...

2012-09-10 meeting: In order to be useful this application should do correct math, accounts for image mask, discard bad events, noisy and "bright" pixels, apply normalization etc, and have a convenient GUI. Below is a list of requirements (marked as R : ) with suggested solutions ( marked as S: ).

Pedestals

R: "dark" run name should be provided by user and pedestals should be evaluated and applied for all runs until the "dark" run name has not changed.
S: For pedestals evaluation: use available ImgAlgos::ImgAverage psana module for "dark" run, which produces file with averaged over events pedestals (also produces the file with rms values).
S: For pedestals subtraction: use ImgAlgos::ImgCalib psana module right before evaluation of pedestals; the pedestals will be subtracted and corrected image will be retained in the event.

Low level threshold.

R: Image pixel intensity physically can't be negative. Low amplitude noise should be suppressed by the threshold. The threshold amplitude should be provided by user (along with substituting amplitude).
S: Add this feature to the ImgAlgos::ImgCalib psana module, right after pedestal evaluation.

...

<--

...

Here

...

is

...

my

...

comments

...

-->

Algorithm

Basic idea is (1) to split image vs time for small parts in image, (2) to process each part on separate computer node, (3) to merge results at the end of processing. It is clear that significant speedup (about T/N_nodes_) is achieved at the 2nd stage. These three stages are performed in separate C++ applications. Wrapping python script allows to submit job by a single command. It takes care about file and sub-process management in this job, as described below.

...