This repository provides a set of notebooks that demonstrates various aspects of PCSE models.
The notebooks include introductory examples:
- 01 Getting Started with PCSE.ipynb provides an impression of how PCSE works and what you can do with it
- 02 Running with custom input data.ipynb shows how you can run a model using your own input data instead of the demonstration data.
- 03 running_LINTUL3.ipynb a similar example, but instead using the LINTUL3 model instead of WOFOST.
- 04 Running PCSE in batch mode.ipynb demonstrates how to run PCSE simulation in batch for a series of crops and year
Some more advanced features of PCSE are demonstrated in:
- 05 Using PCSE WOFOST with a CGMS8 database.ipynb this shows how to retrieve data from a CGMS database and run crop model simulations with WOFOST using that data.
- 06_advanced_agromanagement_with_PCSE.ipynb demonstrates advanced aspects of the agromanagement definitions including scheduling events based on date and state variables.
- 07 Running crop rotations.ipynb provides insight on how to run crop rotations with PCSE models.
Finally, highly advanced subjects are treated that require quite some background knowledge and python programming skills:
- 08_data_assimilation_with_the_EnKF.ipynb provides an introduction to data assimilation with the ensemble Kalman filter.
- 09 Optimizing parameters in a PCSE model.ipynb demonstrates how to do parameter optimizations in PCSE.
Using these notebooks generally require a python environment that includes the following packages:
- PCSE and its dependencies
- pandas, matplotlib and for notebook 09 the NLOPT optimization library.