def showENC():
fname1 = '/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat'
tree1 = TTree()
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan1.dat',
'idX/i:vL/F:vH:A:D/i:R:W')
tree1.ReadFile(
'/data/repos/Mic4Test_KC705/Software/Analysis/data/ENC/ENC_Chip5Col12_scan2_mod.dat'
)
tree1.Show(500)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', 50, 0.12, 0.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
# g1.SetMarkerColor(3)
# g1.SetLineColor(3)
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
# p1.Fit(fun1)
# fun1a = p1.GetFunction('fun1')
fun1a.SetLineColor(2)
# p1.Draw("Esame")
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
lt = TLatex()
lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
def limitCompare(method):
signal, particle, particleP = "XVH", "V", "V'"
channels = [
"ah", "wrhpbb", "wrhpb", "wrlpbb", "wrlpb", "zrhpbb", "zrhpb",
"zrlpbb", "zrlpb"
]
colors = [
1, 610 + 4, 632, 800 + 7, 800, 416 + 1, 860 + 10, 600, 616, 921, 922
]
masses, vals, graphs = {}, {}, {}
for j, c in enumerate(channels):
masses[c], vals[c] = fillValues("./combine/" + method + "/" + signal +
c + "_M%d.txt")
graphs[c] = TGraph()
n = 0
#print vals[c]
for i, m in enumerate(masses[c]):
#if not signals[i] >= 1000: continue
graphs[c].SetPoint(n, m, vals[c][m][3])
n = n + 1
graphs[c].SetLineColor(colors[j])
graphs[c].SetLineWidth(3)
graphs[c].Draw("SAME, L")
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).SetTicks(1, 1)
c1.GetPad(0).SetLogy()
for c in channels:
graphs[c].Draw("AL" if 'ah' in c else "SAME, L")
graphs[channels[0]].GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1])
graphs[channels[0]].GetYaxis().SetRangeUser(0.25, 2.5e4)
graphs[channels[0]].GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
graphs[channels[0]].GetYaxis().SetTitle(
"#sigma(" + particleP + ") #bf{#it{#Beta}}(" + particleP +
" #rightarrow " + particle +
"H) #bf{#it{#Beta}}(H #rightarrow bb) (fb)")
drawAnalysis(signal)
#drawRegion(signal, True)
drawCMS(LUMI, "Preliminary")
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
top = 0.9
leg = TLegend(0.4, top - len(channels) * 0.2 / 5., 0.99, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL expected limits")
for c in channels:
leg.AddEntry(graphs[c], getChannel(c), "l")
leg.Draw()
c1.Update()
c1.Print("plotsLimit/Multi.png")
c1.Print("plotsLimit/Multi.pdf")
hps_event = HpsEvent()
b_hps_event = dst_tree.GetBranch("Event")
b_hps_event.SetAddress(AddressOf(hps_event))
canvas = TCanvas("canvas", "Extrapolation Analysis", 800, 800)
h_track_d0 = TH1F("h_track_d0", "Track D0", 100, -1.5, 1.5)
h_track_d0.SetLineColor(kAzure + 2)
h_track_d0.SetMarkerColor(kAzure + 2)
h_gbl_track_d0 = TH1F("h_gbl_track_d0", "GBL Track D0", 100, -1.5, 1.5)
h_gbl_track_d0.SetLineColor(kRed + 1)
h_gbl_track_d0.SetMarkerColor(kRed + 1)
legend = TLegend(0.7, 0.8, 0.9, 0.9, "", "brNDC")
legend.SetBorderSize(0)
legend.SetFillStyle(0)
legend.AddEntry(h_track_d0, "Seed Tracker", "lp")
legend.AddEntry(h_gbl_track_d0, "GBL Track", "lp")
h_gbl_target_residuals_bp_vs_p_5hit = TH2F(
"h_gbl_target_residuals_bp_vs_p_5hit",
"Bend Plane Residuals at the Target - 5 Hit Tracks", 10, 0, 2.5, 240, -30,
30)
h_gbl_target_residuals_bp_vs_p_6hit = TH2F(
"h_gbl_target_residuals_bp_vs_p_6hit",
"Bend Plane Residuals at the Target - 6 Hit Tracks", 10, 0, 2.5, 240, -30,
30)
h_gbl_sp_residuals_bp_vs_p_5hit = TH2F(
"h_gbl_sp_residuals_bp_vs_p_5hit",
"Bend Plane Residuals at Scoring Plane vs Momentum - 5 Hit Tracks", 10, 0,
class DataMCPlot(object):
'''Handles a Data vs MC plot.
Features a list of histograms (some of them being stacked),
and several Drawing functions.
'''
_f_keeper = {}
_t_keeper = {}
def __init__(self, name, signal2show):
self.histosDict = {}
self.histos = []
self.supportHist = None
self.name = name
self.stack = None
self.legendOn = True
self.legend = None
self.legendBorders = 0.20, 0.43, 0.44, 0.86
self.legendPos = 'left'
# self.lastDraw = None
# self.lastDrawArgs = None
self.nostack = None
self.blindminx = None
self.blindmaxx = None
self.groups = {}
self.axisWasSet = False
self.histPref = histPref
self.signal2show = signal2show
def __contains__(self, name):
return name in self.histosDict
def __getitem__(self, name):
# if name in self.histosDict:
return self.histosDict[name]
def readTree(self, file_name, tree_name='tree'):
'''Cache files/trees'''
if file_name in self.__class__._t_keeper:
ttree = self.__class__._t_keeper[file_name]
else:
tfile = self.__class__._f_keeper[file_name] = TFile.Open(file_name)
ttree = self.__class__._t_keeper[file_name] = tfile.Get(tree_name)
gROOT.cd()
return ttree
def Blind(self, minx, maxx, blindStack):
self.blindminx = minx
self.blindmaxx = maxx
if self.stack and blindStack:
self.stack.Blind(minx, maxx)
if self.nostack:
for hist in self.nostack:
hist.Blind(minx, maxx)
def AddHistogram(self, name, histo, layer=0, legendLine=None, stack=True):
'''Add a ROOT histogram, with a given name.
Histograms will be drawn by increasing layer.'''
tmp = Histogram(name, histo, layer, legendLine, stack=stack)
self.histos.append(tmp)
self.histosDict[name] = tmp
return tmp
def Group(self, groupName, namesToGroup, layer=None, style=None):
'''Group all histos with names in namesToGroup into a single
histo with name groupName. All histogram properties are taken
from the first histogram in namesToGroup.
See UnGroup as well
'''
groupHist = None
realNames = []
actualNamesInGroup = []
for name in namesToGroup:
hist = self.histosDict.get(name, None)
if hist is None:
print 'warning, no histo with name', name
continue
if groupHist is None:
groupHist = hist.Clone(groupName)
self.histos.append(groupHist)
self.histosDict[groupName] = groupHist
else:
groupHist.Add(hist)
actualNamesInGroup.append(name)
realNames.append(hist.realName)
hist.on = False
if groupHist:
self.groups[groupName] = actualNamesInGroup
groupHist.realName = ','.join(realNames)
if style is not None:
groupHist.SetStyle(style)
self._ApplyPrefs()
def UnGroup(self, groupName):
'''Ungroup groupName, recover the histograms in the group'''
group = self.groups.get(groupName, None)
if group is None:
print groupName, 'is not a group in this plot.'
return
for name in group:
self.histosDict[name].on = True
self.histosDict[groupName].on = False
def Replace(self, name, pyhist):
'''Not very elegant... should have a clone function in Histogram...'''
oldh = self.histosDict.get(name, None)
if oldh is None:
print 'histogram', name, 'does not exist, cannot replace it.'
return
pythist = copy.deepcopy(pyhist)
pythist.layer = oldh.layer
pythist.stack = oldh.stack
pythist.name = oldh.name
pythist.legendLine = oldh.legendLine
pythist.SetStyle(oldh.style)
pythist.weighted.SetFillStyle(oldh.weighted.GetFillStyle())
index = self.histos.index(oldh)
self.histosDict[name] = pythist
self.histos[index] = pythist
def _SortedHistograms(self, reverse=False):
'''Returns the histogram dictionary, sorted by increasing layer,
excluding histograms which are not "on".
This function is used in all the Draw functions.'''
byLayer = sorted(self.histos, key=attrgetter('layer'))
byLayerOn = [hist for hist in byLayer if (hist.on is True)]
if reverse:
byLayerOn.reverse()
return byLayerOn
def Hist(self, histName):
'''Returns a histogram.
Print the DataMCPlot object to see which histograms are available.'''
if self.histosDict.has_key(histName):
return self.histosDict[histName]
else:
return None
def DrawNormalized(self, opt=''):
'''All histograms are drawn as PDFs, even the stacked ones'''
same = ''
for hist in self._SortedHistograms():
hist.obj.DrawNormalized(same + opt)
if same == '':
same = 'same'
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawNormalized'
# self.lastDrawArgs = [ opt ]
def Draw(self, opt=''):
'''All histograms are drawn.'''
same = ''
self.supportHist = None
for hist in self._SortedHistograms():
if self.supportHist is None:
self.supportHist = hist
hist.Draw(same + opt)
if same == '':
same = 'same'
yaxis = self.supportHist.GetYaxis()
yaxis.SetRangeUser(0.01, ymax(self._SortedHistograms()))
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'Draw'
# self.lastDrawArgs = [ opt ]
def CreateLegend(self, ratio=False, print_norm=False):
if self.legend is None:
self.legend = TLegend(*self.legendBorders)
self.legend.SetFillColor(0)
self.legend.SetFillStyle(0)
self.legend.SetLineColor(0)
else:
self.legend.Clear()
hists = self._SortedHistograms(reverse=True)
if ratio:
hists = hists[:-1] # removing the last histo.
for index, hist in enumerate(hists):
if print_norm:
if not hist.legendLine:
hist.legendLine = hist.name
hist.legendLine += ' ({norm:.1f})'.format(norm=hist.Yield())
if hist.name.find('LQ')!=-1 or hist.name.find('Zprime')!=-1:
if hist.name in self.signal2show:
hist.AddEntry(self.legend)
else:
hist.AddEntry(self.legend)
def DrawLegend(self, ratio=False, print_norm=False):
'''Draw the legend.'''
if self.legendOn:
self.CreateLegend(ratio=ratio, print_norm=print_norm)
self.legend.Draw('same')
def DrawRatio(self, opt=''):
'''Draw ratios : h_i / h_0.
h_0 is the histogram with the smallest layer, and h_i, i>0 are the other histograms.
if the DataMCPlot object contains N histograms, N-1 ratio plots will be drawn.
To take another histogram as the denominator, change the layer of this histogram by doing:
dataMCPlot.Hist("histName").layer = -99 '''
same = ''
denom = None
self.ratios = []
for hist in self._SortedHistograms():
if denom == None:
denom = hist
continue
ratio = copy.deepcopy(hist)
ratio.obj.Divide(denom.obj)
ratio.obj.Draw(same)
self.ratios.append(ratio)
if same == '':
same = 'same'
self.DrawLegend(ratio=True)
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawRatio'
# self.lastDrawArgs = [ opt ]
def DrawDataOverMCMinus1(self, ymin=-0.5, ymax=0.5):
stackedHists = []
dataHist = None
for hist in self._SortedHistograms():
if hist.stack is False:
dataHist = hist
continue
stackedHists.append(hist)
self._BuildStack(stackedHists, ytitle='Data/MC')
mcHist = self.stack.totalHist
self.dataOverMCHist = copy.deepcopy(dataHist)
# self.dataOverMCHist.Add(mcHist, -1)
self.dataOverMCHist.Divide(mcHist)
self.dataOverMCHist.Draw()
yaxis = self.dataOverMCHist.GetYaxis()
yaxis.SetRangeUser(ymin + 1., ymax + 1.)
yaxis.SetTitle('Data/MC')
yaxis.SetNdivisions(5)
fraclines = 0.2
if ymax <= 0.2 or ymin >= -0.2:
fraclines = 0.1
self.DrawRatioLines(self.dataOverMCHist, fraclines, 1.)
if TPad.Pad():
TPad.Pad().Update()
def DrawRatioStack(self, opt='',
xmin=None, xmax=None, ymin=None, ymax=None):
'''Draw ratios.
The stack is considered as a single histogram.'''
denom = None
# import pdb; pdb.set_trace()
histForRatios = []
denom = None
for hist in self._SortedHistograms():
if hist.stack is False:
# if several unstacked histograms, the highest layer is used
denom = hist
continue
histForRatios.append(hist)
self._BuildStack(histForRatios, ytitle='MC/Data')
self.stack.Divide(denom.obj)
if self.blindminx and self.blindmaxx:
self.stack.Blind(self.blindminx, self.blindmaxx)
self.stack.Draw(opt,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax)
self.ratios = []
for hist in self.nostack:
if hist is denom:
continue
ratio = copy.deepcopy(hist)
ratio.obj.Divide(denom.obj)
ratio.obj.Draw('same')
self.ratios.append(ratio)
self.DrawLegend(ratio=True)
self.DrawRatioLines(denom, 0.2, 1)
if TPad.Pad():
TPad.Pad().Update()
def DrawNormalizedRatioStack(self, opt='',
xmin=None, xmax=None,
ymin=None, ymax=None):
'''Draw ratios.
The stack is considered as a single histogram.
All histograms are normalized before computing the ratio'''
denom = None
histForRatios = []
for hist in self._SortedHistograms():
# taking the first histogram (lowest layer)
# as the denominator histogram.
if denom == None:
denom = copy.deepcopy(hist)
continue
# other histograms will be divided by the denominator
histForRatios.append(hist)
self._BuildStack(histForRatios, ytitle='MC p.d.f. / Data p.d.f.')
self.stack.Normalize()
denom.Normalize()
self.stack.Divide(denom.weighted)
self.stack.Draw(opt,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax)
self.ratios = []
for hist in self.nostack:
# print 'nostack ', hist
ratio = copy.deepcopy(hist)
ratio.Normalize()
ratio.obj.Divide(denom.weighted)
ratio.obj.Draw('same')
self.ratios.append(ratio)
self.DrawLegend(ratio=True)
self.DrawRatioLines(denom, 0.2, 1)
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawNormalizedRatioStack'
# self.lastDrawArgs = [ opt ]
def DrawRatioLines(self, hist, frac=0.2, y0=1.):
'''Draw a line at y = 1, at 1+frac, and at 1-frac.
hist is used to get the x axis range.'''
xmin = hist.obj.GetXaxis().GetXmin()
xmax = hist.obj.GetXaxis().GetXmax()
line = TLine()
line.DrawLine(xmin, y0, xmax, y0)
line.SetLineStyle(2)
line.DrawLine(xmin, y0+frac, xmax, y0+frac)
line.DrawLine(xmin, y0-frac, xmax, y0-frac)
def GetStack(self):
'''Returns stack; builds stack if not there yet'''
if not self.stack:
self._BuildStack(self._SortedHistograms(), ytitle='Events')
return self.stack
def returnTotal(self):
return self.stack.totalHist
def DrawStack(self, opt='',
xmin=None, xmax=None, ymin=None, ymax=None, factor=None, print_norm=False):
'''Draw all histograms, some of them in a stack.
if Histogram.stack is True, the histogram is put in the stack.'''
self._BuildStack(self._SortedHistograms(), ytitle='Events')
same = 'same'
if len(self.nostack) == 0:
same = ''
self.supportHist = None
for hist in self.nostack:
# hist.Draw()
if not self.supportHist:
self.supportHist = hist
self.stack.Draw(opt+same,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax, factor=factor)
if self.supportHist is None:
self.supportHist = self.stack.totalHist
if not self.axisWasSet:
mxsup = self.supportHist.weighted.GetBinContent(
self.supportHist.weighted.GetMaximumBin()
)
mxstack = self.stack.totalHist.weighted.GetBinContent(
self.stack.totalHist.weighted.GetMaximumBin()
)
mx = max(mxsup, mxstack)
if ymin is None:
ymin = 0.01
if ymax is None:
ymax = mx*1.3
self.supportHist.GetYaxis().SetRangeUser(ymin, ymax)
self.axisWasSet = True
for hist in self.nostack:
# import pdb; pdb.set_trace()
if self.blindminx:
hist.Blind(self.blindminx, self.blindmaxx)
# hist.Draw('same')
# print 'Check', hist.name, self.signal2show
if hist.name.find('LQ')!=-1 or hist.name.find('Zprime')!=-1:
if hist.name in self.signal2show:
# print 'PASS!!!!!!!!!!!!!!!!!!!!!!!'
hist.Draw('same')
else:
hist.Draw('same')
if self.supportHist.weighted.GetMaximumBin() < self.supportHist.weighted.GetNbinsX()/2:
# self.legendBorders = 0.62, 0.46, 0.88, 0.89
self.legendPos = 'right'
self.DrawLegend(print_norm=print_norm)
if TPad.Pad():
TPad.Pad().Update()
def DrawNormalizedStack(self, opt='',
xmin=None, xmax=None, ymin=0.001, ymax=None):
'''Draw all histograms, some of them in a stack.
if Histogram.stack is True, the histogram is put in the stack.
all histograms out of the stack, and the stack itself, are shown as PDFs.'''
self._BuildStack(self._SortedHistograms(), ytitle='p.d.f.')
self.stack.DrawNormalized(opt,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax)
for hist in self.nostack:
hist.obj.DrawNormalized('same')
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawNormalizedStack'
# self.lastDrawArgs = [ opt ]
def Rebin(self, factor):
'''Rebin, and redraw.'''
# the dispatching technique is not too pretty,
# but keeping a self.lastDraw function initialized to one of the Draw functions
# when calling it creates a problem in deepcopy.
for hist in self.histos:
hist.Rebin(factor)
self.axisWasSet = False
def NormalizeToBinWidth(self):
'''Normalize each Histograms bin to the bin width.'''
for hist in self.histos:
hist.NormalizeToBinWidth()
def WriteDataCard(self, filename=None, verbose=True,
mode='RECREATE', dir=None):
'''Export current plot to datacard'''
if not filename:
filename = self.name+'.root'
outf = TFile(filename, mode)
if dir:
outf_dir = outf.mkdir(dir)
outf_dir.cd()
for hist in self._SortedHistograms():
'Writing', hist, 'as', hist.name
# blinding !
# if hist.name != 'data_obs':
hist.weighted.Write(hist.name)
outf.Write()
def _BuildStack(self, hists, ytitle=None):
'''build a stack from a list of Histograms.
The histograms for which Histogram.stack is False are put in self.nostack'''
self.stack = None
self.stack = Stack(self.name+'_stack', ytitle=ytitle)
self.nostack = []
for hist in hists:
if hist.stack:
self.stack.Add(hist)
else:
self.nostack.append(hist)
def _GetHistPref(self, name):
'''Return the preference dictionary for a given component'''
thePref = None
for prefpat, pref in self.histPref.iteritems():
if fnmatch.fnmatch(name, prefpat):
if thePref is not None:
print 'several matching preferences for', name
thePref = pref
if thePref is None:
print 'cannot find preference for hist', name
thePref = {'style': Style(), 'layer': 999}
return thePref
def _ApplyPrefs(self):
for hist in self.histos:
pref = self._GetHistPref(hist.name)
hist.layer = pref['layer']
hist.SetStyle(pref['style'])
# print hist.name
hist.legendLine = pref['legend']
def __str__(self):
if self.stack is None:
self._BuildStack(self._SortedHistograms(), ytitle='Events')
tmp = [' '.join(['DataMCPlot: ', self.name])]
tmp.append('Histograms:')
for hist in self._SortedHistograms(reverse=True):
tmp.append(' '.join(['\t', str(hist)]))
tmp.append('Stack yield = {integ:7.1f}'.format(integ=self.stack.integral))
return '\n'.join(tmp)
def limit():
method = ''
channel = "bb"
if INCLUDEACC:
particleP = "X"
else:
particleP = "Z'"
particle = 'b#bar{b}'
multF = ZPTOBB
THEORY = ['A1', 'B3']
if INCLUDEACC: THEORY.append('SSM')
mass_dict = {}
val_dict = {}
for BTAGGING in BTAGGING_WPS:
suffix = "_" + BTAGGING
if ISMC: suffix += "_MC"
if SY: suffix += "_comb"
#if method=="cls": suffix="_CLs"
if INCLUDEACC: suffix += "_acc"
if SY:
filename = "./combine/limits/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt"
else:
filename = "./combine/limits/" + BTAGGING + "/" + YEAR + "_M%d.txt"
if CATEGORY != "":
if SY:
if CATEGORY == 'bb_bq':
filename = filename.replace(
BTAGGING + "/combined_run2/",
BTAGGING + "/combined_run2/" + CATEGORY + "_combined_")
else:
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_dict[BTAGGING], val_dict[BTAGGING] = fillValues(filename)
#Obs0s = TGraph()
#Exp0s = TGraph()
#Exp1s = TGraphAsymmErrors()
#Exp2s = TGraphAsymmErrors()
#Sign = TGraph()
#pVal = TGraph()
#Best = TGraphAsymmErrors()
Theory = {}
Exp0s = {}
for BTAGGING in BTAGGING_WPS:
Exp0s[BTAGGING] = TGraph()
for i, m in enumerate(mass_dict[BTAGGING]):
if not m in val_dict[BTAGGING]:
print "Key Error:", m, "not in value map"
continue
if INCLUDEACC:
acc_factor = ACCEPTANCE[m]
else:
acc_factor = 1.
n = Exp0s[BTAGGING].GetN()
#Obs0s.SetPoint(n, m, val[m][0]*multF*acc_factor)
Exp0s[BTAGGING].SetPoint(
n, m, val_dict[BTAGGING][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])
#print "m =", m, " --> Xsec*Br =", val[m][3]
Exp0s[BTAGGING].SetLineStyle(1)
Exp0s[BTAGGING].SetLineWidth(3)
Exp0s[BTAGGING].SetLineColor(COLORS[BTAGGING])
for t in THEORY:
Theory[t] = TGraphAsymmErrors()
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
Exp0s[BTAGGING_WPS[0]].GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Exp0s[BTAGGING_WPS[0]].GetXaxis().SetTitleSize(
Exp0s[BTAGGING_WPS[0]].GetXaxis().GetTitleSize() * 1.25)
Exp0s[BTAGGING_WPS[0]].GetXaxis().SetNoExponent(True)
Exp0s[BTAGGING_WPS[0]].GetXaxis().SetMoreLogLabels(True)
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetTitle(
"#sigma(" + particleP + ") #bf{#it{#Beta}}(" + particleP +
" #rightarrow " + particle +
"){} (fb)".format(" #times #Alpha" if INCLUDEACC else ""))
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetTitleOffset(1.5)
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetNoExponent(True)
Exp0s[BTAGGING_WPS[0]].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 i, BTAGGING in enumerate(BTAGGING_WPS):
if i == 0:
Exp0s[BTAGGING].Draw("AL")
#if not options.blind: Obs0s.Draw("SAME, L")
else:
Exp0s[BTAGGING].Draw("SAME, L")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
Exp0s[BTAGGING_WPS[0]].GetXaxis().SetTitleSize(0.050)
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetTitleSize(0.050)
Exp0s[BTAGGING_WPS[0]].GetXaxis().SetLabelSize(0.045)
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetLabelSize(0.045)
Exp0s[BTAGGING_WPS[0]].GetXaxis().SetTitleOffset(0.90)
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetTitleOffset(1.25)
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetMoreLogLabels(True)
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetNoExponent(True)
if INCLUDEACC:
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetRangeUser(0.05, 5.e3)
else:
Exp0s[BTAGGING_WPS[0]].GetYaxis().SetRangeUser(0.1, 5.e3)
Exp0s[BTAGGING_WPS[0]].GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1])
#Exp0s.Draw("SAME, L")
#if not options.blind: Obs0s.Draw("SAME, L")
#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)
#Exp2s.GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1])
drawAnalysis("")
drawRegion("", True)
if CATEGORY == "":
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawCMS(LUMI, "Preliminary")
#drawCMS(LUMI, "", suppressCMS=True)
else:
#drawCMS(LUMI, "Work in Progress, "+CAT_LABELS[CATEGORY], suppressCMS=True)
drawCMS(LUMI, "Preliminary " + CAT_LABELS[CATEGORY])
#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")
for BTAGGING in BTAGGING_WPS:
leg.AddEntry(Exp0s[BTAGGING], "Expected {}".format(BTAGGING), "l")
#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 +
"_WP_comparison.png")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR +
"_WP_comparison.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, "(obs)",
# if not (Theory[t].Eval(m) > Exp0s.Eval(m)) == (Theory[t].Eval(m+1) > Exp0s.Eval(m+1)): print m, "(exp)",
# 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 plot_signal( var, cut, norm=False ):
colors = [ 616+4 , 800 , 416+1 , 800+7 , 860+10 , 600 , 616 , 921 , 922 ]
if cut in selection: plotdir = cut
pathname = PLOTDIR+"/signals/"+plotdir
if not os.path.exists(pathname): os.system('mkdir -p %s'%pathname)
histlist={}
plt.cfg.register(sign) #sign is grouptag
samples = plt.cfg.getModule('samples')
CUT = ' '.join(filter( None , selection[cut].split(" ") ))
signlist = plt.cfg.getGroupPlot()['Higgs']['samples']
for i,isample in enumerate(signlist):
WEIGHTS = "%s*(%s)" %( str(float(LUMI)/1000.) , expressAliases(samples[isample]['weight']) )
filelist = [ x for x in samples[isample]['name'] ]
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files); gROOT.cd()
histlist[isample] = makeHisto( df , var , CUT , WEIGHTS , isample )
histlist[isample] = histlist[isample].GetPtr()
histlist[isample].Sumw2()
histlist[isample].SetLineWidth(2)
histlist[isample].SetLineColor(colors[i])
#######################################################################################################
# add total histograms
histlist['Higgs']=None
for i, ihist in enumerate(histlist):
if i==0:
histlist['Higgs'] = histlist[ihist].Clone('totsignal')
histlist['Higgs'].Sumw2()
histlist['Higgs'].Add(histlist[ihist])
leg = TLegend( 0.7 if not options.Statebox else 0.4 , 0.9-0.035*len(signlist), 0.9, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(1001) #1001
leg.SetFillColor(0)
for s in signlist : leg.AddEntry(histlist[s], '%s [%.1f]' %( s , histlist[s].Integral() ) , "l")
c1 = TCanvas("c1", "Signals", 800, 600)
c1.cd().SetLogy() if options.logy else c1.cd()
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
hmax = 0.
for i, s in enumerate(signlist):
if histlist[s].GetMaximum() > hmax: hmax = histlist[s].GetMaximum()
if not options.logy:
histlist[signlist[0]].SetMaximum(hmax*1.2)
histlist[signlist[0]].SetMinimum(0.)
else:
histlist[signlist[0]].SetMaximum(hmax*4)
c1.cd()
for i, s in enumerate(signlist):
histlist[s].Draw("HIST" if i==0 else "SAMES, HIST")
if options.Statebox:
c1.GetPad(0).Update()
if i==1:
lof = histlist[s].GetListOfFunctions()
statbox = lof.FindObject('stats')
statbox.SetX1NDC(0.779026); statbox.SetX2NDC(0.979401)
statbox.SetY1NDC(0.593168); statbox.SetY2NDC(0.754658)
leg.Draw()
drawCMS(LUMI, "Simulation")
printTable_signal( histlist , pathname , '%s_%s.txt' %(plotdir,var) , signlist , histplease=True )
### fitting mass shape?
########################
if gROOT.IsBatch():
c1.Print(pathname+"/"+var.replace('.', '_')+".png")
#c1.Print(pathname+"/"+var.replace('.', '_')+".pdf")
#print col.WARNING+"PURGE OBJECTS IN MEMORY"+col.ENDC
#for process in histlist:
# histlist[process].Delete()
pass
def draw(hist,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L, U - N)
res_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.7, 0.9 - 0.05 * n, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.06)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
#if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
data_graph.Draw("SAME, PE")
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((2. if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
#if log: bkg.SetMinimum(1)
leg.Draw()
drawCMS(lumi, "Preliminary")
drawRegion(channel)
drawAnalysis(channel)
#if nm1 and not cutValue is None: drawCut(cutValue, bkg.GetMinimum(), bkg.GetMaximum()) #FIXME
if len(sign) > 0:
if channel.startswith('X') and len(sign) > 0:
drawNorm(0.9 - 0.04 * (n + 1),
"#sigma(X) #times B(X #rightarrow Vh) = %.1f pb" % snorm)
#elif "SR" in channel: drawNorm(0.9-0.04*(n+1), "DM+bb/tt, scaled by %.0f" % snorm, "m_{#chi}=1 GeV, scalar mediator")
elif "SR" in channel:
drawNorm(0.9 - 0.04 * (n + 1), "DM+bb/tt, m_{#chi}=1 GeV",
"scalar mediator")
setHistStyle(bkg, 1.2 if ratio else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if ratio else 1.1)
if ratio:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if len(data) > 0:
#res.Draw("SAME, PE0")
res_graph.Draw("SAME, PE0")
# if len(err.GetXaxis().GetBinLabel(1))==0: # Bin labels: not a ordinary plot
# drawRatio(hist['data_obs'], hist['BkgSum'])
# drawKolmogorov(hist['data_obs'], hist['BkgSum'])
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, err, errLine, res, data_graph, res_graph]
def DrawOverlap(fileVec,
histVec,
titleVec,
legendtext,
pngname,
logstatus=[0, 0],
xRange=[-99999, 99999, 1]):
gStyle.SetOptTitle(0)
gStyle.SetOptStat(0)
gStyle.SetTitleOffset(1.1, "Y")
#gStyle.SetTitleOffset(1.9,"X");
gStyle.SetLineWidth(3)
gStyle.SetFrameLineWidth(3)
i = 0
histList_ = []
histList = []
histList1 = []
maximum = []
minimum = []
## Legend
leg = TLegend(0.1, 0.80, 0.89, 0.89) #,NULL,"brNDC");
leg.SetBorderSize(0)
leg.SetNColumns(3)
leg.SetLineColor(1)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.05)
c = TCanvas("c1", "c1", 0, 0, 500, 500)
c.SetBottomMargin(0.15)
c.SetLogy(logstatus[1])
c.SetLogx(logstatus[0])
print("you have provided " + str(len(fileVec)) + " files and " +
str(len(histVec)) + " histograms to make a overlapping plot")
print "opening rootfiles"
c.cd()
ii = 0
inputfile = {}
print str(fileVec[(len(fileVec) - 1)])
for ifile_ in range(len(fileVec)):
print("opening file " + fileVec[ifile_])
inputfile[ifile_] = TFile(fileVec[ifile_])
print "fetching histograms"
for ihisto_ in range(len(histVec)):
print("printing histo " + str(histVec[ihisto_]))
histo = inputfile[ifile_].Get(histVec[ihisto_])
#status_ = type(histo) is TGraphAsymmErrors
histList.append(histo)
# for ratio plot as they should nt be normalize
histList1.append(histo)
#print histList[ii].Integral()
#histList[ii].Rebin(xRange[2])
#histList[ii].Scale(1.0/histList[ii].Integral())
maximum.append(histList[ii].GetMaximum())
minimum.append(histList[ii].GetMinimum())
maximum.sort()
ii = ii + 1
print histList
print maximum, " ", max(maximum)
print minimum, " ", min(minimum)
for ih in range(len(histList)):
tt = type(histList[ih])
if logstatus[1] is 1:
histList[ih].SetMaximum(1.5) #1.4 for log
histList[ih].SetMinimum(0.1) #1.4 for log
if logstatus[1] is 0:
maxi = max(maximum)
mini = min(minimum)
if maxi == 0.0: maxi = 0.001
if mini == 0.0: mini = -0.001
histList[ih].SetMaximum(maxi * 3) #1.4 for log
histList[ih].SetMinimum(mini * 3) #1.4 for log
# print "graph_status =" ,(tt is TGraphAsymmErrors)
# print "hist status =", (tt is TH1D) or (tt is TH1F)
if ih == 0:
if tt is TGraphAsymmErrors:
histList[ih].Draw("APL")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("P hist")
if ih > 0:
#histList[ih].SetLineWidth(2)
if tt is TGraphAsymmErrors:
histList[ih].Draw("PL same")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("P hist same")
if tt is TGraphAsymmErrors:
histList[ih].SetMaximum(100)
histList[ih].SetMarkerColor(colors[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetLineWidth(2)
histList[ih].SetMarkerStyle(markerStyle[ih])
histList[ih].SetMarkerSize(1)
leg.AddEntry(histList[ih], legendtext[ih], "PL")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].SetLineStyle(linestyle[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetMarkerColor(colors[ih])
histList[ih].SetMarkerStyle(20)
histList[ih].SetMarkerSize(1.0)
histList[ih].SetLineWidth(3)
leg.AddEntry(histList[ih], legendtext[ih], "P")
histList[ih].GetYaxis().SetTitle(titleVec[1])
histList[ih].GetYaxis().SetTitleSize(0.052)
histList[ih].GetYaxis().SetTitleOffset(0.98)
histList[ih].GetYaxis().SetTitleFont(42)
histList[ih].GetYaxis().SetLabelFont(42)
histList[ih].GetYaxis().SetLabelSize(.052)
histList[ih].GetXaxis().SetRangeUser(xRange[0], xRange[1])
# histList[ih].GetXaxis().SetLabelSize(0.0000);
histList[ih].GetXaxis().SetTitle(titleVec[0])
histList[ih].GetXaxis().SetLabelSize(0.052)
histList[ih].GetXaxis().SetTitleSize(0.052)
#histList[ih].GetXaxis().SetTitleOffset(1.14)
histList[ih].GetXaxis().SetTitleFont(42)
histList[ih].GetXaxis().SetLabelFont(42)
histList[ih].GetYaxis().SetLabelFont(42)
histList[ih].GetXaxis().SetNdivisions(507)
#histList[ih].GetXaxis().SetMoreLogLabels();
#histList[ih].GetXaxis().SetNoExponent();
#histList[ih].GetTGaxis().SetMaxDigits(3);
i = i + 1
pt = TPaveText(0.01, 0.92, 0.95, 0.96, "brNDC")
pt.SetBorderSize(0)
pt.SetTextAlign(12)
pt.SetFillStyle(0)
pt.SetTextFont(42)
pt.SetTextSize(0.046)
#text = pt.AddText(0.12,0.35,"CMS Internal 36 fb^{-1} (2016) ")
text = pt.AddText(0.12, 0.35,
"CMS Internal 41.5 fb^{-1} (2017) ")
#text = pt.AddText(0.12,0.35,"CMS Internal 59.6 fb^{-1} (2018) ")
#text = pt.AddText(0.6,0.5,"41.5 fb^{-1} (2017) ")
pt.Draw()
leg.Draw()
outputdirname = 'UncPlots/'
histname = outputdirname + pngname
c.SaveAs(histname + '.png')
c.SaveAs(histname + '.pdf')
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')
def plotUpperLimits(model, lambdas, helicity, Mmin):
N = len(lambdas)
yellow = TGraph(2 * N) # yellow band
green = TGraph(2 * N) # green band
median = TGraph(N) # median line
up2s = []
xseclist = [1.0] * N
for i in range(N):
file_name = "./%sshapeCards/ee_limit_min%d%s/higgsCombineTest.AsymptoticLimits.mH%d.root" % (
model, Mmin, helicity, lambdas[i])
limit = getLimits(file_name)
up2s.append(limit[4])
if model == "ADD":
yellow.SetPoint(i, lambdas[i] / 1000,
limit[4] * xseclist[i]) # + 2 sigma
green.SetPoint(i, lambdas[i] / 1000,
limit[3] * xseclist[i]) # + 1 sigma
median.SetPoint(i, lambdas[i] / 1000,
limit[2] * xseclist[i]) # median
green.SetPoint(2 * N - 1 - i, lambdas[i] / 1000,
limit[1] * xseclist[i]) # - 1 sigma
yellow.SetPoint(2 * N - 1 - i, lambdas[i] / 1000,
limit[0] * xseclist[i]) # - 2 sigma
else:
yellow.SetPoint(i, lambdas[i], limit[4] * xseclist[i]) # + 2 sigma
green.SetPoint(i, lambdas[i], limit[3] * xseclist[i]) # + 1 sigma
median.SetPoint(i, lambdas[i], limit[2] * xseclist[i]) # median
green.SetPoint(2 * N - 1 - i, lambdas[i],
limit[1] * xseclist[i]) # - 1 sigma
yellow.SetPoint(2 * N - 1 - i, lambdas[i],
limit[0] * xseclist[i]) # - 2 sigma
print "Lambda = %d, Median = %f" % (lambdas[i], limit[2])
W = 800
H = 600
T = 0.08 * H
B = 0.12 * H
L = 0.12 * W
R = 0.04 * W
c = TCanvas("c", "c", 100, 100, W, H)
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
c.SetLeftMargin(L / W)
c.SetRightMargin(R / W)
c.SetTopMargin(T / H)
c.SetBottomMargin(B / H)
c.SetTickx(0)
c.SetTicky(0)
c.SetGrid()
c.cd()
if model == "ADD": frame = c.DrawFrame(4, 0.01, 10, 10)
else: frame = c.DrawFrame(16, 0.1, 40, 8)
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetTitleSize(0.05)
frame.GetXaxis().SetTitleSize(0.05)
frame.GetXaxis().SetLabelSize(0.04)
frame.GetYaxis().SetLabelSize(0.04)
frame.GetYaxis().SetTitleOffset(0.9)
frame.GetXaxis().SetNdivisions(508)
frame.GetYaxis().CenterTitle(True)
#frame.GetYaxis().SetTitle("95% upper limit on #sigma / #sigma_{}")
if model == "ADD":
frame.GetYaxis().SetTitle("95% CL limit on #sigma / #sigma_{ADD}")
frame.GetXaxis().SetTitle("#Lambda_{T} [ADD]")
else:
frame.GetYaxis().SetTitle("95% CL limit on #sigma / #sigma_{CI}")
frame.GetXaxis().SetTitle("#Lambda [CI]")
frame.SetMinimum(0)
#frame.SetMaximum(max(up2s)*1.05)
#frame.GetXaxis().SetLimits(min(values),max(values))
yellow.SetFillColor(ROOT.kOrange)
yellow.SetLineColor(ROOT.kOrange)
yellow.SetFillStyle(1001)
yellow.Draw('F')
green.SetFillColor(ROOT.kGreen + 1)
green.SetLineColor(ROOT.kGreen + 1)
green.SetFillStyle(1001)
green.Draw('Fsame')
median.SetLineColor(1)
median.SetLineWidth(2)
median.SetLineStyle(2)
median.Draw('Lsame')
CMS_lumi.CMS_lumi(c, 13, 11)
ROOT.gPad.SetTicks(1, 1)
frame.Draw('sameaxis')
x1 = 0.15
x2 = x1 + 0.24
y2 = 0.76
y1 = 0.60
legend = TLegend(x1, y1, x2, y2)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextSize(0.041)
legend.SetTextFont(42)
legend.AddEntry(median,
"Asymptotic CL_{s} expected %s%s" % (model, helicity), 'L')
legend.AddEntry(green, "#pm 1 std. deviation", 'f')
legend.AddEntry(yellow, "#pm 2 std. deviation", 'f')
legend.Draw()
c.SaveAs("%slimits/%sLimit_ee_ShapeMin%d%s_201678.png" %
(model, model, Mmin, helicity))
c.Close()
X_axis = "NPU"
print "X_axis= ", X_axis
for obj in (1, 2):
if (obj == 1):
Prefix += "_LCT"
if (obj == 2):
Prefix = Prefix.replace("LCT", "SEG")
c1 = TCanvas("c1", "c1", 600, 600)
c2 = TCanvas("c2", "c2", 600, 600)
leg = TLegend(0.45, 0.25, 0.95, 0.36)
leg.SetNColumns(2)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetShadowColor(0)
leg.SetBorderSize(0)
leg.SetTextFont(132)
leg.SetTextSize(0.04)
leg_ME1 = TLegend(0.45, 0.25, 0.95, 0.36)
leg_ME1.SetNColumns(2)
leg_ME1.SetFillColor(0)
leg_ME1.SetFillStyle(0)
leg_ME1.SetShadowColor(0)
leg_ME1.SetBorderSize(0)
leg_ME1.SetTextFont(132)
leg_ME1.SetTextSize(0.04)
color = 0
def makeBiasPlot(output,
muFile,
chan,
interference,
printStats=False,
obs2="",
ratioLabel=""):
mu = open(muFile, 'r')
limits = {}
mux = []
muy = []
mu1SigLow = []
mu1SigHigh = []
mu2SigLow = []
mu2SigHigh = []
for entry in mu:
massPoint = float(entry.split()[0])
limitEntry = float(entry.split()[1])
if massPoint not in limits: limits[massPoint] = []
limits[massPoint].append(limitEntry)
if printStats: print "len limits:", len(limits)
for massPoint in sorted(limits):
limits[massPoint].sort()
numLimits = len(limits[massPoint])
nrExpts = len(limits[massPoint])
medianNr = int(nrExpts * 0.5)
#get indexes:
upper1Sig = int(nrExpts * (1 - (1 - 0.68) * 0.5))
lower1Sig = int(nrExpts * (1 - 0.68) * 0.5)
upper2Sig = int(nrExpts * (1 - (1 - 0.95) * 0.5))
lower2Sig = int(nrExpts * (1 - 0.95) * 0.5)
if printStats:
print massPoint, ":", limits[massPoint][lower2Sig], limits[
massPoint][lower1Sig], limits[massPoint][medianNr], limits[
massPoint][upper1Sig], limits[massPoint][upper2Sig]
#fill lists:
mux.append(massPoint)
print massPoint, limits[massPoint][medianNr]
muy.append(limits[massPoint][medianNr])
mu1SigLow.append(limits[massPoint][lower1Sig])
mu1SigHigh.append(limits[massPoint][upper1Sig])
mu2SigLow.append(limits[massPoint][lower2Sig])
mu2SigHigh.append(limits[massPoint][upper2Sig])
muX = numpy.array(mux)
muY = numpy.array(muy)
values2 = []
xPointsForValues2 = []
values = []
xPointsForValues = []
if printStats: print "length of mux: ", len(mux)
if printStats: print "length of mu1SigLow: ", len(mu1SigLow)
if printStats: print "length of mu1SigHigh: ", len(mu1SigHigh)
#Here is some Voodoo via Sam:
for x in range(0, len(mux)):
values2.append(mu2SigLow[x])
xPointsForValues2.append(mux[x])
for x in range(len(mux) - 1, 0 - 1, -1):
values2.append(mu2SigHigh[x])
xPointsForValues2.append(mux[x])
if printStats: print "length of values2: ", len(values2)
for x in range(0, len(mux)):
values.append(mu1SigLow[x])
xPointsForValues.append(mux[x])
for x in range(len(mux) - 1, 0 - 1, -1):
values.append(mu1SigHigh[x])
xPointsForValues.append(mux[x])
if printStats: print "length of values: ", len(values)
mu2Sig = numpy.array(values2)
xPoints2 = numpy.array(xPointsForValues2)
mu1Sig = numpy.array(values)
xPoints = numpy.array(xPointsForValues)
if printStats: print "xPoints2: ", xPoints2
if printStats: print "mu2Sig: ", mu2Sig
if printStats: print "xPoints: ", xPoints
if printStats: print "mu1Sig: ", mu1Sig
GraphErr2Sig = TGraphAsymmErrors(len(xPoints), xPoints2, mu2Sig)
GraphErr2Sig.SetFillColor(ROOT.kYellow + 1)
GraphErr1Sig = TGraphAsymmErrors(len(xPoints), xPoints, mu1Sig)
GraphErr1Sig.SetFillColor(ROOT.kGreen)
cCL = TCanvas("cCL", "cCL", 0, 0, 800, 500)
gStyle.SetOptStat(0)
plotPad = ROOT.TPad("plotPad", "plotPad", 0, 0, 1, 1)
plotPad.Draw()
plotPad.cd()
muX = numpy.array(mux)
muY = numpy.array(muy)
GraphMU = TGraph(len(muX), muX, muY)
GraphMU.SetLineWidth(3)
GraphMU.SetLineStyle(2)
GraphMU.SetLineColor(ROOT.kBlue)
#Draw the graphs:
DummyGraph = TH1F("DummyGraph", "", 100, 10, 40)
DummyGraph.GetXaxis().SetTitle("#Lambda [TeV]")
DummyGraph.GetYaxis().SetTitle("#hat{#mu}")
gStyle.SetOptStat(0)
if "Des" in output:
DummyGraph.GetXaxis().SetRangeUser(10, 28)
else:
DummyGraph.GetXaxis().SetRangeUser(10, 40)
DummyGraph.SetMinimum(-2)
DummyGraph.SetMaximum(10)
DummyGraph.GetXaxis().SetLabelSize(0.04)
DummyGraph.GetXaxis().SetTitleSize(0.045)
DummyGraph.GetXaxis().SetTitleOffset(1.)
DummyGraph.GetYaxis().SetLabelSize(0.04)
DummyGraph.GetYaxis().SetTitleSize(0.045)
DummyGraph.GetYaxis().SetTitleOffset(1.)
DummyGraph.Draw()
DummyGraph.SetLineColor(ROOT.kWhite)
if (FULL):
GraphErr2Sig.Draw("F")
GraphErr1Sig.Draw("F")
GraphMU.Draw("lpsame")
else:
GraphMU.Draw("lp")
plCMS = TPaveLabel(.12, .81, .22, .88, "CMS", "NBNDC")
#plCMS.SetTextSize(0.8)
plCMS.SetTextAlign(12)
plCMS.SetTextFont(62)
plCMS.SetFillColor(0)
plCMS.SetFillStyle(0)
plCMS.SetBorderSize(0)
plCMS.Draw()
plPrelim = TPaveLabel(.12, .76, .25, .82, "Preliminary", "NBNDC")
plPrelim.SetTextSize(0.6)
plPrelim.SetTextAlign(12)
plPrelim.SetTextFont(52)
plPrelim.SetFillColor(0)
plPrelim.SetFillStyle(0)
plPrelim.SetBorderSize(0)
plPrelim.Draw()
cCL.SetTickx(1)
cCL.SetTicky(1)
cCL.RedrawAxis()
cCL.Update()
#leg=TLegend(0.65,0.65,0.87,0.87,"","brNDC")
leg = TLegend(0.540517, 0.623051, 0.834885, 0.878644, "", "brNDC")
# leg=TLegend(0.55,0.55,0.87,0.87,"","brNDC")
leg.SetTextSize(0.032)
leg.AddEntry(GraphMU, "median value", "l")
if (FULL):
leg.AddEntry(GraphErr1Sig, "1#sigma quantile", "f")
leg.AddEntry(GraphErr2Sig, "2#sigma quantile", "f")
leg.SetLineWidth(0)
leg.SetLineStyle(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
leg.Draw("hist")
if "Moriond" in output:
if (chan == "mumu"):
plLumi = TPaveLabel(.65, .905, .9, .99,
"36.3 fb^{-1} (13 TeV, #mu#mu)", "NBNDC")
elif (chan == "elel"):
plLumi = TPaveLabel(.65, .905, .9, .99,
"35.9 fb^{-1} (13 TeV, ee)", "NBNDC")
elif (chan == "elmu"):
plLumi = TPaveLabel(
.4, .905, .9, .99,
"35.9 fb^{-1} (13 TeV, ee) + 36.3 fb^{-1} (13 TeV, #mu#mu)",
"NBNDC")
else:
if (chan == "mumu"):
plLumi = TPaveLabel(.65, .905, .9, .99,
"13.0 fb^{-1} (13 TeV, #mu#mu)", "NBNDC")
elif (chan == "elel"):
plLumi = TPaveLabel(.65, .905, .9, .99, "2.7 fb^{-1} (13 TeV, ee)",
"NBNDC")
elif (chan == "elmu"):
plLumi = TPaveLabel(
.4, .905, .9, .99,
"12.4 fb^{-1} (13 TeV, ee) + 13.0 fb^{-1} (13 TeV, #mu#mu)",
"NBNDC")
plLumi.SetTextSize(0.5)
plLumi.SetTextFont(42)
plLumi.SetFillColor(0)
plLumi.SetBorderSize(0)
plLumi.Draw()
maxX = 40
if "Des" in output:
maxX = 28
line = ROOT.TLine(10, 0, maxX, 0)
if "mu1" in output:
line = ROOT.TLine(10, 1, maxX, 1)
line.SetLineStyle(ROOT.kDashed)
line.Draw("same")
plotPad.RedrawAxis()
cCL.Update()
printPlots(cCL, output)
def addSys(var, cut, sys):
binLow = ""
binHigh = ""
binName = ""
if "binned" in cut:
binLow = cut[cut.find("LowVal") + 6:cut.find("HighVal") - 1]
binHigh = cut[cut.find("HighVal") + 7:]
binName = "bin_" + binLow + "_" + binHigh
cut = cut[:cut.find("binned")]
channel = cut
weight = "eventWeightLumi" #+ ("*stitchWeight" if any([x for x in back if x.endswith('b')]) else "")
cut = selection[cut]
if not binLow == "":
cut = cut + " && " + var + " > " + binLow + " && " + var + " < " + binHigh
weightUp = weightDown = weight
varUp = varDown = var
cutUp = cutDown = cut
# Systematics
if sys == 'CMS_scale_j':
if var != "MET_sign": varUp = var.replace('pt', 'ptScaleUp')
else: varUp = var.replace('sign', 'signScaleUp')
if var != "MET_sign": varDown = var.replace('pt', 'ptScaleDown')
else: varDown = var.replace('sign', 'signScaleDown')
cutUp = cut.replace('MET_pt', 'MET_ptScaleUp')
cutUp = cutUp.replace('Jets', 'JetsScaleUp')
cutUp = cutUp.replace('12', '12ScaleUp')
cutUp = cutUp.replace('mT>', 'mTScaleUp>')
cutUp = cutUp.replace('mT2', 'mT2ScaleUp')
cutDown = cut.replace('MET_pt', 'MET_ptScaleDown')
cutDown = cutDown.replace('Jets', 'JetsScaleDown')
cutDown = cutDown.replace('12', '12ScaleDown')
cutDown = cutDown.replace('mT>', 'mTScaleDown>')
cutDown = cutDown.replace('mT2', 'mT2ScaleDown')
elif sys == 'CMS_res_j':
if var != "MET_sign": varUp = var.replace('pt', 'ptResUp')
else: varUp = var.replace('sign', 'signResUp')
if var != "MET_sign": varDown = var.replace('pt', 'ptResDown')
else: varDown = var.replace('sign', 'signResDown')
cutUp = cut.replace('MET_pt', 'MET_ptResUp')
cutUp = cutUp.replace('Jets', 'JetsResUp')
cutUp = cutUp.replace('12', '12ResUp')
cutUp = cutUp.replace('mT>', 'mTResUp>')
cutUp = cutUp.replace('mT2', 'mT2ResUp')
cutDown = cut.replace('MET_pt', 'MET_ptResDown')
cutDown = cutDown.replace('Jets', 'JetsResDown')
cutDown = cutDown.replace('12', '12ResDown')
cutDown = cutDown.replace('mT>', 'mTResDown>')
cutDown = cutDown.replace('mT2', 'mT2ResDown')
elif sys == 'CMS_WqcdWeightRen':
weightUp += "*WqcdWeightRenUp/WqcdWeight"
weightDown += "*WqcdWeightRenDown/WqcdWeight"
elif sys == 'CMS_WqcdWeightFac':
weightUp += "*WqcdWeightFacUp/WqcdWeight"
weightDown += "*WqcdWeightFacDown/WqcdWeight"
elif sys == 'CMS_ZqcdWeightRen':
weightUp += "*ZqcdWeightRenUp/ZqcdWeight"
weightDown += "*ZqcdWeightRenDown/ZqcdWeight"
elif sys == 'CMS_ZqcdWeightFac':
weightUp += "*ZqcdWeightFacUp/ZqcdWeight"
weightDown += "*ZqcdWeightFacDown/ZqcdWeight"
elif sys == 'CMS_WewkWeight':
weightUp += "/WewkWeight"
weightDown += ""
elif sys == 'CMS_ZewkWeight':
weightUp += "/ZewkWeight"
weightDown += ""
elif sys == 'CMS_pdf':
weightUp += "*PDFWeightUp/eventWeight"
weightDown += "*PDFWeightDown/eventWeight"
elif sys == 'CMS_HF':
weightUp += "*1.20"
weightDown += "*0.8"
elif sys == 'CMS_eff_b':
weightUp += "*bTagWeightUp/bTagWeight"
weightDown += "*bTagWeightDown/bTagWeight"
elif sys == 'CMS_scale_pu':
weightUp += "*puWeightUp/puWeight"
weightDown += "*puWeightDown/puWeight"
elif sys == 'CMS_scale_top':
weightUp += "/TopWeight"
weightDown += ""
elif sys == 'CMS_eff_trigger':
weightUp += "*triggerWeightUp/triggerWeight"
weightDown += "*triggerWeightDown/triggerWeight"
elif sys == 'CMS_eff_e' and '2e' in cut or '1e' in channel:
weightUp += "*leptonWeightUp/leptonWeight"
weightDown += "*leptonWeightDown/leptonW\
eight"
elif sys == 'CMS_eff_m' and '2m' in cut or '1m' in channel:
weightUp += "*leptonWeightUp/leptonWeight"
weightDown += "*leptonWeightDown/leptonW\
eight"
elif sys == 'QCDscale_ren':
weightUp += "*QCDRenWeightUp"
weightDown += "*QCDRenWeightDown"
elif sys == 'QCDscale_fac':
weightUp += "*QCDFacWeightUp"
weightDown += "*QCDFacWeightDown"
# elif sys=='EWKscale_Z': weightDown += "/ZewkWeight"
# elif sys=='EWKscale_W': weightDown += "/WewkWeight"
else:
print "Systematic", sys, "not applicable or not recognized."
### Create and fill MC histograms ###
file = {}
tree = {}
hist = {}
histUp = {}
histDown = {}
isBlind = BLIND and 'SR' in channel
for i, s in enumerate(back + sign):
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
tree[s].Add(NTUPLEDIR + ss + ".root")
if not binLow == "":
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''), 1, float(binLow),
float(binHigh))
elif binLow == "" and variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
else:
hist[s] = TH1F(s, ";" + variable[var]['title'],
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
histUp[s] = hist[s].Clone(s + 'Up')
histDown[s] = hist[s].Clone(s + 'Down')
redFactorString = ""
redFactorValue = ""
#if isBlind and 'data' not in s and options.limit:
if isBlind and 'data' not in s:
redFactorValue = " / 15"
cutstring = ("*(" + cut + ")" if len(cut) > 0 else "")
cutstringUp = ("*(" + cutUp + ")" if len(cut) > 0 else "")
cutstringDown = ("*(" + cutDown + ")" if len(cut) > 0 else "")
if '-' in s:
cutstring = cutstring.replace(
cut, cut + "&& nBQuarks==" + s.split('-')[1][0])
tree[s].Project(s, var,
"(" + weight + redFactorValue + ")" + cutstring)
if 'HF' not in sys or 'QCDscale' not in sys:
tree[s].Project(s + 'Up', varUp,
"(" + weightUp + redFactorValue + ")" + cutstring)
tree[s].Project(
s + 'Down', varDown,
"(" + weightDown + redFactorValue + ")" + cutstring)
if 'HF' in sys:
if s.startswith('WJ') or s.startswith('ZJ') or s.startswith(
'DYJets'):
tree[s].Project(
s + 'Up', varUp,
"(" + weightUp + redFactorValue + ")" + cutstringUp)
tree[s].Project(
s + 'Down', varDown,
"(" + weightDown + redFactorValue + ")" + cutstringDown)
else:
tree[s].Project(
s + 'Up', varUp,
"(" + weight + redFactorValue + ")" + cutstringUp)
tree[s].Project(
s + 'Down', varDown,
"(" + weight + redFactorValue + ")" + cutstringDown)
if 'QCDscale' in sys:
if s.startswith('WJ') or s.startswith('ZJ') or s.startswith(
'DYJets'):
tree[s].Project(
s + 'Up', varUp,
"(" + weight + redFactorValue + ")" + cutstringUp)
tree[s].Project(
s + 'Down', varDown,
"(" + weight + redFactorValue + ")" + cutstringDown)
else:
tree[s].Project(
s + 'Up', varUp,
"(" + weightUp + redFactorValue + ")" + cutstringUp)
tree[s].Project(
s + 'Down', varDown,
"(" + weightDown + redFactorValue + ")" + cutstringDown)
hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s].SetLineWidth(2)
histUp[s].SetLineWidth(2)
histDown[s].SetLineWidth(2)
hist[s].SetLineColor(1)
histUp[s].SetLineColor(629)
histDown[s].SetLineColor(602)
# Rescale normalization for QCD scales FIXME
if 'QCDscale' in sys:
for s in back + sign: #['TTbar', 'TTbarSL', 'ST']:
if s in hist and histUp[s].Integral(
) > 0. and histDown[s].Integral() > 0.:
histUp[s].Scale(hist[s].Integral() / histUp[s].Integral())
histDown[s].Scale(hist[s].Integral() / histDown[s].Integral())
hist['BkgSum'] = hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset()
histUp['BkgSum'] = hist['BkgSum'].Clone("BkgSumUp")
histUp['BkgSum'].SetLineColor(629)
histUp['BkgSum'].Reset()
histDown['BkgSum'] = hist['BkgSum'].Clone("BkgSumDown")
histDown['BkgSum'].SetLineColor(602)
histDown['BkgSum'].Reset()
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s], 1)
histUp['BkgSum'].Add(histUp[s], 1)
histDown['BkgSum'].Add(histDown[s], 1)
for i, s in enumerate(back + sign + ['BkgSum']):
addOverflow(hist[s], False)
addOverflow(histUp[s], False)
addOverflow(histDown[s], False)
c1 = TCanvas("c1", "Signals", 800, 600)
c1.cd()
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.06)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
c1.GetPad(bool(RATIO)).SetLogy()
histUp['BkgSum'].SetMaximum(histUp['BkgSum'].GetMaximum() * 5)
histUp['BkgSum'].Draw("HIST")
histDown['BkgSum'].Draw("SAME, HIST")
hist['BkgSum'].Draw("SAME, HIST")
drawCMS(-1, "Simulation", False)
setHistStyle(histUp['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
errUp = histUp['BkgSum'].Clone("BkgUp;")
errUp.Add(hist['BkgSum'], -1)
errUp.Divide(hist['BkgSum'])
errUp.SetTitle("")
errUp.GetYaxis().SetTitle("#frac{shifted-central}{central}")
errUp.GetYaxis().SetNdivisions(503)
setBotStyle(errUp)
errUp.GetYaxis().SetRangeUser(-0.3, 0.3)
errUp.Draw("HIST")
errDown = histDown['BkgSum'].Clone("BkgDown;")
errDown.Add(hist['BkgSum'], -1)
errDown.Divide(hist['BkgSum'])
errDown.Draw("SAME, HIST")
f1 = TF1("myfunc", "[0]", -100000, 10000)
f1.SetLineColor(1)
f1.SetLineStyle(7)
f1.SetLineWidth(1)
f1.SetParameter(0, 0)
f1.Draw("same")
leg = TLegend(0.65, 0.80, 0.95, 0.80)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetHeader(sys.replace('CMS', '').replace('_', ' '))
leg.AddEntry(histUp['BkgSum'], "Up", "l")
leg.AddEntry(hist['BkgSum'], "Central", "l")
leg.AddEntry(histDown['BkgSum'], "Down", "l")
leg.SetY1(0.75 - leg.GetNRows() * 0.045)
c1.cd(1)
leg.Draw()
if options.saveplots:
if not os.path.exists("plotsSys_" + options.name + "/" + channel +
binName):
os.makedirs("plotsSys_" + options.name + "/" + channel + binName)
c1.Print("plotsSys_" + options.name + "/" + channel + binName + "/" +
sys + ".png")
c1.Print("plotsSys_" + options.name + "/" + channel + binName + "/" +
sys + ".pdf")
for i, s in enumerate(back + sign):
c2 = TCanvas(s + "canvas", "Signals", 800, 600)
c2.cd()
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
c2.GetPad(0).SetTopMargin(0.06)
c2.GetPad(0).SetRightMargin(0.06)
c2.GetPad(0).SetTicky(2)
c2.GetPad(0).SetLogy()
histUp[s].SetMaximum(histUp[s].GetMaximum() * 5)
histUp[s].Draw("HIST")
histDown[s].Draw("SAME, HIST")
hist[s].Draw("SAME, HIST")
drawCMS(-1, "Simulation", False)
if options.saveplots:
c2.Print("plotsSys_" + options.name + "/" + channel + binName +
"/" + sys + "_" + s + ".png")
c2.Print("plotsSys_" + options.name + "/" + channel + binName +
"/" + sys + "_" + s + ".pdf")
saveHist(histUp, channel + binName, sys + 'Up')
saveHist(histDown, channel + binName, sys + 'Down')
print "Added systematic", sys, "to channel", channel
def plot(var, cut, norm=False, nm1=False):
### Preliminary Operations ###
doBinned = False
if options.mode == "binned": doBinned = True
fileRead = os.path.exists("combinedCards_" + options.name +
"/fitDiagnostics_" + options.file + ".root")
treeRead = not any(
x == cut
for x in ['0l', '1e', '1m', '2e', '2m', '1e1m', 'Gen', 'Trigger'
]) #(var in variable.keys()) # Read from tree
#signal definition
if fileRead:
sign = ['ttDM_MChi1_MPhi200_scalar',
'tDM_MChi1_MPhi200_scalar'] #for postfit plot
if not fileRead and not options.limit:
sign = ['ttDM_MChi1_MPhi100_scalar',
'tDM_MChi1_MPhi100_scalar'] #for normal plotting
#bkg definition
if fileRead or options.limit:
back = [
"QCD", "DYJetsToNuNu_HT", "DYJetsToLL_HT", "VV", "ST",
"WJetsToLNu_HT", "TTbarSL"
] #for postfit or limit
if (cut).find('>250') or (cut).startswith('AH'): #for hadronic selections
back = [
"QCD", "DYJetsToLL_HT", "VV", "ST", "WJetsToLNu_HT", "TTbarV",
"TTbar2L", "TTbar1L", "DYJetsToNuNu_HT"
]
if fileRead or options.limit:
back = [
"QCD", "DYJetsToLL_HT", "VV", "ST", "WJetsToLNu_HT", "TTbarSL",
"DYJetsToNuNu_HT"
] #for postfit or limit
binLow = ""
binHigh = ""
binName = ""
if "binned" in cut:
binLow = cut[cut.find("LowVal") + 6:cut.find("HighVal") - 1]
binHigh = cut[cut.find("HighVal") + 7:]
binName = "bin_" + binLow + "_" + binHigh
cut = cut[:cut.find("binned")]
useformula = False
if 'formula' in variable[var]:
useformula = True
channel = cut
plotdir = cut
plotname = var
weight = "eventWeightLumi" #*(2.2/35.9)
isBlind = BLIND and ('SR' in channel or 'ps' in channel)
if fileRead:
isBlind = False
options.saveplots = True
RESIDUAL = True
elif isBlind:
RATIO = 0
SIGNAL = 20
else:
RATIO = 4
SIGNAL = 1
RESIDUAL = False
showSignal = True #('SR' in channel)
cutSplit = cut.split()
for s in cutSplit:
if s in selection.keys():
plotdir = s
cut = cut.replace(s, selection[s])
if not binLow == "":
cut = cut + " && " + var + " > " + binLow + " && " + var + " < " + binHigh
#if treeRead and cut in selection: cut = cut.replace(cut, selection[cut])
# Determine Primary Dataset
pd = []
if any(w in cut
for w in ['1l', '1m', '2m', 'isWtoMN', 'isZtoMM', 'isTtoEM']):
pd += [x for x in sample['data_obs']['files'] if 'SingleMuon' in x]
if any(w in cut for w in ['1l', '1e', '2e', 'isWtoEN', 'isZtoEE']):
pd += [x for x in sample['data_obs']['files'] if 'SingleElectron' in x]
if any(w in cut for w in ['0l', 'isZtoNN']):
pd += [x for x in sample['data_obs']['files'] if 'MET' in x]
if len(pd) == 0:
raw_input("Warning: Primary Dataset not recognized, continue?")
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
print " cut :", weight
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(data + back + sign):
if fileRead:
var = 'MET_pt'
if channel.startswith('SL'): var = 'MET_sign'
if channel.endswith('ZR'): var = 'FakeMET_pt'
plotname = var
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
if doBinned:
bins = np.array([])
if 'bins' in variable[var].keys():
bins = np.array(variable[var]['bins'])
else:
binsize = (variable[var]['max'] -
variable[var]['min']) / variable[var]['nbins']
bins = np.arange(variable[var]['min'],
variable[var]['max'] + binsize, binsize)
bins = np.append(bins, 10000)
for i in range(0, len(bins) - 1):
rbin = str(bins[i]) + "_" + str(bins[i + 1])
fileName = "combinedCards_" + options.name + "/fitDiagnostics_" + options.file + ".root" if not any(
t in s for t in ['data', 'tDM']
) else "rootfiles_" + options.name + "/" + channel + "bin_" + rbin + ".root"
histName = "shapes_fit_b/" + channel + "bin_" + rbin + "/" + s if not any(
t in s for t in ['data', 'tDM']) else s
file[s] = TFile(fileName, "READ")
tmphist = file[s].Get(histName)
if 'data' not in s: hist[s].SetMarkerSize(0)
if tmphist:
hist[s].SetBinContent(i + 1, tmphist.GetBinContent(1))
hist[s].SetBinError(i + 1, tmphist.GetBinError(1))
else:
hist[s].SetBinContent(i + 1, 0.)
hist[s].SetBinError(i + 1, 0.)
else:
fileName = "combinedCards_" + options.name + "/fitDiagnostics_" + options.file + ".root" if not s == 'data_obs' else "rootfiles_" + options.name + "/" + channel + binName + ".root"
histName = "shapes_fit_b/" + channel + "/" + s if not s == 'data_obs' else s
file[s] = TFile(fileName, "READ")
tmphist = file[s].Get(histName)
if tmphist == None:
tmphist = hist[back[0]].Clone(s)
tmphist.Reset("MICES")
print "Histogram", histName, "not found in file", fileName
if s == 'data_obs': hist[s] = tmphist
else:
hist[s] = hist['data_obs'].Clone(s)
hist[s].SetMarkerSize(0)
for i in range(tmphist.GetNbinsX() + 1):
hist[s].SetBinContent(i + 1,
tmphist.GetBinContent(i + 1))
elif treeRead: # Project from tree
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
tree[s].Add(NTUPLEDIR + ss + ".root")
if not binLow == "":
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''), 1, float(binLow),
float(binHigh))
elif binLow == "" and variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
else:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''),
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
redFactorString = ""
redFactorValue = ""
#if isBlind and 'data' in s:
if isBlind and 'data' in s and options.limit:
redFactorString = " && Entry$ % 15 == 1"
#if isBlind and 'data' not in s:
if isBlind and 'data' not in s and options.limit:
redFactorValue = " / 15"
cutstring = "(" + weight + redFactorValue + ")" + (
"*(" + cut + redFactorString + ")" if len(cut) > 0 else "")
if '-' in s:
cutstring = cutstring.replace(
cut, cut + "&& nBQuarks==" + s.split('-')[1][0])
if useformula == True:
tree[s].Project(s, variable[var]['formula'], cutstring)
else:
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree() == None:
hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
else: # Histogram written to file
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
file[ss] = TFile(NTUPLEDIR + ss + ".root", "R")
if file[ss].IsZombie():
print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist"
continue
tmphist = file[ss].Get(cut + "/" + var)
if tmphist == None: continue
if not s in hist.keys(): hist[s] = tmphist
else: hist[s].Add(tmphist)
if hist[s].Integral() < 0: hist[s].Scale(0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'])
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
#if 'WJetsToLNu' in s and 'SL' in channel and 'WR' in channel: hist[s].Scale(1.30)
#if 'TTbar' in s and 'SL' in channel and 'TR' in channel: hist[s].Scale(0.91)
hist['BkgSum'] = hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s], 1)
if fileRead:
hist['PreFit'] = hist['BkgSum'].Clone("PreFit")
if doBinned:
for i in range(0, len(bins) - 1):
rbin = str(bins[i]) + "_" + str(bins[i + 1])
tmphist = file[back[0]].Get("shapes_prefit/" + channel +
"bin_" + rbin + "/" +
"total_background")
if tmphist:
hist['PreFit'].SetBinContent(i + 1,
tmphist.GetBinContent(1))
else:
hist['PreFit'].SetBinContent(i + 1, 0.)
else:
tmphist = file[back[0]].Get("shapes_prefit/" + channel + "/" +
"total_background")
for i in range(tmphist.GetNbinsX() + 1):
hist['PreFit'].SetBinContent(i + 1,
tmphist.GetBinContent(i + 1))
addOverflow(hist['PreFit'], False)
hist['PreFit'].SetLineStyle(2)
hist['PreFit'].SetLineColor(617) #923
hist['PreFit'].SetLineWidth(3)
hist['PreFit'].SetFillStyle(0)
hist['BkgSum'].SetFillStyle(3002)
hist['BkgSum'].SetFillColor(1)
# Create data and Bkg sum histograms
# if options.blind or 'SR' in channel:
# hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
# hist['data_obs'].Reset("MICES")
# Set histogram style
hist[data[0]].SetMarkerStyle(20)
hist[data[0]].SetMarkerSize(1.25)
for i, s in enumerate(data + back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
#for i, s in enumerate(sign): sample[s]['plot'] = True#sample[s]['plot'] and s.startswith(channel[:2])
if norm:
for i, s in enumerate(sign):
hist[s].Scale(hist['BkgSum'].Integral() / hist[s].Integral())
# for i, s in enumerate(back):
# hist[s].SetFillStyle(3005)
# hist[s].SetLineWidth(2)
# #for i, s in enumerate(sign):
# # hist[s].SetFillStyle(0)
# if not var=="Events":
# sfnorm = hist[data[0]].Integral()/hist['BkgSum'].Integral()
# print "Applying SF:", sfnorm
# for i, s in enumerate(back+['BkgSum']): hist[s].Scale(sfnorm)
if SIGNAL > 1:
if not var == "Events":
for i, s in enumerate(sign):
hist[s].Scale(SIGNAL)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
#leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg = TLegend(0.45, 0.63, 0.93, 0.92)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetNColumns(3)
leg.SetTextFont(42)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if 'PreFit' not in hist:
leg.AddEntry(hist['BkgSum'], sample['BkgSum']['label'], "f")
else:
leg.AddEntry(hist['BkgSum'], 'MC unc.', "l")
leg.AddEntry(hist['PreFit'], sample['PreFit']['label'], "l")
if showSignal:
for i, s in enumerate(sign):
if SIGNAL > 1:
if sample[s]['plot']:
leg.AddEntry(hist[s],
'%s (x%d)' % (sample[s]['label'], SIGNAL),
"l")
else:
if sample[s]['plot']:
leg.AddEntry(hist[s], sample[s]['label'], "l")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if RATIO else 600)
if RATIO:
if RESIDUAL:
c1.Divide(1, 3)
setFitTopPad(c1.GetPad(1), RATIO)
setFitBotPad(c1.GetPad(2), RATIO)
setFitResPad(c1.GetPad(3), RATIO)
else:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
log = ("log" in hist['BkgSum'].GetZaxis().GetTitle())
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if not isBlind and len(data) > 0:
graph = fixData(hist[data[0]], USEGARWOOD)
graph.Draw("SAME, PE")
#data_graph.Draw("SAME, PE")
if 'PreFit' in hist: hist['PreFit'].Draw("SAME, HIST")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].Draw(
"SAME, HIST"
) # signals Normalized, hist[s].Integral()*sample[s]['weight']
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((5. if log else 1.25) * max(
bkg.GetMaximum(),
hist[data[0]].GetBinContent(hist[data[0]].GetMaximumBin()) +
hist[data[0]].GetBinError(hist[data[0]].GetMaximumBin())))
if len(sign) > 0 and bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(),
hist[sign[-1]].GetMaximum()):
bkg.SetMaximum(
max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()) *
1.25)
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist[data[0]].GetMinimum()), 5.e-1) if log else 0.)
if log:
#bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e3)
bkg.GetYaxis().SetMoreLogLabels(True)
else:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e3)
leg.Draw()
if fileRead and 'SR' in channel:
drawCMS(LUMI / 15., "Preliminary")
else:
drawCMS(LUMI, "Preliminary")
drawRegion(channel, True)
drawAnalysis("DM" + channel[:2])
drawOverflow()
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
if RESIDUAL: setFitBotStyle(err)
else: setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist[data[0]].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
if RESIDUAL: setFitBotStyle(res)
else: setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
if 'PreFit' in hist:
respre = hist[data[0]].Clone("ResiduesPreFit")
respre.Divide(hist['PreFit'])
respre.SetLineStyle(2)
respre.SetLineColor(617) #923
respre.SetLineWidth(3)
respre.SetFillStyle(0)
respre.Draw("SAME, HIST")
errLine.Draw("SAME, HIST")
if not isBlind and len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
c1.Update()
if RATIO and RESIDUAL:
c1.cd(3)
c1.SetGrid(1, 0)
resFit = hist[data[0]].Clone("Residues")
resFit.Reset("MICES")
resFit.SetTitle("")
#resFit.GetYaxis().SetTitle("Residuals")
resFit.GetYaxis().SetTitle(
"#frac{Data - Bkg}{#sqrt{#sigma_{Data}^{2}+#sigma_{Bkg}^{2}}}")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
resFit.SetBinContent(
i, (hist[data[0]].GetBinContent(i) -
hist['BkgSum'].GetBinContent(i)) / (math.sqrt(
math.pow(hist['BkgSum'].GetBinError(i), 2) +
math.pow(hist[data[0]].GetBinError(i), 2))))
setFitResStyle(resFit)
resFit.SetLineColor(15)
resFit.SetFillColor(15)
resFit.SetFillStyle(1001)
resFit.Draw("HIST")
resFitLine = resFit.Clone("resFitLine")
resFitLine.SetLineWidth(1)
resFitLine.SetFillStyle(0)
resFitLine.Draw("SAME, HIST")
c1.Update()
if gROOT.IsBatch(
) and options.saveplots: # and (treeRead and channel in selection.keys()):
AddString = ""
if not os.path.exists("plots_" + options.name + "/" + plotdir):
os.makedirs("plots_" + options.name + "/" + plotdir)
if fileRead:
if RESIDUAL: AddString = "_PostFit_Residual"
else: AddString = "_PostFit"
#c1.Print("plots_"+options.name+"/"+plotdir+"/"+plotname+binName+AddString+".png")
c1.Print("plots_" + options.name + "/" + plotdir + "/" + plotname +
binName + AddString + ".pdf")
# Print table
printTable(hist, sign)
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if gROOT.IsBatch() and not fileRead and (
var == 'MET_pt' or
(channel.startswith('SL') and var == 'MET_sign') or
(channel.endswith('ZR') and var == 'FakeMET_pt')):
saveHist(hist, channel + binName)
def plotPostFitValues(channel,var,DM,year,*parameters,**kwargs):
"""Draw post-fit values for parameter using MultiDimFit and FitDiagnostics output."""
if DM=='DM0' and 'm_2' in var: return
print green("\n>>> plotPostFitValues %s, %s"%(DM, var))
if len(parameters)==1 and isinstance(parameters[0],list): parameters = parameters[0]
parameters = [p.replace('$CAT',DM).replace('$CHANNEL',channel) for p in list(parameters)]
title = kwargs.get('title', "" )
name = kwargs.get('name', "" )
indir = kwargs.get('indir', "output_%d"%year )
outdir = kwargs.get('outdir', "postfit_%d"%year )
tag = kwargs.get('tag', "" )
plotlabel = kwargs.get('plotlabel', "" )
compareFD = kwargs.get('compareFD', False ) and N==1
era = "%d-13TeV"%year
isBBB = any("_bin_" in p for p in parameters)
filename = '%s/higgsCombine.%s_%s-%s%s-%s.MultiDimFit.mH90.root'%(indir,channel,var,DM,tag,era)
filenamesFD = '%s/fitDiagnostics.%s_%s-%s%s-%s_TES*p*.root'%(indir,channel,var,DM,tag,era)
ensureDirectory(outdir)
if not name:
name = formatParameter(parameters[0]).replace('_'+DM,'')
if len(parameters)>1:
name = "comparison_%s"%(name) #re.sub(r"bin_\d+","bin",name)
canvasname = "%s/postfit-%s_%s_%s%s%s"%(outdir,name,var,DM,tag,plotlabel)
print '>>> file "%s"'%(filename)
graphs = [ ]
graphsFD = [ ]
tvals = [ ]
pvals = [ -2.2, +2.2 ]
tes = measureTES(filename)
for parameter in parameters[:]:
graph = getTGraphOfParameter(filename,'tes',parameter,xvals=tvals,yvals=pvals)
if graph:
graphs.append(graph)
else:
parameters.remove(parameter)
if compareFD:
graphFD = getTGraphOfParameter_FD(filenamesFD,parameter,xvals=tvals,yvals=pvals)
if graphFD: graphsFD.append(graphFD)
if len(parameters)!=len(graphs):
warning("plotPostFitValues: len(parameters) = %d != %d = len(graphs)"%(len(parameters),len(graphs)))
exit(1)
N = len(parameters)
compareFD = compareFD and len(graphsFD)>0
parameters = [formatParameter(p).replace('_'+DM,'') for p in parameters]
graphsleg = columnize(graphs,3) if N>6 else columnize(graphs,2) if N>3 else graphs # reordered for two columns
paramsleg = columnize(parameters,3) if N>6 else columnize(parameters,2) if N>3 else parameters # reordered for two columns
xtitle = 'tau energy scale'
ytitle = "%s post-fit value"%(parameters[0] if N==1 else "MultiDimFit")
xmin, xmax = min(tvals), max(tvals)
ymin, ymax = min(pvals), max(pvals)
colors = [ kBlack, kBlue, kRed, kGreen, kMagenta, kOrange, kTeal, kAzure+2, kYellow-3 ]
canvas = TCanvas("canvas","canvas",100,100,800,650)
canvas.SetFillColor(0)
canvas.SetBorderMode(0)
canvas.SetFrameFillStyle(0)
canvas.SetFrameBorderMode(0)
canvas.SetTopMargin( 0.07 ); canvas.SetBottomMargin( 0.13 )
canvas.SetLeftMargin( 0.11 ); canvas.SetRightMargin( 0.05 )
canvas.SetTickx(0)
canvas.SetTicky(0)
canvas.SetGrid()
canvas.cd()
maxmargin = 0.18*abs(ymax-ymin)
minmargin = 0.20*abs(ymax-ymin) #(0.20 if N<5 else 0.10*ceil(N/2))
ymin = 0.0 if ymin>=0 else ymin-minmargin
ymax = ymax + minmargin
textsize = 0.045 if N==1 else 0.036
lineheight = 0.055 if N==1 else 0.045
x1, width = 0.42, 0.25
y1, height = 0.15, lineheight*(ceil(N/3.) if N>6 else ceil(N/2.) if N>3 else N)
if title: height += lineheight
if compareFD: height += lineheight
if N>6:
if isBBB: width = 0.62; x1 = 0.25
else: width = 0.70; x1 = 0.20
elif N>3: width = 0.52; x1 = 0.36
legend = TLegend(x1,y1,x1+width,y1+height)
legend.SetTextSize(textsize)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
legend.SetFillColor(0)
if title:
legend.SetTextFont(62)
legend.SetHeader(title)
legend.SetTextFont(42)
if N>6:
legend.SetNColumns(3)
legend.SetColumnSeparation(0.02)
elif N>3:
legend.SetNColumns(2)
legend.SetColumnSeparation(0.03)
frame = canvas.DrawFrame(xmin,ymin,xmax,ymax)
frame.GetYaxis().SetTitleSize(0.060)
frame.GetXaxis().SetTitleSize(0.060)
frame.GetXaxis().SetLabelSize(0.050)
frame.GetYaxis().SetLabelSize(0.050)
frame.GetXaxis().SetLabelOffset(0.01)
frame.GetYaxis().SetLabelOffset(0.01)
frame.GetXaxis().SetTitleOffset(0.98)
frame.GetYaxis().SetTitleOffset(0.84)
frame.GetXaxis().SetNdivisions(508)
frame.GetYaxis().SetTitle(ytitle)
frame.GetXaxis().SetTitle(xtitle)
for i, (parameter,graph) in enumerate(zip(parameters,graphs)):
graph.SetLineColor(colors[i%len(colors)])
graph.SetLineWidth(2)
graph.SetLineStyle(1)
graph.Draw('LSAME')
for parameter,graph in zip(paramsleg,graphsleg):
if N==1:
legend.AddEntry(graph, "MultiDimFit", 'l')
else:
legend.AddEntry(graph, parameter, 'l')
line = TLine(tes, ymin, tes, ymax)
#line.SetLineWidth(1)
line.SetLineStyle(7)
line.Draw('SAME')
latex = TLatex()
latex.SetTextSize(0.045)
latex.SetTextAlign(13)
latex.SetTextFont(42)
latex.DrawLatex(tes+0.02*(xmax-xmin),ymax-0.04*(ymax-ymin),"tes = %.3f"%tes)
if compareFD:
graphFD.SetLineColor(kBlue)
graphFD.SetLineWidth(2)
graphFD.SetLineStyle(2)
graphFD.Draw('LSAME')
legend.AddEntry(graphFD, "FitDiagnostics", 'l')
legend.Draw()
CMS_lumi.relPosX = 0.12
CMS_lumi.CMS_lumi(canvas,13,0)
gPad.SetTicks(1,1)
gPad.Modified()
frame.Draw('SAMEAXIS')
canvas.SaveAs(canvasname+".png")
if args.pdf: canvas.SaveAs(canvasname+".pdf")
canvas.Close()
def plotInputComparison(files, runs, processes, path):
#clear memory
gROOT.Reset()
#make canvas to save plots to
c1 = TCanvas('c1')
c1.SetLogy()
i = 0
Tfiles = []
while i < len(files):
print "Adding file: %s to list of files to run with Run Number: %s and Process Name: %s" % (
files[i], runs[i], processes[i])
Tfiles.append(TFile(files[i]))
i += 1
j = 0
Thists = []
while j < len(Tfiles):
dirname = "DQMData/Run %s/HLT/Run summary/TimerService/Running 1 processes/process %s/Paths/%s_module_counter" % (
runs[j], processes[j], path)
print dirname
hist = Tfiles[j].Get(dirname)
Thists.append(hist)
j += 1
k = 0
leg = TLegend(0.4, 0.6, 0.9, 0.9, "")
leg.SetFillStyle(0)
leg.SetBorderSize(0)
while k < len(Thists):
if k == 0:
Thists[k].Scale(1.0 / Thists[k].Integral())
Thists[k].GetYaxis().SetRangeUser(0.000008, 0.2)
print "lower bound of bin 100: %i" % Thists[k].GetBinCenter(100)
print "percentage of events running particle flow: %i " % Thists[
k].Integral(100, 500)
Thists[k].SetLineWidth(2)
Thists[k].SetLineColor(k + 1)
if args.ext:
Thists[k].GetXaxis().SetRangeUser(0, 2000)
else:
Thists[k].GetXaxis().SetRangeUser(0, 400)
Thists[k].Draw()
else:
Thists[k].Scale(1.0 / Thists[k].Integral())
Thists[k].SetLineWidth(2)
Thists[k].SetLineColor(k + 1)
Thists[k].Draw("same")
#write name in full
name = "Mean: %f" % Thists[k].GetMean()
if args.ext:
Thists[k].GetXaxis().SetRangeUser(0, 2000)
else:
Thists[k].GetXaxis().SetRangeUser(0, 400)
leg.AddEntry(Thists[k], name, "l")
k += 1
leg.Draw("same")
filename = 'ModuleCounter_%s.pdf' % path
c1.Print(filename)
def plot_histograms(histos,
use_log_y=False,
ratio_=False,
legend_titles=None,
title="",
x_label="",
y_label_up="",
y_label_ratio="",
save_path="./plot.eps",
**kwargs):
"""
Throws all given histograms into one canvas. If desired, a ratio plot will be added.
"""
gStyle.SetOptStat(0)
justratioplot = False
yrange = None
if isinstance(ratio_, list):
ratio = ratio_[0]
justratioplot = ratio_[1]
yrange = ratio_[2]
else:
justratioplot = ratio_
ratio = ratio_
linestyles = kwargs.get("linestyles", None)
markerstyles = kwargs.get("markerstyles", None)
colors = kwargs.get("colors", None)
draw_options = kwargs.get("draw_options", None)
linewidths = kwargs.get("linewidths", None)
fillstyles = kwargs.get("fillstyles", None)
fillcolors = kwargs.get("fillcolors", None)
canvas_name = kwargs.get("canvas_name", "Canvas")
style_histograms(histos, linestyles, markerstyles, colors, linewidths,
fillstyles, fillcolors)
canvas = TCanvas('canvas', canvas_name, 800, 800)
pad_up_start = 0.4 if ratio else 0.
pad_up = TPad("pad_up", "", 0., pad_up_start, 1., 1.)
if ratio:
pad_up.SetBottomMargin(0.)
pad_up.Draw()
x_label_up_tmp = x_label if not ratio else ""
put_in_pad(pad_up,
use_log_y,
histos,
title,
x_label_up_tmp,
y_label_up,
yrange,
draw_options=draw_options)
pad_up.cd()
legend = None
if legend_titles is not None:
if justratioplot:
legend = TLegend(.2, .65, .6, .85)
else:
legend = TLegend(.45, .65, .85, .85)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
legend.SetTextSize(0.02)
for h, l in zip(histos, legend_titles):
if l is not None:
legend.AddEntry(h, l)
legend.Draw()
canvas.cd()
pad_ratio = None
histos_ratio = None
if ratio and justratioplot is False:
histos_ratio = divide_all_by_first(histos)
pad_ratio = TPad("pad_ratio", "", 0., 0.05, 1., pad_up_start)
pad_ratio.SetTopMargin(0.)
pad_ratio.SetBottomMargin(0.3)
pad_ratio.Draw()
put_in_pad(pad_ratio, False, histos_ratio, "", x_label, y_label_ratio)
canvas.SaveAs(save_path)
index = save_path.rfind(".")
# Save also everything into a ROOT file
root_save_path = save_path[:index] + ".root"
root_file = TFile.Open(root_save_path, "RECREATE")
for h in histos:
h.Write()
canvas.Write()
root_file.Close()
canvas.Close()
def comp_fit_pars(do_ratio=False): #pylint: disable-msg=too-many-statements
inputdir = './'
input_files = [
'../ML_DsAnalysis/QM_prel/outputs/3050/raw_yields/RawYieldsDs_3050_pt2_36.root',
'outputs/3_24bin_merge/raw_yields/RawYieldsDs_3050_100419cuts.root'
]
input_files_MC = [
'../ML_DsAnalysis/QM_prel/outputs/3050/raw_yields/RawYieldsDs_3050_MC_pt2_36.root'
]
colors = [kBlack, kGreen + 3, kRed]
markers = [kFullCircle, kFullDiamond, kFullSquare]
legendnames = ['ML', 'Std', 'ML - MC']
suffix = 'compMCStd'
min_pt = 2.
max_pt = 36.
set_style()
hMean, hSigma = [], []
input_files = input_files + input_files_MC
lineMass = TLine(min_pt, 1.96850, max_pt, 1.96850)
lineMass.SetLineWidth(2)
lineMass.SetLineColor(kBlack)
lineMass.SetLineStyle(9)
legSigma = TLegend(0.2, 0.78, 0.8, 0.93)
legSigma.SetFillStyle(0)
legSigma.SetBorderSize(0)
legSigma.SetTextSize(0.04)
legMean = TLegend(0.4, 0.73, 0.7, 0.93)
legMean.SetFillStyle(0)
legMean.SetBorderSize(0)
legMean.SetTextSize(0.04)
legMean.AddEntry(lineMass, "PDG", 'l')
for file_path, color, marker, legend_name in zip(input_files, colors,
markers, legendnames):
input_file = TFile(f'{inputdir}/{file_path}')
histo_mean = input_file.Get('hRawYieldsMean')
histo_sigma = input_file.Get('hRawYieldsSigma')
histo_mean.SetDirectory(0)
histo_mean.SetLineColor(color)
histo_mean.SetLineWidth(2)
histo_mean.SetMarkerColor(color)
histo_mean.SetMarkerStyle(marker)
histo_sigma.SetDirectory(0)
histo_sigma.SetLineColor(color)
histo_sigma.SetLineWidth(2)
histo_sigma.SetMarkerColor(color)
histo_sigma.SetMarkerStyle(marker)
legMean.AddEntry(histo_mean, legend_name, 'p')
legSigma.AddEntry(histo_sigma, legend_name, 'p')
hMean.append(histo_mean)
hSigma.append(histo_sigma)
if do_ratio:
mean_num_list = list(hMean)
mean_num_list.pop()
mean_den_hist = hMean[-1]
mean_ratio_list = create_ratio_hist(mean_num_list, mean_den_hist)
cMeanRatio = TCanvas('cMeanRatio', '', 800, 800)
cMeanRatio.DrawFrame(
min_pt, 0.99, max_pt, 1.01,
';#it{p}_{T} (GeV/#it{c}); peak mean / peak mean MC')
lineRatio = TLine(min_pt, 1., max_pt, 1.)
lineRatio.SetLineWidth(2)
lineRatio.SetLineColor(kBlack)
lineRatio.SetLineStyle(9)
legMeanRatio = TLegend(0.4, 0.73, 0.7, 0.93)
legMeanRatio.SetFillStyle(0)
legMeanRatio.SetBorderSize(0)
legMeanRatio.SetTextSize(0.04)
for hist, color, marker, legend_name in zip(mean_ratio_list,
colors[:-1], markers[:-1],
legendnames[:-1]):
hist.SetLineColor(color)
hist.SetLineWidth(2)
hist.SetMarkerColor(color)
hist.SetMarkerStyle(marker)
hist.Draw('same')
legMeanRatio.AddEntry(hist, legend_name, 'p')
lineRatio.Draw()
legMeanRatio.Draw()
sigma_num_list = list(hSigma)
sigma_num_list.pop()
sigma_den_hist = hSigma[-1]
sigma_ratio_list = create_ratio_hist(sigma_num_list, sigma_den_hist)
cSigmaRatio = TCanvas('cSigmaRatio', '', 800, 800)
cSigmaRatio.DrawFrame(
min_pt, 0.6, max_pt, 1.8,
';#it{p}_{T} (GeV/#it{c}); peak width / peak width MC')
legSigmaRatio = TLegend(0.4, 0.73, 0.7, 0.93)
legSigmaRatio.SetFillStyle(0)
legSigmaRatio.SetBorderSize(0)
legSigmaRatio.SetTextSize(0.04)
for hist, color, marker, legend_name in zip(sigma_ratio_list,
colors[:-1], markers[:-1],
legendnames[:-1]):
hist.SetLineColor(color)
hist.SetLineWidth(2)
hist.SetMarkerColor(color)
hist.SetMarkerStyle(marker)
hist.Draw('same')
legSigmaRatio.AddEntry(hist, legend_name, 'p')
lineRatio.Draw()
legSigmaRatio.Draw()
cMeanRatio.SaveAs(f'{inputdir}/MeanRatio_{suffix}.pdf')
cSigmaRatio.SaveAs(f'{inputdir}/SigmaRatio_{suffix}.pdf')
cMean = TCanvas('cMean', '', 800, 800)
cMean.DrawFrame(min_pt, 1.9641, max_pt, 1.9759,
';#it{p}_{T} (GeV/#it{c}); peak mean (GeV/#it{c}^{2})')
lineMass.Draw("same")
for histo_mean in hMean:
histo_mean.Draw('same')
legMean.Draw()
cSigma = TCanvas('cSigma', '', 800, 800)
cSigma.DrawFrame(min_pt, 0., max_pt, 0.025,
';#it{p}_{T} (GeV/#it{c}); peak width (GeV/#it{c}^{2})')
for histo_sigma in hSigma:
histo_sigma.Draw('same')
legSigma.Draw()
cMean.SaveAs(f'{inputdir}/Mean_{suffix}.pdf')
cSigma.SaveAs(f'{inputdir}/Sigma_{suffix}.pdf')
else:
h2.GetYaxis().SetRangeUser(0, 0.7)
h2.GetXaxis().SetLimits(0, 0.1)
h2.SetTitle("#DeltaR_" + str(list_PT_T[j]))
h2.SetTitle("#DeltaR_" + str(list_PT_T[j]))
h2.SetXTitle("#DeltaR")
h2.SetXTitle("#DeltaR")
h2.SetYTitle("Arbitrary number")
h2.SetYTitle("Arbitrary number")
h2.SetTitle("")
# leg.AddEntry(h1,"Z'("+str(energy[E])+"TeV)#rightarrowq#bar{q}#rightarrow1 subjet","l")
# leg.AddEntry(h2,"Z'("+str(energy[E])+"TeV)#rightarrowW^{+}W^{-}#rightarrow2 subjets","l")
leg = TLegend(0.25, 0.5, 0.45, 0.9)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextSize(0.04)
leg.SetBorderSize(0)
leg.SetTextFont(22)
leg.Draw()
leg.AddEntry("", "Z'#rightarrowq#bar{q}", "")
leg.AddEntry(h1, "Trailing^{1}", "l")
leg.AddEntry(h3, "Trailing^{2}", "l")
leg.AddEntry(h5, "Trailing^{3}", "l")
leg.AddEntry(h7, "Trailing^{4}", "l")
leg.AddEntry(h9, "Trailing^{5}", "l")
leg1 = TLegend(0.55, 0.5, 0.85, 0.9)
leg1.SetFillColor(0)
leg1.SetFillStyle(0)
leg1.SetTextSize(0.04)
def plotDataOverMCEff(hist_mc_tight,
hist_mc_loose,
hist_data_tight,
hist_data_loose,
plot_name='fakerate.pdf'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Fake rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
g_data.GetYaxis().SetTitle('Fake rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i] / g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i) / g_data_vals[i])**2 + (
g.GetErrorYlow(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt(
(g_data.GetErrorYhigh(i) / g_data_vals[i])**2 +
(g.GetErrorYhigh(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() /
xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(
g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
# maxy = 1.1 * min(g.GetMaximum(), g_data.GetMaximum(), 0.2)
g.GetYaxis().SetRangeUser(0.001, 0.2)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), 'MC', 'lep')
legend.AddEntry(g_data.GetName(), 'Observed', 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.51, 1.49)
g_ratio.GetYaxis().SetTitle('Obs/MC')
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
def FitMassPoint(massin, massmin, massmax, nbins=50):
# massin = 800;
# massmin = 400;
# massmax = 1400;
# nbins = 50;
# massin = 600;
# massmin = 200;
# massmax = 1000;
# nbins = 40;
inputFile = ROOT.TFile(
"/uscms_data/d2/andersj/Wjj/2012/data/Moriond2013/ReducedTrees/RD_mu_HWWMH"
+ str(massin) + "_CMSSW532_private.root")
tree = inputFile.Get("WJet")
# tree.Print();
print "n entries: ", tree.GetEntries()
############################################################
# RooFitting
rrv_mass = ROOT.RooRealVar("rrv_mass", "rrv_mass", massmin, massmax)
rrv_weight = RooRealVar("rrv_weight", "rrv_weight", 0., 10000000.)
rrv_mH = ROOT.RooRealVar("rrv_mH", "rrv_mH", massin, massmin, massmax)
rrv_gamma = ROOT.RooRealVar("rrv_gamma", "rrv_gamma", 20., massmax)
rrv_mH2 = ROOT.RooRealVar("rrv_mH2", "rrv_mH2", massin, massmin, massmax)
rrv_gamma2 = ROOT.RooRealVar("rrv_gamma2", "rrv_gamma2", 20., massmax)
# rrv_gamma2 = ROOT.RooRealVar("rrv_gamma2","rrv_gamma2",350.,600.)
rrv_mH3 = ROOT.RooRealVar("rrv_mH3", "rrv_mH3", massin, massmin, massmax)
rrv_gamma3 = ROOT.RooRealVar("rrv_gamma3", "rrv_gamma3", 20., massmax)
rrv_mH4 = ROOT.RooRealVar("rrv_mH4", "rrv_mH4", massin, massmin, massmax)
rrv_gamma4 = ROOT.RooRealVar("rrv_gamma4", "rrv_gamma4", 20., massmax)
rds_raw = ROOT.RooDataSet("rds_raw", "rds_raw",
RooArgSet(rrv_mass, rrv_weight),
RooFit.WeightVar(rrv_weight))
rds_cps = ROOT.RooDataSet("rds_cps", "rds_cps",
RooArgSet(rrv_mass, rrv_weight),
RooFit.WeightVar(rrv_weight))
rds_cps_intf = ROOT.RooDataSet("rds_cps_intf", "rds_cps_intf",
RooArgSet(rrv_mass, rrv_weight),
RooFit.WeightVar(rrv_weight))
model_pdf = ROOT.RooRelBWHighMass("model_pdf", "model_pdf", rrv_mass,
rrv_mH, rrv_gamma)
model2_pdf = ROOT.RooRelBWRunningWidth("model2_pdf", "model2_pdf",
rrv_mass, rrv_mH2, rrv_gamma2)
model3_pdf = ROOT.RooRelBWRunningWidth("model3_pdf", "model3_pdf",
rrv_mass, rrv_mH3, rrv_gamma3)
model4_pdf = ROOT.RooRelBWRunningWidth("model4_pdf", "model4_pdf",
rrv_mass, rrv_mH4, rrv_gamma4)
for i in range(tree.GetEntries()):
if i % 10000 == 0: print "i: ", i
tree.GetEntry(i)
curmass = getattr(tree, "W_H_mass_gen")
if curmass < massmax and curmass > massmin:
rrv_mass.setVal(curmass)
tmpweight_cps = getattr(
tree, "complexpolewtggH" + str(massin)) / getattr(
tree, "avecomplexpolewtggH" + str(massin))
tmpweight_cps_intf = getattr(
tree, "complexpolewtggH" + str(massin)) * getattr(
tree, "interferencewtggH" + str(massin)) / getattr(
tree, "avecomplexpolewtggH" + str(massin))
rds_raw.add(RooArgSet(rrv_mass), 1.)
rds_cps.add(RooArgSet(rrv_mass), tmpweight_cps)
rds_cps_intf.add(RooArgSet(rrv_mass), tmpweight_cps_intf)
############################################################
rfresult = model_pdf.fitTo(rds_cps, RooFit.Save(1),
RooFit.SumW2Error(kTRUE))
rfresult2 = model2_pdf.fitTo(rds_cps, RooFit.Save(1),
RooFit.SumW2Error(kTRUE))
rrv_mH3.setVal(rrv_mH2.getVal())
rrv_gamma3.setVal(0.5 * rrv_gamma2.getVal())
rrv_mH4.setVal(rrv_mH2.getVal())
rrv_gamma4.setVal(0.2 * rrv_gamma2.getVal())
mplot = rrv_mass.frame(RooFit.Title("mass plot"))
rds_raw.plotOn(mplot, RooFit.MarkerColor(kBlack), RooFit.LineColor(kBlack),
RooFit.Binning(nbins, massmin, massmax),
RooFit.DataError(RooAbsData.SumW2))
rds_cps_intf.plotOn(mplot, RooFit.MarkerColor(kBlue),
RooFit.LineColor(kBlue),
RooFit.Binning(nbins, massmin, massmax),
RooFit.DataError(RooAbsData.SumW2))
rds_cps.plotOn(mplot, RooFit.MarkerColor(kRed), RooFit.LineColor(kRed),
RooFit.Binning(nbins, massmin, massmax),
RooFit.DataError(RooAbsData.SumW2))
# model_pdf.plotOn(mplot, RooFit.LineColor(kRed));
model2_pdf.plotOn(mplot, RooFit.LineColor(kRed), RooFit.LineStyle(2))
model3_pdf.plotOn(mplot, RooFit.LineColor(ROOT.kGreen + 2),
RooFit.LineStyle(2))
model4_pdf.plotOn(mplot, RooFit.LineColor(ROOT.kGreen + 2),
RooFit.LineStyle(3))
print "rds_cps_intf.sumEntries() = ", rds_cps_intf.sumEntries()
print "model_pdf: mH = ", rrv_mH.getVal(), ", gamma = ", rrv_gamma.getVal(
)
print "model2_pdf: mH = ", rrv_mH2.getVal(
), ", gamma = ", rrv_gamma2.getVal()
dummy_h1 = ROOT.TH1F("dummy_h1", "dummy_h1", 1, 0, 1)
dummy_h1.SetMarkerColor(ROOT.kBlack)
dummy_h2 = ROOT.TH1F("dummy_h2", "dummy_h2", 1, 0, 1)
dummy_h2.SetMarkerColor(ROOT.kRed)
dummy_h3 = ROOT.TH1F("dummy_h3", "dummy_h3", 1, 0, 1)
dummy_h3.SetMarkerColor(ROOT.kBlue)
dummy_h4 = ROOT.TH1F("dummy_h4", "dummy_h4", 1, 0, 1)
dummy_h4.SetLineColor(ROOT.kRed)
dummy_h5 = ROOT.TH1F("dummy_h5", "dummy_h5", 1, 0, 1)
dummy_h5.SetLineColor(ROOT.kRed)
dummy_h5.SetLineStyle(2)
dummy_h6 = ROOT.TH1F("dummy_h6", "dummy_h6", 1, 0, 1)
dummy_h6.SetLineColor(ROOT.kGreen + 2)
dummy_h6.SetLineStyle(2)
dummy_h7 = ROOT.TH1F("dummy_h7", "dummy_h7", 1, 0, 1)
dummy_h7.SetLineColor(ROOT.kGreen + 2)
dummy_h6.SetLineStyle(3)
L = TLegend(0.65, 0.60, 0.93, 0.85)
L.SetFillStyle(0)
L.AddEntry(dummy_h1, "Powheg", "p")
L.AddEntry(dummy_h2, "w/CPS weight", "p")
L.AddEntry(dummy_h3, "w/CPS,Intf weight", "p")
# L.AddEntry(dummy_h4,"Fit, BW (Mario)","l");
L.AddEntry(dummy_h5, "Fit, BW (running)", "l")
L.AddEntry(dummy_h6, "BW (running), width*0.5", "l")
L.AddEntry(dummy_h7, "Fit, BW (running), width*0.2", "l")
can2 = ROOT.TCanvas("can2", "can2", 800, 800)
mplot.Draw()
L.Draw()
ROOT.gPad.SetLogy()
can2.SaveAs("massFits/mass_rf_" + str(massin) + ".eps")
can2.SaveAs("massFits/mass_rf_" + str(massin) + ".png")
outputpar = []
outputpar.append(rrv_mH2.getVal())
outputpar.append(rrv_gamma2.getVal())
outputpar.append(rrv_mH.getVal())
outputpar.append(rrv_gamma.getVal())
return outputpar
def MakeLegend(can,
x1=.8,
y1=.8,
x2=.9,
y2=.9,
textsize=18,
ncolumns=1,
totalentries=0,
option='f',
skip=[]):
from ROOT import TLegend, TH1, gStyle, TGraph
if can.GetPrimitive('pad_top'):
MakeLegend(can.GetPrimitive('pad_top'),
x1,
y1,
x2,
y2,
textsize,
ncolumns,
totalentries,
skip=skip)
return
if CanvasEmpty(can):
print 'Error: trying to make legend from canvas with 0 plots. Will do nothing.'
return
#
# if a previous version exists from this function, delete it
#
if can.GetPrimitive('legend'):
can.GetPrimitive('legend').Delete()
leg = TLegend(x1, y1, x2, y2)
leg.SetName('legend')
tobject_collector.append(leg)
leg.SetTextFont(43)
leg.SetTextSize(textsize)
leg.SetTextFont(43)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetNColumns(ncolumns)
#
# Add by TH1 GetTitle()
#
the_primitives = can.GetListOfPrimitives()
if can.GetPrimitive('pad_top'):
the_primitives = can.GetPrimitive('pad_top').GetListOfPrimitives()
if can.GetPrimitive('stack'):
the_stack = list(reversed(list(can.GetPrimitive('stack').GetHists())))
the_primitives = the_stack + list(the_primitives)
if type(option) == type(''):
option = [option] * 100
total = 0
for i in the_primitives:
if i.GetName() == 'stack': continue
drawopt = i.GetDrawOption()
if issubclass(type(i), TH1) or issubclass(type(i), TGraph):
if i.GetTitle() in skip:
continue
leg.AddEntry(i, i.GetTitle(), option[total]) # plef
total += 1
#
# Add empty entries to ensure a standard layout
#
for i in range(100):
if totalentries == 0: break
if total >= totalentries: break
leg.AddEntry(None, '', '')
total += 1
# recipe for making roughly square boxes
h = leg.GetY2() - leg.GetY1()
w = leg.GetX2() - leg.GetX1()
leg.SetMargin(leg.GetNColumns() * h / float(leg.GetNRows() * w))
can.cd()
if can.GetPrimitive('pad_top'):
can.GetPrimitive('pad_top').cd()
leg.Draw()
can.Modified()
can.Update()
return
def plotUpperLimits(model, lambdas, helicity):
N = len(lambdas)
#yellow = TGraph(2*N) # yellow band
#green = TGraph(2*N) # green band
#median = TGraph(N) # median line
Mmin = [100 + i * 100 for i in range(40)]
graphs = []
for i in range(N):
thislam = TGraph(len(Mmin))
thislimit = []
for mm in range(len(Mmin)):
file_name = "./%sdataCards/ee_signif%s/higgsCombine%d.Significance.mH%d.root" % (
model, helicity, Mmin[mm], lambdas[i])
limit = getLimits(file_name)
thislam.SetPoint(mm, Mmin[mm], limit[0])
thislam.SetLineColor(i + 2)
thislam.SetLineWidth(2)
thislimit.append(limit[0])
graphs.append(thislam.Clone())
print "this minimum mass cut is %d" % Mmin[mm]
print thislimit
#yellow.SetPoint( i, values[i], limit[4] ) # + 2 sigma
#green.SetPoint( i, values[i], limit[3] ) # + 1 sigma
#median.SetPoint( i, labels[i], limit[0] ) # median
#green.SetPoint( 2*N-1-i, values[i], limit[1] ) # - 1 sigma
#yellow.SetPoint( 2*N-1-i, values[i], limit[0] ) # - 2 sigma
W = 800
H = 600
T = 0.08 * H
B = 0.12 * H
L = 0.12 * W
R = 0.04 * W
c = TCanvas("c", "c", 100, 100, W, H)
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
c.SetLeftMargin(L / W)
c.SetRightMargin(R / W)
c.SetTopMargin(T / H)
c.SetBottomMargin(B / H)
c.SetTickx(0)
c.SetTicky(0)
c.SetGrid()
c.cd()
if model == "ADD": frame = c.DrawFrame(100, 0.01, 4000, 22)
elif "Con" in helicity: frame = c.DrawFrame(100, 0.01, 4000, 15)
else: frame = c.DrawFrame(100, 0.01, 4000, 10)
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetTitleSize(0.05)
frame.GetXaxis().SetTitleSize(0.05)
frame.GetXaxis().SetLabelSize(0.04)
frame.GetYaxis().SetLabelSize(0.04)
frame.GetYaxis().SetTitleOffset(0.9)
frame.GetXaxis().SetNdivisions(508)
frame.GetYaxis().CenterTitle(True)
frame.GetYaxis().SetTitle("Significance")
frame.GetXaxis().SetTitle("M_{min} [GeV]")
frame.SetMinimum(0)
#frame.SetMaximum(max(up2s)*1.05)
#frame.GetXaxis().SetLimits(min(values),max(values))
for gf in graphs:
gf.Draw('Lsame')
'''
yellow.SetFillColor(ROOT.kOrange)
yellow.SetLineColor(ROOT.kOrange)
yellow.SetFillStyle(1001)
yellow.Draw('F')
green.SetFillColor(ROOT.kGreen+1)
green.SetLineColor(ROOT.kGreen+1)
green.SetFillStyle(1001)
green.Draw('Fsame')
median.SetLineColor(1)
median.SetLineWidth(2)
median.SetLineStyle(2)
median.Draw('Lsame')'''
CMS_lumi.CMS_lumi(c, 13, 11)
ROOT.gPad.SetTicks(1, 1)
frame.Draw('sameaxis')
x1 = 0.75
x2 = x1 + 0.24
y2 = 0.86
y1 = 0.60
legend = TLegend(x1, y1, x2, y2)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextSize(0.041)
legend.SetTextFont(42)
for i in range(N):
legend.AddEntry(graphs[i], "#Lambda = %d" % lambdas[i], 'l')
#legend.AddEntry(median, "Asymptotic CL_{s} expected",'L')
#legend.AddEntry(green, "#pm 1 std. deviation",'f')
#legend.AddEntry(green, "Asymptotic CL_{s} #pm 1 std. deviation",'f')
#legend.AddEntry(yellow,"#pm 2 std. deviation",'f')
#legend.AddEntry(green, "Asymptotic CL_{s} #pm 2 std. deviation",'f')
legend.Draw()
print " "
c.SaveAs("%slimits/%sSignificance_ee%s.png" % (model, model, helicity))
c.Close()
def plotDataMC(datahist,mchist,usedata, label1, label2,name,filename):
hCanvas = TCanvas("hCanvas", "Distribution", 800,800)
if usedata==True:
plotPad = ROOT.TPad("plotPad","plotPad",0,0.3,1,1)
ratioPad = ROOT.TPad("ratioPad","ratioPad",0,0.,1,0.3)
setTDRStyle()
plotPad.UseCurrentStyle()
ratioPad.UseCurrentStyle()
plotPad.Draw("hist")
ratioPad.Draw("hist")
plotPad.cd()
else:
plotPad = ROOT.TPad("plotPad","plotPad",0,0,1,1)
setTDRStyle()
plotPad.UseCurrentStyle()
plotPad.Draw()
plotPad.cd()
colors = createMyColors()
legend = TLegend(0.55, 0.6, 0.925, 0.925)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legendEta = TLegend(0.45, 0.75, 0.925, 0.925)
legendEta.SetFillStyle(0)
legendEta.SetBorderSize(0)
legendEta.SetTextFont(42)
legendEta.SetNColumns(2)
latex = ROOT.TLatex()
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(0.04)
latex.SetNDC(True)
latexCMS = ROOT.TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.06)
latexCMS.SetNDC(True)
latexCMSExtra = ROOT.TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(0.045)
latexCMSExtra.SetNDC(True)
legendHists = []
legendHistData = ROOT.TH1F()
category = ROOT.TLatex()
category.SetNDC(True)
category.SetTextSize(0.04)
if usedata==True:
legend.AddEntry(legendHistData,label1,"pe")
legendEta.AddEntry(legendHistData,label1,"pe")
#process.label = process.label.replace("#mu^{+}#mu^{-}","e^{+^{*}}e^{-}")
temphist = ROOT.TH1F()
if label2=="Generated":
temphist.SetFillColor(3)
mchist.SetFillColor(3)
else:
temphist.SetFillColor(2)
mchist.SetFillColor(2)
legendHists.append(temphist.Clone)
legend.AddEntry(temphist,label2,"f")
#legendEta.AddEntry(temphist,process.label,"f")
# Modify plot pad information
nEvents=-1
ROOT.gStyle.SetOptStat(0)
intlumi = ROOT.TLatex()
intlumi.SetTextAlign(12)
intlumi.SetTextSize(0.045)
intlumi.SetNDC(True)
intlumi2 = ROOT.TLatex()
intlumi2.SetTextAlign(12)
intlumi2.SetTextSize(0.07)
intlumi2.SetNDC(True)
scalelabel = ROOT.TLatex()
scalelabel.SetTextAlign(12)
scalelabel.SetTextSize(0.03)
scalelabel.SetNDC(True)
metDiffLabel = ROOT.TLatex()
metDiffLabel.SetTextAlign(12)
metDiffLabel.SetTextSize(0.03)
metDiffLabel.SetNDC(True)
chi2Label = ROOT.TLatex()
chi2Label.SetTextAlign(12)
chi2Label.SetTextSize(0.03)
chi2Label.SetNDC(True)
hCanvas.SetLogy()
# Luminosity information
plotPad.cd()
plotPad.SetLogy(0)
plotPad.SetLogy()
if usedata==True:
yMax = datahist.GetBinContent(datahist.GetMaximumBin())*1000
yMin = 0.00000001
xMax = datahist.GetXaxis().GetXmax()
xMin = datahist.GetXaxis().GetXmin()
else:
yMax = mchist.GetBinContent(datahist.GetMaximumBin())
yMin = 0.00000001
xMax = mchist.GetXaxis().GetXmax()
xMin = mchist.GetXaxis().GetXmin()
yMax = yMax*10000
if name.find("dimuon")!=-1:
plotPad.DrawFrame(xMin,yMin,xMax,yMax,"; m_{#mu#mu}[GeV] ;fb/GeV")
else:
plotPad.DrawFrame(xMin,yMin,xMax,yMax,"; m_{ee}[GeV] ;fb/GeV")
# Draw signal information
# Draw background from stack
mchist.Draw("samehist")
# Draw data
datahist.SetMinimum(0.0001)
if usedata==True:
datahist.SetMarkerStyle(8)
datahist.Draw("samepehist")
# Draw legend
legend.Draw()
plotPad.SetLogx()
latex.DrawLatex(0.95,0.96,"13 TeV")
yLabelPos = 0.85
cmsExtra = "Preliminary"
if not usedata==True:
cmsExtra = "#splitline{Preliminary}{Simulation}"
yLabelPos = 0.82
latexCMS.DrawLatex(0.19,0.89,"CMS")
category.DrawLatex(0.3,0.7,name)
latexCMSExtra.DrawLatex(0.19,yLabelPos,"%s"%(cmsExtra))
#~ print datahist.Integral()
if usedata==True:
try:
ratioPad.cd()
ratioPad.SetLogx()
except AttributeError:
print ("Plot fails. Look up in errs/failedPlots.txt")
outFile =open("errs/failedPlots.txt","a")
outFile.write('%s\n'%plot.filename%("_"+run.label+"_"+dilepton))
outFile.close()
#plot.cuts=baseCut
return 1
if label2=="Generated":
ratioGraphs = ratios.RatioGraph(datahist,mchist, xMin=xMin, xMax=xMax,title=" nokFac/kFac",yMin=0.7,yMax=1.3,ndivisions=10,color=ROOT.kBlack,adaptiveBinning=10000000000000,labelSize=0.125,pull=False)
ratioGraphs.draw(ROOT.gPad,True,False,True,chi2Pos=0.8)
else:
ratioGraphs = ratios.RatioGraph(datahist,mchist, xMin=xMin, xMax=xMax,title="nokFac/kFac",yMin=0.7,yMax=1.3,ndivisions=10,color=ROOT.kBlack,adaptiveBinning=10000000000000,labelSize=0.125,pull=False)
ratioGraphs.draw(ROOT.gPad,True,False,True,chi2Pos=0.8)
ROOT.gPad.RedrawAxis()
plotPad.RedrawAxis()
if usedata==True:
ratioPad.RedrawAxis()
if not os.path.exists("plots"):
os.makedirs("plots")
hCanvas.Print("plots/%s.pdf"%filename)
def limit2HDM():
global signals
signals = range(800, 2000 + 1, 50)
multF = HTOBB
THEORY = ['T1', 'T2']
mass, val = fillValues("./combine/AZh/AZh_M%d.txt")
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
massB, valB = fillValues("./combine/BBAZh/BBAZh_M%d.txt")
Obs0sB = TGraph()
Exp0sB = TGraph()
Exp1sB = TGraphAsymmErrors()
Exp2sB = TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF)
Exp0s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF,
val[m][4] * multF - val[m][3] * multF)
Exp2s.SetPoint(n, m, val[m][3] * multF)
Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF,
val[m][5] * multF - val[m][3] * multF)
Obs0sB.SetPoint(n, m, valB[m][0] * multF)
Exp0sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][2] * multF,
valB[m][4] * multF - valB[m][3] * multF)
Exp2sB.SetPoint(n, m, valB[m][3] * multF)
Exp2sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][1] * multF,
valB[m][5] * multF - valB[m][3] * multF)
col = 629
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp0s.SetLineColor(1)
# Exp1s.SetFillColorAlpha(col, 0.4) #kGreen+1
# Exp1s.SetLineColorAlpha(col, 0.4)
# Exp2s.SetFillColorAlpha(col, 0.2) #kOrange
# Exp2s.SetLineColorAlpha(col, 0.2)
Exp1s.SetFillColor(417)
Exp1s.SetLineColor(417)
Exp2s.SetFillColor(800)
Exp2s.SetLineColor(800)
colB = 922
Exp2sB.SetLineWidth(2)
Obs0sB.SetLineStyle(9)
Obs0sB.SetLineWidth(3)
Obs0sB.SetMarkerStyle(0)
Obs0sB.SetLineColor(colB)
Exp0sB.SetLineStyle(8)
Exp0sB.SetLineWidth(3)
Exp0sB.SetLineColor(colB)
Exp1sB.SetFillColorAlpha(colB, 0.4) #kGreen+1
Exp1sB.SetLineColorAlpha(colB, 0.4)
Exp2sB.SetFillColorAlpha(colB, 0.2) #kOrange
Exp2sB.SetLineColorAlpha(colB, 0.2)
Exp2s.GetXaxis().SetTitle("m_{A} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle(
"#sigma(A) #bf{#it{#Beta}}(A #rightarrow Zh) #bf{#it{#Beta}}(h #rightarrow bb) (fb)"
)
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Theory = {}
#for t in THEORY:
# Theory[t] = TGraphAsymmErrors()
# for m in sorted(THDM[t]['ggA'].keys()):
# if m < mass[0] or m > mass[-1]: continue
# Xs, Xs_Up, Xs_Down = 0., 0., 0.
# Xs = THDM[t]['ggA'][m]
# Xs_Up = Xs*(1.+math.sqrt((THDM['PDF']['ggA'][m][0]-1.)**2 + (THDM['QCD']['ggA'][m][0]-1.)**2))
# Xs_Down = Xs*(1.-math.sqrt((1.-THDM['PDF']['ggA'][m][1])**2 + (1.-THDM['QCD']['ggA'][m][1])**2))
# n = Theory[t].GetN()
# Theory[t].SetPoint(n, m, Xs)
# Theory[t].SetPointError(n, 0., 0., (Xs-Xs_Down), (Xs_Up-Xs))
# Theory[t].SetLineColor(theoryLineColor[t])
# Theory[t].SetFillColor(theoryFillColor[t])
# Theory[t].SetFillStyle(theoryFillStyle[t])
# Theory[t].SetLineWidth(2)
# #Theory[t].SetLineStyle(7)
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).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
Exp0s.Draw("SAME, L")
# Exp2sB.Draw("SAME, 3")
# Exp1sB.Draw("SAME, 3")
Exp0sB.Draw("SAME, L")
if not options.blind:
Obs0s.Draw("SAME, L")
Obs0sB.Draw("SAME, L")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
#setHistStyle(Exp2s)
# Exp2s.GetXaxis().SetTitleSize(0.045)
# Exp2s.GetYaxis().SetTitleSize(0.04)
# Exp2s.GetXaxis().SetLabelSize(0.04)
# Exp2s.GetYaxis().SetLabelSize(0.04)
# Exp2s.GetXaxis().SetTitleOffset(1)
# Exp2s.GetYaxis().SetTitleOffset(1.25)
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)
Exp2s.GetYaxis().SetRangeUser(0.5, 1.e3)
Exp2s.GetXaxis().SetRangeUser(mass[0], mass[-1])
drawAnalysis('AZh')
drawRegion('AZHsl', True)
drawCMS(LUMI, "") #Preliminary
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
leg = TLegend(0.6, 0.90, 0.99, 0.90)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(None, "gg #rightarrow A #rightarrow Zh",
"") #"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")
leg.AddEntry(None, "", "")
leg.AddEntry(None, "bbA #rightarrow Zh", "")
leg.AddEntry(Obs0sB, "Observed", "l")
leg.AddEntry(Exp0sB, "Expected", "l")
leg.SetY1(leg.GetY2() - leg.GetNRows() * 0.045)
leg.Draw()
# latex = TLatex()
# latex.SetNDC()
# latex.SetTextSize(0.040)
# latex.SetTextFont(42)
# latex.DrawLatex(0.65, leg.GetY1()-0.045, "cos(#beta-#alpha)=0.25, tan(#beta)=1")
# legB = TLegend(0.12, 0.4-4*0.3/5., 0.65, 0.4)
legB = TLegend(0.15, 0.27, 0.68, 0.27)
legB.SetBorderSize(0)
legB.SetFillStyle(0) #1001
legB.SetFillColor(0)
for t in THEORY:
legB.AddEntry(Theory[t], theoryLabel[t], "fl")
legB.AddEntry(None, "cos(#beta-#alpha)=0.25, tan(#beta)=1", "")
legB.SetY1(legB.GetY2() - legB.GetNRows() * 0.045)
legB.Draw()
c1.GetPad(0).RedrawAxis()
leg.Draw()
c1.Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("plotsLimit/Exclusion/THDM.png")
c1.Print("plotsLimit/Exclusion/THDM.pdf")
def draw(hist,
fit,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
setHistStyle(hist['BkgSum'], 1.1 if ratio else 1.0)
# Create stack
bkg = THStack(
"Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";" +
hist['BkgSum'].GetYaxis().GetTitle())
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.69, 0.86 - 0.04 * n, 0.95, 0.86)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
### data/bkg ratio and systematics
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("data / bkg")
if fit == "prefit":
err.SetFillColor(ROOT.kOrange - 2)
elif fit == "postfit":
err.SetFillColor(ROOT.kBlue)
err.SetFillStyle(3001)
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
# Legend
leg1 = TLegend(0.12, 0.45, 0.25, 0.5)
leg1.SetBorderSize(0)
leg1.SetFillStyle(0)
leg1.SetFillColor(0)
leg1.SetTextSize(0.05)
leg1.AddEntry(err, "systematic uncertainty (" + fit + ")", "f")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
gStyle.SetOptStat(0)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.08)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
# hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
pass
bkg.SetMaximum((2. if log else 1.1) * max(
bkg.GetMaximum(), hist['data_obs'].GetMaximum() +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 1.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
setHistStyle(bkg, 1.1 if ratio else 1.0)
leg.Draw()
# drawCMS(lumi, "Preliminary",True)
drawCMS(lumi, "Private", True)
drawRegion(channel)
drawAnalysis(channel)
drawFit(fit)
if ratio:
c1.cd(2)
setBotStyle(err, 3, True)
setBotStyle(res)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if len(data) > 0:
res.Draw("SAME, PE0")
drawRatio(hist['data_obs'], hist['BkgSum'])
drawKolmogorov(hist['data_obs'], hist['BkgSum'])
leg1.Draw()
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, leg1, err, errLine, res]
def makeDiTauStack(outDir,
inFile,
rootDir,
s,
labelX,
units="GeV",
left=False,
channel="",
json="Golden",
log=False,
dndm=False,
doRatio=False,
year=2017,
sign='OS',
LTcut=0.,
cat='mmtt',
wait='wait'):
tdrstyle.setTDRStyle()
writeExtraText = True # if extra text
extraText = "Preliminary" # default extra text is "Preliminary"
lumi_sqrtS = "13 TeV"
if json == "Golden": lumi_13TeV = channel + " 41.8 fb^{-1}, 2017"
iPeriod = 4 # 1=7TeV, 2=8TeV, 3=7+8TeV, 7=7+8+13TeV
xR = 0.65 #legend parameters
xR = 0.2 #legend parameters
H = 600
W = 600
H_ref = 600
W_ref = 600
# references for T, B, L, R
T = 0.08 * H_ref
B = 0.12 * H_ref
L = 0.16 * W_ref
R = 0.04 * W_ref
#margins for inbetween the pads in a ratio plot
B_ratio = 0.1 * H_ref
T_ratio = 0.03 * H_ref
#margin required for lebal on bottom of raito plot
B_ratio_label = 0.3 * H_ref
c = TCanvas('c1', 'c1', 50, 50, W, H)
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
if not doRatio:
c.SetLeftMargin(L / W)
c.SetRightMargin(R / W)
c.SetTopMargin(T / H)
c.SetBottomMargin(B / H)
c.cd()
ratioPad = TPad("pad2", "", 0.0, 0.0, 1.0, 0.29)
plotPad = TPad("pad1", "", 0.0016, 0.291, 1.0, 1.0)
if doRatio:
plotPad.SetTicks(0, 0)
plotPad.SetLeftMargin(L / W)
plotPad.SetRightMargin(R / W)
plotPad.SetTopMargin(T / H)
plotPad.SetBottomMargin(B_ratio / H)
plotPad.SetFillColor(0)
plotPad.SetBottomMargin(0)
ratioPad.SetLeftMargin(L / W)
ratioPad.SetRightMargin(R / W)
ratioPad.SetTopMargin(T_ratio / H)
ratioPad.SetTopMargin(0.007)
ratioPad.SetBottomMargin(B_ratio_label / H)
ratioPad.SetGridy(1)
ratioPad.SetFillColor(4000)
else:
plotPad = TPad("pad1", "", 0.0, 0.03, 1.0, 1.0)
plotPad.SetLeftMargin(L / W)
plotPad.SetRightMargin(R / W)
plotPad.SetTopMargin(T / H)
plotPad.SetBottomMargin(B / H)
plotPad.Draw()
plotPad.cd()
print("In makeStack inFile={0:s}".format(inFile))
f = TFile(inFile)
groups = ['data', 'Reducible', 'Rare', 'ZZ4L', 'Signal']
histo = {}
colors = {
'data': 0,
'Reducible': kMagenta - 10,
'Rare': kBlue - 8,
'ZZ4L': kAzure - 9,
'Signal': kRed
}
hs = THStack("hs", "")
for group in groups:
if len(cat) == 4:
histo[group] = f.Get("h{0:s}_{1:s}_Mtt".format(group, cat))
else:
histo[group] = f.Get("h{0:s}_ee{1:s}_Mtt".format(group, cat))
histo2 = f.Get("h{0:s}_mm{1:s}_Mtt".format(group, cat))
histo[group].Add(histo2)
if dndm: convertToDNDM(histo[group])
if group == 'data':
applyDATAStyle(histo[group])
else:
applyStyle(histo[group], colors[group], 1, 1001)
if group != 'data': hs.Add(histo[group])
try:
hMax = 1.2 * max(histo['data'].GetMaximum(), hs.GetMaximum())
except KeyError:
hMax = 1.2 * hs.GetMaximum()
hs.SetMaximum(hMax)
hs.Draw("HIST")
try:
histo['data'].Draw("e,SAME")
except KeyError:
pass
if doRatio:
hs.GetXaxis().SetLabelSize(0)
else:
if units != "":
hs.GetXaxis().SetTitle(labelX + " [" + units + "]")
else:
hs.GetXaxis().SetTitle(labelX)
hs.GetYaxis().SetTitle("Events")
hs.GetYaxis().SetTitleOffset(1)
if dndm: hs.GetYaxis().SetTitle("dN/d" + labelX)
c.cd()
if doRatio:
data2 = histo['data'].Clone("data")
mc = histo['Rare']
mc.Add(histo['Reducible'])
mc.Add(histo['ZZ4L'])
xmin = mc.GetXaxis().GetXmin()
xmax = mc.GetXaxis().GetXmax()
line = TLine(xmin, 1.0, xmax, 1.0)
line.SetLineWidth(1)
line.SetLineColor(kBlack)
ratioPad.Draw()
ratioPad.cd()
data2.Divide(data2, mc)
data2.SetMarkerStyle(20)
data2.SetTitleSize(0.12, "Y")
data2.SetTitleOffset(0.40, "Y")
data2.SetTitleSize(0.12, "X")
data2.SetLabelSize(0.10, "X")
data2.SetLabelSize(0.08, "Y")
data2.GetYaxis().SetRangeUser(0.62, 1.38)
data2.GetYaxis().SetNdivisions(305)
data2.GetYaxis().SetTitle("Obs/Exp ")
if units != "":
data2.GetXaxis().SetTitle(labelX + " [" + units + "]")
else:
data2.GetXaxis().SetTitle(labelX)
data2.Draw("P")
line.Draw()
c.cd()
plotPad.cd()
l = TLegend(xR, 0.55, xR + 0.28, 0.9)
for group in groups:
try:
l.AddEntry(histo[group], group, "F")
except KeyError:
continue
l.SetBorderSize(0)
l.SetFillColor(0)
l.SetFillStyle(0)
l.SetTextSize(0.04)
l.Draw()
lTex1 = TLatex(120., 0.97 * hMax, 'Preliminary {0:d}'.format(year))
lTex1.SetTextSize(0.04)
lTex1.Draw()
signText = 'Same Sign'
if sign == 'OS': signText = 'Opposite Sign'
lTex2 = TLatex(120., 0.90 * hMax, '{0:s}'.format(signText))
lTex2.SetTextSize(0.04)
lTex2.Draw()
if len(cat) == 4:
lt = {
'eeet': '#it{l}#it{l}#it{e}#tau',
'eemt': '#it{l}#it{l}#mu#tau',
'eett': '#it{l}#it{l}#tau#tau',
'mmet': '#mu#mu#it{e}#tau',
'mmmt': '#mu#mu#mu#tau',
'mmtt': '#mu#mu#tau#tau'
}
else:
lt = {
'et': '#it{l}#it{l}#it{e}#tau',
'mt': '#it{l}#it{l}#mu#tau',
'tt': '#it{l}#it{l}#tau#tau'
}
lTex3 = TLatex(120., 0.83 * hMax,
'{0:s} H_LT > {1:d} '.format(lt[cat], int(LTcut)))
lTex3.SetTextSize(0.04)
lTex3.Draw()
plotPad.Draw()
#CMS_lumi(plotPad,4,11)
outFileBase = "Stack_{0:d}_{1:s}_{2:s}_LT{3:02d}".format(
year, cat, sign, int(LTcut))
c.SaveAs("{0:s}.png".format(outFileBase))
c.SaveAs("{0:s}.root".format(outFileBase))
if wait == 'wait':
raw_input()
else:
import time
time.sleep(5.)
def MakePlot(specWithCuts, specWithoutCuts, options):
"""
Creating the final plot. The plot will be a two panel plot:
1st panel shows the comparison of the spectra.
2nd panel shows the ratio of the normalised spectra with and
without pileup rejection (as without/with)
"""
plot = TCanvas("ComparisonPileup",
"Comparison of raw spectra with/without pileup rejection",
1000, 500)
plot.Divide(2, 1)
specpad = plot.cd(1)
specpad.SetGrid(False, False)
specpad.SetLogx(True)
specpad.SetLogy(True)
axispec = TH1F("axispec",
"; p_{t} (GeV/c); 1/N_{ev} dN/dp_{t} ((GeV/c)^{-1})", 1000,
0., 100.)
axispec.SetStats(False)
axispec.GetYaxis().SetRangeUser(1e-10, 100)
axispec.Draw("axis")
gObjects.append(axispec)
specWithCuts.SetMarkerColor(kRed)
specWithCuts.SetLineColor(kRed)
specWithCuts.SetMarkerStyle(24)
specWithoutCuts.SetMarkerColor(kBlue)
specWithoutCuts.SetLineColor(kBlue)
specWithoutCuts.SetMarkerStyle(25)
specWithCuts.Draw("epsame")
specWithoutCuts.Draw("epsame")
gObjects.append(specWithCuts)
gObjects.append(specWithoutCuts)
leg = TLegend(0.1, 0.15, 0.45, 0.3)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.045)
leg.AddEntry(specWithCuts, "With standard cuts", "lep")
leg.AddEntry(specWithoutCuts, "Without standard cuts", "lep")
leg.Draw()
gObjects.append(leg)
label = TPaveText(0.5, 0.75, 0.89, 0.89, "NDC")
label.SetBorderSize(0)
label.SetFillStyle(0)
label.SetTextFont(42)
label.SetTextAlign(12)
label.AddText("Trigger: %s" % (options["Trigger"]))
label.AddText("Pileup rejection: %s" % (options["pileupreject"]))
label.Draw()
gObjects.append(label)
ratiopad = plot.cd(2)
ratiopad.SetGrid(False, False)
ratiopad.SetLogx(True)
axirat = TH1F("axirat", "; p_{t} (GeV/c); Without/with standard cuts",
1000, 0., 100.)
axirat.SetStats(False)
axirat.GetYaxis().SetRangeUser(0., 100)
axirat.Draw("axis")
gObjects.append(axirat)
ratiohist = deepcopy(specWithoutCuts)
ratiohist.SetName("ratioPileup")
ratiohist.Divide(ratiohist, specWithCuts, 1., 1., "b")
ratiohist.SetMarkerColor(kBlack)
ratiohist.SetLineColor(kBlack)
ratiohist.SetMarkerStyle(20)
ratiohist.Draw("epsame")
gObjects.append(ratiohist)
plot.cd()
gObjects.append(plot)
return plot
### A temperature has a list of samples
### when parsing the data, for each sample, we find a T, and add the data to it. Then finally mesure the averge for the temperature
gr2 = getGraph(Ts2, '../data/BandGap_test1a/temprature_test_b3c2.dat')
gr1 = getGraph(Ts1, '../data/BandGap_test1a/temprature_test_b3c1.dat')
gr1.SetLineColor(2)
gr1.SetMarkerColor(2)
gr1.SetMarkerStyle(25)
gr2.SetLineColor(4)
gr2.SetMarkerColor(4)
gr2.SetMarkerStyle(24)
lg = TLegend(0.2, 0.8, 0.4, 0.9)
lg.SetFillStyle(0)
lg.SetBorderSize(0)
lg.AddEntry(gr1, "Chip #1", 'lp')
lg.AddEntry(gr2, "Chip #2", 'lp')
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
gStyle.SetPadLeftMargin(0.16)
gStyle.SetPadRightMargin(0.05)
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadBottomMargin(0.1)
h1 = TH2F('h2', 'h2;T [#circC];Output [V]', 100, -35, 85, 100, 1.116, 1.13)
h1.Draw()
gr2.Draw('P')
gr1.Draw('P')
lg.Draw()
def showENC(self):
tree1 = TTree()
header = 'idX/i:vL/F:vH:A:D/i:R:W'
first = True
for f in self.dataFiles:
if first: tree1.ReadFile(f, header)
else: tree1.ReadFile(f)
p1 = TProfile('p1', 'p1;#DeltaU [V];Prob', self.bins[0],
tree1.GetMinimum('vH-vL') * 0.8,
tree1.GetMaximum('vH-vL') * 1.2)
tree1.Draw("D:(vH-vL)>>p1", "", "profE")
### change it to tgraph
g1 = TGraphErrors()
for i in range(p1.GetNbinsX() + 2):
N = p1.GetBinEntries(i)
if N > 0:
print i, N, p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(
i), p1.GetBinError(i)
n = g1.GetN()
g1.SetPoint(n,
p1.GetXaxis().GetBinCenter(i), p1.GetBinContent(i))
g1.SetPointError(n, 0, p1.GetBinError(i))
p1.Draw("axis")
g1.Draw('Psame')
fun1 = TF1('fun1', '0.5*(1+TMath::Erf((x-[0])/(TMath::Sqrt(2)*[1])))',
0.05, 0.3)
fun1.SetParameter(0, 0.155)
fun1.SetParameter(1, 0.005)
g1.Fit(fun1)
fun1a = g1.GetFunction('fun1')
fun1a.SetLineColor(2)
v0 = fun1a.GetParameter(0)
e0 = fun1a.GetParError(0)
v1 = fun1a.GetParameter(1)
e1 = fun1a.GetParError(1)
print v0, v1
fUnit = 1000.
self.lt.DrawLatexNDC(
0.185, 0.89,
'#mu = {0:.1f} #pm {1:.1f} mV'.format(v0 * fUnit, e0 * fUnit))
self.lt.DrawLatexNDC(
0.185, 0.84,
'#sigma = {0:.1f} #pm {1:.1f} mV'.format(v1 * fUnit, e1 * fUnit))
if self.Info:
self.lt.DrawLatexNDC(0.185, 0.6, self.Info)
print 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1)
fun2 = TF1('gaus1', 'TMath::Gaus(x,{0:.5f},{1:.5f})'.format(v0, v1))
fun2.SetLineColor(4)
fun2.SetLineStyle(2)
fun2.Draw('same')
lg = TLegend(0.7, 0.4, 0.95, 0.5)
lg.SetFillStyle(0)
lg.AddEntry(p1, 'Measurement', 'p')
lg.AddEntry(fun1a, 'Fit', 'l')
lg.AddEntry(fun2, 'Gaus', 'l')
lg.Draw()
waitRootCmdX()
