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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(); |
An example of a non-lasing run is in /reg/g/psdm/tutorials/examplePython/xtcav/xtcavLasingOff.py:
| Code Block | ||
|---|---|---|
| ||
#!/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.
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import psana
# this line is for example purposes only, to allow user to read
# calibration information from local directory called "calib".
# should be deleted for real analysis. It also sets a flag
# ("allow-corrupt-epics") that allows the old example data to be analyzed.
psana.setOptions({'psana.calib-dir':'calib',
'psana.allow-corrupt-epics':True})
from xtcav.ShotToShotCharacterization import *
experiment='xpptut15' #Experiment label
runs='124' #Runs
#Loading the dataset from the "dark" run, this way of working should be compatible with both xtc and hdf5 f
ilesfiles
dataSource=psana.DataSource("exp=%s:run=%s:idx" % (experiment,runs))
#XTCAV Retrieval (setting the data source is useful to get information such as experiment name)
XTCAVRetrieval=ShotToShotCharacterization();
XTCAVRetrieval.SetEnv(dataSource.env())
for r,run in enumerate(dataSource.runs()):
times = run.times()
for t in times:
evt = run.event(t)
if not XTCAVRetrieval.SetCurrentEvent(evt):
continue
time,power,ok=XTCAVRetrieval.XRayPower()
agreement,ok=XTCAVRetrieval.ReconstructionAgreement() |
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