semvec/pypi-packages huggingface dataset:
rmf 0.1.1
Relativistic Mean Field package for neutron star and nuclear matter equation of state
License: MIT
Requires Python: >=3.9

# Relativistic Mean Field

## 1. Purpose

This project provides the code to compute the equation of state (EoS) using Relativistic Mean Field (RMF) Theory. It can automatically generate the RMF coupling constants [ $g_\sigma$, $g_\omega$, $g_\rho$, $g_2$, $g_3$, $\Lambda_v$, $c_3$] based on input saturation properties [ $n_0$, $E$/ $A$, $E_{sym}$, $K_0$, $J_0$, $M^\ast$/ $M_{_N}$, $J_0$]. Using these coupling constants, the code can compute the EoS for neutron star matter or symmetric nuclear matter.

## 2. Installation

### Using Pip
```bash
pip install rmf
```

or

```bash
git clone git@github.com:zzhu-astro/RMF.git; cd RMF
pip install .
```

### Build from Scratch
#### Clone the Repository
```bash
git clone git@github.com:zzhu-astro/RMF.git; cd RMF
```

#### Environment Variable
To compile the project, you must set the `HOME_MF` environment variable. If you are currently in the root directory of `RMF` (e.g. `$HOME/RMF`), you can run:
```bash
export HOME_MF=`pwd`
```

#### Configuration File
To ensure Makefile works correctly, you need to specify the configurations in the config file (e.g. `$HOME/macos_clang-config.mk`). An example can be found at `$HOME_TOV\macos_clang-config.mk`, where the Clang compiler, library paths, includes directories and flags are defined. Once your configuration file is ready, compile the project by running:
```bash
make CONFIG=macos_clang-config.mk python
```

#### Python Package
Then run:
```bash
pip install .
```
This will install the Python package and automatically copy the `librmf.so` shared library you just built into the package directory.


## 3. Dependencies
1. C++ compiler - gcc or clang++ is recommended
2. GSL
3. Python3.9 or newer
4. JAX (Automatic differentiation)

## 4. Related Publications
See the references below for more details on the Relativistic Mean Field model:
1. Zhenyu Zhu, Ang Li, Jinniu Hu, Hong Shen: https://arxiv.org/abs/2305.16058
2. Zhenyu Zhu, Ang Li, Tong Liu: https://arxiv.org/abs/2211.02007
3. Zhenyu Zhu, Ang Li: https://arxiv.org/abs/1802.07441