[SciPy-User] one-sided gauss fit -- or: how to estimate backgound noise ?

Sebastian Haase seb.haase@gmail....
Fri Jul 16 14:34:36 CDT 2010


Zach and Christoph,
thanks for your replies.
I was thinking about 1D-fitting the histogram. And I need this to be
fully automatic so that I can apply that to many subregions of many
images.
I have to think about your suggestions for a while.

Thanks,
Sebastian.


On Fri, Jul 16, 2010 at 1:04 AM, Christoph Deil
<Deil.Christoph@googlemail.com> wrote:
> Hi Sebastian,
>
> in astronomy a method called kappa-sigma-clipping is sometimes used
> to estimate the background level by clipping away most of the signal:
> http://idlastro.gsfc.nasa.gov/ftp/pro/math/meanclip.pro
> I am not aware of a python implementation, but it's just a few lines of code.
>
> If you can identify the background level approximately by eye,
> e.g. by plotting a histogram of your data, you should be able to
> just fit the tail of the Gaussian that only contains background.
>
> Here is my attempt at doing such a fit using scipy.stats.rv_continous.fit(),
> similar to but not exactly what you want:
>
> from scipy.stats import norm, halfnorm, uniform
> signal = - uniform.rvs(0, 3, size=10000)
> background = norm.rvs(size=10000)
> data = hstack((signal, background))
> hist(data, bins=30)
> selection = data[data>0]
> halfnorm.fit(selection)
> x = linspace(-3, 3, 100)
> y = selection.sum() * halfnorm.pdf(x)/3
> plot(x,y)
>
> Good luck!
> Christoph
>
> On Jul 15, 2010, at 10:39 PM, Sebastian Haase wrote:
>
>> Hi,
>> In image analysis one is often faced with (often unknown) background
>> levels (offset) + (Gaussian) background noise.
>> The overall intensity histogram of the image is in fact often Gaussian
>> (Bell shaped), but depending on how many (foreground) objects are
>> present the histogram shows a positive tail of some sort.
>>
>> So, I just got the idea if there was a function (i.e. mathematical
>> algorithm) that would allow to fit only the left half of a Gaussian
>> bell curve to data points !?
>> This would have to be done in a way that the center, the variance (or
>> sigma) and the peak height are free fitting parameters.
>>
>> Any help or ideas are appreciated,
>> thanks
>> Sebastian Haase
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