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pyro-risks

Data science for wildfire risk forecasting and monitoring

https://github.com/pyronear/pyro-risks

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (17.0%) to scientific vocabulary

Keywords

python3 scikit-learn wildfire-forecasting
Last synced: 6 months ago · JSON representation

Repository

Data science for wildfire risk forecasting and monitoring

Basic Info
Statistics
  • Stars: 26
  • Watchers: 4
  • Forks: 9
  • Open Issues: 0
  • Releases: 2
Topics
python3 scikit-learn wildfire-forecasting
Created over 5 years ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing Funding License Code of conduct

README.md

Pyronear Risks

Code style: black

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:

shell 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:

bash 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.

```python from pyrorisks.datasets import NASAFIRMS, NASAFIRMSVIIRS, 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:

shell python scripts/example_ERA5_FIRMS.py --help

You can then run the script with your own arguments:

shell 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.

License

Distributed under the Apache v2 License. See LICENSE for more information.

Owner

  • Name: PyroNear
  • Login: pyronear
  • Kind: organization
  • Location: Paris

Preserving forests from wildfires one commit at a time

GitHub Events

Total
  • Watch event: 2
  • Fork event: 1
Last Year
  • Watch event: 2
  • Fork event: 1

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 62
  • Total Committers: 10
  • Avg Commits per committer: 6.2
  • Development Distribution Score (DDS): 0.79
Past Year
  • Commits: 19
  • Committers: 6
  • Avg Commits per committer: 3.167
  • Development Distribution Score (DDS): 0.579
Top Committers
Name Email 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 (Top 20 + Academic)
camilles-macbook-air.home: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 23
  • Total pull requests: 64
  • 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: 2.22
  • Merged pull requests: 51
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 3
  • Average time to close issues: N/A
  • Average time to close pull requests: 14 minutes
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • jsakv (10)
  • dataJSA (5)
  • frgfm (4)
  • chloeskt (2)
  • miltminz (1)
  • GHCamille (1)
Pull Request Authors
  • GHCamille (18)
  • jsakv (13)
  • frgfm (12)
  • dataJSA (11)
  • chloeskt (9)
  • miltminz (4)
  • Acruve15 (3)
  • juldpnt (3)
  • fe51 (2)
  • TrellixVulnTeam (2)
Top Labels
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)
Pull Request Labels
enhancement (19) module: datasets (14) documentation (13) topic: build (11) module: models (8) module: test (7) module: utils (5) style (3) bug (3) help wanted (3) good first issue (2) module: evaluation (1) topic: app (1)

Packages

  • Total packages: 1
  • Total downloads: unknown
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
proxy.golang.org: github.com/pyronear/pyro-risks
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.4%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 6 months ago

Dependencies

.github/workflows/requirements.txt pypi
  • coverage >=4.5.4
docs/requirements.txt pypi
  • myst-parser ==0.12.10
  • sphinx *
  • sphinx-autobuild ==2020.9.1
  • sphinx-rtd-theme ==0.4.3
requirements-app.txt pypi
  • fastapi ==0.61.1
  • pyro_risks *
  • uvicorn >=0.11.1
requirements.txt pypi
  • 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
.github/workflows/cml.yaml actions
  • actions/cache v2 composite
  • actions/checkout v2 composite
  • actions/setup-python v1 composite
  • iterative/setup-cml v1 composite
  • iterative/setup-dvc v1 composite
.github/workflows/doc-deploy.yaml actions
  • JamesIves/github-pages-deploy-action 3.7.1 composite
  • actions/cache v2 composite
  • actions/checkout v2 composite
  • actions/setup-python v1 composite
  • webfactory/ssh-agent v0.4.1 composite
.github/workflows/gh-page.yaml actions
  • actions/setup-python v1 composite
.github/workflows/main.yml actions
  • actions/cache v2 composite
  • actions/checkout v2 composite
  • actions/setup-python v1 composite
  • actions/setup-python v2 composite
  • codecov/codecov-action v1 composite
  • psf/black stable composite
.github/workflows/web-server.yml actions
  • actions/checkout v2 composite
Dockerfile docker
  • python 3.8.1 build
docker-compose.yml docker
setup.py pypi