Using Python and Astropy for Astronomical Data Analysis
Workshop at the 237th Meeting of the AAS held online
- DATE: , 7-8 January 2021
- TIME: 11:00am - 2:00pm ET both days
- LOCATION: Zoom webinar
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.
Schedule - TO BE UBDATED
|9:00 - 9:30||Install and config help, if needed||All|
|9:30 - 9:45||Intro to Astropy and Code of Conduct||TBD|
|9:45 - 10:15||Astropy Units, Quantities, and Constants||TBD|
|10:15 - 10:30||BREAK|
|10:30 - 11:15||Coordinates||TBD|
|11:15 - 11:45||I/O: FITS and ASCII||TBD|
|11:45 - 12:15||Astropy Tables||TBD|
|1:15 - 1:30||Astropy Communities||TBD|
|1:30 - 1:45||Contributing to Astropy||TBD|
|1:45 - 2:30||WCS and Images||TBD|
|2:30 - 3:00||BREAK|
|3:00 - 4:00||Photutils||TBD|
|4:00 - 4:45||Specutils||TBD|
|4:45 - 5:00||Survey||TBD|
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 to this repository issue tracker by choosing the "Question from workshop participant" issue template.
Materials from past workshops can be found in other branches on this repo and in the past-astropy-workshops repo.