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https://github.com/benjaminlukeclark/sir_model_calculator
SIR Model calculator, using matplotlib, based off of coursework during my OU Degree
https://github.com/benjaminlukeclark/sir_model_calculator
Last synced: 6 days ago
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SIR Model calculator, using matplotlib, based off of coursework during my OU Degree
- Host: GitHub
- URL: https://github.com/benjaminlukeclark/sir_model_calculator
- Owner: benjaminlukeclark
- License: gpl-3.0
- Created: 2022-03-13T19:10:22.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-05-29T16:36:08.000Z (over 2 years ago)
- Last Synced: 2024-11-09T19:45:00.812Z (2 months ago)
- Language: Python
- Homepage:
- Size: 117 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
[](https://circleci.com/gh/Sudoblark/sir_model_calculator/tree/main)
[](https://codecov.io/gh/Sudoblark/SIR_Model_Calculator)
[](https://hub.docker.com/r/sudoblark/sir-model-calculator)
SIR Model Calculator
Program to model an epidemic outbreak, using a basic SIR Model, then visualise in matplotlib
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Table of Contents
## About The Project
This project aims to use the [SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology) to simulate
an epidemic outbreak in a closed population. This was originally a coursework question for my [Degree](https://www.open.ac.uk/courses/computing-it/degrees/bsc-computing-it-software-q62-soft)
which I found particularly intriguing, so I figured I'd redo my answer in Python and add some nifty data visualisation to boot.
Note that instructions in the readme are for setting up a local development environment, whereas releases will contain
instructions on how to utilise outside of a local development environment.
### Built With
* [matplotlib](https://matplotlib.org/)
## Getting Started
The steps below detail how to get a local version up the program up and running.
### Prerequisites
* Windows
* [Python 3](https://www.python.org/downloads/windows/)
* Linux
* [Python 3](https://docs.python-guide.org/starting/install3/linux/)
### Installation
* Windows
* Clone repo and cd into repo
```bash
git clone https://github.com/Sudoblark/SIR_Model_Animation.git
cd SIR_Model_Animation
```
* Setup Python venv and activate
```bash
python3 -m venv venv
venv\Scripts\activate
```
* Install dependencies
```bash
pip3 install -r requirements
```
* Linux
* Clone repo and cd into repo
```bash
git clone https://github.com/Sudoblark/SIR_Model_Animation.git
cd SIR_Model_Animation
```
* Setup Python venv
```bash
python3 -m venv venv
source venv/bin/activate
```
* Install dependencies
```bash
pip3 install -r requirements.txt
```
### Tests
#### Unit Tests
Unit tests are run on every commit, but you can do them yourself if working locally by following the below instructions.
* Setup a venv as per installation instructions
* Install the following packages:
```
pip install pytest
```
* Run pytest with Python executor to implicitly add current path to PYTHONPATH
```
python -m pytest
```
#### Linter
PyLint is run on every commit, but you can run it yoruself if working locally by following the below instructions.
* Setup a venv as per installation instructions
* Install the following packages:
```
pip install pylint
pip install pylint_gitlab
```
* Run PyLint with HTML output
```
pylint src --exit-zero --load-plugins=pylint_gitlab --output-format=gitlab-pages-html --output=index.html
```
## Usage
Now your virtual environment is setup, change to the src directory and run help to determine what options are available:
```
(venv) C:\temp\sir_model_calculator\src>python -m main -h
```
For example, running the below will output a line graph showing SIR data:
```
(venv) C:\temp\SIR_Model_Animation\src>python -m main matplotlib 150 4 60 0.43 0.18
```
## CI
CircleCI is used to run unit tests, generate code coverage reports, run pylint and both build and push docker images to dockerhub.
This follows a rather standard workflow of:
- Commits not on main cause CI to kick in, and to push docker images with a tag of `dev`
- Commits on main (i.e. an approved PR that is merged) causes CI to ckick in, but pushes docker images with a tag of `qa` rather than `dev`
- Marking a release in Github triggers CI, but pushes docker images with the following tags:
- `latest`
- `VERSION` corresponding to the release, e.g. `0.1.0`
### Setup
- Code coverage reports are uploaded to codecov, using `CODECOV_TOKEN` for the authorisation token
- `DOCKER_USER` and `DOCKER_PASS` env vars are needed for dockerhub authorisation
## Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## Releases
Releases are both distributed as source code and a docker image.
### Docker
Images are pushed to [dockerhub](https://hub.docker.com/repository/docker/sudoblark/sir-model-calculator) via circleCI.
Both dockerfile and README.md for dockerhub are inside the `docker` folder
#### Manually building and pushing
To manually build and push run the following commands:
```
docker build -f .\docker\dockerfile . -t sudoblark/sir-model-calculator:tagname
# Done so we also push the readme.md
cd docker
docker push sudoblark/sir-model-calculator:tagname
```
### Source Code
TODO
## License
Distributed under the GNU General Public License. See `LICENSE.txt` for more information.
## Contact
Sudoblark - [@linkedin_handle](https://www.linkedin.com/in/benni/)
Project Link: [sir_model_calculator](https://github.com/Sudoblark/sir_model_calculator)