Science Score: 44.0%
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
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○Academic publication links
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○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 (14.9%) to scientific vocabulary
Repository
Consistent Reproduction of Phenotype
Basic Info
- Host: GitHub
- Owner: PNNL-Predictive-Phenomics
- License: mit
- Language: Python
- Default Branch: main
- Size: 855 KB
Statistics
- Stars: 2
- Watchers: 8
- Forks: 0
- Open Issues: 6
- Releases: 0
Metadata Files
README.md
CROP
Consistent Reproduction of Phenotype (CROP) is an mixed integer linear programming (MILP) algorithm for finding reactions to remove from a model to improve the prediction of no-growth phenotypes while ensuring that correctly predicted growth phenotypes are still preserved.
💪 Getting Started
See the CROP Notebook.
🚀 Installation
The most recent code and data can be installed directly from GitHub with:
bash
$ pip install git+https://github.com/pnnl-predictive-phenomics/crop.git
👐 Contributing
Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.
👋 Attribution
⚖️ License
The code in this package is licensed under the MIT License.
🍪 Cookiecutter
This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.
🛠️ For Developers
See developer instructions
The final section of the README is for if you want to get involved by making a code contribution. ### Development Installation To install in development mode, use the following: ```bash $ git clone git+https://github.com/pnnl-predictive-phenomics/crop.git $ cd crop $ pip install -e . ``` ### 🥼 Testing After cloning the repository and installing `tox` with `pip install tox`, the unit tests in the `tests/` folder can be run reproducibly with: ```shell $ tox ``` Additionally, these tests are automatically re-run with each commit in a [GitHub Action](https://github.com/pnnl-predictive-phenomics/crop/actions?query=workflow%3ATests). ### 📖 Building the Documentation The documentation can be built locally using the following: ```shell $ git clone git+https://github.com/pnnl-predictive-phenomics/crop.git $ cd crop $ tox -e docs $ open docs/build/html/index.html ``` The documentation automatically installs the package as well as the `docs` extra specified in the [`setup.cfg`](setup.cfg). `sphinx` plugins like `texext` can be added there. Additionally, they need to be added to the `extensions` list in [`docs/source/conf.py`](docs/source/conf.py). ### 📦 Making a Release After installing the package in development mode and installing `tox` with `pip install tox`, the commands for making a new release are contained within the `finish` environment in `tox.ini`. Run the following from the shell: ```shell $ tox -e finish ``` This script does the following: 1. Uses [Bump2Version](https://github.com/c4urself/bump2version) to switch the version number in the `setup.cfg`, `src/crop/version.py`, and [`docs/source/conf.py`](docs/source/conf.py) to not have the `-dev` suffix 2. Packages the code in both a tar archive and a wheel using [`build`](https://github.com/pypa/build) 3. Uploads to PyPI using [`twine`](https://github.com/pypa/twine). Be sure to have a `.pypirc` file configured to avoid the need for manual input at this step 4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped. 5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use `tox -e bumpversion -- minor` after.Owner
- Name: pnnl-predictive-phenomics
- Login: pnnl-predictive-phenomics
- Kind: organization
- Repositories: 2
- Profile: https://github.com/pnnl-predictive-phenomics
Citation (CITATION.cff)
cff-version: 1.0.2 message: "If you use this software, please cite it as below." title: "CROP" authors: - name: "Jeremy Zucker" version: 0.0.1-dev doi: url: "https://github.com/pnnl-predictive-phenomics/crop"
GitHub Events
Total
- Issues event: 2
- Push event: 12
- Pull request event: 1
- Create event: 2
Last Year
- Issues event: 2
- Push event: 12
- Pull request event: 1
- Create event: 2
Dependencies
- actions/checkout v2 composite
- actions/setup-python v2 composite
- codecov/codecov-action v1 composite