| Name | SALib JSON |
| Version |
1.5.1
JSON |
| download |
| home_page | None |
| Summary | Tools for global sensitivity analysis. Contains Sobol', Morris, FAST, DGSM, PAWN, HDMR, Moment Independent and fractional factorial methods |
| upload_time | 2024-08-19 16:35:25 |
| maintainer | SALib contributors |
| docs_url | None |
| author | Will Usher, Takuya Iwanaga |
| requires_python | >=3.9 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
.. image:: https://raw.githubusercontent.com/SALib/SALib/main/docs/assets/logo.png
:width: 232px
:align: center
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
:target: https://www.quantifiedcode.com/app/project/ed62e70f899e4ec8af4ea6b2212d4b30
.. |paper status| image:: http://joss.theoj.org/papers/431262803744581c1d4b6a95892d3343/status.svg
:target: http://joss.theoj.org/papers/431262803744581c1d4b6a95892d3343
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"description": ".. image:: https://raw.githubusercontent.com/SALib/SALib/main/docs/assets/logo.png\n :width: 232px\n :align: center\n\n\nSensitivity Analysis Library (SALib)\n====================================\n\nPython implementations of commonly used sensitivity analysis methods.\nUseful in systems modeling to calculate the effects of model inputs or\nexogenous factors on outputs of interest.\n\n**Documentation:** `ReadTheDocs <http://salib.readthedocs.org>`__\n\n**Requirements:** `NumPy <http://www.numpy.org/>`__,\n`SciPy <http://www.scipy.org/>`__,\n`matplotlib <http://matplotlib.org/>`__,\n`pandas <http://https://pandas.pydata.org/>`__,\nPython 3 (from SALib v1.2 onwards SALib does not officially support Python 2)\n\n**Installation:** ``pip install SALib`` or ``pip install .`` or ``conda install SALib``\n\n**Build Status:** |Build Status| **Test Coverage:** |Coverage Status|\n\nIncluded methods\n----------------\n\n* Sobol Sensitivity Analysis (`Sobol 2001 <http://www.sciencedirect.com/science/article/pii/S0378475400002706>`__,\n `Saltelli 2002 <http://www.sciencedirect.com/science/article/pii/S0010465502002801>`__,\n `Saltelli et al. 2010 <http://www.sciencedirect.com/science/article/pii/S0010465509003087>`__)\n\n* Method of Morris, including groups and optimal trajectories (`Morris\n 1991 <http://www.tandfonline.com/doi/abs/10.1080/00401706.1991.10484804>`__,\n `Campolongo et al. 2007 <http://www.sciencedirect.com/science/article/pii/S1364815206002805>`__,\n `Ruano et al. 2012 <https://doi.org/10.1016/j.envsoft.2012.03.008>`__)\n\n* extended Fourier Amplitude Sensitivity Test (eFAST) (`Cukier et al. 1973 <http://scitation.aip.org/content/aip/journal/jcp/59/8/10.1063/1.1680571>`__,\n `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>`__)\n\n* Random Balance Designs - Fourier Amplitude Sensitivity Test (RBD-FAST) (`Tarantola et al. 2006 <https://hal.archives-ouvertes.fr/hal-01065897/file/Tarantola06RESS_HAL.pdf>`__,\n `Plischke 2010 <https://doi.org/10.1016/j.ress.2009.11.005>`__,\n `Tissot et al. 2012 <https://doi.org/10.1016/j.ress.2012.06.010>`__)\n\n* Delta\n Moment-Independent Measure (`Borgonovo 2007 <http://www.sciencedirect.com/science/article/pii/S0951832006000883>`__,\n `Plischke et al. 2013 <http://www.sciencedirect.com/science/article/pii/S0377221712008995>`__)\n\n* Derivative-based Global Sensitivity Measure (DGSM) (`Sobol and\n Kucherenko 2009 <http://www.sciencedirect.com/science/article/pii/S0378475409000354>`__)\n\n* Fractional Factorial Sensitivity Analysis\n (`Saltelli et al. 2008 <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470059974.html>`__)\n\n* High-Dimensional Model Representation (HDMR)\n (`Rabitz et al. 1999 <https://doi.org/10.1016/S0010-4655(98)00152-0>`__, `Li et al. 2010 <https://doi.org/10.1021/jp9096919>`__)\n\n* 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>`__)\n\n* 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>`__)\n\n\n**Contributing:** see `here <CONTRIBUTING.md>`__\n\nQuick Start\n-----------\n\nProcedural approach\n~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n from SALib.sample import saltelli\n from SALib.analyze import sobol\n from SALib.test_functions import Ishigami\n import numpy as np\n\n problem = {\n 'num_vars': 3,\n 'names': ['x1', 'x2', 'x3'],\n 'bounds': [[-np.pi, np.pi]]*3\n }\n\n # Generate samples\n param_values = saltelli.sample(problem, 1024)\n\n # Run model (example)\n Y = Ishigami.evaluate(param_values)\n\n # Perform analysis\n Si = sobol.analyze(problem, Y, print_to_console=True)\n # Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'\n # (first and total-order indices with bootstrap confidence intervals)\n\nIt's also possible to specify the parameter bounds in a file with 3\ncolumns:\n\n::\n\n # name lower_bound upper_bound\n P1 0.0 1.0\n P2 0.0 5.0\n ...etc.\n\nThen the ``problem`` dictionary above can be created from the\n``read_param_file`` function:\n\n.. code:: python\n\n from SALib.util import read_param_file\n problem = read_param_file('/path/to/file.txt')\n # ... same as above\n\nLots of other options are included for parameter files, as well as a\ncommand-line interface. See the `advanced\nsection in the documentation <https://salib.readthedocs.io/en/latest/advanced.html>`__.\n\n\nMethod chaining approach\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nChaining calls is supported from SALib v1.4\n\n.. code:: python\n\n from SALib import ProblemSpec\n from SALib.test_functions import Ishigami\n\n import numpy as np\n\n\n # By convention, we assign to \"sp\" (for \"SALib Problem\")\n sp = ProblemSpec({\n 'names': ['x1', 'x2', 'x3'], # Name of each parameter\n 'bounds': [[-np.pi, np.pi]]*3, # bounds of each parameter\n 'outputs': ['Y'] # name of outputs in expected order\n })\n\n (sp.sample_saltelli(1024, calc_second_order=True)\n .evaluate(Ishigami.evaluate)\n .analyze_sobol(print_to_console=True))\n\n print(sp)\n\n # Samples, model results and analyses can be extracted:\n print(sp.samples)\n print(sp.results)\n print(sp.analysis)\n\n # Basic plotting functionality is also provided\n sp.plot()\n\n\nThe above is equivalent to the procedural approach shown previously.\n\nAlso check out the `FAQ <https://github.com/SALib/SALib/tree/main/FAQ.md>`__ and\n`examples <https://github.com/SALib/SALib/tree/main/examples>`__ for a\nfull description of options for each method.\n\n\nHow to cite SALib\n-----------------\n\nIf you would like to use our software, please cite it using the following:\n\n Iwanaga, T., Usher, W., & Herman, J. (2022).\n Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses.\n Socio-Environmental Systems Modelling, 4, 18155.\n doi:10.18174/sesmo.18155\n\n Herman, J. and Usher, W. (2017) SALib: An open-source Python library for\n sensitivity analysis. Journal of Open Source Software, 2(9).\n doi:10.21105/joss.00097\n\n|paper status|\n\nIf you use BibTeX, cite using the following entries::\n\n @article{Iwanaga2022,\n title = {Toward {SALib} 2.0: {Advancing} the accessibility and interpretability of global sensitivity analyses},\n volume = {4},\n url = {https://sesmo.org/article/view/18155},\n doi = {10.18174/sesmo.18155},\n journal = {Socio-Environmental Systems Modelling},\n author = {Iwanaga, Takuya and Usher, William and Herman, Jonathan},\n month = may,\n year = {2022},\n pages = {18155},\n }\n\n @article{Herman2017,\n doi = {10.21105/joss.00097},\n url = {https://doi.org/10.21105/joss.00097},\n year = {2017},\n month = {jan},\n publisher = {The Open Journal},\n volume = {2},\n number = {9},\n author = {Jon Herman and Will Usher},\n title = {{SALib}: An open-source Python library for Sensitivity Analysis},\n journal = {The Journal of Open Source Software}\n }\n\n\nProjects that use SALib\n-----------------------\n\nMany projects now use the Global Sensitivity Analysis features provided by\nSALib. Here is a selection:\n\nSoftware\n~~~~~~~~\n\n* `The City Energy Analyst <https://github.com/architecture-building-systems/CEAforArcGIS>`_\n* `pynoddy <https://github.com/flohorovicic/pynoddy>`_\n* `savvy <https://github.com/houghb/savvy>`_\n* `rhodium <https://github.com/Project-Platypus/Rhodium>`_\n* `pySur <https://github.com/MastenSpace/pysur>`_\n* `EMA workbench <https://github.com/quaquel/EMAworkbench>`_\n* `Brain/Circulation Model Developer <https://github.com/bcmd/BCMD>`_\n* `DAE Tools <http://daetools.com/>`_\n* `agentpy <https://github.com/JoelForamitti/agentpy>`_\n* `uncertainpy <https://github.com/simetenn/uncertainpy>`_\n* `CLIMADA <https://github.com/CLIMADA-project/climada_python>`_\n\nBlogs\n~~~~~\n\n* `Sensitivity Analysis in Python <http://www.perrygeo.com/sensitivity-analysis-in-python.html>`_\n* `Sensitivity Analysis with SALib <http://keyboardscientist.weebly.com/blog/sensitivity-analysis-with-salib>`_\n* `Running Sobol using SALib <https://waterprogramming.wordpress.com/2013/08/05/running-sobol-sensitivity-analysis-using-salib/>`_\n* `Extensions of SALib for more complex sensitivity analyses <https://waterprogramming.wordpress.com/2014/02/11/extensions-of-salib-for-more-complex-sensitivity-analyses/>`_\n\nVideos\n~~~~~~\n\n* `PyData Presentation on SALib <https://youtu.be/gkR_lz5OptU>`_\n\nIf you would like to be added to this list, please submit a pull request,\nor create an issue.\n\nMany thanks for using SALib.\n\n\nHow to contribute\n-----------------\n\nSee `here <CONTRIBUTING.md>`__ for how to contribute to SALib.\n\n\nLicense\n-------\n\nCopyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and\nlater are released under the `MIT license <LICENSE.md>`__.\n\n.. |Build Status| image:: https://travis-ci.com/SALib/SALib.svg?branch=master\n :target: https://travis-ci.com/SALib/SALib\n.. |Coverage Status| image:: https://img.shields.io/coveralls/SALib/SALib.svg\n :target: https://coveralls.io/r/SALib/SALib\n.. |Code Issues| image:: https://www.quantifiedcode.com/api/v1/project/ed62e70f899e4ec8af4ea6b2212d4b30/badge.svg\n :target: https://www.quantifiedcode.com/app/project/ed62e70f899e4ec8af4ea6b2212d4b30\n.. |paper status| image:: http://joss.theoj.org/papers/431262803744581c1d4b6a95892d3343/status.svg\n :target: http://joss.theoj.org/papers/431262803744581c1d4b6a95892d3343\n",
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