Metadata-Version: 2.3
Name: hy2dl
Version: 2.0.0
Summary: Python library to create hydrological models for rainfall-runoff prediction using deep learning methods
Author: Eduardo Acuña Espinoza, Manuel Álvarez Chaves
Author-email: Eduardo Acuña Espinoza <eduardo.espinoza@kit.edu>, Manuel Álvarez Chaves <manuel.alvarez-chaves@simtech.uni-stuttgart.de>
License: BSD 3-Clause License
         
         Copyright (c) 2026, Hy2DL
         
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Requires-Dist: dask[complete]>=2026.1.0
Requires-Dist: numpy>=2.2.0
Requires-Dist: pandas>=2.2.0
Requires-Dist: pyyaml>=6.0.2
Requires-Dist: torch>=2.2.1
Requires-Dist: tqdm>=4.67.1
Requires-Dist: xarray-tensorstore>=0.3.0
Requires-Dist: xarray[io]>=2026.1.0
Requires-Dist: zarr>=2.18.0,<3.0.0
Requires-Python: >=3.11, <3.14
Project-URL: Documentation, https://hy2dl.readthedocs.io/
Project-URL: Repository, https://github.com/eduardoAcunaEspinoza/Hy2DL
Description-Content-Type: text/markdown

# Hy<sup>2</sup>DL: Hydrological modeling using Deep Learning methods
![Hy2DL Logo](https://raw.githubusercontent.com/eduardoAcunaEspinoza/Hy2DL/main/docs/source/_static/Hy2DL.png)

<p align="justify">
Hy<sup>2</sup>DL is a python library to create hydrological models for rainfall-runoff prediction using deep learning methods. The repository includes implementations with Large-Sample Hydrology datasets such as CAMELS-GB, CAMELS-US, CAMELS-DE, CAMELS-CH and CARAVAN.

The folder structure and some of the code logic presented here is based on [NeuralHydrology](https://github.com/neuralhydrology/neuralhydrology.git).

## Structure of the repository:
The codes presented in the repository are in the form of python scripts. Additionally several experiments are in the form of JupyterNotebooks for easy reproduction and execution. Following is a quick overview of the repository structure:
- **benchmarks**: Comparison of our library against other studies from scientific literature. 
- **data**: Folder where the different datasets (e.g CAMELS-GB, CAMELS-US...) should be added. This information should be independently downloaded by the user.
- **docs**: Library documentation
- **examples**: Codes (.py) and configuration files to run multiple examples.
- **notebooks**: Jupyter notebooks showing implementation examples, for different cases.
- **results**: Folder where the results generated by the codes will be stored.
- **src/hy2dl**: Code of the library.

## Documentation:
Detailed documentation for the repository can be found at [Hy2DL.readthedocs.io](https://hy2dl.readthedocs.io/en/latest/index.html). 

## Installation
A release version is available on [PyPI](https://pypi.org/project/hy2dl/) and can be installed using:

`uv`
```
uv add hy2dl
```

or `pip`.
```
pip install hy2dl
```

The `pyproject.toml` file includes the package requirements. If you want to install an editable version of the package (e.g. for development), please refer to the documentation.

## Citation:
If you find **Hy²DL** useful in your research or applications, please cite it as:

```bibtex
@software{acuna2025,
  author       = {Eduardo Acuna and
                  Manuel Álvarez Chaves and
                  Alexander Dolich and
                  Benedikt Heudorfer and
                  Ashish Manoj J and
                  Wiktoria Brzezińska and
                  Mirko Mälicke},
  title        = {Hy2DL: Hydrological modeling using Deep Learning methods},
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17251944},
  url          = {https://doi.org/10.5281/zenodo.17251944},
}
```
