[SciPy-user] nonlinear fit with non uniform error?

[massimo sandal]

Hi,

We have a set of data that we fit to a nonlinear function using 
scipy.optimize.leastsq that, AFAIK, uses the Levenberg-Marquardt method.

Talking with a collegue of another lab, he pointed me that the dataset 
we fit usually has intrinsically more noise in the first part of the 
data than the latter. So he fitted by taking into account the non 
uniform error -that is, instead of using plain chi-square, giving more 
weight to the distance from points with less intrinsic error. He told me 
that on Origin there is a function that does it. Is there something 
similar on scipy?

m.

-- 
Massimo Sandal
University of Bologna
Department of Biochemistry "G.Moruzzi"

snail mail:
Via Irnerio 48, 40126 Bologna, Italy

email:
massimo.sandal at unibo.it

tel: +39-051-2094388
fax: +39-051-2094387
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