Jordan Mirocha and I have been developing a python-based spectrosopic analysis toolkit. It’s available at pyspeckit.bitbucket.org. It fits profiles interactively or via “clever” guessing. It can fit gaussian, lorentzian, and voigt profiles (built in). It can also do model-grid based fitting and has an NH3 inversion fitter written at the moment. It deals well with unit conversions and can read and write a variety of different file types. It has some interactive data cube capabilities, but those aren’t particularly well refined at the moment. It can link to the SPLATALOGUE radio line database to overplot line locations and has a small optical line database built in.

The big advantages are that it’s designed to be extensible: i.e., you can write your own model function and plug it into the code without having to write any of the overhead stuff (e.g., x-axis units, residual calculations, annotations, etc.).

As Ben pointed out, unhappy spectra can be unhappy in a lot of different ways. PySpecKit is designed to deal with happy spectra, but can deal with flagged data and can be used to subtract baselines.

Finally, because it’s pure python, it is slow for batch-processing large spectra. I’d like to incorporate a fortran implementation of MPFIT if possible, but that’s low on the priority list for now.

http://www.stsci.edu/resources/software_hardware/specview

I fit lines in optical spectra with a Fortran program that uses Levenberg-Marquardt non-linear least squares to fit a Gaussian or Voigt (or multiple lines), with simple automated construction of the initial guesses (important for an iterative routine). One should be able to do the same thing with MPFIT in IDL, less clunkily but slower. I use a subroutine written long ago by my advisor that does a very accurate fast approximation of a Voigt function and its derivatives. Email me if you want a copy of this subroutine.

PAN is particularly useful for IFU/MOS data since:
1. It has the ability to read in multiple spectra at once in an array format, and fit one initial guess to these spectra automatically. This is ideal for IFS data with potentially hundreds of spectra.
2. The spectrum+fit, the residuals and the numerical results are displayed on-screen for each spectrum, meaning you can view and review the fit for each spectrum in a very visual manner.
3. The initial parameter guesses (amplitude, position and width) can be specified visually with the mouse. PAN was written such that the amount of typing done by the user is minimized.