SALib/SALib: Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.

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.

.. code:: python

from SALib.util import read_param_file problem = read_param_file('/path/to/file.txt') # ... same as above

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.

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 ~~~~~~~~

Many thanks for using SALib.

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

License -------