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Code Block | ||
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#!/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 | ||
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#!/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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