def main():
if len(sys.argv) > 1:
fstate = sys.argv[1]
else:
fstate = ''
decor = fstate
if decor != '':
decor += '_'
sources = [('%s/fig_%sbkg.root' % (PATH, decor), 'bkg', kMagenta + 1),
('%s/fig_%shiggs.root' % (PATH, decor), 'higgs', kAzure + 1)]
nsources = 2
fixedSig = False
fixedSigValue = 2.4
# ---------------------------------------
# get data histogram
# ---------------------------------------
filename = '%s/fig_%sdata.root' % (PATH, decor)
fdata = TFile(filename)
if not fdata.IsOpen():
sys.exit("** can't open file %s" % filename)
hdata = fdata.Get(HNAME)
if not hdata: sys.exit("** can't find hbnn")
# ---------------------------------------
# get source histograms
# ---------------------------------------
f = []
h = []
for i, (filename, hname, color) in enumerate(sources):
f.append(TFile(filename))
if not f[-1].IsOpen():
sys.exit("** can't open file %s" % f[-1])
h.append(f[-1].Get(HNAME).Clone(hname))
if not h[-1]: sys.exit("** can't find hbnn")
if FAKEDATA: fakeData(hdata, h, 0.1065)
print '\tcount(%s):\t%8.1f' % ('data', hdata.Integral())
# ---------------------------------------
# set up some standard graphics style
# ---------------------------------------
tdrstyle.setTDRStyle()
#gStyle.SetCanvasPreferGL(True)
gStyle.SetPadRightMargin(0.12)
#gStyle.SetOptStat('ei')
gStyle.SetOptStat('i')
gStyle.SetStatFont(42)
gStyle.SetStatFontSize(0.03)
gStyle.SetStatBorderSize(1)
gStyle.SetStatH(0.2)
gStyle.SetStatW(0.3)
gStyle.SetStatX(0.83)
gStyle.SetStatY(0.93)
# change the CMS_lumi variables (see CMS_lumi.py)
iPeriod = 4
iPos = 0
CMS_lumi.relPosX = 0.12
CMS_lumi.lumi_13TeV = "36.0 fb^{-1}"
CMS_lumi.writeExtraText = 1
CMS_lumi.extraText = "Preliminary"
vdouble = vector("double")
# ---------------------------------------
# plot data
# ---------------------------------------
ymax = 1.2 * hdata.GetMaximum()
ii = int(ymax / 20)
ymax = (ii + 1) * 20
hdata.SetMaximum(ymax)
hdata.SetNdivisions(505, 'X')
xbins = int(hdata.GetNbinsX())
width = hdata.GetXaxis().GetBinWidth(1)
xmin = int(hdata.GetXaxis().GetBinLowEdge(1))
xmax = int(hdata.GetXaxis().GetBinLowEdge(xbins) + width)
postfix = "_%s_%3.3d_%3.3d" % (HNAME, xmin, xmax)
cname = "fig_%sdata_%s" % (decor, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
hdata.Draw("ep gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(0.5)
del canvas
# ---------------------------------------
# plot sources
# ---------------------------------------
for i, (filename, hname, color) in enumerate(sources):
cname = "fig_%s%s%s" % (decor, hname, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
h[i].SetNdivisions(505, 'X')
h[i].Draw("hist gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(0.5)
del canvas
# get counts
nbin = h[0].GetNbinsX()
datum = 0.0
bkg = 0.0
sig = 0.0
fb = []
fs = []
D = []
for ii in xrange(nbin):
bkg += h[0].GetBinContent(ii + 1)
sig += h[1].GetBinContent(ii + 1)
datum += hdata.GetBinContent(ii + 1)
theory = bkg + sig
if theory > 0:
fb.append(bkg)
fs.append(sig)
D.append(datum)
print '%4d\t%10.2f\t%10.2f\t%10.0f' % (len(D), fb[-1], fs[-1],
D[-1])
bkg = 0.0
sig = 0.0
datum = 0.0
theory = bkg + sig
theory = bkg + sig
if theory > 0:
fb[-1] += bkg
fs[-1] += sig
D[-1] += datum
print '%4d\t%10.2f\t%10.2f\t%10.0f' % (len(D), fb[-1], fs[-1], D[-1])
total = sum(D)
# ---------------------------------------
# fit
# ---------------------------------------
t = []
bguess = sum(fb)
t.append(bguess)
fb = map(lambda x: x / bguess, fb)
sguess = sum(fs)
t.append(sguess)
fs = map(lambda x: x / sguess, fs)
if fixedSig: sguess = fixedSigValue
# ---------------------------------------
def Func(npar, grad, fval, xval, flag):
fval[0] = 0.0
B = xval[0]
S = xval[1]
for i in xrange(len(D)):
theory = B * fb[i] + S * fs[i]
x = D[i] - theory
fval[0] += x * x / theory
# ---------------------------------------
minuit = TMinuit(len(sources))
minuit.SetPrintLevel(1)
minuit.SetFCN(Func)
minuit.SetErrorDef(1)
status = Long()
bstep = bguess / 1000
bmin = 0.0
bmax = 2 * total
minuit.mnparm(0, "B", bguess, bstep, bmin, bmax, status)
if status != 0:
sys.exit("** mnparam status code: %d" % status)
if fixedSig: sguess = fixedSigValue
sstep = sguess / 1000
smin = 0.0
smax = 2 * total
minuit.mnparm(1, "S", sguess, sstep, smin, smax, status)
if status != 0:
sys.exit("** mnparam status code: %d" % status)
if fixedSig: minuit.FixParameter(1)
args = array('d')
args.append(MAXITER)
args.append(TOLERANCE)
minuit.mnexcm(METHOD, args, 2, status)
if status != 0:
sys.exit("** mnexcm %s failed" % METHOD)
# Print results
print "-" * 80
print "total observed count: %d" % total
print "%-10s\t%10s" % ('source', " estimated count")
x1 = 0.0
x2 = 0.0
mode = [0] * len(sources)
error = [0] * len(sources)
for index, (fname, src, color) in enumerate(sources):
value = Double()
dvalue = Double()
minuit.GetParameter(index, value, dvalue)
mode[index] = value
error[index] = dvalue
print "%-10s\t%10.1f +/-%- 10.1f" % \
(src, mode[index], error[index])
x1 += mode[index]
x2 += error[index]**2
x2 = sqrt(x2)
print '-' * 35
print "%-10s\t%10.1f +/-%- 10.1f" % ('total', x1, x2)
for index, (fname, src, color) in enumerate(sources):
h[index].SetLineColor(color + 2)
h[index].SetFillColor(color)
h[index].Scale(mode[index] / h[index].Integral())
h[index].SetMaximum(ymax)
hdata.SetMinimum(1.e-3)
hdata.SetMaximum(ymax)
# compute GOF measure
chisq = 0.0
B = mode[0]
S = mode[1]
print B, S
for ii in xrange(len(D)):
bkg = B * fb[ii]
sig = S * fs[ii]
print '%4d\t%10.2f\t%10.2f\t%10.0f' % (ii + 1, bkg, sig, D[ii])
theory = bkg + sig
x = D[ii] - theory
chisq += x * x / theory
ndf = len(D) - len(sources)
print '\nchisq/NDF = %10.1f/%d = %10.2f\n' % (chisq, ndf, chisq / ndf)
ht = h[0].Clone('theory')
ht.Add(h[1])
ht.SetMaximum(ymax)
hs = THStack('hs', '')
hs.Add(h[0])
hs.Add(h[1])
hs.SetMaximum(ymax)
lg = mklegend(0.35, 0.72, 0.25, 0.2)
lg.SetHeader('events(%s)' % FINALSTATES[fstate])
lg.SetTextSize(0.04)
lg.AddEntry(hdata, 'data %4.0f' % hdata.Integral(), 'p')
for ii, (fname, src, color) in enumerate(sources):
src = capitalize(src)
lg.AddEntry(h[ii], '%-5s %6.1f #pm %-6.1f (#color[4]{%6.1f})' \
% (src, mode[ii], error[ii], t[ii]), 'f')
scribe = Scribe(0.43, 0.68)
cname = "fig_%sresult%s" % (decor, postfix)
cfit = TCanvas(cname, cname, 520, 10, 500, 575)
cfit.cd()
pad1 = TPad("pad1", "pad1", 0, 0.3, 1.0, 1.0)
pad1.SetBottomMargin(0)
pad1.Draw()
pad1.cd()
hdata.SetStats(0)
hdata.SetMaximum(ymax)
hdata.Draw('ep')
hs.Draw('histsame')
hdata.Draw('epsame')
lg.Draw()
scribe.write('#chi^{2}/NDF = %5.1f / %d = %5.2f\n' % \
(chisq, ndf, chisq/ndf))
xbins = int(h[0].GetNbinsX())
width = h[0].GetXaxis().GetBinWidth(1)
xmin = int(h[0].GetXaxis().GetBinLowEdge(1))
xmax = int(h[0].GetXaxis().GetBinLowEdge(xbins) + width)
scribe.write('#font[12]{m}_{4#font[12]{l}} #in [%d, %d] GeV' %
(xmin, xmax))
CMS_lumi.CMS_lumi(cfit, iPeriod, iPos)
cfit.SaveAs('.png')
cfit.Update()
gSystem.ProcessEvents()
# plot lower pad (within current canvas)
cfit.cd()
pad2 = TPad("pad2", "pad2", 0, 0.0, 1, 0.3)
pad2.SetTopMargin(0.05)
pad2.SetBottomMargin(0.28)
pad2.SetGridy()
pad2.Draw()
pad2.cd()
hdata.SetStats(0)
hdata.GetXaxis().SetLabelSize(0.12)
hdata.GetXaxis().SetTitleSize(0.12)
hdata.GetYaxis().SetLabelSize(0.12)
hdata.GetYaxis().SetTitleSize(0.12)
hdata.SetNdivisions(505, 'Y')
hratio = hdata.Clone("ratio")
hratio.Divide(ht)
hratio.SetMinimum(0)
hratio.SetMaximum(3)
hratio.Draw('ep')
x = array('d')
x.append(xmin)
x.append(xmax)
y = array('d')
y.append(1)
y.append(1)
hline = TGraph(2, x, y)
hline.SetLineColor(kRed + 1)
hline.SetLineWidth(3)
hline.Draw('lsame')
cfit.Update()
gSystem.ProcessEvents()
cfit.SaveAs('.png')
sleep(5)
def main():
# Load PoissonGammaFit class
gSystem.AddDynamicPath('%s/lib' % os.environ['PGAMMAFIT_PATH'])
gSystem.Load("libpgammafit")
if len(sys.argv) > 1:
fstate = sys.argv[1]
else:
fstate = '_all'
# ---------------------------------------
# set up some standard graphics style
# ---------------------------------------
tdrstyle.setTDRStyle()
#gStyle.SetCanvasPreferGL(True)
gStyle.SetPadRightMargin(0.12)
#gStyle.SetOptStat('ei')
gStyle.SetOptStat('i')
gStyle.SetStatFont(42)
gStyle.SetStatFontSize(0.03)
gStyle.SetStatBorderSize(1)
gStyle.SetStatH(0.2)
gStyle.SetStatW(0.3)
gStyle.SetStatX(0.83)
gStyle.SetStatY(0.93)
# change the CMS_lumi variables (see CMS_lumi.py)
iPeriod = 4
iPos = 0
CMS_lumi.relPosX = 0.12
CMS_lumi.lumi_13TeV = "36.0 fb^{-1}"
CMS_lumi.writeExtraText = 1
CMS_lumi.extraText = "Preliminary"
vdouble = vector("double")
# Get distribution of sources and unfold into 1-d vectors, A[j]
f = []
h = []
A = [] # vector<vector<double> > # counts and uncertainties
t = [] # totals
for i,(filename,hname,c) in \
enumerate([('../fig_bkg.root', 'bkg', 225.60),
('../fig_higgs.root', 'higgs',27.0)]):
f.append(TFile(filename))
h.append(f[-1].Get('hm4l').Clone(hname))
if not h[-1]: sys.exit("** can't find hbnn")
h[-1].SetNdivisions(505, 'X')
t.append(c)
# plot
canvas = TCanvas("fig%s_%s" % (fstate, hname), hname, 10, 10, 500, 500)
canvas.cd()
h[-1].Draw("hist gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(3)
del canvas
# get counts and associated uncertainties and normalize
A.append(getContents(h[-1]))
# get data
# Get histogram of observed counts
filename = '../fig_data.root'
f.append(TFile(filename))
hdata = f[-1].Get('hm4l')
if not hdata: sys.exit("** can't find hbnn")
ymax = 60
hdata.SetMaximum(ymax)
hdata.SetNdivisions(505, 'X')
canvas = TCanvas("fig%s_data" % fstate, "bnn", 10, 10, 500, 500)
canvas.cd()
hdata.Draw("ep gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(3)
del canvas
# Construct a PoissonGamma object
D, dD = getContents(hdata)
pgfit = PoissonGammaFit(D)
# Add sources
for a, da in A:
pgfit.add(a, da)
# Find mode of posterior density
# make some reasonable guesses for fit
total = sum(D)
guess = vdouble(2, total / 3)
pgfit.execute(guess)
if not pgfit.good():
sys.exit('''
** :( boo hoo - fit failed!
** check histograms. If ok, try better starting guesses for
** the source counts in the signal region and/or try rebinning.
''')
# Get mode and width of posterior density, which, because PoissonGammaFit
# uses a flat prior for the yields, are the same as those of the
# Poisson/gamma marginal density.
mode = pgfit.mode()
error = pgfit.width()
logevidence = pgfit.logEvidence()
# Print results
print "-" * 80
print "total observed count: %d" % total
print "%-10s\t%10s" % ('source', " estimated count")
x1 = 0.0
x2 = 0.0
for index, src in enumerate(['bkg', 'higgs']):
print "%-10s\t%10.1f +/-%- 10.1f" % \
(src, mode[index], error[index])
x1 += mode[index]
x2 += error[index]**2
x2 = sqrt(x2)
print '-' * 35
print "%-10s\t%10.1f +/-%- 10.1f" % ('total', x1, x2)
h[0].SetLineColor(kMagenta + 1)
h[0].SetFillColor(kMagenta + 1)
h[0].Scale(mode[0] / h[0].Integral())
h[0].SetMaximum(ymax)
hdata.SetMaximum(ymax)
h[1].SetLineColor(kCyan + 1)
h[1].SetFillColor(kCyan + 1)
h[1].Scale(mode[1] / h[1].Integral())
# compute GOF measure
nbin = h[0].GetNbinsX()
chisq = 0.0
datum = 0.0
theory = 0.0
variance = 0.0
ndf = 0
for ii in xrange(nbin):
d = hdata.GetBinContent(ii + 1)
e0 = h[0].GetBinError(ii + 1)
c0 = h[0].GetBinContent(ii + 1)
e0 = h[0].GetBinError(ii + 1)
c1 = h[1].GetBinContent(ii + 1)
e1 = h[1].GetBinError(ii + 1)
datum += d
theory += c0 + c1
variance += c0 + c1 + e0**2 + e1**2
if theory > 8:
chisq += (theory - datum)**2 / variance
ndf += 1
print '%4d\t%10.1f\t%10.0f' % (ndf, theory, datum)
datum = 0.0
theory = 0.0
variance = 0.0
ndf -= 2
print '\nchisq/NDF = %10.1f/%d = %10.2f\n' % (chisq, ndf, chisq / ndf)
hs = THStack('hs', '')
hs.SetMaximum(ymax)
hs.Add(h[0])
hs.Add(h[1])
lg = mklegend(0.25, 0.65, 0.25, 0.2)
lg.SetHeader('events 4#mu + 2e2#mu, '\
'#font[12]{m}_{4#font[12]{l}} #in [70,170] GeV')
lg.SetTextSize(0.04)
lg.AddEntry(hdata, 'data %4.0f' % hdata.Integral(), 'p')
lg.AddEntry(h[0], 'bkg %6.1f #pm %-6.1f (#color[4]{%6.1f})' \
% (mode[0], error[0], t[0]), 'l')
lg.AddEntry(h[1], 'Higgs %6.1f #pm %-6.1f (#color[4]{%6.1f})' \
% (mode[1], error[1], t[1]), 'l')
scribe = Scribe(0.31, 0.60)
cfit = TCanvas("fig%s_result" % fstate, "", 520, 10, 500, 500)
cfit.cd()
gStyle.SetOptStat('')
hdata.SetMaximum(ymax)
hdata.Draw('ep')
hs.Draw('histsame')
hdata.Draw('epsame')
lg.Draw()
scribe.write('#chi^{2}/NDF = %5.1f/%d = %5.2f\n' % \
(chisq, ndf, chisq/ndf))
CMS_lumi.CMS_lumi(cfit, iPeriod, iPos)
cfit.SaveAs('.png')
cfit.Update()
gSystem.ProcessEvents()
sleep(5)
def main():
if len(sys.argv) > 1:
fstate = sys.argv[1]
else:
fstate = ''
decor = fstate
if decor != '':
decor += '_'
sources = ('%s/fig_%sbkg.root' % (PATH, decor), 'bkg', kMagenta + 1)
# ---------------------------------------
# get data histogram
# ---------------------------------------
filename = '%s/fig_%sdata.root' % (PATH, decor)
fdata = TFile(filename)
if not fdata.IsOpen():
sys.exit("** can't open file %s" % filename)
hdata = fdata.Get(HNAME)
if not hdata: sys.exit("** can't find hbnn")
# ---------------------------------------
# get source histogram
# ---------------------------------------
filename, hname, color = sources
fsrc = TFile(filename)
if not fsrc.IsOpen():
sys.exit("** can't open file %s" % fsrc)
hsrc = fsrc.Get(HNAME).Clone(hname)
if not hsrc: sys.exit("** can't find histogram")
# ---------------------------------------
# set up some standard graphics style
# ---------------------------------------
tdrstyle.setTDRStyle()
#gStyle.SetCanvasPreferGL(True)
gStyle.SetPadRightMargin(0.12)
#gStyle.SetOptStat('ei')
gStyle.SetOptStat('i')
gStyle.SetStatFont(42)
gStyle.SetStatFontSize(0.03)
gStyle.SetStatBorderSize(1)
gStyle.SetStatH(0.2)
gStyle.SetStatW(0.3)
gStyle.SetStatX(0.83)
gStyle.SetStatY(0.93)
# change the CMS_lumi variables (see CMS_lumi.py)
iPeriod = 4
iPos = 0
CMS_lumi.relPosX = 0.12
CMS_lumi.lumi_13TeV = "36.0 fb^{-1}"
CMS_lumi.writeExtraText = 1
CMS_lumi.extraText = "Preliminary"
vdouble = vector("double")
# ---------------------------------------
# plot data
# ---------------------------------------
ymax = 1.2 * hdata.GetMaximum()
ii = int(ymax / 10)
ymax = (ii + 1) * 10
hdata.SetMaximum(ymax)
hdata.SetNdivisions(505, 'X')
xbins = int(hdata.GetNbinsX())
width = hdata.GetXaxis().GetBinWidth(1)
xmin = int(hdata.GetXaxis().GetBinLowEdge(1))
xmax = int(hdata.GetXaxis().GetBinLowEdge(xbins) + width)
postfix = "_%s_%3.3d_%3.3d" % (HNAME, xmin, xmax)
cname = "fig_%sdata_%s" % (decor, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
hdata.Draw("ep gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(1)
del canvas
# ---------------------------------------
# plot source
# ---------------------------------------
cname = "fig_%s%s%s" % (decor, hname, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
hsrc.SetNdivisions(505, 'X')
hsrc.Draw("hist gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(1)
del canvas
# get counts
datum = 0.0
bkg = 0.0
fb = []
D = []
for ii in xrange(xbins):
bkg += hsrc.GetBinContent(ii + 1)
datum += hdata.GetBinContent(ii + 1)
if bkg > 10:
fb.append(bkg)
D.append(datum)
print '%4d\t%10.2f\t%10.0f' % (len(D), bkg, datum)
bkg = 0.0
datum = 0.0
# ---------------------------------------
# fit
# ---------------------------------------
t = []
bguess = sum(fb)
fb = map(lambda x: x / bguess, fb)
def Func(npar, grad, fval, xval, flag):
fval[0] = 0.0
B = xval[0]
for i in xrange(len(D)):
theory = B * fb[i]
x = D[i] - theory
fval[0] += x * x / theory
minuit = TMinuit(1)
minuit.SetPrintLevel(1)
minuit.SetFCN(Func)
minuit.SetErrorDef(1)
# Find mode of posterior density
# make some reasonable guesses for fit
total = sum(D)
status = Long()
bstep = bguess / 1000
bmin = 0.0
bmax = 2 * total
minuit.mnparm(0, "B", bguess, bstep, bmin, bmax, status)
if status != 0:
sys.exit("** mnparam status code: %d" % status)
args = array('d')
args.append(MAXITER)
args.append(TOLERANCE)
minuit.mnexcm(METHOD, args, 2, status)
if status != 0:
sys.exit("** mnexcm %s failed" % METHOD)
# Print results
print "-" * 80
print "total observed count: %d" % total
print "%-10s\t%10s" % ('source', " estimated count")
value = Double()
dvalue = Double()
minuit.GetParameter(0, value, dvalue)
mode = value
error = dvalue
print "%-10s\t%10.1f +/-%- 10.1f" % \
("", mode, error)
color = sources[-1]
hsrc.SetLineColor(color + 2)
hsrc.SetFillColor(color)
hsrc.Scale(mode / hsrc.Integral())
hsrc.SetMaximum(ymax)
# compute GOF measure
chisq = 0.0
for i in xrange(len(D)):
theory = mode * fb[i]
x = D[i] - theory
chisq += x * x / theory
ndf = len(D) - 1
print '\nchisq/NDF = %10.1f/%d = %10.2f\n' % (chisq, ndf, chisq / ndf)
lg = mklegend(0.35, 0.72, 0.25, 0.2)
lg.SetHeader('events(%s)' % FINALSTATES[fstate])
lg.SetTextSize(0.04)
lg.AddEntry(hdata, 'data %4.0f' % hdata.Integral(), 'p')
fname, src, color = sources
src = capitalize(src)
lg.AddEntry(hsrc, '%-5s %6.1f #pm %-6.1f (#color[4]{%6.1f})' \
% (src, mode, error, bguess), 'f')
scribe = Scribe(0.43, 0.68)
cname = "fig_%sresult%s" % (decor, postfix)
cfit = TCanvas(cname, cname, 520, 10, 500, 500)
cfit.cd()
gStyle.SetOptStat('')
hdata.SetMaximum(ymax)
hdata.Draw('ep')
hsrc.Draw('histsame')
hdata.Draw('epsame')
lg.Draw()
scribe.write('#chi^{2}/NDF = %5.1f / %d = %5.2f\n' % \
(chisq, ndf, chisq/ndf))
scribe.write('#font[12]{m}_{4#font[12]{l}} #in [%d, %d] GeV' %
(xmin, xmax))
CMS_lumi.CMS_lumi(cfit, iPeriod, iPos)
cfit.SaveAs('.png')
cfit.Update()
gSystem.ProcessEvents()
sleep(5)
def main():
# Load PoissonGammaFit class
gSystem.AddDynamicPath('%s/lib' % os.environ['PGAMMAFIT_PATH'])
gSystem.Load("libpgammafit")
if len(sys.argv) > 1:
fstate = sys.argv[1]
else:
fstate = ''
decor = fstate
if decor != '':
decor += '_'
sources = [('%s/fig_%sbkg.root' % (PATH, decor), 'bkg', kMagenta + 1),
('%s/fig_%shiggs.root' % (PATH, decor), 'higgs', kAzure + 1)]
# get data histogram
filename = '%s/fig_%sdata.root' % (PATH, decor)
fdata = TFile(filename)
if not fdata.IsOpen():
sys.exit("** can't open file %s" % filename)
hdata = fdata.Get(HNAME)
if not hdata: sys.exit("** can't find hbnn")
# get source histograms
f = []
h = []
for i, (filename, hname, color) in enumerate(sources):
f.append(TFile(filename))
if not f[-1].IsOpen():
sys.exit("** can't open file %s" % f[-1])
h.append(f[-1].Get(HNAME).Clone(hname))
if not h[-1]: sys.exit("** can't find hbnn")
if FAKEDATA: fakeData(hdata, h, 0.1065)
print '\tcount(%s):\t%8.1f' % ('data', hdata.Integral())
# get counts
A = [0] * len(h) # vector<vector<double> > # counts and uncertainties
t = [0] * len(h) # totals
for i, (filename, hname, color) in enumerate(sources):
A[i] = getContents(h[i])
t[i] = h[i].Integral()
print '\tcount(%s):\t%8.1f' % (hname, t[i])
# ---------------------------------------
# set up some standard graphics style
# ---------------------------------------
tdrstyle.setTDRStyle()
#gStyle.SetCanvasPreferGL(True)
gStyle.SetPadRightMargin(0.12)
#gStyle.SetOptStat('ei')
gStyle.SetOptStat('i')
gStyle.SetStatFont(42)
gStyle.SetStatFontSize(0.03)
gStyle.SetStatBorderSize(1)
gStyle.SetStatH(0.2)
gStyle.SetStatW(0.3)
gStyle.SetStatX(0.83)
gStyle.SetStatY(0.93)
# change the CMS_lumi variables (see CMS_lumi.py)
iPeriod = 4
iPos = 0
CMS_lumi.relPosX = 0.12
CMS_lumi.lumi_13TeV = "36.0 fb^{-1}"
CMS_lumi.writeExtraText = 1
CMS_lumi.extraText = "Preliminary"
vdouble = vector("double")
# plot data
ymax = 1.2 * hdata.GetMaximum()
ii = int(ymax / 10)
ymax = (ii + 1) * 10
hdata.SetMaximum(ymax)
hdata.SetNdivisions(505, 'X')
xbins = int(hdata.GetNbinsX())
width = hdata.GetXaxis().GetBinWidth(1)
xmin = int(hdata.GetXaxis().GetBinLowEdge(1))
xmax = int(hdata.GetXaxis().GetBinLowEdge(xbins) + width)
postfix = "_%s_%3.3d_%3.3d" % (HNAME, xmin, xmax)
cname = "fig_%sdata_%s" % (decor, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
hdata.Draw("ep gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(3)
del canvas
for i, (filename, hname, color) in enumerate(sources):
cname = "fig_%s%s%s" % (decor, hname, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
h[i].SetNdivisions(505, 'X')
h[i].Draw("hist gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(3)
del canvas
# ---------------------------------------
# Construct a PoissonGamma object
# ---------------------------------------
D, dD = getContents(hdata)
pgfit = PoissonGammaFit(D)
# Add sources
for a, da in A:
pgfit.add(a, da)
# Find mode of posterior density
# make some reasonable guesses for fit
total = sum(D)
guess = vdouble(len(sources), total / len(sources))
pgfit.execute(guess)
if not pgfit.good():
sys.exit('''
** :( boo hoo - fit failed!
** check histograms. If ok, try better starting guesses for
** the source counts in the signal region and/or try rebinning.
''')
# Get mode and width of posterior density, which, because PoissonGammaFit
# uses a flat prior for the yields, are the same as those of the
# Poisson/gamma marginal density.
mode = pgfit.mode()
error = pgfit.width()
logevidence = pgfit.logEvidence()
# Print results
print "-" * 80
print "total observed count: %d" % total
print "%-10s\t%10s" % ('source', " estimated count")
x1 = 0.0
x2 = 0.0
for index, (fname, src, color) in enumerate(sources):
print "%-10s\t%10.1f +/-%- 10.1f" % \
(src, mode[index], error[index])
x1 += mode[index]
x2 += error[index]**2
x2 = sqrt(x2)
print '-' * 35
print "%-10s\t%10.1f +/-%- 10.1f" % ('total', x1, x2)
for index, (fname, src, color) in enumerate(sources):
h[index].SetLineColor(color + 2)
h[index].SetFillColor(color)
h[index].Scale(mode[index] / h[index].Integral())
h[index].SetMaximum(ymax)
hdata.SetMaximum(ymax)
# compute GOF measure
nbin = h[0].GetNbinsX()
chisq = 0.0
datum = 0.0
theory = 0.0
variance = 0.0
ndf = 0
for ii in xrange(nbin):
d = hdata.GetBinContent(ii + 1)
e0 = h[0].GetBinError(ii + 1)
c0 = h[0].GetBinContent(ii + 1)
e0 = h[0].GetBinError(ii + 1)
c1 = h[1].GetBinContent(ii + 1)
e1 = h[1].GetBinError(ii + 1)
datum += d
theory += c0 + c1
variance += c0 + c1 + e0**2 + e1**2
if datum > 8:
chisq += (theory - datum)**2 / variance
ndf += 1
print '%4d\t%10.1f\t%10.0f' % (ndf, theory, datum)
datum = 0.0
theory = 0.0
variance = 0.0
ndf -= 2
print '\nchisq/NDF = %10.1f/%d = %10.2f\n' % (chisq, ndf, chisq / ndf)
hs = THStack('hs', '')
hs.Add(h[0])
hs.Add(h[1])
hs.SetMaximum(ymax)
lg = mklegend(0.35, 0.72, 0.25, 0.2)
lg.SetHeader('events(%s)' % FINALSTATES[fstate])
lg.SetTextSize(0.04)
lg.AddEntry(hdata, 'data %4.0f' % hdata.Integral(), 'p')
for ii, (fname, src, color) in enumerate(sources):
src = capitalize(src)
lg.AddEntry(h[ii], '%-5s %6.1f #pm %-6.1f (#color[4]{%6.1f})' \
% (src, mode[ii], error[ii], t[ii]), 'f')
scribe = Scribe(0.43, 0.68)
cname = "fig_%sresult%s" % (decor, postfix)
cfit = TCanvas(cname, cname, 520, 10, 500, 500)
cfit.cd()
gStyle.SetOptStat('')
hdata.SetMaximum(ymax)
hdata.Draw('ep')
hs.Draw('histsame')
hdata.Draw('epsame')
lg.Draw()
scribe.write('#chi^{2}/NDF = %5.1f / %d = %5.2f\n' % \
(chisq, ndf, chisq/ndf))
xbins = int(h[0].GetNbinsX())
width = h[0].GetXaxis().GetBinWidth(1)
xmin = int(h[0].GetXaxis().GetBinLowEdge(1))
xmax = int(h[0].GetXaxis().GetBinLowEdge(xbins) + width)
scribe.write('#font[12]{m}_{4#font[12]{l}} #in [%d, %d] GeV' %
(xmin, xmax))
CMS_lumi.CMS_lumi(cfit, iPeriod, iPos)
cfit.SaveAs('.png')
cfit.Update()
gSystem.ProcessEvents()
sleep(5)
def main():
if len(sys.argv) > 1:
fstate = sys.argv[1]
else:
fstate = ''
decor = fstate
if decor != '':
decor += '_'
sources = [('%s/fig_%sbkg.root' % (PATH, decor), 'bkg', kMagenta+1),
('%s/fig_%shiggs.root' % (PATH, decor), 'higgs', kAzure+1)]
# ---------------------------------------
# get data histogram
# ---------------------------------------
filename = '%s/fig_%sdata.root' % (PATH, decor)
fdata = TFile(filename)
if not fdata.IsOpen():
sys.exit("** can't open file %s" % filename)
hdata = fdata.Get(HNAME)
if not hdata: sys.exit("** can't find hbnn")
# ---------------------------------------
# get source histograms
# ---------------------------------------
f = []
h = []
for i,(filename,hname,color) in enumerate(sources):
f.append(TFile(filename))
if not f[-1].IsOpen():
sys.exit("** can't open file %s" % f[-1])
h.append( f[-1].Get(HNAME).Clone(hname) )
if not h[-1]: sys.exit("** can't find hbnn")
if FAKEDATA: fakeData(hdata, h, 0.1065)
print '\tcount(%s):\t%8.1f' % ('data', hdata.Integral())
# ---------------------------------------
# set up some standard graphics style
# ---------------------------------------
tdrstyle.setTDRStyle()
#gStyle.SetCanvasPreferGL(True)
gStyle.SetPadRightMargin(0.12)
#gStyle.SetOptStat('ei')
gStyle.SetOptStat('i')
gStyle.SetStatFont(42)
gStyle.SetStatFontSize(0.03)
gStyle.SetStatBorderSize(1)
gStyle.SetStatH(0.2)
gStyle.SetStatW(0.3)
gStyle.SetStatX(0.83)
gStyle.SetStatY(0.93)
# change the CMS_lumi variables (see CMS_lumi.py)
iPeriod = 4
iPos = 0
CMS_lumi.relPosX = 0.12
CMS_lumi.lumi_13TeV = "36.0 fb^{-1}"
CMS_lumi.writeExtraText = 1
CMS_lumi.extraText = "Preliminary"
vdouble = vector("double")
# ---------------------------------------
# plot data
# ---------------------------------------
ymax = 1.2*hdata.GetMaximum()
ii = int(ymax / 20)
ymax = (ii+1)*20
hdata.SetMaximum(ymax)
hdata.SetNdivisions(505, 'X')
xbins = int(hdata.GetNbinsX())
width = hdata.GetXaxis().GetBinWidth(1)
xmin = int(hdata.GetXaxis().GetBinLowEdge(1))
xmax = int(hdata.GetXaxis().GetBinLowEdge(xbins)+width)
postfix = "_%s_%3.3d_%3.3d" % (HNAME, xmin, xmax)
cname = "fig_%sdata_%s" % (decor, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
hdata.Draw("ep gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(0.5)
del canvas
# ---------------------------------------
# plot sources
# ---------------------------------------
for i,(filename,hname,color) in enumerate(sources):
cname = "fig_%s%s%s" % (decor, hname, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
h[i].SetNdivisions(505, 'X')
h[i].Draw("hist gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(0.5)
del canvas
# create TFractionFitter object
hsrcs = TObjArray(len(sources))
hsrcs.Add(h[0])
hsrcs.Add(h[1])
fitter = TFractionFitter(hdata, hsrcs)
status = fitter.Fit()
if status == 0:
sys.exit("** fit failed: status")
# Print results
total = int(hdata.Integral())
print "-"*80
print "total observed count: %d" % total
print "%-10s\t%10s" % ('source', " estimated count")
x1 = 0.0
x2 = 0.0
mode = [0]*len(sources)
error = [0]*len(sources)
for index, (fname, src, color) in enumerate(sources):
value = Double()
dvalue = Double()
fitter.GetResult(index, value, dvalue)
mode[index] = value * total
error[index]= dvalue * total
print "%-10s\t%10.1f +/-%- 10.1f" % \
(src, mode[index], error[index])
x1 += mode[index]
x2 += error[index]**2
x2 = sqrt(x2)
print '-'*35
print "%-10s\t%10.1f +/-%- 10.1f" % ('total', x1, x2)
t = []
for index, (fname, src, color) in enumerate(sources):
h[index].SetLineColor(color+2)
h[index].SetFillColor(color)
t.append(h[index].Integral())
h[index].Scale(mode[index]/h[index].Integral())
h[index].SetMaximum(ymax)
hdata.SetMinimum(1.e-3)
hdata.SetMaximum(ymax)
# compute GOF measure
chisq = fitter.GetChisquare()
ndf = fitter.GetNDF()
p = fitter.GetProb()
print '\nchisq/NDF = %10.1f/%d = %10.2f\t%8.3f\n' % (chisq, ndf,
chisq/ndf, p)
ht = h[0].Clone('theory')
ht.Add(h[1])
ht.SetMaximum(ymax)
hs = THStack('hs', '')
hs.Add(h[0])
hs.Add(h[1])
hs.SetMaximum(ymax)
lg = mklegend(0.35, 0.72, 0.25, 0.2)
lg.SetHeader('events(%s)' % FINALSTATES[fstate])
lg.SetTextSize(0.04)
lg.AddEntry(hdata, 'data %4.0f' % hdata.Integral(), 'p')
for ii, (fname, src, color) in enumerate(sources):
src = capitalize(src)
lg.AddEntry(h[ii], '%-5s %6.1f #pm %-6.1f (#color[4]{%6.1f})' \
% (src, mode[ii], error[ii], t[ii]), 'f')
scribe = Scribe(0.43, 0.68)
cname = "fig_%sresult%s" % (decor, postfix)
cfit = TCanvas(cname, cname, 520, 10, 500, 575)
cfit.cd()
pad1 = TPad("pad1", "pad1", 0, 0.3, 1.0, 1.0)
pad1.SetBottomMargin(0)
pad1.Draw()
pad1.cd()
hdata.SetStats(0)
hdata.SetMaximum(ymax)
hdata.Draw('ep')
hs.Draw('histsame')
hdata.Draw('epsame')
lg.Draw()
scribe.write('#chi^{2}/NDF = %5.1f / %d = %5.2f\n' % \
(chisq, ndf, chisq/ndf))
xbins = int(h[0].GetNbinsX())
width = h[0].GetXaxis().GetBinWidth(1)
xmin = int(h[0].GetXaxis().GetBinLowEdge(1))
xmax = int(h[0].GetXaxis().GetBinLowEdge(xbins)+width)
scribe.write('#font[12]{m}_{4#font[12]{l}} #in [%d, %d] GeV' % (xmin,xmax))
CMS_lumi.CMS_lumi(cfit, iPeriod, iPos)
cfit.SaveAs('.png')
cfit.Update()
gSystem.ProcessEvents()
# plot lower pad (within current canvas)
cfit.cd()
pad2 = TPad("pad2", "pad2", 0, 0.0, 1, 0.3)
pad2.SetTopMargin(0.05)
pad2.SetBottomMargin(0.28)
pad2.SetGridy()
pad2.Draw()
pad2.cd()
hdata.SetStats(0)
hdata.GetXaxis().SetLabelSize(0.12)
hdata.GetXaxis().SetTitleSize(0.12)
hdata.GetYaxis().SetLabelSize(0.12)
hdata.GetYaxis().SetTitleSize(0.12)
hdata.SetNdivisions(505, 'Y')
hratio = hdata.Clone("ratio")
hratio.Divide(ht)
hratio.SetMinimum(0)
hratio.SetMaximum(3)
hratio.Draw('ep')
x = array('d'); x.append(xmin); x.append(xmax)
y = array('d'); y.append(1); y.append(1)
hline = TGraph(2, x, y)
hline.SetLineColor(kRed+1)
hline.SetLineWidth(3)
hline.Draw('lsame')
cfit.Update()
gSystem.ProcessEvents()
cfit.SaveAs('.png')
sleep(5)
def main():
# Load PoissonGammaFit class
gSystem.AddDynamicPath('$PGAMMAFIT_PATH/lib')
gSystem.Load("libpgammafit")
if len(sys.argv) > 1:
fstate = sys.argv[1]
else:
fstate = ''
decor = fstate
if decor != '':
decor += '_'
sources = [('%s/histos_%sbkg.root' % (PATH, decor), 'bkg', kMagenta + 1),
('%s/histos_%shiggs.root' % (PATH, decor), 'higgs', kAzure + 1)]
# ---------------------------------------
# get data histogram
# ---------------------------------------
filename = '%s/histos_%sdata.root' % (PATH, decor)
fdata = TFile(filename)
if not fdata.IsOpen():
sys.exit("** can't open file %s" % filename)
hdata = fdata.Get(HNAME)
if not hdata: sys.exit("** can't find histogram")
hdata.SetMarkerColor(kBlack)
# ---------------------------------------
# get source histograms
# ---------------------------------------
f = []
h = []
for i, (filename, hname, color) in enumerate(sources):
f.append(TFile(filename))
if not f[-1].IsOpen():
sys.exit("** can't open file %s" % f[-1])
h.append(f[-1].Get(HNAME).Clone(hname))
if not h[-1]: sys.exit("** can't find %s" % HNAME)
h[-1].SetFillColor(color)
h[-1].SetFillStyle(3001)
print '\tcount(%s):\t%8.1f' % ('data', hdata.Integral())
# ---------------------------------------
# set up some standard graphics style
# ---------------------------------------
tdrstyle.setTDRStyle()
#gStyle.SetCanvasPreferGL(True)
gStyle.SetPadRightMargin(0.12)
#gStyle.SetOptStat('ei')
gStyle.SetOptStat('i')
gStyle.SetStatFont(42)
gStyle.SetStatFontSize(0.03)
gStyle.SetStatBorderSize(1)
gStyle.SetStatH(0.2)
gStyle.SetStatW(0.3)
gStyle.SetStatX(0.83)
gStyle.SetStatY(0.93)
# change the CMS_lumi variables (see CMS_lumi.py)
iPeriod = 4
iPos = 0
CMS_lumi.relPosX = 0.12
CMS_lumi.lumi_13TeV = "36.0 fb^{-1}"
CMS_lumi.writeExtraText = 1
CMS_lumi.extraText = "Preliminary"
vdouble = vector("double")
# ---------------------------------------
# plot data
# ---------------------------------------
ymax = 1.5 * hdata.GetMaximum()
ii = int(ymax / 20)
ymax = (ii + 1) * 20
hdata.SetMaximum(ymax)
hdata.SetNdivisions(505, 'X')
xbins, xmin, xmax, width = getm4lrange(fdata)
postfix = "_%s_%3.3d_%3.3d" % (HNAME, xmin, xmax)
if len(sources) == 1: postfix += "_%s" % sources[0][1]
cname = "fig_%sdata%s" % (decor, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
hdata.Draw("ep gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(0.5)
del canvas
# ---------------------------------------
# plot sources
# ---------------------------------------
for i, (filename, hname, color) in enumerate(sources):
cname = "fig_%s%s%s" % (decor, hname, postfix)
canvas = TCanvas(cname, cname, 10, 10, 500, 500)
canvas.cd()
h[i].SetNdivisions(505, 'X')
h[i].Draw("hist gl")
canvas.Update()
canvas.SaveAs('.png')
gSystem.ProcessEvents()
sleep(0.5)
del canvas
# get counts
A = [0] * len(h) # vector<vector<double> > # counts and uncertainties
t = [0] * len(h) # totals
for i, (filename, hname, color) in enumerate(sources):
A[i] = getContents(h[i])
t[i] = h[i].Integral()
print '\tcount(%s):\t%8.1f' % (hname, t[i])
# ---------------------------------------
# Construct a PoissonGamma object
# ---------------------------------------
D, dD = getContents(hdata)
pgfit = PoissonGammaFit(D)
# Add sources
for a, da in A:
pgfit.add(a, da)
# Find mode of posterior density
# make some reasonable guesses for fit
guess = vdouble(len(sources))
total = sum(D)
aguess = total / len(sources)
for i, (filename, hname, color) in enumerate(sources):
guess[i] = aguess
pgfit.execute(guess)
if not pgfit.good():
sys.exit('''
** :( boo hoo - fit failed!
** check histograms. If ok, try better starting guesses for
** the source counts in the signal region and/or try rebinning.
''')
# Get mode and width of posterior density, which, because PoissonGammaFit
# uses a flat prior for the yields, are the same as those of the
# Poisson/gamma marginal density.
mode = pgfit.mode()
error = pgfit.width()
logevidence = pgfit.logEvidence()
result = "result_%sdata%s.txt" % (decor, postfix)
rout = open(result, 'w')
# Print results
print "-" * 80
record = "data\t\t %d" % total
print record
rout.write('%s\n' % record)
record = "%-10s\t%10s" % ('source', " estimated count")
print record
rout.write('%s\n' % record)
x1 = 0.0
x2 = 0.0
for index, (fname, src, color) in enumerate(sources):
record = "%-10s\t%10.1f +/-%- 10.1f" % \
(src, mode[index], error[index])
print record
rout.write('%s\n' % record)
x1 += mode[index]
x2 += error[index]**2
x2 = sqrt(x2)
record = '-' * 35
print record
rout.write('%s\n' % record)
record = "%-10s\t%10.1f +/-%- 10.1f" % ('total', x1, x2)
print record
rout.write('%s\n' % record)
for index, (fname, src, color) in enumerate(sources):
h[index].SetLineColor(color + 2)
h[index].SetFillColor(color)
h[index].Scale(mode[index] / h[index].Integral())
h[index].SetMaximum(ymax)
hdata.SetMinimum(1.e-3)
hdata.SetMaximum(ymax)
# compute GOF measure
chisq, ndf, KS, ht = GOF(hdata, h)
print
rout.write('\n')
record = 'chisq/NDF = %4.1f/%d = %6.2f' % (chisq, ndf, chisq / ndf)
print record
rout.write('%s\n' % record)
record = 'KS-prob = %6.3f' % KS
print record
print
rout.write('%s\n' % record)
rout.close()
ht.SetMaximum(ymax)
hs = THStack('hs', '')
hs.Add(h[0])
if len(sources) > 1: hs.Add(h[1])
hs.SetMaximum(ymax)
lg = mklegend(0.35, 0.72, 0.25, 0.2)
lg.SetHeader('events(%s) %s' % \
(FINALSTATES[fstate],
'#font[12]{m}_{4#font[12]{l}} #in [%d, %d] GeV' %\
(xmin,xmax)))
lg.SetTextSize(0.04)
lg.AddEntry(hdata, 'data %4.0f' % hdata.Integral(), 'p')
for ii, (fname, src, color) in enumerate(sources):
src = capitalize(src)
lg.AddEntry(h[ii], '%-5s %6.1f #pm %-6.1f (#color[4]{%6.1f})' \
% (src, mode[ii], error[ii], t[ii]), 'f')
scribe = Scribe(0.43, 0.68)
cname = "fig_%sresult%s" % (decor, postfix)
cfit = TCanvas(cname, cname, 520, 10, 500, 575)
cfit.cd()
pad1 = TPad("pad1", "pad1", 0, 0.3, 1.0, 1.0)
pad1.SetBottomMargin(0)
pad1.Draw()
pad1.cd()
hdata.SetStats(0)
hdata.SetMaximum(ymax)
hdata.Draw('ep')
lg.Draw()
scribe.write('KS-prob = %6.3f' % KS)
scribe.write('#chi^{2}/NDF = %5.1f / %d = %5.2f\n' % \
(chisq, ndf, chisq/ndf))
hs.Draw('histsame')
hdata.Draw('epsame')
CMS_lumi.CMS_lumi(cfit, iPeriod, iPos)
cfit.Update()
gSystem.ProcessEvents()
# plot lower pad (within current canvas)
cfit.cd()
pad2 = TPad("pad2", "pad2", 0, 0.0, 1, 0.3)
pad2.SetTopMargin(0.05)
pad2.SetBottomMargin(0.28)
pad2.SetGridy()
pad2.Draw()
pad2.cd()
hdata.SetStats(0)
hdata.GetXaxis().SetLabelSize(0.12)
hdata.GetXaxis().SetTitleSize(0.12)
hdata.GetYaxis().SetLabelSize(0.12)
hdata.GetYaxis().SetTitleSize(0.12)
hdata.SetNdivisions(505, 'Y')
hratio = hdata.Clone("ratio")
hratio.Divide(ht)
hratio.SetMinimum(0)
hratio.SetMaximum(3)
hratio.Draw('ep')
drawLine(hratio)
hratio.Draw('epsame')
cfit.Update()
gSystem.ProcessEvents()
cfit.SaveAs('.png')
sleep(5)