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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='102300'
GDB.maxshots=10  #small number for this example, people often use 1000 shots for this.
GDB.SetValidityRange(101300,125302) # delete second run number argument to have the validity range be open-ended ("end")
GDB.Generate();

#!/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='101301'
GLOC.maxshots=2  #small number for this example, people often use 1400 shots for this.
GLOC.nb=1
GLOC.islandsplitmethod = 'scipyLabel'       # see confluence documentation for how to set this parameter
GLOC.groupsize=140             # see confluence documentation for how to set this parameter
GLOC.SetValidityRange(101300,125302) # delete second run number argument to have the validity range be open-ended ("end")
GLOC.Generate();

importfrom 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 files
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()import *
from xtcav.ShotToShotCharacterization import *
ds=DataSource('exp=xpptut15:run=302:smd')
XTCAVRetrieval=ShotToShotCharacterization();
XTCAVRetrieval.SetEnv(ds.env())
import matplotlib.pyplot as plt
ngood = 0
for evt in ds.events():
    if not XTCAVRetrieval.SetCurrentEvent(evt): continue
    time,power,ok=XTCAVRetrieval.XRayPower()  
    if not ok: continue
    agreement,ok=XTCAVRetrieval.ReconstructionAgreement()
    if not ok: continue
    ngood += 1
    # time and power are lists, with each entry corresponding to
    # a bunch number.  The number of bunches is set by the GLOC.nb
    # parameter in the lasing-off analysis.  In general, one should
    # also cut on the "agreement" value, which measures the agreement
    # between the first and second moment analysis (larger is better).
    plt.plot(time[0],power[0])
    plt.xlabel('Time (fs)')
    plt.ylabel('Lasing Power (GW)')
    plt.title('Agreement %4.2f'%agreement)
    plt.show()
    if ngood>1: break