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Code Block
languagepython
#!/usr/bin/env python

# these two lines for example purposes only, to allow user to write
# calibration information to local directory called "calib".
# should be deleted for real analysis.
import psana
psana.setOption('psana.calib-dir','calib')

from xtcav.GenerateDarkBackground import *
GDB=GenerateDarkBackground();
GDB.experiment='xpptut15'
GDB.runs='102'
GDB.maxshots=10  #small number for this example, people often use 1000 shots for this.
GDB.SetValidityRange(101,125) # delete second run number argument to have the validity range be open-ended 
("end")
GDB.Generate();

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Code Block
languagepython
#!/usr/bin/env python

# these two lines for example purposes only, to allow user to write
# calibration information to local directory called "calib".
# should be deleted for real analysis.
import psana
psana.setOption('psana.calib-dir','calib')
 
from xtcav.GenerateLasingOffReference import *
GLOC=GenerateLasingOffReference();
GLOC.experiment='xpptut15'
GLOC.runs='101'
GLOC.maxshots=2  #small number for this example, people often use 1400 shots for this.
GLOC.nb=2
GLOC.islandsplitmethod = 'scipyLabel'       # see confluence documentation for how to set this parameter
GLOC.groupsize=5             # see confluence documentation for how to set this parameter
GLOC.SetValidityRange(101,125) # delete second run number argument to have the validity range be open-ended
 ("end")
GLOC.Generate();

This script can be quite slow.  It can be easily run in parallel by submitting a parallel MPI job to the batch system as described here, however you should change the above script to increase the "maxshots" parameter (so that each core has at least 1 shot to process).  People often use ~1400 shots for this.

Once the dark/lasing-off analysis has been completed, the user can analyze the lasing-on events using the script below.

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