How do I use the standalone build of Sherpa to fit my data?

I've put together a simple IPython notebook showing off the new standalone Sherpa build. The notebook runs through a simple polynomial fit and is based on the "Introduction to NumPy/SciPy" course from the Practical Python for Astronomers site. 

It highlights

• using the Sherpa standalone package along with other Python packages (astropy, matplotlib, numpy)
• how to use the "high-level" API (as described in the CIAO documentation), which provides  data management in a manner similar to the Xspec spectral fitting package
• how to use the "low-level" API, which is unfortunately poorly documented as this time 

One thing it doesn't really cover is how easy it is to switch from a polynomial model to a more-complex one. Oh well, perhaps next time.

Mentioning a next time, hopefully I'll have time to add more notebooks soon; check at the notebook viewer page to see if any have been added, or go to the GitHub page for this project. If you'd like to see any topic covered, please mention it in the comments.

Sherpa 4.7b binary release

Sherpa standalone binary package was released on 26 September, 2014.  It was built and tested on Linux 32, Linux 64 and Mac OSX and can be installed into Anaconda Python. The plan is to migrate the code to github for  December source release.

http://cxc.cfa.harvard.edu/contrib/sherpa47b/

Sherpa Fitting in ds9

You can now use Sherpa fitting directly from ds9. Several basic spectral and image models are loaded via CIAO analysis tools for a quick look at the model parameters. Here is the video from Kenny showing you how to do it: Fitting in ds9