# Introduction

RNS constructs models of rapidly rotating relativistic stars in equilibrium. The ACME RNS training material is authored by [Nikolaos Stergioulas](https://niksterg.github.io/niksterg/) and is available in the [ACME-RNS GitLab repository](https://gitlab.com/niksterg/acme-rns).

The tutorial notebook drives the RNS C executable from Python through `rns_helpers.py`, parses the output, and explores single models, stellar sequences, equilibrium surfaces in mass, angular momentum, and central energy density, and selected astrophysical applications.

## Installation

Create the environment from the tutorial repository:

```bash
conda env create -f environment.yml
conda activate acme-rns
```

Build the RNS executables:

```bash
cd rns_build
make
```

This produces the standard and higher-resolution executables `rns`, `rns_high`, `rns_vh`, and `rns_xh`, plus `hng` for EOS tables. The Python wrapper expects these binaries and the bundled `eos/` directory under `rns_build/`.

The detailed RNS manual is included as {download}`rns_build/manual.pdf`.

## Test Example

The source repository includes several command-line examples in `rns_build/examples.test`. A simple single-model run is:

```bash
cd rns_build
./rns -f eos/eosC -t model -e 2e15 -r 0.59 -d 0
```

The notebook in this section runs the same executable through Python and organizes the parsed output as dictionaries and data frames.

## Citation

For scientific use of RNS, cite Stergioulas & Friedman 1995, ApJ 444, 306, and the RNS documentation bundled with this tutorial.