def TGraph(x, y, yError=None, xError=None, **kwargs):
__parseDrawOptions(kwargs)
import sys
#TODO: Need to check the types
try:
assert (len(x) == len(y))
except AssertionError:
print "Oops! Data points x and y aren't of the same size!"
sys.exit()
nPoints = len(x)
from array import array
if xError is None: xError = __zeros(nPoints)
if yError is None: yError = __zeros(nPoints)
from ROOT import TGraphErrors
graph = TGraphErrors(nPoints, array('d', x), array('d', y),
array('d', xError), array('d', yError))
graph.SetTitle(__drawOptions['title'])
graph.SetMarkerStyle(__drawOptions['mstyle'])
graph.SetMarkerSize(__drawOptions['msize'])
graph.SetMarkerColor(__drawOptions['mcolour'])
graph.SetFillStyle(0)
graph.SetFillColor(0)
return graph
def Sigma_Calc(g,i_,x,y,xe,ye,percentage):
#print'Creating TGraph with new uncertainty band'
# sigma factor. What to multiply uncertainties by, assuming current uncertainties are 1 sigma.
#print'percentage/100. = ',percentage/100.
sf = TMath.ErfInverse(percentage/100.)*TMath.Sqrt(2) # sigma value for desired percentage events contained in distribution
#print"sf = ",sf
#for i in range(i_ - 1):
#print'i_ = ',i_
for i in range(i_):
ye[i] = ye[i]*sf # multiply 1 sigma uncertainties by new sigma value.
#print sf,'sig errors = ',ye
#ng = TGraphErrors(i_ - 1, x, y, xe, ye)
ng = TGraphErrors(i_, x, y, xe, ye)
if percentage == 90: j = 2
if percentage == 99.5: j = 4
ng.SetMarkerStyle(g.GetMarkerStyle())
ng.SetLineStyle(g.GetLineStyle())
#ng.SetMarkerColor(g.GetMarkerColor() + j)
#ng.SetLineColor(g.GetLineColor() + j)
ng.SetFillColor(g.GetFillColor() + j)
#ng.SetName(g.GetName())
ng.SetFillStyle(g.GetFillStyle())
return ng
def plot( self, plotoptions, opt="?" ):
vx= array( "d", self.aostand.getPointsCenter() )
values= self.values
sterrs= self.sterrs
if "m" in opt:
print "AnalysisObservable::plot: use errors from error matrix"
sterrs= array( "d", self.aostand.getErrors( "m" ) )
syerrs= self.syerrs
npoints= len(vx)
if "xshift" in plotoptions:
for i in range(npoints):
vx[i]+= plotoptions["xshift"]
vex= array( "d", npoints*[0.0] )
tgest= TGraphErrors( npoints, vx, values, vex, sterrs )
toterrs= np.sqrt( np.add( np.square( sterrs ), np.square( syerrs ) ) )
tgesy= TGraphErrors( npoints, vx, values, vex, toterrs )
tgesy.SetMarkerStyle( plotoptions["markerStyle"] )
tgesy.SetMarkerSize( plotoptions["markerSize"] )
drawas= plotoptions["drawas"] if "drawas" in plotoptions else "p"
tgesy.SetName( self.obs )
if "fillcolor" in plotoptions:
tgesy.SetFillColor(plotoptions["fillcolor"])
tgest.SetFillColor(plotoptions["fillcolor"])
if "s" in opt:
tgesy.Draw( "psame" )
else:
if "title" in plotoptions:
tgesy.SetTitle( plotoptions["title"] )
else:
tgesy.SetTitle( self.obs )
tgesy.SetMinimum( plotoptions["ymin"] )
tgesy.SetMaximum( plotoptions["ymax"] )
xaxis= tgesy.GetXaxis()
xaxis.SetLimits( plotoptions["xmin"], plotoptions["xmax"] )
if "xlabel" in plotoptions:
xaxis.SetTitle( plotoptions["xlabel"] )
if "ylabel" in plotoptions:
tgesy.GetYaxis().SetTitle( plotoptions["ylabel"] )
tgesy.Draw( "a"+drawas )
optlogx= plotoptions["logx"] if "logx" in plotoptions else 0
gPad.SetLogx( optlogx )
optlogy= plotoptions["logy"] if "logy" in plotoptions else 0
gPad.SetLogy( optlogy )
tgest.Draw( "same"+drawas )
return tgest, tgesy
def graphTruth():
fname = "PionMinusG4.txt"
kineticEnergy = []
crossSec = []
crossSec_el = []
crossSec_inel = []
zero = []
title = ""
with open(fname) as f:
for fLine in f.readlines():
w = fLine.split()
if is_number(w[0]):
runIn = int(w[0])
ke = float(w[1])
xstot = float(w[4])
kineticEnergy.append(ke)
crossSec.append(xstot)
zero.append(0.)
else:
if "for" not in fLine:
continue
title = fLine[9:]
#define some data points . . .
x = array('f', kineticEnergy)
y = array('f', crossSec)
y_el = array('f', crossSec_el)
y_inel = array('f', crossSec_inel)
exl = array('f', zero)
exr = array('f', zero)
nPoints = len(x)
# . . . and hand over to TGraphErros object
gr = TGraphErrors(nPoints, x, y, exl, exr)
gr.SetTitle(title + "; Kinetic Energy [MeV]; Cross Section [barn]")
gr.GetXaxis().SetRangeUser(0, 1000)
gr.GetYaxis().SetRangeUser(0, 2.)
gr.SetLineWidth(2)
gr.SetLineColor(kGreen - 2)
gr.SetFillColor(0)
return gr
def tgrapherrors_from_lists(x, y, ex, ey):
if ex is None and ey is None:
n = min(len(x), len(y))
tg = TGraphErrors(n, array.array('d', x[:n]), array.array('d', y[:n]),
array.array('d', [0] * n), array.array('d', [0] * n))
elif ex is None:
n = min(len(x), len(y), len(ey))
tg = TGraphErrors(n, array.array('d', x[:n]), array.array('d', y[:n]),
array.array('d', [0] * n), array.array('d', ey[:n]))
elif ey is None:
n = min(len(x), len(ex), len(y))
tg = TGraphErrors(n, array.array('d', x[:n]), array.array('d', y[:n]),
array.array('d', ex[:n]), array.array('d', [0] * n))
else:
n = min(len(x), len(ex), len(y), len(ey))
tg = TGraphErrors(n, array.array('d', x[:n]), array.array('d', y[:n]),
array.array('d', ex[:n]), array.array('d', ey[:n]))
keep(tg)
tg.SetFillColor(0)
return tg
def makePlot1D(filepath, foutname, plottitle='', masstitle=''):
br = 1 if 'Resonant' in plottitle else 0.68
limits = parseLimitFiles2D(filepath, br)
xaxis = []
xseclist = []
xsecerr = []
cent = []
obs = []
up1 = []
up2 = []
down1 = []
down2 = []
maxval = 0
minval = 999
for m in sorted(limits):
l = limits[m]
xaxis.append(m)
xseclist.append(l.xsec)
xsecerr.append(l.xsec * .2)
cent.append(l.cent)
up1.append(l.up1 - l.cent)
up2.append(l.up2 - l.cent)
down1.append(l.cent - l.down1)
down2.append(l.cent - l.down2)
obs.append(l.obs)
maxval = max(maxval, l.up2)
minval = min(minval, l.down2)
N = len(xaxis)
up1Sigma = array('f', up1)
up2Sigma = array('f', up2)
down1Sigma = array('f', down1)
down2Sigma = array('f', down2)
cent = array('f', cent)
obs = array('f', obs)
xarray = array('f', xaxis)
xsecarray = array('f', xseclist)
xsecerrarray = array('f', xsecerr)
zeros = array('f', [0 for i in xrange(N)])
graphXsec = TGraphErrors(N, xarray, xsecarray, zeros, xsecerrarray)
graphCent = TGraph(N, xarray, cent)
graphObs = TGraph(N, xarray, obs)
graph1Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down1Sigma,
up1Sigma)
graph2Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down2Sigma,
up2Sigma)
c = TCanvas('c', 'c', 700, 600)
c.SetLogy()
c.SetLeftMargin(.15)
graph2Sigma.GetXaxis().SetTitle(masstitle + ' [GeV]')
graph2Sigma.GetYaxis().SetTitle(
'95% C.L. upper limit [#sigma/#sigma_{theory}]')
c2 = root.kOrange
c1 = root.kGreen + 1
graph2Sigma.SetLineColor(c2)
graph1Sigma.SetLineColor(c1)
graph2Sigma.SetFillColor(c2)
graph1Sigma.SetFillColor(c1)
graph2Sigma.SetMinimum(0.5 * minval)
graph2Sigma.SetMaximum(10 * maxval)
graphCent.SetLineWidth(2)
graphCent.SetLineStyle(2)
graphObs.SetLineColor(1)
graphObs.SetLineWidth(3)
graphObs.SetMarkerStyle(20)
graphObs.SetMarkerSize(1)
graphObs.SetMarkerColor(1)
graph1Sigma.SetLineStyle(0)
graph2Sigma.SetLineStyle(0)
leg = TLegend(0.55, 0.7, 0.9, 0.9)
leg.AddEntry(graphCent, 'Expected', 'L')
if not BLIND:
leg.AddEntry(graphObs, 'Observed', 'Lp')
leg.AddEntry(graph1Sigma, '1 std. dev.', 'F')
leg.AddEntry(graph2Sigma, '2 std. dev.', 'F')
leg.SetFillStyle(0)
leg.SetBorderSize(0)
graph2Sigma.Draw('A3')
graph1Sigma.Draw('3 same')
graphCent.Draw('same L')
if not BLIND:
graphObs.Draw('same Lp')
subscript = 'SR' if 'Resonant' in plottitle else 'FC'
coupling = '0.1' if 'Resonant' in plottitle else '0.25'
graphXsec.SetLineColor(2)
graphXsec.SetLineWidth(2)
graphXsec.SetLineStyle(2)
graphXsec.SetFillColor(2)
graphXsec.SetFillStyle(3005)
graphXsec.Draw('same L3')
'''
if not scale:
if 'Resonant' in plottitle:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=100 GeV}'%(subscript,subscript,coupling),'l')
else:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=10 GeV}'%(subscript,subscript,coupling),'l')
'''
if not BLIND:
findIntersect1D(graphObs, graphXsec, xaxis[0], xaxis[-1])
findIntersect1D(graphCent, graphXsec, xaxis[0], xaxis[-1])
leg.Draw()
label = TLatex()
label.SetNDC()
label.SetTextSize(0.8 * c.GetTopMargin())
label.SetTextFont(62)
label.SetTextAlign(11)
label.DrawLatex(0.15, 0.94, "CMS")
label.SetTextFont(52)
label.SetTextSize(0.6 * c.GetTopMargin())
# label.DrawLatex(0.25,0.94,"Preliminary")
label.SetTextFont(42)
label.SetTextSize(0.7 * c.GetTopMargin())
label.DrawLatex(0.19, 0.83, plottitle)
if 'Resonant' in plottitle:
label.DrawLatex(0.19, 0.75, "a_{SR} = b_{SR} = %s" % coupling)
label.DrawLatex(0.19, 0.68, "m_{#chi}=100 GeV")
else:
label.DrawLatex(0.19, 0.75, "g_{DM}^{V}=1,g_{q}^{V}=0.25")
label.DrawLatex(0.19, 0.68, "m_{#chi}=1 GeV")
label.SetTextSize(0.6 * c.GetTopMargin())
label.SetTextFont(42)
label.SetTextAlign(31) # align right
label.DrawLatex(0.95, 0.94, "%.1f fb^{-1} (13 TeV)" % (plotConfig.lumi))
c.SaveAs(foutname + '.pdf')
c.SaveAs(foutname + '.png')
jetToMETRatePlot.SetPointError(41, 5, 24558.1623237245)
jetToMETRatePlot.SetPointError(42, 5, 25142.8804742893)
jetToMETRatePlot.SetPointError(43, 5, 25727.5986248542)
jetToMETRatePlot.SetPointError(44, 5, 26312.3167754191)
jetToMETRatePlot.SetPointError(45, 5, 26897.034925984)
jetToMETRatePlot.SetPointError(46, 5, 27481.7530765488)
jetToMETRatePlot.SetPointError(47, 5, 28066.4712271137)
jetToMETRatePlot.SetPointError(48, 5, 28651.1893776786)
jetToMETRatePlot.SetPointError(49, 5, 29235.9075282434)
ratePlot = TMultiGraph()
# Setting colours
# Black
jetRatePlot.SetLineColor(1)
jetRatePlot.SetFillColor(1)
# Red
jetToMuonRatePlot.SetLineColor(2)
jetToMuonRatePlot.SetFillColor(2)
# Green
jetToElectronRatePlot.SetLineColor(3)
jetToElectronRatePlot.SetFillColor(3)
# Blue
jetToMETRatePlot.SetLineColor(4)
jetToMETRatePlot.SetFillColor(4)
# Adding a legend
canvas = TCanvas("c1", "canvas")
canvas.SetLogy(True)
canvas.SetGridx()
xx = []
xxer = []
yy = []
yyer = []
for i in range(0, hmctotqcd.GetNbinsX() + 2):
yy.append(float(hmctotqcd.GetBinContent(i)))
yyer.append(float(hmctotqcd.GetBinError(i)))
xx.append(float(hmctotqcd.GetBinCenter(i)))
xxer.append(float(hmctotqcd.GetBinWidth(i) / 2))
x_ = array("d", xx)
xer = array("d", xxer)
y_ = array("d", yy)
yer = array("d", yyer)
gr = TGraphErrors(len(x_), x_, y_, xer, yer)
gr.SetFillColor(ROOT.kBlack)
#gr.SetFillStyle(3144);
gr.SetFillStyle(3244)
gr.Draw("same,2")
c.Print("m3Hist.png")
h_background_m3Hist = h_wjets_m3HistS.Clone("h_background_m3Hist")
#h_background_m3Hist.Add(h_wjets_m3Hist)
h_background_m3Hist.Add(h_singletop_t_m3HistS)
h_background_m3Hist.Add(h_zjets_m3HistS)
rttbar = n_ttbar / (n_ttbar + n_background) #+n_qcd)
x = RooRealVar("x", "x",
h_zjets_m3Hist.GetXaxis().GetXmin(),
#-------end of canvas BS
#--drawing on pad1
print ("gh 6")
pad1.cd()
NSMassPoint.GetXaxis().SetLabelSize(0.15)
NSMassPoint.GetYaxis().SetLabelSize(0.05)
NSMassPoint.GetYaxis().SetTitleOffset(0.42) # 1.2 = 20% larger
NSMassPoint.SetTitleOffset(0.42) # 1.2 = 20% larger
NSMassPoint.SetTitle("Bkg Estimtae in each signal injected mass point: Trijet Inclusive Window: "+str(windowWidth))
NSMassPoint.SetMarkerStyle(1)
NSMassPoint.GetYaxis().SetRange(1, 1000000)
NSMassPoint.SetMarkerColor(0)
NSMassPoint.SetLineColor(0)
NSMassPoint.Draw() #this is to make sure the x axis of pad1 mataches that of pad2
NSErrorBar.SetFillColor(29)
NSErrorBar.SetMarkerColor(1)
NSErrorBar.Draw("LE3 same")
NSErrorBar.Draw("P same")
#NSMassPoint.Draw("E same")
print("N : ", NSErrorBar.GetN())
#NSErrorBar.Draw("P")
#JBMassPoint.SetMarkerStyle(2)
#JBMassPoint.SetMarkerColor(2)
#JBMassPoint.SetLineColor(2)
#JBMassPoint.Draw("P same")
JBGraph.SetMarkerStyle(5)
JBGraph.SetMarkerColor(2)
JBGraph.SetLineColor(2)
JBGraph.Draw("P same")
def plot2BodyDist(theFitter, pars, chi2, ndf,
Err = -1, NP = False, Prefix = "Mjj", Left = False):
from ROOT import gPad, TLatex, TCanvas, kRed, kCyan, kBlue, \
RooFit, RooPlot, RooCurve, RooAbsReal, TGraphErrors, TLine, \
RooWjjMjjFitter
if pars.includeMuons and pars.includeElectrons:
modeString = ''
elif pars.includeMuons:
modeString = 'Muon'
elif pars.includeElectrons:
modeString = 'Electron'
else:
modeString = ''
mf = theFitter.stackedPlot(False, RooWjjMjjFitter.mjj, Left)
mf.SetName("%s_Stacked" % (Prefix));
sf = theFitter.residualPlot(mf, "h_background", "dibosonPdf", False)
sf.SetName("%s_Subtracted" % (Prefix));
pf = theFitter.residualPlot(mf, "h_total", "", True)
pf.SetName("%s_Pull" % (Prefix))
pf2 = pf.emptyClone("%s_Pull_Corrected" % (Prefix))
pf2.SetMinimum(-5.)
pf2.SetMaximum(5.)
corrPull = False
lf = theFitter.stackedPlot(True, RooWjjMjjFitter.mjj, Left)
lf.SetName("%s_Stacked_Log" % (Prefix));
if Err > 0:
totalPdf = theFitter.getWorkSpace().pdf('totalPdf')
## Ntotal = totalPdf.expectedEvents(iset)
## print 'Ntotal:',Ntotal
h_dibosonPdf = sf.getCurve('h_dibosonPdf')
totalPdf.plotOn(sf,
RooFit.ProjWData(theFitter.getWorkSpace().data('data')),
RooFit.Normalization(Err, RooAbsReal.Raw),
#RooFit.AddTo('h_dibosonPdf', 1., 1.),
#RooFit.Invisible(),
RooFit.Name('h_ErrUp'),
RooFit.Range('RangeForPlot'),
RooFit.NormRange('RangeForPlot'),
RooFit.LineColor(kRed), RooFit.LineStyle(3))
h_ErrUp = sf.getCurve('h_ErrUp')
sf.remove('h_ErrUp', False)
ErrBand = TGraphErrors(h_dibosonPdf.GetN(), h_dibosonPdf.GetX(),
h_dibosonPdf.GetY())
for pt in range(1, ErrBand.GetN()):
ErrBand.SetPointError(pt, 0,
h_ErrUp.interpolate(ErrBand.GetX()[pt]))
ErrBand.SetName("ErrBand")
ErrBand.SetTitle("Uncertainty")
ErrBand.SetLineColor(kRed)
## ErrBand.SetLineWidth(0)
## ErrBand.SetLineStyle(0)
ErrBand.SetFillColor(kRed)
ErrBand.SetFillStyle(3353)
#ErrBand.Draw('ap3')
#h_ErrUp.Draw('lp')
#gPad.Update()
#gPad.WaitPrimitive()
## h_ErrUp.Draw("al")
## h_ErrUp.GetXaxis().Set(36, 40., 400.)
## gPad.Update()
## gPad.WaitPrimitive()
## h_UpBand = RooCurve("h_UpBand", "Uncertainty", h_dibosonPdf, h_ErrUp,
## 1., 1.)
## h_UpBand.SetLineStyle(3)
## h_UpBand.SetLineColor(kBlue+1)
## h_DownBand = RooCurve("h_DownBand", "Uncertainty", h_dibosonPdf, h_ErrUp,
## 1., -1.)
## h_DownBand.SetLineStyle(3)
## h_DownBand.SetLineColor(kBlue+1)
## sf.addPlotable(h_UpBand, "L")
## sf.addPlotable(h_DownBand, "L")
sf.addObject(ErrBand, "3")
#sf.Print("v")
sf.drawAfter('h_dibosonPdf', 'ErrBand')
#sf.Print("v")
sf.drawAfter('ErrBand', 'theData')
#sf.Print("v")
sf.findObject('theLegend').AddEntry(ErrBand, 'Uncertainty', 'f')
sf.findObject('theLegend').SetY1NDC(sf.findObject('theLegend').GetY1NDC() - 0.057)
sf.findObject('theLegend').SetY1(sf.findObject('theLegend').GetY1NDC())
corrPull = True
pf2.addObject(sub2pull(sf.getHist('theData'),
sf.findObject('ErrBand')),
'p0')
for item in range(0, int(pf.numItems())):
firstItem = pf.getObject(item)
if (type(firstItem) == TLine):
newLine = TLine(firstItem)
newLine.SetY1(4.)
newLine.SetY2(-4.)
pf2.addObject(newLine, 'l')
#SetOwnership(newLine, False)
if NP:
NPPdf = theFitter.makeNPPdf();
NPNorm = 4.*0.11*46.8/12.*pars.intLumi
if (modeString == 'Electron'):
if pars.njets == 2:
NPNorm *= 0.0381
elif pars.njets == 3:
NPNorm *= 0.0123
else:
if pars.njets == 2:
NPNorm *= 0.0550
elif pars.njets == 3:
NPNorm *= 0.0176
print '**** N_NP:', NPNorm,'****'
NPPdf.plotOn(sf, RooFit.ProjWData(theFitter.getWorkSpace().data('data')),
RooFit.Normalization(NPNorm, RooAbsReal.Raw),
RooFit.AddTo('h_dibosonPdf', 1., 1.),
RooFit.Name('h_NP'),
RooFit.Range('RangeForPlot'),
RooFit.NormRange('RangeForPlot'),
RooFit.LineColor(kBlue), RooFit.LineStyle(2))
h_NP = sf.getCurve('h_NP')
sf.drawBefore('h_dibosonPdf', 'h_NP')
#sf.Print("v")
sf.findObject('theLegend').AddEntry(h_NP, "CDF-like Signal", "L")
sf.findObject('theLegend').SetY1NDC(sf.findObject('theLegend').GetY1NDC() - 0.057)
sf.findObject('theLegend').SetY1(sf.findObject('theLegend').GetY1NDC())
l = TLatex()
l.SetNDC()
l.SetTextSize(0.045)
l.SetTextFont(42)
cstacked = TCanvas("cstacked", "stacked")
mf.Draw()
if (chi2 > 0):
l.DrawLatex(0.55, 0.49,
'#chi^{2}/dof = %0.3f/%d' % (chi2, ndf)
)
pyroot_logon.cmsLabel(cstacked, pars.intLumi/1000, prelim = True)
cstacked.Print('Wjj_%s_%s_%ijets_Stacked.pdf' % (Prefix, modeString,
pars.njets))
cstacked.Print('Wjj_%s_%s_%ijets_Stacked.png' % (Prefix, modeString,
pars.njets))
c2 = TCanvas("c2", "stacked_log")
c2.SetLogy()
lf.Draw()
pyroot_logon.cmsPrelim(c2, pars.intLumi/1000)
c2.Print('Wjj_%s_%s_%ijets_Stacked_log.pdf' % (Prefix, modeString,
pars.njets))
c2.Print('Wjj_%s_%s_%ijets_Stacked_log.png' % (Prefix, modeString,
pars.njets))
c3 = TCanvas("c3", "subtracted")
sf.Draw()
pyroot_logon.cmsLabel(c3, pars.intLumi/1000, prelim = True)
c3.Print('Wjj_%s_%s_%ijets_Subtracted.pdf' % (Prefix, modeString,
pars.njets))
c3.Print('Wjj_%s_%s_%ijets_Subtracted.png' % (Prefix, modeString,
pars.njets))
c4 = TCanvas("c4", "pull")
pf.Draw()
c4.SetGridy()
pyroot_logon.cmsPrelim(c4, pars.intLumi/1000)
c4.Print('Wjj_%s_%s_%ijets_Pull.pdf' % (Prefix, modeString, pars.njets))
c4.Print('Wjj_%s_%s_%ijets_Pull.png' % (Prefix, modeString, pars.njets))
c5 = None
if corrPull:
c5 = TCanvas("c5", "corrected pull")
pf2.Draw()
c5.SetGridy()
pyroot_logon.cmsPrelim(c5, pars.intLumi/1000)
c5.Print('Wjj_%s_%s_%ijets_Pull_Corrected.pdf' % (Prefix, modeString,
pars.njets))
c5.Print('Wjj_%s_%s_%ijets_Pull_Corrected.png' % (Prefix, modeString,
pars.njets))
return ([mf,sf,pf2,lf],[cstacked,c2,c3,c5])
def studyVqqResolution(rootFile):
#get all from file
histos = {}
inF = TFile.Open(rootFile)
keys = inF.GetListOfKeys()
for k in keys:
obj = inF.Get(k.GetName())
obj.SetDirectory(0)
histos[k.GetName()] = obj
inF.Close()
#plot
gROOT.SetBatch()
gROOT.SetStyle('Plain')
gStyle.SetOptStat(0)
gStyle.SetOptFit(1111)
gStyle.SetOptTitle(0)
gStyle.SetStatFont(42)
kin = ['', '30to40', '40to50', '50to75', '75to100', '100toInf']
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
c.SetCanvasSize(1000, 500)
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
c.cd(1)
histos['deta' + k + 'barrel'].SetLineWidth(2)
histos['deta' + k + 'barrel'].SetTitle('barrel')
histos['deta' + k + 'barrel'].Draw('hist')
histos['deta' + k + 'endcap'].SetLineWidth(2)
histos['deta' + k + 'endcap'].SetLineStyle(7)
histos['deta' + k + 'endcap'].SetTitle('endcap')
histos['deta' + k + 'endcap'].Draw('histsame')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['deta' + k + 'barrel'], 'barrel', 'f')
leg.AddEntry(histos['deta' + k + 'endcap'], 'endcap', 'f')
leg.SetNColumns(2)
leg.Draw()
drawHeader()
c.cd(2)
histos['dphi' + k + 'barrel'].SetLineWidth(2)
histos['dphi' + k + 'barrel'].SetTitle('barrel')
histos['dphi' + k + 'barrel'].Draw('hist')
histos['dphi' + k + 'endcap'].SetLineWidth(2)
histos['dphi' + k + 'endcap'].SetLineStyle(7)
histos['dphi' + k + 'endcap'].SetTitle('endcap')
histos['dphi' + k + 'endcap'].Draw('histsame')
c.Modified()
c.Update()
c.SaveAs('dr_%s.png' % k)
labels = []
responseVars = ['dpt', 'den', 'dphi', 'deta', 'dr']
for r in responseVars:
barrelResponse = TGraphErrors()
barrelResponse.SetName(r + 'barrelresponse')
barrelResponse.SetLineWidth(2)
barrelResponse.SetFillStyle(0)
barrelResponse.SetMarkerStyle(20)
barrelCoreResponse = barrelResponse.Clone(r + 'barrelcoreresponse')
endcapResponse = TGraphErrors()
endcapResponse.SetName(r + 'endcapresponse')
endcapResponse.SetLineWidth(2)
endcapResponse.SetFillStyle(0)
endcapResponse.SetMarkerStyle(24)
endcapCoreResponse = endcapResponse.Clone(r + 'endcapresponse')
for k in kin:
c.cd()
c.Clear()
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
for i in [1, 2]:
c.cd(i)
reg = 'barrel'
if i == 2: reg = 'endcap'
h = histos[r + k + reg]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame,
RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
if r == 'dpt' or r == 'den':
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb,
ncb)
else:
mean1.setRange(0, 0.5)
mean2 = RooRealVar("mean2", "mean", 0, 0, 1)
sigma2 = RooRealVar("sigma2", "sigma", 0.1, 0.01, 1.0)
gauss2 = RooGaussian("g2", "g", x, mean2, sigma2)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
if data.sumEntries() < 100:
frac.setVal(1.0)
frac.setConstant(True)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
model_cdf = model.createCdf(RooArgSet(x))
cl = 0.90
ul = 0.5 * (1.0 + cl)
closestToCL = 1.0
closestToUL = -1
closestToMedianCL = 1.0
closestToMedian = -1
for ibin in xrange(1, h.GetXaxis().GetNbins() * 10):
xval = h.GetXaxis().GetXmin() + (
ibin - 1) * h.GetXaxis().GetBinWidth(ibin) / 10.
x.setVal(xval)
cdfValToCL = math.fabs(model_cdf.getVal() - ul)
if cdfValToCL < closestToCL:
closestToCL = cdfValToCL
closestToUL = xval
cdfValToCL = math.fabs(model_cdf.getVal() - 0.5)
if cdfValToCL < closestToMedianCL:
closestToMedianCL = cdfValToCL
closestToMedian = xval
RooAbsPdf.plotOn(model, frame)
frame.Draw()
if i == 1: drawHeader()
labels.append(TPaveText(0.6, 0.92, 0.9, 0.98, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'txt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
kinReg = k.replace('to', '-')
kinReg = kinReg.replace('Inf', '#infty')
labels[ilab].AddText('[' + reg + '] ' + kinReg)
labels[ilab].Draw()
resolutionVal = math.fabs(closestToUL - closestToMedian)
responseGr = barrelResponse
responseCoreGr = barrelCoreResponse
coreResolutionVal = sigma1.getVal()
coreResolutionErr = sigma1.getError()
if frac.getVal() < 0.7 and (sigma2.getVal() < sigma1.getVal()):
coreResolutionVal = sigma2.getVal()
coreResolutionErr = sigma2.getError()
if i == 2:
responseGr = endcapResponse
responseCoreGr = endcapCoreResponse
if k != '':
nrespPts = responseGr.GetN()
kinAvg = 150
kinWidth = 50
if k == '30to40':
kinAvg = 35
kinWidth = 5
if k == '40to50':
kinAvg = 45
kinWidth = 5
if k == '50to75':
kinAvg = 62.5
kinWidth = 12.5
elif k == '75to100':
kinAvg = 87.5
kinWidth = 12.5
responseGr.SetPoint(nrespPts, kinAvg, resolutionVal)
responseCoreGr.SetPoint(nrespPts, kinAvg,
coreResolutionVal)
responseCoreGr.SetPointError(nrespPts, kinWidth,
coreResolutionErr)
labels.append(TPaveText(0.15, 0.7, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'fitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' %
(1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs(r + 'res_' + k + '.png')
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.3)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('frame')
cresp = TCanvas('cresp', 'cresp', 500, 500)
cresp.cd()
frame.Draw('ap')
barrelResponse.Draw('pl')
endcapResponse.Draw('pl')
frame.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Resolution %3.2f C.L." % cl)
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelResponse, 'barrel', 'fp')
leg.AddEntry(endcapResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'res_evol.png')
frameCore = frame.Clone('framecore')
cresp.Clear()
frameCore.Draw('ap')
barrelCoreResponse.Draw('pl')
endcapCoreResponse.Draw('pl')
frameCore.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frameCore.GetYaxis().SetTitle("Core resolution")
frameCore.GetYaxis().SetTitleOffset(1.4)
frameCore.GetYaxis().SetNdivisions(10)
frameCore.GetYaxis().SetRangeUser(0, 0.2)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelCoreResponse, 'barrel', 'fp')
leg.AddEntry(endcapCoreResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'rescore_evol.png')
bosons = ['h', 'z', 'w']
kin = ['', '50', '100']
region = ['', 'bb', 'eb', 'ee']
for k in kin:
for r in region:
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['mjj' + k + r].Rebin()
histos['mjj' + k + r].Draw()
ic = 1
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['mjj' + k + r], 'inclusive', 'f')
for b in bosons:
if histos[b + 'mjj' + k + r].Integral() <= 0: continue
ic = ic + 1
histos[b + 'mjj' + k + r].Rebin()
histos[b + 'mjj' + k + r].SetLineColor(ic)
histos[b + 'mjj' + k + r].SetLineWidth(2)
histos[b + 'mjj' + k + r].SetMarkerColor(ic)
histos[b + 'mjj' + k + r].SetMarkerStyle(1)
histos[b + 'mjj' + k + r].SetFillStyle(3000 + ic)
histos[b + 'mjj' + k + r].SetFillColor(ic)
histos[b + 'mjj' + k + r].Draw('histsame')
leg.AddEntry(histos[b + 'mjj' + k + r], b, "f")
leg.SetNColumns(ic)
leg.Draw()
drawHeader()
labels.append(TPaveText(0.65, 0.8, 0.9, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'mjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('mjj' + k + r + '.png')
massResolutionGrs = []
for r in region:
massResolution = TGraphErrors()
massResolution.SetName(r + 'dm')
massResolution.SetLineWidth(2)
massResolution.SetFillStyle(0)
massResolution.SetMarkerStyle(20 + len(massResolutionGrs))
massResolution.SetMarkerColor(1 + len(massResolutionGrs))
massResolution.SetLineColor(1 + len(massResolutionGrs))
massResolution.SetFillColor(1 + len(massResolutionGrs))
massResolutionGrs.append(massResolution)
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
h = histos['dmjj' + k + r]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame, RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb, ncb)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
RooAbsPdf.plotOn(model, frame)
frame.Draw()
labels.append(TPaveText(0.6, 0.65, 0.85, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'dmfitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' % (1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
drawHeader()
labels.append(TPaveText(0.15, 0.8, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'dmjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('dmjj' + k + r + '.png')
massResolution.SetTitle(regionTitle)
ip = massResolution.GetN()
x = 40
xerr = 10
if k == '50':
x = 75
xerr = 25
elif k == '100':
x = 150
xerr = 50
y = sigma1.getVal()
yerr = sigma1.getError()
if frac.getVal() < 0.8:
if sigma2.getVal() < sigma1.getVal():
y = sigma2.getVal()
ey = sigma2.getError()
massResolution.SetPoint(ip, x, y)
massResolution.SetPointError(ip, xerr, yerr)
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.2)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('dmframe')
cdmevol = TCanvas('cdmevol', 'cdmevol', 500, 500)
cdmevol.cd()
frame.Draw('ap')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
for dmGr in massResolutionGrs:
dmGr.Draw('pl')
leg.AddEntry(dmGr, dmGr.GetTitle(), 'fp')
frame.GetXaxis().SetTitle("Leading quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Core resolution")
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg.SetNColumns(2)
leg.Draw()
cdmevol.Modified()
cdmevol.Update()
cdmevol.SaveAs('dm_evol.png')
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['sel'].Draw('histtext')
drawHeader()
c.Modified()
c.Update()
c.SaveAs('selection.png')
return
]
icol = 0
for iwidth in jitsWidth:
for ilambdaB in jitsLambdaB:
name = "limit_%d_%d" % (ilambdaB, iwidth)
limit = TGraphErrors()
limit.SetName(name)
thefiles = GetFiles(files, ilambdaB, iwidth)
gres = GetGraphs(thefiles)
# print len(gres)
limit.SetMarkerStyle(20)
limit.SetMarkerSize(0.7)
limit.SetMarkerColor(cols[icol])
limit.SetFillColor(cols[icol])
limit.SetLineColor(cols[icol])
GetLimitPlot(gres, limit, fit)
limits.append(limit)
icol += 1
print "number of limits: %d" % len(limits)
limits[0].SetTitle("limit sytematics on #lambda_{B} and #Gamma")
limits[0].GetXaxis().SetTitle("mass [GeV/c^{2}]")
limits[0].GetYaxis().SetTitle("#lambda_{s}")
limits[0].SetMinimum(0)
limits[0].SetMinimum(2)
limits[0].SetMaximum(8)
limits[0].Draw("ape")
def crossSectionRatioPlot(group,groups,keys_no_comb,
sigmas,stats,systs,lumis,tots):
delChars = '#\\/+.~${}><,?[]|&^_!@%*()-`;:\'\"'
sigmas_lcl = array('d',sigmas[:])
stats_lcl = array('d',stats[:])
systs_lcl = array('d',systs[:])
tots_lcl = array('d',tots[:])
lumis_lcl = array('d',lumis[:])
tots_theory = array('d')
if 'combined' in groups[group]:
sig, sta, sys, tot = getCSInfo(groups[group]['combined'])
sigmas_lcl.insert(0,sig)
stats_lcl.insert(0,sta)
systs_lcl.insert(0,sys)
tots_lcl.insert(0,tot)
lumis_lcl.insert(0,sig*lumi_err/100.0)
for i in range(len(sigmas_lcl)):
tots_lcl[i] = tots_lcl[i]/sigmas_lcl[i]
sigmas_lcl[i] = sigmas_lcl[i]/mc_info['sigma']
tots_theory.append(hypot(tots_lcl[i],
mc_info['theory_err']/mc_info['sigma']))
yvals = array('d',[i*2 for i in range(1,len(sigmas_lcl)+1)])
xones = array('d',[1 for i in range(len(sigmas_lcl))])
x1serrs = array('d',[lumi_err/100.0 for i in range(len(sigmas_lcl))])
yerrs = array('d',[0 for i in range(len(sigmas_lcl))])
y1serrs = array('d',[2 for i in range(len(sigmas_lcl))])
gStats = TGraphErrors(len(sigmas_lcl),sigmas_lcl,yvals,tots_lcl,yerrs)
gTotes = TGraphErrors(len(sigmas_lcl),sigmas_lcl,yvals,tots_theory,yerrs)
gLine = TGraphErrors(len(sigmas_lcl),xones,yvals,x1serrs,y1serrs)
lOne = TLine(1,0,1,2*(len(sigmas_lcl)+1))
canv = TCanvas(group+'val','',500,500)
canv.cd()
frame = canv.DrawFrame(0.1,0,2,2*(len(sigmas_lcl)+1))
frame.GetXaxis().SetTitle("Ratio (CMS/Theory)")
unshitify(frame)
gLine.SetFillColor(6)
gLine.SetFillStyle(3004)
gLine.Draw("E2")
lOne.SetLineWidth(2)
lOne.SetLineColor(6)
lOne.Draw()
gTotes.SetMarkerColor(2)
gTotes.SetMarkerStyle(20)
gTotes.SetLineColor(4)
gTotes.SetLineWidth(2)
gTotes.Draw("PE1")
gStats.SetMarkerColor(2)
gStats.SetMarkerStyle(20)
gStats.SetLineColor(1)
gStats.SetLineWidth(2)
gStats.Draw("PE1")
tex1 = TLatex()
tex1.SetTextSize(0.04)
tex2 = TLatex()
tex2.SetTextSize(0.03)
xmin = 0.1
xmax = 2.0
xscale = xmax - xmin
xpad = xmin + xscale*xpadfactor
ymin = 0
ymax = 2*(len(sigmas_lcl)+1)
yscale = ymax-ymin
ypad = ymin + yscale*ypadfactor
for i in range(len(sigmas_lcl)):
if('combined' in groups[group]):
if( i == 0 ):
tex2.DrawLatex(0.75*xpad,yvals[i]-0.009*yscale,
"#font[132]{BLUE Combination}")
else:
tex2.DrawLatex(0.75*xpad,yvals[i]-0.009*yscale,
"#font[132]{%s: %s}"%(group,
keys_no_comb[i-1]))
else:
if 'combined' in group:
tex2.DrawLatex(0.75*xpad,yvals[i]-0.009*yscale,
"#font[132]{BLUE Comb.: %s}"%keys_no_comb[i])
else:
tex2.DrawLatex(0.75*xpad,yvals[i]-0.009*yscale,
"#font[132]{%s: %s}"%(group,
keys_no_comb[i]))
tex2.DrawLatex(4.60*xpad,yvals[i]-0.009*yscale,
"#font[132]{%.2f #pm "%sigmas_lcl[i]+
"%.2f_{exp} #pm "%tots_lcl[i]+
"%.2f_{theo}}"%(mc_info['theory_err']/mc_info['sigma']))
#flavor text
tex1.DrawLatex(0.85*xpad,yvals[-1] + 0.95*ypad,
"#font[132]{%s}"%luminosity)
for i,flav in enumerate(flavor_text.split('\\')):
tex1.DrawLatex(3.7*xpad,
yvals[-1] + (1.40-0.50*i)*ypad,
"#font[132]{%s}"%flav)
tex1.DrawLatex(xmin,ymax+0.08,
"#font[132]{CMS Preliminary 2011}")
tex1.DrawLatex(xmin+0.78*xscale,
ymax+0.08,
"#font[132]{#sqrt{s} = 7 TeV}")
#lumi uncertainty
tex1.SetTextColor(6)
tex1.DrawLatex(3.7*xpad,
yvals[0] - 1.5*ypad,
"#font[132]{lumi. uncertainty %.1f}"%lumi_err
+ "%")
saneName = group.translate(None,delChars)
valName = saneName+"_CS_ratio_plot"
canv.Print(valName+".png")
canv.Print(valName+".eps")
canv.Print(valName+".pdf")
def drawMassRes(data, mc, output, rapidity, ptda, ptmc, trackType, funct,
mcIsData, dataIsMC):
style = setTDRStyle()
pt_e = [0 for x in range(len(data))]
pt_x = [0 for x in range(len(data))]
for i, pt in enumerate(pt_x):
pt_x[i] = ptbins[i] + (ptbins[i + 1] - ptbins[i]) / 2.
pt_e[i] = (ptbins[i + 1] - ptbins[i]) / 2.
if dataIsMC:
(da_mean, da_meane, da_sig, da_sige,
da_nChi2) = doFit(data, output, rapidity, "MC2", trackType, funct)
else:
(da_mean, da_meane, da_sig, da_sige,
da_nChi2) = doFit(data, output, rapidity, "DATA", trackType, funct)
if mcIsData:
(mc_mean, mc_meane, mc_sig, mc_sige,
mc_nChi2) = doFit(mc, output, rapidity, "DATA2", trackType, funct)
else:
(mc_mean, mc_meane, mc_sig, mc_sige,
mc_nChi2) = doFit(mc, output, rapidity, "MC", trackType, funct)
result = {}
result["data_mean"] = da_mean
result["data_meane"] = da_meane
result["data_sig"] = da_sig
result["data_sige"] = da_sige
result["mc_mean"] = mc_mean
result["mc_meane"] = mc_meane
result["mc_sig"] = mc_sig
result["mc_sige"] = mc_sige
result["ptda"] = ptda
result["ptmc"] = ptmc
result["da_nChi2"] = da_nChi2
result["mc_nChi2"] = mc_nChi2
pklFile = open(
output + "/MassResolutionVsPt_%s_%s.pkl" % (trackType, rapidity), "wb")
pickle.dump(result, pklFile)
pklFile.close()
c2 = TCanvas("c2", "c2", 700, 700)
c2.cd()
# Upper plot will be in pad1
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetGrid() # Vertical grid
pad1.SetBottomMargin(0.1)
pad1.Draw() # Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
pad1.SetTicks()
res_data = TGraphAsymmErrors()
res_data.SetName("res_data")
res_mc = TGraphAsymmErrors()
res_mc.SetName("res_mc")
ratio = TGraphErrors()
ratio.SetName("ratio")
#~ print len(pt_x)
for i, pt in enumerate(pt_x):
res_data.SetPoint(i, ptda[i], da_sig[i])
res_data.SetPointError(i, ptda[i] - ptbins[i], ptbins[i + 1] - ptda[i],
da_sige[i], da_sige[i])
res_mc.SetPoint(i, ptmc[i], mc_sig[i])
res_mc.SetPointError(i, ptmc[i] - ptbins[i], ptbins[i + 1] - ptmc[i],
mc_sige[i], mc_sige[i])
if mc_sig[i] > 0:
ratio.SetPoint(i, pt, da_sig[i] / mc_sig[i])
ratio.SetPointError(i, pt_e[i], (da_sig[i] / mc_sig[i]) *
math.sqrt((da_sige[i] / da_sig[i])**2 +
(mc_sige[i] / mc_sig[i])**2))
res_data.SetMarkerStyle(22)
res_data.SetMarkerColor(kBlack)
res_data.SetLineColor(kBlack)
res_data.SetFillColor(0)
res_data.SetTitle("Dimuon mass resolution vs pT for %s tracks" % trackType)
res_data.GetYaxis().SetTitle("Mass resolution at Z peak [GeV]")
res_data.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) [GeV]")
res_data.GetYaxis().SetTitleOffset(1.2)
res_data.GetYaxis().SetRangeUser(0., 6.)
if trackType == "Outer":
res_data.GetYaxis().SetRangeUser(1., 20.)
res_data.GetXaxis().SetRangeUser(ptbins[0], ptbins[len(ptda)])
res_data.Draw("AP E0")
res_mc.SetMarkerStyle(22)
res_mc.SetMarkerColor(kRed)
res_mc.SetLineColor(kRed)
res_mc.SetFillColor(0)
res_mc.SetTitle("Dimuon mass resolution vs pT for %s tracks" % trackType)
res_mc.GetYaxis().SetTitle("Mass resolution at Z peak [GeV]")
res_mc.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) [GeV]")
res_mc.GetYaxis().SetTitleOffset(1.5)
res_mc.Draw("P E0 SAME")
if rapidity == "BB":
leg = TLegend(0.25, 0.6, 0.50, 0.80, "both muons |#eta| < 1.2",
"brNDC")
else:
leg = TLegend(0.25, 0.6, 0.5, 0.8, "at least one muon |#eta| > 1.2",
"brNDC")
if mcIsData:
leg.AddEntry(res_data, "DATA 2017")
leg.AddEntry(res_mc, "DATA 2016")
elif dataIsMC:
leg.AddEntry(res_data, "MC 2017")
leg.AddEntry(res_mc, "MC 2016")
else:
leg.AddEntry(res_data, "DATA", "p")
leg.AddEntry(res_mc, "Simulation")
leg.SetTextFont(42)
leg.SetBorderSize(0)
leg.SetTextSize(.04)
leg.Draw("SAME")
latex = TLatex()
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(0.04)
latex.SetNDC(True)
latexCMS = TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.055 / 0.7)
latexCMS.SetNDC(True)
latexCMSExtra = TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(0.03 / 0.7)
latexCMSExtra.SetNDC(True)
latex.DrawLatex(0.95, 0.96, "(13 TeV)")
cmsExtra = "Preliminary"
latexCMS.DrawLatex(0.78, 0.88, "CMS")
yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.78, yLabelPos, "%s" % (cmsExtra))
c2.cd() # Go back to the main canvas before defining pad2
pad2 = TPad("pad2", "pad2", 0, 0.1, 1, 0.30)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.3)
pad2.SetGrid()
pad2.Draw()
pad2.cd()
pad2.SetTicks()
ratio.SetMarkerColor(kBlue - 4)
ratio.SetFillColor(kBlue - 4)
ratio.SetTitle("")
ratio.GetYaxis().SetTitle("Data/MC")
if mcIsData:
ratio.GetYaxis().SetTitle("Data 2017 / Data 2016")
elif dataIsMC:
ratio.GetYaxis().SetTitle("MC 2017 / MC 2016")
ratio.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) [GeV]")
ratio.GetYaxis().SetRangeUser(0.5, 1.5)
ratio.GetXaxis().SetRangeUser(ptbins[0], ptbins[len(ptda)])
ratio.GetYaxis().SetTitleOffset(0.50)
ratio.GetYaxis().SetTitleSize(0.14)
ratio.GetYaxis().SetLabelSize(0.14)
ratio.GetYaxis().SetNdivisions(506)
ratio.GetXaxis().SetTitleSize(0.12)
ratio.GetXaxis().SetTitleOffset(1.2)
ratio.GetXaxis().SetLabelSize(0.20)
ratio.Draw("A P E2")
pad2.Update()
line = TLine(ptbins[0], 1, ptbins[len(ptda)], 1)
line.SetLineColor(kBlue + 1)
line.SetLineWidth(2)
line.Draw()
saveas = "/MassResolutionVsPt_%s_%s" % (trackType, rapidity)
c2.SaveAs(output + saveas + ".png")
c2.SaveAs(output + saveas + ".pdf")
c2.SaveAs(output + saveas + ".root")
c2.SaveAs(output + saveas + ".C")
gr.SetTitle(
"K^{+}Ar Cross Section; Kinetic Energy [MeV]; Cross Section [barn]")
gr.GetXaxis().SetRangeUser(0, 800)
gr.GetYaxis().SetRangeUser(0, 1.0)
gr.GetYaxis().SetTitleOffset(1.2)
gr.SetLineWidth(2)
grel.SetLineWidth(2)
grinel.SetLineWidth(2)
gr.SetLineColor(kGreen - 2)
grel.SetLineColor(kBlue)
grinel.SetLineColor(kRed)
gr.SetFillColor(0)
grel.SetFillColor(0)
grinel.SetFillColor(0)
gr.Draw("APL")
grel.Draw("PLsame")
grinel.Draw("PLsame")
hTot.Draw("same")
hEla.Draw("same")
hIne.Draw("same")
legend = TLegend(.14, .68, .54, .88)
legend.AddEntry(gr, "G4 Prediction Tot XS")
legend.AddEntry(hTot, "True En Dep -- Tot XS")
def plot(name_f_json, projection=1):
pulse_start = 35000
pulse_width = 15000
sweep_type = 'threshold'
time_scale = 140000
# one for each ASIC
#pixels=[(0,1)]
pixels = get_pixels(name_f_json + '.json')
matrix = [0, 1, 2]
hits = (0, 1, 2, 3, 4, 5, 6, 7)
th_l = 0x0
th_h = 0x800
th_delta = 0x8
thresholdCuts = range(th_l, th_h, th_delta)
data_dic = {}
pulse_end = pulse_start + pulse_width
fast_range = 1500
with open(name_f_json + '.json') as f:
#with open('f_j.json') as f:
hist = json.load(f, object_hook=dic2timep)
data_dic = get_allHists(pixels, matrix, hits, thresholdCuts)
#data_dic=get_allHists(pixels,matrix,thresholdCuts,hits)
for a in hist:
p1 = a.pixel[0]
p2 = a.pixel[1]
data_dic[(p1, p2)][a.matrix][a.index][a.threshold] = a.time
rootf = TFile(name_f_json + '.root', "RECREATE")
for key in data_dic: #pixel
print(key)
for key1 in data_dic[key]: #matri2x
h = "hitnumber_pixel_" + str(key) + "_asic" + str(
key1) + "_THsweep"
h = TH1D(h, ";Hit;Hits number", 8, 0, 8)
h_name_all = "cumulTimeHist_pixel_" + str(key) + "_asic" + str(
key1) + "_THsweep"
#h_name_g="cumulTimeHist_pixel_"+str(key)+"_asic"+str(key1)+"_THsweep_g"
a = []
for key3 in data_dic[key][key1][1]: #threshold
a.append(key3)
a1 = [float(b) / 1241. for b in a]
hist_name_all = TH2D(h_name_all, ";Threshold [V];Time [ns]",
(th_h - th_l) / th_delta, min(a1), max(a1),
2000, 0, time_scale)
for key2 in data_dic[key][key1]: #index
h_name = "cumulTimeHist_pixel_" + str(key) + "_asic" + str(
key1) + "_hit_" + str(key2) + "_THsweep"
hist_name = TH2D(h_name, ";Threshold [V];Time [ns]",
(th_h - th_l) / th_delta, min(a1), max(a1),
800, 0, time_scale)
g_x = []
g_y = []
g_ex = []
g_ey = []
a = data_dic[key][key1][key2].keys()
a.sort()
for key3 in a: #threhsold
#for key3 in data_dic[key][key1][key2]: #threhsold
t_aftercut = []
for val_i in range(len(data_dic[key][key1][key2][key3])):
timev = data_dic[key][key1][key2][key3][val_i]
#if timev>0:
#if (timev<35000):
if timev > 0:
t_aftercut.append(timev)
hist_name.Fill(float(key3) / 1241, timev)
hist_name_all.Fill(float(key3) / 1241, timev)
h.Fill(key2)
if len(t_aftercut) != 0:
t_aftercut_a = np.array(t_aftercut)
g_x.append(float(key3) / 1241)
#print(float(key3)/1241)
g_y.append(np.mean(t_aftercut_a))
g_ey.append(np.std(t_aftercut_a))
g_ex.append(0)
hist_name.Write()
g_ax = np.array(g_x)
g_ay = np.array(g_y)
g_aex = np.array(g_ex)
g_aey = np.array(g_ey)
#hist_name_g=TGraph(len(g_ax),g_ax,g_ay)
# #hist_name_g="cumulTimeHist_pixel_"+str(key)+"_asic"+str(key1)+"_hit_"+str(key2)+"_THsweep_g"
if len(g_ax) != 0:
graph_g = TGraph(len(g_ax), g_ax, g_ay)
graph_g.SetName("cumulTimeHist_pixel_" + str(key) +
"_asic" + str(key1) + "_hit_" + str(key2) +
"_THsweep_graph")
graph_g.SetTitle(";Threshold [V];Time [ns]")
graph_g.SetMarkerStyle(20)
graph_g.SetMarkerSize(0.6)
graph_g.Write()
hist_name_g = TGraphErrors(len(g_ax), g_ax, g_ay, g_aex,
g_aey)
hist_name_g.SetName("cumulTimeHist_pixel_" + str(key) +
"_asic" + str(key1) + "_hit_" +
str(key2) + "_THsweep_g")
hist_name_g.SetTitle(";Threshold [V];Time [ns]")
hist_name_g.SetFillColor(1)
hist_name_g.SetMarkerStyle(20)
hist_name_g.SetMarkerSize(0.6)
hist_name_g.Write()
if projection:
cut1 = [min(a1), 0]
cut2 = [max(a1), 140000]
proj_x = get_proj_X(hist_name, h_name, cut1, cut2, min(a1),
max(a1))
proj_y = get_proj_Y(hist_name, h_name, cut1, cut2, 0,
time_scale)
proj_x.Write()
proj_y.Write()
#hist_name_g="cumulTimeHist_pixel_"+str(key)+"_asic"+str(key1)+"_THsweep_g"
#hist_name_g=TGraphErrors(n,g_ax,g_aex,g_ay,g_aey)
#hist_name_g=TGraphErrors(n,g_ax,g_aex,g_ay,g_aey)
#hist_name_g.SetTitle("cumulTimeHist_pixel_"+str(key)+"_asic"+str(key1)+"_THsweep_g")
#hist_name_g=TGraphErrors(h_name_g,";Threshold [V];Time [ns]",n,g_x,g_y,g_ex,g_ey)
hist_name_all.Write()
h.Write()
if projection:
cut1 = [min(a1), 0]
cut2 = [max(a1), 140000]
proj_x_all = get_proj_X(hist_name_all, h_name_all, cut1, cut2,
min(a1), max(a1))
proj_y_all = get_proj_Y(hist_name_all, h_name_all, cut1, cut2,
0, time_scale)
proj_x_all.Write()
proj_y_all.Write()
rootf.Close()
#define some data points . . .
x = array('f', kineticEnergy)
y = array('f', crossSec)
exl = array('f', zero)
exr = array('f', zero)
nPoints = len(x)
# . . . and hand over to TGraphErros object
gr = TGraphErrors(nPoints, x, y, exl, exr)
gr.SetTitle(title + "; Kinetic Energy [MeV]; Cross Section [barn]")
gr.GetXaxis().SetRangeUser(0, 1000)
gr.GetYaxis().SetRangeUser(0, 2.)
gr.SetLineWidth(2)
gr.SetLineColor(kGreen - 2)
gr.SetFillColor(0)
## Data
data_FileName = "/Volumes/Seagate/Elena/TPC/Data60A.root"
data_File = TFile.Open(data_FileName)
interactingPlotString = "RecoXS/hRecoInteractingKE"
incidentPlotString = "RecoXS/hRecoIncidentKE"
data_Int = data_File.Get(interactingPlotString)
data_Inc = data_File.Get(incidentPlotString)
XSDataRecoPion = data_Int.Clone("pionMCXSData")
XSDataRecoPion.Sumw2()
data_Inc.Sumw2()
XSDataRecoPion.Scale(101.10968)
XSDataRecoPion.Divide(data_Inc)
XSDataRecoPion.SetLineColor(kBlack)
XSDataRecoPion.SetLineWidth(2)
from ROOT import gStyle
jetToElectronRatioGraph = TGraphErrors("jetToElectronFactors.dat",
"%lg %lg %lg %lg")
jetToMuonRatioGraph = TGraphErrors("jetToMuonFactors.dat", "%lg %lg %lg %lg")
jetToMETRatioGraph = TGraphErrors("jetToMETFactors.dat", "%lg %lg %lg %lg")
jetToElectronRatioGraph.SetMarkerColor(2)
jetToElectronRatioGraph.SetMarkerStyle(21)
jetToMuonRatioGraph.SetMarkerColor(3)
jetToMuonRatioGraph.SetMarkerStyle(21)
jetToMETRatioGraph.SetMarkerColor(4)
jetToMETRatioGraph.SetMarkerStyle(21)
jetToElectronRatioGraph.SetFillStyle(1001)
jetToElectronRatioGraph.SetFillColor(2)
jetToMuonRatioGraph.SetFillStyle(1001)
jetToMuonRatioGraph.SetFillColor(3)
jetToMETRatioGraph.SetFillStyle(1001)
jetToMETRatioGraph.SetFillColor(4)
jetToObjectRatioGraphs = TMultiGraph()
jetToObjectRatioGraphs.Add(jetToElectronRatioGraph, "")
jetToObjectRatioGraphs.Add(jetToMuonRatioGraph, "")
jetToObjectRatioGraphs.Add(jetToMETRatioGraph, "")
canvas = TCanvas()
canvas.SetGrid()
canvas.SetLogy()