On 20/10/06, Sebastian Żurek <[EMAIL PROTECTED]> wrote:
> Is there something like that in any numerical python modules (numpy,
> pylab) I could use?
In scipy there are some very convenient spline fitting tools which
will allow you to fit a nice smooth spline through the simulation data
points (or near, if they have some uncertainty); you can then easily
look at the RMS difference in the y values. You can also, less easily,
look at the distance from the curve allowing for some uncertainty in
the x values.
I suppose you could also fit a curve through the experimental points
and compare the two curves in some way.
> I can imagine, I can fit the data with some polynomial or whatever,
> and than compare the fitted data, but my goal is to operate on
> as raw data as it's possible.
If you want to avoid using an a priori model, Numerical Recipes
discuss some possible approaches ("Do two-dimensional distributions
differ?" at http://www.nrbook.com/a/bookcpdf.html is one) but it's not
clear how to turn the problem you describe into a solvable one - some
assumption about how the models vary between sampled x values appears
to be necessary, and that amounts to interpolation.
A. M. Archibald
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