Yao-Yuan Mao

NASA Einstein Fellow @ Rutgers
Yao-Yuan Mao | NASA Einstein Fellow @ Rutgers | yymao.github.io


Ordered alphabetically. If you have any questions regarding these tools (especially those that are not very well documented), please do feel free to contact me.

Also check out my  GitHub profile for other repositories that I develop or contribute to.

A pip-installable Subhalo Abundance Matching (SHAM) Python package. It can interpolate and extrapolate galaxy/halo abundance functions and also applies fiducial deconvolution (with Peter Behroozi's implementation).
A pip-/conda-installable Python package that finds all citation keys in your LaTeX documents and search NASA ADS to generate corresponding bibtex entries. It recognizes author:year, arxiv identifer, DOI, and ADS bibcode.
A simple web interface that allows you to search with arXiv ID, DOI, ADS bibcode, and first author + year, without typing syntax keywords (such as "author" or "year").
A simple bookmarklet that brings you to the corresponding corresponding benty-fields page of the arXiv paper you're reading so that you can vote for it.
[Work in progress!] A web app that uses D3.js to demostrate that, even if two quantities are highly correlated, a subset of the total population can still be biased in one of the quantities but not in another. This project is inspired by my research work on halo assembly bias.
A simple webpage to display image cutouts retrieved from the Dark Energy Camera Legacy Surveys. This tool is similar to the SDSS Image List Tool and can be used for target selection and inspection. Source code is on GitHub.
A pip-/conda-installable Python package to create easy-to-use "query" objects that can apply on NumPy structured arrays, astropy Table, and Pandas DataFrame.
A web interface that uses Google Charts API to show all the scatter plots between any two columns in a multi-column Google Spreadsheet. Source code is on Gist. A downloadable version can be found on GitHub.
A pip-installable Python package that wraps Peter Behroozi's fast3tree C code (taken from Rockstar).
A pip-installable Python package that uses the friends-of-friends algorithm to match multiple sky catalogs without the need to specify a main catalog (i.e., multi-way matching).
A pip-installable Python class that enables easy/fuzzy name comparision, especially in academia.
A Python package that provides an abstract common reader interface for accessing generic catalogs. This package is used by LSST DESC's DESCQA validation framework and GCRCatalogs.
This repository contains a set of useful, but not necessarily related, Python scripts that carry out or accelerate many different tasks in my research. Most of them involve dark matter simulations. In particular, you can find Python scripts that and many mores!
An automatic system which browses through new arXiv astro-ph papers everyday and sends personal suggestions to subscribers. It also sends discussion suggestions to Tea organizers, and discovers new papers that are authored by KIPAC members.
(Currently this system is open to only KIPAC members. If you are a member of KIPAC and want to become a subscriber, just talk to me. I do plan to expand this service and to open it for all, or to make the system easy to install. If you are interested in this, please contact me.)
[Work in progress!] A web app that uses D3.js to visualize the intertwined elements about probing the nature of dark matter with LSST. Read more, see source code, and contribute on GitHub.
An offline web app for quickly browsing through a list of NASA-Sloan Atlas objects.
A hassle-free Python script to quickly start a Jupyter notebook/jupyterlab server on a remote machine over SSH with port tunneling enabled at the same time.
A Python script to run Peter Behroozi's Consistent Trees (replacing "do_merger_tree.pl").
A Python script that generates a page which plots the particle mass as a 2D function of number of particles and box size for N-body simulations.
A pip-/conda-installable Python package that provides the Tracy–Widom distribution functions for β = 1, 2, or 4.