Metadata-Version: 2.1
Name: pyviscous
Version: 2.1.0
Summary: Python codes to implement the VISCOUSm global sensitivity analysis framework
Home-page: https://github.com/CH-Earth/pyviscous
Author: Hongli Liu
Author-email: hongliliu68@gmail.com
License: GNU General Public License v3
Keywords: pyviscous
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: copulae
Requires-Dist: matplotlib
Requires-Dist: jupyter

### Abstract
pyVISCOUS is the open-source code of VISCOUS in Python. VISCOUS (VarIance-based Sensitivity analysis using COpUlaS) is a variance-based global sensitivity analysis framework. It was developed by Sheikholeslami et al. ([2021](https://doi.org/10.1029/2020WR028435)) and improved by Liu et al. (2023).The advantage of VISCOUS is that it can use existing model input-output data (e.g., water model parameters-responses) to estimate the first- and total-order Sobol’ sensitivity indices. 

Within the VISCOUS framework, the following steps are included. Details can be found in Liu et al. (2023).

![flowchart](https://github.com/CH-Earth/pyviscous/assets/48458815/2e8f7575-41d4-4e6a-bac8-fadc2a5b9c7a)

### Installation
#### From PyPI
```pip install pyviscous```

#### From source

Clone pyviscous with: ```git clone https://github.com/CH-Earth/pyviscous.git```

Then navigate to the pyviscous directory and install with: ```python setup.py install```

### Examples
We provide four example notebooks in the example directory. In each example, there are scripts to generate input-output data, set up and run VISCOUS, and evaluate the sensitivity results.

### References
Liu, H., Clark, M. P., Gharari, S., Sheikholeslami, R., Knoben, W. J. M., Freer, J., Marsh C. B., & Papalexiou, S. M. (2023) pyVISCOUS: An open-source tool for computationally frugal global sensitivity analysis. (Submitted to Water Resources Research)

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

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