def onEvent(evt):
# ------------------- #
# INITIAL DIAGNOSTICS #
# ------------------- #
# Time measurement
analysis.event.printProcessingRate()
# ------- #
# RECORDS #
# ------- #
# -------------------- #
# DETECTOR CORRECTIONS #
# -------------------- #
if do_cmc:
analysis.pixel_detector.cmc_pnccd(evt, back_type, back_key)
analysis.pixel_detector.cmc_pnccd(evt, front_type, front_key)
# -------- #
# ANALYSIS #
# -------- #
# Simple hitfinding (Count Nr. of lit pixels)
analysis.hitfinding.countLitPixels(
evt, back_type, back_key,
aduThreshold=adu_threshold,
hitscoreThreshold=min_lit_pixels,
hitscoreMax=max_lit_pixels,
mask=mask_back)
hit = evt["analysis"]["isHit - " + back_key].data
# ------------------------ #
# Send RESULT TO INTERFACE #
# ------------------------ #
# -------------------- #
# RECORD data for hits #
# -------------------- #
if hit:
recorder.append(evt)
def onEvent(evt):
# ------------------- #
# INITIAL DIAGNOSTICS #
# ------------------- #
# Time measurement
analysis.event.printProcessingRate()
#analysis.event.printID(evt["eventID"])
#print evt["photonPixelDetectors"].keys()
#from IPython.core.debugger import Tracer
#Tracer()()
#for k in evt["parameters"].keys(): print k
#print evt["parameters"].keys()
#print evt["parameters"][injector_x_key]
#print evt["parameters"][pnccd_x_key]
#print evt["parameters"][injector_y_key]
#print evt["parameters"][injector_z_key]
#print evt.native_keys()
#evt["psana.PNCCD.FramesV1"]["DetInfo(Camp.0:pnCCD.0)"]
# ------- #
# RECORDS #
# ------- #
# Injector motor positions
inj_x = evt["parameters"][injector_x_key]
inj_y = evt["parameters"][injector_y_key]
inj_z = evt["parameters"][injector_z_key]
# Injector pressures
#p1 = evt["parameters"]["CXI:SDS:REG:01:PRESS"]
#p2 = evt["parameters"]["CXI:SDS:REG:02:PRESS"]
# -------------------- #
# DETECTOR CORRECTIONS #
# -------------------- #
if do_cmc:
# CMC
analysis.pixel_detector.cmc_pnccd(evt, back_type, back_key)
analysis.pixel_detector.cmc_pnccd(evt, front_type, front_key)
back_type_s = "analysis"
back_key_s = "cmc_pnccd - " + back_key
front_type_s = "analysis"
front_key_s = "cmc_pnccd - " + front_key
if not do_cmc:
back_type_s = back_type
back_key_s = back_key
front_type_s = front_type
front_key_s = front_key
# COLLECTING BACKGROUND
if do_stacks:
# Update
bg_front.add(evt[front_type_s][front_key_s].data)
bg_back.add(evt[back_type_s][back_key_s].data)
# Reduce
bg_front.reduce()
bg_back.reduce()
# Write to file
#bg_front.write(evt,directory=bg_dir)
#bg_back.write(evt,directory=bg_dir)
#print data
#data = bg_front.mean()
#bg_front_mean = add_record(evt["analysis"], "analysis", "pnCCD front (mean)", data, unit='')
#plotting.image.plotImage(evt["analysis"]["pnCCD front (mean)"],
# msg='', name="pnCCD front (mean)", vmin=0, vmax=10000)
#data = bg_front.max()
#bg_front_max = add_record(evt["analysis"], "analysis", "pnCCD front (max)", data, unit='')
#plotting.image.plotImage(evt["analysis"]["pnCCD front (max)"],
# msg='', name="pnCCD front (max)", vmin=0, vmax=10000)
#data = bg_back.mean()
#bg_back_mean = add_record(evt["analysis"], "analysis", "pnCCD back (mean)", data, unit='')
#plotting.image.plotImage(evt["analysis"]["pnCCD back (mean)"],
# msg='', name="pnCCD back (mean)", vmin=0, vmax=10000)
data = bg_back.max()
bg_back_max = add_record(evt["analysis"], "analysis", "pnCCD back (max)", data, unit='')
plotting.image.plotImage(evt["analysis"]["pnCCD back (max)"],
msg='', name="pnCCD back (max)")#, vmin=0, vmax=10000)
data = bg_back.sum()
bg_back_sum = add_record(evt["analysis"], "analysis", "pnCCD back (sum)", data, unit='')
plotting.image.plotImage(evt["analysis"]["pnCCD back (sum)"],
msg='', name="pnCCD back (sum)")#, vmin=0, vmax=10000)
# -------- #
# ANALYSIS #
# -------- #
# Simple hitfinding (Count Nr. of lit pixels)
analysis.hitfinding.countLitPixels(evt, back_type, back_key, aduThreshold=1600, hitscoreThreshold=3600, hitscoreMax=500000, mask=mask_back)
# Compute the hitrate
analysis.hitfinding.hitrate(evt, evt["analysis"]["isHit - " + back_key], history=10000)
hit = evt["analysis"]["isHit - " + back_key].data
# ------------------------ #
# Send RESULT TO INTERFACE #
# ------------------------ #
# Plot the hitscore
#plotting.line.plotHistory(evt["analysis"]["hitscore - " + back_key], label='Nr. of lit pixels')
plotting.line.plotHistory(evt["analysis"]["hitscore - " + back_key], runningHistogram=True, hmin=0, hmax=5000, bins=100, window=100, history=1000)
# Plot the hitrate
plotting.line.plotHistory(evt["analysis"]["hitrate"], label='Hit rate [%]')
# Plot the hitrate
add_record(evt["analysis"], "analysis", "sum", evt[back_type][back_key].data.sum(), unit="")
plotting.line.plotHistory(evt["analysis"]["sum"], label='Sum [ADU]')
# Plot MeanMap of hitrate(y,z)
if False:
# TESTING
x = x_min + numpy.random.rand() * (x_max-x_min)
add_record(evt["analysis"], "analysis", "x", x, unit='')
x = evt["analysis"]["x"]
z = z_min + numpy.random.rand() * (z_max-z_min)
add_record(evt["analysis"], "analysis", "z", z, unit='')
z = evt["analysis"]["z"]
h = float(numpy.random.randint(2))
plotting.correlation.plotMeanMap(x, z, h, plotid='hitrateMeanMap', **hitrateMeanMapParams)
# Pulse Energy
#plotting.line.plotHistory(evt["analysis"]["averagePulseEnergy"])
# Perform sizing on hits
if do_showall:
plotting.image.plotImage(evt[front_type_s][front_key_s],
msg='', name="pnCCD front", mask=mask_front)
plotting.image.plotImage(evt[back_type_s][back_key_s],
msg='', name="pnCCD back", mask=mask_back)
# Perform sizing on hits
if hit:
plotting.image.plotImage(evt[front_type_s][front_key_s],
msg='', name="pnCCD front (hit)", vmin=0, vmax=10000, mask=mask_front)
plotting.image.plotImage(evt[back_type_s][back_key_s],
msg='', name="pnCCD back (hit)", vmin=0, vmax=10000, mask=mask_back)
# Record time stamps and other metadata for hits
recorder.append(evt)
def onEvent(evt):
# ------------------- #
# INITIAL DIAGNOSTICS #
# ------------------- #
# Time measurement
analysis.event.printProcessingRate()
#analysis.event.printID(evt["eventID"])
#print evt.native_keys()
# Send Fiducials and Timestamp
plotting.line.plotTimestamp(evt["eventID"]["Timestamp"])
# Spit out a lot for debugging
if do_diagnostics: diagnostics.initial_diagnostics(evt)
# -------- #
# ANALYSIS #
# -------- #
#print evt.native_keys()
# AVERAGE PULSE ENERGY
analysis.beamline.averagePulseEnergy(evt, "pulseEnergies")
# HIT FINDING
#analysis.hitfinding.countTof(evt, "ionTOFs", "Acqiris 0 Channel 0")
# Simple hit finding by counting lit pixels
analysis.hitfinding.countLitPixels(evt, c2x2_type, c2x2_key, aduThreshold=aduThreshold, hitscoreThreshold=hitscoreThreshold, hitscoreDark=hitscoreDark, mask=mask_c2x2)
hit = evt["analysis"]["isHit - " + c2x2_key].data
miss = evt["analysis"]["isMiss - " + c2x2_key].data
# CAMERA
doing_camera = False
if do_camera and "Sc2Questar[camimage]" in evt["camimage"]:
analysis.injection_camera.getMaskedParticles(evt, "camimage", "Sc2Questar[camimage]", "maskedcamera", minX = 200, maxX = 1300, thresh = 30)
analysis.injection_camera.countContours(evt, "image", "Sc2Questar[camimage]", "maskedcamera", "coloredmask", "particlestream")
doing_camera = True
# COUNT PHOTONS
# Count photons in different detector regions
analysis.pixel_detector.totalNrPhotons(evt, c2x2_type, c2x2_key, aduPhoton=20, aduThreshold=10)
if do_front:
analysis.pixel_detector.getCentral4Asics(evt, clarge_type, clarge_key)
if do_assemble_front:
analysis.pixel_detector.assemble(evt, clarge_type, clarge_key, x=x_front, y=y_front, nx=400, ny=400, subset=map(lambda i : (i * 8 + 1) * 2, xrange(4)))
analysis.pixel_detector.totalNrPhotons(evt, clarge_type, clarge_key, aduPhoton=1, aduThreshold=0.5)
analysis.pixel_detector.totalNrPhotons(evt, "analysis", "central4Asics", aduPhoton=1, aduThreshold=0.5)
if miss or bgall:
#print "MISS (hit score %i < %i)" % (evt["analysis"]["hitscore - " + c2x2_key].data, hitscoreThreshold)
# COLLECTING BACKGROUND
# Update
bg.add(evt[c2x2_type][c2x2_key].data)
# Reduce
bg.reduce()
# Write to file
bg.write(evt,directory=bg_dir)
if hit:
print "HIT (hit score %i > %i)" % (evt["analysis"]["hitscore - " + c2x2_key].data, hitscoreThreshold)
good_hit = False
if do_sizing:
if do_cmc:
# CMC
analysis.pixel_detector.cmc(evt, c2x2_type, c2x2_key, mask=beamstops_c2x2)
c2x2_type_s = "analysis"
c2x2_key_s = "cmc - " + c2x2_key
if do_bgsub:
# Running background subtraction
analysis.pixel_detector.bgsub(evt, c2x2_type_s, c2x2_key_s, bg=bg.last_mean)
c2x2_type_s = "analysis"
c2x2_key_s = "bgsub - " + c2x2_key_s
if not do_cmc and not do_bgsub:
c2x2_type_s = c2x2_type
c2x2_key_s = c2x2_key
# RADIAL SPHERE FIT
# Find the center of diffraction
analysis.sizing.findCenter(evt, c2x2_type_s, c2x2_key_s, mask=mask_c2x2, **centerParams)
# Calculate radial average
analysis.pixel_detector.radial(evt, c2x2_type_s, c2x2_key_s, mask=mask_c2x2, cx=evt["analysis"]["cx"].data, cy=evt["analysis"]["cy"].data)
# Fitting sphere model to get size and intensity
analysis.sizing.fitSphereRadial(evt, "analysis", "radial distance - " + c2x2_key_s, "radial average - " + c2x2_key_s, **dict(modelParams, **sizingParams))
# Calculate diffraction pattern from fit result
analysis.sizing.sphereModel(evt, "analysis", "offCenterX", "offCenterY", "diameter", "intensity", (ny_c2x2,nx_c2x2), poisson=False, **modelParams)
# Calculate radial average of diffraction pattern from fit result
analysis.pixel_detector.radial(evt, "analysis", "fit", mask=mask_c2x2, cx=evt["analysis"]["cx"].data, cy=evt["analysis"]["cy"].data)
# Decide whether or not the fit was successful
fit_succeeded = evt["analysis"]["fit error"].data < fit_error_threshold
if fit_succeeded:
# Decide whether or not this was a good hit, i.e. a hit in the expected size range
good_hit = abs(evt["analysis"]["diameter"].data - diameter_expected) <= diameter_error_max
backend.add_record(evt["analysis"], "analysis", "Good hit rate", float(good_hit))
# Record hits together with sizing results
recorder.setup_file_if_needed(evt)
recorder.append(evt)
def onEvent(evt):
# ------------------- #
# INITIAL DIAGNOSTICS #
# ------------------- #
# Time measurement
analysis.event.printProcessingRate()
#analysis.event.printID(evt["eventID"])
#print evt.native_keys()
# Send Fiducials and Timestamp
plotting.line.plotTimestamp(evt["eventID"]["Timestamp"])
# Spit out a lot for debugging
if do_diagnostics: diagnostics.initial_diagnostics(evt)
# -------- #
# ANALYSIS #
# -------- #
#print evt.native_keys()
# AVERAGE PULSE ENERGY
analysis.beamline.averagePulseEnergy(evt, "pulseEnergies")
# HIT FINDING
#analysis.hitfinding.countTof(evt, "ionTOFs", "Acqiris 0 Channel 0")
# Simple hit finding by counting lit pixels
analysis.hitfinding.countLitPixels(evt, c2x2_type, c2x2_key, aduThreshold=aduThreshold, hitscoreThreshold=hitscoreThreshold, hitscoreDark=hitscoreDark, mask=mask_c2x2)
hit = evt["analysis"]["isHit - " + c2x2_key].data
miss = evt["analysis"]["isMiss - " + c2x2_key].data
# CAMERA
doing_camera = False
if do_camera and "Sc2Questar[camimage]" in evt["camimage"]:
analysis.injection_camera.getMaskedParticles(evt, "camimage", "Sc2Questar[camimage]", "maskedcamera", minX = 200, maxX = 1300, thresh = 30)
analysis.injection_camera.countContours(evt, "image", "Sc2Questar[camimage]", "maskedcamera", "coloredmask", "particlestream")
doing_camera = True
# COUNT PHOTONS
# Count photons in different detector regions
analysis.pixel_detector.totalNrPhotons(evt, c2x2_type, c2x2_key, aduPhoton=20, aduThreshold=10)
if do_front:
analysis.pixel_detector.getCentral4Asics(evt, clarge_type, clarge_key)
if do_assemble_front:
analysis.pixel_detector.assemble(evt, clarge_type, clarge_key, x=x_front, y=y_front, nx=400, ny=400, subset=map(lambda i : (i * 8 + 1) * 2, xrange(4)))
analysis.pixel_detector.totalNrPhotons(evt, clarge_type, clarge_key, aduPhoton=1, aduThreshold=0.5)
analysis.pixel_detector.totalNrPhotons(evt, "analysis", "central4Asics", aduPhoton=1, aduThreshold=0.5)
if miss or bgall:
#print "MISS (hit score %i < %i)" % (evt["analysis"]["hitscore - " + c2x2_key].data, hitscoreThreshold)
# COLLECTING BACKGROUND
# Update
bg.add(evt[c2x2_type][c2x2_key].data)
# Reduce
bg.reduce()
# Write to file
bg.write(evt,directory=bg_dir)
if hit:
print "HIT (hit score %i > %i)" % (evt["analysis"]["hitscore - " + c2x2_key].data, hitscoreThreshold)
good_hit = False
if do_sizing:
if do_cmc:
# CMC
analysis.pixel_detector.cmc(evt, c2x2_type, c2x2_key, mask=beamstops_c2x2)
c2x2_type_s = "analysis"
c2x2_key_s = "cmc - " + c2x2_key
if do_bgsub:
# Running background subtraction
analysis.pixel_detector.bgsub(evt, c2x2_type_s, c2x2_key_s, bg=bg.last_mean)
c2x2_type_s = "analysis"
c2x2_key_s = "bgsub - " + c2x2_key_s
if not do_cmc and not do_bgsub:
c2x2_type_s = c2x2_type
c2x2_key_s = c2x2_key
# RADIAL SPHERE FIT
# Find the center of diffraction
analysis.sizing.findCenter(evt, c2x2_type_s, c2x2_key_s, mask=mask_c2x2, **centerParams)
# Calculate radial average
analysis.pixel_detector.radial(evt, c2x2_type_s, c2x2_key_s, mask=mask_c2x2, cx=evt["analysis"]["cx"].data, cy=evt["analysis"]["cy"].data)
# Fitting sphere model to get size and intensity
analysis.sizing.fitSphereRadial(evt, "analysis", "radial distance - " + c2x2_key_s, "radial average - " + c2x2_key_s, **dict(modelParams, **sizingParams))
# Calculate diffraction pattern from fit result
analysis.sizing.sphereModel(evt, "analysis", "offCenterX", "offCenterY", "diameter", "intensity", (ny_c2x2,nx_c2x2), poisson=False, **modelParams)
# Calculate radial average of diffraction pattern from fit result
analysis.pixel_detector.radial(evt, "analysis", "fit", mask=mask_c2x2, cx=evt["analysis"]["cx"].data, cy=evt["analysis"]["cy"].data)
# Decide whether or not the fit was successful
fit_succeeded = evt["analysis"]["fit error"].data < fit_error_threshold
if fit_succeeded:
# Decide whether or not this was a good hit, i.e. a hit in the expected size range
good_hit = abs(evt["analysis"]["diameter"].data - diameter_expected) <= diameter_error_max
backend.add_record(evt["analysis"], "analysis", "Good hit rate", float(good_hit))
# Record hits together with sizing results
recorder.append(evt)
# ------------------------ #
# Send RESULT TO INTERFACE #
# ------------------------ #
# Send stuf from particle stream
if doing_camera:
plotting.line.plotHistogram(evt["analysis"]["particlestream"], log10=True, hmin=1, hmax=8, bins=1000)
plotting.image.plotImage(evt["camimage"]["Sc2Questar[camimage]"], msg="")
plotting.image.plotImage(evt["analysis"]["maskedcamera"], msg="", name="Masked Opal image")
# If not miss or hit, probably dark run -> do not send anything
#if not (miss or hit):
# return
# Pulse Energy
plotting.line.plotHistory(evt["analysis"]["averagePulseEnergy"])
# Injector position (z is along the beam, x is across the beam)
x = evt["parameters"][injector_x_key]
#y = evt["parameters"][injector_y_key]
z = evt["parameters"][injector_z_key]
plotting.line.plotHistory(x)
#plotting.line.plotHistory(y)
plotting.line.plotHistory(z)
# Injector pressures
p1 = evt["parameters"]["CXI:SDS:REG:01:PRESS"]
p2 = evt["parameters"]["CXI:SDS:REG:02:PRESS"]
# HITFINDING
# Keep hit history for hitrate plots
plotting.line.plotHistory(evt["analysis"]["isHit - " + c2x2_key])
# Plot MeanMap of hitrate(y,z)
#plotting.correlation.plotMeanMap(x, z, evt["analysis"]["hitscore - " + ]], plotid='hitrateMeanMap', **hitrateMeanMapParams)
# Keep hitscore history
plotting.line.plotHistory(evt["analysis"]["hitscore - " + c2x2_key], runningHistogram=True, hmin=hitscoreThreshold-100, hmax=hitscoreThreshold+100, bins=100, window=100, history=1000)
# PHOTON COUNTING
# Keep history of number of photons on the back
plotting.line.plotHistory(evt["analysis"]["nrPhotons - " + c2x2_key], runningHistogram=True, hmin=hitscoreThreshold-100, hmax=hitscoreThreshold+100, bins=100, window=100, history=1000)
# Nr. of photons
plotting.line.plotHistory(evt["analysis"]["nrPhotons - " + c2x2_key])
plotting.line.plotHistory(evt["analysis"]["nrPhotons - " + c2x2_key], runningHistogram=True, hmin=0, hmax=100000, bins=100, window=100, history=1000)
if do_front:
plotting.line.plotHistory(evt["analysis"]["nrPhotons - central4Asics"])
# Plot ScatterPlot with colored hitscore
#plotting.correlation.plotScatterColor(x,z, evt["analysis"]["hitscore - " + c2x2_key], plotid='hitscoreScatter', vmin=0, vmax=2000, xlabel='Injector X', ylabel='Injectory Z', zlabel='Hitscore')
if do_showall:
# Image of back detector for all events
plotting.image.plotImage(evt[c2x2_type][c2x2_key], msg="", name="Cspad 2x2: All", mask=mask_c2x2, vmin=vmin_c2x2, vmax=vmax_c2x2)
# Histogram of detector for all events
plotting.line.plotHistogram(evt[c2x2_type][c2x2_key], mask=mask_c2x2, hmin=-100, hmax=100, bins=200, label='Cspad 2x2 pixel value [ADU]')
# THIS HERE CAUSED A CRASH
#plotting.correlation.plotMeanMap(evt["analysis"]["averagePulseEnergy"], evt["analysis"]["nrPhotons - central4Asics"], hit)
#plotting.line.plotHistory(evt["analysis"]["averagePulseEnergy"])
if hit:
# Scatter plot of hitscore vs. injector in Z
plotting.correlation.plotScatter(x, evt["analysis"]["hitscore - " + c2x2_key], plotid='tuneInjectionX', history=10000, xlabel='Injector in X', ylabel='Hitscore')
plotting.correlation.plotScatter(z, evt["analysis"]["hitscore - " + c2x2_key], plotid='tuneInjectionZ', history=10000, xlabel='Injector in Z', ylabel='Hitscore')
# Scatter plot for hitscore vs. injector pressure
plotting.correlation.plotScatter(p1, evt["analysis"]["hitscore - " + c2x2_key], plotid='tuneInjectionP1', history=10000, xlabel='Injector in X', ylabel='Hitscore')
plotting.correlation.plotScatter(p2, evt["analysis"]["hitscore - " + c2x2_key], plotid='tuneInjectionP2', history=10000, xlabel='Injector in Z', ylabel='Hitscore')
# ToF
plotting.line.plotTrace(evt["ionTOFs"]["Acqiris 0 Channel 1"])
# Image of hit
plotting.image.plotImage(evt[c2x2_type][c2x2_key], msg='', name="Cspad 2x2: Hit", vmin=vmin_c2x2, vmax=vmax_c2x2 )
if do_sizing:
if do_cmc or do_bgsub:
# Image of hit (cmc corrected)
plotting.image.plotImage(evt[c2x2_type_s][c2x2_key_s], msg="", mask=mask_c2x2, name="Cspad 2x2: Hit (corrected)", vmin=vmin_c2x2, vmax=vmax_c2x2)
if do_front:
# Front detector image (central 4 asics) of hit
#plotting.image.plotImage(evt[clarge_type][clarge_key])
plotting.image.plotImage(evt["analysis"]["central4Asics"], vmin=vmin_clarge, vmax=vmax_clarge)
if do_assemble_front:
plotting.image.plotImage(evt["analysis"]["assembled - " + clarge_key], msg="", name="Cspad large (central 4 asics): Hit", vmin=vmin_clarge, vmax=vmin_clarge)
if do_sizing:
# Image of fit
msg = "diameter: %.2f nm \nIntensity: %.2f mJ/um2" %(evt["analysis"]["diameter"].data, evt["analysis"]["intensity"].data)
plotting.image.plotImage(evt["analysis"]["fit"], log=True, mask=mask_c2x2, name="Cspad 2x2: Fit result (radial sphere fit)", vmin=vmin_c2x2, vmax=vmax_c2x2, msg=msg)
# Plot measurement radial average
plotting.line.plotTrace(evt["analysis"]["radial average - "+c2x2_key_s], evt["analysis"]["radial distance - "+c2x2_key_s],tracelen=radial_tracelen)
# Plot fit radial average
plotting.line.plotTrace(evt["analysis"]["radial average - fit"], evt["analysis"]["radial distance - fit"], tracelen=radial_tracelen)
# Fit error history
plotting.line.plotHistory(evt["analysis"]["fit error"])
if fit_succeeded:
# Plot parameter histories
plotting.line.plotHistory(evt["analysis"]["offCenterX"])
plotting.line.plotHistory(evt["analysis"]["offCenterY"])
plotting.line.plotHistory(evt["analysis"]["diameter"], runningHistogram=True)
plotting.line.plotHistory(evt["analysis"]["intensity"], runningHistogram=True)
plotting.correlation.plotMeanMap(x,z,evt["analysis"]["diameter"].data, plotid='DiameterMeanMap', **diameterMeanMapParams)
plotting.correlation.plotMeanMap(x,z,evt["analysis"]["intensity"].data, plotid='IntensityMeanMap', **intensityMeanMapParams)
# Diameter vs. intensity scatter plot
plotting.correlation.plotScatter(evt["analysis"]["diameter"], evt["analysis"]["intensity"], plotid='Diameter vs. intensity', history=100)
if good_hit:
# Image of good hit
plotting.image.plotImage(evt[c2x2_type][c2x2_key], msg="", log=True, mask=mask_c2x2, name="Cspad 2x2: Hit and correct particle size", vmin=vmin_c2x2, vmax=vmax_c2x2)
if do_front:
# Front detector image of good hit
plotting.image.plotImage(evt[clarge_type][clarge_key], msg="", name="Cspad large (full): Correct particle size", vmin=vmin_clarge, vmax=vmax_clarge)
# ----------------- #
# FINAL DIAGNOSTICS #
# ----------------- #
# Spit out a lot for debugging
if do_diagnostics: diagnostics.final_diagnostics(evt)