README.md

Self-propagating star formation of spiral galaxies

Demo & introduction

This project is a python implemtation of the self-propagating star formation model. It uses a circular grid where every disk rotates with a velocity of 1/r. This code is use to study under which propagation probabilities spiral formation takes place.

Requirements

This python code uses some common python packages. Make sure you install these or install with pip by running pip install -r requirements.txt

• numy
• matplotlib
• tqdm
• pandas

Sample code

To run a simulation you need to follow 3 steps:

1. Create model
2. Bind a grid to the model
3. Bind a scheduler

from model import Model
model = Model(
        REGEN_TIME, propagation_probability, MAX_RANDOM_STARS, PROPAGATION_SPEED
    )
model.bind_grid(NUM_OF_RINGS, CELLS_PER_RING)
model.bind_scheduler()

model.scheduler.start(TIMESTEP, SIMDURATION)
df = pd.DataFrame(model.scheduler.history.tolist())

To create a visualistion as the gif on the top of this readme you can run

from visualise import Visualise

plotter = Visualise(model.grid)
plotter.animate(df, probability)

The next files produce our results from the presentation, but with changed parameters, otherwise it takes very long to run (1 day)

1. varying_prob.py simulates data and makes animatiions for propagation probabilities 0.1, 0.2, ... , 0.6
2. phaseplots.py creates the plots with average star formation rate vs propagation probability
3. clusters.py creates the plots Average cluster size vs propagation probability, max cluster size vs probability and number of clusters vs probability