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findSeedOld.py
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########################################################################################################################
##### code to make seeds from list of tracks #####
##### The track list must have this order: <BX> <pdgID> <trackId> <stave> <x> <y> <E> <weight> #####
##### Run this code: python findSeed.py -l <trackList.txt> -s <bool, true for signal positron> -e <energyCutinKeV> #####
##### written by: [email protected] #####
########################################################################################################################
import os
import sys
import time
import pprint
import math
from ROOT import *
from collections import OrderedDict
import argparse
import csv, array
import numpy as np
### needed for plotting of tracks and dimension from the spreadsheet
from makeTrackDiagrams import *
### the seed energy width
EseedMin = 0.5 # GeV
EseedMax = 17.5 # GeV
#//MeV mass of electron/positron
meMeV = 0.5109989461
meGeV = meMeV/1000.
### me^2 in GeV
meGeV2 = meGeV*meGeV
### the width of the road around the pivot tracks
yAbsMargins = 0.13 # mm (a "road" of 200 microns around the line between r4 and r1)
xAbsMargins = 0.13 # mm (a "road" of 200 microns around the line between r4 and r1)
zPositionListInner = [z2inner, z3inner]
zPositionListOuter = [z2outer, z3outer]
ddList = []
#### This is the cutflow dictionary
cutFlowDict = OrderedDict(
[('noCut', 0),
('x1Gtx4',0),
('x1*x4Negative',0),
('z1Eqz4', 0),
('yDipoleExitGt15',0),
('xDipoleExitLt25',0),
('xDipoleExitGt165', 0),
('xDipoleExitLt0',0),
('trackEnergy', 0),
('checkClusterTracksMiddleLayers', 0),
('checkClusterFit', 0),
('checkClusterXDistance', 0),
('checkClusterXDistanceLoose', 0),
('checkClusterXDistanceTight', 0)]
)
### read the dEdX vs E curve for the electron
results = csv.reader(open("dEdXSilicon_ForElectron.csv"), delimiter=",")
xdEdX = array.array('f',[]); ydEdX = array.array('f', [])
for eachRow in results:
xdEdX.append(float(eachRow[0]))
ydEdX.append(float(eachRow[1]))
### This is to find the dEdx
def getdEdX(energy):
### the dEdX vs E curve, x axis energy is in MeV, dEdX in y is in keV/um, remember the unit difference
mevEnergy = energy*1e3
interpolateddEdX = np.interp(mevEnergy, xdEdX, ydEdX)
### interpolateddEdX in keV/um
return interpolateddEdX
### This is to find the absorbed energy in the FPC layer and cooling region
def energyAbsorbed(staveId, energy, vtxZ):
if(staveId == 0 or staveId == 8):
zPos = z1inner
elif(staveId == 1 or staveId == 9):
zPos = z1outer
elif(staveId == 2 or staveId == 10):
zPos = z2inner
elif(staveId == 3 or staveId == 11):
zPos = z2outer
elif(staveId == 4 or staveId == 12):
zPos = z3inner
elif(staveId == 5 or staveId == 13):
zPos = z3outer
elif(staveId == 6 or staveId == 14):
zPos = z4inner
elif(staveId == 7 or staveId == 15):
zPos = z4outer
else:
print("No suitable stave found!!! Exiting")
quit()
### front side
if((zPos - vtxZ) > 0):
absorbingFactor = 150
### back side
else:
absorbingFactor = 250
### this dEdX is in keV/um, multiply by the width to get the energy loss
dEdX = getdEdX(energy)
absorbed = dEdX*absorbingFactor
remainingEnergy = energy*1e6 - absorbed
### print("staveId: ", staveId, " zPos: ", zPos, " vtxZ: ", vtxZ, " energy (keV): ", energy*1e6, "dEdX (keV/um): ", dEdX, " factor: ", absorbingFactor, " absorbed (keV): ", absorbed, " remainingEnergy (keV): ", remainingEnergy)
return remainingEnergy
### functions needed for distance cut from the analytical line in x1 vs x4 plane
## get R in m given momentum p
def getR(p):
R = p/(0.3*B)
return R
### get R^2 in m^2 given momentum p
def getR2(p):
R = getR(p)
return R*R
### get the xTangent in m given ZTangent in m and momentum p
def getXTangent(ZT,p):
R2 = getR2(p)
XT = (R2/LB-ZT)*(LB/math.sqrt(R2-LB2))
return XT
### get x in Sasha's coordinate in m given zTangent and momentum p
def getx(ZT,p):
XT = getXTangent(ZT,p)
R = getR(p)
x = R-XT
return x
### get r1 and r2 (x,y --> 2 points) given the Z0 and Z4. This is in m
def getR1R2(p1, p2):
r1 = [getx(Z0,p1), getx(Z4,p1)]
r2 = [getx(Z0,p2), getx(Z4, p2)]
#print("r1: ",r1, " r2: ",r2)
#quit()
return r1, r2
#### find the distance from the analytical line to the r1 and r2 point in m
def Distance(x0Test,x4Test,r1,r2):
aOb = -(r1[1]-r2[1])/(r1[0]-r2[0])
cOb = (r1[1]-r2[1])/(r1[0]-r2[0])*r1[0] - r1[1]
### now find the distance
# d = |a*x0Test + b*x4Test + c|/sqrt(a^2 + b^2) --> d = |(a/b)*x0Test + x4Test + (c/b)|/sqrt((a/b)^2 + 1^2)
d = abs(aOb*x0Test + x4Test + (cOb))/math.sqrt(aOb*aOb + 1)
return d
# get the unit vector along one vector
def rUnit2(r1, r2):
r = (r2-r1).Unit()
return r
### use svd fit
def seed3dfitSVD3Points(r1,r2,r3, name="", dodraw=False):
g = TGraph2D()
g.SetMarkerSize(3)
g.SetMarkerStyle(20)
g.SetMarkerColor(ROOT.kRed)
g.SetLineColor(ROOT.kRed)
g.SetPoint(0,r1[0],r1[1],r1[2])
g.SetPoint(1,r2[0],r2[1],r2[2])
g.SetPoint(2,r3[0],r3[1],r3[2])
g.GetXaxis().SetRangeUser(0, 650)
g.GetYaxis().SetRangeUser(-10,+10)
g.GetZaxis().SetRangeUser(3900, 4400)
x = np.array([r1[0],r2[0],r3[0]])
y = np.array([r1[1],r2[1],r3[1]])
z = np.array([r1[2],r2[2],r3[2]])
data = np.concatenate((x[:, np.newaxis],
y[:, np.newaxis],
z[:, np.newaxis]),
axis=1)
# Calculate the mean of the points, i.e. the 'center' of the cloud
datamean = data.mean(axis=0)
# Do an SVD on the mean-centered data (Singular Value Decomposition)
uu, dd, vv = np.linalg.svd(data - datamean)
# Now vv[0] contains the first principal component, i.e. the direction
# vector of the 'best fit' line in the least squares sense.
# Now generate some points along this best fit line, for plotting.
# I use -7, 7 since the spread of the data is roughly 14
# and we want it to have mean 0 (like the points we did
# the svd on). Also, it's a straight line, so we only need 2 points.
# linepts = vv[0] * np.mgrid[-7:7:2j][:, np.newaxis]
linepts = vv[0] * np.mgrid[-170:170:2j][:, np.newaxis]
# shift by the mean to get the line in the right place
linepts += datamean
if(dodraw):
lfit = TPolyLine3D()
for point in linepts:
lfit.SetNextPoint(point[0],point[1],point[2])
lfit.SetLineColor(ROOT.kBlue)
cnv = TCanvas("","",2000,2000)
view = TView.CreateView(1)
xviewmin = 3900 #if(isel(r1[0])) else xPsideL
xviewmax = 4200 #xEsideR if(isel(r1[0])) else 0
#view.SetAutoRange()
view.SetRange(0,-0.8, xviewmin , 650,+0.8,xviewmax)
view.ShowAxis()
g.Draw("p0")
lfit.Draw("same")
cnv.SaveAs(name+".pdf")
return linepts, dd ## dd is a 1D array of the data singular values
### use svd fit
def seed3dfitSVD(r1,r2,r3,r4, name="", dodraw=False):
g = TGraph2D()
g.SetMarkerSize(3)
g.SetMarkerStyle(20)
g.SetMarkerColor(ROOT.kRed)
g.SetLineColor(ROOT.kRed)
g.SetPoint(0,r1[0],r1[1],r1[2])
g.SetPoint(1,r2[0],r2[1],r2[2])
g.SetPoint(2,r3[0],r3[1],r3[2])
g.SetPoint(3,r4[0],r4[1],r4[2])
g.GetXaxis().SetRangeUser(0, 650)
g.GetYaxis().SetRangeUser(-10,+10)
g.GetZaxis().SetRangeUser(3900, 4400)
x = np.array([r1[0],r2[0],r3[0],r4[0]])
y = np.array([r1[1],r2[1],r3[1],r4[1]])
z = np.array([r1[2],r2[2],r3[2],r4[2]])
data = np.concatenate((x[:, np.newaxis],
y[:, np.newaxis],
z[:, np.newaxis]),
axis=1)
# Calculate the mean of the points, i.e. the 'center' of the cloud
datamean = data.mean(axis=0)
# Do an SVD on the mean-centered data (Singular Value Decomposition)
uu, dd, vv = np.linalg.svd(data - datamean)
# Now vv[0] contains the first principal component, i.e. the direction
# vector of the 'best fit' line in the least squares sense.
# Now generate some points along this best fit line, for plotting.
# I use -7, 7 since the spread of the data is roughly 14
# and we want it to have mean 0 (like the points we did
# the svd on). Also, it's a straight line, so we only need 2 points.
# linepts = vv[0] * np.mgrid[-7:7:2j][:, np.newaxis]
linepts = vv[0] * np.mgrid[-170:170:2j][:, np.newaxis]
# shift by the mean to get the line in the right place
linepts += datamean
if(dodraw):
lfit = TPolyLine3D()
for point in linepts:
lfit.SetNextPoint(point[0],point[1],point[2])
lfit.SetLineColor(ROOT.kBlue)
cnv = TCanvas("","",2000,2000)
view = TView.CreateView(1)
xviewmin = 3900 #if(isel(r1[0])) else xPsideL
xviewmax = 4200 #xEsideR if(isel(r1[0])) else 0
#view.SetAutoRange()
view.SetRange(0,-0.8, xviewmin , 650,+0.8,xviewmax)
view.ShowAxis()
g.Draw("p0")
lfit.Draw("same")
cnv.SaveAs(name+".pdf")
return linepts, dd ## dd is a 1D array of the data singular values
### use svd fit
def seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4):
xList = [r1[0],r4[0]]
yList = [r1[1],r4[1]]
zList = [r1[2],r4[2]]
if len(r2Inner)>0:
xList.append(r2Inner[0])
yList.append(r2Inner[1])
zList.append(r2Inner[2])
if len(r2Outer)>0:
xList.append(r2Outer[0])
yList.append(r2Outer[1])
zList.append(r2Outer[2])
if len(r3Inner)>0:
xList.append(r3Inner[0])
yList.append(r3Inner[1])
zList.append(r3Inner[2])
if len(r3Outer)>0:
xList.append(r3Outer[0])
yList.append(r3Outer[1])
zList.append(r3Outer[2])
x = np.array(xList)
y = np.array(yList)
z = np.array(zList)
data = np.concatenate((x[:, np.newaxis],
y[:, np.newaxis],
z[:, np.newaxis]),
axis=1)
# Calculate the mean of the points, i.e. the 'center' of the cloud
datamean = data.mean(axis=0)
# Do an SVD on the mean-centered data (Singular Value Decomposition)
uu, dd, vv = np.linalg.svd(data - datamean)
# Now vv[0] contains the first principal component, i.e. the direction
# vector of the 'best fit' line in the least squares sense.
# Now generate some points along this best fit line, for plotting.
# I use -7, 7 since the spread of the data is roughly 14
# and we want it to have mean 0 (like the points we did
# the svd on). Also, it's a straight line, so we only need 2 points.
# linepts = vv[0] * np.mgrid[-7:7:2j][:, np.newaxis]
linepts = vv[0] * np.mgrid[-170:170:2j][:, np.newaxis]
# shift by the mean to get the line in the right place
linepts += datamean
#print("linepoints: ", linepts)
return linepts, dd ## dd is a 1D array of the data singular values
### draw the SVD fit
def drawFit(name,linepts,hits):
g = TGraph2D()
g.SetMarkerSize(3)
g.SetMarkerStyle(20)
g.SetMarkerColor(ROOT.kRed)
g.SetLineColor(ROOT.kRed)
for i in range(len(hits)):
g.SetPoint(i,hits[i][0],hits[i][1],hits[i][2])
g.GetXaxis().SetRangeUser(0, 650)
g.GetYaxis().SetRangeUser(-10,+10)
g.GetZaxis().SetRangeUser(3900, 4400)
lfit = TPolyLine3D()
for point in linepts:
lfit.SetNextPoint(point[0],point[1],point[2])
lfit.SetLineColor(ROOT.kBlue)
cnv = TCanvas("","",2000,2000)
view = TView.CreateView(1)
xviewmin = 3900 #if(isel(r1[0])) else xPsideL
xviewmax = 4200 #xEsideR if(isel(r1[0])) else 0
#view.SetAutoRange()
view.SetRange(0,-0.8, xviewmin , 650,+0.8,xviewmax)
view.ShowAxis()
g.Draw("p0")
lfit.Draw("same")
cnv.SaveAs(name)
### get the expected number of hits in the layer 2 and layer 3 of the tracker given the position of the track
def getExpectedHits(r1, r4):
expectedHits2Inner = 0
expectedHits2Outer = 0
expectedHits3Inner = 0
expectedHits3Outer = 0
r1min = [r1[0]-xAbsMargins, r1[1]-yAbsMargins, r1[2]]
r1max = [r1[0]+xAbsMargins, r1[1]+yAbsMargins, r1[2]]
r4min = [r4[0]-xAbsMargins, r4[1]-yAbsMargins, r4[2]]
r4max = [r4[0]+xAbsMargins, r4[1]+yAbsMargins, r4[2]]
# check possible clusters in layer 2, for both inner and outer stave
x2minInner = xofz(r1min, r4min, z2inner)
x2maxInner = xofz(r1max, r4max, z2inner)
x2minOuter = xofz(r1min, r4min, z2outer)
x2maxOuter = xofz(r1max, r4max, z2outer)
#/// check possible clusters in layer 3, for both inner and outer stave
x3minInner = xofz(r1min, r4min, z3inner)
x3maxInner = xofz(r1max, r4max, z3inner)
x3minOuter = xofz(r1min, r4min, z3outer)
x3maxOuter = xofz(r1max, r4max, z3outer)
x2Inner = xofz(r1, r4, z2inner)
x2Outer = xofz(r1, r4, z2outer)
x3Inner = xofz(r1, r4, z3inner)
x3Outer = xofz(r1, r4, z3outer)
for chipName, boundaries in xBoundaries.items():
if "layerid2" in chipName and x2Inner > boundaries[0] and x2Inner < boundaries[1]:
expectedHits2Inner += 1
if "layerid3" in chipName and x2Outer > boundaries[0] and x2Outer < boundaries[1]:
expectedHits2Outer += 1
if "layerid4" in chipName and x3Inner > boundaries[0] and x3Inner < boundaries[1]:
expectedHits3Inner += 1
if "layerid5" in chipName and x3Outer > boundaries[0] and x3Outer < boundaries[1]:
expectedHits3Outer += 1
return expectedHits2Inner, expectedHits2Outer, expectedHits3Inner, expectedHits3Outer
### check if there are hits along one track in layer 2 and layer 3
def check_clusters(r1, r4, allR2Inner, allR2Outer, allR3Inner, allR3Outer, side):
global ddList
expectedHits2Inner, expectedHits2Outer, expectedHits3Inner, expectedHits3Outer = getExpectedHits(r1, r4)
#print(expectedHits2Inner, expectedHits2Outer, expectedHits3Inner, expectedHits3Outer)
hitsOnRoad2Inner = 0 ### how many tracks accepted in the road along the r1 and r4
hitsOnRoad2Outer = 0
hitsOnRoad3Inner = 0 ### how many tracks accepted in the road along the r1 and r4
hitsOnRoad3Outer = 0
r1min = [r1[0]-xAbsMargins, r1[1]-yAbsMargins, r1[2]]
r1max = [r1[0]+xAbsMargins, r1[1]+yAbsMargins, r1[2]]
r4min = [r4[0]-xAbsMargins, r4[1]-yAbsMargins, r4[2]]
r4max = [r4[0]+xAbsMargins, r4[1]+yAbsMargins, r4[2]]
# check possible clusters in layer 2, for both inner and outer stave
y2minInner = yofz(r1min, r4min, z2inner)
y2maxInner = yofz(r1max, r4max, z2inner)
x2minInner = xofz(r1min, r4min, z2inner)
x2maxInner = xofz(r1max, r4max, z2inner)
y2minOuter = yofz(r1min, r4min, z2outer)
y2maxOuter = yofz(r1max, r4max, z2outer)
x2minOuter = xofz(r1min, r4min, z2outer)
x2maxOuter = xofz(r1max, r4max, z2outer)
### separate out staves for allR2, work on the inner stave of layer 2
accept2Inner = False
accept2Outer = False
innerR2FromMatching = []
outerR2FromMatching = []
for i2 in range(0, len(allR2Inner)):
### remember allR2 has x, y, z and E saved in the list
accept2yzInner = ( (allR2Inner[i2][1] >= y2minInner and allR2Inner[i2][1] <= y2maxInner) )
if(not accept2yzInner): continue
accept2xzInner = ( ( allR2Inner[i2][0] >= x2minInner and allR2Inner[i2][0] <= x2maxInner ) )
if(not accept2xzInner): continue
accept2Inner = True
hitsOnRoad2Inner += 1
innerR2FromMatching.append(allR2Inner[i2]) ### collect all matched tracks from layer 2, remove the break
#break
### separate out staves for allR2
### only if there is no suitable track from layer 2 inner stave, go to layer 2 outer stave
#if(not accept2Inner):
if True:
for i2 in range(0, len(allR2Outer)):
### remember allR2 has x, y, z and E saved in the list
accept2yzOuter = ( (allR2Outer[i2][1] >= y2minOuter and allR2Outer[i2][1] <= y2maxOuter) )
if(not accept2yzOuter): continue
accept2xzOuter = ( ( allR2Outer[i2][0] >= x2minOuter and allR2Outer[i2][0] <= x2maxOuter ) )
if(not accept2xzOuter): continue
accept2Outer = True
hitsOnRoad2Outer += 1
outerR2FromMatching.append(allR2Outer[i2]) ### collect all matched tracks from layer 2, remove the break
#break
##### return false if both of the inner and outer acceptances are false
#if(not (accept2Inner or accept2Outer)):
#return False
#/// check possible clusters in layer 3, for both inner and outer stave
y3minInner = yofz(r1min, r4min, z3inner) ### z for inner stave layer 3 is 4064.5125
y3maxInner = yofz(r1max, r4max, z3inner)
x3minInner = xofz(r1min, r4min, z3inner)
x3maxInner = xofz(r1max, r4max, z3inner)
y3minOuter = yofz(r1min, r4min, z3outer) ### z for Outer stave layer 3 is 4076.5125
y3maxOuter = yofz(r1max, r4max, z3outer)
x3minOuter = xofz(r1min, r4min, z3outer)
x3maxOuter = xofz(r1max, r4max, z3outer)
### separate out staves for allR3, work on the inner stave of layer 3
accept3Inner = False
accept3Outer = False
innerR3FromMatching = []
outerR3FromMatching = []
for i3 in range(0, len(allR3Inner)):
### remember allR3 has x, y, z and E saved in the list
accept3yzInner = ( (allR3Inner[i3][1] >= y3minInner and allR3Inner[i3][1] <= y3maxInner) )
if(not accept3yzInner): continue
accept3xzInner = ( ( allR3Inner[i3][0] >= x3minInner and allR3Inner[i3][0] <= x3maxInner ) )
if(not accept3xzInner): continue
accept3Inner = True
hitsOnRoad3Inner += 1
innerR3FromMatching.append(allR3Inner[i3]) ### collect all matched tracks from layer 3, remove the break
#break
### separate out staves for allR3
### only if no track found in layer 3 inner stave, go to layer 3 outer stave
#if(not accept3Inner):
### go to layer 3
if True:
for i3 in range(0, len(allR3Outer)):
### remember allR3 has x, y, z and E saved in the list
accept3yzOuter = ( (allR3Outer[i3][1] >= y3minOuter and allR3Outer[i3][1] <= y3maxOuter) )
if(not accept3yzOuter): continue
accept3xzOuter = ( ( allR3Outer[i3][0] >= x3minOuter and allR3Outer[i3][0] <= x3maxOuter ) )
if(not accept3xzOuter): continue
accept3Outer = True
hitsOnRoad3Outer += 1
outerR3FromMatching.append(allR3Outer[i3]) ### collect all matched tracks from layer 3, remove the break
#break
##### return false if both of the inner and outer acceptances are false
#if(not (accept3Inner or accept3Outer)):
#return False
#### return false if both of the inner and outer acceptances are false
#if(not (accept2Inner or accept2Outer or accept3Inner or accept3Outer)):
#return False
### find the number of actual matched tracks
nMatched = (expectedHits2Inner<=hitsOnRoad2Inner)+(expectedHits2Outer<=hitsOnRoad2Outer)+(expectedHits3Inner<=hitsOnRoad3Inner)+(expectedHits3Outer<=hitsOnRoad3Outer)
### find the number of expected tracks
nExpected = expectedHits2Inner + expectedHits2Outer + expectedHits3Inner + expectedHits3Outer
### only if we have hit in one of the two inner layer of the tracker, we select the seed track, otherwise we reject the track
if(nMatched>=3):
return nMatched, nExpected, innerR2FromMatching, outerR2FromMatching, innerR3FromMatching, outerR3FromMatching
else:
return nMatched, nExpected, [], [], [], []
#### prepare the SVD fit for the seed track
def makeSeedFit(r1, r4, nMatched, nExpected, innerR2FromMatching, outerR2FromMatching, innerR3FromMatching, outerR3FromMatching):
### now check if the SVD match works
allDDList = {'dd':[], 'linepts':[], 'i2Inner':[], 'i2Outer':[], 'i3Inner':[], 'i3Outer':[]}
#### number of matches tracks in each of the staves of the layer 2 and layer 3
n2i = len(innerR2FromMatching)
n2o = len(outerR2FromMatching)
n3i = len(innerR3FromMatching)
n3o = len(outerR3FromMatching)
allHits = {}
for i2Inner in range(len(innerR2FromMatching)):
allHits.update({"Layer2Inner_"+str(i2Inner):innerR2FromMatching[i2Inner]})
for i2Outer in range(len(outerR2FromMatching)):
allHits.update({"Layer2Outer_"+str(i2Outer):outerR2FromMatching[i2Outer]})
for i3Inner in range(len(innerR3FromMatching)):
allHits.update({"Layer3Inner_"+str(i3Inner):innerR3FromMatching[i3Inner]})
for i3Outer in range(len(outerR3FromMatching)):
allHits.update({"Layer3Outer_"+str(i3Outer):outerR3FromMatching[i3Outer]})
if(n2i> 0) :
for i2Inner in range(n2i):
r2Inner = innerR2FromMatching[i2Inner]
if(n2o > 0):
for i2Outer in range(n2o):
r2Outer = outerR2FromMatching[i2Outer]
if(n3i > 0):
for i3Inner in range(n3i):
r3Inner = innerR3FromMatching[i3Inner]
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r3Inner = []
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r2Outer = []
if(n3i > 0):
for i3Inner in range(n3i):
r3Inner = innerR3FromMatching[i3Inner]
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r3Inner = []
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(i2Inner)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r2Inner = []
if(n2o > 0):
for i2Outer in range(n2o):
r2Outer = outerR2FromMatching[i2Outer]
if(n3i > 0):
for i3Inner in range(n3i):
r3Inner = innerR3FromMatching[i3Inner]
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r3Inner = []
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(i2Outer)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r2Outer = []
if(n3i > 0):
for i3Inner in range(n3i):
r3Inner = innerR3FromMatching[i3Inner]
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
r3Outer = []
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(i3Inner)
allDDList['i3Outer'].append(-1)
allDDList['linepts'].append(lfitpts)
else:
r3Inner = []
if(n3o > 0):
for i3Outer in range(n3o):
r3Outer = outerR3FromMatching[i3Outer]
lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
allDDList['dd'].append(dd.tolist())
allDDList['i2Inner'].append(-1)
allDDList['i2Outer'].append(-1)
allDDList['i3Inner'].append(-1)
allDDList['i3Outer'].append(i3Outer)
allDDList['linepts'].append(lfitpts)
else:
return False, {}
#r3Outer = []
#lfitpts, dd = seed3dfitSVDWithList(r1,r2Inner, r2Outer, r3Inner, r3Outer, r4)
#allDDList['dd'].append(dd.tolist())
#allDDList['i2Inner'].append(-1)
#allDDList['i2Outer'].append(-1)
#allDDList['i3Inner'].append(-1)
#allDDList['i3Outer'].append(-1)
#allDDList['linepts'].append(lfitpts)
### start with a very high value, in the end we will take the smallest one
ddValue0 = 1e11
ddValue1 = 1e11
ddValue2 = 1e11
### the index of the best matched track
iWinner = -1
#print("ddList:", allDDList)
for i in range(len(allDDList['dd'])):
ddValue = allDDList['dd'][i]
#print("ddValue1", ddValue)
if ((0.0 < ddValue[1] < 0.1) and (0.0 < ddValue[2] < 0.05)):
if ddValue[1] < ddValue1:
ddValue0 = ddValue[0]
ddValue1 = ddValue[1]
ddValue2 = ddValue[2]
iWinner = i
### only if a good fit is available, return True
if iWinner >= 0:
return True, {"ddValue0": ddValue0, "ddValue1":ddValue1, "ddValue2": ddValue2, "linepts":allDDList['linepts'][iWinner], "r2Inner":allDDList['i2Inner'][iWinner], "r2Outer":allDDList['i2Outer'][iWinner], "r3Inner":allDDList['i3Inner'][iWinner], "r3Outer":allDDList['i3Outer'][iWinner]}
else:
return False, {}
### making the seeds from two tracks from innermost layer and outermost layer, check the seeding cuts in the process
def makeseed(r1, r4, allR2Inner, allR2Outer, allR3Inner, allR3Outer, side, r1GeV, r10GeV):
cutFlowDict['noCut'] += 1
if(abs(r1[0]) >= abs(r4[0])):
return False, {} ### |x1| must be smaller than |x4|
cutFlowDict['x1Gtx4'] += 1
if(r1[0]*r4[0] < 0):
return False, {} ### the x value should have the same sign, i.e. on the same side of the beam
cutFlowDict['x1*x4Negative'] += 1
if(r1[2] == r4[2]):
return False, {} ### if z1=z4..., this is also impossible
cutFlowDict['z1Eqz4'] += 1
yDipoleExit = yofz(r1, r4, zDipoleExit)
xDipoleExit = xofz(r1, r4, zDipoleExit)
### The following cuts are coming because of the distribution of the signal
### the following cannot be 10.8mm/2 according to the signal tracks
if(abs(yDipoleExit) > 10.8/2):
return False, {}
cutFlowDict['yDipoleExitGt15'] += 1
### This is according to the signal x:y at the dipole exit
if(abs(xDipoleExit) < 20.0):
return False, {} # the track should point to |x|<~1.0 at the dipole exit
cutFlowDict['xDipoleExitLt25'] += 1
if(abs(xDipoleExit) > 330.0/2):
return False, {}
cutFlowDict['xDipoleExitGt165'] += 1
### select only positive x or negative x accroding to the particle
if( (side=="Positron" and xDipoleExit < 0) or (side=="Electron" and xDipoleExit > 0)):
return False, {}
cutFlowDict['xDipoleExitLt0'] += 1
#### find the energy of the tracks
x0 = 0
z0 = zofx(r1, r4, x0)
#xExit = abs(xofz(r1, r4, zDipoleActiveExit))
#H = abs((zDipoleActiveExit-z0))/1000.0 ### converting H from mm to m
xExit = abs(xofz(r1, r4, zDipoleActiveExit))
H = abs((zDipoleActiveExit-z0))*mm2m ### converting H from mm to m
xExitInM = xExit*mm2m ### converting xExit from mm to m
R = H*(LB)/xExitInM + xExitInM ### // This is the radius of curvature for a track
P = 0.3*B*R ### here B in Tesla and R in m
v1 = TVector2(r1[2], r1[1])
v4 = TVector2(r4[2], r4[1])
u = rUnit2(v1, v4)
uz = u.X()
uy = u.Y()
px = 0
py = P*uy
pz = P*uz
### The seed lorentz vector, need for energy plotting
p = TLorentzVector()
p.SetPxPyPzE(px, py, pz, math.sqrt(px*px + py*py + pz*pz + meGeV2))
### checking the track energy
if(p.E() < EseedMin or p.E() > EseedMax):
return False, {}
cutFlowDict['trackEnergy'] += 1
nMatched, nExpected, innerR2FromMatching, outerR2FromMatching, innerR3FromMatching, outerR3FromMatching = check_clusters(r1, r4, allR2Inner, allR2Outer, allR3Inner, allR3Outer, side)
if nMatched < 3:
return False, {}
cutFlowDict['checkClusterTracksMiddleLayers'] += 1
#print("2time:", expectedHits2Inner, expectedHits2Outer, expectedHits3Inner, expectedHits3Outer)
passFit, winnerFit = makeSeedFit(r1, r4, nMatched, nExpected, innerR2FromMatching, outerR2FromMatching, innerR3FromMatching, outerR3FromMatching)
### add the nMatched and nExpected to the winnerFit dictionary
winnerFit.update({'nMatched':nMatched, 'nExpected':nExpected})
#if(not passFit):
#return False, {}
winnerFit.update({'passFit':passFit})
cutFlowDict['checkClusterFit'] += 1
winnerFit.update({"pSeed":p})
### if fit is done, then take the x and z from the fit
if(passFit):
xDipoleFromFit = xofz(winnerFit['linepts'][0], winnerFit['linepts'][1], zDipoleExit)*mm2m
xLayer4FromFit = xofz(winnerFit['linepts'][0], winnerFit['linepts'][1], r4[2])*mm2m
#### calculate the distance from x4:x_exit analytical line
d = Distance(xDipoleFromFit,xLayer4FromFit,r1GeV,r10GeV)
#print("The cluster x distance: ",d)
### d is in m
if(d > 5*mm2m):
return False, {}
cutFlowDict['checkClusterXDistance'] += 1
winnerFit.update({"distance":d})
if(winnerFit['nMatched'] == 4):
cutFlowDict['checkClusterXDistanceTight'] += 1
else:
cutFlowDict['checkClusterXDistanceLoose'] += 1
return True, winnerFit
### The main function
def main():
# give the input text file containing all the track information
parser = argparse.ArgumentParser(description='Code to find seed tracks')
parser.add_argument('-l', action="store", dest="inFile", default="list_root_hics_165gev_w0_5000nm_provisional_10xi3_6cee466a_trackInfoClean.txt")
parser.add_argument('-s', action="store_true", dest="needSignal", default=False)
parser.add_argument('-e', action="store", dest="eCut", type=float, default=0.0)
parser.add_argument('-p', action="store", dest="Particle", type=str, default="Positron")
args = parser.parse_args()
for index, row in df.iterrows():
detid = row["detid"]
layerid = row["layerid"]
if(layerid > 7):
continue
xMin, xMax = GetSensorXBoundaries(detid, layerid)
xBoundaries.update({"detid"+str(detid)+"_layerid"+str(layerid):[xMin, xMax]})
#print(xBoundaries)
### open the file containing track file
inTextFile = args.inFile
inputTrackInfo = open(inTextFile)
#energyCutSuffix = str(args.eCut)+"KeVCut"
energyCutSuffix = "VariableEnergyCut"
side = args.Particle
signalCutSuffix = ""
if(args.needSignal):
signalCutSuffix = "OnlySignal"
else:
signalCutSuffix = "SignalAndBackground"
particleSuffix = ""
if(side=="Positron"):
particleSuffix="PositronSide"
else:
particleSuffix="ElectronSide"
### open histogram to know the seed information
if (("_" in inTextFile) and ("hics" in inTextFile)):
eachName = inTextFile.split('.')[0].split('_')
suffixName = "_".join(eachName[2:])
else:
suffixName = inTextFile.split('.')[0]
outFile = TFile("seedingInformation_"+suffixName+"_"+energyCutSuffix+"_"+signalCutSuffix+"_"+particleSuffix+".root", "RECREATE")
outFile.cd()
hAllPossible = TH1D("hAllPossible", "all possible track combination; bunch crossing; number of track combination", 9508, 0, 9508)
hSeedPossible = TH1D("hSeedPossible", "seed track combination; bunch crossing; number of seed track", 9508, 0, 9508)
hSeedMultiplicity = TH1D("hSeedMultiplicity", "seed multiplicity; number of seeds; BX", 2000, 0, 2000)
hSignalMultiplicity = TH1D("hSignalMultiplicity", "number of signals; number of signals; BX", 2000, 0, 2000)
hXLayer4XDipole = TH2D("hXLayer4XDipole", "number of signal distribution; x_{Dipole} [mm]; x_{Layer4}",330,0,330,650,0,650)
hSigEnergy = TH1D("hSigEnergy", "signal energy; Energy [GeV]; Entries", 200, 0, 20)
hSeedEnergy = TH1D("hSeedEnergy", "seed energy; Energy [GeV]; Entries", 200, 0, 20)
hSeedEnergyLoose = TH1D("hSeedEnergyLoose", "seed energy (Loose); Energy [GeV]; Entries", 200, 0, 20)
hSeedEnergyTight = TH1D("hSeedEnergyTight", "seed energy (Tight); Energy [GeV]; Entries", 200, 0, 20)
hSeedDistance = TH1D("hSeedDistance", "seed distance wrt analytical line; d [m]; Entries", 200, 0, 0.005)
hSeedDistanceLoose = TH1D("hSeedDistanceLoose", "seed distance wrt analytical line (loose); d [m]; Entries", 200, 0, 0.005)
hSeedDistanceTight = TH1D("hSeedDistanceTight", "seed distance wrt analytical line (tight); d [m]; Entries", 200, 0, 0.005)
hSVDValues1 = TH1D("hSVDValues1", "output of SVD[0]; fit quality [0]; Events", 50, 200, 250)
hSVDValues2 = TH1D("hSVDValues2", "output of SVD[1]; fit quality [1]; Events", 50, 0, 0.1)
hSVDValues3 = TH1D("hSVDValues3", "output of SVD[2]; fit quality [2]; Events", 25, 0, 0.01)