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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.

Documentation: ReadTheDocs <http://salib.readthedocs.org>__

Requirements: NumPy <http://www.numpy.org/>, SciPy <http://www.scipy.org/>, matplotlib <http://matplotlib.org/>, pandas <http://https://pandas.pydata.org/>, Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)

Installation: pip install SALib or pip install . or conda install SALib

Build Status: |Build Status| Test Coverage: |Coverage Status|

Included methods

• 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>, Ruano et al. 2012 <https://doi.org/10.1016/j.envsoft.2012.03.008>__)

• extended Fourier Amplitude Sensitivity Test (eFAST) (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>, Pujol (2006) in Iooss et al., (2021) <http://scitation.aip.org/content/aip/journal/jcp/59/8/10.1063/1.1680571>__)

• Random Balance Designs - Fourier Amplitude Sensitivity Test (RBD-FAST) (Tarantola et al. 2006 <https://hal.archives-ouvertes.fr/hal-01065897/file/Tarantola06RESS_HAL.pdf>, Plischke 2010 <https://doi.org/10.1016/j.ress.2009.11.005>, Tissot et al. 2012 <https://doi.org/10.1016/j.ress.2012.06.010>__)

• 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>__)

• Fractional Factorial Sensitivity Analysis (Saltelli et al. 2008 <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470059974.html>__)

• High-Dimensional Model Representation (HDMR) (Rabitz et al. 1999 <https://doi.org/10.1016/S0010-4655(98)00152-0>, Li et al. 2010 <https://doi.org/10.1021/jp9096919>)

• PAWN (Pianosi and Wagener 2018 <https://dx.doi.org/10.1016/j.envsoft.2018.07.019>, Pianosi and Wagener 2015 <https://doi.org/10.1016/j.envsoft.2015.01.004>)

• Regional Sensitivity Analysis (based on Hornberger and Spear, 1981 <https://www.osti.gov/biblio/6396608-approach-preliminary-analysis-environmental-systems>, Saltelli et al. 2008 <https://dx.doi.org/10.1002/9780470725184>, Pianosi et al., 2016 <https://dx.doi.org/10.1016/j.envsoft.2016.02.008>__)

Contributing: see here <CONTRIBUTING.md>__

Quick Start

Procedural approach

.. code:: 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': [[-np.pi, np.pi]]*3
    }

    # Generate samples
    param_values = saltelli.sample(problem, 1024)

    # Run model (example)
    Y = Ishigami.evaluate(param_values)

    # Perform analysis
    Si = sobol.analyze(problem, Y, print_to_console=True)
    # 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:

.. code:: 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
section in the documentation <https://salib.readthedocs.io/en/latest/advanced.html>`__.


Method chaining approach

Chaining calls is supported from SALib v1.4

.. code:: python

from SALib import ProblemSpec
from SALib.test_functions import Ishigami

import numpy as np


# By convention, we assign to "sp" (for "SALib Problem")
sp = ProblemSpec({
  'names': ['x1', 'x2', 'x3'],   # Name of each parameter
  'bounds': [[-np.pi, np.pi]]*3,  # bounds of each parameter
  'outputs': ['Y']               # name of outputs in expected order
})

(sp.sample_saltelli(1024, calc_second_order=True)
   .evaluate(Ishigami.evaluate)
   .analyze_sobol(print_to_console=True))

print(sp)

# Samples, model results and analyses can be extracted:
print(sp.samples)
print(sp.results)
print(sp.analysis)

# Basic plotting functionality is also provided
sp.plot()

The above is equivalent to the procedural approach shown previously.

Also check out the FAQ <https://github.com/SALib/SALib/tree/main/FAQ.md>__ and examples <https://github.com/SALib/SALib/tree/main/examples>__ for a full description of options for each method.

How to cite SALib

If you would like to use our software, please cite it using the following:

Iwanaga, T., Usher, W., & Herman, J. (2022).
Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses.
Socio-Environmental Systems Modelling, 4, 18155.
doi:10.18174/sesmo.18155

Herman, J. and Usher, W. (2017) SALib: An open-source Python library for
sensitivity analysis. Journal of Open Source Software, 2(9).
doi:10.21105/joss.00097

|paper status|

If you use BibTeX, cite using the following entries::

@article{Iwanaga2022,
  title = {Toward {SALib} 2.0: {Advancing} the accessibility and interpretability of global sensitivity analyses},
  volume = {4},
  url = {https://sesmo.org/article/view/18155},
  doi = {10.18174/sesmo.18155},
  journal = {Socio-Environmental Systems Modelling},
  author = {Iwanaga, Takuya and Usher, William and Herman, Jonathan},
  month = may,
  year = {2022},
  pages = {18155},
}

@article{Herman2017,
  doi = {10.21105/joss.00097},
  url = {https://doi.org/10.21105/joss.00097},
  year  = {2017},
  month = {jan},
  publisher = {The Open Journal},
  volume = {2},
  number = {9},
  author = {Jon Herman and Will Usher},
  title = {{SALib}: An open-source Python library for Sensitivity Analysis},
  journal = {The Journal of Open Source Software}
}

Projects that use SALib

Many projects now use the Global Sensitivity Analysis features provided by SALib. Here is a selection:

Software

* `The City Energy Analyst <https://github.com/architecture-building-systems/CEAforArcGIS>`_
* `pynoddy <https://github.com/flohorovicic/pynoddy>`_
* `savvy <https://github.com/houghb/savvy>`_
* `rhodium <https://github.com/Project-Platypus/Rhodium>`_
* `pySur <https://github.com/MastenSpace/pysur>`_
* `EMA workbench <https://github.com/quaquel/EMAworkbench>`_
* `Brain/Circulation Model Developer <https://github.com/bcmd/BCMD>`_
* `DAE Tools <http://daetools.com/>`_
* `agentpy <https://github.com/JoelForamitti/agentpy>`_
* `uncertainpy <https://github.com/simetenn/uncertainpy>`_
* `CLIMADA <https://github.com/CLIMADA-project/climada_python>`_

Blogs
~~~~~

* `Sensitivity Analysis in Python <http://www.perrygeo.com/sensitivity-analysis-in-python.html>`_
* `Sensitivity Analysis with SALib <http://keyboardscientist.weebly.com/blog/sensitivity-analysis-with-salib>`_
* `Running Sobol using SALib <https://waterprogramming.wordpress.com/2013/08/05/running-sobol-sensitivity-analysis-using-salib/>`_
* `Extensions of SALib for more complex sensitivity analyses <https://waterprogramming.wordpress.com/2014/02/11/extensions-of-salib-for-more-complex-sensitivity-analyses/>`_

Videos
~~~~~~

* `PyData Presentation on SALib <https://youtu.be/gkR_lz5OptU>`_

If you would like to be added to this list, please submit a pull request,
or create an issue.

Many thanks for using SALib.


How to contribute
-----------------

See `here <CONTRIBUTING.md>`__ for how to contribute to SALib.


License
-------

Copyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and
later are released under the `MIT license <LICENSE.md>`__.

.. |Build Status| image:: https://travis-ci.com/SALib/SALib.svg?branch=master
   :target: https://travis-ci.com/SALib/SALib
.. |Coverage Status| image:: https://img.shields.io/coveralls/SALib/SALib.svg
   :target: https://coveralls.io/r/SALib/SALib
.. |Code Issues| image:: https://www.quantifiedcode.com/api/v1/project/ed62e70f899e4ec8af4ea6b2212d4b30/badge.svg
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.. |paper status| image:: http://joss.theoj.org/papers/431262803744581c1d4b6a95892d3343/status.svg
   :target: http://joss.theoj.org/papers/431262803744581c1d4b6a95892d3343