def plot_residuals(tFile,hist_name,half,side,maxminlist):
#module_L6b_halfmodule_axial_hole_sensor0_hitresglobal
#module_L1t_halfmodule_axial_sensor0_hitresglobal
names = hps_utils.get_module_names()
print 'found ', len(names), ' histogram names'
graphBinNames = []
graphMean = TGraphErrors()
graphMean.SetName('grMean_' + half)
graphRMS = TGraphErrors()
graphRMS.SetName('grRMS_' + half)
for h_name in [hist_name]:
for m_name in names:
m_half = hps_utils.getHalf(m_name)
if half != '' and m_half != half:
continue
if hps_utils.getAxialStereo(m_name) == 'stereo':
continue
if hps_utils.getLayer(m_name) > 3:
if side != '' and hps_utils.getHoleSlot(m_name) != side:
continue
name = m_name + '_' + h_name
h = t_file.Get(name)
if h == None:
print 'no histogram name \"', name, '\" found'
print 'got hist \"', h.GetName(), '\"'
c = TCanvas('c_' + name, 'c_' + name,10,10,1400,900)
if h.GetEntries() > 10:
fitFuncName = 'fg_' + h.GetName()
fitOpt = 'R'
fg = TF1(fitFuncName,'gaus')
bc = plotutils.getHistMaxBinValue(h)
rms = h.GetRMS()
fg.SetParameter(1,bc)
fg.SetParameter(2,rms)
fg.SetRange( bc - rms*2, bc + rms*2 )
h.Fit(fg,fitOpt)
#print 'make graphs of mean and RMS'
mean = fg.GetParameter(1)
meanError = fg.GetParError(1)
rms = fg.GetParameter(2)
rmsError = fg.GetParError(2)
ipoint = graphMean.GetN()
graphMean.SetPoint(ipoint, ipoint, mean)
graphRMS.SetPoint(ipoint, ipoint, rms)
graphMean.SetPointError(ipoint, 0., meanError)
graphRMS.SetPointError(ipoint, 0., rmsError)
graphBinNames.append(hps_utils.getshortsensorname( m_name ) )
print 'mean ', mean, '+-',meanError,' RMS ', rms, '+-', rmsError, ' fg ', fg.GetName()
else:
print 'Not enough entries for histogram \"', name, '\"'
h.Draw()
#ans = raw_input('continue?')
saveName = name
c.SaveAs(saveName + '.png')
c = TCanvas('c_' + hist_name +'_mean_'+half+'_'+side, 'c_' + hist_name +'_mean_'+half+'_'+side,10,10,1400,900)
c.Divide(1,2)
c.cd(1)
gPad.SetBottomMargin(0.3)
gPad.SetGridy()
plotutils.setBinLabels(graphMean,graphBinNames)
plotutils.setGraphStyle(graphMean)
graphMean.SetTitle(hist_name+';;Track residual mean (mm)')
if len(maxminlist) == 4:
graphMean.GetHistogram().SetMaximum(maxminlist[0])
graphMean.GetHistogram().SetMinimum(maxminlist[1])
graphMean.Draw('APL')
c.cd(2)
gPad.SetBottomMargin(0.3)
gPad.SetGridy()
plotutils.setBinLabels(graphRMS,graphBinNames)
plotutils.setGraphStyle(graphRMS)
graphRMS.SetTitle(';;Axial track residual width (mm)')
if len(maxminlist) == 4:
graphRMS.GetHistogram().SetMaximum(maxminlist[2])
graphRMS.GetHistogram().SetMinimum(maxminlist[3])
graphRMS.Draw('APL')
c.SaveAs('summary_' + hist_name+'_mean_'+half+'_'+side +'.png')
ans = raw_input('continue?')
def main():
nametag = os.path.splitext( os.path.basename(args.file) )[0]
if args.name:
nametag = nametag + "-" + args.name
Chi2Sum = 0.
NdfSum = 0.
LostSum = 0.
nTracks = 0
start = time.clock()
np.random.seed(47117)
# Open binary file
binaryFile = None
if args.save:
binaryFileName = "milleBinaryISN" + "_" + nametag
binaryFile = open("%s.dat" % binaryFileName, "wb")
# Open input file
events = hpseventst.readHPSEvents(args.file, args.nevents, args.ntracks)
print 'Read %d events from file' % len(events)
plotsTopBot = hps_plots.plotter(nametag,'pdf',False,True,True, False)
plotsTop = hps_plots.plotter(nametag,'pdf',False,True,False, False)
plotsBot = hps_plots.plotter(nametag,'pdf',False,False,True, False)
# loop over all events
for event in events:
if args.ntracks > 0 and nTracks > args.ntracks:
break
if args.debug or nTracks % 1000 == 0:
print 'Processed %d tracks, now at event id %d with %d tracks ' % (nTracks, event.id, len(event.tracks))
# loop over all tracks in the event
for track in event.tracks:
if args.debug:
print 'track %d in event %d has %d strips' % (track.id, event.id,len(track.strips))
# create the GBL trajectory
traj = GblTrajectory(False)
# point-to-point Jacobian
jacPointToPoint = np.eye(5)
# store projections for later use
proL2m_list = {}
# start trajectory at reference point
# this defines zero path length (s=0)
point = GblPoint(jacPointToPoint)
iLabelRef = traj.addPoint(point)
# save mapping between label and strip object
stripLabelMap = {}
# track direction in global frame
tDirGlobal = track.direction()
sinLambda = math.sin( track.clPar[1] )
sinPhi = math.sin( track.clPar[2] )
cosLambda = math.sqrt(1 - sinLambda ** 2)
cosPhi = math.sqrt(1 - sinPhi ** 2)
if args.debug:
print 'tDir', tDirGlobal
print 'lambda ', track.clPar[1], ' phi ', track.clPar[2]
print 'sinLambda ', sinLambda, ' sinPhi ', sinPhi
# path length
s = 0.
# loop over all strip clusters on the track
for strip in track.strips:
if args.debug:
print 'Processing strip id %d, millepedeId %d on sensor %s ' % (strip.id, strip.millepedeId,strip.deName)
# calculate step from previous point
step = strip.pathLen - s
if args.debug:
print 'step ', step , ' (path length ',strip.pathLen, ' s ', s, ')'
# Find the projection from tracking to measurement frame
mDir = np.array( [strip.u, strip.v] )
if args.debug:
print 'u: ',strip.u
print 'v: ',strip.v
print 'w: ',strip.w
print 'mDir:\n', mDir
# Find the projection from curvilinear to measurement frame
# Track direction in the curvilinear frame (U,V,T)
# U = Z x T / |Z x T|, V = T x U
uvDir = np.array([[-sinPhi, cosPhi, 0.], \
[-sinLambda * cosPhi, -sinLambda * sinPhi, cosLambda]])
if args.debug:
print 'Track direction in curvilinear frame\n',uvDir
# projection from measurement to local (curvilinear uv) directions (duv/dm)
proM2l = np.dot(uvDir, mDir.T)
# projection from local (uv) to measurement directions (dm/duv)
proL2m = np.linalg.inv(proM2l)
proL2m_list[strip.id] = proL2m
if args.debug:
print 'proM2l:\n', proM2l
print 'proL2m:\n', proL2m
# first get the projection from curvilinear to XYZ (or "global") frame
#prjGlobalToCl = track.perToClPrj
#prjClToGlobal = np.linalg.inv(prjGlobalToCl)
#if args.debug:
# print 'prjGlobalToCl\n',prjGlobalToCl
# print 'prjClToGlobal\n',prjClToGlobal
# now get the projection from global frame to measurement (or "sensor") frame
# in HPS we call the measurement frame the "local sensor frame" and denote by "uvw"
#prjGlobalToMeas = np.array( [strip.u, strip.v, strip.w] )
#if args.debug:
# print 'prjGlobalToMeas\n', prjGlobalToMeas
# print 'prjGlobalToMeas.T\n', prjGlobalToMeas.T
# now calculate the projection of the curvilinear to measurement frame
# this is the so-called local to measurement transformation
#prjClToMeas = np.dot( prjClToGlobal, prjGlobalToMeas.T)
#prjMeasToCl = np.linalg.inv( prjClToMeas )
# adjust dimension of the projection, track direction coordinate is always 0
#proL2m = prjClToMeas[:2,:2]
#if args.debug:
# print 'prjClToMeas\n', prjClToMeas
# #print 'prjMeasToCl\n', prjMeasToCl
# residual and errors in measurement frame
meas = np.array( [strip.ures, 0.] )
measErr = np.array( [strip.ures_err, strip.ures_err] )
measPrec = 1.0 / measErr ** 2
measPrec[1] = 0. # no weight for measurement along the strip direction
# Find the Jacobian to be able to propagate the covariance matrix to this strip position
# "cosLambda" is the projection from the difference views
# note that for this Jacobian the cosLambda only enters if there is a B-field
jacPointToPoint = utils.gblSimpleJacobian(step, cosLambda, 0.)
# Create a GBL point
point = GblPoint(jacPointToPoint)
if args.debug:
print 'meas ', meas, ' measErr ', measErr, ' measPrec ', measPrec
print 'jacPointToPoint\n', jacPointToPoint
print 'proL2m \n', proL2m
# Add a measurement to the point
point.addMeasurement([proL2m, meas, measPrec])
# Add scatterer in curvilinear frame to the point
# no direction in this frame
scat = np.array([0., 0.])
# Scattering angle in the curvilinear frame
scatErr = np.array([ strip.scatAngle, strip.scatAngle / cosLambda])
scatPrec = 1.0 / scatErr ** 2
if args.debug:
print 'scatPrec ', scatPrec, ' scatErr ', scatErr, ' cosLambda ', cosLambda
point.addScatterer( [scat, scatPrec] )
# Calculate global derivatives for this point
# needs a few vectors in measurement frame
# Projection matrix from tracking frame to measurement frame
# t_u = dot(u,i)*t_i + dot(u,j)*t_j + dot(u,k)*t_k
# where t_u is component in new u direction and t_i is in the i direction
prjTrkToMeas = np.array([strip.u,strip.v,strip.w])
# rotate to track direction to measurement frame
tDirMeas = np.dot( prjTrkToMeas, tDirGlobal.T)
normalMeas = np.dot( prjTrkToMeas, np.array([strip.w]).T )
# vector coplanar with measurement plane from origin to prediction
tDiff = np.array( [strip.tPos]) - np.array( [strip.origin] )
# rotate to measurement frame
tPosMeas = np.dot( prjTrkToMeas, tDiff.T)
if args.debug:
print 'tDirGlobal ', tDirGlobal
print 'rotation matrix to meas frame\n', prjTrkToMeas
print 'tPosGlobal ', np.array( [strip.tPos]) , ' (origin ', np.array( [strip.origin] ),')'
print 'tDiff ', tDiff
print 'tPosMeas ', tPosMeas
print 'normalMeas ', normalMeas
# non-measured coordinates
vmeas = 0.
wmeas = 0.
# actually calculate the derivatives
glDers = utils.globalDers(strip.millepedeId, strip.meas, vmeas, wmeas, tDirMeas, tPosMeas, normalMeas)
if args.debug:
glDers.dump()
# restructure to two arrays to fit interface
ders = glDers.getDers( track.isTop() )
labGlobal = ders['labels']
addDer = ders['ders']
if args.debug or (1==1 and \
not track.isTop() and \
getAxialStereo(strip.deName) == 'stereo' and \
math.tan( track.clPar[1] ) > -0.011 and \
math.tan( track.clPar[1] ) < -0.01 and \
track.clPar[2] >0.01 and \
track.clPar[2] <0.011):
print '=== Global derivatives ==='
tanLambda = math.tan ( track.clPar[1] )
phi0 = track.clPar[2]
print 'tanLambda ', tanLambda, ' phi0 ', phi0
print 'track dir tracking frame ', tDirGlobal
print 'track dir measurement frame ', tDirMeas
print 'track pred tracking frame ', strip.tPos
print 'track pred measurement frame ', tPosMeas
print 'deName ', strip.deName
print 'normalMeas ', normalMeas
print 'strip.u ', strip.u
print 'strip.ures ', strip.ures
print 'global derivatives for ', strip.deName, ' with id ', strip.id, ' and millepede id ', strip.millepedeId
print labGlobal.shape
for ider in range(labGlobal.shape[1]):
print '%7d %10.3e %s' % (labGlobal[0][ider], addDer[0][ider], strip.deName)
print '====================== ==='
# actually add the global derivatives to the point
point.addGlobals(labGlobal, addDer)
# add point to trajectory
iLabel = traj.addPoint(point)
# save strip and label map
stripLabelMap[strip] = iLabel
# go to next point
s += step
if args.debug:
print 'Done processing strip %d for track %d in event %d' % (strip.id, track.id, event.id)
if args.debug:
print 'Done adding points to trjacetory for track %d in event %d' % (track.id, event.id)
if args.debug:
print 'Do the fit'
Chi2, Ndf, Lost = traj.fit()
#if utils.chi2Prob(Chi2,Ndf) < 0.1:
# continue
if args.save:
traj.milleOut( binaryFile )
# sum up fits
Chi2Sum += Chi2
NdfSum += Ndf
LostSum += Lost
# get corrections and covariance matrix at points; collect the result in one object
result = hpseventst.GBLTrajectory(track,traj)
if nTracks < 2 or args.debug:
print 'fit result: Chi2=%f Ndf=%d Lost=%d' % (Chi2, Ndf, Lost)
result.dump()
# loop over the two halves and the combined to get all plots
for iplot in range(3):
if iplot == 0:
plots = plotsTopBot
elif iplot == 1 and track.isTop():
plots = plotsTop
elif iplot == 2 and not track.isTop():
plots = plotsBot
else:
continue
plots.h_chi2.Fill(Chi2)
plots.h_chi2ndf.Fill(Chi2/Ndf)
plots.h_chi2prob.Fill(utils.chi2Prob(Chi2,Ndf))
# loop over all the strips
for strip in track.strips:
if strip not in stripLabelMap:
raise HpsGblException('this strip is not in the label map?!')
iLabel = stripLabelMap[strip]
point = iLabel
#residual for initial fit
plots.fillSensorPlots("res", strip.deName, strip.ures)
locPar, locCov = result.traj.getResults(point)
kinkLambda = result.kink(point,result.idx_lambda)
kinkPhi = result.kink(point,result.idx_phi)
plots.fillSensorPlots("corr_lambda",strip.deName, locPar[result.idx_lambda])
plots.fillSensorPlots("corrdiff_lambda",strip.deName, kinkLambda)
plots.fillSensorPlots("corr_phi",strip.deName, locPar[result.idx_phi])
plots.fillSensorPlots("corrdiff_phi",strip.deName, kinkPhi)
# correction to xT,yT from GBL fit
plots.fillSensorPlots("xTcorr",strip.deName, locPar[result.idx_xT])
plots.fillSensorPlots("yTcorr",strip.deName, locPar[result.idx_yT])
corr = np.matrix( [ locPar[result.idx_xT], locPar[result.idx_yT] ] )
# project to measurement direction
corr_meas = np.matrix( proL2m_list[strip.id] ) * np.transpose( np.matrix( corr ) )
ures_gbl = strip.ures - corr_meas[0,0] # note minus sign due to definition of residual
plots.fillSensorPlots("res_gbl", strip.deName, ures_gbl)
plots.fillSensorPlots("res_gbl_vs_vpred", strip.deName, [ures_gbl,tPosMeas[1]])
if abs(strip.meas) > 20.:
raise HpsGblException('really, this shouldnt happen? meas= ' + str(strip.meas))
plots.fillSensorPlots("res_gbl_vs_u", strip.deName, [ures_gbl, strip.meas] )
nTracks += 1
if args.debug:
print 'Done processing track %d in event %d' % (track.id, event.id)
if binaryFile != None:
if not binaryFile.closed:
binaryFile.close()
end = time.clock()
print " Processed %d tracks " % nTracks
print " Time [s] ", end - start
if nTracks > 0:
print " Chi2Sum/NdfSum ", Chi2Sum / NdfSum
print " LostSum/nTracks ", LostSum / nTracks
plotsTopBot.show(args.save,args.nopause)
plotsTop.show(args.save,args.nopause)
plotsBot.show(args.save,args.nopause)
if args.save:
hps_plots.saveHistosToFile(gDirectory,'gbltst-hps-plots-%s.root' % nametag)
else:
print 'No tracks processed'
def plotRotations(t_file):
names = hps_utils.getSensorNames()
print ' got ', len(names), ' sensor names'
hname = 'h_res_gbl_vs_u_'
#hname = 'h_res_gbl_vs_vpred_'
c = TCanvas('c','c',10,10,700,500)
grSlopes = TGraphErrors()
grSlopes.SetName('slopes_' + hname)
grSlopesBinLabels = []
for sensorname in names:
name = hname + sensorname
if args.regexp != None:
m = re.match(args.regexp, name)
if m == None:
print 'skip this histogram \"', name, '\"'
continue
h = t_file.Get(name)
if h == None:
print 'no histogram \"', name, '\"'
sys.exit(1)
print 'process \"', name , '\"'
c.Clear()
h.Draw('colz')
c.SaveAs(name + '-' + args.tag + '.png')
#ans = raw_input('continue?')
grMean = TGraphErrors()
grMean.SetName( h.GetName() + '_prjYmean' )
for b in range(1, h.GetNbinsX()+1):
h_p = h.ProjectionY(h.GetName() + '_prjy' + str(b), b, b, 'E')
f = None
if h_p.GetEntries() > 77.:
peakbin = findPeakBin(h_p)
x = h_p.GetBinCenter(peakbin)
minv = x - 1.5*h_p.GetRMS()
maxv = x + 1.5*h_p.GetRMS()
print 'vpred ', h.GetXaxis().GetBinCenter(b) , ' peakbin ', peakbin, ' c ' , h_p.GetBinCenter(peakbin), ' minv ', minv, ' maxv ', maxv
f = TF1('f'+'_'+name,'gaus',minv,maxv)
h_p.Fit(f,'RQ')
ipoint = grMean.GetN()
grMean.SetPoint(ipoint, h.GetXaxis().GetBinCenter(b), f.GetParameter(1))
grMean.SetPointError(ipoint, 0., f.GetParError(1))
c.Clear()
if f != None:
plotutils.myText(0.5,0.85,'<m>=%.2f #sigma=%.2f'%(f.GetParameter(1),f.GetParameter(2)), 0.05, 2)
h_p.Draw()
c.SaveAs(h_p.GetName() + '-' + args.tag + '.png')
#ans = raw_input('continue?')
grMean.SetTitle(name + ';;mean')
grMean.SetMarkerStyle(20)
c.Clear()
fpol1 = TF1('f' + grMean.GetName(),'pol1')
grMean.Fit(fpol1)
grMean.Draw('ALP')
plotutils.myText(0.35,0.8,'slope=%.2e m=%.2e'%(fpol1.GetParameter(1),fpol1.GetParameter(0)), 0.05, 2)
ipoint = grSlopes.GetN()
if args.uflip and hps_utils.getAxialStereo(sensorname) == 'stereo':
print 'flip ', sensorname, ' ', fpol1.GetParameter(1) , ' -> ', -1.*fpol1.GetParameter(1)
grSlopes.SetPoint( ipoint, ipoint, -1.*fpol1.GetParameter(1) )
else:
print 'NO flip ', sensorname, ' ', fpol1.GetParameter(1)
grSlopes.SetPoint( ipoint, ipoint, fpol1.GetParameter(1) )
grSlopes.SetPointError( ipoint, 0., fpol1.GetParError(1) )
grSlopesBinLabels.append( hps_utils.getshortsensorname( sensorname ) )
c.SaveAs(name + '-mean-' + args.tag + '.png')
#ans = raw_input('continue?')
c.Clear()
grSlopes.Draw('ALP')
plotutils.setBinLabels( grSlopes, grSlopesBinLabels )
c.SetBottomMargin(0.2)
if args.uflip:
plotutils.myText(0.35,0.8,'u-flipped', 0.05, 2)
else:
plotutils.myText(0.35,0.8,'NOT u-flipped', 0.05, 2)
if args.uflip:
c.SaveAs(grSlopes.GetName() + '-uflipped-' + args.tag + '.png')
else:
c.SaveAs(grSlopes.GetName() + '-' + args.tag + '.png')
ans = raw_input('continue?')
