class vmagnet(object):
#_____________________________________________________________________________
def __init__(self):
#set magnet from configuration dictionary
self.name = ""
self.center_z = 0.
self.center_x = 0.
self.length = 0.
self.rad1 = 0.
self.rad2 = 0.
self.field = 0.
#electron or hadron
self.is_electron = True
#initial angle
self.theta_0 = 0.
#parameters from MAD-X survey
self.S = 0.
self.L = 0.
self.X = 0.
self.Z = 0.
self.THETA = 0.
self.has_survey = False
#drawing configuration
self.fill_style = 1000
self.label_down = False
self.label = ""
self.no_label = False
self.line_col = rt.kBlue
self.fill_col = rt.kGreen - 2
#_____________________________________________________________________________
def read_survey(self, lin):
#values from MAD-X survey
self.S = float(lin["S"])
self.L = float(lin["L"])
self.X = float(lin["X"])
self.Z = float(lin["Z"])
self.THETA = float(lin["THETA"]) + self.theta_0
self.has_survey = True
#_____________________________________________________________________________
def rotate_translateX(self, theta, xt):
#combined rotation and translation
self.rotate(theta)
self.translateX(xt)
#_____________________________________________________________________________
def translateX(self, xt):
#translate the magnet along x
self.center_x += xt
#_____________________________________________________________________________
def rotate(self, theta):
#rotate by angle theta about the origin
#get new center_z and center_x by TVector2 rotation
vec = TVector2(self.center_z, self.center_x).Rotate(theta)
self.center_z = vec.X()
self.center_x = vec.Y()
#rotate along magnet center
self.THETA = self.THETA - theta
#_____________________________________________________________________________
def draw_2d(self):
#draw only magnets with survey defined
if not self.has_survey: return
#self.draw_box()
self.draw_graph()
#_____________________________________________________________________________
def draw_graph(self):
#inner and outer radius
if self.center_z < 0:
rad_right = self.rad1
rad_left = self.rad2
else:
rad_right = self.rad2
rad_left = self.rad1
#edge points of the magnet
vec = []
vec.append(TVector2(self.length / 2, rad_right))
vec.append(TVector2(self.length / 2, -rad_right))
vec.append(TVector2(-self.length / 2, -rad_left))
vec.append(TVector2(-self.length / 2, rad_left))
#rotate along magnet axis and move to magnet center
vpos = TVector2(self.center_z, self.center_x)
for i in xrange(len(vec)):
vec[i] = vec[i].Rotate(-self.THETA) + vpos
#export points to the graph
self.gbox = TGraph(len(vec) + 1)
self.gbox.SetLineColor(self.line_col)
self.gbox.SetLineWidth(2)
self.gbox.SetFillStyle(self.fill_style)
self.gbox.SetFillColor(self.fill_col)
for i in xrange(len(vec)):
self.gbox.SetPoint(i, vec[i].X(), 100 * vec[i].Y())
#last point same as the first
self.gbox.SetPoint(len(vec), vec[0].X(), 100 * vec[0].Y())
self.gbox.Draw("lfsame")
#label
if self.no_label: return
#lx = (self.center_x + self.rad2)*100 + 4
lx = (self.center_x + (self.rad1 + self.rad2) / 2) * 100 + 4
if lx < 30: lx = 30
align = 12
#left down
if (self.center_z < 0 and not self.is_electron) or self.label_down:
lx = (self.center_x - self.rad2) * 100 - 4
align = 32
#right down
if self.center_z > 0 and self.is_electron:
lx = (self.center_x - self.rad2) * 100 - 4
if lx > -25: lx = -25
align = 32
#label above the magnet
if self.center_x < -0.4:
lx = (self.center_x + self.rad2) * 100 + 4
align = 12
if self.label == "":
self.label = self.name
#self.glabel = TText(self.center_z, lx, self.label)
self.glabel = TLatex(self.center_z, lx, self.label)
self.glabel.SetTextSize(0.03)
#self.glabel.SetTextSize(0.02)
self.glabel.SetTextAngle(90)
self.glabel.SetTextAlign(align)
self.glabel.Draw("same")
#_____________________________________________________________________________
def draw_box(self):
z1 = self.center_z - self.length / 2
z2 = z1 + self.length
x1 = self.center_x - self.rad2
x2 = x1 + 2 * self.rad2
#to cm
x1 *= 100
x2 *= 100
#representation as a box
self.box = TFrame(z1, x1, z2, x2)
self.box.SetBorderMode(0)
self.box.SetFillColor(rt.kGray + 1)
col = rt.kRed
if self.is_electron == True: col = rt.kBlue
self.box.SetLineColor(col)
self.box.SetLineWidth(2)
#self.box.Draw("same")
#label
lx = x2 + 2
align = 11
if lx < 0 and lx > -62:
lx = x1 - 5 # negative lx
align = 31
if self.center_z < 0 and self.center_x < 0.1:
lx = x1 - 2 # negative z and x
align = 31
if lx > 0 and lx < 22: lx = 22 # small positive lx
self.label = TText(z2, lx, self.name)
self.label.SetTextSize(0.03)
self.label.SetTextAngle(90)
self.label.SetTextAlign(align)
gStyle.SetFrameLineWidth(3)
gStyle.SetOptTitle(1)
gStyle.SetOptStat(0)
gStyle.SetFillColor(2)
gStyle.SetLineWidth(1)
gStyle.SetPadColor(1)
#legend=TLegend(.63,.69,.87,.89,"","#gamma #gamma")
#legend=TLegend(0.57, 0.5, 0.94,0.65,"","b b~")
c = TCanvas("c", "First canvas", 2000, 1900)
cmsname = TLatex(0.15, 1.85, "Trials")
#cmsname=TLatex(0.15,1.85,"CMS #it{#bf{Preliminary}}")
cmsname.SetTextSize(0.036)
cmsname.SetTextAlign(12)
cmsname.SetNDC(1)
cmsname.SetTextFont(61)
#lhefdata=LHEFData(float(root.attrib['version']))
#lhefdata=LHEFData(float(root.attrib['version']))
for child in root1:
if (child.tag == 'event'):
lines = child.text.strip().split('\n')
event_header = lines[0].strip()
num_part = int(event_header.split()[0].strip())
a1_1 = [s for s in lines if s.split()[0] == '22']
a1 = a1_1[1::2]
a2 = a1_1[0::2]
b1 = [s for s in lines if s.split()[0] == '5']
def drawHist(hist, confL, name, colz=True, contour=False, dec=False):
can = TCanvas()
can.cd()
if (dec):
gStyle.SetPaintTextFormat("3.2f")
else:
gStyle.SetPaintTextFormat("3.0f")
if "metOmeff" in hist.GetName():
gStyle.SetPaintTextFormat("3.2f")
gStyle.SetStatX(50)
#colors = array("i",[3,30, 40, 20, 46, 5, 6, 7, 8, 9,11,4]) #colors >= #levels - 1
#gStyle.SetPalette(len(colors), colors)
#levels = array("d",[.01, .20, 0.50, 1, 2, 3, 4, 7,9,10])
#hist.SetContour(len(levels), levels)
hist.SetMarkerSize(1.5)
if colz:
hist.Draw("colz,same")
hist.Draw("text30, same")
if SigSample == "GG_ttn1":
xlabel = 850
ylabel = 1400
elif SigSample == "GG_2stepWZ":
xlabel = 600
ylabel = 1400
elif SigSample == "GG_SLN1":
xlabel = 400
ylabel = 1000
elif SigSample == "BB_onestepC1":
xlabel = 350
ylabel = 300
xlabel = 650
ylabel = 1500
if contour:
g = TGraph2D()
TH2toTGraph2D(hist, g)
Ghisto = g.GetHistogram()
level = array('d', [1.64, 3])
Ghisto.SetLineWidth(4)
Ghisto.SetContour(2, level)
Ghisto.Draw("cont1,list ,same")
# confL.SetLineColor(1)
# confL.Draw("colz,same")
tex1 = TLatex()
if SigSample == "BB_onestepC1":
tex1.DrawLatex(xlabel, ylabel + 50, "#color[3]{1.64#sigma}")
else:
tex1.DrawLatex(xlabel, ylabel + 100, "#color[3]{1.64#sigma}")
tex1.DrawLatex(xlabel, ylabel, "#color[2]{3#sigma}")
#sprintf(&title[0],"MET>%d GeV",met);
#myText(.2,.84,kBlue,title);
#sprintf(&title[0],"MEFF>%d GeV",meff);
#myText(.2,.79,kBlue,title);
#sprintf(&title[0],"Bkg: %3.1f evts",_bkg);
#myText(.2,.74,kBlue,title);
# myLineText(.26, .8 , 0.05, kGreen,"1.64#sigma")
# myLineText(.26, .75, 0.05, kRed, "3#sigma")
hist.GetYaxis().SetTitleOffset(1.3)
hist.GetXaxis().SetTitle("Gluino Mass (GeV)")
hist.GetYaxis().SetTitle("Neutralino Mass (GeV)")
gStyle.SetTitleX(100.0) # move title off plot
gStyle.SetTitleW(0.0) # title width =0
tex2 = TLatex()
if SigSample == "BB_onestepC1":
tex2.DrawLatex(
xlabel, ylabel + 100,
name + " " + str(luminosity) + " fb^{-1}: " + hist.GetName())
else:
tex2.DrawLatex(
xlabel, ylabel + 200,
name + " " + str(luminosity) + " fb^{-1}: " + hist.GetName())
# Draw m_gl - m_N1 = 2 (m_W+m_b)
line = TLine(600, 429.6, 1800, 1629.6)
line.SetLineColor(16)
line.SetLineStyle(7)
line.Draw("same")
th1 = TLatex(
1450, 1340,
"m_{#tilde{g}} - m_{#tilde{#chi}_{1}^{0}} = 2 #left(m_{W}+m_{b}#right)"
)
th1.SetTextAngle(30)
th1.SetTextColor(16)
th1.SetTextSize(0.03)
th1.Draw("same")
# Draw m_gl - m_N1 = 2 m_t
line2 = TLine(600, 255, 2050, 1680)
line2.SetLineColor(16)
line2.SetLineStyle(7)
line2.Draw("same")
th2 = TLatex(1550, 1240,
"m_{#tilde{g}} - m_{#tilde{#chi}_{1}^{0}} = 2 m_{t}")
th2.SetTextAngle(30)
th2.SetTextColor(16)
th2.SetTextSize(0.03)
# th2.Draw("same")
if "Dm" in hist.GetName():
line3 = TLine(600, 25, 2100, 25)
line3.SetLineColor(16)
line3.SetLineStyle(7)
line3.Draw("same")
th3 = TLatex(
1600, 40,
"#Delta m = m_{#tilde{#chi}_{1}^{0}} - #left(m_{#tilde{g}} -2 m_{t} #right)"
)
# th3.SetTextAngle(32)
th3.SetTextColor(16)
th3.SetTextSize(0.03)
th3.Draw("same")
hist.GetYaxis().SetTitle(
"#Delta m_{#tilde{#chi}_{1}^{0}} (above diagonal) (GeV)")
can.Update()
directory = "/afs/cern.ch/user/o/othrif/workarea/results/v48/Plots/" + Dirbase #+"/Lumi"+str(luminosity)
if not os.path.exists(directory):
os.makedirs(directory)
can.SaveAs(directory + "/" + name + "_" + hist.GetName() + ".pdf")
def analysis(
hadron="Lambda_c",
collision="pp14p0",
yrange="absy3p0",
brmode="central",
model="Pyhia8mode2",
use_unnorm=1,
):
gStyle.SetOptStat(0)
with open(r"databases/significance.yaml") as filesignificance:
paramsignificance = yaml.safe_load(filesignificance)
ymin = paramsignificance[hadron][collision][yrange]["ymin"]
ymax = paramsignificance[hadron][collision][yrange]["ymax"]
# bin of the final analysis, has to be the binning of efficiency, bkg histos
binanal = array("d",
paramsignificance[hadron][collision][yrange]["binning"])
nfileyieldth = paramsignificance[hadron][collision][yrange]["theoryfile"]
nfileeff = paramsignificance[hadron][collision][yrange]["efffile"]
nhistoeff = paramsignificance[hadron][collision][yrange]["histoeff"]
nfilebkg = paramsignificance[hadron][collision][yrange]["bkgfile"]
nhistobkg = paramsignificance[hadron][collision][yrange]["histobkg"]
nhistoyieldth = paramsignificance[hadron][collision][yrange]["histoyield"]
nhistoyieldth_norm = paramsignificance[hadron][collision][yrange][
"histoyield_norm"]
with open(r"databases/general.yaml") as fileparamgen:
paramgen = yaml.safe_load(fileparamgen)
with open(r"databases/theory_yields.yaml") as fileyields:
paramyields = yaml.safe_load(fileyields)
textcollision = paramgen["text_string"][collision]
textrapid = paramgen["text_string"][yrange]
textmodel = paramgen["text_string"][model]
sigma_aa_b = paramgen["statistics"][collision]["sigmaAA_b"]
lumiaa_monthi_invnb = paramgen["statistics"][collision][
"lumiAA_monthi_invnb"]
nevt = sigma_aa_b * lumiaa_monthi_invnb * 1e9
# nevt = 2.*1e9
bratio = paramgen["branchingratio"][hadron][brmode]
decaychannel = paramgen["latexparticle"][hadron]
if hadron == "Chi_c" and yrange == "absy1p44":
bratio *= 0.8 # chi_c1 and chi_c2 are measured together: non-weighted average of their BRs
decaychannel = "#chi_{c} #rightarrow J/#psi #gamma"
yieldmid = paramyields[model][collision][yrange][hadron]
text = "%s, N_{ev} = %f 10^{9}" % (textmodel, (float)(nevt) / 1.0e9)
text_a = "%s, %s, BR=%.2f%%" % (decaychannel, textrapid, bratio * 100)
text_b = "ALICE3 projection, with IRIS, no PID, %s" % textcollision
fileeff = TFile(nfileeff)
histoeff = fileeff.Get(nhistoeff)
filebkg = TFile(nfilebkg)
hbkgperevent = filebkg.Get(nhistobkg)
fileyieldth = TFile(nfileyieldth)
histoyieldth = None
if use_unnorm == 1:
histodndptth = fileyieldth.Get(nhistoyieldth)
histodndptth.Scale(
1.0 / 70000.0) # TEMPORARY this is a fix to account for the
# conversion from a cross-section in mub
# to yields, sigma=70000 mub
else:
histodndptth = fileyieldth.Get(nhistoyieldth_norm)
integral = 0
for ibin in range(histodndptth.GetNbinsX()):
binwdith = histodndptth.GetBinWidth(ibin + 1)
integral += histodndptth.GetBinContent(ibin + 1) * binwdith
histodndptth.Scale(1.0 / integral)
histodndptth.Scale(yieldmid)
print("yieldmid = %f\n", yieldmid)
integral = 0
for ibin in range(histodndptth.GetNbinsX()):
binwdith = histodndptth.GetBinWidth(ibin + 1)
integral += histodndptth.GetBinContent(ibin + 1) * binwdith
print("yieldmid = %f\n", yieldmid)
if hadron == "Chi_c" and collision == "pp14p0":
print("scaling signal yield by 0.1")
histodndptth.Scale(0.1) # because pythia8 is wrong by a factor ~10
if hadron == "Chi_c" and yrange == "absy1p44":
print("scaling bkg by 2*2, and signal by 3.4")
hbkgperevent.Scale(
2
) # to take approximately into account the worsening of the sig/bkg in the full
# rapidity range (PbWO4 and W+Sci)
hbkgperevent.Scale(
2
) # because in |y| < 1.44 we sum chi_c1 and chi_c2 (states are not resolved)
histodndptth.Scale(
3.4
) # because in |y| < 1.44 we sum chi_c1 and chi_c2 (states are not resolved).
# Assuming chi_c2/chi_c1 (!!) from Pythia8
if hadron == "Chi_c" and yrange == "absy0p33":
print("scaling signal and bkg by 0.23, from |y| < 1.44 to |y| < 0.33")
hbkgperevent.Scale(
0.23
) # to take into account the reduction of the statistics from |y| < 1.44 to |y| < 0.33
# (the input file for chi_c is always |y| < 1.44)
histodndptth.Scale(
0.23
) # to take into account the reduction of the statistics from |y| < 1.44 to |y| < 0.33
# (the input file for chi_c is always |y| < 1.44)
histoyieldth = histodndptth.Clone("histoyieldth")
for ibin in range(histoyieldth.GetNbinsX()):
binwdith = histoyieldth.GetBinWidth(ibin + 1)
yieldperevent = histoyieldth.GetBinContent(ibin +
1) * binwdith * bratio
histoyieldth.SetBinContent(ibin + 1, yieldperevent)
histoyieldth.SetBinError(ibin + 1, 0.0)
histoyieldth = histoyieldth.Rebin(
len(binanal) - 1, "histoyieldth", binanal)
histosignfperevent = histoyieldth.Clone("histosignfperevent")
histosignf = histoyieldth.Clone("histosignf")
histosigoverbkg = histoyieldth.Clone("histosigoverbkg")
canvas = TCanvas("canvas", "A Simple Graph Example", 881, 176, 668, 616)
gStyle.SetOptStat(0)
canvas.SetHighLightColor(2)
canvas.Range(-1.25, -4.625, 11.25, 11.625)
canvas.SetFillColor(0)
canvas.SetBorderMode(0)
canvas.SetBorderSize(2)
canvas.SetLogy()
canvas.SetFrameBorderMode(0)
canvas.SetFrameBorderMode(0)
canvas.cd()
gPad.SetLogy()
hempty = TH2F(
"hempty",
";p_{T} (GeV/c); Significance(3#sigma)",
100,
0.0,
10.0,
100,
ymin,
ymax,
)
hempty.GetXaxis().SetTitle("p_{T} (GeV/c)")
hempty.GetXaxis().SetLabelFont(42)
hempty.GetXaxis().SetTitleOffset(1)
hempty.GetXaxis().SetTitleFont(42)
hempty.GetYaxis().SetLabelFont(42)
hempty.GetYaxis().SetTitleOffset(1.35)
hempty.GetYaxis().SetTitleFont(42)
hempty.GetZaxis().SetLabelFont(42)
hempty.GetZaxis().SetTitleOffset(1)
hempty.GetZaxis().SetTitleFont(42)
hempty.Draw()
histosignf = histosignfperevent.Clone("histosignf")
for ibin in range(histoyieldth.GetNbinsX()):
yieldperevent = histoyieldth.GetBinContent(ibin + 1)
bkgperevent = hbkgperevent.GetBinContent(ibin + 1)
eff = histoeff.GetBinContent(ibin + 1)
signalperevent = eff * yieldperevent
significanceperevent = 0
if bkgperevent > 0:
significanceperevent = signalperevent / sqrt(signalperevent +
bkgperevent)
signaloverbkg = 0
if bkgperevent > 0:
signaloverbkg = signalperevent / bkgperevent
histosignfperevent.SetBinContent(ibin + 1, significanceperevent)
histosignfperevent.SetBinError(ibin + 1, 0.0)
histosignf.SetBinContent(ibin + 1, significanceperevent * sqrt(nevt))
histosignf.SetBinError(ibin + 1, 0.0)
histosigoverbkg.SetBinContent(ibin + 1, signaloverbkg)
histosigoverbkg.SetBinError(ibin + 1, 0.0)
histosignfperevent.SetLineColor(1)
histosignfperevent.SetMarkerColor(1)
histosignfperevent.SetLineWidth(1)
histosignf.SetLineColor(1)
histosignf.SetMarkerColor(1)
histosignf.SetLineWidth(2)
histosignf.Draw("same")
t_b = TLatex()
t_b.SetNDC()
t_b.SetTextFont(42)
t_b.SetTextColor(1)
t_b.SetTextSize(0.035)
t_b.SetTextAlign(12)
t_b.DrawLatex(0.2, 0.85, text_b)
t_c = TLatex()
t_c.SetNDC()
t_c.SetTextFont(42)
t_c.SetTextColor(1)
t_c.SetTextSize(0.03)
t_c.SetTextAlign(12)
t_c.DrawLatex(0.2, 0.80, text)
t_a = TLatex()
t_a.SetNDC()
t_a.SetTextFont(42)
t_a.SetTextColor(1)
t_a.SetTextSize(0.035)
t_a.SetTextAlign(12)
t_a.DrawLatex(0.2, 0.75, text_a)
canvas.SaveAs(hadron + "_" + collision + "_" + yrange + "_results.pdf")
canvas.SaveAs(hadron + "_" + collision + "_" + yrange + "_results.C")
foutput = TFile(
"foutput" + hadron + "_" + collision + "_" + yrange + ".root",
"recreate")
foutput.cd()
histoeff.Write()
hbkgperevent.Write()
histosignfperevent.Write()
histoyieldth.Write()
histosignf.Write()
histodndptth.Write()
histosigoverbkg.Write()
def myText(x, y, color=1, size=0.08, text=""):
l = TLatex()
l.SetTextSize(size)
l.SetNDC()
l.SetTextColor(color)
l.DrawLatex(x, y, text)
can = TCanvas('can', 'can', 1100, 800)
plot_phase_space(can, histos,
keyword='isr',
output_prefix=args.output_prefix)
plot_phase_space(can, histos,
keyword='elastic',
output_prefix=args.output_prefix)
# -----------------------------------------------------------
# Plot W
# -----------------------------------------------------------
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
can.Divide(2,2)
can.cd(1)
color_draw(histos['data']['histos_w_inclusive_'], kGray, "")
color_draw(histos['data']['histos_w_CTOF'], kBlue, "same")
color_draw(histos['data']['histos_w_pass_angle_CTOF'], kRed, "same")
latex.DrawLatex(0.45, 0.02, 'W (GeV/c^{2})')
latex.DrawLatex(0.3, 0.95, 'Data w/ Proton in CTOF')
latex.SetTextColor(kBlue)
latex.DrawLatex(0.15, 0.85, 'w/ proton')
latex.SetTextColor(kRed)
latex.DrawLatex(0.15, 0.80, 'w/ #Delta #phi cut')
latex.SetTextColor(kBlack)
def plot_all_components(workdir, root_file_path, info_file, lumi):
import pandas as pd
import os
histo_names = {
'all': 'gg_zh1_all_{}_generator-level',
'SM_only': 'gg_zh1_SM_only_{}_generator-level',
'A_only': 'gg_zh1_A_only_{}_generator-level',
'qq': 'qq_zh1_{}_generator-level'
}
labels = {
'all': 'gg - all',
'SM_only': 'gg - SM only.',
'A_only': 'gg - A only',
'qq': 'qq'
}
types = {
'mZh': {
'xlabel': 'm_{inv}(Zh) [GeV]',
'name': 'mZh'
},
'h_pt': {
'xlabel': 'p_{T}(h) [GeV]',
'name': 'h_Pt'
},
'Z_pt': {
'xlabel': 'p_{T}(Z) [GeV]',
'name': 'Z_Pt'
}
}
info = pd.read_csv(info_file, delim_whitespace=True)
info['xsec_gg_Zh_qq'] = 0.74829 * info['sinba'] * info['sinba']
info['xsec_gg_Zh_all'] = info['xsec_gg_Zh_all'] * 2.0
info['xsec_gg_Zh_SM_only'] = info['xsec_gg_Zh_SM_only'] * 2.0
info['xsec_gg_Zh_A_only'] = info['xsec_gg_Zh_A_only'] * 2.0
w = {'all': None, 'SM_only': None, 'A_only': None, 'qq': None}
th = {'all': None, 'SM_only': None, 'A_only': None, 'qq': None}
tf = TFile(root_file)
for type in types.keys():
for component in histo_names.keys():
th[component] = tf.Get(histo_names[component].format(type))
xsec = info['xsec_gg_Zh_{}'.format(component)]
w = get_weight_to_xsec(
xsec * 1000, 10000) # xsec * 1000 to convert from pb to fb
th[component].Scale(w, "width")
th[component].SetLineColor(style_color[component])
th[component].SetMarkerColor(style_color[component])
th[component].SetLineWidth(line_width)
print('\nw[key]:', w)
# - Plotting
c = TCanvas('canvas', 'canvas', 800, 600)
th['qq'].GetXaxis().SetTitle(types[type]['xlabel'])
th['qq'].GetYaxis().SetTitle('d#sigma/dm_{Zh^{0}} [fb/bin]')
#th['all'].GetXaxis().SetRangeUser(200,1000)
#th['all'].GetYaxis().SetRangeUser(0.0006,20)
#h_all.GetYaxis().SetRangeUser(200,1000)
gPad.SetLeftMargin(0.14)
gPad.SetBottomMargin(0.15)
label_posx, label_posy = 0.63, 0.6
text1 = "2HDM Type-II"
text2 = "m_{{A}}={:.0f} GeV #Gamma_{{A}}={:.3f} GeV".format(
info.mA[0], info.Gamma_A[0])
text3 = "cos(#beta-#alpha)={:.2f} tan(#beta)={:.2f}".format(
info.cba[0], info.tb[0])
text_vshift = 0.06
label1 = TLatex(label_posx, label_posy, text1)
label1.SetNDC(True)
label1.SetTextSize(text_size)
label2 = TLatex(label_posx, label_posy - text_vshift, text2)
label2.SetNDC(True)
label2.SetTextSize(text_size)
label3 = TLatex(label_posx, label_posy - 2.0 * text_vshift, text3)
label3.SetNDC(True)
label3.SetTextSize(text_size)
legend = TLegend(
0.65,
0.70,
0.85,
0.82,
)
legend.SetTextSize(0.035)
for key in histo_names.keys():
legend.AddEntry(th[key], labels[key])
############################
## --- Linear plot
c.SetLogy(0)
#th['all'].GetYaxis().SetRangeUser(0.0006,3.5)
th['qq'].Draw("SAME hist")
th['all'].Draw("SAME hist")
th['SM_only'].Draw("SAME hist")
th['A_only'].Draw("SAME hist")
#h_A_only.Draw("SAME")
label1.Draw("SAME")
label2.Draw("SAME")
label3.Draw("SAME")
legend.Draw("SAME")
c.SaveAs(
os.path.join(workdir, '{}_lin.pdf'.format(types[type]['name'])))
############################
## --- Log plot
c.Clear()
c.SetLogy()
th['qq'].Draw("hist")
th['all'].Draw("SAME hist")
th['SM_only'].Draw("SAME hist")
th['A_only'].Draw("SAME hist")
label1.Draw("SAME")
label2.Draw("SAME")
label3.Draw("SAME")
legend.Draw("SAME")
c.SaveAs(
os.path.join(workdir, '{}_log.pdf'.format(types[type]['name'])))
h_neutron_long_trans.Draw("colz")
# Eur Phys J (2013) neutron-carbon invariant cross section measurements with pT, xF bins. (NA49, thin target, @158 GeV, so with tgptype particle)
line1 = TLine(0.05, 0, 1, 0)
line2 = TLine(1, 0, 1, 2.4)
line3 = TLine(1, 2.4, 0.475, 2.4)
line4 = TLine(0.475, 2.4, 0.05, 0.6)
line5 = TLine(0.05, 0.6, 0.05, 0)
lineVector = [line1, line2, line3, line4, line5]
for line in lineVector:
line.SetLineColor(ROOT.kBlack)
line.SetLineWidth(3)
line.Draw()
leg = TLegend(.54, .75, .58, .79)
leg.SetFillStyle(0)
leg.SetLineWidth(4)
leg.Draw()
text = TLatex(.6, .75, "NA49 Coverage")
text.SetTextColor(ROOT.kBlack)
text.SetNDC()
text.SetTextSize(1.4 / 30.)
text.SetTextAlign(11)
#text.DrawLatex(.48, .55, "#Square");
text.Draw()
raw_input("Please press enter to exit.")
relPosX = 0.045 relPosY = 0.035 relExtraDY = 1.2 alignY_ = 3 alignX_ = 1 align_ = 10 * alignX_ + alignY_ cv.cd() latex = TLatex() latex.SetNDC() latex.SetTextAngle(0) latex.SetTextColor(1) latex.SetTextFont(42) latex.SetTextAlign(31) latex.SetTextSize(lumiTextSize * t) #latex.DrawLatex(1-r,1-t+lumiTextOffset*t,lumiText); posX_ = 0 posY_ = 1 - t - relPosY * (1 - t - b) latex.SetTextFont(cmsTextFont) latex.SetTextSize(0.8 * cmsTextSize * t) latex.SetTextAlign(align_) #latex.DrawLatex(1.3*l, posY_, cmsText); extraTextSize = extraOverCmsTextSize * cmsTextSize latex.SetTextFont(extraTextFont) latex.SetTextAlign(align_) latex.SetTextSize(0.8 * extraTextSize * t) #print extraText
def main(reader_name,
tags,
lut_path,
xmin,
xmax,
ymin=None,
ymax=None,
tags_name=None,
logx=False,
logy=False,
leg_pos=[0.74, 0.2, 0.90, 0.4],
particles=None,
ind_var=0,
dnch_deta=100,
rmin=None,
add_eta_label=True,
add_alice3_label=True,
save=None,
background=False,
use_p_over_z=True,
aod=None,
styles=None,
study_label="ALICE 3 study"):
gROOT.LoadMacro(reader_name)
gROOT.LoadMacro("style.C")
reader_name = reader_name.split(".")[-2]
reader = getattr(__import__('ROOT', fromlist=[reader_name]), reader_name)
getattr(__import__('ROOT', fromlist=["style"]), "style")()
p = {
"el": "e",
"pi": "#pi",
"ka": "K",
"pr": "p",
"de": "d",
"tr": "t",
"he3": "^{3}He"
}
charge = {"el": 1, "pi": 1, "ka": 1, "pr": 1, "de": 1, "tr": 1, "he3": 2}
p_colors = {
"el": "#e41a1c",
"pi": "#377eb8",
"ka": "#4daf4a",
"pr": "#984ea3",
"de": "#ff7f00",
"tr": "#999999",
"he3": "#a65628"
}
if particles is not None:
to_remove = []
for i in p:
if i not in particles:
to_remove.append(i)
for i in to_remove:
p.pop(i)
latex = TLatex()
latex.SetTextAlign(33)
canvas = reader_name
canvas = canvas.replace("lutRead_", "")
canvas = TCanvas(canvas, canvas, 800, 800)
canvas.Divide(2, 2)
drawn = [canvas]
drawn_graphs = {}
drawn_frames = {}
def set_limit(l, v=0):
if l is None:
return v
return l
if "_dca" in reader_name:
ymin = set_limit(ymin, 0.1)
ymax = set_limit(ymax, 1e4)
elif "_pt" in reader_name:
ymin = set_limit(ymin, 1)
ymax = set_limit(ymax, 100)
elif "_eff" in reader_name:
ymin = set_limit(ymin, 0)
ymax = set_limit(ymax, 115)
def adjust_pad():
if logx:
gPad.SetLogx()
if logy:
gPad.SetLogy()
counter = 1
leg = None
if tags_name is not None:
leg = TLegend(*leg_pos)
if add_eta_label:
label = f"#eta = {int(ind_var)}"
if "vs_eta" in reader_name:
label = "#it{p}_{T} " f"= {int(ind_var)} " "GeV/#it{c}"
label += " dN_{Ch}/d#eta ="
label += f" {int(dnch_deta)}"
if rmin is not None:
label += " R_{min} = " + rmin
else:
leg.SetHeader()
leg.SetHeader(label)
leg.SetLineColor(0)
drawn.append(leg)
def draw_study_label(x=0.5, y=0.9):
latex = TLatex()
latex.SetTextAlign(13)
drawn.append(latex.DrawLatexNDC(x, y, " ".join(study_label)))
for i in p: # Drawing one canvas per particle species
c = f"{canvas.GetName()}_{i}"
c = TCanvas(c, c, 800, 800)
drawn.append(c)
adjust_pad()
frame = c.DrawFrame(xmin, ymin, xmax, ymax, "")
frame.SetDirectory(0)
if leg is not None:
leg.Draw()
drawn_frames[i] = frame
g_list = []
extra = {}
cols = [
'#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33'
]
for k, j in enumerate(tags):
lut = f"{lut_path}/lutCovm.{i}.{j}.dat"
if j == "":
lut = f"{lut_path}/lutCovm.{i}.dat"
if not path.isfile(lut):
print("LUT file", lut, "does not exist")
return
g = reader(lut, ind_var, dnch_deta)
if g.GetN() <= 0:
print("Skipping", g.GetName(), "because empty graph")
continue
if len(g_list) == 0:
frame.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
frame.GetYaxis().SetTitle(g.GetYaxis().GetTitle())
if use_p_over_z:
for j in range(g.GetN()):
if "_pt" in reader_name:
g.SetPoint(j,
g.GetPointX(j) / charge[i],
g.GetPointY(j) / charge[i])
else:
g.SetPoint(j,
g.GetPointX(j) / charge[i], g.GetPointY(j))
frame.GetXaxis().SetTitle("#it{p}_{T}/z (GeV/#it{c})")
col = TColor.GetColor(cols[len(g_list)])
g.SetLineColor(col)
g.SetLineStyle(1)
g.SetLineWidth(3)
g.Draw("samel")
if aod is not None:
f_aod = TFile(aod, "READ")
if "_eff" in reader_name:
extra[g.GetName()] = f_aod.Get(
"qa-tracking-efficiency-kaon/pt/num")
extra[g.GetName()].Divide(
f_aod.Get("qa-tracking-efficiency-kaon/pt/num"),
f_aod.Get("qa-tracking-efficiency-kaon/pt/den"), 1, 1,
"B")
extra[g.GetName()].Scale(100)
extra[g.GetName()].Draw("SAME")
extra[g.GetName()].SetDirectory(0)
elif "_pt" in reader_name:
extra[g.GetName()] = f_aod.Get(
"qa-tracking-efficiency-kaon/pt/num")
extra[g.GetName()].Divide(
f_aod.Get("qa-tracking-efficiency-kaon/pt/num"),
f_aod.Get("qa-tracking-efficiency-kaon/pt/den"), 1, 1,
"B")
extra[g.GetName()].Scale(100)
extra[g.GetName()].Draw("SAME")
extra[g.GetName()].SetDirectory(0)
f_aod.Close()
print("Drawing", g.GetName())
if tags_name is not None and counter == 1:
leg.AddEntry(g, tags_name[k], "l")
g_list.append(g)
drawn_graphs[i] = g_list
if len(g_list) <= 0:
print("Nothing drawn!")
continue
drawn.append(latex.DrawLatexNDC(0.9, 0.9, p[i]))
draw_study_label(.4, .91)
gPad.Update()
canvas.cd(counter)
clone = c.DrawClonePad()
if counter != 1:
l = gPad.GetListOfPrimitives()
for i in l:
cn = i.ClassName()
if cn == "TLegend":
l.Remove(i)
elif cn == "TLatex":
if "ALICE" in i.GetTitle():
l.Remove(i)
drawn.append(clone)
c.SaveAs(f"/tmp/{c.GetName()}.png")
gPad.Update()
counter += 1
canvas_all_species = None
if len(tags) == 1 or styles is not None:
canvas_all_species = "all_spec_" + canvas.GetName()
canvas_all_species = TCanvas(canvas_all_species, canvas_all_species,
800, 800)
drawn.append(canvas_all_species)
canvas_all_species.cd()
drawn_graphs_all_spec = {}
leg_all_spec = TLegend(*leg_pos)
leg_all_spec.SetNColumns(2)
leg_all_spec.SetLineColor(0)
drawn.append(leg_all_spec)
for i in drawn_graphs:
if canvas_all_species.GetListOfPrimitives().GetEntries() == 0:
drawn_frames[i].Draw()
leg_all_spec.Draw()
drawn_graphs_all_spec[i] = []
for k, g in enumerate(drawn_graphs[i]):
g = g.Clone()
drawn_graphs_all_spec[i].append(g)
g.SetName(g.GetName() + "_color")
g.SetLineColor(TColor.GetColor(p_colors[i]))
if styles is not None:
g.SetLineStyle(styles[k])
g.Draw("same")
if k == 0:
leg_all_spec.AddEntry(g, p[i], "L")
if styles is not None:
for j, k in enumerate(tags_name):
g = TGraph()
g.SetLineWidth(3)
g.SetLineColor(1)
g.SetLineStyle(styles[j])
leg_all_spec.AddEntry(g, k, "L")
drawn_graphs_all_spec[i].append(g)
for i in drawn_graphs_all_spec:
drawn_graphs[i + "_allspec"] = drawn_graphs_all_spec[i]
if add_alice3_label:
draw_study_label(.2, .91)
latex = TLatex()
latex.SetTextAlign(13)
latex.SetTextSize(0.04)
if tags_name is not None and styles is None:
drawn.append(latex.DrawLatexNDC(0.5, 0.80, tags_name[0]))
if "vs_eta" in reader_name:
drawn.append(
latex.DrawLatexNDC(
0.42, 0.82, "#splitline{" + "#it{p}_{T}" +
" = {:.1f} GeV/c".format(ind_var) +
" dN_{Ch}/d#eta =" + f" {int(dnch_deta)}" + "}" +
("{R_{min} = " + rmin +
"}" if rmin is not None else "")))
else:
# drawn.append(latex.DrawLatexNDC(0.55, 0.82, "#splitline{" +
drawn.append(
latex.DrawLatexNDC(
0.55, 0.45, "#splitline{" + f"#eta = {int(ind_var)}" +
" dN_{Ch}/d#eta =" + f" {int(dnch_deta)}" + "}" +
("{R_{min} = " + rmin +
"}" if rmin is not None else "")))
adjust_pad()
canvas_all_species.Update()
canvas_all_species.SaveAs(f"/tmp/{canvas_all_species.GetName()}.png")
canvas_all_species.SaveAs(f"/tmp/{canvas_all_species.GetName()}.pdf")
canvas.SaveAs(f"/tmp/lut_{canvas.GetName()}.root")
if save is not None:
print("Saving to", save)
fo = TFile(save, "RECREATE")
fo.cd()
for i in drawn_graphs:
for j in drawn_graphs[i]:
j.Write(j.GetName().split("/")[-1])
if not background:
input("Done, press enter to continue")
def plot2BodyDist(theFitter, pars, chi2, ndf,
Err = -1, NP = False, Prefix = "Mjj", Left = False):
from ROOT import gPad, TLatex, TCanvas, kRed, kCyan, kBlue, \
RooFit, RooPlot, RooCurve, RooAbsReal, TGraphErrors, TLine, \
RooWjjMjjFitter
if pars.includeMuons and pars.includeElectrons:
modeString = ''
elif pars.includeMuons:
modeString = 'Muon'
elif pars.includeElectrons:
modeString = 'Electron'
else:
modeString = ''
mf = theFitter.stackedPlot(False, RooWjjMjjFitter.mjj, Left)
mf.SetName("%s_Stacked" % (Prefix));
sf = theFitter.residualPlot(mf, "h_background", "dibosonPdf", False)
sf.SetName("%s_Subtracted" % (Prefix));
pf = theFitter.residualPlot(mf, "h_total", "", True)
pf.SetName("%s_Pull" % (Prefix))
pf2 = pf.emptyClone("%s_Pull_Corrected" % (Prefix))
pf2.SetMinimum(-5.)
pf2.SetMaximum(5.)
corrPull = False
lf = theFitter.stackedPlot(True, RooWjjMjjFitter.mjj, Left)
lf.SetName("%s_Stacked_Log" % (Prefix));
if Err > 0:
totalPdf = theFitter.getWorkSpace().pdf('totalPdf')
## Ntotal = totalPdf.expectedEvents(iset)
## print 'Ntotal:',Ntotal
h_dibosonPdf = sf.getCurve('h_dibosonPdf')
totalPdf.plotOn(sf,
RooFit.ProjWData(theFitter.getWorkSpace().data('data')),
RooFit.Normalization(Err, RooAbsReal.Raw),
#RooFit.AddTo('h_dibosonPdf', 1., 1.),
#RooFit.Invisible(),
RooFit.Name('h_ErrUp'),
RooFit.Range('RangeForPlot'),
RooFit.NormRange('RangeForPlot'),
RooFit.LineColor(kRed), RooFit.LineStyle(3))
h_ErrUp = sf.getCurve('h_ErrUp')
sf.remove('h_ErrUp', False)
ErrBand = TGraphErrors(h_dibosonPdf.GetN(), h_dibosonPdf.GetX(),
h_dibosonPdf.GetY())
for pt in range(1, ErrBand.GetN()):
ErrBand.SetPointError(pt, 0,
h_ErrUp.interpolate(ErrBand.GetX()[pt]))
ErrBand.SetName("ErrBand")
ErrBand.SetTitle("Uncertainty")
ErrBand.SetLineColor(kRed)
## ErrBand.SetLineWidth(0)
## ErrBand.SetLineStyle(0)
ErrBand.SetFillColor(kRed)
ErrBand.SetFillStyle(3353)
#ErrBand.Draw('ap3')
#h_ErrUp.Draw('lp')
#gPad.Update()
#gPad.WaitPrimitive()
## h_ErrUp.Draw("al")
## h_ErrUp.GetXaxis().Set(36, 40., 400.)
## gPad.Update()
## gPad.WaitPrimitive()
## h_UpBand = RooCurve("h_UpBand", "Uncertainty", h_dibosonPdf, h_ErrUp,
## 1., 1.)
## h_UpBand.SetLineStyle(3)
## h_UpBand.SetLineColor(kBlue+1)
## h_DownBand = RooCurve("h_DownBand", "Uncertainty", h_dibosonPdf, h_ErrUp,
## 1., -1.)
## h_DownBand.SetLineStyle(3)
## h_DownBand.SetLineColor(kBlue+1)
## sf.addPlotable(h_UpBand, "L")
## sf.addPlotable(h_DownBand, "L")
sf.addObject(ErrBand, "3")
#sf.Print("v")
sf.drawAfter('h_dibosonPdf', 'ErrBand')
#sf.Print("v")
sf.drawAfter('ErrBand', 'theData')
#sf.Print("v")
sf.findObject('theLegend').AddEntry(ErrBand, 'Uncertainty', 'f')
sf.findObject('theLegend').SetY1NDC(sf.findObject('theLegend').GetY1NDC() - 0.057)
sf.findObject('theLegend').SetY1(sf.findObject('theLegend').GetY1NDC())
corrPull = True
pf2.addObject(sub2pull(sf.getHist('theData'),
sf.findObject('ErrBand')),
'p0')
for item in range(0, int(pf.numItems())):
firstItem = pf.getObject(item)
if (type(firstItem) == TLine):
newLine = TLine(firstItem)
newLine.SetY1(4.)
newLine.SetY2(-4.)
pf2.addObject(newLine, 'l')
#SetOwnership(newLine, False)
if NP:
NPPdf = theFitter.makeNPPdf();
NPNorm = 4.*0.11*46.8/12.*pars.intLumi
if (modeString == 'Electron'):
if pars.njets == 2:
NPNorm *= 0.0381
elif pars.njets == 3:
NPNorm *= 0.0123
else:
if pars.njets == 2:
NPNorm *= 0.0550
elif pars.njets == 3:
NPNorm *= 0.0176
print '**** N_NP:', NPNorm,'****'
NPPdf.plotOn(sf, RooFit.ProjWData(theFitter.getWorkSpace().data('data')),
RooFit.Normalization(NPNorm, RooAbsReal.Raw),
RooFit.AddTo('h_dibosonPdf', 1., 1.),
RooFit.Name('h_NP'),
RooFit.Range('RangeForPlot'),
RooFit.NormRange('RangeForPlot'),
RooFit.LineColor(kBlue), RooFit.LineStyle(2))
h_NP = sf.getCurve('h_NP')
sf.drawBefore('h_dibosonPdf', 'h_NP')
#sf.Print("v")
sf.findObject('theLegend').AddEntry(h_NP, "CDF-like Signal", "L")
sf.findObject('theLegend').SetY1NDC(sf.findObject('theLegend').GetY1NDC() - 0.057)
sf.findObject('theLegend').SetY1(sf.findObject('theLegend').GetY1NDC())
l = TLatex()
l.SetNDC()
l.SetTextSize(0.045)
l.SetTextFont(42)
cstacked = TCanvas("cstacked", "stacked")
mf.Draw()
if (chi2 > 0):
l.DrawLatex(0.55, 0.49,
'#chi^{2}/dof = %0.3f/%d' % (chi2, ndf)
)
pyroot_logon.cmsLabel(cstacked, pars.intLumi/1000, prelim = True)
cstacked.Print('Wjj_%s_%s_%ijets_Stacked.pdf' % (Prefix, modeString,
pars.njets))
cstacked.Print('Wjj_%s_%s_%ijets_Stacked.png' % (Prefix, modeString,
pars.njets))
c2 = TCanvas("c2", "stacked_log")
c2.SetLogy()
lf.Draw()
pyroot_logon.cmsPrelim(c2, pars.intLumi/1000)
c2.Print('Wjj_%s_%s_%ijets_Stacked_log.pdf' % (Prefix, modeString,
pars.njets))
c2.Print('Wjj_%s_%s_%ijets_Stacked_log.png' % (Prefix, modeString,
pars.njets))
c3 = TCanvas("c3", "subtracted")
sf.Draw()
pyroot_logon.cmsLabel(c3, pars.intLumi/1000, prelim = True)
c3.Print('Wjj_%s_%s_%ijets_Subtracted.pdf' % (Prefix, modeString,
pars.njets))
c3.Print('Wjj_%s_%s_%ijets_Subtracted.png' % (Prefix, modeString,
pars.njets))
c4 = TCanvas("c4", "pull")
pf.Draw()
c4.SetGridy()
pyroot_logon.cmsPrelim(c4, pars.intLumi/1000)
c4.Print('Wjj_%s_%s_%ijets_Pull.pdf' % (Prefix, modeString, pars.njets))
c4.Print('Wjj_%s_%s_%ijets_Pull.png' % (Prefix, modeString, pars.njets))
c5 = None
if corrPull:
c5 = TCanvas("c5", "corrected pull")
pf2.Draw()
c5.SetGridy()
pyroot_logon.cmsPrelim(c5, pars.intLumi/1000)
c5.Print('Wjj_%s_%s_%ijets_Pull_Corrected.pdf' % (Prefix, modeString,
pars.njets))
c5.Print('Wjj_%s_%s_%ijets_Pull_Corrected.png' % (Prefix, modeString,
pars.njets))
return ([mf,sf,pf2,lf],[cstacked,c2,c3,c5])
def main():
gStyle.SetOptStat(0)
c1 = TCanvas('c1', 'c1', 3)
gPad.SetTickx()
gPad.SetTicky()
c1.SetLeftMargin(0.12)
h_frame = TH1F('frame', '', 50, 0, 1000)
h_frame.SetXTitle('P^{miss}_{T} (GeV)')
h_frame.SetYTitle('Arbitrary units')
h_frame.SetMaximum(0.4)
h_higgsPt = TH1F('h_higgsPt', 'h_higgsPt', 50, 0, 1000)
h_higgsPtList = []
for i in range(6):
h_higgsPtList.append(TH1F(histName_zp2HDM[i], '', 50, 0, 1000))
h_higgsPtList[i].SetLineWidth(2)
h_higgsPt_BarList = []
for i in range(6):
h_higgsPt_BarList.append(TH1F(histName_bar[i], '', 40, 0, 800))
h_higgsPt_BarList[i].SetLineWidth(2)
## test code
'''
ivVectList = getPtList(hApath)
for fourV in ivVectList:
h_higgsPt.Fill(fourV.pt)
'''
## loop all combination
leg = TLegend(0.32, 0.57, 0.87, 0.87)
leg.SetBorderSize(0)
for i in range(3):
ivVectList = getPtList(hApathList[i])
h_higgsPtList[i].SetLineColor(87 + 4 * i)
leg.AddEntry(h_higgsPtList[i], legtext[i])
for fourV in ivVectList:
h_higgsPtList[i].Fill(fourV.pt)
for i in range(3):
ivVectList = getPtList(hApathList_5[i])
h_higgsPtList[i + 3].SetLineColor(61 + 4 * i)
leg.AddEntry(h_higgsPtList[i + 3], legtext_5[i])
for fourV in ivVectList:
h_higgsPtList[i + 3].Fill(fourV.pt)
h_frame.Draw('hist')
#h_higgsPt.Draw('histsame')
for i in range(6):
h_higgsPtList[i].DrawNormalized('histsame')
leg.Draw()
c1.Print('Zp2HDM_higgsPt.pdf')
colorList = [95, 91, 87, 61, 65, 69]
#colorList = [61,95,65,91,69,87]
## Baryonic
info_bar = TLatex(0, 0.255, 'CMS')
info_bar_2 = TLatex(750, 0.255, '35.9 fb^{-1} (13 TeV)')
info_bar.SetTextSize(0.03)
info_bar_2.SetTextSize(0.03)
info_bar_2.SetTextAlign(31)
h_frame.SetMaximum(0.25)
h_frame.SetAxisRange(0., 750., "X")
leg.Clear()
for i in range(6):
ivVectList = getPtList('BaryonicFile/' + hApath_barList[i])
h_higgsPt_BarList[i].SetLineColor(colorList[i])
#h_higgsPt_BarList[i].SetLineColor(90-6*i)
leg.AddEntry(h_higgsPt_BarList[i], legtext_bar[i])
for fourV in ivVectList:
h_higgsPt_BarList[i].Fill(fourV.pt)
h_frame.Draw('hist')
for i in range(5, -1, -1):
h_higgsPt_BarList[i].DrawNormalized('histsame')
leg.Draw()
info_bar.Draw()
info_bar_2.Draw()
c1.Print('Baryonic_higgsPt.pdf')
f = TFile('rootFile/Zp2HDM_missPt.root', 'recreate')
for i in range(6):
h_higgsPtList[i].SetLineColor(1)
h_higgsPtList[i].Write()
f.Close()
f = TFile('rootFile/BaryonicZp_missPt.root', 'recreate')
for i in range(6):
h_higgsPt_BarList[i].SetLineColor(1)
h_higgsPt_BarList[i].Write()
f.Close()
def plot(dictHist, option, fitFunction):
c = TCanvas("c", "c", 600, 300)
legend = TLegend(0.12, 0.18, 0.35, 0.85)
legend.AddEntry(None, "#bf{mean[GeV] / sigma[GeV] / ratio}", "")
color = 1
hs = THStack("hs", "corrections;mbb [GeV];Number of events;")
drawOption = "" #Draw Option for THStack
for key in dictHist:
hist = dictHist[key].Clone()
hist.SetLineColor(color) # Setting color for the histogram
# print 'Current Hist Title: ',hist.GetTitle()
# print 'Color Option: ',color
entries = hist.GetEntries()
height = hist.GetMaximum()
mean = hist.GetMean()
rms = hist.GetRMS()
xmin = mean - 3 * rms
xmax = mean + 3 * rms
#Getting Function of Choice
if (fitFunction == 'gauss'):
f = setGauss(mean=mean,
rms=rms,
height=height,
xmin=xmin,
xmax=xmax)
elif (fitFunction == 'bukin'):
f = setBukin(mean=mean,
rms=rms,
height=height,
xmin=xmin,
xmax=xmax)
else:
print "Name of the function was not recognized! Selection can be either 'gauss' or 'bukin'."
assert (False)
########### start code specific to the histo and/or fit choice ##################
if option == "histo":
hs.Add(hist)
result = (height, mean, rms)
drawOption = "nostack"
elif option == "histo+fit":
hist.Fit(fitFunction, "RQ", "HISTS", xmin, xmax)
f = hist.GetFunction(fitFunction)
if (fitFunction == 'gauss'):
result = (f.GetParameter(0), f.GetParameter(1),
f.GetParameter(2))
else:
result = (f.GetParameter(0), f.GetParameter(1),
f.GetParameter(2), f.GetParameter(3),
f.GetParameter(4), f.GetParameter(5))
f.SetLineColor(color)
hs.Add(hist)
drawOption = "nostack"
elif option == "fit":
hist.Fit(fitFunction, "RQ", "RO", xmin, xmax)
f = hist.GetFunction(fitFunction)
if (fitFunction == 'gauss'):
result = (f.GetParameter(0), f.GetParameter(1),
f.GetParameter(2))
else:
result = (f.GetParameter(0), f.GetParameter(1),
f.GetParameter(2), f.GetParameter(3),
f.GetParameter(4), f.GetParameter(5))
f.SetLineColor(color)
hs.Add(hist)
drawOption = "nostackRO"
else:
print "option", option, "not known"
assert (False)
########## end code specific to the histo and/or fit choice ##################
# https://root.cern.ch/doc/master/classTLegend.html
legend.AddEntry(hist, hist.GetName(), "f")
legend_text = "#bf{%-.3f/%-.3f/%-.3f}" % (result[1], result[2],
ratio(result[2], result[1]))
legend.AddEntry(None, legend_text, "")
c.Update()
color += 1
if (color == 10): #10 would not be visible
color = 11
hs.Draw(drawOption)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetTextSize(0.025)
legend.SetTextColor(1)
legend.Draw('Same')
# https://root.cern.ch/doc/master/classTLatex.html#L3
t = TLatex()
t.SetNDC()
t.SetTextSize(10)
t.SetTextFont(43)
t.SetTextAlign(13)
t.DrawLatex(0.7, 0.85, "#it{#bf{ATLAS} Simulation Internal}")
t.Draw("Same")
c.Print("Higgs_" + option + ".pdf")
def CMS_lumi(pad, lumi_sqrtS="", iPosX=0):
outOfFrame = False
if iPosX / 10 == 0: outOfFrame = True
alignY_ = 3
alignX_ = 2
if iPosX / 10 == 0: alignX_ = 1
if iPosX == 0: alignY_ = 1
if iPosX / 10 == 1: alignX_ = 1
if iPosX / 10 == 2: alignX_ = 2
if iPosX / 10 == 3: alignX_ = 3
align_ = 10 * alignX_ + alignY_
H = pad.GetWh()
W = pad.GetWw()
l = pad.GetLeftMargin()
t = pad.GetTopMargin()
r = pad.GetRightMargin()
b = pad.GetBottomMargin()
e = 0.025
pad.cd()
lumiText = ""
if lumi_sqrtS == "":
lumiText += lumi_13TeV
lumiText += " (13 TeV)"
else:
lumiText += lumi_sqrtS
print lumiText
latex = TLatex()
latex.SetNDC()
latex.SetTextAngle(0)
latex.SetTextColor(kBlack)
extraTextSize = extraOverCmsTextSize * cmsTextSize
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(lumiTextSize * t)
latex.DrawLatex(1 - r, 1 - t + lumiTextOffset * t, lumiText)
if outOfFrame:
latex.SetTextFont(cmsTextFont)
latex.SetTextAlign(11)
latex.SetTextSize(cmsTextSize * t)
latex.DrawLatex(l, 1 - t + lumiTextOffset * t, cmsText)
pad.cd()
posX_ = 0
if iPosX % 10 <= 1:
posX_ = l + relPosX * (1 - l - r)
elif iPosX % 10 == 2:
posX_ = l + 0.5 * (1 - l - r)
elif iPosX % 10 == 3:
posX_ = 1 - r - relPosX * (1 - l - r)
posY_ = 1 - t - relPosY * (1 - t - b)
if not outOfFrame:
latex.SetTextFont(cmsTextFont)
latex.SetTextSize(cmsTextSize * t)
latex.SetTextAlign(align_)
latex.DrawLatex(posX_, posY_, cmsText)
if writeExtraText:
latex.SetTextFont(extraTextFont)
latex.SetTextAlign(align_)
latex.SetTextSize(extraTextSize * t)
latex.DrawLatex(posX_, posY_ - relExtraDY * cmsTextSize * t,
extraText)
elif writeExtraText:
if iPosX == 0:
posX_ = l + relPosX * (1 - l - r)
posY_ = 1 - t + lumiTextOffset * t
latex.SetTextFont(extraTextFont)
latex.SetTextSize(extraTextSize * t)
latex.SetTextAlign(align_)
latex.DrawLatex(posX_, posY_, extraText)
pad.Update()
def drawROIGeometry(geometry, is2016):
outfn = "ROILayout%s.pdf" % ("2016" if is2016 else "2015")
global box, c, h, leg
gROOT.Reset()
gStyle.SetOptStat(0)
c = TCanvas('c', "MuCTPi Geometry %s" % "2016" if is2016 else "2015", 1400,
950)
c.Draw()
#h = TH2F("h","Muon Geometry",10,-2.6,2.6,10,-6.4,6.4)
h = TH2F("h", "Muon Geometry %s" % "2016" if is2016 else "2015", 10, -2.6,
2.6, 10, -0.15, 6.4)
h.SetXTitle("#eta")
h.SetYTitle("#phi")
h.Draw()
box = TBox()
box.SetFillStyle(0)
box.SetLineColor(3)
box.SetLineColor(3)
text = TLatex()
text.SetTextSize(0.005)
text.SetTextFont(42)
text.SetTextAlign(22)
secLabel = TLatex()
secLabel.SetTextSize(0.008)
secLabel.SetTextFont(42)
secLabel.SetTextAlign(22)
leg = TLegend(0.7, 0.1, 0.9, 0.4)
leg.SetEntrySeparation(0.05)
leg.SetNColumns(2)
#for MIOCT in geometry.getMIOCTs():
for colorIndex, MioctID in enumerate(drawOrder):
MIOCT = geometry.getMIOCT(MioctID)
firstBox = True
color = colorMap[colorIndex % len(colorMap)]
#print "Using color ",color
box.SetLineColor(color)
box.SetLineWidth(1)
for Sector in MIOCT.Sectors:
ymin = 10
ymax = -10
for ROI in Sector.ROIs:
c1_x = float(ROI["etamin"])
c1_y = float(ROI["phimin"])
c2_x = float(ROI["etamax"])
c2_y = float(ROI["phimax"])
ymin = min(ymin, c1_y)
ymax = max(ymax, c2_y)
#print "eta [%f - %f], phi [%f - %f]" % (c1_x,c2_x,c1_y,c2_y)
b = box.DrawBox(c1_x, c1_y, c2_x, c2_y)
text.DrawText((c1_x + c2_x) / 2, (c1_y + c2_y) / 2,
ROI["roiid"])
if firstBox:
firstBox = False
leg.AddEntry(b, "Slot %s" % MIOCT["slot"], "l")
if Sector["name"].startswith("B"):
if int(Sector["name"][1:]) < 32:
xpos = -0.02
ypos = (ymin + ymax) / 2 - 0.05
else:
xpos = 0.02
ypos = (ymin + ymax) / 2 + 0.03
secLabel.DrawText(xpos, ypos, Sector["name"])
leg.Draw()
c.Update()
c.SaveAs(outfn)
def doULPlot(suffix):
xbins = array('d', [])
xbins_env = array('d', [])
ybins_exp = array('d', [])
ybins_obs = array('d', [])
ybins_1s = array('d', [])
ybins_2s = array('d', [])
ybins_th = array('d', [])
for i in range(len(mass)):
curFile = "higgsCombine_lim_%s%03d%s.Asymptotic.mH%03d.root" % (
options.signalmodel, mass[i], suffix, mass[i])
print "curFile: %s" % curFile
if options.lvqqBR:
sf = table_signalscale[mass[i]] * xsDict_lvj[mass[i]]
#*0.1057*0.577; # BR(W->munu)*BR(H->bb)
else:
sf = table_signalscale[mass[i]] * xsDict[mass[i]]
curAsymLimits = getAsymLimits(curFile)
#print mass[i]
#print curAsymLimits
#raw_input("zixu")
xbins.append(mass[i] / 1000.)
#GeV->TeV
xbins_env.append(mass[i] / 1000.)
ybins_exp.append(curAsymLimits[3] * sf)
ybins_obs.append(curAsymLimits[0] * sf)
ybins_2s.append(curAsymLimits[1] * sf)
ybins_1s.append(curAsymLimits[2] * sf)
#ybins_th.append(sf);#/20.); #*0.25);
if options.lvqqBR:
ybins_th.append(xsDict_lvj[mass[i]])
#/20.); #*0.25);
else:
ybins_th.append(xsDict[mass[i]])
#/20.); #*0.25);
for i in range(len(mass) - 1, -1, -1):
curFile = "higgsCombine_lim_%s%03d%s.Asymptotic.mH%03d.root" % (
options.signalmodel, mass[i], suffix, mass[i])
print "curFile: %s" % curFile
if options.lvqqBR:
sf = table_signalscale[mass[i]] * xsDict_lvj[mass[i]]
#*0.1057*0.577; # BR(W->munu)*BR(H->bb)
else:
sf = table_signalscale[mass[i]] * xsDict[mass[i]]
curAsymLimits = getAsymLimits(curFile)
xbins_env.append(mass[i] / 1000.)
ybins_2s.append(curAsymLimits[5] * sf)
ybins_1s.append(curAsymLimits[4] * sf)
nPoints = len(mass)
curGraph_exp = ROOT.TGraphAsymmErrors(nPoints, xbins, ybins_exp)
curGraph_obs = ROOT.TGraphAsymmErrors(nPoints, xbins, ybins_obs)
curGraph_th = ROOT.TGraph(nPoints, xbins, ybins_th)
curGraph_1s = ROOT.TGraphAsymmErrors(nPoints * 2, xbins_env, ybins_1s)
curGraph_2s = ROOT.TGraphAsymmErrors(nPoints * 2, xbins_env, ybins_2s)
curGraph_obs.SetMarkerStyle(20)
curGraph_obs.SetLineWidth(3)
curGraph_obs.SetLineStyle(1)
curGraph_obs.SetMarkerSize(1.6)
curGraph_exp.SetMarkerSize(1.3)
curGraph_exp.SetMarkerColor(ROOT.kBlack)
curGraph_exp.SetLineStyle(2)
curGraph_exp.SetLineWidth(3)
curGraph_exp.SetMarkerSize(2)
curGraph_exp.SetMarkerStyle(24)
curGraph_exp.SetMarkerColor(ROOT.kBlack)
#curGraph_th.SetLineStyle(ROOT.kDashed);
curGraph_th.SetFillStyle(3344)
curGraph_th.SetLineWidth(2)
curGraph_th.SetMarkerSize(2)
curGraph_th.SetLineColor(ROOT.kRed)
curGraph_1s.SetFillColor(ROOT.kGreen)
curGraph_1s.SetFillStyle(1001)
curGraph_1s.SetLineStyle(ROOT.kDashed)
curGraph_1s.SetLineWidth(3)
curGraph_2s.SetFillColor(ROOT.kYellow)
curGraph_2s.SetFillStyle(1001)
curGraph_2s.SetLineStyle(ROOT.kDashed)
curGraph_2s.SetLineWidth(3)
oneLine = ROOT.TF1("oneLine", "1", 799, 1001)
oneLine.SetLineColor(ROOT.kRed)
oneLine.SetLineWidth(3)
setStyle()
can_SM = ROOT.TCanvas("can_SM", "can_SM", 630, 600)
hrl_SM = can_SM.DrawFrame(mass[0] / 1000. - 0.01, 1e-4,
mass[nPoints - 1] / 1000. + 0.01, 1e2)
if options.signalmodel == "BulkGravWW":
if options.lvqqBR:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow G_{Bulk} #rightarrow WW #rightarrow l#nuqq') (pb)"
)
else:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow G_{Bulk} #rightarrow WW) (pb)")
elif options.signalmodel == "WprimeWZ":
if options.lvqqBR:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow W' #rightarrow WZ #rightarrow l#nuqq') (pb)"
)
else:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow W' #rightarrow WZ) (pb)")
elif options.signalmodel == "WprimeWZ-HVT-A":
if options.lvqqBR:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow W' #rightarrow WZ #rightarrow l#nuqq') (pb)"
)
else:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow W' #rightarrow WZ) (pb)")
hrl_SM.GetYaxis().SetTitleOffset(1.35)
hrl_SM.GetYaxis().SetTitleSize(0.045)
hrl_SM.GetYaxis().SetTitleFont(42)
if options.signalmodel == "BulkGravWW":
hrl_SM.GetXaxis().SetTitle("M_{G_{Bulk}} (TeV)")
elif options.signalmodel == "WprimeWZ":
hrl_SM.GetXaxis().SetTitle("M_{W'} (TeV)")
elif options.signalmodel == "WprimeWZ-HVT-A":
hrl_SM.GetXaxis().SetTitle("M_{W'} (TeV)")
hrl_SM.GetXaxis().SetTitleSize(0.045)
hrl_SM.GetXaxis().SetTitleFont(42)
hrl_SM.GetXaxis().SetNdivisions(510)
hrl_SM.GetYaxis().SetNdivisions(505)
can_SM.SetGridx(1)
can_SM.SetGridy(1)
curGraph_2s.Draw("F")
curGraph_1s.Draw("Fsame")
if options.keepblind == 0:
curGraph_obs.Draw("PLsame")
curGraph_exp.Draw("Lsame")
curGraph_th.Draw("Csame")
leg2 = ROOT.TLegend(0.32, 0.67, 0.9, 0.92)
leg2.SetFillColor(0)
leg2.SetShadowColor(0)
leg2.SetTextFont(42)
leg2.SetTextSize(0.035)
leg2.AddEntry(curGraph_1s, "Asympt. CL_{S} Expected #pm 1 s.d.", "LF")
leg2.AddEntry(curGraph_2s, "Asympt. CL_{S} Expected #pm 2 s.d.", "LF")
if options.signalmodel == "BulkGravWW":
if options.lvqqBR:
leg2.AddEntry(
curGraph_th,
"#sigma_{TH} #times BR(G_{Bulk}#rightarrow WW#rightarrow l#nuqq'), #tilde{k}=0.5",
"L")
else:
leg2.AddEntry(
curGraph_th,
"#sigma_{TH} #times BR(G_{Bulk}#rightarrow WW), #tilde{k}=0.5",
"L")
elif options.signalmodel == "WprimeWZ":
if options.lvqqBR:
leg2.AddEntry(
curGraph_th,
"#sigma_{TH} #times BR(W'_{HVT B}#rightarrow WZ#rightarrow l#nuqq')",
"L")
else:
leg2.AddEntry(curGraph_th,
"#sigma_{TH} #times BR(W'_{HVT B}#rightarrow WZ)",
"L")
elif options.signalmodel == "WprimeWZ-HVT-A":
if options.lvqqBR:
leg2.AddEntry(
curGraph_th,
"#sigma_{TH} #times BR(W'_{HVT A}#rightarrow WZ#rightarrow l#nuqq')",
"L")
else:
leg2.AddEntry(curGraph_th,
"#sigma_{TH} #times BR(W'_{HVT A}#rightarrow WZ)",
"L")
leg2.AddEntry(curGraph_obs, "Asympt. CL_{S} Observed", "LP")
#ROOT.gPad.SetLogx();
#hrl_SM.GetXaxis().SetMoreLogLabels()
#hrl_SM.GetXaxis().SetNoExponent()
ROOT.gPad.SetLogy()
can_SM.Update()
can_SM.RedrawAxis()
can_SM.RedrawAxis("g")
can_SM.Update()
leg2.Draw()
line = TLine(1.1, 1e-4, 1.1, 1e0)
line.SetLineWidth(2)
line.SetLineColor(kBlack)
line.SetLineStyle(9)
line.Draw()
banner = TLatex(0.95, 0.96, "%s fb^{-1} (13 TeV)" % (Lumi))
banner.SetNDC()
banner.SetTextSize(0.038)
banner.SetTextFont(42)
banner.SetTextAlign(31)
banner.SetLineWidth(2)
banner.Draw()
CMStext = TLatex(0.15, 0.96, "CMS")
CMStext.SetNDC()
CMStext.SetTextSize(0.041)
CMStext.SetTextFont(61)
CMStext.SetTextAlign(11)
CMStext.SetLineWidth(2)
CMStext.Draw()
if suffix == "_el_HP":
Extratext = TLatex(0.241, 0.96, "Preliminary W#rightarrow e#nu")
Extratext.SetNDC()
Extratext.SetTextSize(0.032)
Extratext.SetTextFont(52)
Extratext.SetTextAlign(11)
Extratext.SetLineWidth(2)
Extratext.Draw()
elif suffix == "_mu_HP":
Extratext = TLatex(0.241, 0.96, "Preliminary W#rightarrow #mu#nu")
Extratext.SetNDC()
Extratext.SetTextSize(0.032)
Extratext.SetTextFont(52)
Extratext.SetTextAlign(11)
Extratext.SetLineWidth(2)
Extratext.Draw()
elif suffix == "_em_HP":
Extratext = TLatex(0.241, 0.96, "Preliminary W#rightarrow l#nu")
Extratext.SetNDC()
Extratext.SetTextSize(0.032)
Extratext.SetTextFont(52)
Extratext.SetTextAlign(11)
Extratext.SetLineWidth(2)
Extratext.Draw()
#CMS_lumi.lumi_13TeV = "%s fb^{-1}"%(Lumi)
#CMS_lumi.writeExtraText = 1
#CMS_lumi.extraText = "Preliminary"
#iPos = 11
#if(iPos == 0):
# CMS_lumi.relPosX = 0.15
#CMS_lumi.CMS_lumi(can_SM, 4, 11)
os.system("mkdir -p %s/LimitResult/" % (os.getcwd()))
os.system("mkdir -p %s/LimitResult/Limit_%s_sys%s/" %
(os.getcwd(), options.signalmodel, options.Sys))
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s.png" %
(options.signalmodel, options.Sys, suffix))
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s.pdf" %
(options.signalmodel, options.Sys, suffix))
#can_SM.SaveAs("./LimitResult/Limit_sys%s/Lim%s.root"%(options.Sys, suffix));
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s.C" %
(options.signalmodel, options.Sys, suffix))
ROOT.gPad.SetLogx()
hrl_SM.GetXaxis().SetMoreLogLabels()
hrl_SM.GetXaxis().SetNoExponent()
can_SM.Update()
can_SM.RedrawAxis()
can_SM.RedrawAxis("g")
leg2.Draw()
can_SM.Update()
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s_Logx.png" %
(options.signalmodel, options.Sys, suffix))
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s_Logx.pdf" %
(options.signalmodel, options.Sys, suffix))
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s_Logx.C" %
(options.signalmodel, options.Sys, suffix))
def plot_2D_FakeCR():
myfilename = (
"/coepp/cephfs/mel/mmilesi/ttH/MergedDatasets/Merged_v030/Merged_Melb15_ttH_030_DxAOD_p2559/tops/410000.PowhegPythiaEvtGen_P2012_ttbar_hdamp172p5_nonallhad.root",
"/coepp/cephfs/mel/mmilesi/ttH/MiniNTup/25ns_v7/25ns_v7/tops/410000.PowhegPythiaEvtGen_P2012_ttbar_hdamp172p5_nonallhad.root"
)[bool(useGroupNTup)]
#myfilename = ("/coepp/cephfs/mel/mmilesi/ttH/MergedDatasets/Merged_v031/Merged_Melb15_ttH_031-01_DxAOD_p2559/tops/410000.PowhegPythiaEvtGen_P2012_ttbar_hdamp172p5_nonallhad.root","TO_BE_ADDED")[bool(useGroupNTup)]
myfile = TFile(myfilename)
mytree = gDirectory.Get("physics")
gROOT.LoadMacro(
"$ROOTCOREBIN/user_scripts/HTopMultilepAnalysis/ROOT_TTreeFormulas/largeEtaEvent.cxx+"
)
from ROOT import largeEtaEvent
mytree_weight = "3.209 * mcEventWeight * weight_pileup * weight_lepton_trig_HTop[0] * weight_lepton_reco_HTop[0] * weight_lepton_iso_HTop[0] * weight_lepton_ID_HTop[0] * weight_lepton_TTVA_HTop[0] * weight_jet_JVT_HTop[0] * weight_jet_MV2c20_SFFix77[0]"
trig_dec = "( passHLT == 1 )"
trigmatching = "( ( lep_isTrigMatched[0] == 1 && ( ( lep_flavour[0] == 11 && lep_pt[0] > 25e3 ) || ( lep_flavour[0] == 13 && lep_pt[0] > 21e3 ) ) ) || ( lep_isTrigMatched[1] == 1 && ( ( lep_flavour[1] == 11 && lep_pt[1] > 25e3 ) || ( lep_flavour[1] == 13 && lep_pt[1] > 21e3 ) ) ) )"
lep_tag_trigmatch = "( lep_tag_isTightSelected[0] == 1 && lep_tag_isTrigMatched[0] == 1 && ( ( lep_tag_flavour[0] == 11 && lep_tag_pt[0] > 25e3 ) || ( lep_tag_flavour[0] == 13 && lep_tag_pt[0] > 21e3 ) ) )"
el_eta = "( nel == 0 || ( nel > 0 && Max$( TMath::Abs(el_caloCluster_eta) ) < 1.37 ) )"
nbjets = "( njets_mv2c20_Fix77 > 0 )"
njets = "( njets > 1 && njets <= 4 )"
nleptons = "( nlep == 2 && Min$( lep_pt ) > 10e3 )"
flavour_cut = "( 1 )"
same_sign = "( isSS01 == 1 )"
tau_veto = "( ntau == 0 )"
zpeak_veto = "( ( nmuon == 1 && nel == 1 ) || ( TMath::Abs( mll01 - 91.187e3 ) > 7.5e3 ) )"
truth_cut = "( 1 )"
if useTruth:
# Select non-prompt or QMisID probe leptons
#
truth_cut = "( isMC==0 || ( isMC==1 && ( ( lep_probe_truthType[0] != 6 && lep_probe_truthType[0] != 2 ) || ( lep_probe_isChFlip[0] == 1 ) ) ) )"
if useGroupNTup:
mytree_weight = "3.209 * mcWeightOrg * pileupEventWeight_090 * weight_tag * weight_probe * JVT_EventWeight * MV2c20_77_EventWeight"
trig_dec = "( passEventCleaning == 1 && ( isMC == 1 && HLT_e24_lhmedium_L1EM18VH == 1 ) || ( isMC == 0 && HLT_e24_lhmedium_L1EM20VH == 1 ) || ( HLT_e60_lhmedium == 1 ) || ( HLT_e120_lhloose == 1 ) || ( HLT_mu20_iloose_L1MU15 == 1 ) || ( HLT_mu50 == 1 ) )"
trigmatching = "( ( lep_isTrigMatch_0 == 1 && ( ( TMath::Abs( lep_ID_0 ) == 11 && lep_Pt_0 > 25e3 ) || ( TMath::Abs( lep_ID_0 ) == 13 && lep_Pt_0 > 21e3 ) ) ) || ( lep_isTrigMatch_1 == 1 && ( ( TMath::Abs( lep_ID_1 ) == 11 && lep_Pt_1 > 25e3 ) || ( TMath::Abs( lep_ID_1 ) == 13 && lep_Pt_1 > 21e3 ) ) ) )"
lep_tag_trigmatch = "( lep_Tag_isTightSelected == 1 && lep_Tag_isTrigMatch == 1 && ( ( TMath::Abs( lep_Tag_ID ) == 11 && lep_Tag_Pt > 25e3 ) || ( TMath::Abs( lep_Tag_ID ) == 13 && lep_Tag_Pt > 21e3 ) ) )"
el_eta = "( largeEtaEvent( nelectrons,lep_ID_0,lep_ID_1,lep_EtaBE2_0,lep_EtaBE2_1 ) == 0 )"
nbjets = "( nJets_OR_MV2c20_77 > 0 )"
njets = "( nJets_OR > 1 && nJets_OR <= 4 )"
nleptons = "( nleptons == 2 && ( lep_Pt_0 > 10e3 && lep_Pt_1 > 10e3 ) )"
flavour_cut = "( 1 )"
same_sign = "( isSS01 == 1 )"
tau_veto = "( nTaus_OR_Pt25 == 0 )"
zpeak_veto = "( ( nmuons == 1 && nelectrons == 1 ) || ( TMath::Abs( Mll01 - 91.187e3 ) > 7.5e3 ) )"
truth_cut = "( 1 )"
if useTruth:
# Select non-prompt or QMisID probe leptons
#
truth_cut = "( isMC==0 || ( isMC==1 && lep_Probe_isPrompt != 1 ) )"
hist_list = []
basecut = "(" + trig_dec + " && " + trigmatching + " && " + lep_tag_trigmatch + " && " + el_eta + " && " + njets + " && " + nbjets + " && " + nleptons + " && " + flavour_cut + " && " + same_sign + " && " + tau_veto + " && " + zpeak_veto + " && " + truth_cut + " && "
varProbeType = ('lep_probe_truthType', 'TO_BE_ADDED')[bool(useGroupNTup)]
varProbeOrigin = ('lep_probe_truthOrigin',
'TO_BE_ADDED')[bool(useGroupNTup)]
varProbeChFlip = ('lep_probe_isChFlip',
'lep_Probe_isBremsElec')[bool(useGroupNTup)]
varTagType = ('lep_tag_truthType', 'TO_BE_ADDED')[bool(useGroupNTup)]
varTagOrigin = ('lep_tag_truthOrigin', 'TO_BE_ADDED')[bool(useGroupNTup)]
varTagChFlip = ('lep_tag_isChFlip',
'lep_Tag_isBremsElec')[bool(useGroupNTup)]
# ------------
# Muons
# ------------
sel_FAKE_MU_PROBE_T = basecut + "( ( isProbeMuEvent == 1 ) && ( muon_probe_isTightSelected[0] == 1 ) ) )"
sel_FAKE_MU_PROBE_L = basecut + "( ( isProbeMuEvent == 1 ) && ( muon_probe_isTightSelected[0] == 0 ) ) )"
if useGroupNTup:
sel_FAKE_MU_PROBE_T = basecut + "( ( TMath::Abs( lep_Probe_ID ) == 13 ) && ( lep_Probe_isTightSelected == 1 ) ) )"
sel_FAKE_MU_PROBE_L = basecut + "( ( TMath::Abs( lep_Probe_ID ) == 13 ) && ( lep_Probe_isTightSelected == 0 ) ) )"
print "sel_FAKE_MU_PROBE_T: \n", sel_FAKE_MU_PROBE_T
print "sel_FAKE_MU_PROBE_L: \n", sel_FAKE_MU_PROBE_L
h_FAKE_MU_PROBE_T_type_VS_origin = TH2D("FAKE_MU_PROBE_T_type_VS_origin",
"FAKE_MU_PROBE_T_type_VS_origin",
18, 0.0, 18.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_MU_PROBE_T_type_VS_origin)
h_FAKE_MU_PROBE_T_type_VS_origin.GetXaxis().SetTitle(
"Probe lep (T) truthType")
h_FAKE_MU_PROBE_T_type_VS_origin.GetYaxis().SetTitle(
"Probe lep (T) truthOrigin")
h_FAKE_MU_PROBE_L_type_VS_origin = TH2D("FAKE_MU_PROBE_L_type_VS_origin",
"FAKE_MU_PROBE_L_type_VS_origin",
18, 0.0, 18.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_MU_PROBE_L_type_VS_origin)
h_FAKE_MU_PROBE_L_type_VS_origin.GetXaxis().SetTitle(
"Probe lep (L!T) truthType")
h_FAKE_MU_PROBE_L_type_VS_origin.GetYaxis().SetTitle(
"Probe lep (L!T) truthOrigin")
mytree.Project("FAKE_MU_PROBE_T_type_VS_origin",
varProbeOrigin + ":" + varProbeType,
"%s * (%s)" % (mytree_weight, sel_FAKE_MU_PROBE_T), "text")
mytree.Project("FAKE_MU_PROBE_L_type_VS_origin",
varProbeOrigin + ":" + varProbeType,
"%s * (%s)" % (mytree_weight, sel_FAKE_MU_PROBE_L), "text")
# Normalise to unity
#
if doNorm:
if h_FAKE_MU_PROBE_T_type_VS_origin.Integral() > 0:
h_FAKE_MU_PROBE_T_type_VS_origin.Scale(
1.0 / h_FAKE_MU_PROBE_T_type_VS_origin.Integral())
if h_FAKE_MU_PROBE_L_type_VS_origin.Integral() > 0:
h_FAKE_MU_PROBE_L_type_VS_origin.Scale(
1.0 / h_FAKE_MU_PROBE_L_type_VS_origin.Integral())
# -----------------------------------------
h_FAKE_MU_TAG_T_type_VS_origin = TH2D("FAKE_MU_TAG_T_type_VS_origin",
"FAKE_MU_TAG_T_type_VS_origin", 18,
0.0, 18.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_MU_TAG_T_type_VS_origin)
h_FAKE_MU_TAG_T_type_VS_origin.GetXaxis().SetTitle(
"Tag lep ( Probe (T) ) truthType")
h_FAKE_MU_TAG_T_type_VS_origin.GetYaxis().SetTitle(
"Tag lep ( Probe (T) ) truthOrigin")
h_FAKE_MU_TAG_L_type_VS_origin = TH2D("FAKE_MU_TAG_L_type_VS_origin",
"FAKE_MU_TAG_L_type_VS_origin", 18,
0.0, 18.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_MU_TAG_L_type_VS_origin)
h_FAKE_MU_TAG_L_type_VS_origin.GetXaxis().SetTitle(
"Tag lep ( Probe (L!T) ) truthType")
h_FAKE_MU_TAG_L_type_VS_origin.GetYaxis().SetTitle(
"Tag lep ( Probe (L!T) ) truthOrigin")
mytree.Project("FAKE_MU_TAG_T_type_VS_origin",
varTagOrigin + ":" + varTagType,
"%s * (%s)" % (mytree_weight, sel_FAKE_MU_PROBE_T), "text")
mytree.Project("FAKE_MU_TAG_L_type_VS_origin",
varTagOrigin + ":" + varTagType,
"%s * (%s)" % (mytree_weight, sel_FAKE_MU_PROBE_L), "text")
# Normalise to unity
#
if doNorm:
if h_FAKE_MU_TAG_T_type_VS_origin.Integral() > 0:
h_FAKE_MU_TAG_T_type_VS_origin.Scale(
1.0 / h_FAKE_MU_TAG_T_type_VS_origin.Integral())
if h_FAKE_MU_TAG_L_type_VS_origin.Integral() > 0:
h_FAKE_MU_TAG_L_type_VS_origin.Scale(
1.0 / h_FAKE_MU_TAG_L_type_VS_origin.Integral())
#--------------------------------------------------------------------------------
# probe truthOrigin VS tag truthOrigin
"""
h_FAKE_MU_PROBE_T_ProbeOrigin_VS_TagOrigin = TH2D("FAKE_MU_PROBE_T_ProbeOrigin_VS_TagOrigin", "FAKE_MU_PROBE_T_ProbeOrigin_VS_TagOrigin", 40, 0.0, 40.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_MU_PROBE_T_ProbeOrigin_VS_TagOrigin)
h_FAKE_MU_PROBE_T_ProbeOrigin_VS_TagOrigin.GetXaxis().SetTitle("Tag lep (T) truthOrigin")
h_FAKE_MU_PROBE_T_ProbeOrigin_VS_TagOrigin.GetYaxis().SetTitle("Probe lep (T) truthOrigin")
h_FAKE_MU_PROBE_L_ProbeOrigin_VS_TagOrigin = TH2D("FAKE_MU_PROBE_L_ProbeOrigin_VS_TagOrigin", "FAKE_MU_PROBE_L_ProbeOrigin_VS_TagOrigin", 40, 0.0, 40.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_MU_PROBE_L_ProbeOrigin_VS_TagOrigin)
h_FAKE_MU_PROBE_L_ProbeOrigin_VS_TagOrigin.GetXaxis().SetTitle("Tag lep (L!T) truthOrigin")
h_FAKE_MU_PROBE_L_ProbeOrigin_VS_TagOrigin.GetYaxis().SetTitle("Probe lep (L!T) truthOrigin")
mytree.Project("FAKE_MU_PROBE_T_ProbeOrigin_VS_TagOrigin", varProbeOrigin + ":" + varTagOrigin, "%s * (%s)" %(mytree_weight, sel_FAKE_MU_PROBE_T), "text" )
mytree.Project("FAKE_MU_PROBE_L_ProbeOrigin_VS_TagOrigin", varProbeOrigin + ":" + varTagOrigin, "%s * (%s)" %(mytree_weight, sel_FAKE_MU_PROBE_L), "text" )
h_FAKE_MU_PROBE_T_ProbeOrigin_VS_TagOrigin.Scale(1.0/h_FAKE_MU_PROBE_T_ProbeOrigin_VS_TagOrigin.Integral())
h_FAKE_MU_PROBE_L_ProbeOrigin_VS_TagOrigin.Scale(1.0/h_FAKE_MU_PROBE_L_ProbeOrigin_VS_TagOrigin.Integral())
"""
# ------------
# Electrons
# ------------
sel_FAKE_EL_PROBE_T = basecut + "( ( isProbeElEvent == 1 ) && ( el_probe_isTightSelected[0] == 1 ) ) )"
sel_FAKE_EL_PROBE_L = basecut + "( ( isProbeElEvent == 1 ) && ( el_probe_isTightSelected[0] == 0 ) ) )"
if useGroupNTup:
sel_FAKE_EL_PROBE_T = basecut + "( ( TMath::Abs( lep_Probe_ID ) == 11 ) && ( lep_Probe_isTightSelected == 1 ) ) )"
sel_FAKE_EL_PROBE_L = basecut + "( ( TMath::Abs( lep_Probe_ID ) == 11 ) && ( lep_Probe_isTightSelected == 0 ) ) )"
print "sel_FAKE_EL_PROBE_T: \n", sel_FAKE_EL_PROBE_T
print "sel_FAKE_EL_PROBE_L: \n", sel_FAKE_EL_PROBE_L
h_FAKE_EL_PROBE_T_type_VS_origin = TH2D("FAKE_EL_PROBE_T_type_VS_origin",
"FAKE_EL_PROBE_T_type_VS_origin",
18, 0.0, 18.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_EL_PROBE_T_type_VS_origin)
h_FAKE_EL_PROBE_T_type_VS_origin.GetXaxis().SetTitle(
"Probe lep (T) truthType")
h_FAKE_EL_PROBE_T_type_VS_origin.GetYaxis().SetTitle(
"Probe lep (T) truthOrigin")
h_FAKE_EL_PROBE_L_type_VS_origin = TH2D("FAKE_EL_PROBE_L_type_VS_origin",
"FAKE_EL_PROBE_L_type_VS_origin",
18, 0.0, 18.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_EL_PROBE_L_type_VS_origin)
h_FAKE_EL_PROBE_L_type_VS_origin.GetXaxis().SetTitle(
"Probe lep (L!T) truthType")
h_FAKE_EL_PROBE_L_type_VS_origin.GetYaxis().SetTitle(
"Probe lep (L!T) truthOrigin")
h_FAKE_EL_PROBE_T_isChFlip = TH1D("FAKE_EL_PROBE_T_isChFlip",
"FAKE_EL_PROBE_T_isChFlip", 2, -0.5, 1.5)
hist_list.append(h_FAKE_EL_PROBE_T_isChFlip)
h_FAKE_EL_PROBE_T_isChFlip.GetXaxis().SetTitle("Probe lep (T) isChFlip")
h_FAKE_EL_PROBE_L_isChFlip = TH1D("FAKE_EL_PROBE_L_isChFlip",
"FAKE_EL_PROBE_L_isChFlip", 2, -0.5, 1.5)
hist_list.append(h_FAKE_EL_PROBE_L_isChFlip)
h_FAKE_EL_PROBE_L_isChFlip.GetXaxis().SetTitle("Probe lep (L!T) isChFlip")
mytree.Project("FAKE_EL_PROBE_T_type_VS_origin",
varProbeOrigin + ":" + varProbeType,
"%s * (%s)" % (mytree_weight, sel_FAKE_EL_PROBE_T), "text")
mytree.Project("FAKE_EL_PROBE_L_type_VS_origin",
varProbeOrigin + ":" + varProbeType,
"%s * (%s)" % (mytree_weight, sel_FAKE_EL_PROBE_L), "text")
#mytree.Project("FAKE_EL_PROBE_T_isChFlip", varProbeChFlip, "%s * (%s)" %(mytree_weight, sel_FAKE_EL_PROBE_T), "text" )
#mytree.Project("FAKE_EL_PROBE_L_isChFlip", varProbeChFlip, "%s * (%s)" %(mytree_weight, sel_FAKE_EL_PROBE_L), "text" )
# Normalise to unity
#
if doNorm:
if h_FAKE_EL_PROBE_T_type_VS_origin.Integral() > 0:
h_FAKE_EL_PROBE_T_type_VS_origin.Scale(
1.0 / h_FAKE_EL_PROBE_T_type_VS_origin.Integral())
if h_FAKE_EL_PROBE_L_type_VS_origin.Integral() > 0:
h_FAKE_EL_PROBE_L_type_VS_origin.Scale(
1.0 / h_FAKE_EL_PROBE_L_type_VS_origin.Integral())
if h_FAKE_EL_PROBE_T_isChFlip.Integral() > 0:
h_FAKE_EL_PROBE_T_isChFlip.Scale(
1.0 / h_FAKE_EL_PROBE_T_isChFlip.Integral())
if h_FAKE_EL_PROBE_L_isChFlip.Integral() > 0:
h_FAKE_EL_PROBE_L_isChFlip.Scale(
1.0 / h_FAKE_EL_PROBE_L_isChFlip.Integral())
# -----------------------------------------
h_FAKE_EL_TAG_T_type_VS_origin = TH2D("FAKE_EL_TAG_T_type_VS_origin",
"FAKE_EL_TAG_T_type_VS_origin", 18,
0.0, 18.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_EL_TAG_T_type_VS_origin)
h_FAKE_EL_TAG_T_type_VS_origin.GetXaxis().SetTitle(
"Tag lep ( Probe (T) ) truthType")
h_FAKE_EL_TAG_T_type_VS_origin.GetYaxis().SetTitle(
"Tag lep ( Probe (T) ) truthOrigin")
h_FAKE_EL_TAG_L_type_VS_origin = TH2D("FAKE_EL_TAG_L_type_VS_origin",
"FAKE_EL_TAG_L_type_VS_origin", 18,
0.0, 18.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_EL_TAG_L_type_VS_origin)
h_FAKE_EL_TAG_L_type_VS_origin.GetXaxis().SetTitle(
"Tag lep ( Probe (L!T) ) truthType")
h_FAKE_EL_TAG_L_type_VS_origin.GetYaxis().SetTitle(
"Tag lep ( Probe (L!T) ) truthOrigin")
mytree.Project("FAKE_EL_TAG_T_type_VS_origin",
varTagOrigin + ":" + varTagType,
"%s * (%s)" % (mytree_weight, sel_FAKE_EL_PROBE_T), "text")
mytree.Project("FAKE_EL_TAG_L_type_VS_origin",
varTagOrigin + ":" + varTagType,
"%s * (%s)" % (mytree_weight, sel_FAKE_EL_PROBE_L), "text")
# Normalise to unity
#
if doNorm:
if h_FAKE_EL_TAG_T_type_VS_origin.Integral() > 0:
h_FAKE_EL_TAG_T_type_VS_origin.Scale(
1 / h_FAKE_EL_TAG_T_type_VS_origin.Integral())
if h_FAKE_EL_TAG_L_type_VS_origin.Integral() > 0:
h_FAKE_EL_TAG_L_type_VS_origin.Scale(
1 / h_FAKE_EL_TAG_L_type_VS_origin.Integral())
#--------------------------------------------------------------------------------
# probe truthOrigin VS tag truthOrigin
"""
h_FAKE_EL_PROBE_T_ProbeOrigin_VS_TagOrigin = TH2D("FAKE_EL_PROBE_T_ProbeOrigin_VS_TagOrigin", "FAKE_EL_PROBE_T_ProbeOrigin_VS_TagOrigin", 40, 0.0, 40.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_EL_PROBE_T_ProbeOrigin_VS_TagOrigin)
h_FAKE_EL_PROBE_T_ProbeOrigin_VS_TagOrigin.GetXaxis().SetTitle("Tag lep (T) truthOrigin")
h_FAKE_EL_PROBE_T_ProbeOrigin_VS_TagOrigin.GetYaxis().SetTitle("Probe lep (T) truthOrigin")
h_FAKE_EL_PROBE_L_ProbeOrigin_VS_TagOrigin = TH2D("FAKE_EL_PROBE_L_ProbeOrigin_VS_TagOrigin", "FAKE_EL_PROBE_L_ProbeOrigin_VS_TagOrigin", 40, 0.0, 40.0, 40, 0.0, 40.0)
hist_list.append(h_FAKE_EL_PROBE_L_ProbeOrigin_VS_TagOrigin)
h_FAKE_EL_PROBE_L_ProbeOrigin_VS_TagOrigin.GetXaxis().SetTitle("Tag lep (L!T) truthOrigin")
h_FAKE_EL_PROBE_L_ProbeOrigin_VS_TagOrigin.GetYaxis().SetTitle("Probe lep (L!T) truthOrigin")
mytree.Project("FAKE_EL_PROBE_T_ProbeOrigin_VS_TagOrigin", varProbeOrigin + ":" + varTagOrigin, "%s * (%s)" %(mytree_weight, sel_FAKE_EL_PROBE_T), "text" )
mytree.Project("FAKE_EL_PROBE_L_ProbeOrigin_VS_TagOrigin", varProbeOrigin + ":" + varTagOrigin, "%s * (%s)" %(mytree_weight, sel_FAKE_EL_PROBE_L), "text" )
h_FAKE_EL_PROBE_T_ProbeOrigin_VS_TagOrigin.Scale(1.0/h_FAKE_EL_PROBE_T_ProbeOrigin_VS_TagOrigin.Integral())
h_FAKE_EL_PROBE_L_ProbeOrigin_VS_TagOrigin.Scale(1.0/h_FAKE_EL_PROBE_L_ProbeOrigin_VS_TagOrigin.Integral())
"""
# ------------------------------------------------------------------
set_fancy_2D_style()
gStyle.SetPaintTextFormat("2.1f")
for hist in hist_list:
#c = TCanvas("c1","Temp",50,50,700,900)
c = TCanvas("c1", "Temp", 50, 50, 800, 900)
legend = TLegend(0.2, 0.8, 0.7, 0.85)
# (x1,y1 (--> bottom left corner), x2, y2 (--> top right corner) )
legend.SetHeader("2 Lep SS Fake CR")
legend.SetBorderSize(0) # no border
legend.SetFillStyle(0)
# Legend transparent background
legend.SetTextSize(0.04) # Increase entry font size!
legend.SetTextFont(42) # Helvetica
leg_ATLAS = TLatex()
leg_lumi = TLatex()
leg_ATLAS.SetTextSize(0.03)
leg_ATLAS.SetNDC()
leg_lumi.SetTextSize(0.03)
leg_lumi.SetNDC()
hist.Draw("colz text")
legend.Draw()
leg_ATLAS.DrawLatex(0.2, 0.75, "#bf{#it{ATLAS}} Work In Progress")
leg_lumi.DrawLatex(0.2, 0.7,
"#sqrt{s} = 13 TeV, #int L dt = 3.2 fb^{-1}")
canvasname = (hist.GetName(),
hist.GetName() + "_TruthCut")[bool(useTruth)]
c.SaveAs(canvasname + ".png")
print ihist.GetName()
#ihist.SetMaximum(10000)
#ihist.GetYaxis().SetTitle("a.u")
#ihist.Scale(1/300000.)
if not legOnly:
ihist.Draw("same, hist")
xTitle = h_top.GetXaxis().GetTitle()
yTitle = h_top.GetYaxis().GetTitle()
setTitle(hs, xTitle, yTitle)
gPad.RedrawAxis()
ll = TLatex()
ll.SetNDC(kTRUE)
ll.SetTextSize(0.05)
ll.DrawLatex(0.63, 0.92, "3000 fb^{-1} (14 TeV)")
prel = TLatex()
prel.SetNDC(kTRUE)
prel.SetTextFont(52)
prel.SetTextSize(0.05)
prel.DrawLatex(0.18, 0.92, "Simulation")
cms = TLatex()
cms.SetNDC(kTRUE)
cms.SetTextFont(61)
cms.SetTextSize(0.05)
cms.DrawLatex(0.10, 0.92, "CMS")
if var != 'hTPrimeMRecoBoost' and not legOnly:
leg.Draw()
def drawEtaValues():
"""Function to draw the eta.
Function to draw the eta references on top of an already existing
TCanvas. The lines and labels drawn are collected inside a list and
the list is returned to the user to extend the live of the objects
contained, otherwise no lines and labels will be drawn, since they
will be garbage-collected as soon as this function returns.
"""
# Add eta labels
keep_alive = []
etas = [-3.4, -3.0, -2.8, -2.6, -2.4, -2.2,
-2.0, -1.8, -1.6, -1.4, -1.2, -1., -0.8, -0.6, -0.4, -0.2, 0., 0.2,
0.4, 0.6, 0.8, 1., 1.2, 1.4, 1.6, 1.8, 2., 2.2, 2.4, 2.6, 2.8, 3.0,
3.4]
etax = 2940.
etay = 1240.
lineL = 100.
offT = 10.
for ieta in etas:
th = 2*atan(exp(-ieta))
talign = 21
#IP
lineh = TLine(-20.,0.,20.,0.)
lineh.Draw()
linev = TLine(0.,-10.,0.,10.)
linev.Draw()
keep_alive.append(lineh)
keep_alive.append(linev)
x1 = 0
y1 = 0
if ieta>-1.6 and ieta<1.6:
x1 = etay/tan(th)
y1 = etay
elif ieta <=-1.6:
x1 = -etax
y1 = -etax*tan(th)
talign = 11
elif ieta>=1.6:
x1 = etax
y1 = etax*tan(th)
talign = 31
x2 = x1+lineL*cos(th)
y2 = y1+lineL*sin(th)
xt = x2
yt = y2+offT
line1 = TLine(x1,y1,x2,y2)
line1.Draw()
keep_alive.append(line1)
text = "%3.1f" % ieta
t1 = TLatex(xt, yt, '%s' % ('#eta = 0' if ieta == 0 else text))
t1.SetTextSize(0.03)
t1.SetTextAlign(talign)
t1.Draw()
keep_alive.append(t1)
return keep_alive
#c4.SetLogy(1)
#expected95.SetMinimum(0.0001);
expected95.SetMaximum(0.0057)
expected95.Draw("a3")
expected95.GetXaxis().SetTitle("Diphoton Mass [GeV/c^{2}]")
expected95.GetYaxis().SetTitle("#sigma(G_{RS} #rightarrow #gamma #gamma)[pb]")
expected68.Draw("3same")
expected_p.Draw("csame")
observed_p.Draw("cpsame")
theory.Draw("same")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextAlign(31) # align right
latex.DrawLatex(0.45, 0.95, "CMS Preliminary")
latex2 = TLatex()
latex2.SetNDC()
latex2.SetTextSize(0.04)
latex2.SetTextAlign(31) # align right
latex2.DrawLatex(0.87, 0.95, "19.37 fb^{-1} at #sqrt{s} = 8 TeV")
latex4 = TLatex()
latex4.SetNDC()
latex4.SetTextSize(0.04)
latex4.SetTextAlign(31) # align right
latex4.DrawLatex(0.80, 0.87, "")
def show(self, outDir, genPseudoData):
if len(self.mc) == 0:
print '%s is empty' % self.name
return
canvas = TCanvas('c_' + self.name, 'C', 600, 600)
canvas.cd()
t1 = TPad("t1", "t1", 0.0, 0.20, 1.0, 1.0)
t1.SetBottomMargin(0)
t1.Draw()
t1.cd()
self.garbageList.append(t1)
frame = None
# leg = TLegend(0.15,0.9,0.9,0.95)
leg = TLegend(0.6, 0.7, 0.92, 0.89)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.04)
nlegCols = 0
maxY = 1.0
if self.data is not None:
leg.AddEntry(self.data, self.data.GetTitle(), 'p')
frame = self.data.Clone('frame')
self.garbageList.append(frame)
maxY = self.data.GetMaximum() * 1.1
frame.Reset('ICE')
elif genPseudoData:
leg.AddEntry('pseudodata', 'Pseudo-data', 'p')
totalMC = None
stack = THStack('mc', 'mc')
for h in self.mc:
stack.Add(h, 'hist')
leg.AddEntry(h, h.GetTitle(), 'f')
nlegCols = nlegCols + 1
if totalMC is None:
totalMC = h.Clone('totalmc')
self.garbageList.append(totalMC)
totalMC.SetDirectory(0)
else:
totalMC.Add(h)
if totalMC is not None:
maxY = max(totalMC.GetMaximum(), maxY)
if frame is None:
frame = totalMC.Clone('frame')
frame.Reset('ICE')
self.garbageList.append(frame)
if genPseudoData and self.data is None and totalMC.ClassName(
).find('TH1') >= 0:
self.data = totalMC.Clone('pseudodata')
self.data.Sumw2()
self.data.SetLineColor(1)
self.data.SetMarkerStyle(20)
self.data.SetMarkerColor(1)
self.data.SetFillStyle(0)
self.data.SetFillColor(0)
if self.name.find('flow') < 0:
self.data.Reset('ICE')
for i in xrange(0, int(totalMC.Integral())):
self.data.Fill(totalMC.GetRandom())
if self.data is not None: nlegCols = nlegCols + 1
if nlegCols == 0:
print '%s is empty' % self.name
return
frame.GetYaxis().SetRangeUser(1e-2, 1.2 * maxY)
frame.SetDirectory(0)
frame.Draw()
frame.GetYaxis().SetTitleOffset(1.6)
stack.Draw('hist same')
if self.data is not None: self.data.Draw('P same')
# leg.SetNColumns(nlegCols)
leg.SetNColumns(2)
leg.Draw()
## Draw CMS Preliminary label
tlat = TLatex()
tlat.SetNDC()
tlat.SetTextFont(62)
tlat.SetTextSize(0.04)
tlat.SetTextAlign(31)
# prelim_text = 'CMS Preliminary, #sqrt{s} = 8 TeV'
prelim_text = 'CMS Preliminary, #sqrt{s} = 13 TeV'
tlat.DrawLatex(0.92, 0.95, prelim_text)
if totalMC is None or self.data is None:
t1.SetPad(0, 0, 1, 1)
t1.SetBottomMargin(0.12)
else:
canvas.cd()
t2 = TPad("t2", "t2", 0.0, 0.0, 1.0, 0.2)
self.garbageList.append(t2)
t2.SetTopMargin(0)
t2.SetBottomMargin(0.4)
t2.SetGridy()
t2.Draw()
t2.cd()
ratio = self.data.Clone('ratio')
self.garbageList.append(ratio)
ratio.Divide(totalMC)
ratio.SetDirectory(0)
ratio.Draw('e1')
ratio.GetYaxis().SetRangeUser(0.62, 1.38)
ratio.GetYaxis().SetTitle('Data/#SigmaBkg')
ratio.GetYaxis().SetNdivisions(5)
ratio.GetYaxis().SetLabelSize(0.15)
ratio.GetYaxis().SetTitleSize(0.18)
ratio.GetXaxis().SetLabelSize(0.18)
ratio.GetXaxis().SetTitleSize(0.2)
ratio.GetYaxis().SetTitleOffset(0.3)
ratio.GetXaxis().SetTitleOffset(0.8)
canvas.cd()
canvas.Modified()
canvas.Update()
for ext in ['pdf', 'png']:
canvas.SaveAs(outDir + '/' + self.name + '.' + ext)
def draw_atlas_label(self,
luminosity=None,
sqrt_s=None,
custom_label=None,
atlas_label=None):
"""Draws an ATLAS stamp on the plot, with an optional categorization
label.
It is recommended that you construct the Plot with plot_header = True
in order to make space for the label.
Args:
luminosity: The integrated luminosity, in pb^-1
sqrt_s: The center of mass energy, in MeV
label: The label to put after 'ATLAS', None to exclude the 'ATLAS'
categorization entirely
"""
# Change context to the plot pad
self._plot.cd()
# Create the latex object
# TODO: Consider using TPaveText to overwrite drawn graphs and
# histograms. At least for scatter plots.
# TODO: Increase readability: Create two sets of constants -
# one for plots with ratio and one for plots without. Select the
# correct one in draw_ratio_histogram.
stamp = TLatex()
# Style it
stamp.SetTextColor(self.PLOT_ATLAS_STAMP_TEXT_COLOR)
stamp.SetTextSize(
(self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO
if self._ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE))
stamp.SetTextFont(self.PLOT_ATLAS_STAMP_TEXT_FONT)
stamp.SetNDC()
top = (self.PLOT_ATLAS_STAMP_TOP_WITH_RATIO
if self._ratio_plot else self.PLOT_ATLAS_STAMP_TOP)
# Print an ATLAS label on top
if atlas_label is not None:
# Draw the label
stamp.SetTextFont(self.PLOT_ATLAS_STAMP_ATLAS_TEXT_FONT)
stamp.DrawLatex(self.PLOT_ATLAS_STAMP_LEFT, top, 'ATLAS')
stamp.SetTextFont(self.PLOT_ATLAS_STAMP_TEXT_FONT)
stamp.DrawLatex(self.PLOT_ATLAS_STAMP_ATLAS_LABEL_LEFT, top,
atlas_label)
top -= (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self.
_ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE) * 1.3
# Draw the luminosity and sqrt(s)
if luminosity is not None or sqrt_s is not None:
text = ''
if sqrt_s is not None:
text += '#sqrt{{s}} = {0:.0f} TeV'.format(sqrt_s / 1.0e6)
if luminosity is not None:
text += ', '
if luminosity is not None:
if luminosity >= 1000.0:
text += '{0:.1f} fb^{{-1}}'.format(luminosity / 1000.0)
elif luminosity > 100.0:
text += '{0:.2f} fb^{{-1}}'.format(luminosity / 1000.0)
else:
text += '{0:.1f} pb^{{-1}}'.format(luminosity)
stamp.DrawLatex(self.PLOT_ATLAS_STAMP_LEFT, top, text)
top -= (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self.
_ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE) * 1.3
# If requested, draw the custom label or the 'ATLAS' label,
# preferring the former
if custom_label is not None:
# Draw each line of text, decreasing top for each step
for text in [t for t in custom_label if t is not None]:
stamp.DrawLatex(self.PLOT_ATLAS_STAMP_LEFT, top, text)
top -= (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self.
_ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE) * 1.3
# leg_kin.Draw("same")
# cnv.cd(2)
# ROOT.gPad.SetTicks(1,1)
cnv.SetTicks(1, 1)
hmin, hmax = minmax(histos[spotsize + "_tru_E"],
histos[spotsize + "_rec_E"])
histos[spotsize + "_tru_E"].Draw("hist")
histos[spotsize + "_rec_E"].Draw("e same")
leg_kin.Draw("same")
s = TLatex()
s.SetNDC(1)
s.SetTextAlign(13)
s.SetTextColor(ROOT.kBlack)
s.SetTextSize(0.035)
s.DrawLatex(
0.52, 0.65,
ROOT.Form("Tru: #SigmaN_{e^{+}}/BX/Shot=%.2f" % (properties["NtruP"])))
s.DrawLatex(
0.52, 0.60,
ROOT.Form("Acc: #SigmaN_{e^{+}}/BX/Shot=%.2f" % (properties["NaccP"])))
s.DrawLatex(
0.52, 0.55,
ROOT.Form("Rec: #SigmaN_{e^{+}}/BX/Shot=%.2f" % (properties["NrecP"])))
cnv.SaveAs(fn + "_" + spotsize + "_CDR.pdf")
cnv.SaveAs(allpdf)
leg_ntot = TLegend(0.15, 0.70, 0.50, 0.87)
leg_ntot.SetFillStyle(4000) # will be transparent
ROOT.gStyle.SetPadTickX(1)
ROOT.gStyle.SetPadTickY(1)
legend = TLegend(0.15, 0.5, 0.35, 0.6)
legend2 = TLegend(0.65, 0.5, 0.85, 0.6)
l = TLatex()
l.SetNDC()
l.SetTextFont(72)
p = TLatex()
p.SetNDC()
p.SetTextFont(42)
q = TLatex()
q.SetNDC()
q.SetTextFont(42)
q.SetTextSize(0.03)
r = TLatex()
r.SetNDC()
r.SetTextFont(42)
r.SetTextSize(0.03)
s = TLatex()
s.SetNDC()
s.SetTextFont(42)
s.SetTextSize(0.03)
canvas = ROOT.TCanvas('draw', 'draw', 0, 0, 800, 800)
pad = ROOT.TPad()
canvas.cd()
infile = ROOT.TFile('../data/ntuple_v2_500.root', 'open')
intree = infile.Get('SimpleJet')
def setCMSLumiStyle(pad, iPosX, **kwargs):
global outOfFrame, lumiTextSize, lumiText
if iPosX / 10 == 0:
outOfFrame = True
lumiTextSize_ = lumiTextSize
relPosX_ = kwargs.get('relPosX', relPosX)
lumiText_ = kwargs.get('lumiText', lumiText)
if outOfFrame:
lumiTextSize_ *= 0.90
if 'era' in kwargs: # one era
era = kwargs.get('era')
setCMSEra(era, **kwargs)
elif 'eras' in kwargs: # list of multiple eras
eras = kwargs.get('eras')
setCMSEra(*eras, **kwargs)
#if lumiText=="":
# if iPeriod==1:
# lumiText += lumi_7TeV
# lumiText += " (7 TeV)"
# elif iPeriod==2:
# lumiText += lumi_8TeV
# lumiText += " (8 TeV)"
# elif iPeriod==3:
# lumiText = lumi_8TeV
# lumiText += " (8 TeV)"
# lumiText += " + "
# lumiText += lumi_7TeV
# lumiText += " (7 TeV)"
# elif iPeriod==4:
# lumiText += lumi_13TeV
# lumiText += " (13 TeV)"
# elif iPeriod==7:
# if outOfFrame: lumiTextSize_ *= 0.85
# lumiText += lumi_13TeV
# lumiText += " (13 TeV)"
# lumiText += " + "
# lumiText += lumi_8TeV
# lumiText += " (8 TeV)"
# lumiText += " + "
# lumiText += lumi_7TeV
# lumiText += " (7 TeV)"
# elif iPeriod==12:
# lumiText += "8 TeV"
# else:
# if outOfFrame: lumiTextSize_ *= 0.90
# if iPeriod==13:
# lumiText += lumi_13TeV
# lumiText += " (13 TeV)"
# elif iPeriod==2016:
# lumiText += lumi_2016
# lumiText += " (13 TeV)"
# elif iPeriod==2017:
# lumiText += lumi_2017
# lumiText += " (13 TeV)"
# elif iPeriod==2018:
# lumiText += lumi_2018
# lumiText += " (13 TeV)"
# elif iPeriod==14:
# lumiText += lumi_14TeV
# lumiText += " (14 TeV, 200 PU)"
##print lumiText
alignY_ = 3
alignX_ = 2
if iPosX == 0: alignY_ = 1
if iPosX / 10 == 0: alignX_ = 1
elif iPosX / 10 == 1: alignX_ = 1
elif iPosX / 10 == 2: alignX_ = 2
elif iPosX / 10 == 3: alignX_ = 3
align = 10 * alignX_ + alignY_
extraTextSize = extraOverCmsTextSize * cmsTextSize
H = pad.GetWh() * pad.GetHNDC()
W = pad.GetWw() * pad.GetWNDC()
l = pad.GetLeftMargin()
t = pad.GetTopMargin()
r = pad.GetRightMargin()
b = pad.GetBottomMargin()
e = 0.025
scale = float(H) / W if W > H else 1 # float(W)/H
pad.cd()
latex = TLatex()
latex.SetNDC()
latex.SetTextAngle(0)
latex.SetTextColor(kBlack)
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(lumiTextSize_ * t)
if lumiText_:
latex.DrawLatex(1 - r, 1 - t + lumiTextOffset * t, lumiText_)
if iPosX == 0:
relPosX_ = relPosX_ * (50 * t * scale) * (cmsTextSize / 0.84)
posX = l + relPosX_ * (1 - l - r)
posY = 1 - t + lumiTextOffset * t
else:
posX = 0
posY = 1 - t - relPosY * (1 - t - b)
if iPosX % 10 <= 1:
posX = l + relPosX_ * (1 - l - r) # left aligned
elif iPosX % 10 == 2:
posX = l + 0.5 * (1 - l - r) # centered
elif iPosX % 10 == 3:
posX = 1 - r - relPosX_ * (1 - l - r) # right aligned
if outOfFrame:
TGaxis.SetExponentOffset(-0.12 * float(H) / W, 0.015, 'y')
latex.SetTextFont(cmsTextFont)
latex.SetTextAlign(11)
latex.SetTextSize(cmsTextSize * t)
latex.DrawLatex(l, 1 - t + lumiTextOffset * t, cmsText)
if writeExtraText:
latex.SetTextFont(extraTextFont)
latex.SetTextSize(extraTextSize * t)
latex.SetTextAlign(align)
latex.DrawLatex(posX, posY, extraText)
elif drawLogo:
posX = l + 0.045 * (1 - l - r) * W / H
posY = 1 - t - 0.045 * (1 - t - b)
xl_0 = posX
yl_0 = posY - 0.15
xl_1 = posX + 0.15 * H / W
yl_1 = posY
CMS_logo = TASImage("CMS-BW-label.png")
pad_logo = TPad("logo", "logo", xl_0, yl_0, xl_1, yl_1)
pad_logo.Draw()
pad_logo.cd()
CMS_logo.Draw('X')
pad_logo.Modified()
pad.cd()
else:
latex.SetTextFont(cmsTextFont)
latex.SetTextSize(cmsTextSize * t)
latex.SetTextAlign(align)
latex.DrawLatex(posX, posY, cmsText)
if writeExtraText:
latex.SetTextFont(extraTextFont)
latex.SetTextAlign(align)
latex.SetTextSize(extraTextSize * t)
latex.DrawLatex(posX, posY - relExtraDY * cmsTextSize * t,
extraText)
pad.Update()
outHist[i].SetLineWidth(2)
outHist[i].Draw("same")
outName[i] = testFiles[i][:-5]
outEst[i] = BGNormalization
l = TLegend(0.40, 0.70, 0.7, 0.9, "", "brNDC")
l.SetBorderSize(0)
l.SetFillColor(0)
l.SetTextSize(.03)
# l.AddEntry(hist_Data, "Data", "p")
# l.AddEntry(outHist[i], "BG Est.(one tau Iso Reverted)", "lpf")
# l.AddEntry(hist_Estimation_ff, "BG Est.(both tau Iso Reverted)", "lpf")
l.Draw()
#Text in Histogram
t = TLatex()
t.SetNDC()
t.SetTextFont(62)
t.SetTextAlign(12)
t.SetTextSize(0.025)
t.DrawLatex(0.1, .92, "CMS Preliminary 2012")
t.DrawLatex(0.5, .92, "#sqrt{s} = 8 TeV, L = 19.0 fb^{-1}")
t.DrawLatex(0.5, .60, "Data, SS, 10 GeV Tau LooseIso")
t.DrawLatex(0.5, .52, "Data = " + str(DataNormalization))
for i in range(len(testFiles)):
t.SetTextColor(i + 2)
t.DrawLatex(0.5, .52 - ((i + 1) / 20.),
outName[i] + " = " + str(outEst[i]))
canvas.SaveAs("Data_SS_10_GeV_MuTau_LooseIso.pdf")
def limit():
method = ''
channel = "bb"
if INCLUDEACC:
particleP = "X"
else:
particleP = "Z'"
particle = 'b#bar{b}'
multF = ZPTOBB
THEORY = ['A1', 'B3']
if INCLUDEACC: THEORY.append('SSM')
suffix = "_" + BTAGGING
if ISMC: suffix += "_MC"
if SY: suffix += "_comb"
#if method=="cls": suffix="_CLs"
if INCLUDEACC: suffix += "_acc"
if SY:
filename = "./combine/limits/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt"
else:
filename = "./combine/limits/" + BTAGGING + "/" + YEAR + "_M%d.txt"
if CATEGORY != "":
if SY:
if CATEGORY == 'bb_bq':
filename = filename.replace(
BTAGGING + "/combined_run2/",
BTAGGING + "/combined_run2/" + CATEGORY + "_combined_")
else:
filename = filename.replace(
BTAGGING + "/combined_run2/", BTAGGING +
"/single_category/combined_run2/" + CATEGORY + "_")
else:
filename = filename.replace(
BTAGGING + "/",
BTAGGING + "/single_category/" + CATEGORY + "_")
suffix += "_" + CATEGORY
if ISMC: filename = filename.replace(".txt", "_MC.txt")
mass, val = fillValues(filename)
#print "mass =",mass
#print "val =", val
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
Sign = TGraph()
pVal = TGraph()
Best = TGraphAsymmErrors()
Theory = {}
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
if INCLUDEACC:
acc_factor = ACCEPTANCE[m]
else:
acc_factor = 1.
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF * acc_factor)
Exp0s.SetPoint(n, m, val[m][3] * multF * acc_factor)
Exp1s.SetPoint(n, m, val[m][3] * multF * acc_factor)
Exp1s.SetPointError(n, 0., 0.,
(val[m][3] - val[m][2]) * multF * acc_factor,
(val[m][4] - val[m][3]) * multF * acc_factor)
Exp2s.SetPoint(n, m, val[m][3] * multF * acc_factor)
Exp2s.SetPointError(n, 0., 0.,
(val[m][3] - val[m][1]) * multF * acc_factor,
(val[m][5] - val[m][3]) * multF * acc_factor)
if len(val[m]) > 6: Sign.SetPoint(n, m, val[m][6])
if len(val[m]) > 7: pVal.SetPoint(n, m, val[m][7])
if len(val[m]) > 8: Best.SetPoint(n, m, val[m][8])
if len(val[m]) > 10:
Best.SetPointError(n, 0., 0., abs(val[m][9]), val[m][10])
#print "m =", m, " --> Xsec*Br =", val[m][3]
for t in THEORY:
Theory[t] = TGraphAsymmErrors()
Xs_dict = HVT[t]['Z']['XS'] if t != 'SSM' else SSM['Z']
for m in sorted(Xs_dict.keys()):
if INCLUDEACC and t != 'SSM':
acc_factor = ACCEPTANCE[m]
else:
acc_factor = 1.
if m < SIGNALS[0] or m > SIGNALS[-1]: continue
#if m < mass[0] or m > mass[-1]: continue
#if t!= 'SSM' and m>4500: continue ## I don't have the higher mass xs
if m > 4500: continue
XsZ, XsZ_Up, XsZ_Down = 0., 0., 0.
if t != 'SSM':
XsZ = 1000. * HVT[t]['Z']['XS'][m] * SSM["BrZ"][
m] #assuming the same BR as the SSM Z' one
XsZ_Up = XsZ * (1. + math.hypot(HVT[t]['Z']['QCD'][m][0] - 1.,
HVT[t]['Z']['PDF'][m][0] - 1.))
XsZ_Down = XsZ * (1. -
math.hypot(1. - HVT[t]['Z']['QCD'][m][0],
1. - HVT[t]['Z']['PDF'][m][0]))
else:
XsZ = 1000. * SSM['Z'][m] * SSM["BrZ"][m]
XsZ_Up = XsZ * (1. +
math.hypot(HVT['A1']['Z']['QCD'][m][0] - 1.,
HVT['A1']['Z']['PDF'][m][0] - 1.))
XsZ_Down = XsZ * (1. -
math.hypot(1. - HVT['A1']['Z']['QCD'][m][0],
1. - HVT['A1']['Z']['PDF'][m][0]))
n = Theory[t].GetN()
Theory[t].SetPoint(n, m, XsZ * acc_factor)
Theory[t].SetPointError(n, 0., 0., (XsZ - XsZ_Down) * acc_factor,
(XsZ_Up - XsZ) * acc_factor)
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
#Theory[t].SetLineStyle(7)
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp1s.SetFillColor(417) #kGreen+1
Exp1s.SetLineColor(417) #kGreen+1
Exp2s.SetFillColor(800) #kOrange
Exp2s.SetLineColor(800) #kOrange
Exp2s.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle(
"#sigma(" + particleP + ") #bf{#it{#Beta}}(" + particleP +
" #rightarrow " + particle +
"){} (fb)".format(" #times #Alpha" if INCLUDEACC else ""))
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Sign.SetLineWidth(2)
Sign.SetLineColor(629)
Sign.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1)
Sign.GetYaxis().SetTitle("Significance")
pVal.SetLineWidth(2)
pVal.SetLineColor(629)
pVal.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
pVal.GetXaxis().SetTitleSize(pVal.GetXaxis().GetTitleSize() * 1.1)
pVal.GetYaxis().SetTitle("local p-Value")
Best.SetLineWidth(2)
Best.SetLineColor(629)
Best.SetFillColor(629)
Best.SetFillStyle(3003)
Best.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Best.GetXaxis().SetTitleSize(Best.GetXaxis().GetTitleSize() * 1.1)
Best.GetYaxis().SetTitle("Best Fit (pb)")
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
#c1.GetPad(0).SetGridx()
#c1.GetPad(0).SetGridy()
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
Exp0s.Draw("SAME, L")
if not options.blind: Obs0s.Draw("SAME, L")
#setHistStyle(Exp2s)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
if INCLUDEACC:
Exp2s.GetYaxis().SetRangeUser(0.05, 5.e3)
else:
Exp2s.GetYaxis().SetRangeUser(0.1, 5.e3)
#else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2)
#Exp2s.GetXaxis().SetRangeUser(mass[0], min(mass[-1], MAXIMUM[channel] if channel in MAXIMUM else 1.e6))
Exp2s.GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1])
#drawAnalysis(channel)
drawAnalysis("")
#drawRegion(channel, True)
drawRegion("", True)
#drawCMS(LUMI, "Simulation Preliminary") #Preliminary
if CATEGORY == "":
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawCMS(LUMI, "Preliminary")
#drawCMS(LUMI, "", suppressCMS=True)
else:
#drawCMS(LUMI, "Work in Progress, "+CAT_LABELS[CATEGORY], suppressCMS=True)
drawCMS(LUMI, "Preliminary " + CAT_LABELS[CATEGORY])
#drawCMS(LUMI, CAT_LABELS[CATEGORY], suppressCMS=True)
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.98, top)
#leg = TLegend(0.45, top-nitems*0.3/5., 0.98, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
for t in THEORY:
leg.AddEntry(Theory[t], theoryLabel[t], "fl")
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
latex.SetTextFont(42)
#latex.DrawLatex(0.66, leg.GetY1()-0.045, particleP+" #rightarrow "+particle+"h")
leg2 = TLegend(0.12, 0.225 - 2 * 0.25 / 5., 0.65, 0.225)
leg2.SetBorderSize(0)
leg2.SetFillStyle(0) #1001
leg2.SetFillColor(0)
c1.GetPad(0).RedrawAxis()
leg2.Draw()
if not options.blind: Obs0s.Draw("SAME, L")
c1.GetPad(0).Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".png")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".C")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix +
".root")
for t in THEORY:
print "Model", t, ":",
for m in range(mass[0], mass[-1], 1):
if not (Theory[t].Eval(m) > Obs0s.Eval(m)) == (
Theory[t].Eval(m + 1) > Obs0s.Eval(m + 1)):
print m, "(obs)",
if not (Theory[t].Eval(m) > Exp0s.Eval(m)) == (
Theory[t].Eval(m + 1) > Exp0s.Eval(m + 1)):
print m, "(exp)",
print ""
return ##FIXME
# ---------- Significance ----------
c2 = TCanvas("c2", "Significance", 800, 600)
c2.cd()
c2.GetPad(0).SetTopMargin(0.06)
c2.GetPad(0).SetRightMargin(0.05)
c2.GetPad(0).SetTicks(1, 1)
c2.GetPad(0).SetGridx()
c2.GetPad(0).SetGridy()
Sign.GetYaxis().SetRangeUser(0., 5.)
Sign.Draw("AL3")
drawCMS(LUMI, "Preliminary")
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c2.Print("combine/plotsLimit/Significance/" + YEAR + suffix + ".png")
c2.Print("combine/plotsLimit/Significance/" + YEAR + suffix + ".pdf")
# c2.Print("plotsLimit/Significance/"+YEAR+suffix+".root")
# c2.Print("plotsLimit/Significance/"+YEAR+suffix+".C")
# ---------- p-Value ----------
c3 = TCanvas("c3", "p-Value", 800, 600)
c3.cd()
c3.GetPad(0).SetTopMargin(0.06)
c3.GetPad(0).SetRightMargin(0.05)
c3.GetPad(0).SetTicks(1, 1)
c3.GetPad(0).SetGridx()
c3.GetPad(0).SetGridy()
c3.GetPad(0).SetLogy()
pVal.Draw("AL3")
pVal.GetYaxis().SetRangeUser(2.e-7, 0.5)
ci = [
1., 0.317310508, 0.045500264, 0.002699796, 0.00006334, 0.000000573303,
0.000000001973
]
line = TLine()
line.SetLineColor(922)
line.SetLineStyle(7)
text = TLatex()
text.SetTextColor(922)
text.SetTextSize(0.025)
text.SetTextAlign(12)
for i in range(1, len(ci) - 1):
line.DrawLine(pVal.GetXaxis().GetXmin(), ci[i] / 2,
pVal.GetXaxis().GetXmax(), ci[i] / 2)
text.DrawLatex(pVal.GetXaxis().GetXmax() * 1.01, ci[i] / 2,
"%d #sigma" % i)
drawCMS(LUMI, "Preliminary")
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c3.Print("combine/plotsLimit/pValue/" + YEAR + suffix + ".png")
c3.Print("combine/plotsLimit/pValue/" + YEAR + suffix + ".pdf")
# c3.Print("plotsLimit/pValue/"+YEAR+suffix+".root")
# c3.Print("plotsLimit/pValue/"+YEAR+suffix+".C")
# --------- Best Fit ----------
c4 = TCanvas("c4", "Best Fit", 800, 600)
c4.cd()
c4.GetPad(0).SetTopMargin(0.06)
c4.GetPad(0).SetRightMargin(0.05)
c4.GetPad(0).SetTicks(1, 1)
c4.GetPad(0).SetGridx()
c4.GetPad(0).SetGridy()
Best.Draw("AL3")
drawCMS(LUMI, "Preliminary")
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c4.Print("combine/plotsLimit/BestFit/" + YEAR + suffix + ".png")
c4.Print("combine/plotsLimit/BestFit/" + YEAR + suffix + ".pdf")
# c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".C")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if 'ah' in channel:
outFile = TFile("bands.root", "RECREATE")
outFile.cd()
pVal.Write("graph")
Best.Write("best")
outFile.Close()
input_rootfile = args.input_file
output_pdfname = args.output_prefix + '.pdf'
rootfile = TFile(input_rootfile)
histos = load_histos(rootfile)
for k, v in histos.items():
print(k, v)
setup_global_options()
can = TCanvas('can', 'can', 800, 1100)
lab = TLatex()
lab.SetNDC()
lab.SetTextFont(42)
lab.SetTextSize(0.05)
lab.SetTextColor(1)
can.Print('{}['.format(output_pdfname))
# add_text_page(can, lab, text='W Monitoring Plots', save_name=output_pdfname)
plot_sector_page(can,
histos,
'histos_w_CTOF_{}',
lab,
save_name=output_pdfname,
title='(CTOF)',
xtitle='W')
plot_sector_page(can,
def train_and_apply():
np.random.seed(1)
ROOT.gROOT.SetBatch()
#Extract data from root file
tree = uproot.open("out_all.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 = 2
# 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)
x = Reshape((shape[1], ))(x)
outputs = Activation('tanh', name='decoder_output')(x)
# Instantiate Decoder Model
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# Autoencoder = Encoder + Decoder
# Instantiate Autoencoder Model
autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')
autoencoder.summary()
autoencoder.compile(loss='mse', optimizer='adam')
# Train the autoencoder
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)
# Draw Comparision Plots
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.
variable = "P^{B}"
lbin = (max - min) / nbin
lbin = str(float((max - min) / nbin))
xtitle = branch_rec[options.branch - 1]
ytitle = "Events/" + lbin + "GeV"
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_rec.Fill(x_test_noisy[i][options.branch - 1])
h_pre.Fill(x_decoded[i][options.branch - 1])
h_rec = UnderOverFlow1D(h_rec)
h_pre = UnderOverFlow1D(h_pre)
maxY = TMath.Max(h_rec.GetMaximum(), h_pre.GetMaximum())
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_rec.SetStats(0)
h_pre.SetStats(0)
h_rec.GetYaxis().SetRangeUser(0, maxY * 1.1)
h_rec.Draw("HIST")
h_pre.Draw("same HIST")
h_rec.GetYaxis().SetTitleSize(0.06)
h_rec.GetYaxis().SetTitleOffset(0.78)
theLeg = TLegend(0.5, 0.45, 0.95, 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_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 = h_pre.Clone("h_Ratio")
h_Ratio.Divide(h_rec)
h_Ratio.SetLineColor(1)
h_Ratio.SetLineWidth(2)
h_Ratio.SetMarkerStyle(8)
h_Ratio.SetMarkerSize(0.7)
h_Ratio.GetYaxis().SetRangeUser(0, 2)
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_rec.GetBinContent(i)
eD = h_rec.GetBinError(i)
if D == 0: eD = 0.92
B = h_pre.GetBinContent(i)
eB = h_pre.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)
h_Ratio.Draw("e0")
axis1.Draw()
c.SaveAs(branch_rec[options.branch - 1] + "_comparision.png")
def makesob_plots(calcsig,
sigstr_BCuts,
samples,
dirname,
vararray,
signal,
selarray=None,
hist_config=None,
legend_config=None,
extratag=""):
"""
Filter cuts by a significance threshold, plot signal-over-background (SOB) for each cut that passed the
threshold, and save signal and background counts and errors for each "significant" cut into a table.
SOB is defined as $\frac{S}{\sqrt{B}}$.
:param calcsig: whether to recalculate tables of signal and background event counts and errors using `signif`
:param sigstr_BCuts: list of background cut names
:param samples: `SampleManager` containing the (full-dimensional) simulated data
:param dirname: directory to save logs and histograms to
:param vararray: variables of interest (dependent variables) to aggregate counts and error estimates for
:param signal: name of signal (independent variable) to count
:param selarray: list of cuts (see `makeselection` for format)
:param hist_config: dict of `ROOT` plotting options
:param legend_config: legend plotting options
:param extratag: log file ending string (optional)
"""
# Set up ROOT plot
c1 = TCanvas('c1', 'Significance', 200, 10, 1500, 700)
c1.SetBottomMargin(0.4)
c1.SetRightMargin(0.18)
c1.SetLeftMargin(0.1)
# p1 = TPad("p1","p1",0.0,0.0,1.0,1.0,0)#TPad(name, title, xlow, ylow, xup, yup, color )
# p1.SetBottomMargin(1.7)
# p1.GetFrame().SetBorderSize(5)
# p1.SetRightMargin(0.01)#percent of the pad height
# p1.SetLeftMargin(0.1)
# p1.SetBottomMargin(0.5)
# p1.Draw()
# get the cut set name and corresponding significance
xname_all = [] # cut names for x labels
# signame = []
sig_all = []
sig_err_all = []
bkg_all = []
bkg_err_all = []
sigstr_BaseCuts = sigstr_BCuts # base cut string
basecut_listvalue = 0 # index number of the base cut in the xname_all list
sig_cut_plots = []
# xname, ysig, signame = signif(sampManElG,vararray, selarray, hist_config,{}, "log")
# If calcsig != 0 ach
# If recalculating from scratch, call signif to count background and signal events
if calcsig:
xname_all, bkg_all, bkg_err_all, sig_all, sig_err_all, sig_cut_plots = signif(
samples, vararray, signal, dirname, selarray, hist_config, {}, "")
# If loading background and signal events from a file already logged by signif, then load them
else:
ftable = dirname + "/Selected/" + signal + "TOTAL_table"
n = 0
with open(ftable, 'r') as f:
lines = f.readlines()
# print "read first line of the table we just saved" + str(lines[1])
for line in lines:
n = n + 1
listline = line.split()
# print line
if n > 1:
# print listline[5]
# Add background and significance to their respective
# arrays for the plot generation.
xname_all.append(str(listline[0]))
bkg_all.append(float(listline[1]))
bkg_err_all.append(float(listline[2]))
sig_all.append(float(listline[3]))
sig_err_all.append(float(listline[4]))
sig_cut_plots.append(str(listline[5]))
# print sig_all[0]
f.close()
# if (debug):
# print "The bigger lists : sig_all[0]= " + str(sig_all[0])+ "bkg_all[1]=" + str(bkg_all[1])
print "size of bigger array is " + str(len(sig_all))
# check the list of lists is correct - update: it is ot a list of lists anymore but leaving for future.
# row_format = '{:<4}' * len(sig_all)
# for v in zip(*sig_all):
# #if (debug):
# print (row_format.format(*v))
# print sig_all
# Cuts and associated values that are deemed significant by Punzi's criterion
xname = []
# signame = []
sig = [
] # it is alist of lists for significance for all the signal samples
sig_err = [
] # it is alist of lists for significance for all the signal samples
# bkg = [[] for i in range(len(signame))]
bkg = []
bkg_err = []
sig_cut_for_plots = [
] # exact string of selection cuts to be used later to make plots
histall = []
sig_max = 0.
bkg_max = 0.
# for j in range(len(signame)):# loop on signal samples
sig_max = sig_all[0]
bkg_max = bkg_all[0]
signif_max = sig_max / bkg_max
# if (debug):
print "signif_max " + str(signif_max)
# sig[j].append(sig_all[j][0])
sig.append(sig_max)
bkg.append(bkg_all[0])
sig_err.append(sig_err_all[0])
bkg_err.append(bkg_err_all[0])
xname.append(xname_all[0])
sig_cut_for_plots.append(sig_cut_plots[0])
# if (debug):
# print "j " + str(j) + " " + str(sig_all[j][0]/bkg_all[0])
for i in range(1, len(xname_all)): # loop on cuts sets
signif_cut = sig_all[i] / math.sqrt(
bkg_all[i]) # compute variance assuming
# print sigstr_BaseCuts
# print "cut i : " + str(xname_all[i])
if xname_all[i] == sigstr_BaseCuts: # if the cut is a base cut, print
print "Base cut for this signal is : " + xname_all[i]
# print " ith bin in xname_all[i] " + str(i)
basecut_listvalue = i
# if (debug):
print "i " + str(i) + " " + xname_all[i] + " " + str(
signif_cut)
if signif_cut > (
1.015 * signif_max
): # append significant events to list of relevant counts
print "i " + str(i) + " " + xname_all[i] + " " + str(
signif_cut)
signif_max = signif_cut
# if (debug):
print " new signif_max " + str(signif_max) + " " + str(i)
sig.append(sig_all[i])
bkg.append(bkg_all[i])
sig_err.append(sig_err_all[i])
bkg_err.append(bkg_err_all[i])
xname.append(xname_all[i])
sig_cut_for_plots.append(sig_cut_plots[i])
# signame.append(signame_all[i+1])
if debug:
print sig_all[1]
print sig[1]
print "How many cuts ? " + str(len(xname[0]))
# n= int(len(xname))
# nsig= int(len(signame))
nhist = ["_sig", "_bkg", "_bkgsqrt", "_sob", "_sosqrtb"]
# Bin 0 contains the underflow
# Bin 1 contains the first bin with low-edge ( xlow INCLUDED).
# The second to last bin (bin# nbins) contains the upper-edge (xup EXCLUDED).
# The Last bin (bin# nbins+1) contains the overflow.
# Make the SOB plot for ALL cuts
histName_All = signal + "_h_SOB_ALL"
nall = int(len(xname_all))
histNameAll = ROOT.TH1F(histName_All, "", nall + 1, 0, nall + 1)
for i in range(len(xname_all)): # loop on cuts sets
histNameAll.SetBinContent(i + 1, sig_all[i] / (math.sqrt(bkg_all[i])))
# histNameAll.SetBinError(i+1, sig_err[i])#
histNameAll.Draw()
histNameAll.GetYaxis().SetTitle('S/#\sqrt B')
histNameAll.GetXaxis().SetTitle('Cut combination')
savename_all = str(dirname + "/Selected/" + signal + "_SOsqrtB_ALL")
c1.SaveAs(savename_all + ".png")
c1.SaveAs(savename_all + ".pdf")
c1.Modified()
c1.Update()
#################################################
# for i in range(len(signame)):
if debug:
print "Histogram " + signal
n = int(len(xname))
for j in range(len(nhist)):
histName = signal + "_h" + nhist[j]
# if (debug):
# print histName
histall.append(ROOT.TH1F(histName, "", n + 1, 0, n + 1))
# if (debug):
# print"histogram name is = " + histall[j].GetName()
# for j in range(len(signame)):# loop on signal samples
if debug:
print signal
n = int(len(xname))
bscut = basecut_listvalue
print " basecut_listvalue = " + str(basecut_listvalue)
for k in range(len(nhist)):
for i in range(len(xname)): # loop on cuts sets
if (debug):
print "signal and cut set " + str(j) + " " + str(i)
print xname[i] + "has significance" + str(
ysig[i]
) # first index is signal sample, second index is cut
print str(i) + " sig[i] " + str(sig[i])
print nhist[k]
# sig = round(float(ysig[i]),2)
# if (debug):
# print histall[k].GetName()
histall[k].GetXaxis().SetBinLabel(i + 1, xname[i])
# print "bscut =" + str(bscut)
# print "length of xname_all[j] arra =" + str(len(xname_all))
# print "xname_all[j][bscut] " + xname_all[bscut]
histall[k].GetXaxis().SetBinLabel(
n + 1, "X" + xname_all[bscut]
) # notice it is from the initial, bigger cutlist
# print sig_cut_plots[i]
# print "base cut value = " + str(bscut) + str(sig_all[j][bscut])
if nhist[k] == '_sig':
histall[k].SetBinContent(i + 1, sig[i])
histall[k].SetBinError(i + 1, sig_err[i]) #
histall[k].SetBinContent(n + 1, sig_all[bscut])
histall[k].SetBinError(n + 1, sig_err_all[bscut]) #
# histall[j][k].GetXaxis().SetBinLabel(i+1, xname[j][i])
# print "n+1 bin label = " + histall[j][k].GetXaxis().GetBinLabel(n+1)
# if (debug):
# print "n+1 bin content " + str(histall[k].GetBinContent(n+1))
if nhist[k] == '_bkg':
histall[k].SetBinContent(i + 1, bkg[i])
histall[k].SetBinError(i + 1, bkg_err[i]) #
histall[k].SetBinContent(n + 1, bkg_all[bscut])
histall[k].SetBinError(n + 1, bkg_err_all[bscut]) #
if (debug):
print histall[k].GetBinContent(i + 1)
if nhist[k] == '_bkgsqrt':
percenterr = (bkg_err[i] / bkg[i]) / 2
berr = percenterr * math.sqrt(bkg[i])
berr_bscut = ((bkg_err_all[bscut] / bkg_all[bscut]) /
2) * math.sqrt(bkg_all[bscut])
histall[k].SetBinContent(i + 1, math.sqrt(bkg[i]))
histall[k].SetBinError(i + 1, berr) #
histall[k].SetBinContent(n + 1, math.sqrt(bkg_all[bscut]))
histall[k].SetBinError(n + 1, berr_bscut) #
if debug:
print histall[k].GetBinContent(i + 1)
print Sigh.GetXaxis().GetBinLabel(i + 1)
gStyle.SetOptStat(0)
# for j in range(len(signame)):
for k in range(len(nhist)):
if nhist[k] == "_sob":
histall[k] = doratio(histall[0], histall[1])
histall[k].GetYaxis().SetTitle('S/B')
if debug:
print "sob" + str(histall[k].GetBinContent(1))
if nhist[k] == "_sosqrtb":
histall[k] = doratio(histall[0], histall[2])
histall[k].GetYaxis().SetTitle('S/#\sqrt B')
if nhist[k] == "_sig":
histall[k].GetYaxis().SetTitle('Signal')
if nhist[k] == "_bkg":
histall[k].GetYaxis().SetTitle('Total Background')
if nhist[k] == "_bkgsqrt":
histall[k].GetYaxis().SetTitle('Sqrt Total Background')
histall[k].SetLineColor(k + 2)
histall[k].SetLineWidth(4)
# histall[k].GetXaxis().LabelsOption("v")
histall[k].GetXaxis().SetLabelSize(0.025) # percentage of pad size
# histall[k].GetXaxis().SetTitleSize(0.10)
# histall[k].GetXaxis().SetTitleOffset(1.0)
histall[k].GetYaxis().SetTitleSize(0.06)
histall[k].GetYaxis().SetTitleOffset(0.8)
histall[k].GetYaxis().SetLabelSize(0.04)
histall[k].GetYaxis().CenterTitle()
histall[k].GetYaxis().SetNdivisions(510, True)
if debug:
print histall[k].GetXaxis().GetBinLabel(i + 1)
maxy = 0.0
miny = 0.0
# for j in range(len(signame)):
sn = signal
# sob bin with max value
sob_max_bin = 0
for k in range(len(nhist)):
maxvalue = histall[k].GetBinContent((histall[k].GetMaximumBin()))
minvalue = histall[k].GetBinContent((histall[k].GetMinimumBin()))
maxy = maxvalue
miny = minvalue
if maxvalue > maxy:
maxy = maxvalue
if minvalue > miny:
miny = minvalue
histall[k].SetMaximum(maxvalue + 0.5 * maxvalue)
histall[k].SetMinimum(minvalue - 0.5 * minvalue)
if k == 4:
sob_max_bin = histall[k].GetMaximumBin()
# sigstr_BaseCut[]
# = histall[k].SetBinContent((n+a, histall[k].GetXaxis().GetBinLabel(i+1)))
# for j in range(len(signame)):
sn = signal
for k in range(len(nhist)):
if k == 2:
continue
c1.Modified()
c1.Update()
if k == 1:
gPad.SetLogy()
else:
gPad.SetLogy(0)
legend = TLegend(0.1, 0.7, 0.5, 0.89, " ") # x1,y1,x2,y2
maxcut = histall[k].GetXaxis().GetBinLabel(sob_max_bin)
maxvalue = round(histall[k].GetBinContent(sob_max_bin), 2)
# legend. AddEntry(histall[j][k] , signame[j] + ' (' + maxcut + ')' , "")
print signal
# p1.cd()
histall[k].Draw()
c1.Modified()
c1.Update()
BC = 0
BC = round(histall[k].GetBinContent(n + 1), 2)
print str(c1.GetUxmax()) + " " + str(BC)
# p1.Update();
l = ROOT.TLine(c1.GetUxmin(), BC, c1.GetUxmax(), BC)
l.SetLineColor(4)
l.SetLineWidth(2)
l.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextAlign(12) # 12 center, 31align right
latex.DrawLatex(0.2, 0.85, signal)
latex.DrawLatex(0.2, 0.75, 'Max Cut ' + maxcut)
latex.DrawLatex(
0.2, 0.65,
'Base Value = ' + str(BC) + ' Max Value ' + str(maxvalue))
latex.SetTextAlign(31) # align right
latex.DrawLatex(0.25, 0.93, " 36 fb^{-1} at #sqrt{s} = 13 TeV")
latex.DrawLatex(0.8, 0.93, "CMS Internal")
# legend.SetShadowColor(0);
# legend.SetFillColor(0);
# legend.SetLineColor(0);
# legend.Draw('same')
savename = str(dirname + "/Selected/" + signal + str(nhist[k]))
c1.SaveAs(savename + ".png")
c1.SaveAs(savename + ".pdf")
# c1.SaveAs(savename + ".C")
# c1.SaveAs(savename + ".root")
c1.Modified()
c1.Update()
# for n in range(len(signame)):
ftable = dirname + "/Selected/" + signal + "_selected_table"
with open(ftable, 'w+') as f:
f.write(
"cutname total_b total_b_err total_s total_s_err cut_array_used\n"
)
for j in range(len(xname)):
# print signal
print('%15s %5s %5s %5s %5s %5s' %
(str(xname[j]), str(bkg[j]), str(bkg_err[j]), str(
sig[j]), str(sig_err[j]), sig_cut_for_plots[j]))
# f.write('%15s %5s %5s %5s %5s %5s' %(str(xname[j]), str(bkg[j]), str(bkg_err[j]) , str(sig[j]) , str(sig_err[j]) , sig_cut_for_plots[j]))
f.write('%15s %5f %5f %5f %5f %5s\n' %
(xname[j], bkg[j], bkg_err[j], sig[j], sig_err[j],
sig_cut_for_plots[j]))
f.close()
print "Total number of cut combinations used for plotting: " + str(
len(xname_all))
