{"id":13948310,"url":"https://github.com/pyronear/pyro-risks","last_synced_at":"2025-08-20T12:38:12.611Z","repository":{"id":37819717,"uuid":"299996448","full_name":"pyronear/pyro-risks","owner":"pyronear","description":"Data science for wildfire risk forecasting and monitoring","archived":false,"fork":false,"pushed_at":"2024-08-19T16:19:27.000Z","size":29053,"stargazers_count":26,"open_issues_count":0,"forks_count":9,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-08-03T14:42:11.724Z","etag":null,"topics":["python3","scikit-learn","wildfire-forecasting"],"latest_commit_sha":null,"homepage":"https://pyronear.github.io/pyro-risks","language":"Jupyter 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Notebook","funding_links":["https://github.com/sponsors/pyronear"],"categories":["Biosphere"],"sub_categories":["Wildfire"],"readme":"\u003ch1 align=\"center\"\u003ePyronear Risks\u003c/h1\u003e\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"LICENSE\" alt=\"License\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/License-Apache_2.0-blue.svg\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://github.com/pyronear/pyro-risks/actions?query=workflow%3Apython-package\"\u003e\n        \u003cimg src=\"https://github.com/pyronear/pyro-risks/workflows/python-package/badge.svg\" /\u003e\u003c/a\u003e\n   \u003ca href=\"https://www.codacy.com/gh/pyronear/pyro-risks/dashboard?utm_source=github.com\u0026utm_medium=referral\u0026utm_content=pyronear/pyro-risks\u0026utm_campaign=Badge_Grade\"\u003e\n        \u003cimg src=\"https://camo.githubusercontent.com/6361a174bbd36acd5ee8c24b0ef27ba6a84803c2ac9354d57d60d1264d78a31a/68747470733a2f2f6170702e636f646163792e636f6d2f70726f6a6563742f62616467652f47726164652f6532623936393836356539663439633561623934343435643765346132613637\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://codecov.io/gh/pyronear/pyro-risks\"\u003e\n  \t\t\u003cimg src=\"https://codecov.io/gh/pyronear/pyro-risks/branch/master/graph/badge.svg\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://github.com/psf/black\"\u003e\n        \u003cimg alt=\"Code style: black\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://pyronear.github.io/pyro-risks\"\u003e\n  \t\t\u003cimg src=\"https://img.shields.io/badge/docs-available-blue.svg\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\nThe pyro-risks project aims at providing the pyronear-platform with a machine learning based wildfire forecasting capability. \n\n## Table of Contents\n\n- [Table of Contents](#table-of-contents)\n- [Getting started](#getting-started)\n  - [Prerequisites](#prerequisites)\n  - [Installation](#installation)\n- [Usage](#usage)\n  - [Web server](#web-server)\n- [Examples](#examples)\n  - [datasets](#datasets)\n  - [Scripts](#scripts)\n- [Documentation](#documentation)\n- [Contributing](#contributing)\n- [Credits](#credits)\n- [License](#license)\n\n## Getting started\n\n### Prerequisites\n\n-   Python 3.6 (or more recent), but \u003c 3.12.0\n-   [pip](https://pip.pypa.io/en/stable/)\n### Installation\n\nYou can install the package from github as follows:\n\n```shell\npip install git+https://github.com/pyronear/pyro-risks\n```\n\n## Usage\n\nBeforehand, you will need to set a few environment variables either manually or by writing an `.env` file in the root directory of this project, like in the example below:\n\n```\nCDS_UID=my_secret_uid\nCDS_API_KEY=my_very_secret_key\n```\nThose values will allow your web server to connect to CDS [API](https://github.com/ecmwf/cdsapi), which is mandatory for your datasets access to be fully operational.\n\n### Web server\n\nTo be able to expose model inference, you can run a web server using docker containers with this command:\n\n```bash\nPORT=8003 docker-compose up -d --build\n```\n\nOnce completed, you will notice that you have a docker container running on the port you selected, which can process requests just like any web server.\n\n## Examples\n### datasets\n\nAccess the main pyro-risks datasets locally. \n\n```python\nfrom pyro_risks.datasets import NASAFIRMS, NASAFIRMS_VIIRS, GwisFwi, ERA5T, ERALand\n\nmodis = NASAFIRMS()\nviirs = NASAFIRMS_VIIRS()\n\nfdi = GwisFwi()\n\nera = ERA5T()\nera_land = ERA5Land()\n```\n### Scripts\n\nYou are free to merge the datasets however you want and to implement any zonal statistic you want, but some are already provided for reference. In order to use them check the example scripts options as follows:\n\n```shell\npython scripts/example_ERA5_FIRMS.py --help\n```\n\nYou can then run the script with your own arguments:\n\n```shell\npython scripts/example_ERA5_FIRMS.py --type_of_merged departements\n```\n\n## Documentation\n\nThe full package documentation is available [here](https://pyronear.org/pyro-risks/) for detailed specifications. The documentation was built with [Sphinx](https://www.sphinx-doc.org) using a [theme](https://github.com/readthedocs/sphinx_rtd_theme) provided by [Read the Docs](https://readthedocs.org).\n\n## Contributing\n\nPlease refer to the [`CONTRIBUTING`](./CONTRIBUTING.md) guide if you wish to contribute to this project.\n\n## Credits\n\nThis project is developed and maintained by the repo owner and volunteers from [Data for Good](https://dataforgood.fr/).\n\nThis project uses data from EFFIS (European Forest Fire Information System) for the FWI (Fire Weather Index). This data is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. \n\n- Dataset: [EFFIS FWI Dataset](https://effis.jrc.ec.europa.eu/applications/data-and-services)\n- License: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)\n\n## License\n\nDistributed under the Apache v2 License. See [`LICENSE`](./LICENSE) for more information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpyronear%2Fpyro-risks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpyronear%2Fpyro-risks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpyronear%2Fpyro-risks/lists"}