Metadata-Version: 1.1
Name: mcerp3
Version: 1.0.2
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
          
        - 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
        ============
        
        Required Packages
        -----------------
        
        The following packages should be installed automatically (if using ``pip``
        or ``easy_install``), otherwise they will need to be installed manually:
        
        - `NumPy`_ : Numeric Python
        - `SciPy`_ : Scientific Python
        - `Matplotlib`_ : Python plotting library
        
        These packages come standard in *Python(x,y)*, *Spyder*, and other 
        scientific computing python bundles.
        
        How to install
        --------------
        
        You have **several easy, convenient options** to install the ``mcerp3`` 
        package (administrative privileges may be required)
        
        #. Simply copy the unzipped ``mcerp3-XYZ`` directory to any other location that
           python can find it and rename it ``mcerp3``.
            
        #. From the command-line, do one of the following:
           
           a. Manually download the package files below, unzip to any directory, and 
              run::
           
               $ [sudo] python setup.py install
        
           b. If ``setuptools`` is installed, run::
        
               $ [sudo] easy_install [--upgrade] mcerp3
            
           c. If ``pip`` is installed, run::
        
               $ [sudo] pip install [--upgrade] mcerp3
        
        See also
        ========
        
        - `uncertainties`_ : First-order error propagation
        - `soerp`_ : Second-order error propagation
        
        Contact
        =======
        
        Please send **Python 3 related issues** 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
        
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
