Metadata-Version: 1.1
Name: SALib
Version: 0.5
Summary: Tools for sensitivity analysis. Contains Sobol, Morris, and FAST methods.
Home-page: https://github.com/jdherman/SALib
Author: Jon Herman
Author-email: jdherman8@gmail.com
License: The MIT License (MIT)

Copyright (c) 2013-2015 Jon Herman and others.

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Description: ##Sensitivity Analysis Library (SALib)
        
        Python implementations of commonly used sensitivity analysis methods. Useful in systems modeling to calculate the effects of model inputs or exogenous factors on outputs of interest.
        
        **Requirements:** [NumPy](http://www.numpy.org/), [SciPy](http://www.scipy.org/)
        
        **Installation:** `pip install SALib` or `python setup.py install`
        
        **Build Status:** [![Build Status](https://travis-ci.org/jdherman/SALib.svg?branch=master)](https://travis-ci.org/jdherman/SALib)    **Test Coverage:** [![Coverage Status](https://img.shields.io/coveralls/jdherman/SALib.svg)](https://coveralls.io/r/jdherman/SALib)
        
        **Methods included:**
        * Sobol Sensitivity Analysis ([Sobol 2001](http://www.sciencedirect.com/science/article/pii/S0378475400002706), [Saltelli 2002](http://www.sciencedirect.com/science/article/pii/S0010465502002801), [Saltelli et al. 2010](http://www.sciencedirect.com/science/article/pii/S0010465509003087))
        * Method of Morris, including groups and optimal trajectories ([Morris 1991](http://www.tandfonline.com/doi/abs/10.1080/00401706.1991.10484804), [Campolongo et al. 2007](http://www.sciencedirect.com/science/article/pii/S1364815206002805))
        * Fourier Amplitude Sensitivity Test (FAST) ([Cukier et al. 1973](http://scitation.aip.org/content/aip/journal/jcp/59/8/10.1063/1.1680571), [Saltelli et al. 1999](http://amstat.tandfonline.com/doi/abs/10.1080/00401706.1999.10485594))
        * Delta Moment-Independent Measure ([Borgonovo 2007](http://www.sciencedirect.com/science/article/pii/S0951832006000883), [Plischke et al. 2013](http://www.sciencedirect.com/science/article/pii/S0377221712008995))
        * Derivative-based Global Sensitivity Measure (DGSM) ([Sobol and Kucherenko 2009](http://www.sciencedirect.com/science/article/pii/S0378475409000354))
        
        **Contributing:** see [here](CONTRIBUTING.md)
        
        ### Quick Start
        ```python
        from SALib.sample import saltelli
        from SALib.analyze import sobol
        from SALib.test_functions import Ishigami
        import numpy as np
        
        problem = {
          'num_vars': 3, 
          'names': ['x1', 'x2', 'x3'], 
          'bounds': [[-3.14159265359, 3.14159265359], 
                    [-3.14159265359, 3.14159265359], 
                     [-3.14159265359, 3.14159265359]]
        }
        
        # Generate samples
        param_values = saltelli.sample(problem, 1000, calc_second_order=True)
        
        # Run model (example)
        Y = Ishigami.evaluate(param_values)
        
        # Perform analysis
        Si = sobol.analyze(problem, Y, print_to_console=False)
        # Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'
        # (first and total-order indices with bootstrap confidence intervals)
        ```
        
        It's also possible to specify the parameter bounds in a file with 3 columns:
        ```
        # name lower_bound upper_bound
        P1 0.0 1.0
        P2 0.0 5.0
        ...etc.
        ```
        
        Then the `problem` dictionary above can be created from the `read_param_file` function:
        ```python
        from SALib.util import read_param_file
        problem = read_param_file('/path/to/file.txt')
        # ... same as above
        ```
        
        Lots of other options are included for parameter files, as well as a command-line interface. See the [advanced readme](README-advanced.md).
        
        Also check out the [examples](https://github.com/jdherman/SALib/tree/master/examples) for a full description of options for each method.
        
        ### License
        Copyright (C) 2013-2015 Jon Herman and others. Versions v0.5 and later are released under the [MIT license](LICENSE.md).
        
Platform: UNKNOWN
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Mathematics
