This is a data-oriented approach to Python. The focus is on showing one how to quickly get up and running reading, manipulating and displaying data learning the minimum amount of Python initially. Gradually, more Python language is introduced as more complex examples are worked through.
No Python background is required.
Video of all the presentations is available on the STScI webpage.
Completed homework and demo IPython Notebooks are available in the homework_notebooks and lecture_notebooks directories.
| Session 1 |
|---|
| Lecture Nov. 28, 10 AM Auditorium |
| Problem Review Dec. 5, 1 PM Cafe Con |
| Session 2 |
|---|
| Lecture Dec. 12, 9 AM Auditorium |
| Problem Review Dec. 19, 9 AM Boardroom |
| Session 3 |
|---|
| Lecture Jan. 16, 10 AM Auditorium |
| Problem Review Jan. 23, 10 AM Cafe Con |
| Session 4 |
|---|
| Lecture Jan. 30, 10 AM Auditorium |
| Problem Review Feb. 6, 10 AM Boardroom |
| Session 5 |
|---|
| Lecture Feb. 13, 10 AM Auditorium |
| Problem Review Feb. 20, 10 AM Boardroom |
| Session 6 |
|---|
| Lecture Mar. 13, 10 AM Auditorium |
| Problem Review Mar. 20, 10 AM Boardroom |
Session 1: Introduction
- Goals
- Sources of information
- IPython Notebook basics
- Examples of capabilities
- Reading data
- Displaying images
- Plotting data
- General Python practicalities
- Exercises part of all sessions
Session 2: Basic Tools
Introduction to:
- pyfits
- numpy
- matplotlib
- ascii tables
Session 3: Source finding example part 1
- Calling IRAF tasks, manipulating and displaying results
- Python topics covered:
- strings and lists
- writing functions, modules, and scripts
Session 4: Source finding example part 2
- Doing completeness tests on previous results and displaying results
- Python topics covered:
- intermediate numpy
- looping, conditional expressions
- random distributions
Session 5: STIS Long-Slit spectral extraction example
- Identify location of spectral sources in STIS long-slit data, call xxx with fit locations
- Python topics covered
- fitting
- numpy techniques and libraries
Session 6: Data elsewhere
- Doodle poll for potential topics is at http://doodle.com/78vi8b6rzruarcwb
There are many sources of information. That's sometime part of the problem (as compared to integrated tools like IDL or IRAF).
- AstroPy: relatively new; software specifically for astronomy (with documentation)
- Using Python for Interactive Data Analysis: short book by STSCI/SSB
- Python4Astronomers: tutorials by CfA
- Numpy and SciPy: general website containing software and documentation for scientific python
- matplotlib: 2-d plotting (and some 3-d capability)
- IPython: enhanced interactive python environments
- Python for Data Analysis by Wes McKinney
- SciPy and NumPy by Eli Bressert
- A Primer on Scientific Programming with Python by Hans Petter Langtangen (Also: Python Scripting for Computational Science)
- Beginning Python Visualization: Crafting Visual Transformation Scripts by Shai Vaingast
- Matplotlib for Python Developers by Sandro Tosi
- Numpy 1.5 Beginner's Guide by Ivan Idris
- Numerical Methods in Engineering with Python by Jaan Kiusalaas
- Python: The Python mother ship
- Standard Python Docs
- Standard Python Library: Bookmark this!
There are a large number of books about Python.
These two versions of Python differ in non-trivial ways. Eventually we expect that we will migrate to Python 3 (the process has been underway for a while), but we expect it will still be a couple years before a significant number of science users will be using Python 3. This course will use only Python 2 for all its examples. Discussions regarding the differences are beyond the scope of this course.
If you are using Ureka download the AstroPy Ureka add-on and install it with:
ur-install astropy-2012-12-05-addon.tar.gz commonDownload and run the AstroPy windows installer.
Those using their own setups will need to install Astropy from source. Download the source tarball, extract it, and run python setup.py install in the astropy-2012-12-05 directory.