VISCOUS is a variance-based global sensitivity analysis framework (Sheikholeslami et al. 2021, Liu et al. 2024). Functioning as a "given-data" method, VISCOUS utilizes existing model input and output data, such as water model parameters and responses, to provide valuable approximations of first- and total-order Sobol’ sensitivity indices. The input-output data can be from previous model runs conducted for various modeling purposes (e.g., model calibration and uncertainty analysis).
From PyPI:
pip install pyviscous
From source:
# Fork the repository on GitHub (assuming you have the rights to do so).
# You can fork it manually from the GitHub interface.
# Clone your forked repository
git clone https://github.com/your_username/pyviscous.git
# Navigate to the pyviscous repository directory
cd pyviscous
# Install
python setup.py install
Replace your_username with your GitHub username. This assumes that you have forked the repository on GitHub manually.
Do not want to install pyviscous?
If you prefer not to install pyviscous, you can still use it by adding the pyviscous source code to the system path. For example:
import sys
sys.path.insert(path_to_directory)
import pyviscous
Replace path_to_directory with the absolute path to the folder where the pyviscous repository is located on your computer. Note: before utilizing pyviscous in this manner, ensure that you have installed all the necessary Python packages as listed in the setup.py file. These packages include numpy, pandas, scipy, scikit-learn, copulae, matplotlib, jupyter.
NOTE: Please install the copulae package using pip, not conda. The conda distribution of copulae lacks its full source code/functions. We recommend using pip for a comprehensive installation. We will notify the copulae developer about this issue for resolution.
We provide five demonstration notebooks in the example directory, including the Rosenbrock function and four Sobol’ functions from Liu et al. (2024). Additionally, a real case study of the Bow at Banff basin, Alberta, Canada, is included to show the real-world application of VISCOUS. Each example includes scripts for input-output data generation or reading, VISCOUS setup and execution, and evaluation of sensitivity results.
VISCOUS was originally developed by Sheikholeslami et al. (2021) and enhanced by Liu et al. (2023).
- Sheikholeslami, R., Gharari, S., Papalexiou, S. M., & Clark, M. P. (2021) VISCOUS: A variance-based sensitivity analysis using copulas for efficient identification of dominant hydrological processes. Water Resources Research, 57, e2020WR028435. https://doi.org/10.1029/2020WR028435
- Liu, H., Clark, M. P., Gharari, S., Sheikholeslami, R., Freer, J., Knoben, W. J. M., Marsh C. B., & Papalexiou, S. M. (2024) An improved copula-based framework for efficient global sensitivity analysis. Water Resources Research, 60, e2022WR033808. https://doi.org/10.1029/2022WR033808
This package was created with Cookiecutter and the https://github.com/audreyr/cookiecutter-pypackage project template.