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ToolBox.py
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import numpy as np
from scipy.optimize import minimize,basinhopping
from scipy.stats import skew
from ROOT import *
from math import *
from array import array
from EudetData import *
from Constant import *
import time
###############################################################################################################################
#
# Box with tools usefull for the analysis
#
###############################################################################################################################
def Chi2Distribution(x,par):
res = []
for X in x :
A = (TMath.Power(TMath.Power(X,2),(-1+par[0]/2)))
B = (1.0/((TMath.Power(2.,par[0]))*TMath.Gamma(par[0]/2.)))
C = TMath.Exp(-(X*X)/2.)
res.append(A*B*C)
return res
def drange(start, stop, step):
r = start
while r < stop:
yield r
r += step
def CountClusterSize(dataSet):
# Count the number of clusters for the different topologies and draw histograms of the results
n_s1x1y1 = 0.
n_s2x1y2 = 0.
n_s2x2y1 = 0.
n_s2x2y2 = 0.
n_s3x2y2 = 0.
n_s4x2y2 = 0.
n_else = 0.
hClusterSize = TH1D("hClusterSize","Cluster size distribution",4,1,5)
hClusterSizeX = TH1D("hClusterSizeX","Cluster size X distribution",4,1,5)
hClusterSizeY = TH1D("hClusterSizeY","Cluster size Y distribution",4,1,5)
hClusterSizeXvsSizeY = TH2D("hClusterSizeXvsSizeY","Cluster size X vs Y distribution",4,1,5,4,1,5)
last_time = time.time()
for i,tracks in enumerate(dataSet.AllTracks) :
for track in tracks :
if track.cluster!=-11 and len(dataSet.AllClusters[i])!=0 :
aCluster = dataSet.AllClusters[i][track.cluster]
hClusterSize.Fill(aCluster.size)
hClusterSizeX.Fill(aCluster.sizeX)
hClusterSizeY.Fill(aCluster.sizeY)
hClusterSizeXvsSizeY.Fill(aCluster.sizeX, aCluster.sizeY)
if(aCluster.size==1) :
n_s1x1y1 = n_s1x1y1 + 1.
elif(aCluster.size==2 and aCluster.sizeY==2 and aCluster.sizeX==1) :
n_s2x1y2 = n_s2x1y2 + 1.
elif(aCluster.size==2 and aCluster.sizeY==1 and aCluster.sizeX==2) :
n_s2x2y1 = n_s2x2y1 + 1.
elif(aCluster.size==2 and aCluster.sizeY==2 and aCluster.sizeX==2) :
n_s2x2y2 = n_s2x2y2 + 1.
elif(aCluster.size==3 and aCluster.sizeY==2 and aCluster.sizeX==2) :
n_s3x2y2 = n_s3x2y2 + 1.
elif(aCluster.size==4 and aCluster.sizeY==2 and aCluster.sizeX==2) :
n_s4x2y2 = n_s4x2y2 + 1.
else :
n_else = n_else + 1.
if(i%10000==0):
print "Elapsed time for Counting Cluster Size , event %i: %f s"%(i,(time.time()-last_time))
n_tot = n_s1x1y1 + n_s2x1y2 + n_s2x2y1 + n_s2x2y2 + n_s3x2y2 + n_s4x2y2 + n_else
allSizes = [n_s1x1y1 ,n_s2x1y2 , n_s2x2y1 , n_s2x2y2 , n_s3x2y2 , n_s4x2y2 , n_else]
allSizesPercent = [n_s1x1y1/n_tot*100. ,n_s2x1y2/n_tot*100. , n_s2x2y1/n_tot*100. , n_s2x2y2/n_tot*100. , n_s3x2y2/n_tot*100. , n_s4x2y2/n_tot*100. , n_else/n_tot*100.]
for size in allSizes :
print"cluster sizes number..."
print size
for size in allSizesPercent :
print"cluster sizes percentage..."
print size
hClusterSize.GetXaxis().SetTitle("Cluster size")
hClusterSizeX.GetXaxis().SetTitle("Cluster size X")
hClusterSizeY.GetXaxis().SetTitle("Cluster size Y")
hClusterSizeXvsSizeY.GetXaxis().SetTitle("Cluster size X")
hClusterSizeXvsSizeY.GetYaxis().SetTitle("Cluster size Y")
hClusterSizeCounter = TH1D("hClusterSizeCounter","Number of clusters for different sizes",7,0.,7.)
hClusterSizeCounter.GetXaxis().SetTitle("Cluster size")
hClusterSizeCounter.GetYaxis().SetTitle("Number of events")
hClusterSizeCounter.SetBinContent(1,n_s1x1y1)
hClusterSizeCounter.SetBinContent(2,n_s2x1y2)
hClusterSizeCounter.SetBinContent(3,n_s2x2y1)
hClusterSizeCounter.SetBinContent(4,n_s2x2y2)
hClusterSizeCounter.SetBinContent(5,n_s3x2y2)
hClusterSizeCounter.SetBinContent(6,n_s4x2y2)
hClusterSizeCounter.SetBinContent(7,n_else)
hClusterSizeCounter.GetXaxis().SetNdivisions(7,kTRUE)
hClusterSizeCounter.GetXaxis().SetBinLabel(1,"size 1 (1x1)")
hClusterSizeCounter.GetXaxis().SetBinLabel(2,"size 2 (1x2)")
hClusterSizeCounter.GetXaxis().SetBinLabel(3,"size 2 (2x1)")
hClusterSizeCounter.GetXaxis().SetBinLabel(4,"size 2 (2x2)")
hClusterSizeCounter.GetXaxis().SetBinLabel(5,"size 3 (2x2)")
hClusterSizeCounter.GetXaxis().SetBinLabel(6,"size 4 (2x2)")
hClusterSizeCounter.GetXaxis().SetBinLabel(7,"else")
hClusterSizeCounter.SetStats(0)
hClusterSizeCounter_percent = TH1D("hClusterSizeCounter_percent","Percentage of clusters for different sizes",7,0.,7.)
hClusterSizeCounter_percent.GetXaxis().SetTitle("Cluster size")
hClusterSizeCounter_percent.GetYaxis().SetTitle("Percent of events")
hClusterSizeCounter_percent.SetBinContent(1,n_s1x1y1/n_tot*100.)
hClusterSizeCounter_percent.SetBinContent(2,n_s2x1y2/n_tot*100.)
hClusterSizeCounter_percent.SetBinContent(3,n_s2x2y1/n_tot*100.)
hClusterSizeCounter_percent.SetBinContent(4,n_s2x2y2/n_tot*100.)
hClusterSizeCounter_percent.SetBinContent(5,n_s3x2y2/n_tot*100.)
hClusterSizeCounter_percent.SetBinContent(6,n_s4x2y2/n_tot*100.)
hClusterSizeCounter_percent.SetBinContent(7,n_else/n_tot*100.)
hClusterSizeCounter_percent.GetXaxis().SetNdivisions(7,kTRUE)
hClusterSizeCounter_percent.GetXaxis().SetBinLabel(1,"size 1 (1x1)")
hClusterSizeCounter_percent.GetXaxis().SetBinLabel(2,"size 2 (1x2)")
hClusterSizeCounter_percent.GetXaxis().SetBinLabel(3,"size 2 (2x1)")
hClusterSizeCounter_percent.GetXaxis().SetBinLabel(4,"size 2 (2x2)")
hClusterSizeCounter_percent.GetXaxis().SetBinLabel(5,"size 3 (2x2)")
hClusterSizeCounter_percent.GetXaxis().SetBinLabel(6,"size 4 (2x2)")
hClusterSizeCounter_percent.GetXaxis().SetBinLabel(7,"else")
hClusterSizeCounter_percent.SetStats(0)
return hClusterSize, hClusterSizeX, hClusterSizeY, hClusterSizeXvsSizeY, hClusterSizeCounter,hClusterSizeCounter_percent
def RotationMatrix(theta):
tx,ty,tz = theta
tx = 2*pi*tx/360.
ty = 2*pi*ty/360.
tz = 2*pi*tz/360.
Rx = np.array([[1,0,0], [0, cos(tx), -sin(tx)], [0, sin(tx), cos(tx)]])
Ry = np.array([[cos(ty), 0, -sin(ty)], [0, 1, 0], [sin(ty), 0, cos(ty)]])
Rz = np.array([[cos(tz), -sin(tz), 0], [sin(tz), cos(tz), 0], [0,0,1]])
return np.dot(Rx, np.dot(Ry, Rz))
def rms(x):
r"""Compute root mean square."""
x = np.asanyarray(x)
rms = np.sqrt(np.sum(x ** 2) / x.size)
return rms
#
#compute the number of tracks falling in the detector acceptance
#first parameter: a data set (class EudetData)
#second parameter: position of the device under test in the list of planes (the timepix detector in our case)
#
def ComputeDetectorAcceptance(dataSet, dut=6, edges = 0):
n_tracks_in = 0
last_time = time.time()
for i,tracks in enumerate(dataSet.AllTracks) :
for track in tracks :
if (track.trackX[track.iden.index(dut)]>=(-halfChip_X-edges) and track.trackX[track.iden.index(dut)]<=(halfChip_X+edges)) and (track.trackY[track.iden.index(dut)]>=(-halfChip_Y-edges) and track.trackY[track.iden.index(dut)]<=(halfChip_Y+edges)) :
n_tracks_in+=1
return n_tracks_in
def ComputeChargeDistance(dataSet,d=0.005,dut=6):
# Compute the distance between the track and the edge of the pixel with highest energy
# d: the value is used to remove the tracks which are in the corner of the pixel
QrelWrtMindistance = TH2D("QrelWrtMindistance","",110,0,0.0275,100,0.5,1)
for i,tracks in enumerate(dataSet.AllTracks) :
for track in tracks :
if track.cluster!=-11 :
if(dataSet.AllClusters[i][track.cluster].size==2 and dataSet.AllClusters[i][track.cluster].aspectRatio!=1) :
cluster = dataSet.AllClusters[i][track.cluster]
# computing the track positions X and Y in the pixel and the relative charge
X = (track.trackX[track.iden.index(dut)])%pitchX
Y = (track.trackY[track.iden.index(dut)])%pitchY
Qrel = max(cluster.tot) / cluster.totalTOT
if ((X<=d and Y<=d) or (X>=(pitchX-d) and Y<=d) or (X>=(pitchX-d) and Y>=(pitchY-d)) or (X<=d and Y>=(pitchY-d))):
# removing tracks in the corners of the pixel
continue
else :
# computing the distance between the track and the closest pixel edge
if(Y<X and Y<(-X+pitchY)):
d = Y
elif(Y<X and Y>=(-X+pitchY)):
d = pitchX - X
elif(Y>=X and Y<(-X+pitchY)):
d = X
elif(Y>=X and Y>=(-X+pitchY)):
d = pitchY - Y
QrelWrtMindistance.Fill(d, Qrel)
return QrelWrtMindistance
#
#compute the correlation in X i.e. X position of the tracks as a function of the X position of the clusters
#param 1: a data set (class EudetData)
#param 2: number of bins in the histograms
#param 3: position of the device under test in the list of planes
#
def HitProbCorrelationX(dataSet,nbin,dut=6):
HitProb_1_correlationX = TH2D("HitProb_1_correlationX_nbin%i"%nbin,"Hit probability, cluster size 1",nbin,0.,pitchX,nbin,0.,pitchX)
#HitProb_1_correlationX.GetXaxis().SetRangeUser(0.,0.055)
HitProb_1_correlationX.GetXaxis().SetTitle("Track X position within pixel [mm]")
#HitProb_1_correlationX.GetYaxis().SetRangeUser(0.,0.055)
HitProb_1_correlationX.GetYaxis().SetTitle("Cluster X position within pixel [mm]")
HitProb_2_correlationX = TH2D("HitProb_2_correlationX_nbin%i"%nbin,"Hit probability, cluster size 2",nbin,0.,pitchX,nbin,0.,pitchX)
#HitProb_2_correlationX.GetXaxis().SetRangeUser(0.,0.055)
HitProb_2_correlationX.GetXaxis().SetTitle("Track X position within pixel [mm]")
#HitProb_2_correlationX.GetYaxis().SetRangeUser(0.,0.055)
HitProb_2_correlationX.GetYaxis().SetTitle("Cluster X position within pixel [mm]")
HitProb_3_correlationX = TH2D("HitProb_3_correlationX_nbin%i"%nbin,"Hit probability, cluster size 3",nbin,0.,pitchX,nbin,0.,pitchX)
#HitProb_3_correlationX.GetXaxis().SetRangeUser(0.,0.055)
HitProb_3_correlationX.GetXaxis().SetTitle("Track X position within pixel [mm]")
#HitProb_3_correlationX.GetYaxis().SetRangeUser(0.,0.055)
HitProb_3_correlationX.GetYaxis().SetTitle("Cluster X position within pixel [mm]")
HitProb_4_correlationX = TH2D("HitProb_4_correlationX_nbin%i"%nbin,"Hit probability, cluster size 4",nbin,0.,pitchX,nbin,0.,pitchX)
#HitProb_4_correlationX.GetXaxis().SetRangeUser(0.,0.055)
HitProb_4_correlationX.GetXaxis().SetTitle("Track X position within pixel [mm]")
#HitProb_4_correlationX.GetYaxis().SetRangeUser(0.,0.055)
HitProb_4_correlationX.GetYaxis().SetTitle("Cluster X position within pixel [mm]")
for i,tracks in enumerate(dataSet.AllTracks) :
for track in tracks :
if track.cluster!=-11 :
if(dataSet.AllClusters[i][track.cluster].size==1) :
HitProb_1_correlationX.Fill((track.trackX[track.iden.index(dut)])%pitchX,(dataSet.AllClusters[i][track.cluster].absX)%pitchX)
elif(dataSet.AllClusters[i][track.cluster].size==2) :
HitProb_2_correlationX.Fill((track.trackX[track.iden.index(dut)])%pitchX,(dataSet.AllClusters[i][track.cluster].absX)%pitchX)
elif(dataSet.AllClusters[i][track.cluster].size==3 and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
HitProb_3_correlationX.Fill((track.trackX[track.iden.index(dut)])%pitchX,(dataSet.AllClusters[i][track.cluster].absX)%pitchX)
elif(dataSet.AllClusters[i][track.cluster].size==4 and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
HitProb_4_correlationX.Fill((track.trackX[track.iden.index(dut)])%pitchX,(dataSet.AllClusters[i][track.cluster].absX)%pitchX)
return HitProb_1_correlationX,HitProb_2_correlationX,HitProb_3_correlationX,HitProb_4_correlationX
#
#compute the correlation in Y i.e. Y position of the tracks as a function of the Y position of the clusters
#param 1: a data set (class EudetData)
#param 2: number of bins in the histograms
#param 3: position of the device under test in the list of planes
#
def HitProbCorrelationY(dataSet,nbin,dut=6):
HitProb_1_correlationY = TH2D("HitProb_1_correlationY_nbin%i"%nbin,"Hit probability, cluster size 1",nbin,0.,pitchY,nbin,0.,pitchY)
#HitProb_1_correlationY.GetXaxis().SetRangeUser(0.,0.055)
HitProb_1_correlationY.GetXaxis().SetTitle("Track Y position within pixel [mm]")
#HitProb_1_correlationY.GetYaxis().SetRangeUser(0.,0.055)
HitProb_1_correlationY.GetYaxis().SetTitle("Cluster Y position within pixel [mm]")
HitProb_2_correlationY = TH2D("HitProb_2_correlationY_nbin%i"%nbin,"Hit probability, cluster size 2",nbin,0.,pitchY,nbin,0.,pitchY)
#HitProb_2_correlationY.GetXaxis().SetRangeUser(0.,0.055)
HitProb_2_correlationY.GetXaxis().SetTitle("Track Y position within pixel [mm]")
#HitProb_2_correlationY.GetYaxis().SetRangeUser(0.,0.055)
HitProb_2_correlationY.GetYaxis().SetTitle("Cluster Y position within pixel [mm]")
HitProb_3_correlationY = TH2D("HitProb_3_correlationY_nbin%i"%nbin,"Hit probability, cluster size 3",nbin,0.,pitchY,nbin,0.,pitchY)
#HitProb_3_correlationY.GetXaxis().SetRangeUser(0.,0.055)
HitProb_3_correlationY.GetXaxis().SetTitle("Track Y position within pixel [mm]")
#HitProb_3_correlationY.GetYaxis().SetRangeUser(0.,0.055)
HitProb_3_correlationY.GetYaxis().SetTitle("Cluster Y position within pixel [mm]")
HitProb_4_correlationY = TH2D("HitProb_4_correlationY_nbin%i"%nbin,"Hit probability, cluster size 4",nbin,0.,pitchY,nbin,0.,pitchY)
#HitProb_4_correlationY.GetXaxis().SetRangeUser(0.,0.055)
HitProb_4_correlationY.GetXaxis().SetTitle("Track Y position within pixel [mm]")
#HitProb_4_correlationY.GetYaxis().SetRangeUser(0.,0.055)
HitProb_4_correlationY.GetYaxis().SetTitle("Cluster Y position within pixel [mm]")
for i,tracks in enumerate(dataSet.AllTracks) :
for track in tracks :
if track.cluster!=-11 :
if(dataSet.AllClusters[i][track.cluster].size==1) :
HitProb_1_correlationY.Fill((track.trackY[track.iden.index(dut)])%pitchY,(dataSet.AllClusters[i][track.cluster].absY)%pitchY)
elif(dataSet.AllClusters[i][track.cluster].size==2) :
HitProb_2_correlationY.Fill((track.trackY[track.iden.index(dut)])%pitchY,(dataSet.AllClusters[i][track.cluster].absY)%pitchY)
elif(dataSet.AllClusters[i][track.cluster].size==3 and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
HitProb_3_correlationY.Fill((track.trackY[track.iden.index(dut)])%pitchY,(dataSet.AllClusters[i][track.cluster].absY)%pitchY)
elif(dataSet.AllClusters[i][track.cluster].size==4 and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
HitProb_4_correlationY.Fill((track.trackY[track.iden.index(dut)])%pitchY,(dataSet.AllClusters[i][track.cluster].absY)%pitchY)
return HitProb_1_correlationY,HitProb_2_correlationY,HitProb_3_correlationY,HitProb_4_correlationY
#
#this function computes the position hit probability coming from the tracks in the pixel plan
#i.e. Track X and Track Y positions within pixel
#it returns 4 histograms for the different cluster sizes
#param 1: a data set (class EudetData)
#param 2: number of bins in the histograms
#param 3: position of the device under test in the list of planes
#
def TrackHitProb(dataSet,nbin,dut=6):
HitProb_1_track = TH2D("HitProb_1_track_nbin%i"%nbin,"Hit probability, cluster size 1",nbin,0.,pitchX,nbin,0.,pitchY)
#HitProb_1_track.GetXaxis().SetRangeUser(0.,0.055)
HitProb_1_track.GetXaxis().SetTitle("Track X position within pixel [mm]")
#HitProb_1_track.GetYaxis().SetRangeUser(0.,0.055)
HitProb_1_track.GetYaxis().SetTitle("Track Y position within pixel [mm]")
HitProb_2_track = TH2D("HitProb_2_track_nbin%i"%nbin,"Hit probability, cluster size 2",nbin,0.,pitchX,nbin,0.,pitchY)
#HitProb_2_track.GetXaxis().SetRangeUser(0.,0.055)
HitProb_2_track.GetXaxis().SetTitle("Track X position within pixel [mm]")
#HitProb_2_track.GetYaxis().SetRangeUser(0.,0.055)
HitProb_2_track.GetYaxis().SetTitle("Track Y position within pixel [mm]")
HitProb_3_track = TH2D("HitProb_3_track_nbin%i"%nbin,"Hit probability, cluster size 3",nbin,0.,pitchX,nbin,0.,pitchY)
#HitProb_3_track.GetXaxis().SetRangeUser(0.,0.055)
HitProb_3_track.GetXaxis().SetTitle("Track X position within pixel [mm]")
#HitProb_3_track.GetYaxis().SetRangeUser(0.,0.055)
HitProb_3_track.GetYaxis().SetTitle("Track Y position within pixel [mm]")
HitProb_4_track = TH2D("HitProb_4_track_nbin%i"%nbin,"Hit probability, cluster size 4",nbin,0.,pitchX,nbin,0.,pitchY)
#HitProb_4_track.GetXaxis().SetRangeUser(0.,0.055)
HitProb_4_track.GetXaxis().SetTitle("Track X position within pixel [mm]")
#HitProb_4_track.GetYaxis().SetRangeUser(0.,0.055)
HitProb_4_track.GetYaxis().SetTitle("Track Y position within pixel [mm]")
last_time = time.time()
for i,tracks in enumerate(dataSet.AllTracks) :
if(i%1000==0):
print "Elapsed time for Hitprob Calculation , event %i: %f s"%(i,(time.time()-last_time))
for track in tracks :
if track.cluster!=-11 :
if(dataSet.AllClusters[i][track.cluster].size==1) :
HitProb_1_track.Fill((track.trackX[track.iden.index(dut)])%pitchX,(track.trackY[track.iden.index(dut)])%pitchY)
elif(dataSet.AllClusters[i][track.cluster].size==2) :
HitProb_2_track.Fill((track.trackX[track.iden.index(dut)])%pitchX,(track.trackY[track.iden.index(dut)])%pitchY)
elif(dataSet.AllClusters[i][track.cluster].size==3 and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
HitProb_3_track.Fill((track.trackX[track.iden.index(dut)])%pitchX,(track.trackY[track.iden.index(dut)])%pitchY)
elif(dataSet.AllClusters[i][track.cluster].size==4 and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
HitProb_4_track.Fill((track.trackX[track.iden.index(dut)])%pitchX,(track.trackY[track.iden.index(dut)])%pitchY)
return HitProb_1_track,HitProb_2_track,HitProb_3_track,HitProb_4_track
def TOTProfile(dataSet,nbin,dut=6):
TOTProfileX_1 = TH2D("TOTProfileX_1_nbin%i"%nbin,"Hit probability, cluster size 1",nbin,0.,pitchX,1000,0.,1000)
#TOTProfileX_1.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileX_1.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileX_1.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileX_1.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfileX_2 = TH2D("TOTProfileX_2_nbin%i"%nbin,"Hit probability, cluster size 2",nbin,0.,pitchX,1000,0.,1000)
#TOTProfileX_2.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileX_2.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileX_2.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileX_2.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfileX_3 = TH2D("TOTProfileX_3_nbin%i"%nbin,"Hit probability, cluster size 3",nbin,0.,pitchX,1000,0.,1000)
#TOTProfileX_3.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileX_3.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileX_3.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileX_3.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfileX_4 = TH2D("TOTProfileX_4_nbin%i"%nbin,"Hit probability, cluster size 4",nbin,0.,pitchX,1000,0.,1000)
#TOTProfileX_4.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileX_4.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileX_4.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileX_4.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfileY_1 = TH2D("TOTProfileY_1_nbin%i"%nbin,"Hit probability, cluster size 1",nbin,0.,pitchY,1000,0.,1000)
#TOTProfileY_1.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileY_1.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileY_1.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileY_1.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfileY_2 = TH2D("TOTProfileY_2_nbin%i"%nbin,"Hit probability, cluster size 2",nbin,0.,pitchY,1000,0.,1000)
#TOTProfileY_2.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileY_2.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileY_2.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileY_2.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfileY_3 = TH2D("TOTProfileY_3_nbin%i"%nbin,"Hit probability, cluster size 3",nbin,0.,pitchY,1000,0.,1000)
#TOTProfileY_3.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileY_3.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileY_3.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileY_3.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfileY_4 = TH2D("TOTProfileY_4_nbin%i"%nbin,"Hit probability, cluster size 4",nbin,0.,pitchY,1000,0.,1000)
#TOTProfileY_4.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileY_4.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileY_4.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileY_4.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfileX = TH2D("TOTProfileX_nbin%i"%nbin,"Hit probability, cluster size 4",nbin,0.,pitchY,1000,0.,2000)
#TOTProfileY_4.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileX.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileY_4.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileX.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfileY = TH2D("TOTProfileY_nbin%i"%nbin,"Hit probability, cluster size 4",nbin,0.,pitchY,1000,0.,2000)
#TOTProfileY_4.GetXaxis().SetRangeUser(0.,0.055)
TOTProfileY.GetXaxis().SetTitle("Track X position within pixel [mm]")
#TOTProfileY_4.GetYaxis().SetRangeUser(0.,0.055)
TOTProfileY.GetYaxis().SetTitle("TOT (A.U.)]")
TOTProfile= TH2D("TOTProfile","2D TOT Profile",nbin,0.,pitchX,nbin,0.,pitchY)
TOTProfileN= TH2D("TOTProfileN","2D TOT Profile N",nbin,0.,pitchX,nbin,0.,pitchY)
last_time = time.time()
for i,tracks in enumerate(dataSet.AllTracks) :
if(i%1000==0):
print "Elapsed time for Hitprob Calculation , event %i: %f s"%(i,(time.time()-last_time))
for track in tracks :
if track.cluster!=-11 :
TOTProfileX.Fill((track.trackX[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
TOTProfileY.Fill((track.trackY[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
TOTProfile.Fill((track.trackX[track.iden.index(dut)])%pitchX,(track.trackY[track.iden.index(dut)])%pitchY,dataSet.AllClusters[i][track.cluster].totalTOT)
TOTProfileN.Fill((track.trackX[track.iden.index(dut)])%pitchX,(track.trackY[track.iden.index(dut)])%pitchY)
if(dataSet.AllClusters[i][track.cluster].size==1) :
TOTProfileX_1.Fill((track.trackX[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
TOTProfileY_1.Fill((track.trackY[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
elif(dataSet.AllClusters[i][track.cluster].size==2) :
TOTProfileX_2.Fill((track.trackX[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
TOTProfileY_2.Fill((track.trackY[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
elif(dataSet.AllClusters[i][track.cluster].size==3 and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
TOTProfileX_3.Fill((track.trackX[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
TOTProfileY_3.Fill((track.trackY[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
elif(dataSet.AllClusters[i][track.cluster].size==4 and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
TOTProfileX_4.Fill((track.trackX[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
TOTProfileY_4.Fill((track.trackY[track.iden.index(dut)])%pitchX,dataSet.AllClusters[i][track.cluster].totalTOT)
TOTProfileAv = TOTProfile.Clone("efficiency")
TOTProfileAv.Divide(TOTProfile,TOTProfileN,1.,1.,"B")
return TOTProfileX_1,TOTProfileX_2,TOTProfileX_3,TOTProfileX_4,TOTProfileY_1,TOTProfileY_2,TOTProfileY_3,TOTProfileY_4,TOTProfileX,TOTProfileY,TOTProfileAv
#
#this function computes the position hit probability reconstructed from the clusters in the pixel plan
#i.e. cluster X and cluster Y positions within pixel
#it returns 4 histograms for the different cluster sizes
#param 1: a data set (class EudetData)
#param 2: number of bins in the histograms
#param 3: position of the device under test in the list of planes
#
def ClusterHitProb(dataSet,nbin,dut=6):
HitProb_1_cluster = TH2D("HitProb_1_cluster_nbin%i"%nbin,"Hit probability, cluster size 1",nbin,0.,pitchX,nbin,0.,pitchY)
#HitProb_1_cluster.GetXaxis().SetRangeUser(0.,0.055)
HitProb_1_cluster.GetXaxis().SetTitle("Cluster X position within pixel [mm]")
#HitProb_1_cluster.GetYaxis().SetRangeUser(0.,0.055)
HitProb_1_cluster.GetYaxis().SetTitle("Cluster Y position within pixel [mm]")
HitProb_2_cluster = TH2D("HitProb_2_cluster_nbin%i"%nbin,"Hit probability, cluster size 2",nbin,0.,pitchX,nbin,0.,pitchY)
#HitProb_2_cluster.GetXaxis().SetRangeUser(0.,0.055)
HitProb_2_cluster.GetXaxis().SetTitle("Cluster X position within pixel [mm]")
#HitProb_2_cluster.GetYaxis().SetRangeUser(0.,0.055)
HitProb_2_cluster.GetYaxis().SetTitle("Cluster Y position within pixel [mm]")
HitProb_3_cluster = TH2D("HitProb_3_cluster_nbin%i"%nbin,"Hit probability, cluster size 3",nbin,0.,pitchX,nbin,0.,pitchY)
#HitProb_3_cluster.GetXaxis().SetRangeUser(0.,0.055)
HitProb_3_cluster.GetXaxis().SetTitle("Cluster X position within pixel [mm]")
#HitProb_3_cluster.GetYaxis().SetRangeUser(0.,0.055)
HitProb_3_cluster.GetYaxis().SetTitle("Cluster Y position within pixel [mm]")
HitProb_4_cluster = TH2D("HitProb_4_cluster_nbin%i"%nbin,"Hit probability, cluster size 4",nbin,0.,pitchX,nbin,0.,pitchY)
#HitProb_4_cluster.GetXaxis().SetRangeUser(0.,0.055)
HitProb_4_cluster.GetXaxis().SetTitle("Cluster X position within pixel [mm]")
#HitProb_4_cluster.GetYaxis().SetRangeUser(0.,0.055)
HitProb_4_cluster.GetYaxis().SetTitle("Cluster Y position within pixel [mm]")
for i,tracks in enumerate(dataSet.AllTracks) :
for track in tracks :
if track.cluster!=-11 :
# print 'len(dataSet.AllClusters[i]) : '
# print len(dataSet.AllClusters[i])
# print 'track.cluster : '
# print track.cluster
if(dataSet.AllClusters[i][track.cluster].size==1) :
HitProb_1_cluster.Fill((dataSet.AllClusters[i][track.cluster].absX)%pitchX,(dataSet.AllClusters[i][track.cluster].absY)%pitchY)
elif(dataSet.AllClusters[i][track.cluster].size==2) :
HitProb_2_cluster.Fill((dataSet.AllClusters[i][track.cluster].absX)%pitchX,(dataSet.AllClusters[i][track.cluster].absY)%pitchY)
elif((dataSet.AllClusters[i][track.cluster].size==3) and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
HitProb_3_cluster.Fill((dataSet.AllClusters[i][track.cluster].absX)%pitchX,(dataSet.AllClusters[i][track.cluster].absY)%pitchY)
elif(dataSet.AllClusters[i][track.cluster].size==4 and (dataSet.AllClusters[i][track.cluster].sizeX==2 and dataSet.AllClusters[i][track.cluster].sizeY==2)) :
HitProb_4_cluster.Fill((dataSet.AllClusters[i][track.cluster].absX)%pitchX,(dataSet.AllClusters[i][track.cluster].absY)%pitchY)
return HitProb_1_cluster,HitProb_2_cluster,HitProb_3_cluster,HitProb_4_cluster
def TrackClusterCorrelation(dataSet,dut=6,imax=1000):
histox = TH2D("corX","Track-cluster x correlation",npix_X,-halfChip_X,halfChip_X,npix_X,-halfChip_X,halfChip_X)
histoy = TH2D("corY","Track-cluster y correlation",npix_Y,-halfChip_Y,halfChip_Y,npix_Y,-halfChip_Y,halfChip_Y)
for h in [histox,histoy] :
h.GetXaxis().SetTitle("Cluster position (mm)")
h.GetYaxis().SetTitle("Track position (mm)")
last_time = time.time()
for i,tracks in enumerate(dataSet.AllTracks[0:imax]) :
if(i%1000==0) :
print "Correlation, event %i %f s elapsed"%(i,time.time()-last_time)
for track in tracks :
if(i<len(dataSet.AllClusters)):
for cluster in dataSet.AllClusters[i]:
histox.Fill(cluster.absX, track.trackX[track.iden.index(dut)])
histoy.Fill(cluster.absY, track.trackY[track.iden.index(dut)])
return histox,histoy
def TotalMeanFunctionX(Translations,Rotations,aDataDet,nevents,skip,cut = 0.1,dut=6):
totaldist_evaluator = 0.
n = 0
dist_tmp_x = []
dist_tmp_y = []
rotationMatrix = RotationMatrix(Rotations)
h_dist_x_1 = TH1D("h_dist_x_1","TotalMeanFunctionX: dist_x",8000,-4.,4.)
h_dist_y_1 = TH1D("h_dist_y_1","TotalMeanFunctionX: dist_y",8000,-4.,4.)
cut2 = cut**2
for i,clusters in enumerate(aDataDet.AllClusters[0:nevents]) :
for index,cluster in enumerate(clusters) :
if i%skip==0 :
for track in aDataDet.AllTracks[i] :
tmp=np.dot(rotationMatrix,[track.trackX[track.iden.index(dut)],track.trackY[track.iden.index(dut)],0])
tmp[0] = tmp[0] + Translations[0]
tmp[1] = tmp[1]
distx=cluster.absX -tmp[0]
disty=cluster.absY -tmp[1]
h_dist_x_1.Fill(distx)
h_dist_y_1.Fill(disty)
dist_tmp_x.append(distx)
dist_tmp_y.append(disty)
maxx_bin = h_dist_x_1.GetMaximumBin()
maxx = h_dist_x_1.GetXaxis().GetBinCenter(maxx_bin)
maxy_bin = h_dist_y_1.GetMaximumBin()
maxy = h_dist_y_1.GetXaxis().GetBinCenter(maxy_bin)
for index,eventx in enumerate(dist_tmp_x) :
if((eventx)**2 < cut2) :
totaldist_evaluator+=eventx
n+=1
print "Evaluating for Trans : %.9f %.9f [mm] metric = %.9f n = %i"%(Translations[0],0,fabs(totaldist_evaluator/n),n)
return fabs(totaldist_evaluator/n)
def TotalMeanFunctionY(Translations,Tx,Rotations,aDataDet,nevents,skip,cut = 0.1,dut=6):
totaldist_evaluator = 0.
n = 0
dist_tmp_x = []
dist_tmp_y = []
rotationMatrix = RotationMatrix(Rotations)
h_dist_x_2 = TH1D("h_dist_x_2","TotalMeanFunctionY: dist_x",8000,-4.,4.)
h_dist_y_2 = TH1D("h_dist_y_2","TotalMeanFunctionY: dist_y",8000,-4.,4.)
cut2 = cut**2
for i,clusters in enumerate(aDataDet.AllClusters[0:nevents]) :
for index,cluster in enumerate(clusters) :
if i%skip==0 :
for track in aDataDet.AllTracks[i] :
tmp=np.dot(rotationMatrix,[track.trackX[track.iden.index(dut)],track.trackY[track.iden.index(dut)],0])
tmp[0] = tmp[0] + Tx
tmp[1] = tmp[1] + Translations[0]
distx=cluster.absX -tmp[0]
disty=cluster.absY -tmp[1]
h_dist_x_2.Fill(distx)
h_dist_y_2.Fill(disty)
dist_tmp_x.append(distx)
dist_tmp_y.append(disty)
maxx_bin = h_dist_x_2.GetMaximumBin()
maxx = h_dist_x_2.GetXaxis().GetBinCenter(maxx_bin)
maxy_bin = h_dist_y_2.GetMaximumBin()
maxy = h_dist_y_2.GetXaxis().GetBinCenter(maxy_bin)
for index,eventy in enumerate(dist_tmp_y) :
if((eventy)**2 < cut2) :
totaldist_evaluator+=eventy
n+=1
print "Evaluating for Trans : %.9f %.9f [mm] metric = %.9f n = %i"%(Tx,Translations[0],fabs(totaldist_evaluator/n),n)
return fabs(totaldist_evaluator/n)
def TotalRotationFunctionZ(zang,Rotations,Translations,aDataDet,nevents,skip=1,cut=0.1,dut=6):
# function to find the best starting Z angle
# returns the mertic to be minimised
n = 0
Rotations = np.concatenate((Rotations,zang),axis=0)
rotationMatrix = RotationMatrix(Rotations)
h_dist_x = TH1D("h_dist_x","TotalRotationFunction: dist_x",100,-0.5,0.5)
h_dist_y = TH1D("h_dist_y","TotalRotationFunction: dist_y",100,-0.5,0.5)
for i,clusters in enumerate(aDataDet.AllClusters[0:nevents]) :
for cluster in clusters :
if i%skip==0 :
for track in aDataDet.AllTracks[i] :
tmp=np.dot(rotationMatrix,[track.trackX[track.iden.index(dut)],track.trackY[track.iden.index(dut)],0])
tmp[0] = tmp[0] + Translations[0]
tmp[1] = tmp[1] + Translations[1]
distx = cluster.absX - tmp[0]
disty = cluster.absY - tmp[1]
if sqrt(distx**2 + disty**2) < cut:
n = n + 1
h_dist_x.Fill(distx)
h_dist_y.Fill(disty)
gx = TF1("gx","gaus",-0.1,0.1)
if h_dist_x.GetEffectiveEntries() > 10.:
rX = h_dist_x.Fit(gx,"RSQ","")
sigmaResX = rX.Parameter(2)
else:
print "TotalRotationFunctionZ: not enough events to fit"
sigmaResX = 9.
gy = TF1("gy","gaus",-0.1,0.1)
if h_dist_y.GetEffectiveEntries() > 10.:
rY = h_dist_y.Fit(gy,"RSQ","")
sigmaResY = rY.Parameter(2)
else:
print "TotalRotationFunctionZ: not enough events to fit"
sigmaResY = 9.
result = sqrt(sigmaResX**2 + sigmaResY**2)/n
print "Evaluating for Rotation : %.9f %.9f %.9f [deg] Trans : %f %f [mm] metric = %.9f n = %i"%(Rotations[0],Rotations[1],Rotations[2],Translations[0],Translations[1],result,n)
return result
def TotalRotationFunction(Rotations,Translations,aDataDet,nevents,skip=1,cut = 0.1,dut=6):
n = 0
rotationMatrix = RotationMatrix(Rotations)
h_dist_x_3 = TH1D("h_dist_x_3","TotalRotationFunction: dist_x",100,-0.5,0.5)
h_dist_y_3 = TH1D("h_dist_y_3","TotalRotationFunction: dist_y",100,-0.5,0.5)
for i,clusters in enumerate(aDataDet.AllClusters[0:nevents]) :
for cluster in clusters :
if i%skip==0 :
for track in aDataDet.AllTracks[i] :
tmp=np.dot(rotationMatrix,[track.trackX[track.iden.index(dut)],track.trackY[track.iden.index(dut)],0])
tmp[0] = tmp[0] + Translations[0]
tmp[1] = tmp[1] + Translations[1]
distx=cluster.absX -tmp[0]
disty=cluster.absY -tmp[1]
if((distx*distx + disty*disty) < (cut*cut)):
n+=1
h_dist_x_3.Fill(distx)
h_dist_y_3.Fill(disty)
gx = TF1("gx","gaus",-0.1,0.1)
if h_dist_x_3.GetEffectiveEntries() > 10.:
rX = h_dist_x_3.Fit(gx,"RSQ","")
sigmaResX = rX.Parameter(2)
else:
print "TotalRotationFunctionZ: not enough events to fit"
sigmaResX = 9.
gy = TF1("gy","gaus",-0.1,0.1)
if h_dist_y_3.GetEffectiveEntries() > 10.:
rY = h_dist_y_3.Fit(gy,"RSQ","")
sigmaResY = rY.Parameter(2)
else:
print "TotalRotationFunctionZ: not enough events to fit"
sigmaResY = 9.
result = sqrt(sigmaResX**2 + sigmaResY**2)/n
print "Evaluating for Rotation : %.9f %.9f %.9f [deg] Trans : %f %f [mm] metric = %.9f n = %i"%(Rotations[0],Rotations[1],Rotations[2],Translations[0],Translations[1],result,n)
return result
#
#return the X resolution (sigma of the X residuals distribution) for a given value of the sigma for the charge sharing (eta correction)
#param 1: value of the sigma for the charge sharing (eta correction)
#param 2: a data set (class EudetData)
#param 3: number of skiped events (compute residuals for 1 event over 'skip' events)
#param 4: position of the device under test in the list of planes
#
def TotalSigmaFunctionX(sigmaCharge_tmp,dataSet,skip,dut=6,mode="tot"):
tmpx = TH1D("resX_2","Unbiased residual X, cluster size Y = 2",300,-0.150,0.150)
for j,tracks in enumerate(dataSet.AllTracks) :
if j%skip==0 :
for track in tracks :
if track.cluster!=-11 and len(dataSet.AllClusters[j])!=0 :
aCluster = dataSet.AllClusters[j][track.cluster]
if(aCluster.sizeX==2 and aCluster.sizeY==1) :
if mode == "tot":
aCluster.GetEtaCorrectedQWeightedCentroid(sigmaCharge_tmp)
dataSet.ComputeResiduals(j, dut)
tmpx.Fill(aCluster.resX)
elif mode == "gc":
aCluster.GetEtaCorrectedQWeightedCentroid(0.0,sigmaCharge_tmp)
dataSet.ComputeResiduals(j, dut)
tmpx.Fill(aCluster.resX_energyGC)
elif mode == "pbpc":
aCluster.GetEtaCorrectedQWeightedCentroid(0.0,0.0,sigmaCharge_tmp)
dataSet.ComputeResiduals(j, dut)
tmpx.Fill(aCluster.resX_energyPbPC)
# for i in range(dataSet.p_nEntries) :
# if i%skip==0 :
# dataSet.ClusterEvent(i,method_name,sigmaCharge_tmp_Y)
# dataSet.ComputeResiduals(i)
# resY_cs = []
# n_cs = 3
# for i in range(1,n_cs+2) : #n_cs+2 excluded
# tmpy = TH1D("resY_%i"%i,"Unbiased residual Y, cluster size Y = %i"%i,300,-0.150,0.150)
# tmpy.GetXaxis().SetTitle("Y_{track} - Y_{Timepix} (mm)")
# tmpy.GetYaxis().SetTitle("Number of hits")
# tmpy.SetLineColor(i)
# resY_cs.append(tmpy)
# for j,tracks in enumerate(dataSet.AllTracks) :
# if j%skip==0 :
# for track in tracks :
# if track.cluster!=-11 and len(dataSet.AllClusters[j])!=0 :
# aCluster = dataSet.AllClusters[j][track.cluster]
# for i in range(1,n_cs+2) :
# if(aCluster.sizeY==i and aCluster.sizeX==1) :
# resY_cs[i-1].Fill(aCluster.resY)
g2 = TF1("m1","gaus",-0.03,0.03)
if tmpx.GetEffectiveEntries() > 10.:
rX = tmpx.Fit(g2,"RSQ","")
sigmaResX = rX.Parameter(2)
sigmaResX_err = rX.ParError(2)
#print "resolution = %f for sigma=%f"%(sigmaResX,sigmaCharge_tmp_X)
else:
print "not enough events to fit."
sigmaResX = 9.
return sigmaResX
#
#return the Y resolution (sigma of the Y residuals distribution) for a given value of the sigma for the charge sharing (eta correction)
#param 1: value of the sigma for the charge sharing (eta correction)
#param 2: a data set (class EudetData)
#param 3: number of skiped events (compute residuals for 1 event over 'skip' events)
#param 4: position of the device under test in the list of planes
#
def TotalSigmaFunctionY(sigmaCharge_tmp,dataSet,skip,dut=6,mode="tot"):
tmpy = TH1D("resY_1","Unbiased residual Y, cluster size Y = 2",300,-0.150,0.150)
for j,tracks in enumerate(dataSet.AllTracks) :
if j%skip==0 :
for track in tracks :
if track.cluster!=-11 and len(dataSet.AllClusters[j])!=0 :
aCluster = dataSet.AllClusters[j][track.cluster]
if(aCluster.sizeY==2 and aCluster.sizeX==1) :
if mode=="tot":
aCluster.GetEtaCorrectedQWeightedCentroid(sigmaCharge_tmp)
dataSet.ComputeResiduals(j, dut)
tmpy.Fill(aCluster.resY)
elif mode=="gc":
aCluster.GetEtaCorrectedQWeightedCentroid(0.0,sigmaCharge_tmp)
dataSet.ComputeResiduals(j, dut)
tmpy.Fill(aCluster.resY_energyGC)
elif mode=="pbpc":
aCluster.GetEtaCorrectedQWeightedCentroid(0.0,0.0,sigmaCharge_tmp)
dataSet.ComputeResiduals(j, dut)
tmpy.Fill(aCluster.resY_energyPbPC)
# for i in range(dataSet.p_nEntries) :
# if i%skip==0 :
# dataSet.ClusterEvent(i,method_name,sigmaCharge_tmp_Y)
# dataSet.ComputeResiduals(i)
# resY_cs = []
# n_cs = 3
# for i in range(1,n_cs+2) : #n_cs+2 excluded
# tmpy = TH1D("resY_%i"%i,"Unbiased residual Y, cluster size Y = %i"%i,300,-0.150,0.150)
# tmpy.GetXaxis().SetTitle("Y_{track} - Y_{Timepix} (mm)")
# tmpy.GetYaxis().SetTitle("Number of hits")
# tmpy.SetLineColor(i)
# resY_cs.append(tmpy)
# for j,tracks in enumerate(dataSet.AllTracks) :
# if j%skip==0 :
# for track in tracks :
# if track.cluster!=-11 and len(dataSet.AllClusters[j])!=0 :
# aCluster = dataSet.AllClusters[j][track.cluster]
# for i in range(1,n_cs+2) :
# if(aCluster.sizeY==i and aCluster.sizeX==1) :
# resY_cs[i-1].Fill(aCluster.resY)
g2 = TF1("m1","gaus",-0.03,0.03)
rY = tmpy.Fit(g2,"RSQ","")
sigmaResY = rY.Parameter(2)
sigmaResY_err = rY.ParError(2)
#print "resolution = %f for sigma=%f"%(sigmaResY,sigmaCharge_tmp_Y)
return sigmaResY
def TotalDistanceFunction(parameters,aDataDet,nevents,skip,dut=6):
totaldist_evaluator = 0.
n = 0
for i, tracks in enumerate(aDataDet.AllTracks[0:nevents]):
if i%skip == 0:
for track in tracks:
closestcluster = 999
for cluster in aDataDet.AllClusters[i]:
tmp=np.dot(RotationMatrix([0.,0.,parameters[0]]),[track.trackX[track.iden.index(dut)],track.trackY[track.iden.index(dut)],0])
tmp[0] = tmp[0] + parameters[1]
tmp[1] = tmp[1] + parameters[2]
distx=cluster.absX - tmp[0]
disty=cluster.absY - tmp[1]
r = sqrt(distx**2 + disty**2)
if r < closestcluster:
closestcluster = r
if closestcluster < 0.5:
totaldist_evaluator += closestcluster
n += 1
result = totaldist_evaluator
print "Evaluating for Rotation : %f %f %f [deg] Trans : %f %f [mm] metric = %f n = %i"%(0., 0., parameters[0],parameters[1],parameters[2],result,n)
return result
def ReadAlignment(filename) :
f = open(filename,'r')
alignments = []
for line in f.readlines() :
result = line.split(" ")
#print result
alignments.append([float(result[2]),float(result[3]),float(result[4]),float(result[8]),float(result[9])])
return alignments
def PerformPreAlignment(aDataSet,nevents,skip=1,filename='Alignment.txt',dut=6,Rotations=[0,0,0]):
# will plot the x and y distance between every cluster-track pair in an event
# returns the central values of the x, y bins with the most entries, and hists
last_time=time.time()
h_dist_x = TH1D("h_dist_x","Track-cluster distance x",800,-20.,20.)
h_dist_y = TH1D("h_dist_y","Track-cluster distance y",800,-20.,20.)
for i in xrange(nevents):
if i%skip == 0:
clusters = aDataSet.AllClusters[i]
for cluster in clusters:
for track in aDataSet.AllTracks[i]:
tmp=[track.trackX[track.iden.index(dut)],track.trackY[track.iden.index(dut)],0]
tmp=np.dot(RotationMatrix(Rotations),tmp)
distx=cluster.absX -tmp[0]
disty=cluster.absY -tmp[1]
h_dist_x.Fill(distx)
h_dist_y.Fill(disty)
if i%1000 == 0 :
print "Event %d"%i
print "Elapsed time/1000 Event. Prealignment : %f s"%(time.time()-last_time)
last_time = time.time()
maxx_bin = h_dist_x.GetMaximumBin()
maxx = h_dist_x.GetXaxis().GetBinCenter(maxx_bin)
maxy_bin = h_dist_y.GetMaximumBin()
maxy = h_dist_y.GetXaxis().GetBinCenter(maxy_bin)
f = open(filename,'w')
f.write("Rotation : %f %f %f [deg] Trans : %f %f [mm] \n"%(Rotations[0],Rotations[1],Rotations[2],maxx,maxy))
f.close()
print "Prealignment yield Translations : %.9f %.9f [mm] Rotation : %f %f %f [deg] "%(maxx,maxy,Rotations[0],Rotations[1],Rotations[2])
print "Time for Prealignment : %f s"%(time.time()-last_time)
return [[Rotations[0],Rotations[1],Rotations[2],maxx,maxy]], h_dist_x, h_dist_y
def PerformAlignement(aDataSet,nevent,skip,max_matched_dist=0.1,filename='Alignment.txt', dut=6) :
x0 = np.array([0.,0.,0.]) # [zrot, xtrans, ytrans]
argTuple = aDataSet,nevent,skip
res = minimize(TotalDistanceFunction,x0,argTuple,method='Nelder-Mead',options={'xtol': 1e-5,'disp': True})
return [0.,0.,res.x[0]],[res.x[1],res.x[2]]
def Perform3StepAlignment(aDataSet,boundary,nevent,skip,cut = 0.1,filename='Alignment.txt',Rotations=[0,0,0], dut=6) :
xr= np.array(Rotations)
x_txang = [0.]
argTuple = [0.,0.],[0.,0.],aDataSet,nevent,skip,cut,dut
resrx = minimize(TotalRotationFunctionZ,x_txang,argTuple,method='Nelder-Mead',options={'xtol': 1e-6,'disp': True})
rZ = resrx.x[0]
print "Starting guess Z angle : %f"%rZ
xr= np.array([0,0,rZ])
x_tx = np.array([0.])
x_ty = np.array([0.])
argTuple = [x_tx,x_ty],aDataSet,nevent,skip,cut,dut
resr = minimize(TotalRotationFunction,xr,argTuple,method='Nelder-Mead',options={'xtol': 1e-6,'disp': True})
print "resr", resr
print "best guess for rotation matrix: resr.x", resr.x
if resr.success == False:
print "Minimisation didn't converge. Press any key to continue, ctrl D to exit"
b=raw_input()
argTuple = resr.x,aDataSet,nevent,skip,cut,dut
rest = minimize(TotalMeanFunctionX,x_tx, argTuple,method='Nelder-Mead',options={'xtol': 1e-5,'disp': True})
print "rest", rest
print "best guess for x translation: rest.x", rest.x
if rest.success == False:
print "Minimisation didn't converge. Press any key to continue, ctrl D to exit"
b=raw_input()
argTuple = rest.x[0],resr.x,aDataSet,nevent,skip,cut,dut
rest2= minimize(TotalMeanFunctionY,x_ty, argTuple,method='Nelder-Mead',options={'xtol': 1e-5,'disp': True})
print "rest2", rest2
print "best guess for y translation: rest2.x", rest2.x
if rest2.success == False:
print "Minimisation didn't converge. Press any key to continue, ctrl D to exit"
b=raw_input()
f = open(filename,'a')
f.write("Rotation : %f %f %f [deg] Trans : %f %f [mm] \n"%(resr.x[0],resr.x[1],resr.x[2],rest.x[0],rest2.x[0]))
f.close()
return resr.x ,[rest.x[0],rest2.x[0],0]
def Perform2StepAlignment(aDataSet,boundary,nevent,skip,cut = 0.1,filename='Alignment.txt') :
x_tx = np.array([0.])
x_ty = np.array([0.])
xr= np.array([0.,0.,0.])
#resr = minimize(TotalRotationFunction,xr,[x_tx,aDataSet,nevent,skip],method='Nelder-Mead',options={'xtol': 1e-5,'disp': True})
rest = minimize(TotalMeanFunctionX,x_tx,[xr,aDataSet,nevent,skip,cut],method='Nelder-Mead',options={'xtol': 1e-5,'disp': True})
rest2 = minimize(TotalMeanFunctionY,x_ty,[rest.x[0],xr,aDataSet,nevent,skip,cut],method='Nelder-Mead',options={'xtol': 1e-5,'disp': True})
# rest = minimize(TotalMeanFunctionX,x_tx,[xr,aDataSet,nevent,skip,cut],method='Nelder-Mead',options={'xtol': 1e-3,'disp': True})
# rest2 = minimize(TotalMeanFunctionY,x_ty,[rest.x[0],xr,aDataSet,nevent,skip,cut],method='Nelder-Mead',options={'xtol': 1e-3,'disp': True})
f = open(filename,'a')
f.write("Rotation : %f %f %f [deg] Trans : %f %f [mm] \n"%(xr[0],xr[1],xr[2],rest.x[0],rest.x[1]))
f.close()
return xr,[rest.x[0],rest2.x[0],0]
#
#optimize the sigma for the eta correction
#i.e. find the sigma for charge sharing which gives the best detector resolution (minimum value for the sigma of the residuals distributions)
#param 1: a data set (class EudetData)
#param 2: number of events we are running on
#param 3: number of skiped events
#
def FindSigmaMin(dataSet,nevent,PlotPath,RunNumber,method_name, skip=1, dut=6) :
bestsigma=1000
bestres=1000
sigmaint_max = 500