def writeFullUnc(pred_file):
''' Update the input root file, add a hist with total prediction and full uncertainty. '''
f = rt.TFile(pred_file, 'UPDATE')
h = TGraphAsymmErrors(f.Get(pred_total_name).Clone('bkgtotal_unc_sr'))
h_pieces = {}
for hname, sample in zip(graph_names, all_samples):
h_pieces[sample] = TGraphAsymmErrors(
f.Get(hname).Clone(sample + '_unc_sr'))
print "%30s %10s %16s" % ('bin', 'total pred', 'total unc.')
for ibin in xrange(0, h.GetN()):
bin = binlist[ibin]
val = h.GetY()[ibin]
e_low, e_up = fullUnc[bin]
h.SetPointEYlow(ibin, e_low)
h.SetPointEYhigh(ibin, e_up)
print "%30s %10.2f +%8.2f -%8.2f" % (bin, val, e_up, e_low)
allVals[bin] = {'bkg': (val, e_low, e_up)}
for sample in all_samples:
val = yields[bin][sample]
e_low, e_up = fullUnc_pieces[sample][bin]
h_pieces[sample].SetPointEYlow(ibin, e_low)
h_pieces[sample].SetPointEYhigh(ibin, e_up)
allVals[bin][sample] = (val, e_low, e_up)
h.Write('bkgtotal_unc_sr', rt.TObject.kOverwrite)
for sample in all_samples:
h_pieces[sample].Write(sample + '_unc_sr', rt.TObject.kOverwrite)
f.Close()
def GetTGraphAsymmErrors(histo, offset):
graph_in = TGraphAsymmErrors(histo)
graph_in.SetName(histo.GetName() + '_tmp')
graph_out = TGraphAsymmErrors(histo)
graph_out.SetName(histo.GetName() + '_offset')
for i in range(graph_in.GetN()):
graph_out.GetX()[i] = graph_in.GetX()[i] + offset
graph_out.GetEXlow()[i] = graph_in.GetEXlow()[i] + offset
graph_out.GetEXhigh()[i] = graph_in.GetEXhigh()[i] - offset
x_min = graph_out.GetX()[0] - graph_out.GetEXlow()[0]
if (x_min > 0): x_min = 0
x_max = graph_out.GetX()[graph_out.GetN() -
1] + graph_out.GetEXhigh()[graph_out.GetN() - 1]
graph_out.GetXaxis().SetLimits(x_min, x_max)
return graph_out
def writeFullUnc(pred_file):
''' Update the input root file, add a hist with total prediction and full uncertainty. '''
f = rt.TFile(pred_file, 'UPDATE')
h = TGraphAsymmErrors(f.Get(pred_total_name).Clone('bkgtotal_unc_sr'))
h_syst = TGraphAsymmErrors(
f.Get(pred_total_name).Clone('bkgtotal_syst_unc_sr'))
h_pieces = {}
h_syst_pieces = {}
for hname, sample in zip(graph_names, all_samples):
h_pieces[sample] = TGraphAsymmErrors(
f.Get(hname).Clone(sample + '_unc_sr'))
h_syst_pieces[sample + "_up"] = TH1F(sample + '_syst_up',
sample + '_syst_up', 183, 0, 183)
h_syst_pieces[sample + "_dn"] = TH1F(sample + '_syst_dn',
sample + '_syst_dn', 183, 0, 183)
print "%30s %10s %16s" % ('bin', 'total pred', 'total unc.')
for ibin in xrange(0, h.GetN()):
bin = binlist[ibin]
val = h.GetY()[ibin]
e_low, e_up = fullUnc[bin]
h.SetPointEYlow(ibin, e_low)
h.SetPointEYhigh(ibin, e_up)
print "%30s %10.4f +%8.4f -%8.4f" % (bin, val, e_up, e_low)
allVals[bin] = {'bkg': (val, e_low, e_up)}
# Test for only syst histograms
val = h_syst.GetY()[ibin]
e_low, e_up = systUnc[bin]
h_syst.SetPointEYlow(ibin, e_low)
h_syst.SetPointEYhigh(ibin, e_up)
for sample in all_samples:
val = yields[bin][sample]
e_low, e_up = fullUnc_pieces[sample][bin]
#print "%11s %30s %10.4f +%8.4f -%8.4f" % (sample, bin, val, e_up, e_low)
h_pieces[sample].SetPointEYlow(ibin, e_low)
h_pieces[sample].SetPointEYhigh(ibin, e_up)
allVals[bin][sample] = (val, e_low, e_up)
# Test for only syst histograms
e_low, e_up = systUnc_rel_pieces[sample][bin]
#if sample in test_samp and bin in test_bin and type in test_type and debug:
#if 'TTZ' in sample and e_up > 8:
# print("%11s %30s %10.4f +%8.4f -%8.4f" % (sample, bin, val, e_up, e_low))
h_syst_pieces[sample + "_up"].SetBinContent(ibin + 1, e_up)
h_syst_pieces[sample + "_dn"].SetBinContent(ibin + 1, e_low)
h_syst_pieces[sample + "_up"].SetBinError(ibin + 1, 0)
h_syst_pieces[sample + "_dn"].SetBinError(ibin + 1, 0)
h.Write('bkgtotal_unc_sr', rt.TObject.kOverwrite)
h.Write('bkgtotal_syst_unc_sr', rt.TObject.kOverwrite)
for sample in all_samples:
h_pieces[sample].Write(sample + '_unc_sr', rt.TObject.kOverwrite)
h_syst_pieces[sample + "_up"].Write(sample + '_syst_up',
rt.TObject.kOverwrite)
h_syst_pieces[sample + "_dn"].Write(sample + '_syst_dn',
rt.TObject.kOverwrite)
f.Close()
def normalizeGraph(graph, binwidth):
xsecSum = 0.
for pt in range(1, graph.GetN()):
xsecSum += graph.GetY()[pt] * binwidth[pt]
normGraph = TGraphAsymmErrors(graph.GetN() - 1)
for pt in range(0, normGraph.GetN()):
normGraph.SetPoint(pt,
graph.GetX()[pt + 1],
graph.GetY()[pt + 1] / xsecSum)
normGraph.SetPointEYhigh(pt, graph.GetErrorYhigh(pt + 1) / xsecSum)
normGraph.SetPointEYlow(pt, graph.GetErrorYlow(pt + 1) / xsecSum)
return normGraph
def calculate(self, histo):
from ROOT import TGraphAsymmErrors
frac = self.__getWeightOneHisto(histo, self.__name)
total = self.__getWeightOneHisto( histo, self.__norm)
if(frac.GetEntries() > total.GetEntries()):
raise StandardError," comparing '%s' to '%s' in '%s' makes no sense eff > 1!"%(self.__name, self.__norm, histo.GetName())
eff = TGraphAsymmErrors(1)
eff.BayesDivide(frac, total)
if eff.GetN() < 1:
raise StandardError,"Efficiency cannot be calculated '%s' in '%s'"%(self.__name, histo.GetName())
return ( eff.GetY()[0], (eff.GetEYlow()[0],eff.GetEYhigh()[0]) )
def diffGraph(self, g, h, opt):
gdiff = TGraphAsymmErrors(g)
gdiff.SetName(g.GetName() + h.GetName() + '_diff')
for i in range(0, gdiff.GetN()):
x = Double(0.)
y = Double(0.)
g.GetPoint(i, x, y)
gdiff.SetPoint(i, x, y - h.GetBinContent(i + 1))
if opt == 1: # keep bin errors
pass
elif opt == 2:
gdiff.SetPointEYhigh(
i,
math.sqrt(g.GetErrorYhigh(i)**2 + h.GetBinError(i + 1)**2))
return gdiff
def createTGraphPoisson(self, h, w=1.):
self.tfile.cd()
g = TGraphAsymmErrors(h)
for i in range(0, g.GetN()):
y = h.GetBinContent(i + 1)
binWidth = h.GetBinWidth(i + 1)
# From https://twiki.cern.ch/twiki/bin/view/CMS/PoissonErrorBars
alpha = 1. - 0.6827
n = int(
round(y * binWidth /
w)) # Round is necessary due to, well, rounding errors
l = Math.gamma_quantile(alpha / 2., n, 1.) if n != 0. else 0.
u = Math.gamma_quantile_c(alpha, n + 1, 1)
# print y*binWidth/w, n, y, u, l
g.SetPointEYlow(i, (n - l) / binWidth * w)
g.SetPointEYhigh(i, (u - n) / binWidth * w)
return g
def totalBkgWithStatErrors( self ):
total_bkg_hist = None
low, high = None, None
for p in self.__processes:
if p.isSignal():
continue
nbins = p.nominal().GetXaxis().GetNbins()
if low is None: low = [0]*nbins
if high is None: high = [0]*nbins
if total_bkg_hist is None:
total_bkg_hist = p.nominal().Clone()
else:
total_bkg_hist.Add( p.nominal() )
gr = None
if p.name() in ['nonpromptDF']: gr = self.__dfGraph
elif p.name() in ['nonpromptSF']: gr = self.__sfGraph
if gr:
for ib in range(nbins):
low[ib] += math.pow(gr.GetEYlow()[ib], 2)
high[ib] += math.pow(gr.GetEYhigh()[ib], 2)
else:
for ib in range(nbins):
low[ib] += math.pow(p.nominal().GetBinError(ib), 2)
high[ib] += math.pow(p.nominal().GetBinError(ib), 2)
for ib in range(len(low)):
low[ib] = math.sqrt(low[ib])
high[ib] = math.sqrt(high[ib])
total_bkg_graph = TGraphAsymmErrors(total_bkg_hist)
for ib in range(total_bkg_graph.GetN()):
total_bkg_graph.SetPointEYlow(ib, low[ib])
total_bkg_graph.SetPointEYhigh(ib, high[ib])
return [total_bkg_hist, total_bkg_graph]
def readFile(fname, maxenergy=4.8):
infile = file(fname, 'r')
inlines = infile.readlines()
graph = TGraphAsymmErrors()
for iline in range(0, len(inlines)):
line = inlines[iline]
if '*' in line: continue
lineparts = ' '.join(line.split()).strip().split(' ')
if not check_number(lineparts[0:5]): continue
energy = float(lineparts[0])
R = float(lineparts[3])
if (energy > maxenergy): continue
statupp = abs(float(lineparts[4]))
statlow = abs(float(lineparts[5]))
(systlow, systupp) = systematics(iline, inlines)
ipoint = graph.GetN()
graph.SetPoint(ipoint, energy, R)
graph.SetPointError(
ipoint, 0, 0,
math.sqrt(statlow * statlow + 1e-4 * systlow * systlow * R * R),
math.sqrt(statupp * statupp + 1e-4 * systupp * systupp * R * R))
return graph
def ROC_GEN(load_s, load_b):
n_scan = 10000000
n_bins = 300 #100
bin_i = 0
bin_f = 1
draw = 1
param = {}
param['n_scan'] = n_scan
param['n_bins'] = n_bins
param['bin_i'] = bin_i
param['bin_f'] = bin_f
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Loading data...'
#load_s = joblib.load(pth+'/dumps/s.pkl')
#load_b = joblib.load(pth+'/dumps/b.pkl')
print len(load_b)
# Calculate the bin vector:
bin_width = float(bin_f - bin_i) / n_bins
bin_value_dict = {}
for j in xrange(n_bins):
bin_value_dict[j] = bin_i + (0.5 + j) * bin_width
#print bin_value_dict
# if you want to use the GetCumulative() of a histogram and set its error: do not call the Sumw2() of it
h_after_selection = TH1F('h_after_selection', 'hist_after_selection',
n_bins, bin_i, bin_f)
#h_after_selection.Sumw2()
h_before_selection = TH1F('h_before_selection', 'hist_before_selection',
n_bins, bin_i, bin_f)
#h_before_selection.Sumw2()
h_true_positive = TH1F('h_true_positive', 'True_Positives', n_bins, bin_i,
bin_f)
h_true = TH1F('h_true', 'Trues', n_bins, bin_i, bin_f)
# see if reversing the bin_min and bin_max will cause the histogram axis to reverse(no)
#h_true = TH1F('h_true' ,'Trues' , n_bins, bin_f, bin_i)
h_c_b = TH1F('h_c_b', 'hist_after_selection_cum_rev', n_bins, bin_i, bin_f)
#h_c_b.Sumw2()
h_c_s = TH1F('h_c_s', 'hist_true_positives_cum_rev', n_bins, bin_i, bin_f)
#h_c_s.Sumw2()
####################################
####################################
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Filling histogram...'
zeitA = time()
df_b = load_b.copy()[:n_scan]
df_b['bin'] = df_b['signal']
#df_s = load_s.copy()
#df_s['bin'] = df_s['signal']
df_list_pre_sel = []
df_list_pos_sel = []
def Appnd(tupl_in):
df_list_pre_sel.append(tupl_in[0])
df_list_pos_sel.append(tupl_in[1])
def DataFrameExpand(k):
mask_k_b = (bin_value_dict[k] - 0.5 * bin_width <= df_b['bin']) & (
bin_value_dict[k] + 0.5 * bin_width > df_b['bin'])
df_b['bin'][mask_k_b] = k
#mask_k_s = ( bin_value_dict[k] - 0.5*bin_width <= df_s['bin'] ) & ( bin_value_dict[k] + 0.5*bin_width > df_s['bin'] )
#df_s['bin'][mask_k_s] = k
mask_k = df_b.bin == k
df_b_w_k = df_b.weight[mask_k]
df_list_k_pre_sel = []
df_list_k_pos_sel = []
for kk in xrange(n_bins):
df_tmp_kk_pre_sel = pd.DataFrame()
df_tmp_kk_pre_sel['weight'] = df_b_w_k
df_tmp_kk_pre_sel['bin'] = bin_value_dict[kk]
df_list_k_pre_sel.append(df_tmp_kk_pre_sel)
if kk > k: continue
df_tmp_kk_pos_sel = pd.DataFrame()
df_tmp_kk_pos_sel['weight'] = df_b_w_k
df_tmp_kk_pos_sel['bin'] = bin_value_dict[kk]
df_list_k_pos_sel.append(df_tmp_kk_pos_sel)
df_tmp_k_pre_sel = pd.concat(df_list_k_pre_sel)
df_tmp_k_pos_sel = pd.concat(df_list_k_pos_sel)
return df_tmp_k_pre_sel, df_tmp_k_pos_sel
# Parallelization:
'''
pool_dfe = mp.Pool()
for i in xrange(n_bins):
pool_dfe.apply_async(DataFrameExpand, args=(i, ), callback=Appnd)
pool_dfe.close()
pool_dfe.join()
'''
for ii in xrange(n_bins):
callback = DataFrameExpand(ii)
Appnd(callback)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~alternatives:
#process = mp.Process(target=F, args=(k,))
#manager = mp.Manager()
#pool.map(FT,L)
df_before_selection = pd.concat(df_list_pre_sel)
df_after_selection = pd.concat(df_list_pos_sel)
rnp.fill_hist(h_before_selection, df_before_selection.bin,
df_before_selection.weight)
rnp.fill_hist(h_c_b, df_after_selection.bin, df_after_selection.weight)
zeitB = time()
print 'Time taken for filling histogram(for #events: ' + str(
n_scan) + '): ', str(zeitB - zeitA)
####################################
####################################
print '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Filling histogram (for tpr)...'
zeitA = time()
for index, row in load_s.iterrows():
tmp_weight = row['weight']
tmp_signal = row['signal']
for k in xrange(n_bins):
h_true.Fill(bin_value_dict[k], tmp_weight)
if bin_value_dict[
k] - 0.5 * bin_width <= tmp_signal and bin_value_dict[
k] + 0.5 * bin_width > tmp_signal:
for kk in xrange(k):
h_c_s.Fill(bin_value_dict[kk], tmp_weight)
g_fpr = GAE()
g_tpr = GAE()
g_fpr.Divide(h_c_b, h_before_selection, "cl=0.683 b(1,1) mode")
g_tpr.Divide(h_c_s, h_true, "cl=0.683 b(1,1) mode")
g_size = g_fpr.GetN()
x = Double()
y = Double()
x_s = Double()
y_s = Double()
arr_x = np.zeros(g_size)
arr_y = np.zeros(g_size)
arr_x_s = np.zeros(g_size)
arr_y_s = np.zeros(g_size)
for i in xrange(g_size):
g_fpr.GetPoint(i, x, y)
arr_x[i] = x
arr_y[i] = y
g_tpr.GetPoint(i, x_s, y_s)
arr_x_s[i] = x_s
arr_y_s[i] = y_s
buffer_l = g_fpr.GetEYlow()
buffer_l.SetSize(g_size)
arr_l = np.array(buffer_l, copy=True)
buffer_h = g_fpr.GetEYhigh()
buffer_h.SetSize(g_size)
arr_h = np.array(buffer_h, copy=True)
#print arr_h
#print arr_l
buffer_l_s = g_tpr.GetEYlow()
buffer_l_s.SetSize(g_size)
arr_l_s = np.array(buffer_l_s, copy=True)
buffer_h_s = g_tpr.GetEYhigh()
buffer_h_s.SetSize(g_size)
arr_h_s = np.array(buffer_h_s, copy=True)
print len(arr_h)
print len(arr_l)
#######################
# Calculate AOC #
#######################
'''
x = np.array(arr_y_s)
y = np.array(arr_y)
exl = np.array(arr_l_s)
eyl = np.array(arr_l)
exh = np.array(arr_h_s)
eyh = np.array(arr_h)
'''
#######################
# Export ROC Position #
#######################
roc_dict = {}
roc_dict['param'] = param
roc_dict['tpr'] = np.array(arr_y_s)
roc_dict['fpr'] = np.array(arr_y)
roc_dict['e_tpr_l'] = np.array(arr_l_s)
roc_dict['e_fpr_l'] = np.array(arr_l)
roc_dict['e_tpr_h'] = np.array(arr_h_s)
roc_dict['e_fpr_h'] = np.array(arr_h)
roc_dict['threshold'] = bin_value_dict
roc_dict['cut_based'] = {}
#roc_dict['cut_based']['lc'] = LC_dict
#roc_dict['cut_based']['hc'] = HC_dict
#roc_dict['aoc'] = aoc
#roc_dict['aoc_l'] = aoc_l
#roc_dict['aoc_h'] = aoc_h
#raw_data = {}
#raw_data['load_s'] = load_s
#raw_data['load_b'] = load_b
#roc_dict['raw'] = raw_data
'''
path_dump = '/beegfs/desy/user/hezhiyua/2bBacked/roc_data/'
name_dump = 'roc.pkl'
joblib.dump(roc_dict, path_dump+name_dump)
'''
return roc_dict
name = ["300", "400", "600", "1000", "1200", "1400", "1600"]
err_sigeff_M1 = []
xM1 = ROOT.Double(0)
yM1 = ROOT.Double(0)
for i in range(nfile):
preselect = file.Get("h_preselect_"+str(i))
med1 = file.Get("h_med1_"+str(i))
effM1 = TGraphAsymmErrors(med1,preselect,"cl=0.683 b(1,1) mode")
effM1.SetTitle("Medium 1")
binM1 = effM1.GetN()
for j in range(binM1):
effM1.GetPoint(j, xM1, yM1)
if (yM1 == 0):
continue
else:
errM1 = effM1.GetErrorY(j)
err_sigeff_M1.append(errM1)
#print "errM1_"+str(name[i]), errM1
'''
print "####################################"
print "err_sigeff_M1", err_sigeff_M1
print "####################################"
'''
h1.Delete()
h2.Delete()
print xl
print yl
print hl
print ll
exit()
#g_fpr = GAE()
g_tpr = GAE()
#g_fpr.Divide(h_c_b, h_before_selection, "cl=0.683 b(1,1) mode")
g_tpr.Divide(h1, h2, "cl=0.683 b(1,1) mode")
g_size = g_tpr.GetN()
#x = Double()
#y = Double()
x_s = Double()
y_s = Double()
#arr_x = np.zeros(g_size)
#arr_y = np.zeros(g_size)
arr_x_s = np.zeros(g_size)
arr_y_s = np.zeros(g_size)
for i in xrange(g_size):
#g_fpr.GetPoint(i,x,y)
#arr_x[i] = x
#arr_y[i] = y
xM1 = ROOT.Double(0)
yM1 = ROOT.Double(0)
xM2 = ROOT.Double(0)
yM2 = ROOT.Double(0)
for i in range(nfile):
preselect = file.Get("h_preselect_"+str(i))
loo = file.Get("h_loo_"+str(i))
med1 = file.Get("h_med1_"+str(i))
med2 = file.Get("h_med2_"+str(i))
#gPad.Modified()
effL = TGraphAsymmErrors(loo,preselect,"cl=0.683 b(1,1) mode")
effL.SetTitle("Loose")
binL = effL.GetN()
for j in range(binL):
effL.GetPoint(j, xL, yL)
if (yL == 0):
continue
else:
errL = effL.GetErrorY(j)
err_sigeff_L.append(errL)
#print "errL_"+str(name[i]), errL
effM1 = TGraphAsymmErrors(med1,preselect,"cl=0.683 b(1,1) mode")
effM1.SetTitle("Medium 1")
binM1 = effM1.GetN()
for j in range(binM1):
effM1.GetPoint(j, xM1, yM1)
if (yM1 == 0):
def plotQIcalibration(results, outDir = "results", currentMode = ""):
gROOT.SetBatch(True)
calibGraphs = []
gStyle.SetMarkerColor(kBlue)
gStyle.SetLineColor(kBlack)
#for ch in xrange(1, len(results)+1): # Number of channels
for ch in sorted(results.keys()):
gr = TGraphAsymmErrors()
gr.SetMarkerStyle(22)
gr.SetMarkerSize(1.2)
gr.SetName("Channel_%d_Charge_vs_DAC" % ch)
gr.SetTitle("Channel %d Charge vs DAC value%s" % (ch, " %s current mode" % currentMode if currentMode in ["low", "high"] else ""))
res = results[ch]
for i, dacVal in enumerate(sorted(res.keys())):
mean = res[dacVal]["mean"] * 25.e6 # Convert to fC
err = res[dacVal]["std"] * 25.e6
# Index of new point
np = gr.GetN()
# Set value and error
gr.SetPoint(np, dacVal, mean)
gr.SetPointError(np, 0., 0., err, err)
calibGraphs.append((gr,ch))
if outDir[-1] == "/": outDir = outDir[:-1]
os.system("mkdir -p %s/calibGraphs" % outDir )
c1 = TCanvas('c1','c1', 1200, 800)
c1.SetLeftMargin(0.15);
c1.SetRightMargin(0.25)
c1.SetBottomMargin(0.25);
pad1=TPad('p1','p1',0.,0.,1.0,1.0)
pad1.Draw()
outF = TFile.Open(outDir + "/calibration.root", "RECREATE")
fitParams = {} # Converted to charge by * 25ns
for i,(gr,ch) in enumerate(calibGraphs):
gr.Fit('pol1', "Q") # Q: Quiet mode
f = gr.GetFunction("pol1")
p0 = f.GetParameter(0)
p0_err = f.GetParError(0)
p1 = f.GetParameter(1)
p1_err = f.GetParError(1)
fitline = "offset %g #pm %g slope %g #pm %g" % (p0, p0_err, p1, p1_err)
# Convert to fC
fitParams[ch] = {"slope":(p1), "offset":(p0)}
#fitParams[(i+1)] = {"slope":(p1), "offset":(p0)}
gr.GetXaxis().SetTitle("DAC value")
gr.GetYaxis().SetTitle("Charge [fC]")
if currentMode == "low":
gr.GetYaxis().SetTitleOffset(1.37)
pad1.cd()
gr.Draw("AP")
gr.Write()
txt=TLatex()
txt.SetNDC(True)
txt.SetTextFont(43)
txt.SetTextSize(20)
txt.SetTextAlign(12)
txt.DrawLatex(0.40,0.87, fitline)
Quiet(c1.SaveAs)(outDir + "/calibGraphs/channel_%d.png" % (i+1))
# for g in adcGraphs:
#
# g.Write()
# for g in chargeGraphs:
# g.Write()
#outF.Write()
outF.Close()
return fitParams
def limit(method, channel):
particle = channel[1:2]
particleP = particle + "'" if channel.startswith('X') else channel[0]
THEORY = ['A1', 'B3'] if channel.startswith('X') else []
suffix = ""
if method == "hvt": suffix = "_HVT"
if method == "cls": suffix = "_CLs"
if method == "monoH": suffix = "_monoH"
filename = "./combine/" + method + "/" + channel + "_M%d.txt"
mass, val = fillValues(filename)
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
Sign = TGraph()
pVal = TGraph()
Best = TGraphAsymmErrors()
Theory = {}
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0])
Exp0s.SetPoint(n, m, val[m][3])
Exp1s.SetPoint(n, m, val[m][3])
Exp1s.SetPointError(n, 0., 0., val[m][3] - val[m][2],
val[m][4] - val[m][3])
Exp2s.SetPoint(n, m, val[m][3])
Exp2s.SetPointError(n, 0., 0., val[m][3] - val[m][1],
val[m][5] - val[m][3])
if len(val[m]) > 6: Sign.SetPoint(n, m, val[m][6])
if len(val[m]) > 7: pVal.SetPoint(n, m, val[m][7])
if len(val[m]) > 8: Best.SetPoint(n, m, val[m][8])
if len(val[m]) > 10:
Best.SetPointError(n, 0., 0., abs(val[m][9]), val[m][10])
for t in THEORY:
Theory[t] = TGraphAsymmErrors()
for m in sorted(HVT[t]['Z']['XS'].keys()):
if m < mass[0] or m > mass[-1]: continue
XsW, XsW_Up, XsW_Down, XsZ, XsZ_Up, XsZ_Down = 0., 0., 0., 0., 0., 0.
XsZ = 1000. * HVT[t]['Z']['XS'][m] * HVT[t]['Z']['BR'][m]
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]))
n = Theory[t].GetN()
Theory[t].SetPoint(n, m, XsW + XsZ)
Theory[t].SetPointError(n, 0., 0.,
(XsW - XsW_Down) + (XsZ - XsZ_Down),
(XsW_Up - XsW) + (XsZ_Up - XsZ))
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
#Theory[t].SetLineStyle(7)
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp1s.SetFillColor(417) #kGreen+1
Exp1s.SetLineColor(417) #kGreen+1
Exp2s.SetFillColor(800) #kOrange
Exp2s.SetLineColor(800) #kOrange
Exp2s.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle("#sigma(" + particleP + ") #bf{#it{#Beta}}(" +
particleP + " #rightarrow " + particle +
"h) (fb)")
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Sign.SetLineWidth(2)
Sign.SetLineColor(629)
Sign.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1)
Sign.GetYaxis().SetTitle("Significance")
pVal.SetLineWidth(2)
pVal.SetLineColor(629)
pVal.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
pVal.GetXaxis().SetTitleSize(pVal.GetXaxis().GetTitleSize() * 1.1)
pVal.GetYaxis().SetTitle("local p-Value")
Best.SetLineWidth(2)
Best.SetLineColor(629)
Best.SetFillColor(629)
Best.SetFillStyle(3003)
Best.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Best.GetXaxis().SetTitleSize(Best.GetXaxis().GetTitleSize() * 1.1)
Best.GetYaxis().SetTitle("Best Fit (pb)")
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
#c1.GetPad(0).SetGridx()
#c1.GetPad(0).SetGridy()
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
Exp0s.Draw("SAME, L")
if not options.blind: Obs0s.Draw("SAME, L")
#setHistStyle(Exp2s)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.01, 5.e3)
#else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2)
Exp2s.GetXaxis().SetRangeUser(
mass[0], min(mass[-1],
MAXIMUM[channel] if channel in MAXIMUM else 1.e6))
drawAnalysis(channel)
drawRegion(channel, True)
drawCMS(LUMI, YEAR, "Preliminary") #Preliminary
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.98, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
for t in THEORY:
leg.AddEntry(Theory[t], theoryLabel[t], "fl")
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
latex.SetTextFont(42)
#latex.DrawLatex(0.66, leg.GetY1()-0.045, particleP+" #rightarrow "+particle+"h")
leg2 = TLegend(0.12, 0.225 - 2 * 0.25 / 5., 0.65, 0.225)
leg2.SetBorderSize(0)
leg2.SetFillStyle(0) #1001
leg2.SetFillColor(0)
c1.GetPad(0).RedrawAxis()
"""
if True and channel.endswith('sl'):
mass, val = fillValues("./combine/alpha/X"+particle+"Hbonly_M%d.txt")
Exp, Obs = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val: continue
Exp.SetPoint(Exp.GetN(), m, val[m][3])
Obs.SetPoint(Obs.GetN(), m, val[m][0])
Exp.SetLineWidth(3)
Exp.SetLineColor(602) #602
Exp.SetLineStyle(5)
Obs.SetLineWidth(3)
Obs.SetLineColor(602)
Exp.Draw("SAME, L")
Obs.Draw("SAME, L")
mass15, val15 = fillValues("./combine/Vh_2015/X"+particle+"h_M%d.txt")
Exp15, Obs15 = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(mass15):
if not m in val: continue
Exp15.SetPoint(Exp15.GetN(), m, val15[m][3]*multF*Theory['B3'].GetY()[i]*(0.625 if particle=='V' and m>3000 else 1.))
Obs15.SetPoint(Obs15.GetN(), m, val15[m][0]*multF*Theory['B3'].GetY()[i]*(0.625 if particle=='V' and m>3000 else 1.))
Exp15.SetLineWidth(3)
Exp15.SetLineColor(856) #602
Exp15.SetLineStyle(6)
Obs15.SetLineWidth(3)
Obs15.SetLineColor(856)
Exp15.Draw("SAME, L")
#Obs15.Draw("SAME, L")
leg2.AddEntry(Exp, "1+2 b-tag", "l")
"""
if True and channel == 'AZh':
massLL, valLL = fillValues("./combine/AZh/AZhll_M%d.txt")
ExpLL, ObsLL = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massLL):
if not m in val: continue
ExpLL.SetPoint(ExpLL.GetN(), m, valLL[m][3] * multF)
ObsLL.SetPoint(ObsLL.GetN(), m, valLL[m][0] * multF)
ExpLL.SetLineWidth(3)
ExpLL.SetLineColor(833) #602
ExpLL.SetLineStyle(5)
ObsLL.SetLineWidth(3)
ObsLL.SetLineColor(833)
ExpLL.Draw("SAME, L")
#ObsLL.Draw("SAME, L")
massNN, valNN = fillValues("./combine/AZh/AZhnn_M%d.txt")
ExpNN, ObsNN = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massNN):
if not m in val: continue
ExpNN.SetPoint(ExpNN.GetN(), m, valNN[m][3] * multF)
ObsNN.SetPoint(ObsNN.GetN(), m, valNN[m][0] * multF)
ExpNN.SetLineWidth(3)
ExpNN.SetLineColor(855) #602
ExpNN.SetLineStyle(6)
ObsNN.SetLineWidth(3)
ObsNN.SetLineColor(855)
ExpNN.Draw("SAME, L")
#ObsNN.Draw("SAME, L")
leg2.AddEntry(ExpLL,
"Expected, A #rightarrow Zh #rightarrow llb#bar{b}", "l")
leg2.AddEntry(ExpNN,
"Expected, A #rightarrow Zh #rightarrow #nu#nub#bar{b}",
"l")
if method == 'combo':
massAH, valAH = fillValues("./combine/dijet/X" + particle +
"Hah_M%d.txt")
ExpAH = TGraphAsymmErrors()
for i, m in enumerate(massAH):
if not m in val: continue
ExpAH.SetPoint(ExpAH.GetN(), m, valAH[m][3] * multF)
ExpAH.SetLineWidth(2)
ExpAH.SetLineColor(602) #602
ExpAH.SetLineStyle(4)
ExpAH.Draw("SAME, L")
massSL, valSL = fillValues("./combine/alpha/X" + particle +
"Hsl_M%d.txt")
ExpSL = TGraphAsymmErrors()
for i, m in enumerate(massSL):
if not m in val: continue
ExpSL.SetPoint(ExpSL.GetN(), m, valSL[m][3] * multF)
ExpSL.SetLineWidth(3)
ExpSL.SetLineColor(860 - 9) #602
ExpSL.SetLineStyle(7)
ExpSL.Draw("SAME, L")
leg2.AddEntry(ExpAH, "B2G-17-002", "l")
leg2.AddEntry(ExpSL, "B2G-17-004", "l")
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("plotsLimit/Exclusion/" + channel + suffix + ".png")
c1.Print("plotsLimit/Exclusion/" + channel + suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("plotsLimit/Exclusion/" + channel + suffix + ".C")
c1.Print("plotsLimit/Exclusion/" + channel + suffix + ".root")
for t in THEORY:
print "Model", t, ":",
for m in range(mass[0], mass[-1], 1):
if not (Theory[t].Eval(m) > Obs0s.Eval(m)) == (
Theory[t].Eval(m + 1) > Obs0s.Eval(m + 1)):
print m,
print ""
print "p1s[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i] + Exp1s.GetErrorYhigh(i), ",",
print "],"
print "m1s[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i] - Exp1s.GetErrorYlow(i), ",",
print "],"
print "[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i], ",",
print "]"
#if not 'ah' in channel and not 'sl' in channel: return
# ---------- 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, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c2.Print("plotsLimit/Significance/" + channel + suffix + ".png")
c2.Print("plotsLimit/Significance/" + channel + suffix + ".pdf")
# c2.Print("plotsLimit/Significance/"+channel+suffix+".root")
# c2.Print("plotsLimit/Significance/"+channel+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, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c3.Print("plotsLimit/pValue/" + channel + suffix + ".png")
c3.Print("plotsLimit/pValue/" + channel + suffix + ".pdf")
# c3.Print("plotsLimit/pValue/"+channel+suffix+".root")
# c3.Print("plotsLimit/pValue/"+channel+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, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c4.Print("plotsLimit/BestFit/" + channel + suffix + ".png")
c4.Print("plotsLimit/BestFit/" + channel + suffix + ".pdf")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+channel+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 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 makeplot_single(
h1_sig=None,
h1_bkg=None,
h1_data=None,
sig_legends_=None,
bkg_legends_=None,
sig_colors_=None,
bkg_colors_=None,
hist_name_=None,
sig_scale_=1.0,
dir_name_="plots",
output_name_=None,
extraoptions=None
):
if h1_sig == None or h1_bkg == None:
print("nothing to plot...")
return
os.system("mkdir -p "+dir_name_)
os.system("cp index.php "+dir_name_)
s_color = [632, 617, 839, 800, 1]
b_color = [920, 2007, 2005, 2003, 2001, 2011]
if sig_colors_:
s_color = sig_colors_
if bkg_colors_:
b_color = bkg_colors_
for idx in range(len(h1_sig)):
h1_sig[idx].SetLineWidth(3)
h1_sig[idx].SetLineColor(s_color[idx])
for idx in range(len(h1_bkg)):
h1_bkg[idx].SetLineWidth(2)
h1_bkg[idx].SetLineColor(b_color[idx])
h1_bkg[idx].SetFillColorAlpha(b_color[idx], 1)
if h1_data:
h1_data.SetBinErrorOption(1)
h1_data.SetLineColor(1)
h1_data.SetLineWidth(2)
h1_data.SetMarkerColor(1)
h1_data.SetMarkerStyle(20)
myC = r.TCanvas("myC","myC", 600, 600)
myC.SetTicky(1)
pad1 = r.TPad("pad1","pad1", 0.05, 0.33,0.95, 0.97)
pad1.SetBottomMargin(0.027)
pad1.SetRightMargin( rightMargin )
pad1.SetLeftMargin( leftMargin )
pad2 = r.TPad("pad2","pad2", 0.05, 0.04, 0.95, 0.31)
pad2.SetBottomMargin(0.4)
pad2.SetTopMargin(0.05)
pad2.SetRightMargin( rightMargin )
pad2.SetLeftMargin( leftMargin )
pad2.Draw()
pad1.Draw()
pad1.cd()
for idx in range(len(h1_sig)):
print("before signal scaling",h1_sig[idx].Integral())
h1_sig[idx].Scale(sig_scale_)
print("after signal scaling",h1_sig[idx].Integral())
stack = r.THStack("stack", "stack")
nS = np.zeros(h1_bkg[0].GetNbinsX())
eS = np.zeros(h1_bkg[0].GetNbinsX())
#hist_all is used to make the data/mc ratio. remove signal for the moment due to signal is scaled right now
hist_all = h1_sig[0].Clone("hist_all")
hist_all.Scale(0.0)
hist_s = h1_sig[0].Clone("hist_s")
hist_b = h1_bkg[0].Clone("hist_b")
for idx in range(len(h1_bkg)):
stack.Add(h1_bkg[idx])
for ib in range(h1_bkg[0].GetNbinsX()):
nS[ib] += h1_bkg[idx].GetBinContent(ib+1)
eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_bkg[idx].GetBinError(ib+1)*h1_bkg[idx].GetBinError(ib+1))
hist_all.Add(h1_bkg[idx])
if idx > 0:
hist_b.Add(h1_bkg[idx])
for idx in range(len(h1_sig)):
print("ggH signal yield: ", hist_s.Integral())
if idx > 0:
hist_temp = h1_sig[idx].Clone(h1_sig[idx].GetName()+"_temp")
#hist_all.Add(hist_temp)
hist_s.Add(h1_sig[idx])
print("all signal yield: ", hist_s.Integral())
stack.SetTitle("")
maxY = 0.0
if "stack_signal" in extraoptions and extraoptions["stack_signal"]:
for idx in range(len(h1_sig)):
h1_sig[idx].SetFillColorAlpha(s_color[idx], 1)
stack.Add(h1_sig[idx])
for ib in range(h1_bkg[0].GetNbinsX()):
nS[ib] += h1_sig[idx].GetBinContent(ib+1)
eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_sig[idx].GetBinError(ib+1)*h1_sig[idx].GetBinError(ib+1))
if stack.GetMaximum() > maxY:
maxY = stack.GetMaximum()
#if "SR" in h.GetTitle():
stack.Draw("hist")
else:
stack.Draw("hist")
if stack.GetMaximum() > maxY:
maxY = stack.GetMaximum()
for idx in range(len(h1_sig)):
if h1_sig[idx].GetMaximum() > maxY:
maxY = h1_sig[idx].GetMaximum()
if "SR" in h1_bkg[0].GetTitle():
#h1_sig[idx].Draw("samehist")
hist_s.Draw("samehist")
##draw stack total unc on top of total histogram
box = r.TBox(0,0,1,1,)
box.SetFillStyle(3002)
box.SetLineWidth(0)
box.SetFillColor(r.kBlack)
for idx in range(h1_bkg[0].GetNbinsX()):
box.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]-eS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]+eS[idx])
if h1_data:
if h1_data.GetMaximum() > maxY:
maxY = h1_data.GetMaximum()+np.sqrt(h1_data.GetMaximum())
#if not "SR" in h1_data.GetTitle() or "fail" in h1_data.GetTitle():
if True:
#print("debug h1_data.GetName()",h1_data.GetName(), h1_data.GetTitle())
TGraph_data = TGraphAsymmErrors(h1_data)
for i in range(TGraph_data.GetN()):
#data point
var_x, var_y = Double(0.), Double(0.)
TGraph_data.GetPoint(i,var_x,var_y)
if np.fabs(var_y) < 1e-5:
TGraph_data.SetPoint(i,var_x,-1.0)
TGraph_data.SetPointEYlow(i,-1)
TGraph_data.SetPointEYhigh(i,-1)
#print("zero bins in the data TGraph: bin",i+1)
else:
TGraph_data.SetPoint(i,var_x,var_y)
err_low = var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y))
TGraph_data.SetPointEYlow(i, var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)))
TGraph_data.SetPointEYhigh(i, (0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y)
TGraph_data.SetMarkerColor(1)
TGraph_data.SetMarkerSize(1)
TGraph_data.SetMarkerStyle(20)
TGraph_data.Draw("same P")
stack.GetYaxis().SetTitle("Events")
stack.GetYaxis().SetTitleOffset(1.05)
stack.GetYaxis().SetTitleSize(0.08)
stack.GetYaxis().SetLabelSize(0.06)
#stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetLabelSize(0.)
#stack.GetXaxis().SetLabelOffset(0.013)
#if "xaxis_range" in extraoptions:
# stack.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1]))
leg = r.TLegend(0.2, 0.60, 0.9, 0.88)
leg.SetNColumns(3)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.SetTextSize(0.05)
for idx in range(len(h1_bkg)):
leg.AddEntry(h1_bkg[idx], bkg_legends_[idx], "F")
if "SR" in hist_s.GetTitle():
leg.AddEntry(hist_s, 'HH #times {:1.2}'.format(sig_scale_), "L")
leg.AddEntry(box, "Total unc", "F")
if h1_data:
leg.AddEntry(h1_data, "Data", "ep")
leg.Draw()
pad2.cd()
pad2.SetGridy(1)
ratio = None
ratio_Low = 0.0
ratio_High = 4
if h1_data:
ratio = TGraphAsymmErrors(h1_data)
for i in range(ratio.GetN()):
#bkg prediction
imc = Double(hist_all.GetBinContent(i+1))
#data point
var_x, var_y = Double(0.), Double(0.)
if not ("SR" in h1_data.GetTitle() and (i>5 and i<9)):
ratio.GetPoint(i,var_x,var_y)
if var_y == 0.:
ratio.SetPoint(i,var_x,-1.0)
ratio.SetPointEYlow(i,-1)
ratio.SetPointEYhigh(i,-1)
continue
ratio.SetPoint(i,var_x,var_y/imc)
err_low = (var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)))/imc
err_high = ((0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y)/imc
ratio.SetPointEYlow(i, err_low)
ratio.SetPointEYhigh(i, err_high)
ratio.SetMarkerColor(1)
ratio.SetMarkerSize(1)
ratio.SetMarkerStyle(20)
ratio.GetXaxis().SetTitle("j_{2} regressed mass [GeV]")
#myC.Update()
if "ratio_range" in extraoptions:
ratio_Low = extraoptions["ratio_range"][0]
ratio_High = extraoptions["ratio_range"][1]
ratio.GetYaxis().SetTitle("data/mc")
ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High)
ratio.GetXaxis().SetRangeUser(50, 220)
ratio.SetTitle("")
ratio.Draw("same AP")
pad2.Update()
print(ratio.GetTitle(),ratio.GetName(),"debug")
else:
ratio = h1_sig[0].Clone("ratio")
ratio_High = 0.0
for ibin in range(1,ratio.GetNbinsX()+1):
s = hist_s.GetBinContent(ibin)
b = hist_b.GetBinContent(ibin)
L = 0.0
if b > 0.0:
L = s/math.sqrt(b)
if L > ratio_High:
ratio_High = L
ratio.SetBinContent(ibin, L)
if ratio_High > 1.0:
ratio_High = 1.0
ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High*1.2)
ratio.GetYaxis().SetTitle("S/#sqrt{B}")
ratio.Draw("samehist")
ratio.SetLineColor(1)
ratio.SetLineWidth(2)
ratio.SetMarkerStyle(20)
ratio.SetMarkerColor(1)
ratio.SetFillColorAlpha(1, 0)
ratio.GetXaxis().SetTitleOffset(0.94)
ratio.GetXaxis().SetTitleSize(0.18)
ratio.GetXaxis().SetLabelSize(0.12)
ratio.GetXaxis().SetLabelOffset(0.013)
ratio.GetYaxis().SetTitleOffset(0.40)
ratio.GetYaxis().SetTitleSize(0.17)
ratio.GetYaxis().SetLabelSize(0.13)
ratio.GetYaxis().SetTickLength(0.01)
ratio.GetYaxis().SetNdivisions(505)
#if "xaxis_range" in extraoptions:
# ratio.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1]))
#draw stack total unc on the ratio plot to present the background uncertainty
box_ratio = r.TBox(0,0,1,1,)
box_ratio.SetFillStyle(3002)
box_ratio.SetLineWidth(0)
box_ratio.SetFillColor(r.kBlack)
for idx in range(h1_bkg[0].GetNbinsX()):
if np.fabs(nS[idx])> 1e-06:
box_ratio.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]-eS[idx])/nS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]+eS[idx])/nS[idx])
else:
print("blinded Higgs peak region")
if "xaxis_label" in extraoptions and extraoptions["xaxis_label"] != None:
x_title = extraoptions["xaxis_label"]
ratio.GetXaxis().SetTitle(x_title)
ratio.GetYaxis().CenterTitle()
##########draw CMS preliminary
pad1.cd()
tex1 = r.TLatex(leftMargin, 0.91, "CMS")
tex1.SetNDC()
tex1.SetTextFont(61)
tex1.SetTextSize(0.070)
tex1.SetLineWidth(2)
tex1.Draw()
tex2 = r.TLatex(leftMargin+0.12,0.912,"Internal")
tex2.SetNDC()
tex2.SetTextFont(52)
tex2.SetTextSize(0.055)
tex2.SetLineWidth(2)
tex2.Draw()
lumi_value = 137
if "lumi_value" in extraoptions:
lumi_value = extraoptions["lumi_value"]
tex3 = r.TLatex(0.72,0.912,"%d"%lumi_value+" fb^{-1} (13 TeV)")
tex3.SetNDC()
tex3.SetTextFont(42)
tex3.SetTextSize(0.055)
tex3.SetLineWidth(2)
tex3.Draw()
outFile = dir_name_
if output_name_:
outFile = outFile + "/" +output_name_
else:
outFile = outFile + "/" + hist_name_
#print("maxY = "+str(maxY))
stack.SetMaximum(maxY*1.7)
#print everything into txt file
text_file = open(outFile+"_linY.txt", "w")
text_file.write("bin | x ")
for idx in range(len(h1_bkg)):
text_file.write(" | %21s"%bkg_legends_[idx])
text_file.write(" | %21s"%("total B"))
for idx in range(len(sig_legends_)):
text_file.write(" | %25s"%sig_legends_[idx])
if h1_data:
text_file.write(" | data | data/mc")
text_file.write("\n-------------")
for idx in range(24*(len(h1_bkg) + 1)+ 29*len(sig_legends_)):
text_file.write("-")
if h1_data:
text_file.write("-------")
text_file.write("\n")
for ibin in range(0,h1_sig[0].GetNbinsX()+1):
text_file.write("%3d"%ibin+" ")
text_file.write(" | %6.3f"%h1_data.GetBinCenter(ibin)+" ")
for idx in range(len(h1_bkg)):
text_file.write(" | %7.3f "%h1_bkg[idx].GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%h1_bkg[idx].GetBinError(ibin))
text_file.write(" | %7.3f "%hist_b.GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%hist_b.GetBinError(ibin))
for idx in range(len(sig_legends_)):
text_file.write(" | %9.3f "%h1_sig[idx].GetBinContent(ibin)+"$\\pm$"+ " %9.3f"%h1_sig[idx].GetBinError(ibin))
if h1_data:
text_file.write(" | %d"%h1_data.GetBinContent(ibin) + " | %7.3f "%h1_data.GetBinContent(ibin) +"$\\pm$"+ " %7.3f"%h1_data.GetBinError(ibin))
text_file.write("\n\n")
#print yield table for AN
text_file.write("print yield table for AN\n")
bkg_all = 0
bkg_all_errsq = 0
for idx in range(len(h1_bkg)):
bkg_tmp = h1_bkg[idx].GetBinContent(7)+h1_bkg[idx].GetBinContent(8)+h1_bkg[idx].GetBinContent(9)
bkg_errsq_tmp = h1_bkg[idx].GetBinError(7)*h1_bkg[idx].GetBinError(7)+h1_bkg[idx].GetBinError(8)*h1_bkg[idx].GetBinError(8)+h1_bkg[idx].GetBinError(9)*h1_bkg[idx].GetBinError(9)
bkg_all += bkg_tmp
bkg_all_errsq += bkg_errsq_tmp
text_file.write("%s"%(bkg_legends_[idx])+"& %7.2f"%(bkg_tmp)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_errsq_tmp)+"\n")
text_file.write("total background & %7.2f"%(bkg_all)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_all_errsq)+"\n")
text_file.write("\ggHH SM ($\kapl=1$) & %7.2f"%((h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9)))+"\n")
text_file.write("\VBFHH SM ($\kapl=1$) & %7.2f"%((h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9)))+"\n")
text_file.write("HH bin 8 value %s"%h1_sig[0].GetBinContent(8)+"\n")
text_file.write("HH bin 9 value %s"%h1_sig[0].GetBinContent(9)+"\n")
text_file.write("HH bin 7 value %s"%h1_sig[0].GetBinContent(7)+"\n")
text_file.write("HH bin 8 error %s"%h1_sig[0].GetBinError(8)+"\n")
text_file.write("HH bin 9 error %s"%h1_sig[0].GetBinError(9)+"\n")
text_file.write("HH bin 7 error %s"%h1_sig[0].GetBinError(7)+"\n")
text_file.write("total & %7.2f"%(bkg_all+(h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9)+h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.2f"%(np.sqrt((h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9))/(sig_scale_*sig_scale_)+(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9))/(sig_scale_*sig_scale_)+bkg_all_errsq))+"\n")
text_file.close()
os.system("cp "+outFile+"_linY.txt "+outFile+"_logY.txt")
pad1.RedrawAxis()
myC.SaveAs(outFile+"_linY.png")
myC.SaveAs(outFile+"_linY.pdf")
myC.SaveAs(outFile+"_linY.C")
pad1.cd()
stack.SetMaximum(maxY*100.0)
stack.SetMinimum(0.5)
pad1.SetLogy()
pad1.RedrawAxis()
myC.SaveAs(outFile+"_logY.png")
myC.SaveAs(outFile+"_logY.pdf")
myC.SaveAs(outFile+"_logY.C")
#save histogram and ratio to root file
outFile_root = r.TFile(outFile+".root", "recreate")
outFile_root.cd()
for idx in range(len(h1_bkg)):
h1_bkg[idx].Write()
for idx in range(len(sig_legends_)):
h1_sig[idx].Write()
if h1_data:
h1_data.Write()
ratio.Write()
#outFile_root.Write()
outFile_root.Close()
#slp(33)
g_tpr.SetTitle('True Positive Rate')
g_tpr.SetFillColor(5)
g_tpr.SetMarkerStyle(21)
g_tpr.SetMarkerColor(4)
#g_tpr.Draw('ALP')
#g_tpr.Draw('3A')a
#slp(33)
'''
from ROOT import Double
g_size = g_efficiency.GetN()
g_size_s = g_tpr.GetN()
x = Double()
y = Double()
x_s = Double()
y_s = Double()
arr_x = np.zeros(g_size)
arr_y = np.zeros(g_size)
arr_x_s = np.zeros(g_size_s)
arr_y_s = np.zeros(g_size_s)
for i in range( g_size ):
h14.GetBinError(b + 1) * h14.GetBinWidth(b + 1))
origh14 = h14.Rebin(
len(chi_binnings[massbin]) - 1,
h14.GetName() + "rebinorig", chi_binnings[massbin])
h14 = rebin2(h14,
len(chi_binnings[massbin]) - 1, chi_binnings[massbin])
h14G = TGraphAsymmErrors(h14.Clone(histname + "G"))
new_hists += [h14G]
h14G.SetMarkerStyle(21)
h14G.SetMarkerSize(0.4)
h14G.SetMarkerColor(1)
h14G.SetLineColor(1)
alpha = 1. - 0.6827
nevents = 0
for b in range(h14G.GetN()):
if unfoldedData:
N = origh14.GetBinContent(b + 1)
else:
N = 1. / pow(
h14.GetBinError(b + 1) / h14.GetBinContent(b + 1), 2)
print N
nevents += N
L = 0
if N > 0:
L = ROOT.Math.gamma_quantile(alpha / 2., N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2., N + 1, 1.)
h14G.SetPointEYlow(b, (N - L) / N * h14.GetBinContent(b + 1))
h14G.SetPointEYhigh(b, (U - N) / N * h14.GetBinContent(b + 1))
print "data events:", nevents
def plotDataOverMCEff(hist_mc_tight, hist_mc_loose, hist_data_tight, hist_data_loose, plot_name='fakerate.pdf', mc_leg='MC', obs_leg='Observed', ratio_leg='Obs/MC'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Misidentification 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)
# if g_data.GetN() != hist_data_tight.GetNbinsX():
# import pdb; pdb.set_trace()
g_data.GetYaxis().SetTitle('Misidentification 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 = 1.3 * max(g.GetMaximum(), g_data.GetMaximum(), 0.05)
g.GetYaxis().SetRangeUser(0.0011, maxy)
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_leg, 'lep')
legend.AddEntry(g_data.GetName(), obs_leg, 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.01, 1.99)
g_ratio.GetYaxis().SetTitle(ratio_leg)
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
g.GetYaxis().SetRangeUser(0.0001, 1)
pad.SetLogy(True)
cv.Print(plot_name.replace('.', '_log.'))
f = ROOT.TFile(plot_name.replace('.', '_log.').replace('.pdf', '.root'), 'RECREATE')
g.Write()
g_data.Write()
cv.Write()
f.Close()
g_fpr.SetMarkerStyle(21)
g_fpr.SetMarkerColor(4)
#g_fpr.Draw('ALP')
#g_fpr.Draw('ALPE1')
#g_fpr.Draw('3A')a
#slp(33)
g_tpr.SetTitle('True Positive Rate')
g_tpr.SetFillColor(5)
g_tpr.SetMarkerStyle(21)
g_tpr.SetMarkerColor(4)
#g_tpr.Draw('ALP')
#g_tpr.Draw('3A')a
#slp(33)
'''
g_size = g_fpr.GetN()
x = Double()
y = Double()
x_s = Double()
y_s = Double()
arr_x = np.zeros(g_size)
arr_y = np.zeros(g_size)
arr_x_s = np.zeros(g_size)
arr_y_s = np.zeros(g_size)
for i in xrange(g_size):
g_fpr.GetPoint(i, x, y)
arr_x[i] = x
arr_y[i] = y
def limit(channel, signal):
multF = 1. # in fb
filename = "./limitOutput_" + options.name + "/" + signal + "_MChi1_MPhi%d_scalar" + options.bjets + "_" + channel + "_AsymptoticLimits_grepOutput.txt"
if (options.mediator == 'SC'):
filename = "./limitOutput_" + options.name + "/" + signal + "_MChi1_MPhi%d_scalar" + options.bjets + "_" + channel + "_AsymptoticLimits_grepOutput.txt"
elif (options.mediator == 'PS'):
filename = "./limitOutput_" + options.name + "/" + signal + "_MChi1_MPhi%d_pseudo" + options.bjets + "_" + channel + "_AsymptoticLimits_grepOutput.txt"
else:
print 'WRONG mediator type'
mass, val = fillValues(filename)
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
Sign = TGraph()
pVal = TGraph()
Best = 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)
#Sign.SetPoint(n, m, val[m][6])
#pVal.SetPoint(n, m, val[m][7])
#Best.SetPoint(n, m, val[m][8])
#Best.SetPointError(m, 0., 0., abs(val[m][9]), abs(val[m][10]))
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp1s.SetFillColor(417) #kGreen+1
Exp1s.SetLineColor(417) #kGreen+1
Exp2s.SetFillColor(800) #kOrange
Exp2s.SetLineColor(800) #kOrange
Exp2s.GetXaxis().SetTitle("m_{#phi} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle("#sigma/#sigma_{th}")
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Sign.SetLineWidth(2)
Sign.SetLineColor(629)
Sign.GetXaxis().SetTitle("m_{#phi} (GeV)")
Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1)
Sign.GetYaxis().SetTitle("Significance")
pVal.SetLineWidth(2)
pVal.SetLineColor(629)
pVal.GetXaxis().SetTitle("m_{#phi} (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_{#phi} (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).SetTicks(1, 1)
#c1.GetPad(0).SetGridx()
#c1.GetPad(0).SetGridy()
c1.GetPad(0).SetLogx()
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
Exp0s.Draw("SAME, L")
if not options.blind: Obs0s.Draw("SAME, L")
#Theory[0].Draw("SAME, L")
#Theory[1].Draw("SAME, L")
#setHistStyle(Exp2s)
Exp2s.GetXaxis().SetTitleSize(0.045)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetXaxis().SetNoExponent(True)
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.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.1, 1000.)
#else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2)
Exp2s.GetXaxis().SetRangeUser(mass[0], mass[-1])
drawAnalysis("tDM")
drawRegion(channel, True)
drawCMS(LUMI, "Preliminary")
if True:
if (options.mediator == 'SC'):
massT, valT = fillValues("./limitOutput_" + options.name + "/" +
signal.replace('tttDM', 'tDM') +
"_MChi1_MPhi%d_scalar" + options.bjets +
"_" + channel +
"_AsymptoticLimits_grepOutput.txt")
elif (options.mediator == 'PS'):
massT, valT = fillValues("./limitOutput_" + options.name + "/" +
signal.replace('tttDM', 'tDM') +
"_MChi1_MPhi%d_pseudo" + options.bjets +
"_" + channel +
"_AsymptoticLimits_grepOutput.txt")
ExpT, ObsT = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massT):
if not m in val: continue
ExpT.SetPoint(ExpT.GetN(), m, valT[m][3] * multF)
ObsT.SetPoint(ObsT.GetN(), m, valT[m][0] * multF)
ExpT.SetLineWidth(3)
ExpT.SetLineColor(602) #602
ExpT.SetLineStyle(5)
ObsT.SetLineWidth(3)
ObsT.SetLineColor(602)
ExpT.SetMarkerStyle(21)
ObsT.SetMarkerStyle(22)
ExpT.SetMarkerColor(602)
ObsT.SetMarkerColor(602)
ExpT.Draw("SAME, PC")
#if not options.blind: ObsT.Draw("SAME, P")
if (options.mediator == 'SC'):
massTTT, valTTT = fillValues("./limitOutput_" + options.name +
"/" +
signal.replace('tttDM', 'ttDM') +
"_MChi1_MPhi%d_scalar" +
options.bjets + "_" + channel +
"_AsymptoticLimits_grepOutput.txt")
elif (options.mediator == 'PS'):
massTTT, valTTT = fillValues("./limitOutput_" + options.name +
"/" +
signal.replace('tttDM', 'ttDM') +
"_MChi1_MPhi%d_pseudo" +
options.bjets + "_" + channel +
"_AsymptoticLimits_grepOutput.txt")
ExpTTT, ObsTTT = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massTTT):
if not m in val: continue
ExpTTT.SetPoint(ExpTTT.GetN(), m, valTTT[m][3] * multF)
ObsTTT.SetPoint(ObsTTT.GetN(), m, valTTT[m][0] * multF)
ExpTTT.SetLineWidth(3)
ExpTTT.SetLineColor(634) #602
ExpTTT.SetLineStyle(5)
ObsTTT.SetLineWidth(3)
ObsTTT.SetLineColor(634)
ExpTTT.SetMarkerStyle(21)
ObsTTT.SetMarkerStyle(22)
ExpTTT.SetMarkerColor(634)
ObsTTT.SetMarkerColor(634)
ExpTTT.Draw("SAME, PC")
#if not options.blind: ObsTTT.Draw("SAME, P")
# legend
top = 0.9
nitems = 4 + 2
leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.95, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL limits")
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected (t+DM, tt+DM)", "l")
leg.AddEntry(Exp1s, "#pm 1 s. d.", "f")
leg.AddEntry(Exp2s, "#pm 2 s. d.", "f")
if True:
leg.AddEntry(ExpT, "Expected (t+DM)", "p")
leg.AddEntry(ExpTTT, "Expected (tt+DM)", "p")
leg.Draw()
c1.GetPad(0).RedrawAxis()
c1.GetPad(0).Update()
if gROOT.IsBatch():
c1.Print("plotsLimit_" + options.name + "/Exclusion_" + channel + "_" +
options.mediator + "_" + options.bjets + ".png")
c1.Print("plotsLimit_" + options.name + "/Exclusion_" + channel + "_" +
options.mediator + "_" + options.bjets + ".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
# print "p1s[",
# for i in range(Exp0s.GetN()):
# print Exp0s.GetY()[i]+Exp1s.GetErrorYhigh(i), ",",
# print "],"
# print "m1s[",
# for i in range(Exp0s.GetN()):
# print Exp0s.GetY()[i]-Exp1s.GetErrorYlow(i), ",",
# print "],"
# print "[",
# for i in range(Exp0s.GetN()):
# print Exp0s.GetY()[i], ",",
# print "]"
return
# ---------- 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")
drawAnalysis(channel[1:3])
if gROOT.IsBatch():
c2.Print("plotsLimit_" + options.name + "/Significance/" + channel +
"_" + options.mediator + "_" + options.bjets + ".png")
c2.Print("plotsLimit_" + options.name + "/Significance/" + channel +
"_" + options.mediator + "_" + options.bjets + ".pdf")
# c2.Print("plotsLimit/Significance/"+channel+suffix+".root")
# c2.Print("plotsLimit/Significance/"+channel+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")
drawAnalysis(channel[1:3])
if gROOT.IsBatch():
c3.Print("plotsLimit_" + options.name + "/pValue/" + channel + suffix +
"_" + options.mediator + "_" + options.bjets + ".png")
c3.Print("plotsLimit_" + options.name + "/pValue/" + channel + suffix +
"_" + options.mediator + "_" + options.bjets + ".pdf")
# c3.Print("plotsLimit/pValue/"+channel+suffix+".root")
# c3.Print("plotsLimit/pValue/"+channel+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")
drawAnalysis(channel[1:3])
if gROOT.IsBatch():
c4.Print("plotsLimit_" + options.name + "/BestFit/" + channel +
suffix + "_" + options.mediator + "_" + options.bjets +
".png")
c4.Print("plotsLimit_" + options.name + "/BestFit/" + channel +
suffix + "_" + options.mediator + "_" + options.bjets +
".pdf")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".C")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
#slp(33)
g_tpr.SetTitle('True Positive Rate')
g_tpr.SetFillColor(5)
g_tpr.SetMarkerStyle(21)
g_tpr.SetMarkerColor(4)
#g_tpr.Draw('ALP')
#g_tpr.Draw('3A')a
#slp(33)
'''
from ROOT import Double
g_size = g_efficiency.GetN()
#g_size_s = g_tpr.GetN()
#print g_size
#print g_size_s
x = Double()
y = Double()
#x_s = Double()
#y_s = Double()
arr_x = np.zeros(g_size)
arr_y = np.zeros(g_size)
#arr_x_s = np.zeros(g_size_s)
