======================== mcerp Python Package

.. image:: https://dev.azure.com/tisimst/tisimst/_apis/build/status/tisimst.mcerp :target: https://dev.azure.com/tisimst/tisimst/_build/latest?definitionId=1 :alt: Build Status

• Code: https://github.com/tisimst/mcerp
• Documentation: (not online yet, for now, see the doc folder on Github)
• License: BSD-3-Clause

Overview

mcerp is a stochastic calculator for Monte Carlo methods_ that uses latin-hypercube sampling_ to perform non-order specific error propagation_ (or uncertainty analysis).

With this package you can easily and transparently track the effects of uncertainty through mathematical calculations. Advanced mathematical functions, similar to those in the standard math_ module, and statistical functions like those in the scipy.stats_ module, can also be evaluated directly.

If you are familiar with Excel-based risk analysis programs like @Risk, Crystal Ball, ModelRisk, etc., this package will work wonders for you (and probably even be faster!) and give you more modelling flexibility with the powerful Python language. This package also doesn't cost a penny, compared to those commercial packages which cost thousands of dollars for a single-seat license. Feel free to copy and redistribute this package as much as you desire!

Main Features

1. Transparent calculations. No or little modification to existing code required.

2. Basic NumPy_ support without modification. (I haven't done extensive testing, so please let me know if you encounter bugs.)

3. Advanced mathematical functions supported through the mcerp.umath sub-module. If you think a function is in there, it probably is. If it isn't, please request it!

4. Easy statistical distribution constructors. The location, scale, and shape parameters follow the notation in the respective Wikipedia articles and other relevant web pages.

5. Correlation enforcement and variable sample visualization capabilities.

6. Probability calculations using conventional comparison operators.

7. Advanced Scipy statistical function compatibility with package functions. Depending on your version of Scipy, some functions might not work.

Installation

mcerp works on Linux, MacOS and Windows, with Python 2.7 or with Python 3.5 or later.

To install it, use pip::

pip install mcerp

The mcerp dependencies should be installed automatically if using pip, otherwise they will need to be installed manually:

• NumPy_ : Numeric Python
• SciPy_ : Scientific Python
• Matplotlib_ : Python plotting library

See also

• uncertainties_ : First-order error propagation
• soerp_ : Second-order error propagation

Contact

Please send feature requests, bug reports, or feedback to Abraham Lee_.

.. _Monte Carlo methods: http://en.wikipedia.org/wiki/Monte_Carlo_method .. _latin-hypercube sampling: http://en.wikipedia.org/wiki/Latin_hypercube_sampling .. _soerp: http://pypi.python.org/pypi/soerp .. _error propagation: http://en.wikipedia.org/wiki/Propagation_of_uncertainty .. _math: http://docs.python.org/library/math.html .. _NumPy: http://www.numpy.org/ .. _SciPy: http://scipy.org .. _Matplotlib: http://matplotlib.org/ .. _scipy.stats: http://docs.scipy.org/doc/scipy/reference/stats.html .. _uncertainties: http://pypi.python.org/pypi/uncertainties .. _source code: https://github.com/tisimst/mcerp .. _Abraham Lee: mailto:[email protected] .. _package documentation: http://pythonhosted.org/mcerp .. _GitHub: http://github.com/tisimst/mcerp