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https://github.com/lmiconsulting/cosasi

COntagion Simulation And Source Identification: a Python package for graph diffusion source inference
https://github.com/lmiconsulting/cosasi

cyber disease-simulation epidemics graph-algorithms graph-theory message-passing network-analysis network-science python

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COntagion Simulation And Source Identification: a Python package for graph diffusion source inference

Host: GitHub
URL: https://github.com/lmiconsulting/cosasi
Owner: lmiconsulting
License: mit
Created: 2022-09-25T13:08:08.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2023-06-30T10:12:05.000Z (12 months ago)
Last Synced: 2024-03-17T19:45:50.615Z (3 months ago)
Topics: cyber, disease-simulation, epidemics, graph-algorithms, graph-theory, message-passing, network-analysis, network-science, python
Language: Python
Homepage: https://cosasi.readthedocs.io/
Size: 2.68 MB
Stars: 4
Watchers: 3
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-stars - cosasi

README

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# cosasi: Graph Diffusion Source Inference in Python

``cosasi`` (COntagion Simulation And Source Identification) is a Python package for graph diffusion source inference, allowing users to:

- **perform and evaluate** source inference using standard techniques from literature,

- **contribute** innovative localization methods to a growing core library, and

- **benchmark** new techniques against a battery of comparable schemes.

![logo](./docs/_assets/grasshopper.png)

## Table of Contents

* [Table of Contents](#table-of-contents)

* [Installation](#installation)

* [Getting Started](#getting-started)

* [Code Snippet](#code-snippet)

* [Testing](#testing)

* [Contributions](#contributions)

* [Citing](#citing)

* [Support](#support)

* [Contact](#contact)

* [License](#license)

## Installation

#### Installation via PyPI

```bash

pip install cosasi

```

#### Installation via GitHub

Clone the repo from [here](https://github.com/lmiconsulting/cosasi) (this repo).

Install requirements:

```bash

pip install -r requirements.txt

```

## Getting Started

Once `cosasi` is installed, feel free to review our [tutorial](https://cosasi.readthedocs.io/en/latest/tutorial.html) introducing major functionality. Official documentation, including a detailed [API reference](https://cosasi.readthedocs.io/en/latest/apiref.html), is available on [Read the Docs](https://cosasi.readthedocs.io/).

## Code Snippet

| ![carbon](./docs/_assets/carbon.png) |

|:--:|

| Above: [Carbon](https://github.com/carbon-app/carbon) image of example code snippet; copy-and-paste-able version below. |

```python

import networkx as nx

import cosasi

G = nx.fast_gnp_random_graph(100, 0.25)

contagion = cosasi.StaticNetworkContagion(

   G=G,

   model="si",

   infection_rate=0.01,

   number_infected=3,

)

contagion.forward(100)

I = contagion.get_infected_subgraph(step=15)

result = cosasi.source_inference.multiple_source.netsleuth(G=G, I=I)

result.evaluate(contagion.get_source())

```

## Testing

Extensive unit testing is employed throughout, with ~97% code coverage.

If you've cloned our repo from [GitHub](https://github.com/lmiconsulting/cosasi), you can cd into the root directory and run [pytest](https://docs.pytest.org/en/7.1.x/contents.html) via [coverage](https://coverage.readthedocs.io/en/6.3.2/):

```bash

    coverage run -m pytest

```

To read the .coverage file:

```bash

    coverage report

```

## Contributions

We’d love your help! If you’d like to make an addition or improvement, please submit a pull request consisting of an atomic commit and a brief message describing your contribution.

Our contributor guide is [here](https://cosasi.readthedocs.io/en/latest/developer_pages/contributing.html), and we itemize a few areas of development we’d like to prioritize for the future of `cosasi` [here](https://cosasi.readthedocs.io/en/latest/developer_pages/roadmap.html). If you find something wrong, please submit a bug report to the [issue tracker](https://github.com/lmiconsulting/cosasi/issues). For other questions or comments, feel free to [contact us](#contact) directly.

## Citing

If you found `cosasi` helpful in your work, please consider citing it with:

```bibtex

@article{McCabe2022joss,

  doi = {10.21105/joss.04894},

  url = {https://doi.org/10.21105/joss.04894},

  year = {2022},

  publisher = {The Open Journal},

  volume = {7},

  number = {80},

  pages = {4894},

  author = {Lucas H. McCabe},

  title = {cosasi: Graph Diffusion Source Inference in Python}, journal = {Journal of Open Source Software}

}

```

McCabe, L. H., (2022). cosasi: Graph Diffusion Source Inference in Python. Journal of Open Source Software, 7(80), 4894, https://doi.org/10.21105/joss.04894

## Support

`cosasi` was developed in [Forge](https://www.lmi.org/technology-innovation/forge), the technology accelerator of the [Logistics Management Institute](https://www.lmi.org/about-lmi).

## Contact

Questions? Reach out:

- Lucas ([email](mailto:[email protected]))

## License

[MIT](https://choosealicense.com/licenses/mit/)