![OMBREA(16)](https://private-user-images.githubusercontent.com/105670904/282431343-d68a7c73-4bb7-4a15-8385-dd82508ce496.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjQ0MDg2MDAsIm5iZiI6MTcyNDQwODMwMCwicGF0aCI6Ii8xMDU2NzA5MDQvMjgyNDMxMzQzLWQ2OGE3YzczLTRiYjctNGExNS04Mzg1LWRkODI1MDhjZTQ5Ni5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwODIzJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDgyM1QxMDE4MjBaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1lMWQxNjQ3YThkZjBkOTI4YTAzNGIxMWVjYTQzZGUxNDM5MjhhMTNiMzdkZGEwMTExNjc3NGJjNzljMjM0N2JhJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.Qvr5hEcpCBxvVQiNxZlwqugVVaCvlrapRDKmNbaUxKQ)
Python Implementation of the STICS crop model (https://eng-stics.paca.hub.inrae.fr).
See Notebooks/run_simulation.ipynb to see a simulation example on wheat.
Two simulation types are possible :
- Simulation from STICS USM examples associated to a species and variety chosen by the user.
- Simulation with plant/soil parameters from USM examples and weather data from a location and a year chosen by the user. Weather data are then requested from ERA5 API (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels).
- Create and activate a conda environment:
conda create --name envi_name python=3.11 pip
conda activate envi_name
- Clone repository and install pystics with pip:
git clone [email protected]:OmbreaPV/pySTICS.git
cd pySTICS
pip install -e .