HaverfordGalaxyGroup

Code collaboration for the Galaxy Group at Haverford College.

Warning - this README is not automatic, so sometimes there's code here which is not documented/sorted corrected.

• 1ADVICE.txt - advice to please read this README file!

Example Codes Either Written to help, or output of student papers

Basic Statistics/Coding Techniques

• BinnedMedianExample.ipynb - functions to apply binned statistics to data with an example use on some random data. By Emmy Wisz with some modifications from Karen Masters. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/BasicCode/BinnedMedianExample.ipynb

• Fitsinput_simpleplots.ipynb -- Example code for how to read in a fits file, slice the data on selection from a column, make basic histograms and scatter plots. Written for mangaHIall.fits file... but should be easily modified to any .fits table. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/BasicCode/Fitsinput_simpleplots.ipynb

• basic_stats.py -- Various useful statistics functions from Brooke Simmons. Includes a function to calculate proper errors on fractions, and to match two distributions (e.g. by mass). https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/BasicCode/basic_stats.py

• mpl_style.py -- Style file for Matplotlib which may be useful (from Coleman Krawcyzk) https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/BasicCode/mpl_style.py

• SurvivalAnalysisTutorial.ipynb -- Work in progress tutorial on Survival Analysis. Will likely use HI-MaNGA data as an example at some point. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/BasicCode/SurvivalAnalysisTutorial.ipynb

Working with MaNGA Data

• FourPanelGalaxyPlot.ipynb -- Code to make a four panel plot for a MaNGA galaxy, showing the gri image and three other MaNGA maps. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/MaNGA/FourPanelGalaxyPlot.ipynb

• MaNGAGalaxyofTheDay.ipynb -- Code by Karen Masters which mades a four panel plot of any input MaNGA galaxy. Hoping to use for a Bluesky bot for 'MaNGA galaxy of the day'. There are enough galaxies in the MaNGA sample to do this for almost 30 years. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/MaNGA/MaNGAGalaxyofTheDay.ipynb

• Pipe3D_access.ipynb -- example notebook to access some Pipe3D analysis of MaNGA data (by KLM) https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/MaNGA/Pipe3d_access.ipynb

• RadialGradientsMaNGAEditedMar20.ipynb -- create a radial metallicity trend for one or multiple galaxies. Originally from KLM, minor changes by Emmy Wisz. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/MaNGA/RadialGradientsMaNGAEditedMar20.ipynb

• Metallicity_BinnedStatisticOverSamples.ipynb -- sample selection for spiral galaxies binned into arm winding level, loops over all galaxies in a sample and saves a metallicity trend for each. From Emmy Wisz BMC'23 senior thesis https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/MaNGA/Metallicity_BinnedStatisticOverSamples.ipynb

• Metallicity_MassBinning_LinearRegressionFits_FitsofFits.ipynb -- mass binning within subsamples, fitting a straight line to trends. From Emmy Wisz '23 senior thesis. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/MaNGA/Metallicity_MassBinning_LinearRegressionFits_FitsofFits.ipynb

• Radial_Metallicity_Rainbow_ex.ipynb - the rainbow plot of metallicity trends in bins of galaxy stellar mass. From Emmy Wisz. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/MaNGA/Radial_Metallicity_Rainbow_ex.ipynb

Working with Galaxy Zoo

• LaurenKocurek_GZ_SGAplots.ipynb -- code by Lauren Kocurek'27 from Summer2025 in the group showing how to match the Sienna Galaxy Atlas with GZDESI (LSDR8) Zoobot predictions of morphology. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/GalaxyZoo/LaurenKocurek_GZ_SGAplots.ipynb

Working with Galaxy Zoo 3D Masks (and MaNGA Data)

• VAC_GZ3D_tutorial_KLMedits.ipynb -- edits to the GZ3D VAC tutorial in Marvin so that it works on Sciserver.org (by KLM) https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/GZ3D/VAC_GZ3D_tutorial_KLMedits.ipynb

• Arm_Interarm_Tutorial.ipynb -- tutorial to learn how to calculate excess in stellar mass density of spiral galaxy arms. By Maša Kilibarda '26 with edits by KLM https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/GZ3D/Arm_Interarm_Tutorial.ipynb

• KLM_RRL_PAandLength_Notebook.ipynb -- Rachel Langgin BMC'23 uploaded her code to measure bar position angles from GZ:3D masks https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/GZ3D/KLM_RRL_PAandLength_Notebook.ipynb

Working with HI-MaNGA Data/HI Data

• HI-MassFraction-StellarMassPlot.ipynb -- Simplified from HIMaNGA_First_Paper_EmilyHarrington_KLM.ipynb - just makes the HI mass fraction against stellar mass plot. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/HIMaNGA/HI-MassFraction-StellarMassPlot.ipynb

• HIMaNGA_First_Paper_EmilyHarrington_KLM.ipynb -- Code to make plots in Masters et al. 2019 on HI-MaNGA. Mostly written by Emily Harrington BMC '20. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/HIMaNGA/HIMaNGA_First_Paper_EmilyHarrington_KLM.ipynb

• HI-Estimate Code Template.ipynb-- Code by Elisabeth Brann BMC'26 which applied different techniques (model estimates and survival analysis) to HI scaling relations. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/HIMaNGA/HI-Estimate%20Code%20Template.ipynb

• Model_HI_GlobalProfiles.ipynb - Toy model for a HI global profile based on a model rotation curve and HI radial profile (by Karen Masters, with some help from Jessica Washington KNAC student summer 2021) https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/HIMaNGA/Model_HI_GlobalProfiles.ipynb

• SurvivalAnalysisTutorial.ipynb -- Work in progress tutorial on Survival Analysis. Will likely use HI-MaNGA data as an example at some point. See below for instructions on how to set up a conda environment for this notebook. https://github.com/karenlmasters/HaverfordGalaxyGroup/blob/main/BasicCode/SurvivalAnalysisTutorial.ipynb

Other Useful Repositories

ASTR352 (Extragalactic Data Science Class) https://github.com/karenlmasters/ASTR352JupyterActivities

Masha's Spiral Arm Project Respository: https://github.com/mashakilibarda/Spiral_Arms_Mass_Project

Research Group Members also might like to check out Group Google Drive, which has some more private code sharing stuff.

David Stark's survival analysis code (https://github.com/dvstark/survival/tree/main)

Instructions for setting up an Conda environment for survival analysis codes

Note: These steps were performed using an Apply Macbook Pro with M1 processor and Sequoia 15.5

0. Install R from here

1. This may be optional, but here is how conda was set up in this working example:

   • Install miniconda. Anaconda is probably fine too, just takes longer.

   • Add conda-forge to the conda channels:

     >> conda config --add channels conda-forge
     

   • Install a few packages that improve conda's speed when solving environments, and improve how it handles packages installed via pip

     >> conda install -n base conda-libmamba-solver
     >> conda config --set solver libmamba
     >> conda config --set pip_interop_enabled true 
     

2. Setup new conda environment w/ some standard packages. I called my environment rpy2. We're not actually installing rpy2 yet though. Conda-forge installs an older version that seems to force use of an older version of R that's incompatible with packages we need…

   >> conda create -n rpy2 python=3.12 jupyterlab numpy matplotlib lifelines fortranformat
   

3. Activate the environment

   >> conda activate rpy2
   

4. Now, install rpy2. Set to the latest version, e.g.,

   >> pip install rpy2==3.6.1
   

5. Check the version of R. You can do this just by opening R and seeing what it prints:

   >> R
   R version 4.5.1 (2025-06-13) -- "Great Square Root"
    Copyright (C) 2025 The R Foundation for Statistical Computing
    Platform: aarch64-apple-darwin20
   

6. open python and import rpy2. Make sure there are no errors

   import rpy2