Metadata-Version: 2.1
Name: cosmic-web-classifier
Version: 1.0.1
Summary: A Python package for classifying cosmic web structures
Author-email: Yangyao Chen <yangyaochen.astro@foxmail.com>
Project-URL: Repository, https://github.com/ChenYangyao/cosmic-web-classifier.git
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pyhipp
Requires-Dist: pytest>=7.0.0
Requires-Dist: flake8

# CosmicWebClassifier - A Python package for classifying cosmic web structures

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The package provides a set of tools to compute the density and tidal field from a sample of particles (usually obtained by N-body/hydrodynamical simulations), and classify the cosmic web structures into knots, filaments, sheets and voids based on the fields.

To install, run:
```bash
pip install cosmic-web-classifier
```
Alternatively, you can clone the repository and install the package locally via `pip install -e /path/to/the/repo`.


## Usage 

See the Jupyter notebooks under `docs/`.
- `cosmic_web_types.ipynb`: demonstrates how to generate a sample of dark matter particles from a particle-mesh code, and perform the classification based on the particle distribution.

## Example images

<div align="center">
    <div align="middle">
        <img width="33%" src="https://raw.githubusercontent.com/ChenYangyao/cosmic-web-classifier/master/docs/figures/density.jpeg"/>
        <img width="33%" src="https://raw.githubusercontent.com/ChenYangyao/cosmic-web-classifier/master/docs/figures/voids.jpeg"/>
    </div>
    <div width="80%">Left: The evaluated density field. Right: The regions classified as voids (shown in white).</div>
</div>


## Contributors

- Yangyao Chen (USTC, [yangyaochen.astro@foxmail.com](mailto:yangyaochen.astro@foxmail.com)).

## Citation

If you use this package in your research, please cite the following papers:
- Huiyuan Wang et al. 2016, ApJ 831, 164 ([https://arxiv.org/abs/1608.01763](https://arxiv.org/abs/1608.01763))
- Yangyao Chen et al. 2020, ApJ, 899, 81 ([https://arxiv.org/abs/2003.05137](https://arxiv.org/abs/2003.05137))
