SALib 1.5.1

Description:

SALib 1.5.1

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
Requirements: NumPy,
SciPy,
matplotlib,
pandas,
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: Test Coverage:

Included methods

Sobol Sensitivity Analysis (Sobol 2001,
Saltelli 2002,
Saltelli et al. 2010)
Method of Morris, including groups and optimal trajectories (Morris
1991,
Campolongo et al. 2007,
Ruano et al. 2012)
extended Fourier Amplitude Sensitivity Test (eFAST) (Cukier et al. 1973,
Saltelli et al. 1999, Pujol (2006) in Iooss et al., (2021))
Random Balance Designs - Fourier Amplitude Sensitivity Test (RBD-FAST) (Tarantola et al. 2006,
Plischke 2010,
Tissot et al. 2012)
Delta
Moment-Independent Measure (Borgonovo 2007,
Plischke et al. 2013)
Derivative-based Global Sensitivity Measure (DGSM) (Sobol and
Kucherenko 2009)
Fractional Factorial Sensitivity Analysis
(Saltelli et al. 2008)
High-Dimensional Model Representation (HDMR)
(Rabitz et al. 1999, Li et al. 2010)
PAWN (Pianosi and Wagener 2018, Pianosi and Wagener 2015)
Regional Sensitivity Analysis (based on Hornberger and Spear, 1981, Saltelli et al. 2008, Pianosi et al., 2016)

Contributing: see here


Quick Start

Procedural approach
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:
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.


Method chaining approach
Chaining calls is supported from SALib v1.4
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 and
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


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
pynoddy
savvy
rhodium
pySur
EMA workbench
Brain/Circulation Model Developer
DAE Tools
agentpy
uncertainpy
CLIMADA



Blogs

Sensitivity Analysis in Python
Sensitivity Analysis with SALib
Running Sobol using SALib
Extensions of SALib for more complex sensitivity analyses



Videos

PyData Presentation on SALib

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 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

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

Share

• Share on Facebook
• Share on Twitter