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 run_zbi_stat(self):
n_on = self.observation()
mu_b_hat = self.background
sigma_b = self.fullunc()
tau = mu_b_hat / (sigma_b * sigma_b)
n_off = tau * mu_b_hat
P_Bi = TMath.BetaIncomplete(1. / (1. + tau), n_on, n_off + 1)
Z_Bi = math.sqrt(2.) * TMath.ErfInverse(1 - 2. * P_Bi)
return str(round(Z_Bi, 4))
def plotSignificance(observed_test_stat,
dir=None,
plot_format='png',
infile=None):
#
# Plots sampling distribution and test statistic from data
# Used for the trials factor-corrected significance
#
# Input either from a file or from a dir with *.ascii files
#
legend = '[significance]:'
intLumiEe = 4.7
intLumiMumu = 5.0
if observed_test_stat == None:
print 'Observed test statistic value not specified (use -t option)'
_limits = []
if dir:
if infile:
print legend, 'cannot process both dir and file, specify just one'
sys.exit(-1)
for _subdir, _dir, files in os.walk(dir):
print legend, files
for filename in files:
if filename.rfind('ascii') == -1:
continue
with open(dir + '/' + filename) as file:
lines = file.readlines()
for line in lines:
_words = line.strip().split()
if _words[0][0] == '#':
continue
_number = []
for word in _words:
_number.append(float(word))
_limits.append(_number)
if infile:
with open(infile) as file:
lines = file.readlines()
for line in lines:
_words = line.strip().split()
if _words[0][0] == '#':
continue
_number = []
for word in _words:
_number.append(float(word))
_limits.append(_number)
_limits.sort(compare_columns)
_h = TH1F("-2 ln L/L_{0}", "", 50, 0, 10)
_p = observed_test_stat
_count = 0
for l in _limits:
if float(l[1]) < _p:
_count += 1
_h.Fill(l[1])
_nup = len(_limits) - _count
_pval = _nup / float(len(_limits))
print 'test_stat:', _p
print 'p-value:', _pval
_z = math.sqrt(2.0) * TMath.ErfInverse(1.0 - 2.0 * _pval)
print 'significance:', _z
c = TCanvas("c", "c", 600, 400)
c.SetFrameLineWidth(2)
c.SetRightMargin(0.05)
c.cd()
_h.SetLineWidth(2)
_h.SetLineColor(ROOT.kBlue + 2)
_h.SetFillStyle(1001)
_h.SetFillColor(ROOT.kBlue - 9)
_h.Draw("")
_l = TLine(_p, 0, _p, _h.GetMaximum() * 0.9)
_l.SetLineWidth(2)
_l.SetLineColor(ROOT.kRed)
_l.Draw("same")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
latex.SetTextAlign(5) # align left
latex.DrawLatex(0.45, 0.45, "#font[62]{p-value = " + '%.2f' % _pval + "}")
latex.DrawLatex(0.45, 0.4, "#font[62]{Z = " + '%.1f' % _z + "}")
# x axis
latex.SetTextAlign(31) # align left
latex.DrawLatex(0.95, 0.01, 'test statistic')
# y axis
latex.SetTextAlign(31) # align left
latex.SetTextAngle(90)
latex.DrawLatex(0.04, 0.95, 'number of pseud-experiments')
# legend
xLegend = 0.65
yLegend = 0.75
legendWidth = 0.3
legendHeight = 0.15
tLegend = TLegend(xLegend, yLegend, xLegend + legendWidth,
yLegend + legendHeight)
tLegend.AddEntry(_h, "pseudo-experiments", 'l')
tLegend.AddEntry(_l, "observed data", 'l')
tLegend.Draw()
# CMS preliminary
cms.CmsPrel()
cms.Title(
'Z-score trials [600-2500] GeV, ee (4.7fb^{-1}) + #mu^{+}#mu^{-} (5.0fb^{-1`})'
)
#cms.Title('Z-score trials [600-2500] GeV, ee (4.7fb^{-1})')
#cms.Title('Z-score trials [600-2500] GeV, #mu^{+}#mu^{-} (5.0fb^{-1`})')
#cms.Title('Z-score trials 965 GeV, ee (4.7fb^{-1}) + #mu^{+}#mu^{-} (5.0fb^{-1`})')
#cms.Title('Z-score trials [600-2500] GeV, ee (4.7fb^{-1})')
#cms.Title('Z-score trials [600-2500] GeV, #mu^{+}#mu^{-} (5.0fb^{-1`})')
c.SaveAs("lee." + plot_format)
def getZbi(n_on, n_off, tau):
P_Bi = TMath.BetaIncomplete(1. / (1. + tau), n_on, n_off + 1)
Z_Bi = TMath.Sqrt(2) * TMath.ErfInverse(1 - 2 * P_Bi)
return (Z_Bi)
def shiftDiag(sigma_tmp, Qrel_tmp):
return sigma_tmp * TMath.ErfInverse(2. * Qrel_tmp - 1.) * 1. / sqrt(
2.), sigma_tmp * TMath.ErfInverse(2. * Qrel_tmp - 1.) * 1. / sqrt(2.)
def shiftLat(sigma_tmp, Qrel_tmp):
return sigma_tmp * TMath.ErfInverse(2. * Qrel_tmp - 1.)
def look_elsewhere(observed_test_stat, dir, plot_format='png'):
sys.argv.pop(1)
sys.argv.pop(1)
sys.argv.pop(1)
sys.argv.pop(1)
sys.argv.pop(1)
intLumiEe = 1.1
intLumiMumu = 1.1
if observed_test_stat == None:
print 'Observed test statistic value not specified (use -t value)'
_limits = []
for line in fileinput.input():
#print legend, line.strip()
_words = line.strip().split()
if _words[0][0] == '#':
continue
_number = []
for word in _words:
_number.append(float(word))
_limits.append(_number)
_limits.sort(compare_columns)
_h = TH1F("-2 ln L/L_{0}", "", 50, 0, 10)
# print sorted result
#_p = 3.734019
#_p = 3.308118 # 3 events
#_p = 3.798105 # 4 events
#_p = 3.55 # 4 events + flat
#_p = 3.219322 # 5 events fake 600/pb strange mumu bg
#_p = 3.512337 # 5 events 350+370/pb strange mumu bg
#_p = 2.313430 # 5 events fake 600/pb
#_p = 2.645160 # 5 events 350+370/pb strange mumu bg
_p = observed_test_stat
_count = 0
for l in _limits:
#for n in l:
#print n, ' ',
#print
if float(l[1]) < _p:
_count += 1
_h.Fill(l[1])
_nup = len(_limits) - _count
_pval = _nup / float(len(_limits))
print 'test_stat:', _p
print 'p-value:', _pval
_z = math.sqrt(2.0) * TMath.ErfInverse(1.0 - 2.0 * _pval)
print 'significance:', _z
c = TCanvas("c", "c", 600, 400)
c.SetFrameLineWidth(2)
c.SetRightMargin(0.05)
c.cd()
_h.SetLineWidth(2)
_h.SetLineColor(ROOT.kBlue + 2)
_h.SetFillStyle(1001)
_h.SetFillColor(ROOT.kBlue - 9)
_h.Draw("")
_l = TLine(_p, 0, _p, _h.GetMaximum() * 0.9)
_l.SetLineWidth(2)
_l.SetLineColor(ROOT.kRed)
_l.Draw("same")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
latex.SetTextAlign(5) # align left
#latex.DrawLatex(0.45,0.5,"#font[62]{M = 954.3 GeV}")
latex.DrawLatex(0.45, 0.45, "#font[62]{p-value = " + '%.2f' % _pval + "}")
latex.DrawLatex(0.45, 0.4, "#font[62]{Z = " + '%.1f' % _z + "}")
#latex.DrawLatex(0.45,0.5,"#font[62]{M = 962.2 GeV}")
#latex.DrawLatex(0.45,0.45,"#font[62]{p-value = 0.0068}")
#latex.DrawLatex(0.45,0.4,"#font[62]{Z = 2.5}")
# x axis
latex.SetTextAlign(31) # align left
latex.DrawLatex(0.95, 0.01, 'test statistic')
# y axis
latex.SetTextAlign(31) # align left
latex.SetTextAngle(90)
latex.DrawLatex(0.04, 0.95, 'number of pseud-experiments')
# legend
xLegend = 0.65
yLegend = 0.75
legendWidth = 0.3
legendHeight = 0.15
tLegend = TLegend(xLegend, yLegend, xLegend + legendWidth,
yLegend + legendHeight)
tLegend.AddEntry(_h, "pseudo-experiments", 'l')
tLegend.AddEntry(_l, "observed data", 'l')
tLegend.Draw()
# CMS preliminary
latex.SetTextAngle(0)
#latex.DrawLatex(0.9,0.929,"#font[62]{CMS preliminary, #int #font[42]{L}dt = "+'%.1ffb^{-1} (#mu^{+}#mu^{-})'%intLumiMumu+"}")
#latex.DrawLatex(0.9,0.929,"#font[62]{CMS preliminary, #int #font[42]{L}dt = "+'%.1ffb^{-1} (ee)'%intLumiEe+"}")
latex.DrawLatex(
0.9, 0.929, "#font[62]{CMS preliminary, #int #font[42]{L}dt = " +
'%.1ffb^{-1} (#mu^{+}#mu^{-}), %.1fpb^{-1} (ee)' %
(intLumiMumu, intLumiEe) + "}")
#latex.DrawLatex(0.9,0.929,"#font[62]{CMS, #int #font[42]{L}dt = "+'%.1ffb^{-1} (#mu^{+}#mu^{-})'%intLumiMumu+"}")
#latex.DrawLatex(0.9,0.929,"#font[62]{CMS, #int #font[42]{L}dt = "+'%.1ffb^{-1} (ee)'%intLumiEe+"}")
#latex.DrawLatex(0.9,0.929,"#font[62]{CMS, #int #font[42]{L}dt = "+'%.1ffb^{-1} (#mu^{+}#mu^{-}), %.1fpb^{-1} (ee)'%(intLumiMumu,intLumiEe)+"}")
# _p = 2.67
# _count = 0
# for l in _limits:
#
# if float(l[1])<_p:
# _count += 1
#
# _h.Fill(l[1])
#
# _nup = len(_limits) - _count
# _pval = _nup/float(len(_limits))
# print 'test_stat:', _p
# print 'p-value:', _pval
# print 'significance:', math.sqrt(2.0)*TMath.ErfInverse(1.0-2.0*_pval)
# _l2 = TLine(_p,0,_p,1000)
# _l2.Draw("same")
# latex.DrawLatex(0.15,0.3,"#font[62]{M = 942.2 GeV}")
# latex.DrawLatex(0.15,0.25,"#font[62]{p-value = 0.05}")
c.SaveAs("lee.pdf")
