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05:54 | Bertl_oO | off to bed now ... have a good one everyone!
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05:54 | Bertl_oO | changed nick to: Bertl_zZ
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08:30 | se6astian | good da
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08:30 | se6astian | y
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09:22 | vup | some more flatfield analysis
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09:23 | vup | there seem to be a lot of pixels that get very low pixel values and show very nonlinear response curves in that region
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09:23 | vup | https://files.niemo.de/corr_1.9932959999999997_by_13.993392.pdf
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09:24 | vup | https://files.niemo.de/corr_2.9964_by_13.993392.pdf
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09:24 | vup | https://files.niemo.de/corr_6.996432_by_13.993392.pdf
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09:25 | vup | (The flatfields for each exposure is a average of 256 flatfields)
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09:27 | vup | The plots show a 2d hexbin histogram of the original pixel value on the x axis and the ratio between the original pixel value divided by the value of that pixel for the 13ms exposure
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09:28 | vup | If the response curve of all pixels were the same we would expect no correlation between x and y
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09:28 | vup | But there clearly is
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09:29 | vup | (Or atleast no correlation if the response curve were linear)
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09:38 | vup | Any thoughts?
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09:39 | vup | I guess we will see more when se6astian has time to capture more flatfields
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09:39 | vup | All in all it looks like flatfield calibration will be very important atm
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11:07 | se6astian | Great progress
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11:07 | se6astian | I am in Linz currently demanteling the kitchen of the mother in law
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11:08 | se6astian | Will capture more next week
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11:49 | anuejn | vup: veery interesting
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11:50 | anuejn | looks kinda bad though
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11:50 | anuejn | I hoped everything would be much more linear
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11:51 | anuejn | though I dont know how to interpret these plots exactly
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13:37 | Bertl_zZ | changed nick to: Bertl
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13:37 | Bertl | morning folks!
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13:43 | vup | anuejn: well i don't think its bad really
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13:44 | vup | Looks pretty easy to model actually
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13:44 | vup | For exactly linear you would expect the same y for every x
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13:44 | vup | (If you double the exposure, the value should double)
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13:45 | vup | And it seems like this actually is the case for many pixels
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13:45 | vup | (Atleast roughly)
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13:46 | vup | Oh also the per color version of this is pretty interesting
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14:06 | Bertl | hmm, it looks like half of the sensel have one factor and the other half a different one, no?
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14:10 | vup | ah whoops
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14:11 | vup | these were already just one color
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14:11 | vup | i updated them inplace to show all pixels and each of {even,odd} rows, {even,odd} columns
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14:11 | vup | Bertl: well not really, there seem to be pixels that are more sensitive, that seem to have a approx linear factor
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14:13 | vup | and then there are pixels that are less sensitive, that seem nonlinear, maybe exponential? (ie doubeling the original pixel value seems to double the factor between the exposrues
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14:13 | vup | anuejn: ^ updated them implace with per color plots
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14:16 | anuejn | thats interesting
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14:23 | Bertl | vup: hmm, is there a pattern to the linear vs the nonlinear one? what's the ratio between the 'linear' and the 'nonlinear' ones?
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14:23 | se6astian | Do you consider an offset image and a factor image is still the best way for compensation?
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16:34 | vup | Well darkframe / offset image seems definitely like a good choice
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16:34 | vup | Maybe with a constant and a nonuniform (exposure scaled) part
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16:35 | vup | But for linearization it seems like a simple factor probably wont cut it
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16:36 | vup | Bertl: the obvious pattern is that the nonlinear ones seem to only occur in odd rows
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16:36 | vup | The rest ill look into later
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