[SciPy-User] Fitting a curve on a log-normal distributed data
Tue Nov 17 16:03:14 CST 2009
> Date: Mon, 16 Nov 2009 23:44:17 -0600
> From: G?khan Sever <firstname.lastname@example.org>
> Subject: [SciPy-User] Fitting a curve on a log-normal distributed data
> To: Discussion of Numerical Python <email@example.com>, SciPy
> Users List <firstname.lastname@example.org>
> Content-Type: text/plain; charset="utf-8"
> 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
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