21st Century Software and Data Analysis Tools for Physics and Astronomy

Apr 14-18, 2019

9:00am - 5:00pm

Instructors: Azalee Bostroem, Griffin Hosseinzadeh, Dalya Baron

Helpers: Iair Arcavi, Griffin Hosseinzadeh, Dalya Baron, Itamar Reis, Natalie Lubelchick, Sahar Shahaf

General Information

A hands-on workshop to be held on April 14-18, 2019 at Tel Aviv University, hosted by the School of Physics and Astronomy. The workshop aims to help you get your work done in less time and with less pain by using basic software and data analysis concepts and tools, including program design, version control, data management, and task automation, and is aimed at physicists at all stages of their education and career. The instructors for days 1-4 are Azalee Bostroem from UC Davis and Griffin Hosseinzadeh from the Center for Astrophysics at Harvard. Day 5 will be taught by our own Dalya Baron.

The first three days of the workshop will be based on the Software Carpentry curriculum. For more information on what we teach and why, please see our paper "Best Practices for Scientific Computing".

Who: The course is aimed at graduate students, postdocs, and faculty. The workshop will be taught with the assumption that participants have written or edited code in some language, and can navigate directories using the Unix command line. Knowledge of Git is not required.

Where: Wolfson 206 (Enginerring Building) and Kaplun 118 (Physics Building), Tel Aviv University (see schedule for details).

When: Apr 14-18, 2019. Add to your Google Calendar.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).

Code of Conduct: Everyone who participates in Carpentries activities is required to conform to the Code of Conduct. This document also outlines how to report an incident if needed.

Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.

Contact: Please email arcavi@gmail.com for more information.


Please be sure to complete these surveys before and after the workshop.

Pre-workshop Survey

Post-workshop Survey


Sunday: Shell Scripting and Version Control

Before Pre-workshop survey
08:00 OPTIONAL: Installation help Kaplun 118
09:10 Automating Tasks with the Unix Shell Kaplun 118
11:00 Lunch break
13:10 Version Control with Git Wolfson 206
14:00 Afternoon break
15:10 Version Control with GitHub Wolfson 206
17:00 END

Monday: Python

09:30 Building Programs with Python (notebook) Wolfson 206
11:30 Lunch break
13:10 Building Programs with Python (notebook) Wolfson 206
14:00 Afternoon break
15:30 Building Programs with Python (command line) Wolfson 206
16:00 Plotting in Python Wolfson 206
17:00 END

Tuesday: SQL/ Git

09:10 Managing Data with SQL Wolfson 206
10:30 Morning break
11:10 Managing Data with SQL Wolfson 206
12:00 Lunch break
13:30 Managing Data with SQL Wolfson 206
14:15 Advanced git Wolfson 206
15:00 Afternoon break
15:30 Advanced git Wolfson 206
16:45 Post-workshop Survey
17:00 END

Wednesday: MCMC and Gaussian Processes

09:10 Intro to Bayesian Statistics Kaplun 118
09:40 Monte Carlo Approach to Fitting a Line Kaplun 118
10:40 Morning break
11:10 Intro to MCMC Packages Kaplun 118
11:40 MCMC Approach to Fitting a Line Kaplun 118
12:40 Lunch break
14:10 Intro to Gaussian Processes Kaplun 118
14:40 Gaussian Process Regression of a Light Curve Kaplun 118
15:40 Afternoon break
16:10 Apply These Techniques to Your Own Dataset Kaplun 118
17:00 END

Thursday: Machine Learning

09:10 Introduction and Supervised Machine Learning Wolfson 206
10:40 Morning break
11:10 Hands on: Supervised Machine Learning Wolfson 206
12:10 Lunch break
13:40 Hands on: Supervised Machine Learning Wolfson 206
14:40 Afternoon break
15:10 Unsupervised Learning and advanced topics Wolfson 206
16:10 Hands on: Unsupervised Learning Wolfson 206
17:00 END

We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.


The Unix Shell

  • Files and Directories
  • History and Tab Completion
  • Pipes and Redirection
  • Looping Over Files
  • Creating and Running Shell Scripts
  • Finding Things
  • Reference...

Programming in Python

  • Using Libraries
  • Working with Arrays
  • Reading and Plotting Data
  • Creating and Using Functions
  • Loops and Conditionals
  • Defensive Programming
  • Using Python from the Command Line
  • Reference...

Version Control with Git

  • Creating a Repository
  • Recording Changes to Files: add, commit, ...
  • Viewing Changes: status, diff, ...
  • Ignoring Files
  • Working on the Web: clone, pull, push, ...
  • Resolving Conflicts
  • Open Licenses
  • Where to Host Work, and Why
  • Reference...

Managing Data with SQL

  • Reading and Sorting Data
  • Filtering with where
  • Calculating New Values on the Fly
  • Handling Missing Values
  • Combining Values Using Aggregation
  • Combining Information From Multiple Tables Using join
  • Creating, Modifying, and Deleting Data
  • Programming with Databases
  • Reference...

Intro to MCMC and Gaussian Processes

  • Intro to Bayesian Statistics
  • Monte Carlo Approach to Fitting a Line
  • Intro to MCMC Packages
  • MCMC Approach to Fitting a Line
  • Intro to Gaussian Processes
  • Gaussian Process Regression of a Light Curve
  • Apply These Techniques to Your Own Data Set

Introduction to Machine Learning

  • Introduction: why do we need Machine learning?
  • Supervised Learning: evaluation metrics and input dataset
  • Supervised Learning: Support Vector Machine, Random Forests, and Neural Networks
  • Supervised Learning: hands-on exercises with jupyter notebook
  • Unsupervised Learning: introduction
  • Unsupervised Learning: clustering, dimensionality reduction, and outlier detection
  • Unsupervised Learning: hands-on exercises with jupyter notebook


To participate in a Software Carpentry workshop, you will need access to the software described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

The Bash Shell

Bash is a commonly-used shell that gives you the power to do simple tasks more quickly.

Video Tutorial
  1. Download the Git for Windows installer.
  2. Run the installer and follow the steps below:
    1. Click on "Next" four times (two times if you've previously installed Git). You don't need to change anything in the Information, location, components, and start menu screens.
    2. Select "Use the nano editor by default" and click on "Next".
    3. Keep "Use Git from the Windows Command Prompt" selected and click on "Next". If you forgot to do this programs that you need for the workshop will not work properly. If this happens rerun the installer and select the appropriate option.
    4. Click on "Next".
    5. Keep "Checkout Windows-style, commit Unix-style line endings" selected and click on "Next".
    6. Select "Use Windows' default console window" and click on "Next".
    7. Click on "Install".
    8. Click on "Finish".
  3. If your "HOME" environment variable is not set (or you don't know what this is):
    1. Open command prompt (Open Start Menu then type cmd and press [Enter])
    2. Type the following line into the command prompt window exactly as shown:

      setx HOME "%USERPROFILE%"

    3. Press [Enter], you should see SUCCESS: Specified value was saved.
    4. Quit command prompt by typing exit then pressing [Enter]

This will provide you with both Git and Bash in the Git Bash program.

The default shell in all versions of macOS is Bash, so no need to install anything. You access Bash from the Terminal (found in /Applications/Utilities). See the Git installation video tutorial for an example on how to open the Terminal. You may want to keep Terminal in your dock for this workshop.

The default shell is usually Bash, but if your machine is set up differently you can run it by opening a terminal and typing bash. There is no need to install anything.


Git is a version control system that lets you track who made changes to what when and has options for easily updating a shared or public version of your code on github.com. You will need a supported web browser.

You will need an account at github.com for parts of the Git lesson. Basic GitHub accounts are free. We encourage you to create a GitHub account if you don't have one already. Please consider what personal information you'd like to reveal. For example, you may want to review these instructions for keeping your email address private provided at GitHub.

Git should be installed on your computer as part of your Bash install (described above).

Video Tutorial

For OS X 10.9 and higher, install Git for Mac by downloading and running the most recent "mavericks" installer from this list. Because this installer is not signed by the developer, you may have to right click (control click) on the .pkg file, click Open, and click Open on the pop up window. After installing Git, there will not be anything in your /Applications folder, as Git is a command line program. For older versions of OS X (10.5-10.8) use the most recent available installer labelled "snow-leopard" available here.

If Git is not already available on your machine you can try to install it via your distro's package manager. For Debian/Ubuntu run sudo apt-get install git and for Fedora run sudo dnf install git.

Text Editor

When you're writing code, it's nice to have a text editor that is optimized for writing code, with features like automatic color-coding of key words. The default text editor on macOS and Linux is usually set to Vim, which is not famous for being intuitive. If you accidentally find yourself stuck in it, hit the Esc key, followed by :+Q+! (colon, lower-case 'q', exclamation mark), then hitting Return to return to the shell.

nano is a basic editor and the default that instructors use in the workshop. It is installed along with Git.

Others editors that you can use are Notepad++ or Sublime Text. Be aware that you must add its installation directory to your system path. Please ask your instructor to help you do this.

nano is a basic editor and the default that instructors use in the workshop. See the Git installation video tutorial for an example on how to open nano. It should be pre-installed.

Others editors that you can use are BBEdit or Sublime Text.

nano is a basic editor and the default that instructors use in the workshop. It should be pre-installed.

Others editors that you can use are Gedit, Kate or Sublime Text.


Python is a popular language for research computing, and great for general-purpose programming as well. Installing all of its research packages individually can be a bit difficult, so we recommend Anaconda, an all-in-one installer. If you have already installed Anaconda (or Astroconda), then you do not need to installed it again. You can create an environment for this class with the following command: conda create -n py37 python=3.7 anaconda

Regardless of how you choose to install it, please make sure you install Python version 3.x (e.g., 3.6 is fine).

We will teach Python using the Jupyter notebook, a programming environment that runs in a web browser. For this to work you will need a reasonably up-to-date browser. The current versions of the Chrome, Safari and Firefox browsers are all supported (some older browsers, including Internet Explorer version 9 and below, are not).

  1. Open https://www.anaconda.com/download/#linux with your web browser.
  2. Download the Python 3 installer for Linux.
    (The installation requires using the shell. If you aren't comfortable doing the installation yourself stop here and request help at the workshop.)
  3. Open a terminal window.
  4. Type
    bash Anaconda3-
    and then press Tab. The name of the file you just downloaded should appear. If it does not, navigate to the folder where you downloaded the file, for example with:
    cd Downloads
    Then, try again.
  5. Press Return. You will follow the text-only prompts. To move through the text, press Spacebar. Type yes and press enter to approve the license. Press enter to approve the default location for the files. Type yes and press enter to prepend Anaconda to your PATH (this makes the Anaconda distribution the default Python).
  6. Close the terminal window.

Once you are done installing the software listed above, please go to this page, which has instructions on how to test that everything was installed correctly.


SQL is a specialized programming language used with databases. We use a simple database manager called SQLite in our lessons.

  • Run git-bash from the start menu
  • Copy the following curl https://abostroem.github.io/2019-04-14-tau/getsql.sh | bash
  • Paste it into the window that git bash opened. If you're unsure, ask an instructor for help
  • You should see something like 3.27.2 2019-02-25 16:06:06 ...

If you want to do this manually, download sqlite3, make a bin directory in the user's home directory, unzip sqlite3, move it into the bin directory, and then add the bin directory to the path.

SQLite comes pre-installed on macOS.

SQLite comes pre-installed on Linux.

  • In case of problems: register for an account at Python Anywhere
  • Download survey.db
  • Click on files and upload survey.db
  • Click on dashboard and Choose new console $ bash

If you installed Anaconda, it also has a copy of SQLite without support to readline. Instructors will provide a workaround for it if needed.

Bayesian Statistics Modules

emcee is an pure-Python implementation of a Markov chain Monte Carlo (MCMC) Ensemble sampler designed for Bayesian parameter estimation. corner makes it very easy to plot results from emcee. George is a fast and flexible Python library for Gaussian Process Regression. All three modules are written by Dan Foreman-Mackey, and much of this paragraph is plagiarized from his webpages.

Now that you've installed Python, it's very easy to install all three. If you installed Anaconda, run conda install -c conda-forge emcee corner george. For any other version of Python, run pip install emcee corner george.

To check if this worked, run python -c 'import emcee, corner, george'. If that returns without errors, you're all set!