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

Lorenzo Isella lorenzo.isella@gmail....
Tue Nov 17 16:03:14 CST 2009

> Date: Mon, 16 Nov 2009 23:44:17 -0600
> From: G?khan Sever <gokhansever@gmail.com>
> Subject: [SciPy-User] Fitting a curve on a log-normal distributed data
> To: Discussion of Numerical Python <numpy-discussion@scipy.org>,	SciPy
> 	Users List <scipy-user@scipy.org>
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> 	<49d6b3500911162144x1193e04cj1a103776092c4471@mail.gmail.com>
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> Hello,
> I have a data which represents aerosol size distribution in between 0.1 to
> 3.0 micrometer ranges. I would like extrapolate the lower size down to 10
> nm. The data in this context is log-normally distributed. Therefore I am
> looking a way to fit a log-normal curve onto my data. Could you please give
> me some pointers to solve this problem?
> Thank you.
I have not followed the many replies to this long post in detail, but by 
chance I happen to know quite in detail what you are talking about 
(probably SMPS data or similar).
I normally resort to R for this kind of tasks 
(http://www.r-project.org/), but nothing prevents you from using Python 
instead. You just want to compare your empirical data binning with what 
would be expected from a lognormal distribution. Please have a look at
and at the functions defined there (A1, mu1 and myvar1 are the overall 
concentration, the geometric mean and the std of the number-size 
distribution, respectively).


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