Pyronear Risks
The pyro-risks project aims at providing the pyronear-platform with a machine learning based wildfire forecasting capability.
https://github.com/pyronear/pyro-risks
Category: Biosphere
Sub Category: Wildfire
Keywords
python3 scikit-learn wildfire-forecasting
Keywords from Contributors
wildfire jekyll wildfire-management
Last synced: about 20 hours ago
JSON representation
Repository metadata
Data science for wildfire risk forecasting and monitoring
- Host: GitHub
- URL: https://github.com/pyronear/pyro-risks
- Owner: pyronear
- License: apache-2.0
- Created: 2020-09-30T17:19:23.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-08-19T16:19:27.000Z (8 months ago)
- Last Synced: 2025-02-07T22:36:03.616Z (3 months ago)
- Topics: python3, scikit-learn, wildfire-forecasting
- Language: Jupyter Notebook
- Homepage: https://pyronear.github.io/pyro-risks
- Size: 27.7 MB
- Stars: 24
- Watchers: 5
- Forks: 9
- Open Issues: 0
- Releases: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
README.md
The pyro-risks project aims at providing the pyronear-platform with a machine learning based wildfire forecasting capability.
Table of Contents
Getting started
Prerequisites
- Python 3.6 (or more recent), but < 3.12.0
- pip
Installation
You can install the package from github as follows:
pip install git+https://github.com/pyronear/pyro-risks
Usage
Beforehand, 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.
Web server
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 --build
Once 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.
Examples
datasets
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()
Scripts
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 --help
You can then run the script with your own arguments:
python scripts/example_ERA5_FIRMS.py --type_of_merged departements
Documentation
The full package documentation is available here for detailed specifications. The documentation was built with Sphinx using a theme provided by Read the Docs.
Contributing
Please refer to the CONTRIBUTING guide if you wish to contribute to this project.
Credits
This project is developed and maintained by the repo owner and volunteers from Data for Good.
This 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.
- Dataset: EFFIS FWI Dataset
- License: CC BY 4.0
License
Distributed under the Apache v2 License. See LICENSE for more information.
Owner metadata
- Name: PyroNear
- Login: pyronear
- Email:
- Kind: organization
- Description: Preserving forests from wildfires one commit at a time
- Website: https://pyronear.org/
- Location: Paris
- Twitter: pyro_near
- Company:
- Icon url: https://avatars.githubusercontent.com/u/61667887?v=4
- Repositories: 23
- Last ynced at: 2024-10-30T02:43:17.535Z
- Profile URL: https://github.com/pyronear
GitHub Events
Total
- Fork event: 1
Last Year
- Fork event: 1
Committers metadata
Last synced: 6 days ago
Total Commits: 62
Total Committers: 10
Avg Commits per committer: 6.2
Development Distribution Score (DDS): 0.79
Commits in past year: 19
Committers in past year: 6
Avg Commits per committer in past year: 3.167
Development Distribution Score (DDS) in past year: 0.579
| Name | Commits | |
|---|---|---|
| F-G Fernandez | f****3@h****r | 13 |
| Camille | 3****e | 9 |
| Joshua Sant'Anna | 4****A | 9 |
| Camille Modeste | c****e@c****e | 8 |
| chloeskt | 5****t | 7 |
| Joshua Sant'Anna | 4****v | 5 |
| Milton Minervino | m****o@g****m | 4 |
| Alexis Cruveiller | 3****5 | 3 |
| Jules | 1****t | 3 |
| fe51 | 5****1 | 1 |
Committer domains:
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 23
Total pull requests: 65
Average time to close issues: over 1 year
Average time to close pull requests: about 2 months
Total issue authors: 6
Total pull request authors: 10
Average comments per issue: 2.43
Average comments per pull request: 2.18
Merged pull request: 51
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 11
Past year average time to close issues: N/A
Past year average time to close pull requests: 17 days
Past year issue authors: 0
Past year pull request authors: 3
Past year average comments per issue: 0
Past year average comments per pull request: 0.45
Past year merged pull request: 8
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- jsakv (10)
- dataJSA (5)
- frgfm (4)
- chloeskt (2)
- miltminz (1)
- GHCamille (1)
Top Pull Request Authors
- GHCamille (15)
- dataJSA (11)
- frgfm (11)
- chloeskt (9)
- jsakv (7)
- miltminz (4)
- juldpnt (3)
- Acruve15 (2)
- fe51 (2)
- TrellixVulnTeam (1)
Top Issue Labels
- enhancement (22)
- help wanted (13)
- module: models (9)
- good first issue (7)
- topic: build (6)
- module: datasets (5)
- documentation (4)
- question (3)
- module: pipeline (2)
- module: test (2)
- module: predict (1)
- module: utils (1)
- style (1)
- discussion (1)
Top Pull Request Labels
- enhancement (17)
- module: datasets (14)
- documentation (13)
- topic: build (10)
- module: models (7)
- module: test (7)
- module: utils (5)
- style (3)
- bug (3)
- help wanted (2)
- module: evaluation (1)
- topic: app (1)
- good first issue (1)
Dependencies
- coverage >=4.5.4
- myst-parser ==0.12.10
- sphinx *
- sphinx-autobuild ==2020.9.1
- sphinx-rtd-theme ==0.4.3
- fastapi ==0.61.1
- pyro_risks *
- uvicorn >=0.11.1
- Rtree >=0.9.4
- Shapely >=1.7.1
- cdsapi ==0.4.0
- dvc >=2.0.5
- geopandas >=0.8.1
- imbalanced-learn >=0.7.0
- netCDF4 >=1.5.4
- numpy >=1.18.5
- pandas >=1.1.4
- plot-metric ==0.0.6
- python-dotenv >=0.15.0
- requests >=2.24.0
- scikit-learn >=0.23.2
- scipy >=1.5.4
- xarray >=0.16.1
- xgboost ==1.2.1
- xlrd ==1.2.0
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- psf/black stable composite
- actions/checkout v2 composite
- python 3.8.1 build
Score: 5.480638923341992