def example2():
#
binaryFile = open("milleBinaryISN.dat", "rb")
nRec = 0
maxRec = 10 #: maximum number of records to read
Chi2Sum = 0.
NdfSum = 0
LostSum = 0.
start = time.clock()
try:
while(nRec < maxRec):
# create trajectory
traj = GblTrajectory(0)
# read from file
traj.milleIn(binaryFile) # get data blocks from file
nRec += 1
# fit trajectory
Chi2, Ndf, Lost = traj.fit()
print " Record, Chi2, Ndf, Lost", nRec, Chi2, Ndf, Lost
# sum up
Chi2Sum += Chi2
NdfSum += Ndf
LostSum += Lost
except EOFError:
pass
print " records read ", nRec
end = time.clock()
print " Time [s] ", end - start
print " Chi2Sum/NdfSum ", Chi2Sum / NdfSum
print " LostSum/nTry ", LostSum / nRec
def example2():
#
binaryFile = open("milleBinaryISN.dat", "rb")
nRec = 0
maxRec = 10 #: maximum number of records to read
Chi2Sum = 0.
NdfSum = 0
LostSum = 0.
start = time.clock()
try:
while (nRec < maxRec):
# create trajectory
traj = GblTrajectory(0)
# read from file
traj.milleIn(binaryFile) # get data blocks from file
nRec += 1
# fit trajectory
Chi2, Ndf, Lost = traj.fit()
print " Record, Chi2, Ndf, Lost", nRec, Chi2, Ndf, Lost
# sum up
Chi2Sum += Chi2
NdfSum += Ndf
LostSum += Lost
except EOFError:
pass
print " records read ", nRec
end = time.clock()
print " Time [s] ", end - start
print " Chi2Sum/NdfSum ", Chi2Sum / NdfSum
print " LostSum/nTry ", LostSum / nRec
def example1():
#
np.random.seed(47117)
nTry = 1000 #: number of tries
nLayer = 5 #: number of detector layers
print " Gbltst $Rev: 95 $ ", nTry, nLayer
start = time.clock()
# track direction
sinLambda = 0.3
cosLambda = math.sqrt(1.0 - sinLambda ** 2)
sinPhi = 0.
cosPhi = math.sqrt(1.0 - sinPhi ** 2)
# Curvilinear system: track direction T, U = Z x T / |Z x T|, V = T x U
tuvDir = np.array([[cosLambda * cosPhi, cosLambda * sinPhi, sinLambda], \
[-sinPhi, cosPhi, 0.], \
[-sinLambda * cosPhi, -sinLambda * sinPhi, cosLambda]])
# measurement resolution
measErr = np.array([ 0.001, 0.001]) # 10 mu
measPrec = 1.0 / measErr ** 2
# scattering error
scatErr = 0.001 # 1 mread
# RMS of CurviLinear track parameters (Q/P, slopes, offsets)
clErr = np.array([0.001, -0.1, 0.2, -0.15, 0.25])
# precision matrix for external seed (in local system)
locSeed = None
seedLabel = 0 # label of point with seed
if seedLabel != 0:
print " external seed at label ", seedLabel
#
bfac = 0.2998 # Bz*c for Bz=1
step = 1.5 / cosLambda # constant steps in RPhi
#
Chi2Sum = 0.
NdfSum = 0
LostSum = 0.
#
binaryFile = open("milleBinaryISN.dat", "wb")
#
for iTry in range(nTry):
# generate (CurviLinear) track parameters
clNorm = np.random.normal(0., 1., 5)
clPar = clErr * clNorm
# covariance matrix
clCov = np.eye(5)
for i in range(5):
clCov[i, i] = clErr[i] ** 2
# arclength
s = 0.
# point-to-point jacobian (from previous point)
jacPointToPoint = np.eye(5)
# additional (local or global) derivatives
addDer = np.array([[1.0], [0.0]])
labGlobal = np.array([[4711], [4711]])
# create trajectory
traj = GblTrajectory(bfac != 0.)
# at previous point: transformation from local to curvilinear system
oldL2c = np.eye(5)
for iLayer in range(nLayer):
#print " layer ", iLayer
# measurement directions (J,K) from stereo angle
sinStereo = (0. if iLayer % 2 == 0 else 0.1)
cosStereo = math.sqrt(1.0 - sinStereo ** 2)
# measurement system: I, J ,K
ijkDir = np.array([[1., 0., 0.], \
[0., cosStereo, sinStereo], \
[0., -sinStereo, cosStereo]])
# local system: measurement or curvilinear
#local = gblMeasSystem(ijkDir, tuvDir)
local = gblCurviSystem(ijkDir, tuvDir)
# projections
proL2m = local.getTransLocalToMeas()
proL2c = local.getTransLocalToCurvi()
proC2l = np.linalg.inv(proL2c)
# projection curvilinear to measurement directions
proC2m = local.getTransCurviToMeas()
# measurement - prediction in measurement system with error
measNorm = np.random.normal(0., 1., 2)
meas = np.dot(proC2m, clPar[3:5]) + measErr * measNorm
# jacobian is calculated in curvilinear system and transformed
jac = np.dot(proC2l, np.dot(jacPointToPoint, oldL2c))
# point with (independent) measurements (in measurement system)
point = GblPoint(jac)
point.addMeasurement([proL2m, meas, measPrec])
# additional local parameters?
# point.addLocals(addDer)
# additional global parameters?
point.addGlobals(labGlobal, addDer)
addDer = -addDer # locDer flips sign every measurement
# add point to trajectory
iLabel = traj.addPoint(point)
if iLabel == abs(seedLabel):
clSeed = np.linalg.inv(clCov)
locSeed = np.dot(proL2c, np.dot(clSeed, proL2c.T))
# propagate to scatterer
jacPointToPoint = gblSimpleJacobian(step, cosLambda, bfac)
clPar = np.dot(jacPointToPoint, clPar)
clCov = np.dot(jacPointToPoint, np.dot(clCov, jacPointToPoint.T))
s += step
if (iLayer < nLayer - 1):
scat = np.array([0., 0.])
# point with scatterer
jac = np.dot(proC2l, np.dot(jacPointToPoint, proL2c))
point = GblPoint(jac)
scatP = local.getScatPrecision(scatErr)
point.addScatterer([scat, scatP])
iLabel = traj.addPoint(point)
if iLabel == abs(seedLabel):
clSeed = np.linalg.inv(clCov)
locSeed = np.dot(proL2c, np.dot(clSeed, proL2c.T))
# scatter a little
scatNorm = np.random.normal(0., 1., 2)
clPar[1:3] = clPar[1:3] + scatErr * scatNorm
# propagate to next measurement layer
clPar = np.dot(jacPointToPoint, clPar)
clCov = np.dot(jacPointToPoint, np.dot(clCov, jacPointToPoint.T))
s += step
oldL2c = proL2c
# add external seed
if locSeed is not None:
traj.addExternalSeed(seedLabel, locSeed)
# dump trajectory
# traj.dump()
# fit trajectory
Chi2, Ndf, Lost = traj.fit()
print " Record, Chi2, Ndf, Lost", iTry, Chi2, Ndf, Lost
# write to MP binary file
# traj.milleOut(binaryFile)
# sum up
Chi2Sum += Chi2
NdfSum += Ndf
LostSum += Lost
# get corrections and covariance matrix at points
if (iTry == 0):
for i in range(1, nLayer + 1):
locPar, locCov = traj.getResults(-i)
print " >Point ", i
print " locPar ", locPar
#print " locCov ", locCov
locPar, locCov = traj.getResults(i)
print " Point> ", i
print " locPar ", locPar
#print " locCov ", locCov
# check residuals
for i in range(traj.getNumPoints()):
numData, aResiduals, aMeasErr, aResErr, aDownWeight = traj.getMeasResults(i + 1)
for j in range(numData):
print " measRes " , i, j, aResiduals[j], aMeasErr[j], aResErr[j], aDownWeight[j]
numData, aResiduals, aMeasErr, aResErr, aDownWeight = traj.getScatResults(i + 1)
for j in range(numData):
print " scatRes " , i, j, aResiduals[j], aMeasErr[j], aResErr[j], aDownWeight[j]
#
end = time.clock()
print " Time [s] ", end - start
print " Chi2Sum/NdfSum ", Chi2Sum / NdfSum
print " LostSum/nTry ", LostSum / nTry
def example1():
#
np.random.seed(47117)
nTry = 1000 #: number of tries
nLayer = 5 #: number of detector layers
print " Gbltst $Rev$ ", nTry, nLayer
start = time.clock()
# track direction
sinLambda = 0.3
cosLambda = math.sqrt(1.0 - sinLambda**2)
sinPhi = 0.
cosPhi = math.sqrt(1.0 - sinPhi**2)
# Curvilinear system: track direction T, U = Z x T / |Z x T|, V = T x U
tuvDir = np.array([[cosLambda * cosPhi, cosLambda * sinPhi, sinLambda], \
[-sinPhi, cosPhi, 0.], \
[-sinLambda * cosPhi, -sinLambda * sinPhi, cosLambda]])
# measurement resolution
measErr = np.array([0.001, 0.001]) # 10 mu
measPrec = 1.0 / measErr**2
# scattering error
scatErr = 0.001 # 1 mrad
# RMS of CurviLinear track parameters (Q/P, slopes, offsets)
clErr = np.array([0.001, -0.1, 0.2, -0.15, 0.25])
# precision matrix for external seed (in local system)
locSeed = None
seedLabel = 0 # label of point with seed
if seedLabel != 0:
print " external seed at label ", seedLabel
#
bfac = 0.2998 # Bz*c for Bz=1
step = 1.5 / cosLambda # constant steps in RPhi
#
Chi2Sum = 0.
NdfSum = 0
LostSum = 0.
#
binaryFile = open("milleBinaryISN.dat", "wb")
#
for iTry in range(nTry):
# generate (CurviLinear) track parameters
clNorm = np.random.normal(0., 1., 5)
clPar = clErr * clNorm
# covariance matrix
clCov = np.eye(5)
for i in range(5):
clCov[i, i] = clErr[i]**2
# arclength
s = 0.
# point-to-point jacobian (from previous point)
jacPointToPoint = np.eye(5)
# additional (local or global) derivatives
addDer = np.array([[1.0], [0.0]])
labGlobal = np.array([[4711], [4711]])
# create trajectory
traj = GblTrajectory(bfac != 0.)
# at previous point: transformation from local to curvilinear system
oldL2c = np.eye(5)
for iLayer in range(nLayer):
#print " layer ", iLayer
# measurement directions (J,K) from stereo angle
sinStereo = (0. if iLayer % 2 == 0 else 0.1)
cosStereo = math.sqrt(1.0 - sinStereo**2)
# measurement system: I, J ,K
ijkDir = np.array([[1., 0., 0.], \
[0., cosStereo, sinStereo], \
[0., -sinStereo, cosStereo]])
# local system: measurement or curvilinear
#local = gblMeasSystem(ijkDir, tuvDir)
local = gblCurviSystem(ijkDir, tuvDir)
# projections
proL2m = local.getTransLocalToMeas()
proL2c = local.getTransLocalToCurvi()
proC2l = np.linalg.inv(proL2c)
# projection curvilinear to measurement directions
proC2m = local.getTransCurviToMeas()
# measurement - prediction in measurement system with error
measNorm = np.random.normal(0., 1., 2)
meas = np.dot(proC2m, clPar[3:5]) + measErr * measNorm
# jacobian is calculated in curvilinear system and transformed
jac = np.dot(proC2l, np.dot(jacPointToPoint, oldL2c))
# point with (independent) measurements (in measurement system)
point = GblPoint(jac)
point.addMeasurement([proL2m, meas, measPrec])
# additional local parameters?
# point.addLocals(addDer)
# additional global parameters?
point.addGlobals(labGlobal, addDer)
addDer = -addDer # locDer flips sign every measurement
# add point to trajectory
iLabel = traj.addPoint(point)
if iLabel == abs(seedLabel):
clSeed = np.linalg.inv(clCov)
locSeed = np.dot(proL2c, np.dot(clSeed, proL2c.T))
# propagate to scatterer
jacPointToPoint = gblSimpleJacobian(step, cosLambda, bfac)
clPar = np.dot(jacPointToPoint, clPar)
clCov = np.dot(jacPointToPoint, np.dot(clCov, jacPointToPoint.T))
s += step
if (iLayer < nLayer - 1):
scat = np.array([0., 0.])
# point with scatterer
jac = np.dot(proC2l, np.dot(jacPointToPoint, proL2c))
point = GblPoint(jac)
if scatErr > 0:
scatP = local.getScatPrecision(scatErr)
point.addScatterer([scat, scatP])
iLabel = traj.addPoint(point)
if iLabel == abs(seedLabel):
clSeed = np.linalg.inv(clCov)
locSeed = np.dot(proL2c, np.dot(clSeed, proL2c.T))
# scatter a little
scatNorm = np.random.normal(0., 1., 2)
clPar[1:3] = clPar[1:3] + scatErr * scatNorm
# propagate to next measurement layer
clPar = np.dot(jacPointToPoint, clPar)
clCov = np.dot(jacPointToPoint, np.dot(clCov,
jacPointToPoint.T))
s += step
oldL2c = proL2c
# add external seed
if locSeed is not None:
traj.addExternalSeed(seedLabel, locSeed)
# fit trajectory
Chi2, Ndf, Lost = traj.fit()
print " Record, Chi2, Ndf, Lost", iTry, Chi2, Ndf, Lost
# dump trajectory
#traj.printPoints()
#traj.printData()
# write to MP binary file
# traj.milleOut(binaryFile)
# sum up
Chi2Sum += Chi2
NdfSum += Ndf
LostSum += Lost
# get corrections and covariance matrix at points
if (iTry == 0):
for i in range(1, nLayer + 1):
locPar, locCov = traj.getResults(-i)
print " >Point ", i
print " locPar ", locPar
#print " locCov ", locCov
locPar, locCov = traj.getResults(i)
print " Point> ", i
print " locPar ", locPar
#print " locCov ", locCov
# check residuals
for i in range(traj.getNumPoints()):
numData, aResiduals, aMeasErr, aResErr, aDownWeight = traj.getMeasResults(
i + 1)
for j in range(numData):
print " measRes ", i, j, aResiduals[j], aMeasErr[
j], aResErr[j], aDownWeight[j]
numData, aResiduals, aMeasErr, aResErr, aDownWeight = traj.getScatResults(
i + 1)
for j in range(numData):
print " scatRes ", i, j, aResiduals[j], aMeasErr[
j], aResErr[j], aDownWeight[j]
#
end = time.clock()
print " Time [s] ", end - start
print " Chi2Sum/NdfSum ", Chi2Sum / NdfSum
print " LostSum/nTry ", LostSum / nTry
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 main(args):
'''
Read initial fit and points from test file
Create trajectory from points,
fit and write trajectory to MP-II binary file,
get track parameter corrections and covariance matrix at points.
Detector arrangement from text file
'''
Chi2Sum = 0.
NdfSum = 0
LostSum = 0.
np.random.seed(47117)
if args.save:
binaryFileName = "milleBinaryISN" + "_" + nametag
binaryFile = open("%s.dat" % binaryFileName, "wb")
inputFile = open(args.file, 'r')
events = hpsevent.readHPSEvents(inputFile, args.nevents, args.ntracks)
print 'Read %d events from file' % len(events)
if len(events) > 0:
Bz = events[0].Bz
bfac = 0.0002998 * Bz # for Bz in Tesla, momentum in GeV and Radius in mm
print Bz, bfac
plots = hps_plots.plotter(nametag,'pdf',args.testrun,True,True, args.beamspot)
plotsTop = hps_plots.plotter(nametag,'pdf',args.testrun,True,False, args.beamspot)
plotsBot = hps_plots.plotter(nametag,'pdf',args.testrun,False,True,args.beamspot)
#print " GblHpsTest $Rev: 234 $ ", nTry, nLayer
nTry = 0
start = time.clock()
# loop over events
for event in events:
# loop over tracks in event
for track in event.tracks:
if ((nTry % 500) == 0 and nTry > 0) or args.debug:
print '\nProcessed %d events: now on event id %d and track id %d' % (nTry, event.id, track.id)
# if there's no truth info -> skip the track
# use the track parameters and check curvature
if args.mc and track.curvature_truth() == 0.:
print 'Track curvature is zero for this MC track, skip track ', track.id, ' in event ', event.id, ' (perigee pars truth: ', track.perParTruth,')'
continue
# check if it is top or bottom
if args.debug:
print 'track with strip0 origin ', track.strips[0].origin
if args.notop and track.isTop():
if args.debug:
print 'not ok'
continue
if args.nobottom and not track.isTop():
if args.debug:
print 'not ok'
continue
if args.debug:
print 'ok'
# check if it has enough hits
if len(track.strips) < args.minStrips:
if args.debug:
print 'not enough strip clusters'
continue
if args.minP != None and track.p(bfac) < args.minP:
continue
# create the trajectory
traj = GblTrajectory(True)
# save mapping between label and strip object
stripLabelMap = {}
if args.debug:
print 'Track has %d strip clusters' % len(track.strips)
# arc length
s = 0.
# point-to-point jacobian (from previous point)
jacPointToPoint = np.eye(5)
#start trajectory at reference point (defining s=0)
point = GblPoint(jacPointToPoint)
refLabel = traj.addPoint(point)
# multiple scattering covariance matrix (for curvilinear track parameters)
msCov = np.zeros((5, 5))
# store projections for later use
proL2m_list = {}
# loop over strip clusters on the track
for strip in track.strips:
if args.debug:
print '\nProcessing strip id %d, millepedeId %d on sensor %s origin (%f,%f,%f)' % (strip.id, strip.millepedeId,strip.deName,strip.origin[0],strip.origin[1],strip.origin[2])
step = strip.pathLen3D - s
if args.debug: print 'Path length step %f from %f to %f ' % (step, s, strip.pathLen3D)
# measurement direction (in YZ plane: perpendicular/parallel to strip direction)
mDir = np.array( [strip.u, strip.v] )
if args.debug: print 'mDir:\n', mDir
# track direction: in x directon
sinLambda = strip.sinLambda
cosLambda = math.sqrt(1.0 - sinLambda ** 2)
sinPhi = strip.sinPhi
cosPhi = math.sqrt(1.0 - sinPhi ** 2)
if args.debug: print 'Track direction sinLambda=%f sinPhi=%f' % (sinLambda, sinPhi)
# 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
# measurement/residual in the measurement system
meas = np.array([strip.ures, 0.]) # only measurement in u-direction
#meas[0] += deltaU[iLayer] # misalignment
measErr = np.array([strip.ures_err, strip.ures_err])
measPrec = 1.0 / measErr ** 2
measPrec[1] = 0. # 1D measurement perpendicular to strip direction
if args.debug:
print 'meas ', meas, ' measErr ', measErr, ' measPrec ', measPrec
# cross-check track position and residuals
if nTry < 10:
uResIter = utils.getMeasurementResidualIterative(track.perPar,strip.origin,strip.u,strip.w,strip.meas,1.0e-8)
#predIter = utils.getXPlanePositionIterative(track.perPar,strip.origin,strip.w,1.0e-8)
#diffTrk = predIter - strip.origin
#uPredIter = np.dot(strip.u , diffTrk.T)
#uResIter = strip.meas - uPredIter
if abs(uResIter - strip.ures) > 1.0e-6:
print 'WARNING diff %.10f uResIter %.10f compared to %.10f' % (uResIter - strip.ures,uResIter,strip.ures)
#print 'predIter ', predIter, ' origin ', strip.origin, ' diffTrk ',diffTrk,' u ', strip.u, ' diffTrk ',diffTrk.T
sys.exit(1)
# Find the Jacobian to be able to propagate the covariance matrix to this strip position
jacPointToPoint = utils.gblSimpleJacobianLambdaPhi(step, cosLambda, abs(bfac))
if args.debug:
print 'jacPointToPoint to extrapolate to this point:'
print jacPointToPoint
# propagate MS covariance matrix (in the curvilinear frame) to this strip position
msCov = np.dot(jacPointToPoint, np.dot(msCov, jacPointToPoint.T))
# Get the MS covariance for measurements in the measurement frame
measMsCov = np.dot(proL2m, np.dot(msCov[3:, 3:], proL2m.T))
# Plot the MS variance in the u-direction
plots.h_measMsCov.Fill(float(strip.id+1),measMsCov[0,0])
if track.isTop():
plotsTop.h_measMsCov.Fill(float(strip.id+1),measMsCov[0,0])
else:
plotsBot.h_measMsCov.Fill(float(strip.id+1),measMsCov[0,0])
if args.debug:
print 'msCov at this point:'
print msCov
print 'measMsCov at this point:'
print measMsCov
# Option to blow up measurement error according to multiple scattering
if args.useuncorrms:
measPrec[0] = 1.0 / (measErr[0] ** 2 + measMsCov[0, 0])
if args.debug:
print 'Adding measMsCov ', measMsCov[0,0]
# Create a GBL point
point = GblPoint(jacPointToPoint)
# Add 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
# Note the cosLambda to correct for the projection in the phi direction
scatErr = np.array([ strip.scatAngle, strip.scatAngle / cosLambda])
scatPrec = 1.0 / scatErr ** 2
# add scatterer if not using the uncorrelated MS covariances
if not args.useuncorrms:
point.addScatterer([scat, scatPrec])
if args.debug:
print 'adding scatError to this point:'
print scatErr
# Update MS covariance matrix
msCov[1, 1] += scatErr[0] ** 2; msCov[2, 2] += scatErr[1] ** 2
#####
## Calculate global derivatives for this point
# track direction in tracking/global frame
tDirGlobal = np.array( [ [cosPhi * cosLambda, sinPhi * cosLambda, sinLambda] ] )
# Cross-check that the input is consistent
if( np.linalg.norm( tDirGlobal - strip.tDir) > 0.00001):
print 'ERROR: tDirs are not consistent!'
sys.exit(1)
# 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 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
plots.fillSensorPlots("pred_meas", strip.deName, tPosMeas)
if track.isTop():
plotsTop.fillSensorPlots("pred_meas", strip.deName, tPosMeas)
else:
plotsBot.fillSensorPlots("pred_meas", strip.deName, tPosMeas)
# rotate track direction to measurement frame
# non-measured directions
vmeas = 0.
wmeas = 0.
# calculate and add derivatives to point
glDers = utils.globalDers(strip.millepedeId,strip.meas,vmeas,wmeas,tDirMeas,tPosMeas,normalMeas)
if args.debug:
glDers.dump()
ders = glDers.getDers(track.isTop())
labGlobal = ders['labels']
addDer = ders['ders']
if args.debug:
print 'global derivatives for strip ', strip.id, ' which has millepede id ', strip.millepedeId
print labGlobal.shape
for ider in range(labGlobal.shape[1]):
print labGlobal[0][ider], '\t', addDer[0][ider]
point.addGlobals(labGlobal, addDer)
#####
# add point to trajectory
iLabel = traj.addPoint(point)
#if nTry==0:
#print 'uRes ', strip.id, ' uRes ', strip.ures, ' pred ', tPosMeas, ' s(3D) ', strip.pathLen3D
#print 'uRes ', strip.id, ' uRes ', strip.ures, ' pred ', strip.tPos, ' s(3D) ', strip.pathLen3D
# go to next point
s += step
# save strip and label map
stripLabelMap[strip] = iLabel
if args.debug: print 'Do the fit'
Chi2, Ndf, Lost = traj.fit()
# write to millepede
if args.save:
traj.milleOut(binaryFile)
# sum up
Chi2Sum += Chi2
NdfSum += Ndf
LostSum += Lost
if nTry == 0 or args.debug:
print 'fit result: Chi2=%f Ndf=%d Lost=%d' % (Chi2, Ndf, Lost)
# get corrections and covariance matrix at points; collect the result in one object
result = hpsevent.GBLResults(track)
#traj.dump()
for i in range(1, traj.getNumPoints() + 1):
# label start at 1
locPar, locCov = traj.getResults(-i)
if nTry == 0:
print " >Point ", i
print " locPar ", locPar
print " locCov ", locCov
result.addPoint(-i,locPar,locCov)
locPar, locCov = traj.getResults(i)
if nTry == -1:
print " Point> ", i
print " locPar ", locPar
print " locCov ", locCov
result.addPoint(i, locPar, locCov)
if nTry == 0 or args.debug:
result.printVertexCorr()
result.printCorrection()
# calculate the truth chi2 from initial fit
# get the truth and fitted params with indexes same as cov matrix of initial fit (dca,phi0,curv,z0,slope)
perParInitialVec = np.matrix([track.d0(), track.phi0(), track.curvature(), track.z0(), track.slope()])
perParVecTruth = np.matrix([track.d0_truth(), track.phi0_truth(), track.curvature_truth(), track.z0_truth(), track.slope_truth()])
perParInitialVecRes = perParInitialVec-perParVecTruth
chi2_initial_truth = perParInitialVecRes * np.linalg.inv(track.perCov) * np.transpose(perParInitialVecRes)
# calculate the truth chi2 from gbl fit at vertex
clParGBLVtx = np.array(track.clPar) + np.array(result.locPar[1])
clParTruth = np.array(track.clParTruth)
clParGBLRes = clParGBLVtx - clParTruth
chi2_gbl_truth = np.dot(clParGBLRes, np.dot(np.linalg.inv(result.locCov[1]), clParGBLRes))
label = 1 #refLabel
chi2_res = np.dot(result.locPar[label], np.dot(np.linalg.inv(result.locCov[label]), result.locPar[label]))
if nTry == 0:
print " Chi2: ", event.id, chi2_res, chi2_gbl_truth, chi2_initial_truth
# plots
for iplot in range(3):
plot = None
if iplot==0:
plot = plots
elif iplot==1 and track.isTop():
plot = plotsTop
elif iplot==2 and not track.isTop():
plot = plotsBot
if plot is None:
continue
# reject some tracks
#if abs(track.d0())<2.0:
# continue
#if(track.q<0):
# continue
plot.h_clPar_initial_xT.Fill(track.clPar[3])
plot.h_clPar_initial_yT.Fill(track.clPar[4])
plot.h_clPar_initial_qOverP.Fill(track.clPar[0])
plot.h_clPar_initial_lambda.Fill(track.clPar[1])
# transform phi to plot nicer
if track.clPar[2]<math.pi:
plot.h_clPar_initial_phi.Fill(track.clPar[2])
else:
plot.h_clPar_initial_phi.Fill(track.clPar[2]-math.pi*2)
plot.h_clParGBL_res_qOverP.Fill(clParGBLRes[0])
plot.h_clParGBL_res_lambda.Fill(clParGBLRes[1])
plot.h_clParGBL_res_phi.Fill(clParGBLRes[2])
plot.h_clParGBL_res_xT.Fill(clParGBLRes[3])
plot.h_clParGBL_res_yT.Fill(clParGBLRes[4])
plot.h_clParGBL_pull_qOverP.Fill(clParGBLRes[0]/math.sqrt(math.fabs(result.locCov[1][0,0])))
plot.h_clParGBL_pull_lambda.Fill(clParGBLRes[1]/math.sqrt(math.fabs(result.locCov[1][1,1])))
plot.h_clParGBL_pull_phi.Fill(clParGBLRes[2]/math.sqrt(math.fabs(result.locCov[1][2,2])))
plot.h_clParGBL_pull_xT.Fill(clParGBLRes[3]/math.sqrt(math.fabs(result.locCov[1][3,3])))
plot.h_clParGBL_pull_yT.Fill(clParGBLRes[4]/math.sqrt(math.fabs(result.locCov[1][4,4])))
plot.h_perPar_res_initial_d0.Fill(perParInitialVecRes[0,0])
plot.h_perPar_res_initial_phi0.Fill(perParInitialVecRes[0,1])
plot.h_perPar_res_initial_kappa.Fill(perParInitialVecRes[0,2])
plot.h_perPar_res_initial_z0.Fill(perParInitialVecRes[0,3])
plot.h_perPar_res_initial_slope.Fill(perParInitialVecRes[0,4])
plot.h_chi2_initial.Fill(track.chi2Initial)
if track.ndfInitial != 0:
plot.h_chi2ndf_initial.Fill(track.chi2Initial/track.ndfInitial)
plot.h_chi2prob_initial.Fill(utils.chi2Prob(track.chi2Initial,track.ndfInitial))
else:
plot.h_chi2ndf_initial.Fill(0.)
plot.h_chi2prob_initial.Fill(-1.)
plot.h_chi2_initial_truth.Fill(chi2_initial_truth)
plot.h_chi2ndf_initial_truth.Fill(chi2_initial_truth/5.0)
plot.h_chi2prob_initial_truth.Fill(utils.chi2Prob(chi2_initial_truth,5))
plot.h_chi2_gbl_truth.Fill(chi2_gbl_truth)
plot.h_chi2ndf_gbl_truth.Fill(chi2_gbl_truth/5.0)
plot.h_chi2prob_gbl_truth.Fill(utils.chi2Prob(chi2_gbl_truth,5))
plot.h_chi2.Fill(Chi2)
plot.h_chi2ndf.Fill(Chi2/Ndf)
plot.h_chi2prob.Fill(utils.chi2Prob(Chi2,Ndf))
plot.h_p.Fill(track.p(bfac))
plot.h_qOverP.Fill(track.qOverP(bfac))
if args.mc:
plot.h_qOverP_truth_res.Fill(track.qOverP(bfac) - track.q()/track.p_truth(bfac))
plot.h_p_truth.Fill(track.p_truth(bfac))
plot.h_p_truth_res.Fill(track.p(bfac)-track.p_truth(bfac))
plot.h_p_truth_res_vs_p.Fill(track.p_truth(bfac),track.p(bfac)-track.p_truth(bfac))
plot.h_qOverP_corr.Fill(result.curvCorr())
plot.h_qOverP_gbl.Fill(result.qOverP_gbl(bfac))
plot.h_p_gbl.Fill(result.p_gbl(bfac))
if args.mc:
plot.h_qOverP_truth_res_gbl.Fill(result.qOverP_gbl(bfac) - result.track.qOverP_truth(bfac))
plot.h_p_truth_res_gbl.Fill(result.p_gbl(bfac) - result.track.p_truth(bfac))
plot.h_p_truth_res_gbl_vs_p.Fill(result.track.p_truth(bfac), result.p_gbl(bfac) - result.track.p_truth(bfac))
vtx_idx = 1 # first point is at s=0
plot.h_vtx_xT_corr.Fill(result.xTCorr(vtx_idx))
plot.h_vtx_yT_corr.Fill(result.yTCorr(vtx_idx))
plot.h_d0_corr.Fill(result.d0Corr(vtx_idx))
plot.h_z0_corr.Fill(result.z0Corr(vtx_idx))
plot.h_d0_initial.Fill(track.d0())
plot.h_z0_initial.Fill(track.z0())
plot.h_d0_gbl.Fill(result.d0_gbl(vtx_idx))
plot.h_z0_gbl.Fill(result.z0_gbl(vtx_idx))
if args.debug:
print 'curvCorr ', result.curvCorr(), ' xT_corr ', result.xTCorr(vtx_idx), ' yT_corr ', result.yTCorr(vtx_idx)
print 'd0_corr ', result.d0Corr(vtx_idx), ' z0_corr ', result.z0Corr(vtx_idx)
print 'd0_gbl ', result.d0_gbl(vtx_idx), ' (', result.track.d0(), ') z0_gbl ' , result.z0_gbl(vtx_idx), ' (', result.track.z0(), ')'
print 'locPar ', result.locPar[1]
for label,corr in result.locPar.iteritems():
if label>0:
lbl = 2*(label-1) + 1
else:
lbl = -1*2*label
plot.h_xT_corr.Fill(lbl, corr[result.idx_xT])
plot.h_yT_corr.Fill(lbl, corr[result.idx_yT])
for istrip in range(len(track.strips)):
strip = track.strips[istrip]
# find the label, if not found it's the vertex
if strip in stripLabelMap:
iLabel = stripLabelMap[strip]
else:
iLabel = 1
#residuals
plot.fillSensorPlots("res", strip.deName, strip.ures)
plot.fillSensorPlots("res_truth", strip.deName, strip.uresTruth)
#track direction corrections
point = istrip + 2
plot.fillSensorPlots("corr_lambda",strip.deName, result.locPar[point][result.idx_lambda])
plot.fillSensorPlots("corrdiff_lambda",strip.deName, result.locPar[point][result.idx_lambda]-result.locPar[point-1][result.idx_lambda])
plot.fillSensorPlots("corr_phi",strip.deName, result.locPar[point][result.idx_phi])
plot.fillSensorPlots("corrdiff_phi",strip.deName, result.locPar[point][result.idx_phi]-result.locPar[point-1][result.idx_phi])
# correction to xT,yT from GBL fit
corr = np.matrix( [result.locPar[iLabel][3], result.locPar[iLabel][4] ] )
# 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
plot.fillSensorPlots("res_gbl", strip.deName, ures_gbl)
plot.fillSensorPlots("res_gbl_vs_vpred", strip.deName, [ures_gbl,tPosMeas[1]])
if abs(strip.meas) > 20.:
print 'really, this shouldnt happen? ', strip.meas
sys.exit(1)
#if abs(tPosMeas[1]) > 50.:
# print 'really2? ', tPosMeas
# #sys.exit(1)
plot.fillSensorPlots("res_gbl_vs_u", strip.deName, [ures_gbl, strip.meas] )
plot.fillSensorPlots("iso", strip.deName, strip.iso)
# plot residuals of the seed vs the corrected seed
if nTry < 999999:
if args.debug: print '========= START DEBUG TRACK RESIDUALS ======== '
# get the residual from the seed track perigee track parameters
uResSeed = utils.getMeasurementResidualIterative(track.perPar,strip.origin,strip.u,strip.w,strip.meas,1.0e-8)
# get the GBL corrections to the perigee track parameters at this point
# NOTE: this is wrong!
perParCorr = result.getPerParCorr(iLabel,bfac)
if args.debug: print 'perPar ', track.perPar
if args.debug: print 'perParCorr ', perParCorr
# get the residual from the *corrected* (see note above) seed track perigee track parameters
uResSeedCorrWrong = utils.getMeasurementResidualIterative(perParCorr,strip.origin,strip.u,strip.w,strip.meas,1.0e-8)
uResSeedCorrCmpWrong = abs(uResSeedCorrWrong) - abs(uResSeed)
# plot the difference in residuals b/w the corrected and uncorrected track
plot.fillSensorPlots("res_diff_wrong_gbl_seed", strip.deName, abs(uResSeedCorrWrong) - abs(uResSeed) )
if args.debug: print 'WRONG diff ', uResSeedCorrCmpWrong, ' uResSeedCorrWrong', uResSeedCorrWrong, ' uResSeed ', uResSeed
# This is the correct way of getting the corrected track parameters in perigee frame
# Create a SimpleHelix object from the original seed track parameters
# note that it uses slope instead of theta and difference ordering
# [C,phi0,dca,slope,z0]
helixSeed = simpleHelix.SimpleHelix([ track.perPar[0], track.perPar[2], track.perPar[3], math.tan(math.pi/2.0 - track.perPar[1]), track.perPar[4] ])
if args.debug:
print 'helixSeed '
helixSeed.dump()
# define reference points
# global origin
refPointAtOrg = [ 0., 0. ]
# intersection of seed track with plane
refPointAtPlane = [ strip.tPos[0],strip.tPos[1] ]
if args.debug: print 'move helix to interception of seed track and plane which is at x,y ', refPointAtPlane, ' ( tPos ',strip.tPos,')'
helixSeedParsAtPoint = helixSeed.moveToL3( refPointAtPlane )
# create the helix at the new ref point
helixSeedAtPoint = simpleHelix.SimpleHelix( helixSeedParsAtPoint, refPointAtPlane )
if args.debug:
print 'helixSeedAtPoint'
helixSeedAtPoint.dump()
# compare to the other propagation function
if args.debug:
helixSeedParsAtPointOther = helixSeed.moveTo( refPointAtPlane )
helixSeedAtPointOther = simpleHelix.SimpleHelix( helixSeedParsAtPointOther, refPointAtPlane )
print 'helixSeedAtPointOther'
helixSeedAtPointOther.dump()
print 'diff b/w propagations: ', np.array(helixSeedParsAtPoint) - np.array(helixSeedParsAtPointOther)
# find the GBL corrections in perigee frame
if args.debug: print 'get corrections in perFrame'
perCorrections = result.getPerCorrections(iLabel, bfac)
if args.debug: print 'perCorrection [C,theta,phi,d0,z0] ', perCorrections
# get the corrections in the simpleHelix representation (slope instead of theta and different ordering)
perCorrections = np.array( [ perCorrections[0], perCorrections[2], perCorrections[3], result.getSlopeCorrection(iLabel), perCorrections[4] ] )
if args.debug: print 'perCorrection [C,phi,slope,d0,z0] ', perCorrections
# cross-check the corrections in perigee frame with different formula
perCorrectionsDiff = result.getPerCorrectionsOther(iLabel, bfac) - perCorrections
#if args.debug: print 'perCorrectionOther ', perCorrectionsOther
if args.debug: print 'Cross check perCorrections w/ different formula: ', perCorrectionsDiff
if all( abs(i)>1e-5 for i in perCorrectionsDiff[:]):
raise HpsGblException('Correction ', i, ' in perCorrections is different ', perCorrectionsDiff )
# correct the perigee helix parameters at this point
helixParsAtPoint = np.array( helixSeedAtPoint.getParameters() )
helixParsAtPointCorrected = helixParsAtPoint + perCorrections
if args.debug:
print 'helixParsAtPoint', helixParsAtPoint
print 'perCorrections', perCorrections
print 'helixParsAtPointCorrected', helixParsAtPointCorrected
# create a SimpleHelix object from the corrected parameters
helixCorrAtPoint = simpleHelix.SimpleHelix( helixParsAtPointCorrected, refPointAtPlane )
if args.debug:
print 'helixCorrAtPoint'
helixCorrAtPoint.dump()
# change reference point of the corrected helix to the original one
#delta_refPointAtOrg = np.array( refPointAtOrg ) - np.array( refPointAtPlane )
helixParsAtOrg = helixCorrAtPoint.moveToL3( refPointAtOrg )
# create a SimpleHelix object from the corrected parameters at org
helixCorr = simpleHelix.SimpleHelix( helixParsAtOrg, refPointAtOrg )
if args.debug:
print 'helixCorr'
helixCorr.dump()
# get the residual from the corrected track parameters at this point
perParCorrSH = helixCorr.getParameters()
if args.debug: print 'get residuals for parameters perParCorrSH ', perParCorrSH
perParCorr = [ perParCorrSH[0], math.pi / 2.0 - math.atan( perParCorrSH[3] ), perParCorrSH[1], perParCorrSH[2], perParCorrSH[4] ]
if args.debug: print ' and in [C,theta,phi,d0,z0] ', perParCorr
uResSeedCorr = utils.getMeasurementResidualIterative(perParCorr,strip.origin,strip.u,strip.w,strip.meas,1.0e-8)
uResSeedCorrCmp = abs(uResSeedCorr) - abs(uResSeed)
# plot the difference in residuals b/w the corrected and uncorrected track
plot.fillSensorPlots("res_diff_gbl_seed", strip.deName, abs(uResSeedCorr) - abs(uResSeed) )
if args.debug: print 'CORR diff ', abs(uResSeedCorr) - abs(uResSeed), ' uResSeedCorr', uResSeedCorr, ' uResSeed ', uResSeed
uResSeedCorrCmpVal = abs(uResSeedCorrCmp) - abs(uResSeedCorrCmpWrong)
if args.debug: print 'CORR diff cmp ', uResSeedCorrCmpVal, ' uResSeedCorrCmp ', uResSeedCorrCmp, ' uResSeedCorrCmpWrong ', uResSeedCorrCmpWrong
if args.debug: print '========= END DEBUG TRACK RESIDUALS ======== '
# make plots for a given track only
if nTry==0:
plot.gr_ures.SetPoint(istrip,strip.pathLen3D,strip.ures)
plot.gr_ures.SetPointError(istrip,0.,strip.ures_err)
plot.gr_ures_truth.SetPoint(istrip,strip.pathLen3D,strip.uresTruth)
plot.gr_ures_simhit.SetPoint(istrip,strip.pathLen3D,strip.uresSimHit)
meass = np.array([strip.ures, 0.])
# find corrections to xT and yT
plot.gr_corr_ures.SetPoint(istrip, strip.pathLen3D, corr_meas[0,0]) #u-direction
ures_corr = meass - corr_meas.T
plot.gr_ures_corr.SetPoint(istrip, strip.pathLen3D, ures_corr[0,0]) #u-direction
nTry += 1
#
end = time.clock()
print " Processed %d tracks " % nTry
print " Time [s] ", end - start
if nTry > 0:
print " Chi2Sum/NdfSum ", Chi2Sum / NdfSum
print " LostSum/nTry ", LostSum / nTry
print " Make plots "
if nTry > 0 and not args.noshow:
plots.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)
def exampleHpsTest(inputfile):
'''
Read initial fit and points from test file
Create trajectory from points,
fit and write trajectory to MP-II binary file,
get track parameter corrections and covariance matrix at points.
Detector arrangement from text file
'''
Chi2Sum = 0.
NdfSum = 0
LostSum = 0.
Bz = -0.5 # full detector -0.491 for test run detector
bfac = 0.0002998 * Bz # for Bz in Tesla, momentum in GeV and Radius in mm
np.random.seed(47117)
binaryFile = open("milleBinaryISN.dat", "wb")
inputFile = open(inputfile, 'r')
events = utils.readHPSEvents(inputFile, nEventsMax)
print 'Read %d events from file' % len(events)
#print " GblHpsTest $Rev: 234 $ ", nTry, nLayer
nTry = 0
start = time.clock()
h_chi2prob_gbl_truth = TH1F('h_chi2prob_gbl_truth','h_chi2prob_gbl_truth',50,0,1)
h_chi2prob_initial_truth = TH1F('h_chi2prob_initial_truth','h_chi2prob_initial_truth',50,0,1)
for event in events:
if debug: print '\nEvent %d has %d tracks ' % (event.id, len(event.tracks))
for track in event.tracks:
if debug: print '\nProcessing track %d \n p = %.3f p(truth)=%.3f' % (track.id, track.p(bfac), track.p_truth(bfac))
# if there's no truth info -> skip it
if track.p_truth(bfac) == 0.:
print 'No truth info, skip track %d in event %d ' % (track.id, event.id)
continue
traj = GblTrajectory(True)
#print " perPar ", track.perParTruth
# hlxPar = [ cmp(bfac, 0.) * track.curvature(), track.phi0(), track.d0(), track.slope(), track.z0()]
hlxPar = [ cmp(bfac, 0.) * track.curvature(), track.phi0(), -track.d0(), track.slope(), track.z0()]
#hlxPar = [ cmp(bfac, 0.) * track.curvature_truth(), track.phi0_truth(), track.d0_truth(), track.slope_truth(), track.z0_truth()]
#print " hlxPar ", hlxPar
hlx = SimpleHelix(hlxPar)
cosLambda = hlx.getZSDirection()[0]
# stupid error for now
#clCov = np.eye(5)
#for i in range(5):
# clCov[i, i] = clErr[i] ** 2
#clCov = track.clCov
stripLabelMap = {}
if debug: print 'Track has %d strip clusters' % len(track.strips)
# arc length
s = 0.
# point-to-point jacobian (from previous point)
jacPointToPoint = np.eye(5)
#start trajectory at reference point (defining s=0)
point = GblPoint(jacPointToPoint)
refLabel = traj.addPoint(point)
# multiple scattering covariance matrix (for curvilinear track parameters)
msCov = np.zeros((5, 5))
# store projection for later use
proM2l_list = {}
proL2m_list = {}
for strip in track.strips:
if debug: print '\nProcessing strip %d at layer %d ' % (strip.id, strip.layer)
# direction in detector plane in XY: (xDir, yDir, 0.)
xDir = -strip.w[1]
yDir = strip.w[0]
# direction in detector plane in Z: (0., 0., 1.)
# position of detector (center)
xDet = strip.origin[0]
yDet = strip.origin[1]
zDet = strip.origin[2]
# get prediction along the 2 directions (in the detector plane)
pred = hlx.getExpectedPlanePos(xDet, yDet, xDir, yDir, zDet)
if pred is None:
continue # no intersection of track and detector
# prediction position (in global system)
xPred = xDet + pred[0] * xDir
yPred = yDet + pred[0] * yDir
zPred = zDet + pred[1]
# project onto u-direction
uPred = pred[0] * (xDir * strip.u[0] + yDir * strip.u[1]) + pred[1] * strip.u[2]
# stupid iterative intercept
predIter = utils.getXPlanePositionIterative(track.perPar,strip.origin,strip.w,1.0e-8)
diffIter = predIter - strip.origin
uPredIter = np.dot(strip.u , diffIter.T )
#xPred = predIter[0]
#yPred = predIter[1]
#zPred = predIter[2]
#pred0 = (predIter[0] - xDet) / xDir
#pred1 = (predIter[1] - zDet)
#uPredIter = pred0 * (xDir * strip.u[0] + yDir * strip.u[1]) + pred1 * strip.u[2]
# u residuum
uRes = strip.meas - uPred
uResIter = strip.meas - uPredIter
#uRes = uResIter
# (3D) arc-length
sArc = pred[3] / cosLambda
phi = pred[4]
#print " pred ", sArc, xPred, yPred, zPred
if nTry == 0:
print " uRes ",strip.id, ' uRes ', uRes, ' pred ', xPred, yPred, zPred, ' s(3D) ', sArc
#print " uRes ", strip.id, 'uRes(Cl) ', uRes, ' uRes ', strip.ures, ' uResIter ', uResIter, ' pred ', xPred, yPred, zPred, ' predIter ', predIter, ' s(3D) ', sArc
#print -1*track.d0(),track.z0(),track.phi0(),track.slope(),track.curvature()
#print predIter, diffIter, strip.u, uPredIter, strip.meas
#print " uRes ", strip.id, uRes, uResIter, strip.ures, strip.ures_err, ' pred ', xPred, yPred, zPred, ' s ', pred[3], ' s3D ', sArc, ' on plane ', np.dot(np.array([[xPred, yPred, zPred]]) - strip.origin , np.array([strip.w]).T )
#print ' predIter ', predIter
#print ' predIter diff ', (np.array([xPred,yPred,zPred]) - predIter)
#print " java ", strip.id , strip.ures, ' pred ', strip.tPos, ' on plane ', np.dot(np.array([strip.tPos]) - strip.origin , np.array([strip.w]).T )
step = sArc - s
if debug: print 'Step %f (s %f pathLen %f)' % (step, s, sArc)
# measurement direction(s): (m[0]=u, m[1]=v)
if debug: print 'Strip udir', strip.u
if debug: print 'Strip vdir', strip.v
mDir = np.array([strip.u, strip.v])
if debug:
print 'mDir:\n', mDir
# track direction: in x directon
sinLambda = strip.sinLambda
cosLambda = math.sqrt(1.0 - sinLambda ** 2)
sinPhi = strip.sinPhi
cosPhi = math.sqrt(1.0 - sinPhi ** 2)
if debug: print 'Track direction sinLambda=%f sinPhi=%f' % (sinLambda, sinPhi)
# tDir = np.array([cosLambda * cosPhi, cosLambda * sinPhi, sinLambda])
# U = Z x T / |Z x T|, V = T x U
uvDir = np.array([[-sinPhi, cosPhi, 0.], \
[-sinLambda * cosPhi, -sinLambda * sinPhi, cosLambda]])
# projection measurement to local (curvilinear uv) directions (duv/dm)
proM2l = np.dot(uvDir, mDir.T)
proM2l_list[strip.id] = proM2l
if debug: print 'proM2l:\n', proM2l
# projection local (uv) to measurement directions (dm/duv)
proL2m = np.linalg.inv(proM2l)
proL2m_list[strip.id] = proL2m
if debug: print 'proL2m:\n', proL2m
# measurement/residual in the measurement system
#meas = np.array([strip.ures, 0.])
meas = np.array([uRes, 0.])
#meas[0] += deltaU[iLayer] # misalignment
measErr = np.array([strip.ures_err, strip.ures_err])
measPrec = 1.0 / measErr ** 2
measPrec[1] = 0. # 1D measurement perpendicular to strip direction
if debug: print 'meas ', meas, ' measErr ', measErr, ' measPrec ', measPrec
#propagate to this strip
#jacPointToPoint = utils.gblSimpleJacobianLambdaPhi(step, cosLambda, bfac)
jacPointToPoint = hlx.getPropagatorSimple(step, abs(bfac))
#print jacPointToPoint
point = GblPoint(jacPointToPoint)
if debug:
print 'jacPointToPoint to extrapolate to this point:'
print point.getP2pJacobian()
#propagate MS covariance matrix
msCov = np.dot(jacPointToPoint, np.dot(msCov, jacPointToPoint.T))
# MS covariance for measurements
measMsCov = np.dot(proL2m, np.dot(msCov[3:, 3:], proL2m.T))
if debug:
print " uPred ", strip.id, pred[3], uPred, strip.meas, strip.ures, strip.ures_err, measMsCov[0, 0]
#plots.h_measMsCov.Fill(float(strip.layer),measMsCov[0,0])
if debug:
print 'msCov propagated to this point:'
print msCov
print 'measMsCov at this point to be used in measPrec:'
print measMsCov
if useUncorrMS:
# blow up measurement errors according to multiple scattering
measPrec[0] = 1.0 / (measErr[0] ** 2 + measMsCov[0, 0])
point.addMeasurement([proL2m, meas, measPrec])
if debug:
print 'measMsCov ', measMsCov[0, 0]
scat = np.array([0., 0.])
scatErr = np.array([ strip.scatAngle, strip.scatAngle / cosLambda])
scatPrec = 1.0 / scatErr ** 2
if not useUncorrMS:
point.addScatterer([scat, scatPrec])
#update MS covariance matrix
msCov[1, 1] += scatErr[0] ** 2; msCov[2, 2] += scatErr[1] ** 2
if debug:
print 'adding scatError to the msCov from this point:'
print scatErr
addDer = np.array([[1.0], [0.0]])
#top or bottom half
if math.copysign(1, sinLambda) > 0:
offset = 11101
else:
offset = 21101
labGlobal = np.array([[offset + strip.layer], [0]])
point.addGlobals(labGlobal, addDer)
# add point to trajectory
iLabel = traj.addPoint(point)
s += step
stripLabelMap[strip] = iLabel
if debug: print 'Do the fit'
Chi2, Ndf, Lost = traj.fit()
# write to millepede
traj.milleOut(binaryFile)
# sum up
Chi2Sum += Chi2
NdfSum += Ndf
LostSum += Lost
# get corrections and covariance matrix at points
result = utils.GBLResults(track)
#traj.dump()
if nTry == 0:
print 'fit result: Chi2=%f Ndf=%d Lost=%d' % (Chi2, Ndf, Lost)
print 'get corrections and covariance matrix for %d points:' % 1 #traj.getNumPoints()
for i in range(1, traj.getNumPoints() + 1):
# label start at 1
locPar, locCov = traj.getResults(-i)
if nTry < 0:
print " >Point ", i
print " locPar ", locPar
#print " locCov ", locCov
result.addPoint(-i, locPar, locCov)
locPar, locCov = traj.getResults(i)
if nTry < 0:
print " Point> ", i
print " locPar ", locPar
#print " locCov ", locCov
result.addPoint(i, locPar, locCov)
# calculate the truth chi2 from initial fit
# get the truth and fitted params with indexes same as cov matrix of initial fit (dca,phi0,curv,z0,slope)
perParVec = np.array([track.d0(), track.phi0(), track.curvature(), track.z0(), track.slope()])
perParVecTruth = np.array([track.d0_truth(), track.phi0_truth(), track.curvature_truth(), track.z0_truth(), track.slope_truth()])
perParVecRes = perParVec - perParVecTruth
chi2_initial_truth = np.dot(perParVecRes, np.dot(np.linalg.inv(track.perCov) , perParVecRes))
# calculate the truth chi2 from gbl fit at vertex
clParVtx = np.array(track.clPar) + np.array(result.locPar[1])
clParTruth = np.array(track.clParTruth)
clParRes = clParVtx - clParTruth
chi2_gbl_truth = np.dot(clParRes, np.dot(np.linalg.inv(result.locCov[1]), clParRes))
#print " truth ", track.clParTruth
#print " res ", refLabel, result.locPar[refLabel], result.locCov[refLabel]
# calculate chi2 for seeding by truth
label = 1#refLabel
chi2_res = np.dot(result.locPar[label], np.dot(np.linalg.inv(result.locCov[label]), result.locPar[label]))
#chi2_res4 = np.dot(result.locPar[label][:4], np.dot(np.linalg.inv(result.locCov[label][:4, :4]), result.locPar[label][:4]))
print " Chi2: ",
#for i in range(5):
# print track.clParTruth[i], result.locPar[label][i] / math.sqrt(result.locCov[label][i][i]),
print event.id, chi2_res, chi2_gbl_truth, chi2_initial_truth
#print clParRes
#print track.clPar
#print result.locPar[1]
#print clParVtx
#print clParTruth
#print result.locCov[label]
h_chi2prob_gbl_truth.Fill(TMath.Prob(chi2_gbl_truth,5))
h_chi2prob_initial_truth.Fill(TMath.Prob(chi2_initial_truth,5))
'''
print " clPar ", track.clPar
print " clParTruth ", track.clParTruth
print " clParVtx ", clParVtx
print " clParRes ", clParRes
print " res[1] ", np.array(result.locPar[1])
print " cov[1] ", result.locCov[1]
'''
'''
# plots
plots.h_clPar_xT.Fill(track.clPar[3])
plots.h_clPar_yT.Fill(track.clPar[4])
plots.h_clPar_qOverP.Fill(track.clPar[0])
plots.h_clPar_lambda.Fill(track.clPar[1])
# transform phi to plot nicer
if track.clPar[2]<math.pi:
plots.h_clPar_phi.Fill(track.clPar[2])
else:
plots.h_clPar_phi.Fill(track.clPar[2]-math.pi*2)
plots.h_clPar_res_qOverP.Fill(clParRes[0,0])
plots.h_clPar_res_lambda.Fill(clParRes[0,1])
plots.h_clPar_res_phi.Fill(clParRes[0,2])
plots.h_clPar_res_xT.Fill(clParRes[0,3])
plots.h_clPar_res_yT.Fill(clParRes[0,4])
plots.h_clPar_pull_qOverP.Fill(clParRes[0,0]/math.sqrt(result.locCov[1][0,0]))
plots.h_clPar_pull_lambda.Fill(clParRes[0,1]/math.sqrt(result.locCov[1][1,1]))
plots.h_clPar_pull_phi.Fill(clParRes[0,2]/math.sqrt(result.locCov[1][2,2]))
plots.h_clPar_pull_xT.Fill(clParRes[0,3]/math.sqrt(result.locCov[1][3,3]))
plots.h_clPar_pull_yT.Fill(clParRes[0,4]/math.sqrt(result.locCov[1][4,4]))
plots.h_perPar_res_d0.Fill(perParVecRes[0,0])
plots.h_perPar_res_phi0.Fill(perParVecRes[0,1])
plots.h_perPar_res_kappa.Fill(perParVecRes[0,2])
plots.h_perPar_res_z0.Fill(perParVecRes[0,3])
plots.h_perPar_res_slope.Fill(perParVecRes[0,4])
plots.h_chi2_initial.Fill(track.chi2Initial)
plots.h_chi2ndf_initial.Fill(track.chi2Initial/track.ndfInitial)
plots.h_chi2_initial_truth.Fill(chi2_initial_truth)
plots.h_chi2ndf_initial_truth.Fill(chi2_initial_truth/5.0)
plots.h_chi2prob_initial_truth.Fill(utils.chi2Prob(chi2_initial_truth,5))
plots.h_chi2_gbl_truth.Fill(chi2_gbl_truth)
plots.h_chi2ndf_gbl_truth.Fill(chi2_gbl_truth/5.0)
plots.h_chi2prob_gbl_truth.Fill(utils.chi2Prob(chi2_gbl_truth,5))
plots.h_chi2.Fill(Chi2)
plots.h_chi2ndf.Fill(Chi2/Ndf)
plots.h_p.Fill(track.p(bfac))
plots.h_qOverP.Fill(track.qOverP(bfac))
plots.h_qOverP_truth_res.Fill(track.qOverP(bfac) - track.q()/track.p_truth(bfac))
plots.h_p_truth.Fill(track.p_truth(bfac))
plots.h_p_truth_res.Fill(track.p(bfac)-track.p_truth(bfac))
plots.h_qOverP_corr.Fill(result.qOverPCorr())
plots.h_qOverP_gbl.Fill(result.qOverP_gbl(bfac))
plots.h_qOverP_truth_res_gbl.Fill(result.qOverP_gbl(bfac) - result.track.q()/result.track.p_truth(bfac))
plots.h_p_corr.Fill(result.pCorr(bfac))
plots.h_p_gbl.Fill(result.p_gbl(bfac))
plots.h_p_truth_res_gbl.Fill(result.p_gbl(bfac) - result.track.p_truth(bfac))
vtx_idx = 1 # first point is at s=0 (the "vtx" is at -670mm in test run)
plots.h_vtx_xT_corr.Fill(result.xTCorr(vtx_idx))
plots.h_vtx_yT_corr.Fill(result.yTCorr(vtx_idx))
plots.h_d0_corr.Fill(result.d0Corr(vtx_idx))
plots.h_z0_corr.Fill(result.z0Corr(vtx_idx))
plots.h_d0.Fill(track.d0())
plots.h_z0.Fill(track.z0())
plots.h_d0_gbl.Fill(result.d0_gbl(vtx_idx))
plots.h_z0_gbl.Fill(result.z0_gbl(vtx_idx))
for label,corr in result.locPar.iteritems():
if label>0:
lbl = 2*(label-1) + 1
else:
lbl = -1*2*label
plots.h_xT_corr.Fill(lbl, corr[result.idx_xT])
plots.h_yT_corr.Fill(lbl, corr[result.idx_yT])
for istrip in range(len(track.strips)):
strip = track.strips[istrip]
# find the label, if not found it's the vertex
if strip in stripLabelMap:
iLabel = stripLabelMap[strip]
else:
iLabel = 1
#residuals
plots.h_res_layer.Fill(strip.layer,strip.ures)
# correction to xT,yT from GBL fit
corr = np.matrix( [result.locPar[iLabel][3], result.locPar[iLabel][4] ] )
# 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.h_res_gbl_layer.Fill(strip.layer,ures_gbl)
# make plots for a given track only
if nTry==0:
plots.gr_ures.SetPoint(istrip,strip.pathLen,strip.ures)
plots.gr_ures.SetPointError(istrip,0.,strip.ures_err)
plots.gr_ures_truth.SetPoint(istrip,strip.pathLen,strip.uresTruth)
plots.gr_ures_simhit.SetPoint(istrip,strip.pathLen,strip.uresSimHit)
meas = np.array([strip.ures, 0.])
#locRes = np.matrix(proM2l_list[strip.id]) * np.transpose(np.matrix(meas))
#xT_res = locRes[0,0]
#yT_res = locRes[1,0]
# find corrections to xT and yT
plots.gr_corr_ures.SetPoint(istrip, strip.pathLen, corr_meas[0,0]) #u-direction
ures_corr = meas - corr_meas.T
plots.gr_ures_corr.SetPoint(istrip, strip.pathLen, ures_corr[0,0]) #u-direction
'''
nTry += 1
#
end = time.clock()
print " Processed %d tracks " % nTry
print " Time [s] ", end - start
print " Chi2Sum/NdfSum ", Chi2Sum / NdfSum
print " LostSum/nTry ", LostSum / nTry
c = TCanvas('c','c',10,10,700,500)
c.Divide(1,2)
c.cd(1)
h_chi2prob_initial_truth.Draw()
c.cd(2)
h_chi2prob_gbl_truth.Draw()
ans = raw_input('kill...')
#
'''
def exampleHpsTest():
'''
Create points on initial trajectory, create trajectory from points,
fit and write trajectory to MP-II binary file,
get track parameter corrections and covariance matrix at points.
Detector arrangement according to HPS test setup 2012, B=0.
'''
def gblSimpleJacobian(ds, cosl, bfac):
'''
Simple jacobian: quadratic in arc length difference (parabola instead of circle),
assuming constant magnetic field in Z direction.
@param ds: arc length difference
@type ds: float
@param cosl: cos(lambda)
@type cosl: float
@param bfac: Bz*c
@type bfac: float
@return: jacobian to move by 'ds' on trajectory
@rtype: matrix(float)
'''
jac = np.eye(5)
jac[1, 0] = -bfac * ds * cosl
jac[3, 0] = -0.5 * bfac * ds * ds * cosl
jac[3, 1] = ds
jac[4, 2] = ds
return jac
#
np.random.seed(47117)
nTry = 1000 #: number of tries
nLayer = 10 #: number of detector layers
# positions (perpendicular to detector plane)
positions = [88., 95., 188., 195., 288., 295., 488., 495., 688., 695.]
# stereo angles
angles = [0., 0.005, 0., 0.005, 0., 0.005, 0., 0.01, 0., 0.01 ]
# misalignment in measurement direction
deltaU = [0., 0., 0.0, 0.00, 0., 0., 0., 0., 0., 0.]
print " GblHpsTest $Rev: 234 $ ", nTry, nLayer
start = time.clock()
# track direction: in x direction
sinLambda = 0.
cosLambda = math.sqrt(1.0 - sinLambda ** 2)
sinPhi = 0.
cosPhi = math.sqrt(1.0 - sinPhi ** 2)
# tDir = np.array([cosLambda * cosPhi, cosLambda * sinPhi, sinLambda])
# U = Z x T / |Z x T|, V = T x U
uvDir = np.array([[-sinPhi, cosPhi, 0.], \
[-sinLambda * cosPhi, -sinLambda * sinPhi, cosLambda]])
# measurement resolution
measErr = np.array([ 0.006, 0.006]) # 6 mu
measPrec = 1.0 / measErr ** 2
measPrec[1] = 0.0 # only 1D measurement (perpendicular to strip direction)
# scattering error
scatErr = np.array([ 0.000070, 0.000070]) # 70 micro-rad
scatPrec = 1.0 / scatErr ** 2
# RMS of track parameters
clErr = np.array([0.001, 0.05, 0.05, 1., 1.])
clSeed = np.eye(5)
for i in range(5):
clSeed[i, i] = 1.0 / clErr[i] ** 2
#
bfac = 0.#2998 # Bz*c for Bz=1 (units:MeV, T, mm)
#
Chi2Sum = 0.
NdfSum = 0
LostSum = 0.
#
binaryFile = open("milleBinaryISN.dat", "wb")
#
for iTry in range(nTry):
# generate (CurviLinear) track parameters
clNorm = np.random.normal(0., 1., 5)
clPar = clErr * clNorm
# arclength
s = 0.
sPoint = []
# point-to-point jacobian (from previous point)
jacPointToPoint = np.eye(5)
# additional (local or global) derivatives
addDer = np.array([[1.0], [0.0]])
labGlobal = np.array([[4711], [4711]])
# create trajectory
traj = GblTrajectory(bfac != 0.)
for iLayer in range(nLayer):
# step
step = positions[iLayer] / cosLambda - s
prop = np.array([[1., step], [0., 1.]])
#print " layer ", iLayer, step, varMs
# measurement directions (in YZ plane: perpendicular/parallel to strip direction)
sinStereo = angles[iLayer]
cosStereo = math.sqrt(1.0 - sinStereo ** 2)
mDir = np.array([[0., sinStereo, cosStereo], [0., cosStereo, -sinStereo]])
# projection measurement to local (curvilinear uv) directions (duv/dm)
proM2l = np.dot(uvDir, mDir.T)
# projection local (uv) to measurement directions (dm/duv)
proL2m = np.linalg.inv(proM2l)
# measurement - prediction in measurement system with error
measNorm = np.random.normal(0., 1., 2)
meas = np.dot(proL2m, clPar[3:5]) + measErr * measNorm
meas[0] += deltaU[iLayer] # misalignment
# point with measurement
#measPrec[0] = 1.0 / (varMs[0][0] + measErr[0] * measErr[0])
point = GblPoint(jacPointToPoint)
point.addMeasurement([proL2m, meas, measPrec])
# point with scatterer
scat = np.array([0., 0.])
point.addScatterer([scat, scatPrec])
# scatter a little
scatNorm = np.random.normal(0., 1., 2)
clPar[1:3] = clPar[1:3] + scatErr * scatNorm
# additional global parameters?
addDer = np.array([[1.0], [0.0]])
labGlobal = np.array([[11101 + iLayer], [0]])
point.addGlobals(labGlobal, addDer)
# add point to trajectory
iLabel = traj.addPoint(point)
sPoint.append(s)
# propagate to scatterer
jacPointToPoint = gblSimpleJacobian(step, cosLambda, bfac)
clPar = np.dot(jacPointToPoint, clPar)
s += step
# add external seed
# traj.addExternalSeed(1, clSeed)
# dump trajectory
# traj.dump()
# fit trajectory
Chi2, Ndf, Lost = traj.fit()
#print " Record, Chi2, Ndf, Lost", iTry, Chi2, Ndf, Lost
# write to MP binary file
traj.milleOut(binaryFile)
# sum up
Chi2Sum += Chi2
NdfSum += Ndf
LostSum += Lost
# get corrections and covariance matrix at points
if (iTry == 0):
for i in range(1, 1):
locPar, locCov = traj.getResults(i)
print " Point< ", i
print " locPar ", locPar
print " locCov ", locCov
traj.getResults(-i)
print " Point> ", i
print " locPar ", locPar
print " locCov ", locCov
#
end = time.clock()
print " Time [s] ", end - start
print " Chi2Sum/NdfSum ", Chi2Sum / NdfSum
print " LostSum/nTry ", LostSum / nTry
def example1():
'''
Create points on initial trajectory, create trajectory from points,
fit and write trajectory to MP-II binary file,
get track parameter corrections and covariance matrix at points.
Equidistant measurement layers and thin scatterers, propagation
with simple jacobian (quadratic in arc length differences).
Curvilinear system (U,V,T) as local coordinate system.
'''
def gblSimpleJacobian(ds, cosl, bfac):
'''
Simple jacobian: quadratic in arc length difference.
@param ds: arc length difference
@type ds: float
@param cosl: cos(lambda)
@type cosl: float
@param bfac: Bz*c
@type bfac: float
@return: jacobian to move by 'ds' on trajectory
@rtype: matrix(float)
'''
jac = np.eye(5)
jac[1, 0] = -bfac * ds * cosl
jac[3, 0] = -0.5 * bfac * ds * ds * cosl
jac[3, 1] = ds
jac[4, 2] = ds
return jac
#
np.random.seed(47117)
nTry = 1000 #: number of tries
nLayer = 5 #: number of detector layers
print " Gbltst $Rev: 234 $ ", nTry, nLayer
start = time.clock()
# track direction
sinLambda = 0.3
cosLambda = math.sqrt(1.0 - sinLambda ** 2)
sinPhi = 0.
cosPhi = math.sqrt(1.0 - sinPhi ** 2)
# tDir = np.array([cosLambda * cosPhi, cosLambda * sinPhi, sinLambda])
# U = Z x T / |Z x T|, V = T x U
uvDir = np.array([[-sinPhi, cosPhi, 0.], \
[-sinLambda * cosPhi, -sinLambda * sinPhi, cosLambda]])
# measurement resolution
measErr = np.array([ 0.001, 0.001]) # 10 mu
measPrec = 1.0 / measErr ** 2
# scattering error
scatErr = np.array([ 0.001, 0.001]) # 1 mread
scatPrec = 1.0 / scatErr ** 2
# RMS of CurviLinear track parameters (Q/P, slopes, offsets)
clErr = np.array([0.001, -0.1, 0.2, -0.15, 0.25])
clSeed = None
seedLabel = 0 # label of point with seed
if seedLabel != 0:
print " external seed at label ", seedLabel
#
bfac = 0.2998 # Bz*c for Bz=1
step = 1.5 / cosLambda # constant steps in RPhi
#
Chi2Sum = 0.
NdfSum = 0
LostSum = 0.
#
binaryFile = open("milleBinaryISN.dat", "wb")
#
for iTry in range(nTry):
# generate (CurviLinear) track parameters
clNorm = np.random.normal(0., 1., 5)
clPar = clErr * clNorm
# covariance matrix
clCov = np.eye(5)
for i in range(5):
clCov[i, i] = clErr[i] ** 2
# arclength
s = 0.
sPoint = []
# point-to-point jacobian (from previous point)
jacPointToPoint = np.eye(5)
# additional (local or global) derivatives
addDer = np.array([[1.0], [0.0]])
labGlobal = np.array([[4711], [4711]])
# create trajectory
traj = GblTrajectory(bfac != 0.)
for iLayer in range(nLayer):
# measurement directions
sinStereo = (0. if iLayer % 2 == 0 else 0.5)
cosStereo = math.sqrt(1.0 - sinStereo ** 2)
mDir = np.array([[sinStereo, cosStereo, 0.0], [0., 0, 1.]])
# projection measurement to local (curvilinear uv) directions (duv/dm)
proM2l = np.dot(uvDir, mDir.T)
# projection local (uv) to measurement directions (dm/duv)
proL2m = np.linalg.inv(proM2l)
# measurement - prediction in measurement system with error
measNorm = np.random.normal(0., 1., 2)
meas = np.dot(proL2m, clPar[3:5]) + measErr * measNorm
# point with measurement
point = GblPoint(jacPointToPoint)
point.addMeasurement([proL2m, meas, measPrec])
# additional local parameters?
# point.addLocals(addDer)
# additional global parameters?
point.addGlobals(labGlobal, addDer)
addDer = -addDer # locDer flips sign every measurement
# add point to trajectory
iLabel = traj.addPoint(point)
sPoint.append(s)
if iLabel == abs(seedLabel):
clSeed = np.linalg.inv(clCov)
# propagate to scatterer
jacPointToPoint = gblSimpleJacobian(step, cosLambda, bfac)
clPar = np.dot(jacPointToPoint, clPar)
clCov = np.dot(jacPointToPoint, np.dot(clCov, jacPointToPoint.T))
s += step
if (iLayer < nLayer - 1):
scat = np.array([0., 0.])
# point with scatterer
point = GblPoint(jacPointToPoint)
point.addScatterer([scat, scatPrec])
iLabel = traj.addPoint(point)
sPoint.append(s)
if iLabel == abs(seedLabel):
clSeed = np.linalg.inv(clCov)
# scatter a little
scatNorm = np.random.normal(0., 1., 2)
clPar[1:3] = clPar[1:3] + scatErr * scatNorm
# propagate to next measurement layer
clPar = np.dot(jacPointToPoint, clPar)
clCov = np.dot(jacPointToPoint, np.dot(clCov, jacPointToPoint.T))
s += step
# add external seed
if clSeed is not None:
traj.addExternalSeed(seedLabel, clSeed)
# dump trajectory
# traj.dump()
# fit trajectory
Chi2, Ndf, Lost = traj.fit()
print " Record, Chi2, Ndf, Lost", iTry, Chi2, Ndf, Lost
# write to MP binary file
# traj.milleOut(binaryFile)
# sum up
Chi2Sum += Chi2
NdfSum += Ndf
LostSum += Lost
# get corrections and covariance matrix at points
if (iTry == 0):
for i in range(1, 1):
locPar, locCov = traj.getResults(i)
print " Point< ", i
print " locPar ", locPar
print " locCov ", locCov
locPar, locCov = traj.getResults(-i)
print " Point> ", i
print " locPar ", locPar
print " locCov ", locCov
#
end = time.clock()
print " Time [s] ", end - start
print " Chi2Sum/NdfSum ", Chi2Sum / NdfSum
print " LostSum/nTry ", LostSum / nTry
