Model calibration (or model updating) is a task most scientists are facing when building parameterized models and then updating the parameters based on some experimental data in order to generalize the model and make accurate predictions. For complex models and complex data sets (different experimental tests with different data structures, sensor types and different models) this task is often tedious, difficult to reproduce and often error prone due to complex challenges related to data processing, forward model development and inference. The aim of this roject is to make the process more transparent, easier to setup and work with and more transparent when different people are jointly performing this task. Further information can be found in the documentation.
Clone the repository and make sure to include the submodules
git clone --recurse-submodules https://github.com/BAMresearch/ModelCalibration.git
Create environment within the main project folder
conda env create --prefix ./conda-env -f environment.yml
Update environment within main project folder (if environment.yml was changed)
conda env update --prefix ./conda-env -f environment.yml --prune
Activate environment
conda activate ./conda-env
Install submodule Bayes
git pull --recurse-submodules
pip install -e BayesianInference