Preparation Instructions for Tutorial Participants
(last updated 2019 Sep 6)
Thank you for your interest in our tutorial on pywwt, powered by the AAS WorldWide Telescope! This post lists the things you should do to prepare before the tutorial.
Prerequisite knowledge
This tutorial will assume familiarity with the Python programming language and the Jupyter computation environment. No complex skills will be required, but you should at least have some experience with their basic usage. If you’re unsure about your knowledge level, please email the instructor to discuss. We will not assume any familiarity with pywwt or the AAS WorldWide Telescope ecosystem.
Additional familiarity with the standard astronomical Python stack — namely, AstroPy and its applications — will be helpful. If you’d like to gain more experience with AstroPy, we recommend visiting http://learn.astropy.org/.
pywwt is fundamentally software for working with astronomical data, so familiarity with the concepts of astronomy observing — especially coordinate systems and data formats — will be valuable as well. The final activity of the tutorial will encourage independent exploration of your own astronomical data with pywwt (see below for details). So, if you bring your own data, familiarity with the Python tools to manipulate them will be beneficial.
Equipment
You should bring a personal laptop if at all possible. If you don’t have a laptop, we’ll see if we can set you up to share one with another tutorial participant.
You should also bring your laptop’s charger. Because WWT runs a realtime simulation of the known Universe, it will keep your CPU busy.
On your laptop, all you need is a web browser. Google Chrome is the officially supported choice, but other browsers work as well, on a variety of operating systems. If you have any concerns, you can test that your browser has the needed capabilities by visiting the AAS WorldWide Telescope web client and checking that the site’s functionality seems to work correctly.
For all of the Python coding, we will be running JupyterLab instances pre-loaded with pywwt and related packages in the cloud using BinderHub, so no local software installation will be necessary. However, if you would like to install the software locally, there is certainly nothing wrong with
that! Please consult the pywwt installation instructions. We strongly recommend installation in an isolated conda environment. For maximum compatibility with the tutorial, we encourage you to verify that you can use pywwt in the JupyterLab environment, which requires a bit of specialized setup right now. See the pywwt installation instructions for details.
Bring some data to visualize, if you can!
In the final part of the tutorial, we’ll have some freeform time for you to use your newly-learned skills to visualize some of your own astronomical data. Therefore, we strongly encourage you to identify a dataset that you want to visualize and make sure that it is available on your laptop! But don’t worry:
we’ll have sample data sets to play with if you don’t have anything of your own to bring.
If you would like to bring your own data, here are some guidelines:
- We will be visualizing both data tables on the sky (e.g. source catalogs) and images, so you can bring either kind of data. Better yet, bring both!
- Because we’ll be running our Python code in the cloud, you will have to upload your data to the BinderHub server, so your dataset should not be too large. More than 30 MB might be risky. If in doubt, please email the instructor.
- That being said, the WWT engine does have support for streaming and exploring very large (gigabyte and above) images on the fly, which is one of its unique capabilities. If you have a very large image that you would like to try visualizing, please email the instructor and we can discuss some extra preparation to explore this functionality.
- For tabular data, WWT supports both 2D and 3D points, as well as coordinates relative to the Earth and other planets, not just in RA/Dec space. (For instance, one of our demos involves the locations of earthquakes below Earth’s crust.) So feel free to get creative in what you’d like to visualize!
Think about web hosting
We will also do an activity where we export data from a pywwt visualization to a freestanding web site. So it would be ideal if you could arrive at the tutorial prepared with some web space where you can upload HTML and JavaScript files and they’ll be accessible with a web browser on the open Internet.
If you’re not sure whether you have such a space, one option would be to use the GitHub Pages service with a temporary repository, and we’ll be prepared to walk you through the setup during the tutorial. So, if you have a GitHub account, you should be all set.
If you don’t personal web space and you don’t have or want a GitHub account, we can try to set you up with a partner who does. Or, email the instructor to discuss options.
Any questions?
If you have any questions about any of this, feel free to reach out to the instructor, Peter Williams, either by posting in this thread, or by emailing him at pwilliams@cfa.harvard.edu.
We’re looking forward to seeing you in Groningen!