Skip to content

moira-andrews/Pathak_2021

 
 

Repository files navigation

Quenching, Mergers and Age Profiles for z=2 Galaxies in IllustrisTNG

In this repository, you can find resources to replicate the analysis of IllustrisTNG data presented in Pathak et al. (2021) https://arxiv.org/abs/2105.02234.

We also provide some resources for streamlining your analysis of relevant IllustrisTNG data with detailed instructions to download and process this data.


1. Download all code and data included in the repository.

All data acquisition and analysis in this project was done in Python 3.8.3.

We suggest downloading all notebooks, modules, and data files included in this repository before starting your analysis.

2. Run the notebook titled Data_Acquisition.ipynb.

Follow the steps outlined in the notebook. If this is your first time working with IllustrisTNG data, do not skip Step 0. Go to www.tng-project.org to set up an account and get an API key. Detailed steps are included in the notebook. You will need this API key to download TNG data from the server.

Note that downloading data on individual galaxies from the TNG server requires a stable internet connection. Most of the subsequent analysis can be done offline.

This notebook also provides instructions for storing data on individual galaxies, calculating halo properties from downloaded data, and compiling data on selected populations.

3. Run the notebook titled Figures.ipynb.

This notebook analyzes the data that was downloaded and processed in Data_Acquisition.ipynb. This notebook will help you generate all five figures from the paper https://arxiv.org/abs/2105.02234.


If you have any questions or comments, please reach out to pathakde@grinnell.edu!

About

This repository provides detailed instructions to download and analyze IllustrisTNG data to generate all five figures from the paper https://arxiv.org/abs/2105.02234.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages

  • Jupyter Notebook 97.6%
  • Python 2.4%