I have been an IDL programmer for sometime now and looking to transition to Python. I find that MPFIT's IDL version exists in Python. However, I am looking for MPFITFUN version in Python (http://www.physics.wisc.edu/~craigm/idl/down/mpfitfun.pro) or something similar.
Basically, I am looking for a Python function that takes a user-defined function and uses like Levenberg-Marquardt least-squared fit (like MPFIT).
Thanks,
There are fitting functions built into SciPy but I do not know of any which account for uncertainties in data as MPFITFUN does. I have found Sherpa to be an excellent modeling and fitting package for Python which accounts for uncertainties and replaces MPFITFUN: http://cxc.harvard.edu/contrib/sherpa/
Since Sherpa is produced by astronomers it has a lot of built in astrophysical models, but you can build your own function to fit with Sherpa's Levenberg-Marquardt, Nelder-Mead or Monte Carlo algorithms. I used the template from the pysherpa blog: http://pysherpa.blogspot.com/2010/06/user-defined-sherpa-model-types-using.html