[AstroPy] Image combination with sigma clipping

[Lisa Alborghetti]

Yes, I think it works now! 
Thank you very much. 

Lisa 

> On 6 Feb 2024, at 17:00, Slavin, Jonathan via AstroPy <astropy at python.org> wrote:
> 
> Hi Lisa,
> 
> Looks to me like the method sigma_clipping of the Combiner object acts on the data in place and has no return statement (and so returns None).
> If that's true then you want something like:
> 
> combiner.sigma_clipping(low_thresh=2, high_thresh=5, func='median', dev_func='mad_std')
> 
> combined_average = combiner.average_combine() 
> 
> Regards,
> Jon
> 
>  
>> Date: Tue, 6 Feb 2024 16:06:40 +0100
>> From: Lisa Alborghetti <lisi.alborghetti at gmail.com <mailto:lisi.alborghetti at gmail.com>>
>> To: astropy at python.org <mailto:astropy at python.org>
>> Subject: [AstroPy] Image combination with sigma clipping
>> Message-ID: <DB96B4E2-270E-4982-9414-EEAF0B56DC05 at gmail.com <mailto:DB96B4E2-270E-4982-9414-EEAF0B56DC05 at gmail.com>>
>> Content-Type: text/plain; charset="utf-8"
>> 
>> Hi everybody,
>> I?m new to AstroPy, and I have what I believe to be a very simple question: I have a .fits file, containing a series of 50 images that I want to combine to create a dark image of my CCD. Before averaging, I would like to perform a sigma_clipping using mad_std. 
>> I was trying with something like this:
>> 
>> from astropy import units as u
>> from astropy.nddata import CCDData
>> from ccdproc import Combiner
>> import numpy as np
>> 
>> ccd = CCDData.read('Dark Current/minus_10/5_sec/Background_fits.fits', unit="adu")
>> 
>> combiner = Combiner(ccd)
>> 
>> combiner_clipped = combiner.sigma_clipping(low_thresh=2, high_thresh=5, func='median', dev_func='mad_std')
>> 
>> combined_average = combiner_clipped.average_combine()  
>> 
>> 
>> But it doesn?t seem to be the right way to do it. In fact, I get:
>> 
>> 
>> ---> 12 combined_average = combiner_clipped.average_combine()  
>> 
>> AttributeError: 'NoneType' object has no attribute ?average_combine?
>> 
>> Can someone help me with this, please?
>> Thank you!
>> 
>> Lisa
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