Data and Code

On this page, I plan to provide access to various codes that I have used throughout my research. I aim to release two different types of code: (i) tools that may be of use to others in their own work and (ii) scripts, Jupyter notebooks, etc. used to generate my results, in the interest of scientific reproducibility.

I’m making an active effort to build GitHub repositories for my various projects.

Tools:

Databases:

The DASH database of subhalo evolution, released in Ogiya et al. (2019).

Reproducibility:

I have put the code and data up on GitHub for some of my older projects, and I plan to focus on being more systematic on current and future projects so that all necessary materials for reproducing my published results are make publicly available.

Analysis notebooks, FORTRAN codes, and Python scripts used to generate results of non-thermal pressure evolution on the \(Y_\mathrm{SZ}-M\) relation in Green et al. (2020): GitHub Repository

Analysis notebooks used to generate results of evolved subhalo density profiles discussed in Green et al. (2019a): GitHub Repository

Codes used for generating a trained random forest model for predicting mock Magneticum X-ray cluster masses and the Jupyter notebooks used to generate results and figures in Green et al. (2019b): GitHub Repository

Side Projects:

I’ve been exploring some additional ML/AI/NLP projects using Kaggle. Check out my Kaggle user profile.

I’m working with text data from the r/wallstreetbets subreddit to do some sentiment analysis on stock prices. Here is my working GitHub Repository.