Metadata-Version: 2.4
Name: diffwofost
Version: 0.1.1
Summary: Differentiable WOFOST
Author-email: Sarah Alidoost <f.alidoost@esciencecenter.nl>
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Project-URL: Repository, https://github.com/WUR-AI/diffwofost
Project-URL: Issues, https://github.com/WUR-AI/diffwofost/issues
Keywords: pytorch, differentiable, crop, optimization
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: pcse
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Dynamic: license-file

# diffWOFOST

The python package `diffWOFOST` is a differentiable implementation of WOFOST models using [`torch`](https://pytorch.org/), 
allowing gradients to flow through the simulations for optimization and data assimilation.

The python implementation of WOFOST is available at
[`PCSE`](https://pcse.readthedocs.io/en/stable/). For more information about the
models, the functional components of PCSE, and documentation have a look at the
following links:

- [Models available in PCSE](https://pcse.readthedocs.io/en/stable/available_models.html#models-available-in-pcse)
- [The Engine](https://pcse.readthedocs.io/en/stable/reference_guide.html#the-engine)
- [PCSE source code](https://github.com/ajwdewit/pcse)
- [PCSE example notebooks](https://github.com/ajwdewit/pcse_notebooks): examples of optimization and data assimilation are notebooks number 8 and 9.
- [WOFOST_crop_parameters](https://github.com/ajwdewit/WOFOST_crop_parameters): each branch contains the crop parameters for a specific crop model version

  
