Metadata-Version: 2.1
Name: stats-arrays
Version: 0.6
Summary: Standard NumPy array interface for defining uncertain parameters
Home-page: https://bitbucket.org/cmutel/stats_arrays
Author: Chris Mutel
Author-email: cmutel@gmail.com
License: BSD 3-Clause License
Description: The `stats_arrays` package provides a standard NumPy array interface for defining uncertain parameters used in models, and classes for Monte Carlo sampling. It also plays well with others.
        
        # Motivation
        
        * Want a consistent interface to SciPy and NumPy statistical function
        * Want to be able to quickly load and save many parameter uncertainty distribution definitions in a portable format
        * Want to manipulate and switch parameter uncertainty distributions and variables
        * Want simple Monte Carlo random number generators that return a vector of parameter values to be fed into uncertainty or sensitivity analysis
        * Want something simple, extensible, documented and tested
        
        The `stats_arrays package was originally developed for the [Brightway2 life cycle assessment framework](http://brightwaylca.org/), but can be applied to any stochastic model.
        
        # Example
        
        ```python
        
        >>> from stats_arrays import *
        >>> my_variables = UncertaintyBase.from_dicts(
        ...     {'loc': 2, 'scale': 0.5, 'uncertainty_type': NormalUncertainty.id},
        ...     {'loc': 1.5, 'minimum': 0, 'maximum': 10, 'uncertainty_type': TriangularUncertainty.id}
        ... )
        >>> my_variables
        array([(2.0, 0.5, nan, nan, nan, False, 3),
               (1.5, nan, nan, 0.0, 10.0, False, 5)],
            dtype=[('loc', '<f8'), ('scale', '<f8'), ('shape', '<f8'),
                   ('minimum', '<f8'), ('maximum', '<f8'), ('negative', '?'),
                   ('uncertainty_type', 'u1')])
        >>> my_rng = MCRandomNumberGenerator(my_variables)
        >>> my_rng.next()
        array([ 2.74414022,  3.54748507])
        >>> # can also be used as an interator
        >>> zip(my_rng, xrange(10))
        [(array([ 2.96893108,  2.90654471]), 0),
         (array([ 2.31190619,  1.49471845]), 1),
         (array([ 3.02026168,  3.33696367]), 2),
         (array([ 2.04775418,  3.68356226]), 3),
         (array([ 2.61976694,  7.0149952 ]), 4),
         (array([ 1.79914025,  6.55264372]), 5),
         (array([ 2.2389968 ,  1.11165296]), 6),
         (array([ 1.69236527,  3.24463981]), 7),
         (array([ 1.77750176,  1.90119991]), 8),
         (array([ 2.32664152,  0.84490754]), 9)]
        
        ```
        
        # More
        
        * Source code: https://bitbucket.org/cmutel/stats_arrays
        * Online documentation: https://stats_arrays.readthedocs.io/en/latest/
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/markdown
