bayesvalidrox 1.1.0

An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.

BayesValidRox

An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models. This framework provides an automated workflow for surrogate-based sensitivity analysis, Bayesian calibration, and validation of computational models with a modular structure.

Features

• Surrogate modeling with Polynomial Chaos Expansion
• Global sensitivity analysis using Sobol Indices
• Bayesian calibration with MCMC using emcee package
• Bayesian validation with model weights for multi-model setting

Resources

The following resources are useful to get started on working with BayesValidRox:

• BayesValidRox website
• User guide
• Tutorial

Important links:

• GitLab
• Changelog

Authors

• @farid
• @RKohlhaas

Installation

The best practive is to create a virtual environment and install the package inside it.

To create and activate the virtual environment run the following command in the terminal:

  python3 -m venv bayes_env
  cd bayes_env
  source bin/activate

You can replace bayes_env with your preferred name. For more information on virtual environments see this link.

Now, you can install the latest release of the package on PyPI inside the venv with:

  pip install bayesvalidrox

and installing the version on the master branch can be done by cloning this repo and installing:

  git clone https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox.git
  cd bayesvalidrox
  pip install .

Requirements

python 3.10:

• numpy>=1.23.5
• pandas==1.4.4
• joblib==1.1.1
• matplotlib==3.8.0
• seaborn==0.11.1
• scipy>=1.11.1
• scikit-learn==1.3.1
• tqdm>=4.61.1
• chaospy==4.3.3
• emcee==3.0.2
• corner==2.2.1
• h5py==3.9.0
• statsmodels==0.14.2
• multiprocess==0.70.16
• datasets==2.20.0
• umbridge==1.2.4

TexLive for Plotting with matplotlib

Here you need super user rights

sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended cm-super