def plotAllData(self, trigger):
# paramlist == fits
missing_fit = False
if not self.plotting_data.has_key(trigger):
print "\tERROR: Trigger not found in plotting data - %s" % trigger
return False
else:
data = self.plotting_data[
trigger] # { run_number: ( [x_vals], [y_vals], [det_status], [phys_status] ) }
bad_ls_x = array.array('f')
bad_ls_y = array.array('f')
# Separate the good and bad LS points
if self.ls_options['rm_bad_beams']:
# Remove 'non-stable' beams LS
for run in data:
res = self.fitFinder.removeBadLS(data[run][0], data[run][1],
data[run][3])
data[run][0] = res[0]
data[run][1] = res[1]
bad_ls_x += res[2]
bad_ls_y += res[3]
elif self.ls_options['rm_bad_det']:
# Remove 'bad' detector LS
# NOTE: These LS are a super-set of the 'non-stable' LS
for run in data:
res = self.fitFinder.removeBadLS(data[run][0], data[run][1],
data[run][2])
data[run][0] = res[0]
data[run][1] = res[1]
bad_ls_x += res[2]
bad_ls_y += res[3]
skip_bad_ls_plot = (len(bad_ls_x) == 0 or len(bad_ls_y) == 0
or not self.ls_options['show_bad_ls'])
run_count = 0
num_pts = 0
for run in data:
x_pts, y_pts = self.fitFinder.removePoints(data[run][0],
data[run][1])
x_pts, y_pts = self.fitFinder.getGoodPoints(x_pts, y_pts)
num_pts += len(y_pts)
if len(data[run][0]) > 0:
run_count += 1
if num_pts < self.min_plot_pts:
#print "\tSkipping %s: Not enough plot points, %d" % (trigger,num_pts)
return False
if self.use_fit and not self.fits.has_key(trigger):
# No fit found for this plot
print "\tWARNING: Missing fit - %s" % trigger
missing_fit = True
# Find max and min values
maximumRR = array.array('f')
maximumVals = array.array('f')
minimumVals = array.array('f')
max_xaxis_val = 0
min_xaxis_val = 9999
max_yaxis_val = 0
cut_sigma = 6 # Sigma used to remove points from the plot points
display_sigma = 4 # Sigma used to cap max y-axis value
# Calculate the avg and std dev using only the upper half of points
# NOTE: This leverages the assumption that trigger rates increase with PU
# TODO: Only use the x_cut when we have some minimum number of plot points
xVals, yVals = self.combinePoints(data)
xVals, yVals = self.fitFinder.removePoints(xVals, yVals, 0)
x_cut = (max(xVals) - min(xVals)) / 2
xVals, yVals = self.combinePoints(data, x_cut)
avg_y, std_y = self.fitFinder.getSD(yVals)
# Remove points that are extremely far outside of the avg.
for run in data:
data[run][0], data[run][1] = self.fitFinder.getGoodPoints(
data[run][0], data[run][1], avg_y, std_y, cut_sigma)
# Recalculate the avg and std dev
xVals, yVals = self.combinePoints(data)
avg_y, std_y = self.fitFinder.getSD(yVals)
# Find minima and maxima so we create graphs of the right size
for run in data:
if len(data[run][0]) > 0:
maximumVals.append(max(data[run][0]))
minimumVals.append(min(data[run][0]))
tmp_max_y = max(data[run][1])
if abs(tmp_max_y - avg_y) < std_y * display_sigma:
# Don't let the maximum be set by rate 'spikes'
maximumRR.append(max(data[run][1]))
else:
maximumRR.append(avg_y + std_y * display_sigma)
if not skip_bad_ls_plot:
# TODO: Possibly apply same 'spike' check to the bad LS lists
maximumVals.append(max(bad_ls_x))
minimumVals.append(min(bad_ls_x))
maximumRR.append(max(bad_ls_y))
if len(maximumRR) > 0:
max_yaxis_val = max(maximumRR)
else:
print "\tERROR: Invalid boundary for plot axis!"
return False
if len(maximumVals) > 0:
max_xaxis_val = max(maximumVals)
min_xaxis_val = min(minimumVals)
else:
print "\tERROR: Invalid boundary for plot axis!"
return False
if max_xaxis_val == 0 or max_yaxis_val == 0:
print "\tERROR: Invalid boundary for plot axis!"
return False
canvas = TCanvas(self.var_X, self.var_Y, 1000, 600)
canvas.SetName(trigger + "_" + self.var_X + "_vs_" + self.var_Y)
if self.use_fit and not missing_fit:
plot_func_str = {}
func_str = {}
fit_func = {}
fit_mse = {}
for fit_type in self.fits[trigger]:
fit_params = self.fits[trigger][fit_type]
plot_func_str[fit_type], func_str[fit_type] = self.getFuncStr(
fit_params)
fit_func[fit_type] = TF1("Fit_" + trigger,
plot_func_str[fit_type], 0.,
1.1 * max_xaxis_val)
fit_mse[fit_type] = fit_params[5]
graphList = []
color_map = self.getColorMap()
leg_entries = 0
if self.color_by_fill:
leg_entries += self.fill_count
else:
leg_entries += run_count
if self.use_fit and not missing_fit:
if self.use_multi_fit:
leg_entries += len(self.fits[trigger].keys())
else:
leg_entries += 1
legend = self.getLegend(num_entries=leg_entries)
old_fill = -1
counter = 0
for run in sorted(data):
#data[run][0],data[run][1] = self.fitFinder.getGoodPoints(data[run][0],data[run][1])
num_LS = len(data[run][0])
if num_LS == 0: continue
graphList.append(TGraph(num_LS, data[run][0], data[run][1]))
graphColor = color_map[run]
graphList[-1].SetMarkerStyle(7)
graphList[-1].SetMarkerSize(1.0)
graphList[-1].SetLineColor(graphColor)
graphList[-1].SetFillColor(graphColor)
graphList[-1].SetMarkerColor(graphColor)
graphList[-1].SetLineWidth(2)
#graphList[-1].GetXaxis().SetTitle(self.name_X+" "+self.units_X)
graphList[-1].GetXaxis().SetTitle(self.label_X)
graphList[-1].GetXaxis().SetLimits(0, 1.1 * max_xaxis_val)
graphList[-1].GetYaxis().SetTitle(self.label_Y)
graphList[-1].GetYaxis().SetTitleOffset(1.2)
graphList[-1].SetMinimum(0)
graphList[-1].SetMaximum(1.2 * max_yaxis_val)
graphList[-1].SetTitle(trigger)
if counter == 0: graphList[-1].Draw("AP")
else: graphList[-1].Draw("P")
canvas.Update()
if self.bunch_map.has_key(run):
bunches = str(self.bunch_map[run])
else:
bunches = "-"
legendStr = ""
if self.color_by_fill:
new_fill = self.fill_map[run]
if new_fill != old_fill:
old_fill = new_fill
legendStr = "%s (%s b)" % (new_fill, bunches)
legend.AddEntry(graphList[-1], legendStr, "f")
else:
legendStr = "%s (%s b)" % (run, bunches)
legend.AddEntry(graphList[-1], legendStr, "f")
counter += 1
if self.use_fit and not missing_fit: # Display all the fit functions
color_counter = 0
for fit_type in sorted(self.fits[trigger]):
legend.AddEntry(
fit_func[fit_type],
"%s Fit ( %s \sigma )" % (fit_type, self.sigmas))
fit_func[fit_type].SetLineColor(
self.fit_color_list[color_counter %
len(self.fit_color_list)])
fit_func[fit_type].Draw("same")
color_counter += 1
if self.show_errors and not self.use_multi_fit: # Display the error band
fit_error_band = self.getErrorGraph(
fit_func[fit_type], fit_mse[fit_type])
fit_error_band.Draw("3")
if self.show_eq and not self.use_multi_fit: # Display the fit equation
func_leg = TLegend(.146, .71, .47, .769)
func_leg.SetHeader("f(x) = " + func_str[fit_type])
func_leg.SetFillColor(0)
#func_leg.SetFillColorAlpha(0,0.5)
func_leg.Draw()
canvas.Update()
if not skip_bad_ls_plot:
bad_ls_graph = TGraph(len(bad_ls_x), bad_ls_x, bad_ls_y)
bad_ls_graph.SetMarkerStyle(self.ls_options['bad_marker_style'])
bad_ls_graph.SetMarkerSize(self.ls_options['bad_marker_size'])
bad_ls_graph.SetMarkerColor(self.ls_options['bad_marker_color'])
bad_ls_graph.SetLineWidth(2)
bad_ls_graph.GetXaxis().SetTitle(self.label_X)
bad_ls_graph.GetXaxis().SetLimits(0, 1.1 * max_xaxis_val)
bad_ls_graph.GetYaxis().SetTitle(self.label_Y)
bad_ls_graph.SetMinimum(0)
bad_ls_graph.SetMaximum(1.2 * max_yaxis_val)
bad_ls_graph.SetTitle(trigger)
bad_ls_graph.Draw("P")
canvas.Update()
# draw text
latex = TLatex()
latex.SetNDC()
latex.SetTextColor(1)
latex.SetTextAlign(11)
latex.SetTextFont(62)
latex.SetTextSize(0.05)
latex.DrawLatex(0.15, 0.84, "CMS")
latex.SetTextSize(0.035)
latex.SetTextFont(52)
latex.DrawLatex(0.15, 0.80, "Rate Monitoring")
canvas.SetGridx(1)
canvas.SetGridy(1)
canvas.Update()
# Draw Legend
if self.color_by_fill:
legend.SetHeader("%s fills (%s runs):" %
(self.fill_count, run_count))
else:
legend.SetHeader("%s runs:" % (run_count))
legend.SetFillColor(0)
legend.Draw()
canvas.Update()
if self.save_root_file:
self.saveRootFile(canvas)
if self.save_png:
self.savePlot(trigger, canvas)
return True
line1, line2, line3, line4, line5, line6, line7, line8, line9, line10,
line11, line12, line13, line14, line15, line16, line17, line18, line19,
line20, line21, line22, line23, line24, line25, line26, line27, line28,
line29, line30, line31, line32, line33, line34, line35, line36, line37,
line38, line39, line40, line41, line42, line43, line44, line45, line46,
line47, line48, line49, line50, line51, line52, line53, line54, line55,
line56, line57, line58, line59, line60, line61, line62, line63, line64
]
for line in lineVector:
line.SetLineColor(ROOT.kBlack)
line.SetLineWidth(3)
line.Draw()
leg = TLegend(.64, .82, .68, .86)
leg.SetFillStyle(0)
leg.SetLineWidth(4)
leg.Draw()
text = TLatex(.41, .825, "NA49 Coverage")
text.SetTextColor(ROOT.kBlack)
text.SetNDC()
text.SetTextSize(1.4 / 30.)
text.SetTextAlign(11)
#text.DrawLatex(.48, .55, "#Square");
text.Draw()
c4.Print("plots/pionplus_NA49.pdf")
# raw_input("Please press enter to exit.")
extraText = ""
extraOverCmsTextSize = 0.76
extraTextFont = 52
if year == '2016':
lumiText = "35.9 fb^{#minus1} (13 TeV)"
if year == '2017':
lumiText = "41.50 fb^{#minus1} (13 TeV)"
if year == '2018':
lumiText = "59.74 fb^{#minus1} (13 TeV)"
lumiTextSize = 0.55
lumiTextOffset = 0.2
relExtraDY = 1.2
latex.SetTextAngle(0)
latex.SetTextColor(kBlack)
extraTextSize = extraOverCmsTextSize * cmsTextSize
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(lumiTextSize * t)
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(lumiTextSize * t)
latex.DrawLatex(1 - r, 1 - t + lumiTextOffset * t, lumiText)
latex.SetTextFont(cmsTextFont)
latex.SetTextAlign(11)
latex.SetTextSize(cmsTextSize * t)
latex.DrawLatex(1.15 * l, 1 - t + lumiTextOffset * t, cmsText)
def limit():
method = ''
channel = "bb"
if INCLUDEACC:
particleP = "X"
else:
particleP = "Z'"
particle = 'b#bar{b}'
multF = ZPTOBB
THEORY = ['bstar']
if INCLUDEACC: THEORY.append('SSM')
suffix = "_" + BTAGGING
if ISMC: suffix += "_MC"
if SY: suffix += "_comb"
#if method=="cls": suffix="_CLs"
if INCLUDEACC: suffix += "_acc"
if SY:
filename = "./combine/limits/bstar/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt"
else:
filename = "./combine/limits/bstar/" + BTAGGING + "/" + YEAR + "_M%d.txt"
if CATEGORY != "":
if SY:
filename = filename.replace(
BTAGGING + "/combined_run2/",
BTAGGING + "/single_category/combined_run2/" + CATEGORY + "_")
else:
filename = filename.replace(
BTAGGING + "/",
BTAGGING + "/single_category/" + CATEGORY + "_")
suffix += "_" + CATEGORY
if ISMC: filename = filename.replace(".txt", "_MC.txt")
mass, val = fillValues(filename)
#print "mass =",mass
#print "val =", val
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
Sign = TGraph()
pVal = TGraph()
Best = TGraphAsymmErrors()
Theory = {}
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
if INCLUDEACC:
acc_factor = ACCEPTANCE[m]
else:
acc_factor = 1.
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF * acc_factor)
Exp0s.SetPoint(n, m, val[m][3] * multF * acc_factor)
Exp1s.SetPoint(n, m, val[m][3] * multF * acc_factor)
Exp1s.SetPointError(n, 0., 0.,
(val[m][3] - val[m][2]) * multF * acc_factor,
(val[m][4] - val[m][3]) * multF * acc_factor)
Exp2s.SetPoint(n, m, val[m][3] * multF * acc_factor)
Exp2s.SetPointError(n, 0., 0.,
(val[m][3] - val[m][1]) * multF * acc_factor,
(val[m][5] - val[m][3]) * multF * acc_factor)
if len(val[m]) > 6: Sign.SetPoint(n, m, val[m][6])
if len(val[m]) > 7: pVal.SetPoint(n, m, val[m][7])
if len(val[m]) > 8: Best.SetPoint(n, m, val[m][8])
if len(val[m]) > 10:
Best.SetPointError(n, 0., 0., abs(val[m][9]), val[m][10])
for t in THEORY:
Theory[t] = TGraphAsymmErrors()
if 'bstar' == t:
x = []
y = []
with open('bstar_deta1p1_lhc13TeV2.txt') as fin:
for line in fin.readlines():
x.append(float(line.split()[0]))
y.append(float(line.split()[1]) * 1000)
for ind in range(len(x)):
Theory[t].SetPoint(ind + 1, x[ind], y[ind])
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
continue
Xs_dict = HVT[t]['Z']['XS'] if t != 'SSM' else SSM['Z']
for m in sorted(Xs_dict.keys()):
if INCLUDEACC and t != 'SSM':
acc_factor = ACCEPTANCE[m]
else:
acc_factor = 1.
if m < SIGNALS[0] or m > SIGNALS[-1]: continue
#if m < mass[0] or m > mass[-1]: continue
#if t!= 'SSM' and m>4500: continue ## I don't have the higher mass xs
if m > 4500: continue
XsZ, XsZ_Up, XsZ_Down = 0., 0., 0.
if t != 'SSM':
XsZ = 1000. * HVT[t]['Z']['XS'][m] * SSM["BrZ"][
m] #assuming the same BR as the SSM Z' one
XsZ_Up = XsZ * (1. + math.hypot(HVT[t]['Z']['QCD'][m][0] - 1.,
HVT[t]['Z']['PDF'][m][0] - 1.))
XsZ_Down = XsZ * (1. -
math.hypot(1. - HVT[t]['Z']['QCD'][m][0],
1. - HVT[t]['Z']['PDF'][m][0]))
else:
XsZ = 1000. * SSM['Z'][m] * SSM["BrZ"][m]
XsZ_Up = XsZ * (1. +
math.hypot(HVT['A1']['Z']['QCD'][m][0] - 1.,
HVT['A1']['Z']['PDF'][m][0] - 1.))
XsZ_Down = XsZ * (1. -
math.hypot(1. - HVT['A1']['Z']['QCD'][m][0],
1. - HVT['A1']['Z']['PDF'][m][0]))
n = Theory[t].GetN()
Theory[t].SetPoint(n, m, XsZ * acc_factor)
Theory[t].SetPointError(n, 0., 0., (XsZ - XsZ_Down) * acc_factor,
(XsZ_Up - XsZ) * acc_factor)
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
#Theory[t].SetLineStyle(7)
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp1s.SetFillColor(417) #kGreen+1
Exp1s.SetLineColor(417) #kGreen+1
Exp2s.SetFillColor(800) #kOrange
Exp2s.SetLineColor(800) #kOrange
Exp2s.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle(
"#sigma(" + particleP + ") #bf{#it{#Beta}}(" + particleP +
" #rightarrow " + particle +
"){} (fb)".format(" #times #Alpha" if INCLUDEACC else ""))
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Sign.SetLineWidth(2)
Sign.SetLineColor(629)
Sign.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1)
Sign.GetYaxis().SetTitle("Significance")
pVal.SetLineWidth(2)
pVal.SetLineColor(629)
pVal.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
pVal.GetXaxis().SetTitleSize(pVal.GetXaxis().GetTitleSize() * 1.1)
pVal.GetYaxis().SetTitle("local p-Value")
Best.SetLineWidth(2)
Best.SetLineColor(629)
Best.SetFillColor(629)
Best.SetFillStyle(3003)
Best.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Best.GetXaxis().SetTitleSize(Best.GetXaxis().GetTitleSize() * 1.1)
Best.GetYaxis().SetTitle("Best Fit (pb)")
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
#c1.GetPad(0).SetGridx()
#c1.GetPad(0).SetGridy()
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
Exp0s.Draw("SAME, L")
if not options.blind: Obs0s.Draw("SAME, L")
#setHistStyle(Exp2s)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
if INCLUDEACC:
Exp2s.GetYaxis().SetRangeUser(0.05, 5.e3)
else:
Exp2s.GetYaxis().SetRangeUser(0.1, 5.e3)
#else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2)
#Exp2s.GetXaxis().SetRangeUser(mass[0], min(mass[-1], MAXIMUM[channel] if channel in MAXIMUM else 1.e6))
Exp2s.GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1])
#drawAnalysis(channel)
drawAnalysis("")
#drawRegion(channel, True)
drawRegion("", True)
#drawCMS(LUMI, "Simulation Preliminary") #Preliminary
if CATEGORY == "":
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawCMS(LUMI, "", suppressCMS=True)
else:
#drawCMS(LUMI, "Work in Progress, "+CAT_LABELS[CATEGORY], suppressCMS=True)
drawCMS(LUMI, CAT_LABELS[CATEGORY], suppressCMS=True)
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.98, top)
#leg = TLegend(0.45, top-nitems*0.3/5., 0.98, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
for t in THEORY:
leg.AddEntry(Theory[t], theoryLabel[t], "fl")
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
latex.SetTextFont(42)
#latex.DrawLatex(0.66, leg.GetY1()-0.045, particleP+" #rightarrow "+particle+"h")
leg2 = TLegend(0.12, 0.225 - 2 * 0.25 / 5., 0.65, 0.225)
leg2.SetBorderSize(0)
leg2.SetFillStyle(0) #1001
leg2.SetFillColor(0)
c1.GetPad(0).RedrawAxis()
leg2.Draw()
if not options.blind: Obs0s.Draw("SAME, L")
c1.GetPad(0).Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".png")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".C")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix +
".root")
for t in THEORY:
print "Model", t, ":",
for m in range(mass[0], mass[-1], 1):
if not (Theory[t].Eval(m) > Obs0s.Eval(m)) == (
Theory[t].Eval(m + 1) > Obs0s.Eval(m + 1)):
print m,
print ""
return ##FIXME
# ---------- Significance ----------
c2 = TCanvas("c2", "Significance", 800, 600)
c2.cd()
c2.GetPad(0).SetTopMargin(0.06)
c2.GetPad(0).SetRightMargin(0.05)
c2.GetPad(0).SetTicks(1, 1)
c2.GetPad(0).SetGridx()
c2.GetPad(0).SetGridy()
Sign.GetYaxis().SetRangeUser(0., 5.)
Sign.Draw("AL3")
#drawCMS(LUMI, "Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c2.Print("combine/plotsLimit/Significance/" + YEAR + suffix + ".png")
c2.Print("combine/plotsLimit/Significance/" + YEAR + suffix + ".pdf")
# c2.Print("plotsLimit/Significance/"+YEAR+suffix+".root")
# c2.Print("plotsLimit/Significance/"+YEAR+suffix+".C")
# ---------- p-Value ----------
c3 = TCanvas("c3", "p-Value", 800, 600)
c3.cd()
c3.GetPad(0).SetTopMargin(0.06)
c3.GetPad(0).SetRightMargin(0.05)
c3.GetPad(0).SetTicks(1, 1)
c3.GetPad(0).SetGridx()
c3.GetPad(0).SetGridy()
c3.GetPad(0).SetLogy()
pVal.Draw("AL3")
pVal.GetYaxis().SetRangeUser(2.e-7, 0.5)
ci = [
1., 0.317310508, 0.045500264, 0.002699796, 0.00006334, 0.000000573303,
0.000000001973
]
line = TLine()
line.SetLineColor(922)
line.SetLineStyle(7)
text = TLatex()
text.SetTextColor(922)
text.SetTextSize(0.025)
text.SetTextAlign(12)
for i in range(1, len(ci) - 1):
line.DrawLine(pVal.GetXaxis().GetXmin(), ci[i] / 2,
pVal.GetXaxis().GetXmax(), ci[i] / 2)
text.DrawLatex(pVal.GetXaxis().GetXmax() * 1.01, ci[i] / 2,
"%d #sigma" % i)
#drawCMS(LUMI, "Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c3.Print("combine/plotsLimit/pValue/" + YEAR + suffix + ".png")
c3.Print("combine/plotsLimit/pValue/" + YEAR + suffix + ".pdf")
# c3.Print("plotsLimit/pValue/"+YEAR+suffix+".root")
# c3.Print("plotsLimit/pValue/"+YEAR+suffix+".C")
# --------- Best Fit ----------
c4 = TCanvas("c4", "Best Fit", 800, 600)
c4.cd()
c4.GetPad(0).SetTopMargin(0.06)
c4.GetPad(0).SetRightMargin(0.05)
c4.GetPad(0).SetTicks(1, 1)
c4.GetPad(0).SetGridx()
c4.GetPad(0).SetGridy()
Best.Draw("AL3")
#drawCMS(LUMI, "Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c4.Print("combine/plotsLimit/BestFit/" + YEAR + suffix + ".png")
c4.Print("combine/plotsLimit/BestFit/" + YEAR + suffix + ".pdf")
# c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".C")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if 'ah' in channel:
outFile = TFile("bands.root", "RECREATE")
outFile.cd()
pVal.Write("graph")
Best.Write("best")
outFile.Close()
def PoissonError(nObs, ErrorType, plot=""):
# Prepare histograms
LambdaMin = 0.
LambdaMax = nObs + 6*np.sqrt(nObs)
Nsteps = 1000
h_likelihood = TH1D( "h_likelihood","",Nsteps,LambdaMin,LambdaMax )
h_2loglik = TH1D( "h_2loglik","",Nsteps,LambdaMin,LambdaMax )
h_pdf_full = TH1D( "h_pdf_full","",Nsteps,LambdaMin,LambdaMax )
IntFraction =ROOT.Math.gaussian_cdf(-1,1,0)
# loop over possible Lambda values
for iBin in range(1,Nsteps+1):
Lambda=h_likelihood.GetBinCenter(iBin)
PoissonProb = TMath.Poisson(nObs,Lambda)
LogLikelihood = -2.*TMath.Log(PoissonProb)
h_likelihood.Fill( Lambda,PoissonProb )
h_2loglik.Fill( Lambda, LogLikelihood )
h_pdf_full.Fill( Lambda, PoissonProb*1. )
# get characteristic values
bin_central = h_2loglik.GetMinimumBin()
LoglikMin = h_2loglik.GetBinContent(bin_central)
Lambda_central = h_2loglik.GetBinCenter(bin_central)
LambdaLow = -1.
LambdaUp = -1.
if ErrorType=="ClassicalCentral": # Frequentist
NobsMax = nObs+100
for iBin in range(1,h_pdf_full.GetNbinsX()+1):
Lambda = h_pdf_full.GetBinCenter(iBin)
PoissonSumLow = 0.
PoissonSumUp = 0.
for i in range(nObs,NobsMax+1):
PoissonSumLow += TMath.Poisson(i,Lambda)
if( PoissonSumLow>IntFraction and LambdaLow<0 ):
LambdaLow = Lambda
for i in range(0,nObs+1):
PoissonSumUp += TMath.Poisson(i,Lambda)
if( PoissonSumUp<IntFraction and LambdaUp<0 ):
LambdaUp = Lambda
cvs = TCanvas("Standard Canvas","",600,600)
cvs.Divide(1,2)
cvs.cd(1)
hLow = TH1D("hLow","CLASSICAL CENTRAL",1000,0,17)
for i in range(1,hLow.GetNbinsX()+1):
hLow.SetBinContent(i, TMath.Poisson( hLow.GetBinCenter(i),LambdaLow ))
hLow.SetLineColor(1)
hLow.GetXaxis().SetTitle(r"#mu")
h_conf = hLow.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)<=nObs): h_conf.SetBinContent(i, 0)
h_conf.SetFillColorAlpha(9,0.5)
h_conf.SetLineWidth(1)
h_conf.SetLineColor(1)
LowLine = TLine(LambdaLow,0,LambdaLow,hLow.GetBinContent(hLow.FindBin(LambdaLow)))
hLow.SetStats(kFALSE)
hLow.Draw("l")
h_conf.Draw("l same")
LowLine.Draw()
text = TLatex()
text.SetTextSize(0.5)
text.SetTextColor(1)
text.DrawLatex(0.5,0.8, "LOL")
cvs.cd(2)
hUp = TH1D("hUp","",1000,0,17)
for i in range(1,hUp.GetNbinsX()+1):
hUp.SetBinContent(i, TMath.Poisson( hLow.GetBinCenter(i),LambdaUp ))
hUp.SetLineColor(1)
hUp.GetXaxis().SetTitle(r"#mu")
h_conf1 = hUp.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>=nObs): h_conf1.SetBinContent(i, 0)
h_conf1.SetFillColorAlpha(9,0.5)
h_conf1.SetLineWidth(1)
h_conf1.SetLineColor(1)
UpLine = TLine(LambdaUp,0,LambdaUp,hUp.GetBinContent(hUp.FindBin(LambdaUp)))
hUp.SetStats(kFALSE)
hUp.Draw("l")
h_conf1.Draw("l same")
UpLine.Draw()
cvs.cd()
leg1 = TLegend(0.35,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
if ErrorType=="LikelihoodRatio":
for i in range(1,h_2loglik.GetNbinsX()+1):
if (h_2loglik.GetBinCenter(i)<LoglikMin and h_2loglik.GetBinContent(i)-LoglikMin>=1.): LambdaLow=h_2loglik.GetBinCenter(i)
if (h_2loglik.GetBinCenter(i)>LoglikMin and h_2loglik.GetBinContent(i)-LoglikMin<=1.): LambdaUp=h_2loglik.GetBinCenter(i)
cvs = TCanvas("Standard Canvas","",600,400)
cvs.cd()
LowLine = TLine(LambdaLow,2,LambdaLow,h_2loglik.GetBinContent(h_2loglik.FindBin(LambdaLow))); LowLine.SetLineWidth(1)
UpLine = TLine(LambdaUp,2,LambdaUp,h_2loglik.GetBinContent(h_2loglik.FindBin(LambdaUp))); UpLine.SetLineWidth(1)
ObsLine = TLine(nObs,2,nObs,h_2loglik.GetBinContent(h_2loglik.FindBin(nObs))); ObsLine.SetLineWidth(1); ObsLine.SetLineColor(2); ObsLine.SetLineStyle(7)
h_2loglik.SetFillColor(0)
h_2loglik.SetStats(kFALSE)
h_2loglik.SetTitle("LIKELIHOOD RATIO")
h_2loglik.SetAxisRange(0.8,8,"X")
h_2loglik.SetAxisRange(2,7,"Y")
h_2loglik.Draw("hist lp")
h_2loglik.GetXaxis().SetTitle(r"#mu")
h_conf = h_2loglik.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>LambdaLow and h_conf.GetBinCenter(i)<LambdaUp): h_conf.SetBinContent(i,0)
h_conf.SetFillColor(9)
h_conf.Draw("hist same")
leg1 = TLegend(0.55,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
LowLine.Draw()
UpLine.Draw()
ObsLine.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
if ErrorType=="BayesCentral":
# Work on likelihood as PDF
Integral = h_likelihood.Integral()
for i in range(1,h_likelihood.GetNbinsX()+1):
h_likelihood.SetBinContent(i,h_likelihood.GetBinContent(i)/Integral)
# Sum over bins until reached CL
Sum_low = 0.
Sum_up = 0.
for i in range(1,bin_central+1):
if Sum_low<=IntFraction:
Sum_low += h_likelihood.GetBinContent(i)
LambdaLow = h_likelihood.GetBinCenter(i)
for i in range(h_likelihood.GetNbinsX(),bin_central,-1):
if Sum_up<=IntFraction:
Sum_up += h_likelihood.GetBinContent(i)
LambdaUp = h_likelihood.GetBinCenter(i)
cvs = TCanvas("Standard Canvas","",600,400)
cvs.cd()
LowLine = TLine(LambdaLow,0,LambdaLow,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaLow))); LowLine.SetLineWidth(1)
UpLine = TLine(LambdaUp,0,LambdaUp,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaUp))); UpLine.SetLineWidth(1)
ObsLine = TLine(nObs,0,nObs,h_likelihood.GetBinContent(h_2loglik.FindBin(nObs))); ObsLine.SetLineWidth(1); ObsLine.SetLineColor(2); ObsLine.SetLineStyle(7)
h_likelihood.SetFillColor(0)
h_likelihood.SetStats(kFALSE)
h_likelihood.SetTitle("BAYES CENTRAL")
#h_likelihood.SetAxisRange(0.8,8,"X")
#h_likelihood.SetAxisRange(2,7,"Y")
h_likelihood.Draw("hist lp")
h_likelihood.GetXaxis().SetTitle(r"#mu")
h_conf = h_likelihood.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>LambdaLow and h_conf.GetBinCenter(i)<LambdaUp): h_conf.SetBinContent(i,0)
h_conf.SetFillColor(9)
h_conf.Draw("hist same")
leg1 = TLegend(0.55,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
LowLine.Draw()
UpLine.Draw()
ObsLine.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
if ErrorType=="BayesHDI":
# Work on likelihood as PDF
Integral = h_likelihood.Integral()
for i in range(1,h_likelihood.GetNbinsX()+1):
h_likelihood.SetBinContent(i,h_likelihood.GetBinContent(i)/Integral)
Area = 0.
RightIndex=bin_central
while Area<=(1-2*IntFraction):
RightIndex+=1
# find corresponding bin on left side
for i in range(1,bin_central):
if h_likelihood.GetBinContent(i)<=h_likelihood.GetBinContent(RightIndex):
LeftIndex=i
Area = h_likelihood.Integral( LeftIndex,RightIndex )
LambdaLow = h_likelihood.GetBinCenter(LeftIndex)
LambdaUp = h_likelihood.GetBinCenter(RightIndex)
cvs = TCanvas("Standard Canvas","",600,400)
cvs.cd()
LowLine = TLine(LambdaLow,0,LambdaLow,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaLow))); LowLine.SetLineWidth(1)
UpLine = TLine(LambdaUp,0,LambdaUp,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaUp))); UpLine.SetLineWidth(1)
ObsLine = TLine(nObs,0,nObs,h_likelihood.GetBinContent(h_2loglik.FindBin(nObs))); ObsLine.SetLineWidth(1); ObsLine.SetLineColor(2); ObsLine.SetLineStyle(7)
h_likelihood.SetFillColor(0)
h_likelihood.SetStats(kFALSE)
h_likelihood.SetTitle("BAYES SHORTEST")
h_likelihood.Draw("hist lp")
h_likelihood.GetXaxis().SetTitle(r"#mu")
h_conf = h_likelihood.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>LambdaLow and h_conf.GetBinCenter(i)<LambdaUp): h_conf.SetBinContent(i,0)
h_conf.SetFillColor(9)
h_conf.Draw("hist same")
leg1 = TLegend(0.55,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
LowLine.Draw()
UpLine.Draw()
ObsLine.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
return [LambdaLow, LambdaUp]
return
