Science Score: 67.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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○Scientific vocabulary similarity
Low similarity (7.0%) to scientific vocabulary
Repository
Network Inpainting via Optimal Transport
Basic Info
- Host: GitHub
- Owner: enricofacca
- License: mit
- Language: Python
- Default Branch: main
- Size: 11.1 MB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 2
Metadata Files
README.md
Description
Network Inpainting via Optimal Transport (NIOT) is a python package for reconstructing corrupted networks based on optimal (branched) transport principles. See [^1] for all details (preprint available here).
Installation
The code is written in python and it is based on Firedrake. Check the dedicated page for installation guidance. Consider also installing using
firedrake-install --doi 10.5281/zenodo.11242360
to ensure having the same software used in the manuscript [^1].
Clone this repository with
git clone https://github.com/enricofacca/niot.git
move in the niot directory, and install the niot package with
pip install -e .
Examples
Move in the directory examples/2024FaccaNordbottenHanson and follow the instruction to reproduce the results of the paper.
Authors
Enrico Facca, enrico.facca@uib.no : Departement of Mathematics, University of Bergen, Bergen, Norway.
[^1]:"Network Inpainting via Optimal Transport" Enrico Facca, Jan Martin Nordbotten, Erik Andreas Hanson
Owner
- Name: enrico facca
- Login: enricofacca
- Kind: user
- Company: INRIA Lille, France
- Website: https://enricofacca.github.io/
- Repositories: 4
- Profile: https://github.com/enricofacca
Citation (citations/citations.bib)
@article{FCP2018,
author = {Facca, Enrico and Cardin, Franco and Putti, Mario},
title = {Towards a Stationary Monge--Kantorovich Dynamics: The Physarum Polycephalum Experience},
journal = {SIAM Journal on Applied Mathematics},
volume = {78},
number = {2},
pages = {651-676},
year = {2018},
doi = {10.1137/16M1098383},
URL = {https://doi.org/10.1137/16M1098383},
eprint = {https://doi.org/10.1137/16M1098383}
}
@article{FCP2021,
title = {Branching structures emerging from a continuous optimal transport model},
journal = {Journal of Computational Physics},
volume = {447},
pages = {110700},
year = {2021},
issn = {0021-9991},
doi = {https://doi.org/10.1016/j.jcp.2021.110700},
url = {https://www.sciencedirect.com/science/article/pii/S0021999121005957},
author = {Enrico Facca and Franco Cardin and Mario Putti},
eprint={1811.12691},
archivePrefix={arXiv},
primaryClass={math.NA}
}
@inproceedings{dahl2023fast,
title={Fast Local Thickness},
author={Dahl, Vedrana Andersen and Dahl, Anders Bjorholm},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
pages={4335--4343},
year={2023}
}
@article{van2014scikit,
title={scikit-image: image processing in Python},
author={Van der Walt, Stefan and Sch{\"o}nberger, Johannes L and Nunez-Iglesias, Juan and Boulogne, Fran{\c{c}}ois and Warner, Joshua D and Yager, Neil and Gouillart, Emmanuelle and Yu, Tony},
journal={PeerJ},
volume={2},
pages={e453},
year={2014},
publisher={PeerJ Inc.}
}
GitHub Events
Total
- Push event: 159
Last Year
- Push event: 159
Dependencies
- actions/checkout v2 composite
- numpy *