[JUNGFRAU][DARK][BURST] Convert to float only within each process to save memory for > 4800 trains
Fixing error with memory allocation for dark processing of adaptive burst mode runs.
Description
@mramilli contacted me regarding dark processing failure for SPB adaptive burst mode dark processing.
For normal operation in adaptive burst mode, medium and low runs are of 300 trains X 16 memory cells.
I am not sure why I didn't face this issue while developing this part, but I assume that these runs were not as big as we normally operate.
For the FXE reference run it had the same issue and it failed in the automated tests but I mistakenly overlooked it as all jungfraus failed due to h5file validation as expected.
To avoid this issue I am basically converting the images type inside the memory cells parallel processes. This leads to using half of the previously used memory.
FXE_XAD_JF1M-DARK-BURST_220303_151642.pdf
How Has This Been Tested?
Test with automated test against for all reference runs at: https://git.xfel.eu/detectors/pycalibration/-/merge_requests/610
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Types of changes
- Bug fix (non-breaking change which fixes an issue)
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added 1 commit
- 177c196b - convert to float only within each process to save memory
- Resolved by Karim Ahmed
added 1 commit
- fe3579c6 - convert to float only within each process to save memory
Thanks for the fast review!
Edited by Karim Ahmedenabled an automatic merge when the pipeline for fe3579c6 succeeds
mentioned in commit 22023f3f