2026-10-09 APP 2.0.0-beta48 has been released !
This is mainly a bug fix release for the reported bugs in beta47. In addition it has a significant User Interface performance improvement noticable when you need to process 1000s of images in APP.
2026-10-03 APP 2.0.0-beta47 has been released !
This is a very big release... many code optimizations/rewrites, many parts of APP are much faster and more precise, especially star analysis and registration are much faster ! Big mosaics will go much faster 🙂
And we introduce a new preview filter, which includes the new Adaptive Deep Sky Stretch and a General Levels Stretch, this has been long over due. The old preview filter (the panel on the right side to stretch and preview your data) has not been updated for a couple of years and it is a critical part of any APP session.
Adaptive Deep Sky Stretch or ADSS is a much more intuitive stretch algorithm. It is also color preserving (which is a great new addition in itself ! ) which means that the highlights are no longer bleached, color is maintained from the linear data while stretching. You can simply stretch your data using the sky background level and signal width sliders which work much easier and more intuitive. You no longer need to fiddle with the old complicated sliders with zoom buttons. All the zoom buttons for the sliders are made redundant. And you can enter slider values in the slider value fields yourself. I will make a youtube video in the coming days to explain and fully introduce ADSS. Once the video is ready, it will be highlighted on the forum.
RGB Combine upgrade !. The RGB Combine Tool has been rewritten mostly. The tool is faster and easier to use and most importantly, the Luminance slider to inject a luminance channel or channels into the RGB composite is now working correctly. It no longer bleaches the RGB colors. Combined with the new ADSS preview, you will get much better and easier great composites with great color! I will also explain this in a new youtube video.
Hi,
I wanted to integrate data from two different instruments. Different scopes and different cameras with different formats and resolutions. I followed a tutorial here:
However, when I came to the integration part I had this error message:
Encountered error in module:
Integrator
Error message:
java.lang.ArrayIndexOutOfBoundsException: Index 1 out of bounds for length 1
Cause:
Index 1 out of bounds for length 1
rD.a(Unknown Source)
rD.doInBackground(Unknown Source)
java.desktop/javax.swing.SwingWorker$1.call(Unknown Source)
java.base/java.util.concurrent.FutureTask.run(Unknown Source)
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I am running the 2.0.0 beta 6 version. According to the tutorial this integration should work, but it didn't.
However, the images were also of different types, one was OSC color, and the other was mono. Could it be that
this is the reason for the crash?
Regards
Ulf Granlund
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Could you try to first separate the OSC data into mono channels? This can be done by selecting "split channels" in tab 2 (all the way down) and then save those. Load in the split frames as mono and add the other mono data and try again.
Hi, thanks, yes splitting the color image in channels does make it possible to integrate them separately, as well as the mono nb data. But the only way I can see to put it all toghether is to use RGB combination. This means that I can not get the advantages from a local normalization correction when integrating data from different instruments with different image sizes and orientation. The tutorial I mentioned show the great advantages of this. But it uses only color images. Is there a way to get local normalization while working with both color data and nb mono data from several different intstruments?
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And, secondly, while it may not be possible to integrate mixed types of data, I think APP should say this in some other way then the "Out of bounds" error. Â
You can actually, if you have all mono data you can load those in again as lights and just process all those together. Easiest is to have those calibrated first with their own calibration data (in the case of different sensors).
