Documentation

Because github pages are publicly accessible, I moved the documentation to the "doc" branch. To download the documentation either

1. Download the zip file from the "doc" branch

2. clone the repo with

git clone -b doc http:// https://[email protected]/lukaskiwitz/thesis

To clone the repo from the cli you will need to set up a personal acces token in your github settings. Then you can download any update with

git pull

3. Alternatively, you can build the documentation yourself, provided that you have a properly configured conda environment. Move to the docsrc folder and run

make html

When done open index.html in your web browser to access the title page.

Requirements

1. All python dependencies (provided as an anaconda yml-file)
2. correctly set LD_LIBRARY_PATH environment variable
3. installed thesisproject-package (provided as wheel file for pip)

Installation Instructions (with anaconda)

1. Install anaconda: https://www.anaconda.com/products/individual

2. Run

conda env create -f fenics.yml -n "enviroment_name"

to create a new anaconda environment with the necessary packages. This could take a moment.

3.Run

conda activate "enviroment_name"

to activate the newly created environment.

4.Install the package using pip with

pip install wheel-file

if you got the wheel file or

pip install -e "repository_dir"

if you cloned this repository.

5.Run

conda env config vars set LD_LIBRARY_PATH=~/anaconda3/envs/enviroment_name/lib

and if you want to use paraview rendering

conda env config vars set PARAVIEW_PATH=~path_to_paraview_dir

to set up necessary environment variables.

6.Reactivate the conda environment to apply changes

conda activate enviroment_name

Usage

For clarification, a fully documented example_parameters.py file has be created at model/scripts/paper_models/example_parameters.py. Refer to it if questions regarding the parameters and how to change them arise.
If a parameter is not chosen its value will revert to its standard value chosen in the parameter template. These templates can be found at model/scenarios/box_grid.py, together with their unit conversion.

To replicate Figure 1 of the correspoding publication, follow these steps:

1. Navigate to model/scripts/paper_models/Brunner_etal_Figure1/run.
2. Depending on which panel you want to recreate, choose a folder, e.g. B-E. Here you will find all necessary files to reproduce the data needed for the plotting.
3. Open the parameters.py file and edit the path variable at the top to point to the desired top level directoy for your simulation results.
4. Execute python run_all.py && python list_post_process.py && python create_df.py. Note: This is a parameter scan which will do multiple scans in parallel. How many are done simultaneously can be adjusted in the run_all.py script, line 33.
5. These scripts will first run the scan, perform the post_processing and create the data frames for each sub-run.
6. To combine the dataframes from the sub-runs, first edit the path variable in the combine_dfs.py file to point to your results, then execute it. This will then combine them into one cell_df.hf and global_df.h5 each.
7. After this has finished, navigate to model/scripts/paper_models/Brunner_etal_Figure1/plot/B, adjust the paths according to the path you chose in the parameters.py file and execute both files here to create the plots of panel B.

The first time any model is run it will need to

• create a mesh
• mark the boundary parts
• run the FFC compiler

this may take some time. For large meshes this can be considerable (1h or so), but subsequent runs should be much faster.

Paraview Integration

Only works for simulation data generated with the current version

1. Download an omesa build of paraview (necessary to run without X-server)
2. Set the PARAVIEW_PATH env. variable to your paraview directory. For instance in you conda env. or in the post_process.py
3. pass the keyword argument "render_paraview=True" to run_post_process()
4. various settings can be passed. "example_min" contains a commented example

The cell type markers are floats and will be set according to "SimContainer.marker_lookup". SimContainer now has the "markers" attribute, which lists entity properties (parameters or python attributes) for which to export a MeshFunction. Assigment of non numerical values is controlled by marker_lookup.