Metadata-Version: 2.1
Name: SpyDust
Version: 1.0.0
Summary: A code for modeling spinning dust radiation
Home-page: https://github.com/zzhang0123/SpyDust
Author: Zheng Zhang
Author-email: zheng.zhang@manchester.ac.uk
Keywords: astrophysics,astronomy
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7, <4
Description-Content-Type: text/markdown
Requires-Dist: numpy<=2.0
Requires-Dist: scipy
Requires-Dist: numba
Requires-Dist: logging
Requires-Dist: mpi4py
Requires-Dist: pandas

# SpyDust

[**SpyDust**](https://arxiv.org/abs/2412.03431) is an advanced Python package for modeling spinning dust radiation in astrophysical environments. Building upon previous models (the IDL [**SPDUST**](https://arxiv.org/pdf/1003.4732)), SpyDust offers enhanced capabilities and corrections, making it a valuable tool for researchers in astrophysics and related fields.

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## Features

- **Comprehensive Grain Shape Modeling**: Considers a wide range of grain geometries, providing corresponding grain dynamics, directional radiation fields, and angular momentum transports.

- **Updated Physical Processes**: Incorporates corrections and extensions, including updated expressions for the effects of electrical dipole radiation back-reaction and plasma drag on angular momentum dissipation.

- **Parallisation**: Functions can be run in parallel (implemented by mpi4py) by simply setting the corresponding keyword to True.

- **SPDUST as is** mode: Provides a submodule, **SPDUST_as_is**, which is exactly the full equivalent of the IDL spdust. Just in case the user wants to stick with the spdust simulation.

- **Example notebooks**: Provide some Jupyter notebooks to help users get started with this software package [paper](https://arxiv.org/abs/2412.03431).

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## Requirements

SpyDust requires Python 3.7 or higher (up to Python 3.9) and the following dependencies:

- `numpy <= 2.0`
- `scipy`
- `numba`
- `logging`
- `mpi4py`
- `pandas`

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## Installation

You can install SpyDust using pip:

```bash
pip install SpyDust
```

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# Usage

Import the package in your project and explore its functionalities for modeling spinning dust radiation. Data files required for computations are bundled with the package.

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## Example usage:

```python

```

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# Resources

- **Author**: Zheng Zhang
- **License**: MIT License
- **Paper**: https://arxiv.org/abs/2412.03431 (Z Zhang and J Chluba 2024)

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## History

- **Version 1.0.0**: Initial release of SpyDust, introducing comprehensive grain shape modeling, updated physical processes, and degeneracy analysis tools.

## TODO (Future Versions)
- Get rid of numba. Use jax instead.
- Future upgrades will include features such as the SED fitting tool (based on perturbation statistics tools like moment expansion) and improve the treatment of the grain rotation distribution.

---

## Resources

- **Author**: Zheng Zhang
- **License**: MIT License
- **Collaboration**: [SpyDust GitHub Collaboration](https://github.com/SpyDust/SpyDust)
- ** Spdust Documentation**: We refer users to the website of [spdust](https://cosmo.nyu.edu/yacine/spdust/spdust.html) for better documentation of environmental parameters 

---

## License

This project is licensed under the MIT License - see the LICENSE file for details.

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For a detailed understanding of the underlying models and theoretical background, refer to the publication: [SpyDust: an improved and extended implementation for modeling spinning dust radiation](https://arxiv.org/abs/2412.03431).
