example-paper-fenics
Example paper using FEniCS
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.9%) to scientific vocabulary
Keywords
Repository
Example paper using FEniCS
Basic Info
- Host: GitHub
- Owner: scientificcomputing
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://scientificcomputing.github.io/example-paper-fenics/
- Size: 230 KB
Statistics
- Stars: 2
- Watchers: 2
- Forks: 2
- Open Issues: 0
- Releases: 4
Topics
Metadata Files
README.md
Supplementary code for the paper: Title of paper
This repository contains supplementary code for the paper
Finsberg, H., Dokken, J. 2022. Title of paper, Journal of ..., volume, page, url
Abstract
Provide the abstract of the paper
Getting started
We provide a pre-build Docker image which can be used to run the the code in this repository. First thing you need to do is in ensure that you have docker installed.
To start an interactive docker container you can execute the following command
bash
docker run --rm -it ghcr.io/scientificcomputing/example-paper-fenics:latest
Data
Data is available in a dropbox folder. Use the script download_data.sh in the data folder to download the data.
The data folder should have the following structure after the data is downloaded.
├── README.md
├── data.tar
├── download_data.sh
└── mesh
├── heart01.msh
└── heart02.msh
These meshes are originally taken from https://ora.ox.ac.uk/objects/uuid:951b086c-c4ba-41ef-b967-c2106d87ee06, but since the original data is about 26GB we decided to make a smaller dataset for this example.
Eventually when you publish a paper you could put this data on e.g Zenodo. That will make sure the data gets it's own DOI.
Scripts
All the scripts are located in the folder called code in the repository. Is is assumed that you run the script from within this folder.
Pre-processing
In order to reproduce the results you need to first run the pre-processing script
python3 pre_processing.py
This will convert the meshes from Gmsh to a dolfin format.
Fiber generation
The next step is to run the fiber generation. You can do this by running the script
python3 run_fiber_generation.py
This will create a new folder code/results containing files called microstructure_<heart_nr>.h5.
Postprocessing
The final step is to postprocess the results by running the script
python3 postprocess.py
This will generate a file for visualizing the fibers in the Paraview (inside code/results called fiber_<heart_nr>.xdmf). This script will also compare some features computed from the fibers with the results published in the (artificial) paper. If the results differ, then the program will raise an error.
Citation
@software{Lisa_My_Research_Software_2017,
author = {Lisa, Mona and Bot, Hew},
doi = {10.5281/zenodo.1234},
month = {12},
title = {{My Research Software}},
url = {https://github.com/scientificcomputing/example-paper},
version = {2.0.4},
year = {2017}
}
Having issues
If you have any troubles please file and issue in the GitHub repository.
License
MIT
Owner
- Name: Scientific Computing at Simula Research Laboratory
- Login: scientificcomputing
- Kind: organization
- Repositories: 7
- Profile: https://github.com/scientificcomputing
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Lisa" given-names: "Mona" orcid: "https://orcid.org/0000-0000-0000-0000" - family-names: "Bot" given-names: "Hew" orcid: "https://orcid.org/0000-0000-0000-0000" title: "My Research Software" version: 0.3.0 doi: 10.5281/zenodo.1234 date-released: 2017-12-18 url: "https://github.com/scientificcomputing/example-paper"
GitHub Events
Total
Last Year
Dependencies
- jupyter-book *
- sphinxcontrib-bibtex *
- actions/cache v3 composite
- actions/checkout v4 composite
- actions/configure-pages v3 composite
- actions/deploy-pages v2 composite
- actions/upload-pages-artifact v2 composite
- actions/checkout v3 composite
- docker/build-push-action v3 composite
- docker/login-action v2 composite
- docker/metadata-action 98669ae865ea3cffbcbaa878cf57c20bbf1c6c38 composite
- docker/setup-buildx-action v2 composite
- docker/setup-qemu-action v2 composite
- actions/cache v3 composite
- actions/checkout v4 composite
- actions/upload-artifact v3 composite
- ghcr.io/scientificcomputing/example-paper-fenics v0.1.6 build
- ghcr.io/scientificcomputing/fenics-gmsh 2023-08-16 build
- cardiac-geometries >=0.11.0
- h5py ==3.9.0
- ldrb *
- requests *
- tqdm *
- cardiac-geometries ==0.11.0
- certifi ==2023.7.22
- charset-normalizer ==3.3.2
- click ==8.1.3
- commonmark ==0.9.1
- h5py ==3.9.0
- idna ==3.4
- ldrb ==2023.4.0
- llvmlite ==0.40.1
- meshio ==5.3.4
- numba ==0.57.1
- numpy ==1.21.6
- pygments ==2.13.0
- requests ==2.31.0
- rich ==12.6.0
- rich-click ==1.5.2
- tqdm ==4.66.1
- urllib3 ==2.0.7