lines[-txtid].SetLineStyle(style)
lines[-txtid].SetLineColor(color)
if 'Axis' in param:
paxis = param['Axis']
axis_margin = 0.015 * (plot_range['ymax'] - plot_range['ymin'])
axis = TGaxis(-param['TextWidth'] - param['space'],
paxis['range_min'],
-param['TextWidth'] - param['space'],
paxis['range_max'], paxis['range_min'],
paxis['range_max'], paxis['ticks'], "")
axis.SetTitle(paxis['title'])
axis.SetLabelSize(paxis['label_size'])
axis.SetTitleSize(paxis['title_size'])
axis.SetTitleOffset(paxis['title_offset'])
axis.Draw()
for line in lines:
line.SetLineWidth(param['LineWidth'])
alllines.append(line)
for text in texts:
text.SetTextSize(param['TextSize'])
text.Draw()
for i_g, g in enumerate(gamma):
#python2
#level1 = filter(lambda x: x.GetY1() == g['from'],lines)[0]
#level2 = filter(lambda x: x.GetY1() == g['to'],lines)[0]
#python3
level1 = next(filter(lambda x: x.GetY1() == g['from'], lines))
mg.GetYaxis().SetMoreLogLabels()
mg.GetYaxis().SetTitleOffset(1.9)
legend.SetBorderSize(0)
legend.SetTextSize(0.03)
legend.Draw("same")
c.SetGrid()
gStyle.SetGridStyle(2)
gStyle.SetGridColor(ROOT.kGray)
latex = Text(gPad, 0.88,0.65, "#scale[1.2]{#bf{CMS}} #font[50]{Preliminary}", align = 33)
latex = Text(gPad, 0.88,0.60,"Run2 Data (13 TeV)", align = 33,size = 0.03)
if args.glob: latex = Text(gPad, 0.88,0.55,"Z#rightarrow ee events, EB", align = 33,size = 0.03)
if args.loc: latex = Text(gPad, 0.88,0.55,"Same cluster, EB", align = 33,size = 0.03)
latex = Text(gPad, 0.88,0.48,"#sigma = #frac{N}{A_{eff}} #font[62]{#oplus} #sqrt{2}C", align = 33,size = 0.04)
top_axis = TGaxis(xmin ,mg.GetHistogram().GetMaximum(),xmax,mg.GetHistogram().GetMaximum(),ADC2GEV_B*xmin,ADC2GEV_B*xmax,510,"G-");
top_axis.SetTitle("E [GeV]")
top_axis.SetTitleSize(0.035)
top_axis.SetLabelSize(0.035)
top_axis.SetTitleFont(42)
top_axis.SetLabelFont(42)
top_axis.Draw()
Frame(gPad)
c.Update()
c.Modified()
input()
c.SaveAs("plots/"+args.tag+"/allYears/sigma_"+name+".png")
c.SaveAs("plots/"+args.tag+"/allYears/sigma_"+name+".pdf")
graph_exclusion_cms_8TeV_2g = TGraph(6, mass_cms_8TeV_2g, ctau_cms_8TeV_2g)
graph_exclusion_cms_8TeV_2g.SetFillColorAlpha(kGray, 0.6)
graph_exclusion_cms_8TeV_2g.SetLineColorAlpha(kGray, 0.6)
graph_exclusion_cms_8TeV_2g.Draw("Fsames")
#Lambda axis
f1_lambda = TF1("f1", "(x+6.00)/1.454", 72.902, 416.78)
A1_lambda = TGaxis(100.0, 0.02, 600.0, 0.02, "f1", 1010, "NI")
A1_lambda.SetLabelFont(42)
A1_lambda.SetLabelSize(0.035)
A1_lambda.SetTextFont(42)
A1_lambda.SetTextSize(1.2)
A1_lambda.SetTitle("#Lambda [TeV]")
A1_lambda.SetTitleSize(0.04)
A1_lambda.SetTitleOffset(0.9)
A1_lambda.Draw()
### only
graph_exclusion_exp_ = TGraph(
len(lambda_point_boundary_exp_),
np.array(1.454 * lambda_point_boundary_exp_ - 6.0),
np.array(ctau_points))
graph_exclusion_obs_ = TGraph(
len(lambda_point_boundary_obs_),
np.array(1.454 * lambda_point_boundary_obs_ - 6.0),
np.array(ctau_points))
graph_exclusion_exp_.GetXaxis().SetTitleSize(axisTitleSize)
graph_exclusion_exp_.GetXaxis().SetTitleOffset(axisTitleOffset)
graph_exclusion_exp_.GetYaxis().SetTitleSize(axisTitleSize)
graph_exclusion_exp_.GetYaxis().SetTitleOffset(axisTitleOffset)
if drawObs:
leg_2d_exclusion_.AddEntry(graph_exclusion_obs_,
"CMS observed " + label_this_withoutY, "L")
leg_2d_exclusion_.AddEntry(
graph_exclusion_atlas_8TeV_2g,
"ATLAS observed 20.3 fb^{-1} (8 TeV), #gamma#gamma", "L")
#leg_2d_exclusion_.AddEntry(graph_exclusion_cms_8TeV_2g, "CMS Obs 8 TeV #gamma#gamma", "F")
#leg_2d_exclusion_.AddEntry(graph_exclusion_cms_8TeV_1g, "CMS Obs 8 TeV #gamma", "F")
leg_2d_exclusion_.AddEntry(graph_exclusion_cms_7TeV_1g,
"CMS observed 4.9 fb^{-1} (7 TeV), #gamma", "L")
leg_2d_exclusion_.Draw("same")
#drawCMS3(myC2D, 13, lumi_this)
A1_lambda.Draw("same")
myC2D.SaveAs(outputDir + "/limits" + "/limit_exclusion_region_2D_" +
year_this + plot_tag + ".pdf")
myC2D.SaveAs(outputDir + "/limits" + "/limit_exclusion_region_2D_" +
year_this + plot_tag + ".png")
myC2D.SaveAs(outputDir + "/limits" + "/limit_exclusion_region_2D_" +
year_this + plot_tag + ".C")
##draw only this analysis, with color map
##2D color map
myC2D.SetLogz(1)
#stops = np.array([0.0, 1.0])
#red = np.array([0.0, 0.8])
#green = np.array([0.3, 1.0])
tHalfLow_y = SensClass.GetTHalf(mbbMax_meV * 1.e-3 * CLHEP.eV, isotope,
nmeModel, ga) / CLHEP.year
tHalfHigh_y = SensClass.GetTHalf(mbbMin_meV * 1.e-3 * CLHEP.eV, isotope,
nmeModel, ga) / CLHEP.year
nLabels = 4
gAxis = TGaxis(expMax_ty * 1.01, mbbMax_meV,
expMax_ty - (expMax_ty - expMin_ty) / 10000., mbbMin_meV,
tHalfLow_y, tHalfHigh_y, nLabels, "-G")
if (options.xenon136):
gAxis.SetTitle("^{136}Xe T^{0#nu}_{1/2} sensitivity (90% CL) [years]")
else:
gAxis.SetTitle("^{76}Ge T^{0#nu}_{1/2} sensitivity (90% CL) [years]")
gAxis.SetTitleOffset(1.4)
gAxis.SetBit(TAxis.kRotateTitle)
gAxis.SetBit(TAxis.kCenterTitle)
gAxis.Draw()
else:
mbbHi_meV = SensClass.GetMbb(tHalfMin_y * CLHEP.year, isotope, nmeModel,
ga) / CLHEP.meV
mbbLow_meV = SensClass.GetMbb(tHalfMax_y * CLHEP.year, isotope, nmeModel,
ga) / CLHEP.meV
nLabels = 4
gAxis = TGaxis(expMax_ty * 1.01, tHalfMax_y,
expMax_ty - (expMax_ty - expMin_ty) / 10000., tHalfMin_y,
mbbLow_meV, mbbHi_meV, nLabels, "-G")
gAxis.SetTitle("m_{#beta#beta} sensitivity (90% CL) [meV]")
gAxis.SetTitleOffset(1.2)
gAxis.SetTitleFont(hiBGFunc.GetYaxis().GetTitleFont())
gAxis.SetTitleSize(hiBGFunc.GetYaxis().GetTitleSize())
gAxis.SetLabelFont(hiBGFunc.GetYaxis().GetLabelFont())
gAxis.SetLabelSize(hiBGFunc.GetYaxis().GetLabelSize())
def makeTwoScalesGraph(file):
print(file)
ch1 = OPData.fromPath('../data/part3/%s' % file, 1)
ch2 = OPData.fromPath('../data/part3/%s' % file, 2)
print('make canvas + pad')
c = TCanvas('c-%s' % file, '', 1280, 720)
pad = TPad('pad-%s' % file, '', 0, 0, 1, 1)
pad.Draw()
pad.cd()
print('frame')
frame = pad.DrawFrame(0,
min(ch1.getY()) * 1.1, 0.051,
max(ch1.getY()) * 1.1)
frame.SetXTitle('Zeit t / s')
frame.GetXaxis().CenterTitle()
frame.SetYTitle('Spannung Spule 2: U_{2} / V')
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetLabelColor(4)
frame.GetYaxis().SetTitleColor(4)
print('g1')
g1 = ch1.makeGraph('g1-%s' % file)
prepareGraph(g1)
g1.Draw('PX')
print('overlay')
c.cd()
overlay = TPad('overlay-%s' % file, '', 0, 0, 1, 1)
overlay.SetFillStyle(4000) # transparent
overlay.SetFillColor(0) # white
overlay.SetFrameFillStyle(4000) # transparent
overlay.Draw()
overlay.cd()
print('g2')
g2 = ch2.makeGraph('g2-%s' % file)
prepareGraph(g2, 2)
g2ymin = min(ch2.getY())
xmin = pad.GetUxmin()
xmax = pad.GetUxmax()
ymin = 1.1 * g2ymin
ymax = abs(ymin)
if file == '07.tab': # same scale like 06.tab
ymin, ymax = -0.07128, 0.07128
oframe = overlay.DrawFrame(xmin, ymin, xmax, ymax)
oframe.GetXaxis().SetLabelOffset(99)
oframe.GetYaxis().SetLabelOffset(99)
oframe.GetYaxis().SetTickLength(0)
g2.Draw('PX')
print('axis')
axis = TGaxis(xmax, ymin, xmax, ymax, ymin, ymax, 510, "+L")
axis.SetTitle('Spannung Photodiode: U_{ph} / V')
axis.CenterTitle()
axis.SetTitleOffset(1.2)
axis.Draw()
print('print')
c.Update()
c.Print('../img/part3/%s.pdf' % file[:-4], 'pdf')
def train_and_apply():
np.random.seed(1)
ROOT.gROOT.SetBatch()
#Extract data from root file
tree = uproot.open("SIM_100.root")["outA/Tevts"]
branch_mc = [
"MC_B_P", "MC_B_eta", "MC_B_phi", "MC_B_pt", "MC_D0_P", "MC_D0_eta",
"MC_D0_phi", "MC_D0_pt", "MC_Dst_P", "MC_Dst_eta", "MC_Dst_phi",
"MC_Dst_pt", "MC_Est_mu", "MC_M2_miss", "MC_mu_P", "MC_mu_eta",
"MC_mu_phi", "MC_mu_pt", "MC_pis_P", "MC_pis_eta", "MC_pis_phi",
"MC_pis_pt", "MC_q2"
]
branch_rec = [
"B_P", "B_eta", "B_phi", "B_pt", "D0_P", "D0_eta", "D0_phi", "D0_pt",
"Dst_P", "Dst_eta", "Dst_phi", "Dst_pt", "Est_mu", "M2_miss", "mu_P",
"mu_eta", "mu_phi", "mu_pt", "pis_P", "pis_eta", "pis_phi", "pis_pt",
"q2"
]
nvariable = len(branch_mc)
x_train = tree.array(branch_mc[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_train = np.vstack(
(x_train, tree.array(branch_mc[i], entrystop=options.maxevents)))
x_test = tree.array(branch_rec[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_test = np.vstack(
(x_test, tree.array(branch_rec[i], entrystop=options.maxevents)))
x_train = x_train.T
x_test = x_test.T
x_test = array2D_float(x_test)
#Different type of reconstruction variables
#BN normalization
gamma = 0
beta = 0.2
ar = np.array(x_train)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_train = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_train = np.vstack((x_train, a))
x_train = x_train.T
ar = np.array(x_test)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_test = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_test = np.vstack((x_test, a))
x_test = x_test.T
#Add noise, remain to be improved
noise = np.random.normal(loc=0.0, scale=0.01, size=x_train.shape)
x_train_noisy = x_train + noise
noise = np.random.normal(loc=0.0, scale=0.01, size=x_test.shape)
x_test_noisy = x_test + noise
x_train = np.clip(x_train, -1., 1.)
x_test = np.clip(x_test, -1., 1.)
x_train_noisy = np.clip(x_train_noisy, -1., 1.)
x_test_noisy = np.clip(x_test_noisy, -1., 1.)
# Network parameters
input_shape = (x_train.shape[1], )
batch_size = 128
latent_dim = 100
# Build the Autoencoder Model
# First build the Encoder Model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
# Shape info needed to build Decoder Model
shape = K.int_shape(x)
# Generate the latent vector
latent = Dense(latent_dim, name='latent_vector')(x)
# Instantiate Encoder Model
encoder = Model(inputs, latent, name='encoder')
encoder.summary()
# Build the Decoder Model
latent_inputs = Input(shape=(latent_dim, ), name='decoder_input')
x = Dense(shape[1])(latent_inputs)
outputs = Activation('tanh')(x)
# Instantiate Decoder Model
decoder = Model(latent_inputs, outputs)
decoder.summary()
# Autoencoder = Encoder + Decoder
# Instantiate Autoencoder Model
autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')
autoencoder.summary()
# Train the autoencoder - 'mse'
autoencoder.compile(loss='mse', optimizer='adam')
autoencoder.fit(x_train_noisy,
x_train,
validation_data=(x_test_noisy, x_test),
epochs=options.epochs,
batch_size=batch_size)
# Predict the Autoencoder output from corrupted test imformation
x_decoded = autoencoder.predict(x_test_noisy) # mean
# Train the autoencoder - quantile_loss
autoencoder.compile(loss=quantile_loss, optimizer='adam')
autoencoder.fit(x_train_noisy,
x_train,
validation_data=(x_test_noisy, x_test),
epochs=options.epochs,
batch_size=batch_size)
# Predict the Autoencoder output from corrupted test imformation
x_decoded_quantile_loss = autoencoder.predict(x_test_noisy) # mean+sigma
# Draw Comparision Plots
os.system("rm -rf Plots")
os.system("mkdir Plots")
for j in range(0, nvariable):
c = TCanvas("c", "c", 700, 700)
fPads1 = TPad("pad1", "Run2", 0.0, 0.29, 1.00, 1.00)
fPads2 = TPad("pad2", "", 0.00, 0.00, 1.00, 0.29)
fPads1.SetBottomMargin(0.007)
fPads1.SetLeftMargin(0.10)
fPads1.SetRightMargin(0.03)
fPads2.SetLeftMargin(0.10)
fPads2.SetRightMargin(0.03)
fPads2.SetBottomMargin(0.25)
fPads1.Draw()
fPads2.Draw()
fPads1.cd()
nbin = 50
min = -1.
max = 1.
lbin = (max - min) / nbin
lbin = str(float((max - min) / nbin))
xtitle = branch_rec[j]
ytitle = "Events/" + lbin
h_gen = TH1D("h_gen", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_gen.Sumw2()
h_rec = TH1D("h_rec", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_rec.Sumw2()
h_pre = TH1D("h_pre", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_pre.Sumw2()
for i in range(x_test_noisy.shape[0]):
h_gen.Fill(x_train_noisy[i][j])
h_rec.Fill(x_test_noisy[i][j])
h_pre.Fill(x_decoded[i][j])
h_gen = UnderOverFlow1D(h_gen)
h_rec = UnderOverFlow1D(h_rec)
h_pre = UnderOverFlow1D(h_pre)
maxR = 2
maxY = TMath.Max(h_rec.GetMaximum(), h_pre.GetMaximum())
h_gen.SetLineColor(4)
h_gen.SetFillStyle(0)
h_gen.SetLineWidth(2)
h_gen.SetLineStyle(1)
h_rec.SetLineColor(2)
h_rec.SetFillStyle(0)
h_rec.SetLineWidth(2)
h_rec.SetLineStyle(1)
h_pre.SetLineColor(3)
h_pre.SetFillStyle(0)
h_pre.SetLineWidth(2)
h_pre.SetLineStyle(1)
h_gen.SetStats(0)
h_rec.SetStats(0)
h_pre.SetStats(0)
h_gen.GetYaxis().SetRangeUser(0, maxY * 1.1)
h_gen.Draw("HIST")
h_rec.Draw("same HIST")
h_pre.Draw("same HIST")
h_gen.GetYaxis().SetTitleSize(0.06)
h_gen.GetYaxis().SetTitleOffset(0.85)
theLeg = TLegend(0.58, 0.45, 0.99, 0.82, "", "NDC")
theLeg.SetName("theLegend")
theLeg.SetBorderSize(0)
theLeg.SetLineColor(0)
theLeg.SetFillColor(0)
theLeg.SetFillStyle(0)
theLeg.SetLineWidth(0)
theLeg.SetLineStyle(0)
theLeg.SetTextFont(42)
theLeg.SetTextSize(.05)
theLeg.AddEntry(h_gen, "Generation", "L")
theLeg.AddEntry(h_rec, "Reconstruction", "L")
theLeg.AddEntry(h_pre, "Prediction", "L")
theLeg.SetY1NDC(0.9 - 0.05 * 6 - 0.005)
theLeg.SetY1(theLeg.GetY1NDC())
fPads1.cd()
theLeg.Draw()
title = TLatex(
0.91, 0.93, "AE prediction compare with reconstruction, epochs=" +
str(options.epochs))
title.SetNDC()
title.SetTextSize(0.05)
title.SetTextFont(42)
title.SetTextAlign(31)
title.SetLineWidth(2)
title.Draw()
fPads2.cd()
h_Ratio = TH1D("h_Ratio", "" + ";%s;%s" % (xtitle, ytitle), nbin, min,
max)
h_Ratio.SetLineColor(1)
h_Ratio.SetLineWidth(2)
h_Ratio.SetMarkerStyle(8)
h_Ratio.SetMarkerSize(0.7)
h_Ratio.GetYaxis().SetRangeUser(0, maxR)
h_Ratio.GetYaxis().SetNdivisions(504, 0)
h_Ratio.GetYaxis().SetTitle("Pre/Rec")
h_Ratio.GetYaxis().SetTitleOffset(0.35)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetLabelSize(0.11)
h_Ratio.GetXaxis().SetLabelSize(0.1)
h_Ratio.GetXaxis().SetTitleOffset(0.8)
h_Ratio.GetXaxis().SetTitleSize(0.14)
h_Ratio.SetStats(0)
axis1 = TGaxis(min, 1, max, 1, 0, 0, 0, "L")
axis1.SetLineColor(1)
axis1.SetLineWidth(1)
for i in range(1, h_Ratio.GetNbinsX() + 1, 1):
D = h_pre.GetBinContent(i)
eD = h_pre.GetBinError(i)
if D == 0: eD = 0.92
B = h_rec.GetBinContent(i)
eB = h_rec.GetBinError(i)
if B < 0.1 and eB >= B:
eB = 0.92
Err = 0.
if B != 0.:
Err = TMath.Sqrt((eD * eD) / (B * B) + (D * D * eB * eB) /
(B * B * B * B))
h_Ratio.SetBinContent(i, D / B)
h_Ratio.SetBinError(i, Err)
if B == 0.:
Err = TMath.Sqrt((eD * eD) / (eB * eB) + (D * D * eB * eB) /
(eB * eB * eB * eB))
h_Ratio.SetBinContent(i, D / 0.92)
h_Ratio.SetBinError(i, Err)
if D == 0 and B == 0:
h_Ratio.SetBinContent(i, -1)
h_Ratio.SetBinError(i, 0)
if h_Ratio.GetBinContent(i) > maxR:
h_Ratio.SetBinContent(i, maxR)
h_Ratio.Draw("e0")
axis1.Draw()
c.SaveAs("Plots/" + branch_rec[j] + "_comparision.png")
#2D plots
c2 = TCanvas("c2", "c2", 700, 700)
fPads3 = TPad("pad3", "Run2", 0.0, 0.0, 1.00, 1.00)
fPads3.SetBottomMargin(0.09)
fPads3.SetLeftMargin(0.10)
fPads3.SetRightMargin(0.15)
fPads3.Draw()
fPads3.cd()
xtitle = branch_rec[j] + " (Rec)"
ytitle = branch_rec[j] + " (Pre)"
h_2D = TH2D("h_2D", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max,
nbin, min, max)
h_2D.Sumw2()
h_2D.SetStats(0)
for i in range(x_test_noisy.shape[0]):
h_2D.Fill(x_test_noisy[i][j], x_decoded[i][j])
h_2D.GetYaxis().SetTitleSize(0.04)
h_2D.GetYaxis().SetTitleOffset(1.20)
h_2D.Draw("colz")
title = TLatex(
0.93, 0.93, "AE prediction compare with reconstruction, epochs=" +
str(options.epochs))
title.SetNDC()
title.SetTextSize(0.04)
title.SetTextFont(42)
title.SetTextAlign(31)
title.SetLineWidth(2)
title.Draw()
c2.SaveAs("Plots/2D_" + branch_rec[j] + "_comparision.png")
';#it{p}_{T} (GeV/#it{c});RMS (%)')
hRMS.DrawCopy('same')
axisSoverB = TGaxis(gPad.GetUxmax(), gPad.GetUymin(), gPad.GetUxmax(),
gPad.GetUymax(), 0.01, 20., 510, "+LG")
axisSoverB.SetLineColor(kRed + 1)
axisSoverB.SetLabelColor(kRed + 1)
axisSoverB.SetLabelFont(42)
axisSoverB.SetLabelSize(0.045)
axisSoverB.SetTitle('S/B (3#sigma)')
axisSoverB.SetTitleOffset(1.2)
axisSoverB.SetLabelOffset(0.012)
axisSoverB.SetTitleColor(kRed + 1)
axisSoverB.SetTitleFont(42)
axisSoverB.SetTitleSize(0.05)
axisSoverB.SetMaxDigits(3)
axisSoverB.Draw()
hSoverB.DrawCopy('same')
cRMS.Update()
cSyst = TCanvas('cSyst', '', 800, 800)
cSyst.DrawFrame(hSyst.GetBinLowEdge(1), 0.1, ptMax, 20.,
';#it{p}_{T} (GeV/#it{c}); Syst (%)')
SetObjectStyle(hRMS, color=kRed + 1, fillstyle=0, linewidth=3)
SetObjectStyle(hMeanShift, color=kBlack, fillstyle=0, linewidth=3)
SetObjectStyle(hSyst, color=kBlue + 1, fillstyle=0, linewidth=3)
hMeanShift.DrawCopy('same')
hSyst.DrawCopy('same')
hRMS.DrawCopy('same')
leg = TLegend(0.5, 0.8, 0.8, 0.95)
leg.SetTextSize(0.04)
leg.SetFillStyle(0)
def AddShadedProfile(can, hist):
tobject_collector = []
if can.GetPrimitive('pad_top'):
GetTopPad(can).SetRightMargin(0.08)
GetBotPad(can).SetRightMargin(0.08)
tobject_collector.extend(
AddShadedProfile(can.GetPrimitive('pad_top'), hist))
GetBotPad(can).Modified()
GetBotPad(can).Update()
return tobject_collector
from ROOT import TH1, TGraph, THStack, TColor, kGray, kBlue, TGaxis, TText
listOfPlottedObjects = [
o for o in can.GetListOfPrimitives() if isinstance(o, (TH1, THStack))
]
listOfPlottedObjects += [
o.GetHistogram() for o in can.GetListOfPrimitives()
if isinstance(o, TGraph)
]
#print listOfPlottedObjects
minValue = 0
maxValue = 1
if listOfPlottedObjects:
#maxValue = max([o.GetBinContent(o.GetMaximumBin()) for o in listOfPlottedObjects])
#minValue = min([o.GetBinContent(o.GetMinimumBin()) for o in listOfPlottedObjects])
maxValue = max([
o.GetBinContent(o.GetMaximumBin()) +
o.GetBinError(o.GetMaximumBin()) for o in listOfPlottedObjects
])
minValue = min([
o.GetBinContent(o.GetMinimumBin()) -
o.GetBinError(o.GetMinimumBin()) for o in listOfPlottedObjects
])
# #print [o.GetMaximum() for o in listOfPlottedObjects]
#maxValue = can.GetUymax()
#minValue = can.GetUymin()
#print maxValue, minValue
temp = hist.Clone()
temp.SetName("ShadedProfile")
temp.SetFillStyle(1001)
lightgray = 1001
#color = TColor(lightgray, 0.956, 0.956, 0.956)
temp.SetFillColorAlpha(kGray, 0.15)
temp.SetLineColor(kGray)
#temp.Scale(1./16000)
temp.SetStats(0)
origMax = temp.GetMaximum()
if origMax > 0:
temp.Scale(1. / origMax)
minCanValue = can.GetUymin()
temp.Scale(maxValue - minCanValue)
# Get new min value
for x in range(temp.GetNbinsX() + 1):
temp.AddBinContent(x, minCanValue)
#temp.Scale(0.9*maxValue)
#print maxValue
#for (int x=1; x<=temp->GetXaxis()->GetNbins(); x++)
# temp->SetBinContent(x,temp->GetBinContent(x)+0.61)
can.cd()
temp.Draw("hist same")
tobject_collector.append(temp)
axis = TGaxis(can.GetUxmax(), can.GetUymin(), can.GetUxmax(), maxValue,
0, origMax, 510, "+L")
axis.SetLineColor(kGray)
axis.SetLabelColor(kGray)
#axis.SetTitle("count")
axis.Draw()
tobject_collector.append(axis)
can.SetTicks(can.GetTickx(), 0)
can.Modified()
can.Update()
#text = TText(0,0, "count");
#text.SetTextAlign(13)
#text.SetTextAngle(90)
#text.SetTextColor(kGray)
#text.Draw()
#tobject_collector.append(text)
return tobject_collector
def PlotOnCanvas(self, pdf_name):
#gROOT.SetBatch(False)
tdrstyle.setTDRStyle()
canvas = TCanvas("c1", "c1", 600, 600)
pad1 = TPad("pad1", "pad1", 0, 0.0, 1, 1.0)
pad1.SetBottomMargin(0.32)
pad1.SetGridx()
pad1.Draw()
pad1.cd()
self.histo1.SetStats(0)
self.histo1.Draw(self.option)
self.histo2.Draw(self.option + " same")
if self.logx:
pad1.SetLogx()
if self.logy:
pad1.SetLogy()
legend = TLegend(0.65, 0.7, 0.85, 0.83)
legend.SetHeader("Legend", "C")
legend.AddEntry(self.histo1, self.legend1, "l")
legend.AddEntry(self.histo2, self.legend2, "l")
legend.Draw()
# Redraw axis to avoid clipping 0
self.histo1.GetXaxis().SetLabelSize(0.)
self.histo1.GetXaxis().SetTitle('')
axis = TGaxis(-9, -2.8, -9, 2.8, 0, 10000, 50510, "")
axis.SetLabelFont(43)
axis.SetLabelSize(15)
axis.Draw("same")
pad2 = TPad("pad2", "pad2", 0, 0.0, 1, 0.3)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.4)
pad2.SetGridx()
pad2.Draw()
pad2.cd()
if self.logx:
pad2.SetLogx()
self.ratio.SetLineColor(ROOT.kBlack)
self.ratio.SetMinimum(0.5)
self.ratio.SetMaximum(1.5)
self.ratio.SetStats(0)
self.ratio.SetMarkerStyle(21)
self.ratio.Draw("ep")
self.histo1.SetLineColor(ROOT.kBlue + 1)
self.histo1.SetLineWidth(2)
max_y = max(self.histo1.GetMaximum(), self.histo2.GetMaximum())
self.histo1.SetMaximum(max_y * 1.1)
self.histo1.GetYaxis().SetNdivisions(505)
self.histo1.GetYaxis().SetTitleSize(20)
self.histo1.GetYaxis().SetTitleFont(43)
self.histo1.GetYaxis().SetTitleOffset(1.8)
self.histo2.SetLineColor(ROOT.kRed)
self.histo2.SetLineWidth(2)
self.ratio.SetTitle("")
self.ratio.GetYaxis().SetTitle("Ratio")
self.ratio.GetYaxis().SetNdivisions(505)
self.ratio.GetYaxis().SetTitleSize(20)
self.ratio.GetYaxis().SetTitleFont(43)
self.ratio.GetYaxis().SetTitleOffset(1.8)
self.ratio.GetYaxis().SetLabelFont(43)
self.ratio.GetYaxis().SetLabelSize(15)
self.ratio.GetXaxis().SetNdivisions(510)
self.ratio.GetXaxis().SetTitleSize(20)
self.ratio.GetXaxis().SetTitleFont(43)
self.ratio.GetXaxis().SetTitleOffset(4.)
self.ratio.GetXaxis().SetLabelFont(43)
self.ratio.GetXaxis().SetLabelSize(15)
ROOT.SetOwnership(canvas, False)
ROOT.SetOwnership(pad1, False)
ROOT.SetOwnership(pad2, False)
canvas.Print(pdf_name, 'Title:' + self.title)
canvas.Close()
class Differential1D:
""" Base class for displaying 1D differential plots.
Produces a plot comparing the baseline geometry (base) to the geometry specified by
the geom arguement. The quantity plotted is given by the "stat" option, and is
limited to the different statistics created by the StarBASE application. At present,
these include:
stat="radlen" :: plots number of radiation lengths of material encountered
... need to add more stats
The plot will show a solid histogram for the baseline geometry, with the comparison
geometry shown with red hashes. The fractional difference (baseline-comp)/baseline
is shown at the bottom in blue, scaled according to the alternate axis at the right.
base: selects the baseline geometry. [Mandatory]
geom: selects the comparison geometry. [Mandatory]
volume: selects the volume to be compared. [Mandatory]
geomvolume: selects the volume in the comparison geometry. [Default: same]
stat: selects the statistic to compare. [Default: radlen]
xmin, xmax: x-axis range. [Optional]
ymin, ymax: y-axis range. [Optional]
"""
def __init__(self,
base, geom,
volume="EMSS", geomvolume="same",
stat="radlen",
xmin=+0., xmax=-1.,
ymin=+0., ymax=-1.,
canvas=0,
legend=False
):
self.name = volume
self.base = base
self.geom = geom
baseFile = get_geom_file(base) # get the files
compFile = get_geom_file(geom)
hname = "h_"+stat+"_"+volume+"_eta" # retrive the histograms from the files
print "Get histo: " + str(hname)
self.histo_base = stat_histo( stat, volume, file=baseFile )
if ( geomvolume == "same" ):
self.histo_comp = stat_histo( stat, volume, compFile )
else:
self.histo_comp = stat_histo( stat, geomvolume, compFile )
self.histo_diff = self.histo_base.Clone( hname + "_diff" ) # difference the histograms
self.histo_diff.Add( self.histo_comp, -1.0 )
self.histo_diff.Divide(self.histo_base) # diff will be (old-new)/old * 100%
if ( canvas == 0 ):
canvas = TCanvas("temp"+base+geom+volume+geomvolume,"Differential 1D: baseline="+base+" compare="+geom,500,400);
self.canvas = canvas
# Detect and apply optional x-axis range
if ( xmax > xmin ):
self.histo_base.GetXaxis().SetRangeUser(xmin,xmax)
self.histo_comp.GetXaxis().SetRangeUser(xmin,xmax)
# ... or zoom in on an appropriate scale
else:
# auto_range( self.histo_base )
# auto_range( self.histo_comp )
xmin = auto_min( self.histo_base )
xmax = auto_max( self.histo_base )
self.histo_base.GetXaxis().SetRangeUser(xmin,xmax)
self.histo_comp.GetXaxis().SetRangeUser(xmin,xmax)
# xmin = TMath.Min( self.histo_base.GetXaxis().GetXmin(),self.histo_comp.GetXaxis().GetXmin() )
# xmax = TMath.Max( self.histo_base.GetXaxis().GetXmax(),self.histo_comp.GetXaxis().GetXmax() )
# print "xmin="+str(xmin)
# print "xmax="+str(xmax)
# Detect and apply optional y-axis range
if ( ymax > ymin ):
self.histo_base.GetYaxis().SetRangeUser(ymin,ymax)
else:
ymin = self.histo_base.GetMinimum()
ymax = TMath.Max( self.histo_base.GetMaximum(),
self.histo_comp.GetMaximum())
ymax *= 1.05
# Current range in y-axis extends from 0 to ymax. We want
# to expand this to go from -0.2*ymax to ymax.
ymin = -0.2 * ymax
self.histo_base.GetYaxis().SetRangeUser(ymin,ymax)
# Draw the baseline and comparison histograms
self.histo_base.SetLineWidth(2)
self.histo_base.SetFillStyle(1001)
self.histo_base.SetFillColor(22)
self.histo_base.Draw()
self.histo_comp.SetLineColor(2)
self.histo_comp.SetLineStyle(2)
self.histo_comp.SetFillStyle(3345)
self.histo_comp.SetFillColor(2)
self.histo_comp.Draw("same")
# Rescale difference histogram so that it is in percent
self.histo_diff.Scale(100.0)
# This is the maximum of the histogram.
yfull_max = self.histo_diff.GetMaximum()
yfull_min = self.histo_diff.GetMinimum()
yfull = TMath.Max( yfull_max, TMath.Abs( yfull_min ) )
self.max_differential = yfull
yfull *= 1.3
if ( yfull == 0. ):
yfull = 1.0
# We need to rescale the histogram so that it fits w/in 10% of ymax
self.histo_diff.Scale( 0.10 * ymax / yfull )
# Next we shift the histogram down by 0.1 * ymax
nbinx=self.histo_diff.GetNbinsX();
i=1
while ( i<=nbinx ):
self.histo_diff[i] -= 0.1 * ymax
i+=1
# Reset the line color and draw on the same plot
self.histo_diff.SetLineColor(4)
self.histo_diff.Draw("same")
self.line = TLine(xmin, -0.1 * ymax, xmax, -0.1*ymax )
self.line.SetLineStyle(3)
self.line.Draw()
# And superimpose an axis on the new plot
xa = xmax
self.axis = TGaxis( xa, -0.2*ymax, xa, 0.0, -yfull, +yfull, 50510, "-+L" )
self.axis.SetLabelSize(0.03)
self.axis.SetLabelOffset(-0.02)
self.axis.SetLineColor(4)
self.axis.SetTextColor(4)
self.axis.SetLabelColor(4)
self.axis.SetNdivisions(4)
# self.axis.SetTitle("(base-comp)/base [%]")
self.axis.SetTitleSize(0.0175)
self.axis.SetTitleOffset(-0.5)
self.axis.Draw();
# Add the legend if requested
if ( legend ):
self.legend = TLegend( 0.78, 0.80, 0.98, 0.98 )
self.legend.AddEntry( self.histo_base, base )
self.legend.AddEntry( self.histo_comp, geom )
self.legend.AddEntry( self.histo_diff, "#frac{"+base+"-"+geom+"}{"+base+"} [%]" )
self.legend.Draw()
def main(): # pylint: disable=too-many-locals, too-many-statements, too-many-branches
"""
Main plotting function
"""
gROOT.SetBatch(True)
# pylint: disable=unused-variable
parser = argparse.ArgumentParser()
parser.add_argument("--database-analysis",
"-d",
dest="database_analysis",
help="analysis database to be used",
required=True)
parser.add_argument("--analysis",
"-a",
dest="type_ana",
help="choose type of analysis",
required=True)
parser.add_argument("--input",
"-i",
dest="input_file",
help="results input file",
required=True)
args = parser.parse_args()
typean = args.type_ana
shape = typean[len("jet_"):]
print("Shape:", shape)
file_in = args.input_file
with open(args.database_analysis, "r") as file_db:
data_param = yaml.safe_load(file_db)
case = list(data_param.keys())[0]
datap = data_param[case]
logger = get_logger()
i_cut = file_in.rfind("/")
rootpath = file_in[:i_cut]
# plotting
# LaTeX string
p_latexnhadron = datap["analysis"][typean]["latexnamehadron"]
p_latexbin2var = datap["analysis"][typean]["latexbin2var"]
v_varshape_latex = datap["analysis"][typean]["var_shape_latex"]
# first variable (hadron pt)
lpt_finbinmin = datap["analysis"][typean]["sel_an_binmin"]
lpt_finbinmax = datap["analysis"][typean]["sel_an_binmax"]
var1ranges = lpt_finbinmin.copy()
var1ranges.append(lpt_finbinmax[-1])
# second variable (jet pt)
v_var2_binning = datap["analysis"][typean]["var_binning2"] # name
lvar2_binmin_reco = datap["analysis"][typean].get("sel_binmin2_reco", None)
lvar2_binmax_reco = datap["analysis"][typean].get("sel_binmax2_reco", None)
p_nbin2_reco = len(lvar2_binmin_reco) # number of reco bins
lvar2_binmin_gen = datap["analysis"][typean].get("sel_binmin2_gen", None)
lvar2_binmax_gen = datap["analysis"][typean].get("sel_binmax2_gen", None)
p_nbin2_gen = len(lvar2_binmin_gen) # number of gen bins
var2ranges_reco = lvar2_binmin_reco.copy()
var2ranges_reco.append(lvar2_binmax_reco[-1])
var2binarray_reco = array(
"d",
var2ranges_reco) # array of bin edges to use in histogram constructors
var2ranges_gen = lvar2_binmin_gen.copy()
var2ranges_gen.append(lvar2_binmax_gen[-1])
var2binarray_gen = array(
"d",
var2ranges_gen) # array of bin edges to use in histogram constructors
# observable (z, shape,...)
v_varshape_binning = datap["analysis"][typean][
"var_binningshape"] # name (reco)
v_varshape_binning_gen = datap["analysis"][typean][
"var_binningshape_gen"] # name (gen)
lvarshape_binmin_reco = \
datap["analysis"][typean].get("sel_binminshape_reco", None)
lvarshape_binmax_reco = \
datap["analysis"][typean].get("sel_binmaxshape_reco", None)
p_nbinshape_reco = len(lvarshape_binmin_reco) # number of reco bins
lvarshape_binmin_gen = \
datap["analysis"][typean].get("sel_binminshape_gen", None)
lvarshape_binmax_gen = \
datap["analysis"][typean].get("sel_binmaxshape_gen", None)
p_nbinshape_gen = len(lvarshape_binmin_gen) # number of gen bins
varshaperanges_reco = lvarshape_binmin_reco.copy()
varshaperanges_reco.append(lvarshape_binmax_reco[-1])
varshapebinarray_reco = array(
"d", varshaperanges_reco
) # array of bin edges to use in histogram constructors
varshaperanges_gen = lvarshape_binmin_gen.copy()
varshaperanges_gen.append(lvarshape_binmax_gen[-1])
varshapebinarray_gen = array(
"d", varshaperanges_gen
) # array of bin edges to use in histogram constructors
file_results = TFile.Open(file_in)
if not file_results:
logger.fatal(make_message_notfound(file_in))
ibin2 = 1
suffix = "%s_%g_%g" % (v_var2_binning, lvar2_binmin_gen[ibin2],
lvar2_binmax_gen[ibin2])
# HF data
nameobj = "%s_hf_data_%d_stat" % (shape, ibin2)
hf_data_stat = file_results.Get(nameobj)
if not hf_data_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_hf_data_%d_syst" % (shape, ibin2)
hf_data_syst = file_results.Get(nameobj)
if not hf_data_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# HF PYTHIA
nameobj = "%s_hf_pythia_%d_stat" % (shape, ibin2)
hf_pythia_stat = file_results.Get(nameobj)
if not hf_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
# HF ratio
nameobj = "%s_hf_ratio_%d_stat" % (shape, ibin2)
hf_ratio_stat = file_results.Get(nameobj)
if not hf_ratio_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_hf_ratio_%d_syst" % (shape, ibin2)
hf_ratio_syst = file_results.Get(nameobj)
if not hf_ratio_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive data
nameobj = "%s_incl_data_%d_stat" % (shape, ibin2)
incl_data_stat = file_results.Get(nameobj)
if not incl_data_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_data_%d_syst" % (shape, ibin2)
incl_data_syst = file_results.Get(nameobj)
if not incl_data_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive PYTHIA
nameobj = "%s_incl_pythia_%d_stat" % (shape, ibin2)
incl_pythia_stat = file_results.Get(nameobj)
if not incl_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_pythia_%d_syst" % (shape, ibin2)
incl_pythia_syst = file_results.Get(nameobj)
if not incl_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive ratio
nameobj = "%s_incl_ratio_%d_stat" % (shape, ibin2)
incl_ratio_stat = file_results.Get(nameobj)
if not incl_ratio_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_ratio_%d_syst" % (shape, ibin2)
incl_ratio_syst = file_results.Get(nameobj)
if not incl_ratio_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# quark PYTHIA
nameobj = "%s_quark_pythia_%d_stat" % (shape, ibin2)
quark_pythia_stat = file_results.Get(nameobj)
if not quark_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_quark_pythia_%d_syst" % (shape, ibin2)
quark_pythia_syst = file_results.Get(nameobj)
if not quark_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# gluon PYTHIA
nameobj = "%s_gluon_pythia_%d_stat" % (shape, ibin2)
gluon_pythia_stat = file_results.Get(nameobj)
if not gluon_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_gluon_pythia_%d_syst" % (shape, ibin2)
gluon_pythia_syst = file_results.Get(nameobj)
if not gluon_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# plot the results with systematic uncertainties and models
size_can = [800, 800]
size_can_double = [800, 1000]
offsets_axes = [0.8, 1.1]
offsets_axes_double = [0.8, 0.8]
margins_can = [0.1, 0.13, 0.1, 0.03]
margins_can_double = [0.1, 0.1, 0.1, 0.1]
margins_can_double = [0., 0., 0., 0.]
size_thg = 0.05
offset_thg = 0.85
gStyle.SetErrorX(0) # do not plot horizontal error bars of histograms
fontsize = 0.035
opt_leg_g = "FP"
opt_plot_g = "2"
list_new = [] # list to avoid loosing objects created in loops
# labels
x_latex = 0.16
y_latex_top = 0.83
y_step = 0.055
title_x = v_varshape_latex
title_y = "(1/#it{N}_{jet}) d#it{N}/d%s" % v_varshape_latex
title_full = ";%s;%s" % (title_x, title_y)
title_full_ratio = ";%s;data/MC: ratio of %s" % (title_x, title_y)
title_full_ratio_double = f";{title_x};MC/data"
text_alice = "#bf{ALICE}, pp, #sqrt{#it{s}} = 13 TeV"
text_alice_sim = "#bf{ALICE} Simulation, pp, #sqrt{#it{s}} = 13 TeV"
text_pythia = "PYTHIA 8 (Monash)"
text_pythia_short = "PYTHIA 8"
text_pythia_split = "#splitline{PYTHIA 8}{(Monash)}"
text_jets = "charged jets, anti-#it{k}_{T}, #it{R} = 0.4"
text_ptjet = "%g #leq %s < %g GeV/#it{c}, |#it{#eta}_{jet}| #leq 0.5" % (
lvar2_binmin_reco[ibin2], p_latexbin2var, lvar2_binmax_reco[ibin2])
text_pth = "%g #leq #it{p}_{T}^{%s} < %g GeV/#it{c}, |#it{y}_{%s}| #leq 0.8" % (
lpt_finbinmin[0], p_latexnhadron,
min(lpt_finbinmax[-1], lvar2_binmax_reco[ibin2]), p_latexnhadron)
text_ptcut = "#it{p}_{T, incl. ch. jet}^{leading track} #geq 5.33 GeV/#it{c}"
text_ptcut_sim = "#it{p}_{T, incl. ch. jet}^{leading h^{#pm}} #geq 5.33 GeV/#it{c} (varied)"
text_sd = "Soft Drop (#it{z}_{cut} = 0.1, #it{#beta} = 0)"
title_thetag = "#it{#theta}_{g} = #it{R}_{g}/#it{R}"
radius_jet = 0.4
# colour and marker indeces
c_hf_data = 0
c_incl_data = 1
c_hf_mc = 2
c_incl_mc = 6
c_quark_mc = 5
c_gluon_mc = 0
# markers
m_hf_data = get_marker(0)
m_incl_data = get_marker(1)
m_hf_mc = get_marker(0, 2)
m_incl_mc = get_marker(1, 2)
m_quark_mc = get_marker(2)
m_gluon_mc = get_marker(3)
# make the horizontal error bars smaller
if shape == "nsd":
for gr in [
hf_data_syst, incl_data_syst, hf_ratio_syst, incl_ratio_syst,
incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst
]:
for i in range(gr.GetN()):
gr.SetPointEXlow(i, 0.1)
gr.SetPointEXhigh(i, 0.1)
# data, HF and inclusive
hf_data_syst_cl = hf_data_syst.Clone()
leg_pos = [.72, .75, .85, .85]
list_obj = [hf_data_syst, incl_data_syst, hf_data_stat, incl_data_stat]
labels_obj = ["%s-tagged" % p_latexnhadron, "inclusive", "", ""]
colours = [
get_colour(i, j) for i, j in zip((c_hf_data, c_incl_data, c_hf_data,
c_incl_data), (2, 2, 1, 1))
]
markers = [m_hf_data, m_incl_data, m_hf_data, m_incl_data]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_data, list_obj_data_new = make_plot("cshape_data_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip((hf_data_syst, incl_data_syst), (c_hf_data, c_incl_data)):
gr.SetMarkerColor(get_colour(c))
list_obj_data_new[0].SetTextSize(fontsize)
if shape == "nsd":
hf_data_syst.GetXaxis().SetNdivisions(5)
# Draw a line through the points.
if shape == "nsd":
for h in (hf_data_stat, incl_data_stat):
h_line = h.Clone(h.GetName() + "_line")
h_line.SetLineStyle(2)
h_line.Draw("l hist same")
list_new.append(h_line)
cshape_data.Update()
if shape == "rg":
# plot the theta_g axis
gr_frame = hf_data_syst
axis_rg = gr_frame.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data = []
for text_latex in [
text_alice, text_jets, text_ptjet, text_pth, text_ptcut, text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data.Update()
cshape_data.SaveAs("%s/%s_data_%s.pdf" % (rootpath, shape, suffix))
# data and PYTHIA, HF
leg_pos = [.72, .65, .85, .85]
list_obj = [hf_data_syst_cl, hf_data_stat, hf_pythia_stat]
labels_obj = ["data", "", text_pythia_split]
colours = [
get_colour(i, j)
for i, j in zip((c_hf_data, c_hf_data, c_hf_mc), (2, 1, 1))
]
markers = [m_hf_data, m_hf_data, m_hf_mc]
y_margin_up = 0.4
y_margin_down = 0.05
cshape_data_mc_hf, list_obj_data_mc_hf_new = make_plot("cshape_data_mc_hf_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip([hf_data_syst_cl], [c_hf_data]):
gr.SetMarkerColor(get_colour(c))
leg_data_mc_hf = list_obj_data_mc_hf_new[0]
leg_data_mc_hf.SetHeader("%s-tagged" % p_latexnhadron)
leg_data_mc_hf.SetTextSize(fontsize)
if shape == "nsd":
hf_data_syst_cl.GetXaxis().SetNdivisions(5)
#axis_nsd = hf_data_syst_cl.GetHistogram().GetXaxis()
#x1 = axis_nsd.GetBinLowEdge(1)
#x2 = axis_nsd.GetBinUpEdge(axis_nsd.GetNbins())
#axis_nsd.Set(5, x1, x2)
#for ibin in range(axis_nsd.GetNbins()):
# axis_nsd.SetBinLabel(ibin + 1, "%d" % ibin)
#axis_nsd.SetNdivisions(5)
cshape_data_mc_hf.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_data_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data_mc_hf.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data_mc_hf.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data_mc_hf = []
for text_latex in [text_alice, text_jets, text_ptjet, text_pth, text_sd]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data_mc_hf.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data_mc_hf.Update()
cshape_data_mc_hf.SaveAs("%s/%s_data_mc_hf_%s.pdf" %
(rootpath, shape, suffix))
# data and PYTHIA, inclusive
#leg_pos = [.68, .65, .85, .85]
list_obj = [
incl_data_syst, incl_pythia_syst, incl_data_stat, incl_pythia_stat
]
labels_obj = ["data", text_pythia_split]
colours = [
get_colour(i, j) for i, j in zip((c_incl_data, c_incl_mc, c_incl_data,
c_incl_mc), (2, 2, 1, 1))
]
markers = [m_incl_data, m_incl_mc, m_incl_data, m_incl_mc]
y_margin_up = 0.4
y_margin_down = 0.05
cshape_data_mc_incl, list_obj_data_mc_incl_new = make_plot("cshape_data_mc_incl_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip([incl_data_syst, incl_pythia_syst],
[c_incl_data, c_incl_mc]):
gr.SetMarkerColor(get_colour(c))
leg_data_mc_incl = list_obj_data_mc_incl_new[0]
leg_data_mc_incl.SetHeader("inclusive")
leg_data_mc_incl.SetTextSize(fontsize)
if shape == "nsd":
incl_data_syst.GetXaxis().SetNdivisions(5)
cshape_data_mc_incl.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = incl_data_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data_mc_incl.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data_mc_incl.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data_mc_incl = []
for text_latex in [text_alice, text_jets, text_ptjet, text_ptcut, text_sd]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data_mc_incl.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data_mc_incl.Update()
cshape_data_mc_incl.SaveAs("%s/%s_data_mc_incl_%s.pdf" %
(rootpath, shape, suffix))
# Ratios data/MC, HF and inclusive
line_1 = TLine(lvarshape_binmin_reco[0], 1, lvarshape_binmax_reco[-1], 1)
line_1.SetLineStyle(9)
line_1.SetLineColor(1)
line_1.SetLineWidth(3)
#leg_pos = [.72, .7, .85, .85] # with header
leg_pos = [.72, .75, .85, .85] # without header
list_obj = [
hf_ratio_syst, line_1, incl_ratio_syst, hf_ratio_stat, incl_ratio_stat
]
labels_obj = ["%s-tagged" % p_latexnhadron, "inclusive"]
colours = [
get_colour(i, j) for i, j in zip((c_hf_data, c_incl_data, c_hf_data,
c_incl_data), (2, 2, 1, 1))
]
markers = [m_hf_data, m_incl_data, m_hf_data, m_incl_data]
y_margin_up = 0.52
y_margin_down = 0.05
if shape == "nsd":
y_margin_up = 0.22
cshape_ratio, list_obj_ratio_new = make_plot("cshape_ratio_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full_ratio)
cshape_ratio.Update()
for gr, c in zip((hf_ratio_syst, incl_ratio_syst),
(c_hf_data, c_incl_data)):
gr.SetMarkerColor(get_colour(c))
leg_ratio = list_obj_ratio_new[0]
leg_ratio.SetTextSize(fontsize)
#leg_ratio.SetHeader("data/MC")
if shape == "nsd":
hf_ratio_syst.GetXaxis().SetNdivisions(5)
cshape_ratio.Update()
if shape == "rg":
# plot the theta_g axis
gr_frame = hf_ratio_syst
axis_rg = gr_frame.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_ratio.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_ratio.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_ratio = []
for text_latex in [
text_alice, text_jets, text_ptjet, text_pth, text_ptcut, text_sd,
text_pythia
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_ratio.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_ratio.Update()
cshape_ratio.SaveAs("%s/%s_ratio_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, inclusive, quark, gluon
incl_pythia_syst_cl = incl_pythia_syst.Clone()
y_min_h, y_max_h = get_y_window_his([
hf_pythia_stat, incl_pythia_stat, quark_pythia_stat, gluon_pythia_stat
])
y_min_g, y_max_g = get_y_window_gr(
[incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst])
y_min = min(y_min_h, y_min_g)
y_max = max(y_max_h, y_max_g)
y_margin_up = 0.46
y_margin_down = 0.05
y_min_plot, y_max_plot = get_plot_range(y_min, y_max, y_margin_down,
y_margin_up)
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .55, .85, .85]
list_obj = [
incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst, hf_pythia_stat,
incl_pythia_stat, quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["inclusive", "quark", "gluon", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_mc, c_quark_mc, c_gluon_mc, c_hf_mc, c_incl_mc,
c_quark_mc, c_gluon_mc), (2, 2, 2, 1, 1, 1, 1))
]
markers = [
m_incl_mc, m_quark_mc, m_gluon_mc, m_hf_mc, m_incl_mc, m_quark_mc,
m_gluon_mc
]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
cshape_mc.Update()
for gr, c in zip((incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst),
(c_incl_mc, c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
incl_pythia_syst.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, quark, gluon
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .61, .85, .85]
list_obj = [
quark_pythia_syst, gluon_pythia_syst, hf_pythia_stat,
quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["quark", "gluon", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_quark_mc, c_gluon_mc, c_hf_mc, c_quark_mc,
c_gluon_mc), (2, 2, 1, 1, 1))
]
markers = [m_quark_mc, m_gluon_mc, m_hf_mc, m_quark_mc, m_gluon_mc]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_qgd_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
cshape_mc.Update()
for gr, c in zip((quark_pythia_syst, gluon_pythia_syst),
(c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
quark_pythia_syst.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_qgd_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, inclusive
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .67, .85, .85]
list_obj = [incl_pythia_syst_cl, incl_pythia_stat, hf_pythia_stat]
labels_obj = ["inclusive", "", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_mc, c_incl_mc, c_hf_mc), (2, 1, 1))
]
markers = [m_incl_mc, m_incl_mc, m_hf_mc]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_id_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
# Draw a line through the points.
if shape == "nsd":
for h in (incl_pythia_stat, hf_pythia_stat):
h_line = h.Clone(h.GetName() + "_line")
h_line.SetLineStyle(2)
h_line.Draw("l hist same")
list_new.append(h_line)
cshape_mc.Update()
incl_pythia_syst_cl.SetMarkerColor(get_colour(c_incl_mc))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
incl_pythia_syst_cl.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_id_%s.pdf" % (rootpath, shape, suffix))
# data inclusive vs PYTHIA, quark, gluon
#leg_pos = [.6, .65, .75, .85]
#leg_pos = [.72, .55, .85, .85]
leg_pos = [.6, .7, .85, .85]
list_obj = [
incl_data_syst, quark_pythia_syst, gluon_pythia_syst, incl_data_stat,
quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["inclusive (data)", "quark (PYTHIA 8)", "gluon (PYTHIA 8)"]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_data, c_quark_mc, c_gluon_mc, c_incl_data,
c_quark_mc, c_gluon_mc), (2, 2, 2, 1, 1, 1))
]
markers = [
m_incl_data, m_quark_mc, m_gluon_mc, m_incl_data, m_quark_mc,
m_gluon_mc
]
y_margin_up = 0.3
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_data_iqg" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip((incl_data_syst, quark_pythia_syst, gluon_pythia_syst),
(c_incl_data, c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_data_i_mc_qg_%s.pdf" % (rootpath, shape, suffix))
# data + MC/data, HF and inclusive
# print relative syst. unc.
for name, gr in zip(("HF", "inclusive"), (hf_data_syst, incl_data_syst)):
print(f"Rel. syst. unc. for {name} {shape}")
e_plus_min = float("inf")
e_minus_min = float("inf")
e_plus_max = 0.
e_minus_max = 0.
for i in range(gr.GetN()):
y = gr.GetPointY(i)
e_plus = 100 * gr.GetErrorYhigh(i)
e_minus = 100 * gr.GetErrorYlow(i)
e_plus /= y
e_minus /= y
e_plus_min = min(e_plus_min, e_plus)
e_minus_min = min(e_minus_min, e_minus)
e_plus_max = max(e_plus_max, e_plus)
e_minus_max = max(e_minus_max, e_minus)
# print(f"Point {i}, up {e_plus:.2g} %, down {e_minus:.2g} %")
# print(f"Minima: up {e_plus_min:.2g} %, down {e_minus_min:.2g} %")
# print(f"Maxima: up {e_plus_max:.2g} %, down {e_minus_max:.2g} %")
print(
f"Absolutes: min: {min(e_plus_min, e_minus_min):.2g} %, max {max(e_plus_max, e_minus_max):.2g} %"
)
# explicit y ranges [zg, rg, nsd]
list_range_y = [[0, 9], [0, 6], [0, 0.7]] # data
list_range_y_rat = [[0, 2], [0, 2], [0, 2]] # mc/data ratios
i_shape = 0 if shape == "zg" else 1 if shape == "rg" else 2
print(f"Index {i_shape}")
# data
leg_pos = [.7, .75, .82, .85]
list_obj = [hf_data_syst, incl_data_syst, hf_data_stat, incl_data_stat]
labels_obj = ["%s-tagged" % p_latexnhadron, "inclusive", "", ""]
colours = [
get_colour(i, j) for i, j in zip((c_hf_data, c_incl_data, c_hf_data,
c_incl_data), (2, 2, 1, 1))
]
markers = [m_hf_data, m_incl_data, m_hf_data, m_incl_data]
y_margin_up = 0.42
y_margin_down = 0.05
cshape_datamc_all = TCanvas("cshape_datamc_" + suffix,
"cshape_datamc_" + suffix)
cshape_datamc_all.Divide(1, 2)
pad1 = cshape_datamc_all.cd(1)
pad2 = cshape_datamc_all.cd(2)
pad1.SetPad(0., 0.3, 1, 1)
pad2.SetPad(0., 0., 1, 0.3)
pad1.SetBottomMargin(0.)
pad2.SetBottomMargin(0.25)
pad1.SetTopMargin(0.1)
pad2.SetTopMargin(0.)
pad1.SetLeftMargin(0.12)
pad2.SetLeftMargin(0.12)
pad1.SetTicks(1, 1)
pad2.SetTicks(1, 1)
cshape_datamc_all, list_obj_data_new = make_plot("cshape_datamc_" + suffix, size=size_can_double, \
can=cshape_datamc_all, pad=1, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=[0.8, 1.1], \
colours=colours, markers=markers, leg_pos=leg_pos, margins_c=margins_can_double, \
# margins_y=[y_margin_down, y_margin_up], \
range_y=list_range_y[i_shape], \
title=title_full)
for gr, c in zip((hf_data_syst, incl_data_syst), (c_hf_data, c_incl_data)):
gr.SetMarkerColor(get_colour(c))
list_obj_data_new[0].SetTextSize(fontsize)
hf_data_syst.GetYaxis().SetLabelSize(0.1 * 3 / 7)
#hf_data_syst.GetYaxis().SetTitleSize(0.1)
if shape == "nsd":
hf_data_syst.GetXaxis().SetNdivisions(5)
# Draw a line through the points.
if shape == "nsd":
for h in (hf_data_stat, incl_data_stat):
h_line = h.Clone(h.GetName() + "_line")
h_line.SetLineStyle(2)
h_line.Draw("l hist same")
list_new.append(h_line)
cshape_datamc_all.Update()
if shape == "rg":
# plot the theta_g axis
gr_frame = hf_data_syst
axis_rg = gr_frame.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = pad1.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_datamc_all.cd(1).SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data = []
for text_latex in [
text_alice, text_jets, text_ptjet, text_pth, text_ptcut, text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_datamc_all.Update()
# MC/data
leg_pos = [.15, .85, .85, .95]
hf_ratio_pythia_stat = hf_pythia_stat.Clone(
f"{hf_pythia_stat.GetName()}_rat")
hf_ratio_pythia_stat.Divide(hf_data_stat)
# hf_ratio_pythia_stat = hf_data_stat.Clone(f"{hf_data_stat.GetName()}_rat") # version data/MC
# hf_ratio_pythia_stat.Divide(hf_pythia_stat) # version data/MC
# create a graph with PYTHIA points and with zero syst. unc.
hf_pythia_stat_zero = hf_pythia_stat.Clone(
f"{hf_pythia_stat.GetName()}_zero")
for i in range(hf_pythia_stat_zero.GetNbinsX()):
hf_pythia_stat_zero.SetBinError(i + 1, 0)
gStyle.SetErrorX(
0.5
) # we have to restore the histogram bin width to propagate it to graph
hf_pythia_syst = TGraphAsymmErrors(
hf_pythia_stat_zero) # convert histogram into a graph
gStyle.SetErrorX(0) # set back the intended settings
hf_ratio_pythia_syst = divide_graphs(hf_pythia_syst, hf_data_syst)
# hf_ratio_pythia_syst = divide_graphs(hf_data_syst, hf_pythia_syst) # version data/MC
incl_ratio_pythia_stat = incl_pythia_stat.Clone(
f"{incl_pythia_stat.GetName()}_rat")
incl_ratio_pythia_stat.Divide(incl_data_stat)
incl_ratio_pythia_syst = divide_graphs(incl_pythia_syst, incl_data_syst)
# incl_ratio_pythia_stat = incl_data_stat.Clone(f"{incl_data_stat.GetName()}_rat") # version data/MC
# incl_ratio_pythia_stat.Divide(incl_pythia_stat) # version data/MC
# incl_ratio_pythia_syst = divide_graphs(incl_data_syst, incl_pythia_syst) # version data/MC
list_obj = [
hf_ratio_pythia_syst, incl_ratio_pythia_syst, hf_ratio_pythia_stat,
incl_ratio_pythia_stat, line_1
]
labels_obj = [
f"{p_latexnhadron}-tagged {text_pythia_short}",
f"inclusive {text_pythia_short}", "", ""
]
colours = [
get_colour(i, j)
for i, j in zip((c_hf_mc, c_incl_mc, c_hf_mc, c_incl_mc), (2, 2, 1, 1))
]
markers = [m_hf_mc, m_incl_mc, m_hf_mc, m_incl_mc]
y_margin_up = 0.2
y_margin_down = 0.05
cshape_datamc_all, list_obj_data_mc_hf_new = make_plot("cshape_data_mc_hf_" + suffix, size=size_can_double, \
can=cshape_datamc_all, pad=2, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=[1, 1.3 * 3/7], \
colours=colours, markers=markers, leg_pos=leg_pos, margins_c=margins_can_double, \
# margins_y=[y_margin_down, y_margin_up], \
range_y=list_range_y_rat[i_shape], \
title=title_full_ratio_double)
hf_ratio_pythia_syst.GetXaxis().SetLabelSize(0.1)
hf_ratio_pythia_syst.GetXaxis().SetTitleSize(0.1)
hf_ratio_pythia_syst.GetYaxis().SetLabelSize(0.1)
hf_ratio_pythia_syst.GetYaxis().SetTitleSize(0.1)
for gr, c in zip([hf_ratio_pythia_syst, incl_ratio_pythia_syst],
[c_hf_mc, c_incl_mc]):
gr.SetMarkerColor(get_colour(c))
leg_data_mc_hf = list_obj_data_mc_hf_new[0]
#leg_data_mc_hf.SetHeader("%s-tagged" % p_latexnhadron)
leg_data_mc_hf.SetTextSize(fontsize * 7 / 3)
leg_data_mc_hf.SetNColumns(2)
if shape == "nsd":
hf_ratio_pythia_syst.GetXaxis().SetNdivisions(5)
cshape_datamc_all.Update()
# Draw LaTeX
#y_latex = y_latex_top
#list_latex_data_mc_hf = []
#for text_latex in [text_alice, text_jets, text_ptjet, text_pth, text_sd]:
# latex = TLatex(x_latex, y_latex, text_latex)
# list_latex_data_mc_hf.append(latex)
# draw_latex(latex, textsize=fontsize)
# y_latex -= y_step
#cshape_datamc_all.Update()
pad1.RedrawAxis()
pad2.RedrawAxis()
cshape_datamc_all.SaveAs("%s/%s_datamc_all_%s.pdf" %
(rootpath, shape, suffix))
graph_pratio_allmases.Draw("AP")
graph_pratio_allmases.GetXaxis().SetTitle("Nr of tracks")
graph_pratio_allmases.GetYaxis().SetTitle("<ratio>")
graph_pratio_allmases.GetYaxis().SetRangeUser(0., 0.7)
graph_pratio_allmases.Draw("AP")
graph_ntr_allmases.Draw("*")
leg_all = TLegend(0.65, 0.35, 0.9, 0.55)
leg_all.AddEntry(graph_pratio_allmases, "pratio", "l")
leg_all.AddEntry(graph_ntr_allmases, "number of tracks", "p")
leg_all.SetTextSize(0.015)
leg_all.Draw()
axis = TGaxis(10.8, 0., 10.8, 0.7, 0, 0.7 * rightmax / leftmax, 510, "+L")
axis.SetLineColor(2)
axis.SetLabelColor(2)
axis.Draw()
c_allmases.cd(4)
graph_deltar_allmases.Draw("AP")
graph_deltar_allmases.GetXaxis().SetTitle("Nr of tracks")
graph_deltar_allmases.GetYaxis().SetTitle("deltaR")
graph_deltar_allmases.GetYaxis().SetRangeUser(0., 0.09)
graph_deltar_allmases.Draw("AP")
graph_ntr_allmases2.Draw("*")
axis2 = TGaxis(10.8, 0., 10.8, 0.09, 0, 0.09 * rightmax2 / leftmax2, 510, "+L")
axis2.SetLineColor(2)
axis2.SetLabelColor(2)
axis2.Draw()
c_allmases.Modify()
def ratio_plot(h1,
h2,
legend,
name='c',
dim=(800, 600),
mode='',
l_range=(0, 0),
l_y_title=''):
if not isinstance(dim, collections.abc.Sequence):
raise TypeError("The variable 'dim' has a wrong type")
elif len(dim) != 2:
raise ValueError("Wrong length given for list 'dim'")
if not isinstance(l_range, collections.abc.Sequence):
raise TypeError("The variable 'l_range' has a wrong type")
elif len(l_range) != 2:
raise ValueError("Wrong length given for list 'l_range'")
if name == 'c':
name = name + '_' + random_string()
c = TCanvas(name, '', dim[0], dim[1])
pad1 = TPad('pad1', 'pad1', 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0) # Upper and lower plot are joined
pad1.Draw() # Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
h1.SetStats(0)
h1.Draw()
h2opt = h2.GetOption() + 'SAME'
h2.Draw(h2opt)
legend.Draw()
axis = TGaxis(-5, 20, -5, 220, 20, 220, 510, '')
axis.SetLabelFont(43) # Absolute font size in pixel (precision 3)
axis.SetLabelSize(20)
axis.SetTitleFont(43)
axis.SetTitleSize(20)
axis.SetTitleOffset(1.3)
axis.Draw()
c.Update()
# lower plot will be in pad
c.cd() # Go back to the main canvas before defining pad2
pad2 = TPad('pad2', 'pad2', 0, 0.01, 1., 0.30)
pad2.SetTopMargin(0.0)
pad2.SetBottomMargin(0.28)
pad2.SetGridy(1)
pad2.Draw()
pad2.cd() # pad2 becomes the current pad
# Define the ratio plot
h3 = h1.Clone('h3')
h3.SetLineColor(kPurpleC)
h3.SetMarkerColor(kPurpleC)
h3.SetMarkerSize(0.5)
h3.Sumw2()
h3.SetStats(0) # No statistics on lower plot
if (mode == 'ratio'):
h3.Divide(h2)
if (mode == 'diff'):
h3.Add(h2, -1.)
h3.SetMarkerStyle(21)
h3.Draw('ep') # Draw the ratio plot
# Remove the ratio title
h3.SetTitle('')
# Y axis ratio plot settings
l_title = ''
if l_y_title == '':
l_title = mode + ' '
else:
l_title = l_y_title
h3.GetYaxis().SetTitle(l_title)
h3.GetYaxis().SetNdivisions(505)
h3.GetYaxis().SetTitleSize(20)
h3.GetYaxis().SetTitleFont(43)
h3.GetYaxis().SetTitleOffset(1.2)
h3.GetYaxis().SetLabelFont(43)
# Absolute font size in pixel(precision 3)
h3.GetYaxis().SetLabelSize(16)
if l_range != (0, 0):
h3.GetYaxis().SetRangeUser(l_range[0], l_range[1])
# X axis ratio plot settings
h3.GetXaxis().SetTitleSize(20)
h3.GetXaxis().SetTitleFont(43)
h3.GetXaxis().SetTitleOffset(3.5)
h3.GetXaxis().SetLabelFont(43)
# Absolute font size in pixel(precision 3)
h3.GetXaxis().SetLabelSize(18)
c.Write()
c.Close()
corrected_nue.SetLineWidth(4)
simple_nue.SetLineWidth(4)
simple_nue.GetXaxis().SetRangeUser(0, 4)
corrected_nue.GetXaxis().SetRangeUser(0, 4)
simple_nue.Draw("hist")
corrected_nue.Draw("hist same")
# Do not draw the Y axis label on the upper plot and redraw a small
# axis instead, in order to avoid the first label (0) to be clipped.
corrected_nue.GetYaxis().SetLabelSize(0.)
axis = TGaxis(-5, 20, -5, 220, 20, 220, 510, "")
axis.SetLabelFont(43) # Absolute font size in pixel (precision 3)
axis.SetLabelSize(15)
axis.Draw()
leg_nue = TLegend(0.75, 0.95, 0.75, 0.95)
leg_nue.AddEntry(simple_nue, "nue std. Flux", "l")
leg_nue.AddEntry(corrected_nue, "nue cor. Flux", "l")
leg_nue.Draw()
c6.cd()
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0.1)
pad2.SetBottomMargin(0.1)
pad2.SetGridx() # vertical grid
pad2.Draw()
pad2.cd() # pad2 becomes the current pad
ratio_nue.SetLineColor(12)
