2023-03-15: APP 2.0.0-beta14 has been released !
IMPROVED FRAME LIST, sorting on column header click and you can move the columns now which will be preserved between restarts.
We are very close now to releasing APP 2.0.0 stable with a complete printable manual...
Astro Pixel Processor Windows 64-bit
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Integration messed up for 1 of 5 channels
I have a fairly large set of files I am trying to integrate up using 1.082. There are 760 L, 149 R, 131 G, 108 B, and 376 Ha. All files register properly and no errors are generated when the integration runs. It takes a while given the large number of large files (camera is QHY600 which has 122 MB FITS files). I am running on Ubuntu 20.04 with 64 MB of RAM 56 MB of which is assigned to app. It is a Ryzen 8 core (16 processes - 15 assigned to app).
After the integration completes I have 4 beautiful integrated FITS: RGB and Ha but the L FITS is basically all white with three black horizontal lines one pixel wide. I was a little worried app might choke on so much data but there is nothing in the console to indicate a problem.
Is there a way to integrate in smaller batches and then register and integrate the integrated files?
That is a strange result, but yes you can chop the data in chunks. Which is what I would advise anyway when you have a lot of data. If each stack consists of something like 40-50 at least, all calibration should work fine and the resulting calibrated stack can then be combined later on with the other subsets.
Stacking into 7 sets of 110 each came out OK but when I tried to stack the subsets the gradients produced artifacts - I think because outlier rejection was behaving badly with only 7 images and cutting out some portions of the images as the gradients moved around due to meridian flips etc.
I was able to stack the majority of the images - I removed 60 and then another 60 subs based on the quality score and background levels and while 760 and 700 images didn't work once I got down to 640 subs it stacked fine without artifacts.
Ok interesting, that might indicate a data problem somehow as usually the gradients shouldn't pose that much of an issue (unless really super apparent).