SALib 1.4.6.dev0

Tools for global sensitivity analysis. Contains Sobol', Morris, FAST, DGSM, PAWN, HDMR, Moment Independent and fractional factorial methods

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)

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 Analyis 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.