Metadata-Version: 1.1
Name: mcerp3
Version: 1.0.3
Summary: Real-time latin-hypercube-sampling-based Monte Carlo Error Propagation
Home-page: https://github.com/paul-freeman/mcerp3
Author: Paul Freeman
Author-email: paul.freeman.cs@gmail.com
License: BSD License
Description: ================================
        ``mcerp3`` Package Documentation
        ================================
        
        Overview
        ========
        
        ``mcerp3`` 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!
        
        What's New In This Release
        ==========================
        
        - this is a Python 3 release of the mcerp package by Abraham Lee
        
        - available via ``conda`` or ``pip``
          
        - officially adds the 3-clause BSD licesnse text to the software
          (this license has been specified in the mcerp PyPI package for years)  
        
        - supports SciPy >= 1.0 by removing the scipy.stats.signaltonoise function
        
        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.
        
        8. Python 3 support
        
        Installation
        ============
        
        How to install
        --------------
        
        Effort has been made to ensure ``mcerp3`` is easy to install.
        
        #. From the command-line, do one of the following:
           
           a. Install the `conda package`_::
           
               $ conda install mcerp3 -c freemapa
            
           b. Install the `PyPI package`_::
        
               $ pip install mcerp3
        
        The `source code`_ is also freely available, in case you would like to
        incorporate it directly into your project. However, when possible, it is
        usually easier to let your package manager handle things for you.
        
        Required Packages
        -----------------
        
        The following packages are required, but should be installed automatically
        (if using ``conda`` or ``pip``). Otherwise, they may 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
        =======
        
        Bugs should be reported on the `GitHub issues`_ page. Python 3 related
        requests can be sent to `Paul Freeman`_. Other issues should be referred to
        the original author, `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/paul-freeman/mcerp
        .. _Abraham Lee: mailto:tisimst@gmail.com
        .. _Paul Freeman: mailto:paul.freeman.cs@gmail.com
        .. _package documentation: http://pythonhosted.org/mcerp3
        .. _GitHub: http://github.com/paul-freeman/mcerp
        .. _GitHub issues: http://github.com/paul-freeman/mcerp/issues
        .. _conda package: https://anaconda.org/freemapa/mcerp3
        .. _PyPI package: https://pypi.org/project/mcerp3/
        
Keywords: monte carlo,latin hypercube,sampling calculator,error propagation,uncertainty,risk analysis,error,real-time
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
Classifier: Environment :: Win32 (MS Windows)
Classifier: Environment :: X11 Applications
Classifier: Intended Audience :: Customer Service
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Manufacturing
Classifier: Intended Audience :: Other Audience
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: OS Independent
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Electronic Design Automation (EDA)
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
