Metadata-Version: 2.4
Name: spherical-deepkriging
Version: 1.0.4
Summary: Deep learning package for FRK
Author-email: Wen-Ting Wang <egpivo@gmail.com>, Hao-Yun Huang <hhuscout@gms.ndhu.edu.tw>, Ping-Hsun Chiang <andrew501228@gmail.com>, Wu-Wei Ying <wuweiying1011@gms.ndhu.edu.tw>
License-Expression: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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License-File: LICENSE
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Dynamic: requires-python

# Spherical DeepKriging

[![Tests](https://github.com/STLABTW/spherical-deepkriging/workflows/Test/badge.svg)](https://github.com/STLABTW/spherical-deepkriging/actions)
[![codecov](https://codecov.io/github/STLABTW/spherical-deepkriging/graph/badge.svg?token=OF0LKVDII6)](https://codecov.io/github/STLABTW/spherical-deepkriging)

Code for **DeepKriging on the Global Data** ([arXiv:2604.01689](https://arxiv.org/abs/2604.01689)): spherical spatial prediction with DeepKriging, MRTS-sphere / Wendland bases, and universal kriging. Implementation lives under `spherical_deepkriging/`.

## Setup

Needs [Miniconda](https://docs.conda.io/en/latest/miniconda.html). On Windows, use WSL.

```bash
git clone https://github.com/STLABTW/spherical-deepkriging.git
cd spherical-deepkriging
make install-dev
```

`make install-dev` creates the conda env and installs dependencies; `make build-spherical-cpp` builds the MRTS-sphere C++ extension.

## Examples

- Smoke test: `examples/toy/toy_sphere_deepkriging.ipynb`
- Simulations: `examples/simulation/`
- Real data: `examples/real_data/`

See `examples/README.md` for run notes.

## Citation

```bibtex
@misc{huang2026deepkrigingglobaldata,
      title={DeepKriging on the Global Data},
      author={Hao-Yun Huang and Wen-Ting Wang and Ping-Hsun Chiang and Wei-Ying Wu},
      year={2026},
      eprint={2604.01689},
      archivePrefix={arXiv},
      primaryClass={stat.ME},
      url={https://arxiv.org/abs/2604.01689},
}
```
