Metadata-Version: 2.4
Name: ClebschGordanSUd
Version: 0.0.1
Summary: Package to symbolically compute Clebsch-Gordan coefficients for SU(d) representations.
Author-email: Tommaso Aschieri <tommaso.aschieri@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/Aschie4589/ClebschGordanSUd
Project-URL: Issues, https://github.com/Aschie4589/ClebschGordanSUd/issues
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: sympy>=1.14.0
Dynamic: license-file

# ClebschGordanSUd

This project is a Python library for computing Clebsch-Gordan coefficients for SU(d) irreducible representations (irreps). It provides tools for working with irreducible representations (irreps), Gelfand-Tsetlin patterns, and the decomposition of tensor products of irreps.

The procedure used is detailed in the paper "A numerical algorithm for the explicit calculation of SU(N) and SL(N,C) Clebsch-Gordan coefficients" by Alex, Kalus, Huckleberry, von Delft, which can be found [here](https://arxiv.org/abs/1009.0437v2). The code in this library is more or less a rewriting in Python of the code provided in the paper's supplementary material, which is written in C++. The main difference is that this library uses the SymPy library for symbolic computation, which allows for exact arithmetic and symbolic manipulation of expressions.

### Installation

To install the library, you can use pip:

```bash
pip install ClebschGordanSUd
```

### Usage

For detailed usage examples, please refer to the [documentation](docs/Usage.md).

### Limitations

The current implementation leverages SymPy for symbolic computation. This can be extremely slow for large irreps. As a rough estimate, for two irreps of dimension $n_1$ and $n_2$, the computation of any Clebsch-Gordan coefficient apart from the top one will involve internally working with matrices of size roughly $n_1 \cdot n_2 \times d$, where $d$ is the distance from the highest weight to the requested weight. It is suggested that you check the size of the irreps you need to work with before computing the CG coefficients, since the computation of the dimension is much cheaper.

### Disclaimer

This code is provided as-is, and contributions are welcome. It was originally written for personal use, and while it has been tested for correctness, there may be edge cases or bugs that have not been encountered. Please report any issues you find.
