# Galaxy Ninja

A efficient binning and computation tool for numerical simulations

Galaxy ninja uses `numba` for fast processing of huge simulation dataset.

## Download

The easiest way to get Galaxy Ninja is to clone this repository

```
git clone https://git.brucy.fr/noe/galaxy_ninja.git
```


## Install 

Assuming `pip` is available on your system, just run 

``` pip install . ```

in the downloaded folder.

## Getting started

### Load data

The first step is to write a loader for your simulation dataset. 
A loader is a python function that for a given simulation snapshot returns a dictionnary with the following structure:    

```python
        gas:
            position (Ngas, 3) [kpc], centered
            volume   (Ngas)    [pc^3]
            velocity (Ngas, 3) [km/s]
            mass     (Ngas)    [Msun]
        stars:
            position (Nstar, 3) [kpc], centered
            velocity (Nstar, 3) [km/s]
            mass     (Nstar)    [Msun]
            birth_time      (Nstar)    [Myr]
        dm:
            position (Ngas, 3) [kpc], centered
            velocity (Ngas, 3) [km/s]
            mass     (Ngas)    [Msun]
        maps:
            extent      (xmin, xmax, ymin, ymax) Coordinates of the edges of the map, centered
            gas_coldens (Nx, Ny) [Msun/pc^2], map of column densityructure:
```

Example loaders for Ramses and Arepo are provided.

### Compute binned quantities

1. Instanciate a galsec object

```python
from galaxy_ninja import Galsec

data = your_loader(path)
galsec = Galsec(data, copy=False)
```

2. Choose your binning method.
Pre-set binning methods includes radial rings, sectors and cartesian binning.

```python
rings = galex.rings_analysis(
    delta_r=0.1 * u.kpc,
    rmax=30 * u.kpc,
    zmin=-4 * u.kpc,
    zmax=4 * u.kpc,
)
```

### Access averaged binned quantities

Example for a velocity curve:
```python 
import matplotlib.pyplot as plt
plt.plot(rings["gas"]["r"], sectors["gas"]["velphi"])
```