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            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Education",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Electronic Design Automation (EDA)",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Physics",
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            "Topic :: Software Development :: Libraries",
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        "description": "========================\n``mcerp`` Python Package\n========================\n\n.. image:: https://dev.azure.com/tisimst/tisimst/_apis/build/status/tisimst.mcerp\n    :target: https://dev.azure.com/tisimst/tisimst/_build/latest?definitionId=1\n    :alt: Build Status\n\n- Code: https://github.com/tisimst/mcerp\n- Documentation: (not online yet, for now, see the doc folder on Github)\n- License: BSD-3-Clause\n\nOverview\n========\n\n``mcerp`` is a stochastic calculator for `Monte Carlo methods`_ that uses \n`latin-hypercube sampling`_ to perform non-order specific \n`error propagation`_ (or uncertainty analysis). \n\nWith this package you can **easily** and **transparently** track the effects\nof uncertainty through mathematical calculations. Advanced mathematical \nfunctions, similar to those in the standard `math`_ module, and statistical\nfunctions like those in the `scipy.stats`_ module, can also be evaluated \ndirectly.\n\nIf you are familiar with Excel-based risk analysis programs like *@Risk*, \n*Crystal Ball*, *ModelRisk*, etc., this package **will work wonders** for you\n(and probably even be faster!) and give you more modelling flexibility with \nthe powerful Python language. This package also *doesn't cost a penny*, \ncompared to those commercial packages which cost *thousands of dollars* for a \nsingle-seat license. Feel free to copy and redistribute this package as much \nas you desire!\n\nMain Features\n=============\n\n1. **Transparent calculations**. **No or little modification** to existing \n   code required.\n    \n2. Basic `NumPy`_ support without modification. (I haven't done extensive \n   testing, so please let me know if you encounter bugs.)\n\n3. Advanced mathematical functions supported through the ``mcerp.umath`` \n   sub-module. If you think a function is in there, it probably is. If it \n   isn't, please request it!\n\n4. **Easy statistical distribution constructors**. The location, scale, \n   and shape parameters follow the notation in the respective Wikipedia \n   articles and other relevant web pages.\n\n5. **Correlation enforcement** and variable sample visualization capabilities.\n\n6. **Probability calculations** using conventional comparison operators.\n\n7. Advanced Scipy **statistical function compatibility** with package \n   functions. Depending on your version of Scipy, some functions might not\n   work.\n\nInstallation\n============\n\n``mcerp`` works on Linux, MacOS and Windows, with Python 2.7 or with Python 3.5 or later.\n\nTo install it, use ``pip``::\n\n    pip install mcerp\n\nThe ``mcerp`` dependencies should be installed automatically if using ``pip``,\notherwise they will need to be installed manually:\n\n- `NumPy`_ : Numeric Python\n- `SciPy`_ : Scientific Python\n- `Matplotlib`_ : Python plotting library\n\nSee also\n========\n\n- `uncertainties`_ : First-order error propagation\n- `soerp`_ : Second-order error propagation\n\nContact\n=======\n\nPlease send **feature requests, bug reports, or feedback** to \n`Abraham Lee`_.\n\n.. _Monte Carlo methods: http://en.wikipedia.org/wiki/Monte_Carlo_method\n.. _latin-hypercube sampling: http://en.wikipedia.org/wiki/Latin_hypercube_sampling\n.. _soerp: http://pypi.python.org/pypi/soerp\n.. _error propagation: http://en.wikipedia.org/wiki/Propagation_of_uncertainty\n.. _math: http://docs.python.org/library/math.html\n.. _NumPy: http://www.numpy.org/\n.. _SciPy: http://scipy.org\n.. _Matplotlib: http://matplotlib.org/\n.. _scipy.stats: http://docs.scipy.org/doc/scipy/reference/stats.html\n.. _uncertainties: http://pypi.python.org/pypi/uncertainties\n.. _source code: https://github.com/tisimst/mcerp\n.. _Abraham Lee: mailto:tisimst@gmail.com\n.. _package documentation: http://pythonhosted.org/mcerp\n.. _GitHub: http://github.com/tisimst/mcerp",
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