Using Python and Astropy for Astronomical Data Analysis
Workshop at the 233rd Meeting of the AAS in Seattle
DATE: Sunday, 6 January 2019
TIME: 9:00am - 5:00pm
LOCATION: ROOM 4C-4 at the Washington State Convention Center
Please be sure your laptop is properly configured before the workshop by following the installation and setup instructions.
This could take as long as one hour depending on your current configuration and internet speeds.
DO NOT WAIT UNTIL THE DAY OF THE WORKSHOP.
|9:00 - 9:30||Install and config help, if needed||Juan Cabanela|
|9:30 - 9:45||Intro to Astropy and Code of Conduct||Erik Tollerud|
|9:45 - 10:15||Introduction to Python||Clare Shanahan|
|10:15 - 10:30||BREAK|
|10:30||Last call on breakfast|
|10:30 - 11:00||Astropy Units, Quantities, and Constants||Brett Morris|
|11:00 - 11:30||Coordinates||Brett Morris|
|11:30 - 12:00||I/O: FITS and ASCII||Lauren Chambers|
|12:00 - 1:00||LUNCH||On your own|
|1:00 - 1:30||Astropy Tables||Clare Shanahan|
|1:30 - 2:00||WCS and Images||Clare Shanahan|
|2:00 - 2:45||Photutils||Lauren Chambers|
|2:45 - 3:15||BREAK||Snacks Provided|
|3:15 - 4:00||Specutils||Erik Tollerud|
|3:30||Last call on snacks|
|4:00 - 4:15||Astropy Communities||Adrian Price-Whelan|
|4:15 - 4:45||Contributing to Astropy||Adrian Price-Whelan|
|4:45 - 5:00||Survey||Adrian Price-Whelan|
This workshop covers the use of Python tools for astronomical data analysis and visualization, with the focus primarily on UV, Optical, and IR data. Data analysis tools for JWST are being written in Python and distributed as part of Astropy, a community developed Python library for astronomy, and its affiliated packages.
The workshop goals introduce you to the variety of tools which are already available inside the Astropy library as well as provide ample hands-on time during which you’ll be able to explore the science analysis capabilities which the greater Python environment and community provide.
We plan on accomplishing this with brief overview talks on the main tools followed by extended instructor guided tutorials where you’ll be able to try them out for yourself and ask questions in the company of expert users and developers.
Some basic Python experience is highly recommended to be able to effectively participate in the exercises, but those without Python experience will still get much useful information about the capabilities for data analysis in Python and perhaps pick up some pointers on where they can get started learning more scientific Python and integrating it into their work flow.
If you would like to get a head start with the tools we will be concentrating on you can check out their documentation on readthedocs:
Other tools we can answer questions about but probably won't discuss explicitly:
Problems or Questions?
We encourage you to submit any problems or questions you have either to this repository issue tracker.
Materials from past workshops can be found here: https://github.com/astropy/past-astropy-workshops