The pyro-risks project aims at providing the pyronear-platform with a machine learning based wildfire forecasting capability.
- Python 3.6 (or more recent)
- pip
You can install the package from github as follows:
pip install git+https://github.com/pyronear/pyro-risksBeforehand, 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:
CDS_UID=my_secret_uid
CDS_API_KEY=my_very_secret_key
Those values will allow your web server to connect to CDS API, which is mandatory for your datasets access to be fully operational.
To be able to expose model inference, you can run a web server using docker containers with this command:
PORT=8003 docker-compose up -d --buildOnce 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.
Access the main pyro-risks datasets locally.
from pyro_risks.datasets import NASAFIRMS, NASAFIRMS_VIIRS, GwisFwi, ERA5T, ERALand
modis = NASAFIRMS()
viirs = NASAFIRMS_VIIRS()
fdi = GwisFwi()
era = ERA5T()
era_land = ERA5Land()You 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:
python scripts/example_ERA5_FIRMS.py --helpYou can then run the script with your own arguments:
python scripts/example_ERA5_FIRMS.py --type_of_merged departementsThe full package documentation is available here for detailed specifications. The documentation was built with Sphinx using a theme provided by Read the Docs.
Please refer to the CONTRIBUTING guide if you wish to contribute to this project.
This project is developed and maintained by the repo owner and volunteers from Data for Good.
Distributed under the AGPLv3 Licenses. See LICENSE for more information.