[SciPy-User] [Numpy-discussion] Fitting a curve on a log-normal distributed data

Gökhan Sever gokhansever@gmail....
Tue Nov 17 16:42:44 CST 2009


On Tue, Nov 17, 2009 at 4:27 PM, Robert Kern <robert.kern@gmail.com> wrote:

> On Tue, Nov 17, 2009 at 16:21, Gökhan Sever <gokhansever@gmail.com> wrote:
>
> > Besides, what is wrong with using the spline interpolation technique? It
> > fits nicely on my sample data. See the resulting image here:
> > http://img197.imageshack.us/img197/9638/sizeconcsplinefit.png    (Green
> line
> > represents the fit spline)
>
> What spline interpolation technique?


>From here
http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html

Spline interpolation in 1-d (interpolate.splXXX)


That certainly doesn't look like
> a good spline fit.


True, because I used only 30 points. It looks much smoother with alot more
point as you might expected.


> In any case, splines may be fine for
> *interpolation*, but you need *extrapolation*, and splines are useless
> for that.
>
You need a physically-motivated model like the distributions
> recommended by your textbook.
>
>
Using spline-interp is a test case to see how good it will do on my data. I
will use log-normal way as was in the original intention. Let me check with
someone else in the department to get some feedback on this before I
completely get lost in the matter.

One quick question: "extrapolation" means to estimate a data both "beyond"
and "below" the given limits, right? (For my example to guess less than
0.1um should I say downward-extrapolation and above 3.0 um
upward-extrapolation or just extrapolation is enough?)



> --
> Robert Kern
>
> "I have come to believe that the whole world is an enigma, a harmless
> enigma that is made terrible by our own mad attempt to interpret it as
> though it had an underlying truth."
>  -- Umberto Eco
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-- 
Gökhan
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