varstool 2.3.5

VARS-TOOL Sensitivity and Uncertainty Analysis Package

VARS-TOOL Python Library

1. Overview of VARS-TOOL

VARS-TOOL is a next-generation, multi-method sensitivity and uncertainty analysis software toolbox, applicable to the full range of computer simulation models, including Dynamical Earth Systems Models (DESMs). Developed primarily around the powerful “Variogram Analysis of Response Surfaces” (VARS) framework, VARS-TOOL provides a comprehensive suite of algorithms and methods for global sensitivity analysis (GSA), including ones based on the derivative-based (such as the method of Morris), distribution-based (particularly the variance-based method of Sobol’), and variogram-based (such as VARS) approaches.

The underlying approach of the VARS-TOOL toolkit is described in detail in the following publications:

1. A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory
2. A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application
3. VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis
4. Correlation Effects? A Major but Often Neglected Component in Sensitivity and Uncertainty Analysis (GVARS)
5. A Fresh Look at Variography: Measuring Dependence and Possible Sensitivities Across Geophysical Systems From Any Given Data (DVARS)

2. Installation

2.1. Installing with pip (preferred)

If you have Python3 and pip installed on your machine, then the VARS-TOOL package can be installed as following:

foo@bar:~$ pip install varstool

2.2. Installing from source code

To install the lastest VARS-TOOL code from the source code, you will need to clone the github repository onto your local device using the command:

foo@bar:~$ git clone https://github.com/vars-tool/vars-tool.git

To install the package, enter the VARS-TOOL directory and run:

foo@bar:~$ cd vars-tool
foo@bar:~$ pip install .

If pip is not available on your device use:

foo@bar:~$ python setup.py install

:point_up:	If installation does not work due to limited permissions, add the --user option to the install commands.

If you do not have anaconda or git installed, there is a guide on how to install them located here

3. Documentation

The documentation of the project is located on readthedocs.

4. Examples and Tutorials

You can find examples/tutorials on how to use various aspects of the vars-tool package in the tutorials folder of the github repository. These can be used by cloning the repository to your own device and opening the jupyter notebook tutorials there.

5. Your Contributions

Contributions to VARS-TOOL are welcome! To contribute new features submit a pull request. To get started it is recommended to install the packages in requirements.txt by using the following command:

foo@bar:~$ pip install -r requirements.txt

Once the packages are installed to contribute do the following:

1. Fork the repository (here). A fork makes it possible to make changes to the source code through creating a copy,
2. Create a new branch on your fork,
3. Commit your changes and push them to your branch, and
4. When the branch is ready to be merged, you can create a Pull Request (how to create a pull request).

6. Citations

• Do, N. C., & Razavi, S. (2020). Correlation effects? A major but often neglected component in sensitivity and uncertainty analysis. Water Resources Research, 56(3), e2019WR025436. https://doi.org/10.1029/2019WR025436

• Razavi, S., & Gupta, H. V. (2019). A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models. Environmental modelling & software, 114, 1-11. https://doi.org/10.1016/j.envsoft.2018.12.002

• Razavi, S., & Gupta, H. V. (2016). A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory. Water Resources Research, 52(1), 423-439. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015WR017558

• Razavi, S., & Gupta, H. V. (2016). A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application. Water Resources Research, 52(1), 440-455. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2015WR017559

• Razavi, S., Sheikholeslami, R., Gupta, H. V., & Haghnegahdar, A. (2019). VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis. Environmental modelling & software, 112, 95-107.

7. License

7.1 Software

VARS-TOOL is licensed under the GNU General Public License, Version 3.0 or later.

Copyright (C) 2015-21 Saman Razavi, University of Saskatchewan

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 1, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston MA 02110-1301 USA.

7.2 Documentation

Copyright (C) 2015-21 Saman Razavi, University of Saskatchewan

This documentation is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.