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-------
 [](https://www.python.org)   [](https://ramannoodle.readthedocs.io/en/latest/?badge=latest) [](https://opensource.org/license/mit)
## About
**Ramannoodle** is a Python API for efficiently calculating Raman spectra from first principles calculations. Ramannoodle supports molecular-dynamics- and phonon-based Raman calculations. It includes interfaces with VASP but can easily be used with other codes using IO from external libraries, such as [pymatgen](https://pymatgen.org/) or [ase](https://wiki.fysik.dtu.dk/ase/).
Ramannoodle aims to be:
1. **EFFICIENT**
Ramannoodle provides `PolarizabilityModel`'s to reduce the required number of first-principles polarizability calculations.
2. **FLEXIBLE**
Ramannoodle provides a simple, object-oriented API that makes calculations a breeze while offering plenty of flexibility to carry out advanced analyses and add new functionality.
3. **TRANSPARENT**
Ramannoodle is designed to give the user a good understanding of what is being calculated at varying levels of abstraction.
## Installation
The base version of ramannoodle can be installed with pip:
```
$ pip install ramannoodle
```
Ramannoodle's machine learning modules are implemented with PyTorch. To use these modules:
1. Install [PyTorch](https://pytorch.org/get-started/locally/).
2. Install [torch-scatter](https://pypi.org/project/torch-scatter/) and [torch-sparse](https://pypi.org/project/torch-sparse/) corresponding to the PyTorch version/implementation.
3. Install ramannoodle using the `torch` options group.
For example, installation on a Linux system using PyTorch 2.4.1 (cpu implementation) is done as follows:
```
$ pip install torch==2.4.1+cpu --index-url https://download.pytorch.org/whl/cpu
$ pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.4.0+cpu.html
$ pip install ramannoodle[torch]
```
## Documentation
[https://ramannoodle.readthedocs.io/](https://ramannoodle.readthedocs.io/)
## Contributing
Contributions in the form of bug reports, feature suggestions, and pull requests are always welcome! Those contributing code should check out the [dev guide](https://ramannoodle.readthedocs.io/en/latest/development.html).
## Citing
To acknowledge use of ramannoodle, please cite
>> **Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors** <br>
W. O’Leary, M. Grumet, W. Kaiser, T. Bučko, J.L.M. Rupp, D.A. Egger <br>
Journal of the American Chemical Society (2024) <br>
doi: [10.1021/jacs.4c07812](https://pubs.acs.org/doi/10.1021/jacs.4c07812)
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