Contents

Class Overview & Syllabus

Below, we’ve compiled the topics we covered throughout the semester, with references to the Lecture and Lab links found in the side bar.

Modules

  1. Fundamentals recap and review. We began with an assignment (Lab 1) which was distributed prior to the semester starting, and which focused on setting up conda environments for the students to use during the course, ensured everyone had the proper libraries installed, and reviewed basic operations such as numpy array manipulation, list comprehensions, plotting, and fitting.

  2. Astronomical Imaging. Our first core lab and lecture were focused on the analysis of astronomical images (e.g., FITS imaging from HST). The programming goals of this module were to emphasize file handling with FITS, and the creation of robust functions in Python. Students wrote functions to plot astronomical imaging which handled WCS coordinate transformations, scaling, and optional arguments for colorbars and other features. They then used array manipulation and the astropy photutils package to perform aperture photometry on sources in the provided imaging.

  3. Object Oriented Programming. From functions, we moved to Object Oriented Programming (OOP) and the construction of class objects with attributes and methods. We discussed the structure and use cases for classes, and the lab carried the students through the creation of a full pipeline to perform PSF Photometry on the stars in an image. Their pipelines, created as a series of methods for a class, performed calibrations (dark subtraction, flatfielding), background estimation and subtraction, masking of galaxies in the frame, algorithmic detection of the stars, centroiding of those stars, calculation of the second moments, and ultimately the creation of PSF models that were used to estimate the flux of each star in the frame.

  4. Sampling and MCMC. A key task in general astronomy research is fitting models to data. In this module, we introduced various means of fitting, from linear least squares fits using simple polynomial functions, to more complex models fit using student-built Metropolis Hastings MCMC samplers, and more complex models still fit using the popular fitting code emcee (Dan Foreman-Mackey). We spent time parsing the concept of sampling, and how it relates to the estimation of integrals, the concept of the Likelihood, the Prior, and the Evidence. In the lab, students first used Chi-squared methods to fit models to data, and then used self-made MCMC samplers to do the same. We assigned problems from two papers, Hogg, Bovy, and Lang (2010) on fitting, and Hogg & Foreman-Mackey (2018) on MCMC, then extended these with comparisons to Chi-squared fitting. Students also got their first taste of emcee here.

  5. Stellar Spectroscopy and MCMC Fitting. Having practiced the fundamentals of fitting and sampling in module four, we approached a more complex problem: fitting stellar spectral templates to Keck/DEIMOS data. Students used skills developed through the first three labs to sucessfully read in and modify the real and synthetic data and match their continuua and smooth the synthetic spectra to the instrumental resolution. They then used both Chi-squared and emcee to find the best fit radial velocities of the stars.

  6. Pandas. Pandas is a popular data management library, used often in the data science industry, which extends the functionality of basic arrays. We taught the fundamentals of creating and operating on pandas DataFrames, including row and column based indexing, filtering on conditions, and joining tables using set theory / SQL style commands. Our lab used two datasets, including the 3DHST catalog, to exemplify some of the handy features of DataFrames that would be difficult to carry out with numpy arrays. Using the pandas tools, students were able to investigate the 3DHST catalog, creating UVJ diagrams of massive galaxies in the sample conditioned on different mass ranges, redshift ranges, etc.

  7. Packages. At this point in the semester, students began working on their final projects, a requirement of which was that installable packages were created. We carried out an interactive lecture, which followed roughly the guide in Jo Bovy’s handbook on package development, in which the students created and installed an example package from scratch during the lecture. For their lab, students investigated the code structure, import syntax, and documentation of several astronomical software packages, namely, ArtPop (Danieli & Greco). By investigating the documentation and API reference for this unfamiliar software, students were able to create stellar populations in an odd shape (a ring) and inject this system into a real sky image. The ultimate goal was to gain familiarity with package structure, and how to write documentation useful for others using your code.

  8. Interactivity. Our final module focused on interactivity in Python. We focused on two areas within this topic — interactivity in matplotlib, including the use of sliders and animations, and interactive websites made using the streamlit package, which allows python scripts to seamlessly interact with buttons, sliders, and other forms of user input. A majority of the class used one of these two tools (or both) in their final projects.

Guest Speakers

We are grateful to the speakers who met with our students this semester - below is a running list, including some of the packages they are associated with:

  • Prof. X Prochaska (UCSC); pypeit — used in lab 5

  • Prof. David Hogg (NYU/Flatiron CCA); emcee — used in labs 4 and 5

  • Shany Danieli (IAS/Princeton); artpop — used in lab 7

  • Yao-Yuan Mao (Rutgers;adstex)

  • Adrian Price-Whelan (CCA/Flatiron); gala,astropy — used in every lab.

Our guest lectures consisted primarily of us asking our guest about their programming environments (do they use editors? IDEs? Notebooks? and how do they stay git synced), before moving on to broader questions of their coding philosophy, advice for students, and questions about their specific areas of expertise. It was especially nice to have the people “behind the curtains” of the software being used in the course provide their perspectives.

Final Projects

For more on final projects, you can see the tab on the left. Generally, students spent the final month and a half of the semester forming the idea for, fleshing out, and then building a python package (or interactive website). We had a variety of fantastic projects, ranging from astrophysics (jeans modeling, drone flight path control for radio telescopes, image handling and alignment, radial velocity tools) to tracking the buses in New Haven or creating custom spotify playlists. All projects used tenants of functional programming, classes, code organization and structure, and github integration that was emphasized throughout the semester.

We are happy to highlight some of the student projects here, and invite you to check them out!