Mar 28 2026 APP 2.0.0-beta40 will be released in 7 days.
It did take a long time to have the work finished on this and it will have a major performance boost of 30-50% over 2.0.0-beta39 from calibration to integration. We extensively optimized many critical parts of APP. All has been tested to guarantee correct optimizations. Drizzle and image resampling is much faster for instance, those modules have been completely rewritten. Much less memory usage. LNC 2.0 will be released which works much better and faster than LNC in it's current state. And more, all will be added to the release notes in the coming weeks...
Update on the 2.0.0 release & the full manual
We are getting close to the 2.0.0 stable release and the full manual. The manual will soon become available on the website and also in PDF format. Both versions will be identical and once released, will start to follow the APP release cycle and thus will stay up-to-date to the latest APP version.
Once 2.0.0 is released, the price for APP will increase. Owner's license holders will not need to pay an upgrade fee to use 2.0.0, neither do Renter's license holders.
It would be very helpful if you can provide us with what you were doing, what OS you're using, what workflow you have, type of data you're trying to process etc. 🙂
<slaps forehead> Oh, sorry. Of course all that would be helpful.
iMac (Retina, 27", 2020), 3.6Mhz 8-Core Intel Core i7 with 64Gb RAM
My usual workflow is to set options in each tab, as required, and then click the Integrate button.
The data I was trying to process was 188 24Mb FITS files created by ASIair Pro using an ASI294MC (cooled.)
The data was transferred from the ASIair to my Mac on a thumb drive and copied to an external SSD
If they're useful, attached is a .zip file, not a txt file, containing several Astro Pixel Processor wakeups_resource.diag reports and one cpu_resource.diag
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Ok, thanks very much. 🙂 So, could you test if this also happens for a small set like 30 images? And if not, if it'll work without calibration data?
Did not get error with smaller set of lights and no calibration frames, nor with small set with calibration frames.
Just ran a test with 249 lights, 20 darks, master flat and master bias. Ran to completion with no error
Interesting. Maybe a good idea to process in smaller subsets with larger amount of data. Like 100 per integration and then combining those. Should work with more data, but depending on the system it seems to help sometimes when dividing the data.
