Sensitivity Analysis Library

SALib是常用灵敏度分析方法的Python实现，包括 Sobol、Morris 和 FAST 方法。在系统建模中很有用，可以计算模型输入或外生因素对感兴趣的输出的影响。

Supported Methods

SALib支持以下10种方法：

• Sobol Sensitivity Analysis

• Method of Morris, including groups and optimal trajectories

• Fourier Amplitude Sensitivity Test (FAST)

• Random Balance Designs - Fourier Amplitude Sensitivity Test (RBD-FAST)

• Delta Moment-Independent Measure

• Derivative-based Global Sensitivity Measure (DGSM)

• Fractional Factorial Sensitivity Analysis

• High Dimensional Model Representation

• PAWN

• Regional Sensitivity Analysis

Installation

pip install SALib  

or

conda install SALib

Requirements

NumPy

SciPy

matplotlib

pandas

Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)

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.00097If you would like to use our software, please cite it using the following:

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

Reference

• 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
• Fourier Amplitude Sensitivity Test (FAST)
  Cukier et al. 1973, Saltelli et al. 1999
• Random Balance Designs - Fourier Amplitude Sensitivity Test (RBD-FAST)
  Tarantola et al. 2006 Elmar 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
  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