def _doProfile2d( solver, ipar1=0, type1="u", ipar2=1, type2= "m" ):
from ConstrainedFit import clsq
from ROOT import TGraph2D, TMarker, gPad
from array import array
global tg2d, hist, te1, te2, te3, tm
par1= clsq.createClsqPar( ipar1, type1, solver )
par2= clsq.createClsqPar( ipar2, type2, solver )
parval1, parerr1, name1= _getUMParErrName( par1 )
parval2, parerr2, name2= _getUMParErrName( par2 )
print( "\nChi^2 profile plot " + name1 + " - " + name2 + ":" )
corr= solver.getCorrMatrix()
icorr1= ipar1
icorr2= ipar2
if type1 == "u" or type2 == "u":
nmpar= len(solver.getMpars())
if type1 == "u":
icorr1= nmpar + ipar1
if type2 == "u":
icorr2= nmpar + ipar2
rho= corr[icorr1,icorr2]
te1= _makeEllipse( parval1, parval2, parerr1, parerr2, rho )
te2= _makeEllipse( parval1, parval2, 2.0*parerr1, 2.0*parerr2, rho )
te3= _makeEllipse( parval1, parval2, 3.0*parerr1, 3.0*parerr2, rho )
ca= clsq.clsqAnalysis( solver )
points= ca.profile2d( par1, par2 )
npoints= len(points)
tg2d= TGraph2D( npoints )
for i in range( npoints ):
point= points[i]
tg2d.SetPoint( i, point[0], point[1], point[2] )
hist= tg2d.GetHistogram()
contourlevels= array( "d", [ 1.0, 4.0, 9.0 ] )
hist.SetContour( 3, contourlevels )
hist.GetXaxis().SetTitle( name1 )
hist.GetYaxis().SetTitle( name2 )
hist.SetTitle( "Triangle fit "+name2+" vs "+name1 )
hist.Draw( "cont1" )
tm= TMarker( parval1, parval2, 20 )
tm.Draw( "s" )
te1.Draw( "s" )
te2.Draw( "s" )
te3.Draw( "s" )
gPad.Print( "triangle_errorellipse.png" )
return
def myMarkerTextSmall2(x, y, lcolor, lstyle, mcolor, mstyle, msize, size,
text):
l = TLatex()
l.SetTextAlign(12)
l.SetTextSize(size / 2.5)
l.SetNDC()
l.DrawLatex(x, y, text)
yb1 = y - 0.15 * size
yb2 = y + 0.15 * size
xb1 = x - 0.28 * size - 0.45 * size
xb2 = x + 0.28 * size - 0.45 * size
print "box = ", xb1, yb1, xb2, yb2
#print "line = ",xl1,yl1,xl2,yl2
mline1 = TLine(xb1, yb1, xb2, yb2)
mline1.SetLineColor(lcolor)
mline1.SetLineStyle(lstyle)
mline1.SetLineWidth(2)
y_new = (yb1 + yb2) / 2.
mline1.DrawLineNDC(xb1, y_new, xb2, y_new)
mline2 = TLine(xb1, yb1, xb2, yb2)
mline2.SetLineColor(lcolor)
mline2.SetLineStyle(lstyle)
mline2.SetLineWidth(2)
x_new = (xb1 + xb2) / 2.
mline2.DrawLineNDC(x_new, yb1, x_new, yb2)
marker = TMarker((xb1 + xb2) / 2.0, y, mstyle)
marker.SetNDC()
marker.SetMarkerColor(mcolor)
marker.SetMarkerStyle(mstyle)
marker.SetMarkerSize(msize)
marker.Draw()
return marker
xp = -xp
xm = math.sqrt(r2**2 - ym**2)
if abs((xm - x1)**2 + (ym - y1)**2 - r1**2) > 1e-9:
xm = -xm
if switchxy:
xm, ym = ym, xm
xp, yp = yp, xp
return xm, ym, xp, yp
if __name__ == '__main__':
from ROOT import TEllipse, TH2F, TCanvas, TMarker
can = TCanvas("can", "", 600, 600)
suph = TH2F("suph", "", 10, -5, 5, 10, -5, 5)
suph.Draw()
x1, y1, r1, r2 = 0., 1.8, 1., 2.
results = circle_intersection(x1, y1, r1, r2)
c1 = TEllipse(x1, y1, r1)
c1.Draw('same')
c2 = TEllipse(0., 0., r2)
c2.Draw('same')
c1.SetFillStyle(0)
c2.SetFillStyle(0)
mm = TMarker(results[0], results[1], 8)
mp = TMarker(results[2], results[3], 21)
mm.Draw('same')
mp.Draw('same')
can.Update()
ROCwmass.SetLineColor(2)
ROCwmass.SetLineWidth(2)
ROCwmass.SetTitle(";signal efficiency;background rejection")
c2 = TCanvas("c2")
ptlimstrs = [str(int(2000.0*ptlims[0])),str(int(2000.0*ptlims[1]))]
ROCwmass.Draw()
prelim.DrawLatex( 0.15, 0.85, ptlimstrs[0]+" GeV < p_{T} < "+ptlimstrs[1]+" GeV" )
if (cust=="w" or cust=="top") and Drawmarker:
if cust=="w":
prelim.DrawLatex( 0.44, 0.55, "N_{2}^{DDT}+msd" )
TM = TMarker(0.4, 0.01, 23)
if cust=="top":
prelim.DrawLatex( 0.44, 0.60, "#tau_{32}+msd+subjetb" )
TM = TMarker(0.4, 0.017, 23)
TM.SetMarkerSize(2)
TM.SetMarkerColor(4)
TM.Draw()
c2.SetLogy()
c2.Write("rocwmass")
ROC.Draw()
c2.Write("roccomp")
dnnsig.Write()
dnnbkg.Write()
output.Write()
output.Close()
l_5percent.SetLineWidth(3)
l_10percent.SetLineColor(2)
l_10percent.SetLineWidth(3)
l_10percent.SetLineStyle(2)
l_25percent.SetLineColor(2)
l_25percent.SetLineStyle(3)
l_25percent.SetLineWidth(3)
p_5percent.SetMarkerStyle(20)
p_5percent.SetMarkerColor(2)
p_10percent.SetMarkerStyle(21)
p_10percent.SetMarkerColor(2)
p_25percent.SetMarkerStyle(22)
p_25percent.SetMarkerColor(2)
p_5percent.Draw()
l_5percent.Draw()
p_10percent.Draw()
l_10percent.Draw()
p_25percent.Draw()
l_25percent.Draw()
g_width.GetXaxis().SetTitle('1/tan(#beta)^{2}') # #prop coupling^{2}
g_width.GetYaxis().SetTitle('Width (GeV)')
g_width.GetYaxis().SetTitleSize(0.05)
g_width.GetYaxis().SetLabelSize(0.05)
g_width.GetYaxis().SetTitleOffset(0.8)
g_width.GetYaxis().SetRangeUser(0., mass * 0.12)
g_width.GetXaxis().SetTitleSize(0.05)
g_width.GetXaxis().SetLabelSize(0.05)
def MuFit(Nbins,irebin=1.):
# Get histrograms
h_bgr = GetMassDistribution(1)
h_data = GetMassDistribution(2)
h_sig = GetMassDistribution(125)
h_bgr.Rebin(irebin)
h_data.Rebin(irebin)
h_sig.Rebin(irebin)
h_sf = TH2D("scalefactor","title",Nbins,0.5,2.,Nbins,0.,5.)
for i in range(1,h_sf.GetNbinsX()+1):
for j in range(1,h_sf.GetNbinsY()+1):
sf_bgr = h_sf.GetXaxis().GetBinCenter(i)
sf_sig = h_sf.GetYaxis().GetBinCenter(j)
# Loop over bins, compute likelihood
loglik = 0.
for iDataBin in range(1,h_data.GetNbinsX()+1):
m4lepBin = h_data.GetBinCenter(iDataBin)
NObsBin = h_data.GetBinContent(iDataBin)
MeanBin = sf_bgr*h_bgr.GetBinContent(iDataBin) + sf_sig*h_sig.GetBinContent(iDataBin)
if (MeanBin>0): loglik += TMath.Log( TMath.Poisson(NObsBin,MeanBin) )
h_sf.SetBinContent(i,j,-2.*loglik)
# Get best SF parameters
x=ctypes.c_int(0)
y=ctypes.c_int(0)
z=ctypes.c_int(0)
h_sf.GetBinXYZ(h_sf.GetMinimumBin(),x,y,z)
Minimum = h_sf.GetBinContent(x.value,y.value)
best_alpha = h_sf.GetXaxis().GetBinCenter(x.value)
best_mu = h_sf.GetYaxis().GetBinCenter(y.value)
print(Minimum,best_alpha,best_mu)
# Rescale histogram
for i in range(1,h_sf.GetNbinsX()+1):
for j in range(1,h_sf.GetNbinsY()+1):
h_sf.SetBinContent( i,j, h_sf.GetBinContent(i,j)-Minimum )
canvas = TCanvas("canvas","Standard Canvas",600,400)
canvas.cd()
Min = TMarker(best_alpha,best_mu,29)
Min.SetMarkerSize(2)
h_sf.SetStats(kFALSE)
h_sf.SetTitle("HIGGS PRODUCTION CROSS SECTION - PARAMETERS")
h_sf.GetXaxis().SetTitle(r"#alpha")
h_sf.GetYaxis().SetTitle(r"#mu")
h_sf.Draw("COLZ")
h_sigma = h_sf.Clone("h_sigma")
h_sigma.Reset()
for i in range(1,h_sigma.GetNbinsX()+1):
for j in range(1,h_sigma.GetNbinsY()+1):
if( h_sf.GetBinContent(i,j)<=1.): h_sigma.SetBinContent(i,j, 1.)
h_sigma.SetMarkerColorAlpha(kRed,0.40)
h_sigma.SetMarkerSize(10)
h_sigma.Draw("same L")
Min.Draw()
leg1 = TLegend(0.65,0.85,0.85,0.75)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
leg1a = leg1.AddEntry(Min, r"optimal (#alpha,#mu)", "p"); leg1a.SetTextSize(0.04);
leg1.Draw()
canvas.Print("Plots/MuFit.pdf")