As reported by @tapio-not:
I encountered a new issue with the astropy/ccdproc, specifically
subtract_overscan.
The "problem" is that the data I have has occasional bad first line
with values of 4e9 instead of about 10 000 (ADU). This is a CCD-controller
issue, so I just have to live with that.
So, if I fit " models.Polynomial1D(1)" this fails because I have not found
"sigma clipping" option in this "task" as with iraf/ccdproc there is
"Low and high sigma rejection factors for rejecting deviant
points from the overscan fit."
Of course I can replace the 4e9 values with something smaller, but much
more elegant solution would be to have "sigma clipping" while fitting the
data. I guess that using "NDData uncertainty" could do the job,
but I'm not familiar enough with that and anyway this might be an
overkill in comparison to reject one/few points from a fit.
As reported by @tapio-not:
I encountered a new issue with the astropy/ccdproc, specifically
subtract_overscan.The "problem" is that the data I have has occasional bad first line
with values of 4e9 instead of about 10 000 (ADU). This is a CCD-controller
issue, so I just have to live with that.
So, if I fit " models.Polynomial1D(1)" this fails because I have not found
"sigma clipping" option in this "task" as with iraf/ccdproc there is
"Low and high sigma rejection factors for rejecting deviant
points from the overscan fit."
Of course I can replace the 4e9 values with something smaller, but much
more elegant solution would be to have "sigma clipping" while fitting the
data. I guess that using "NDData uncertainty" could do the job,
but I'm not familiar enough with that and anyway this might be an
overkill in comparison to reject one/few points from a fit.