Metadata-Version: 2.4
Name: aidsorb
Version: 2.0.0
Summary: Python package for deep learning on molecular point clouds.
Author-email: "Antonios P. Sarikas" <antonios.sarikas@gmail.com>
License-Expression: GPL-3.0-only
Project-URL: Homepage, https://github.com/adosar/aidsorb
Project-URL: Issues, https://github.com/adosar/aidsorb/issues
Project-URL: Documentation, https://aidsorb.readthedocs.io/en/stable/
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: ase>=3.23.0
Requires-Dist: plotly>=5.19.0
Requires-Dist: tqdm>=4.66.2
Requires-Dist: pandas>=2.2.0
Requires-Dist: roma>=1.5.1
Requires-Dist: lightning>=2.5.0
Requires-Dist: jsonargparse[signatures]>=4.39.0
Requires-Dist: torchmetrics>=1.7.1
Requires-Dist: numpy>=1.26.4
Provides-Extra: docs
Requires-Dist: sphinx>=8.2.3; extra == "docs"
Requires-Dist: sphinx-rtd-theme==3.0.2; extra == "docs"
Requires-Dist: sphinx-copybutton>=0.5.2; extra == "docs"
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Requires-Dist: sphinx-gallery>=0.16.0; extra == "docs"
Requires-Dist: kaleido==0.2.1; extra == "docs"
Dynamic: license-file

<h1 align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/adosar/aidsorb/master/docs/source/images/aidsorb_logo_dark.svg">
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    <img alt="AIdsorb logo" src="https://raw.githubusercontent.com/adosar/aidsorb/master/docs/source/images/aidsorb_logo_light.svg" width=40%/>
  </picture>
</h1>

<p align="center">
  <img src="https://readme-typing-svg.demolab.com?font=Roboto+Slab&weight=700&duration=3000&pause=1000&color=FFFFFF&background=000000&center=true&vCenter=true&height=40&lines=%F0%9F%9A%80+Simple%2C+easy+to+use+and+reproduce;%F0%9F%94%A5+Supports+PyTorch;%E2%9A%A1+Supports+PyTorch+Lightning;%F0%9F%8E%89+A+.yaml+is+all+you+need!" />
</p>

<h4 align="center">
	
  ![Static Badge](https://img.shields.io/badge/PYTHON%203.11+-black?style=for-the-badge&logo=python&logoColor=cyan)
  ![Static Badge](https://img.shields.io/badge/GPL--3.0--ONLY-black?style=for-the-badge&logo=gnu&logoColor=cyan)
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  [![coverage](https://img.shields.io/codecov/c/gh/adosar/aidsorb?style=for-the-badge&logo=codecov&logoColor=cyan&label=CODECOV&labelColor=black&color=purple)](https://app.codecov.io/gh/adosar/aidsorb)
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  [![App](https://img.shields.io/badge/online%20app-purple?style=for-the-badge&logo=streamlit&logoSize=auto&logoColor=cyan&label=streamlit&labelColor=black)](https://aidsorb-online.streamlit.app)

</h4>

**AIdsorb** is a Python package for **deep learning on molecular point clouds**.

This package aims to provide a **simple, easy-to-use and reproduce** interface for:

-   📥 **Creating molecular point clouds**
  
-   🤖 **Training DL algorithms on molecular point clouds**

<p align="center">
  <img alt="IRMOF-1" src="https://raw.githubusercontent.com/adosar/aidsorb/master/docs/source/images/IRMOF-1.gif" width="25%"/>
  <img alt="Cu-BTC" src="https://raw.githubusercontent.com/adosar/aidsorb/master/docs/source/images/Cu-BTC.gif" width="25%"/>
  <img alt="UiO-66" src="https://raw.githubusercontent.com/adosar/aidsorb/master/docs/source/images/UiO-66.gif" width="25%"/>
</p>

## ⚙️  Installation
> [!IMPORTANT] 
> It is strongly recommended to **perform the installation inside a virtual environment**.

Assuming an activated virtual environment:
```bash
pip install aidsorb
```

## 🚀 Usage
> [!NOTE] 
> Refer to the 📚 [Documentation](https://aidsorb.readthedocs.io/en/stable/) for more information.

Here is a summary of what you can do from the command line:

1. Visualize a point cloud:
	```bash
	aidsorb visualize path/to/structure_or_pcd  # Structure (.xyz, .cif, etc) or .npy
	```

2.  Create and prepare point clouds:
	```bash
	aidsorb create path/to/structures path/to/pcd_data  # Create and store point clouds
	aidsorb prepare path/to/pcd_data  # Split point clouds to train, valdation and test
	```
	
3. Train and test a model:
	```bash
	aidsorb-lit fit --config=path/to/config.yaml
	aidsorb-lit test --config=path/to/config.yaml --ckpt_path=path/to/ckpt
	```
 
## 💡 Questions and Contributing

### Questions
If you have any questions about how to use **AIdsorb**, we encourage you to post them in the 💬 [Discussions](https://github.com/adosar/aidsorb/discussions)
section of the repository.

> [!NOTE]
> Please make sure to **read the documentation carefully first** before asking your question.

### Contributing
We welcome contributions from the community! Please read our 🙌 [Contributing Guidelines](CONTRIBUTING.md) before submitting PRs or opening issues.

## 📑 Citing
* **To cite the software**, please refer to the [citation file](./CITATION.cff) or click the citation button.
* **To cite the paper**, please use the following BibTeX entry:
<details>
<summary>Show BibTex entry</summary>
	
```bibtex
@article{Sarikas2024,
  title = {Gas adsorption meets geometric deep learning: points, set and match},
  volume = {14},
  ISSN = {2045-2322},
  url = {http://dx.doi.org/10.1038/s41598-024-76319-8},
  DOI = {10.1038/s41598-024-76319-8},
  number = {1},
  journal = {Scientific Reports},
  publisher = {Springer Science and Business Media LLC},
  author = {Sarikas,  Antonios P. and Gkagkas,  Konstantinos and Froudakis,  George E.},
  year = {2024},
  month = nov
}
```
</details>

## ⚖️ License
**AIdosrb** is released under the [GNU General Public License v3.0 only](https://spdx.org/licenses/GPL-3.0-only.html).
