[SciPy-User] How to fit data obtained from a Monte Carlo simulation?

[D K]

Hi everyone

I would like to fit data obtained from a Monte Carlo simulation to 
experimental data, in order to extract two parameters from the 
experiments. There are several issues with this:

a) There is a small element of randomness to each simulated data point; 
we don't actually have a function describing the curve (the overall 
curve shape is reproducible though).
b) I have never performed curve fitting before, and I haven't got a clue 
how to even go about looking for the required information.
b) I don't have a strong maths background.

I tried using optimize.leastsq, but I learnt that, apparently, I ought 
to know the function describing my data to be able to use this (I kept 
researching, as it exited with code 2, claiming that the fit had been 
successful, but it mainly returned the initial guess as the fitting 
result). So I switched to optimize.fmin (having read that it only uses 
the function values); this, however, does not converge and simply exits 
after the maximum number of iterations have been performed.

I can post code or further details if required, but perhaps someone on 
here might already be able to guess what I might be doing wrong, and/or 
point me in the right direction (different fitting function? entirely 
different approach to parameter determination?). I would be very 
grateful for your help.

Thanks a lot in advance!

Kind regards,

Donata


PS: This is the first time I'm writing to this mailing list, I hope I 
will be forgiven in case I made some daft mistake...