def browse_mode():
cartoon_selector = 0
cartoon = []
cartoon_cursor = 1
cartoons_number = selection.count_files("/media/PAP/cartoons/")
c_path = "/media/PAP/cartoons/pap_"
# if no USB key named PAP containing cartoons is found, displays an error and returns.
# This will crash if it contains images that are not names pap_x and are not 1280 / 1024 size.
if not os.path.exists("/media/PAP/cartoons/"):
cv2.imshow("window", cv2.imread("./load_failed.png"))
cv2.waitKey(0)
return
while (len(cartoon) == 0):
cv2.imshow("window", cv2.imread("background2.png"))
cv2.waitKey(10)
# builds and displays the image containing thumbnails of the cartoons
selection.build_selection(cartoon_selector, c_path, cartoons_number)
cv2.waitKey(10)
button_value = button.waitpressedbutton("browse")
if (button_value == "mode"): # the mode is no longer browse, so we return to go to the right mode
return
if (button_value == "next"): # sets the cursor to the next cartoon
cartoon_selector += 1
elif (button_value == "prev"): # sets the cursor to the previous cartoon
cartoon_selector -= 1
elif (button_value == "play"): # plays the cartoon
cartoon = playing.load_selected(c_path + str(cartoon_selector % cartoons_number + 1) + "/")
playing.play(cartoon)
def browse_mode():
cartoon_selector = -1
cartoon = []
cartoon_cursor = 1
cartoons_number = selection.count_files("/media/pi/PAP/cartoons/")
c_path = "/media/pi/PAP/cartoons/pap_"
# if no USB key named PAP containing cartoons is found, displays an error and returns.
# This will crash if it contains images that are not names pap_x and are not 1280 / 1024 size.
if not os.path.exists("/media/pi/PAP/cartoons/"):
cv2.imshow("window", cv2.imread("./load_failed.png"))
cv2.waitKey(0)
return
while (len(cartoon) == 0):
cartoons_number = selection.count_files("/media/pi/PAP/cartoons/")
cv2.imshow("window", cv2.imread("background2.png"))
cv2.waitKey(10)
# builds and displays the image containing thumbnails of the cartoons
print (cartoons_number)
selection.build_selection(cartoon_selector, c_path, cartoons_number)
cv2.waitKey(10)
button_value = button.waitpressedbutton("browse")
if (button_value == "mode"): # the mode is no longer browse, so we return to go to the right mode
return
if (button_value == "next"): # sets the cursor to the next cartoon
cartoon_selector += 1
elif (button_value == "prev"): # sets the cursor to the previous cartoo
cartoon_selector -= 1
elif (button_value == "delete"):
if ((cartoon_selector % cartoons_number + 1) == cartoons_number):
ani_supp = cv2.imread("ani_supp.png", 1)
cv2.imshow("window", ani_supp)
cv2.waitKey(10)
shutil.rmtree(c_path + str(cartoon_selector % cartoons_number + 1))
elif (button_value == "play"): # plays the cartoon
if ((cartoon_selector % cartoons_number + 1) <= 99):
num = cv2.imread("/home/pi/Pas_a_pas/img/num/num" + str(cartoon_selector % cartoons_number + 1) + ".png", 1)
cv2.imshow("window", num)
cv2.waitKey(10)
cartoon = playing.load_selected(c_path + str(cartoon_selector % cartoons_number + 1) + "/")
cartoon_selector = playing.play_other(cartoon, cartoon_selector)
def plot_stack(channel,
name,
var,
bin,
low,
high,
ylabel,
xlabel,
setLog=False):
lumi = 41500.
lumi_str = 41.5
if not os.path.exists(folder):
os.mkdir(folder)
yield_dic = {}
## setup stack plot
stack = THStack('a', 'a')
if var.startswith('mjj'):
added = TH1D('added', 'added', nb, array('d', binLowE))
else:
added = TH1D('added', 'added', bin, low, high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard = build_selection(channel)
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard, "\n"
print 'INFO time is:', datetime.datetime.fromtimestamp(time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
for Type in reordered_physics_processes:
histName = Type + '_' + name + '_' + channel
if var.startswith('mjj'):
Variables[Type] = TH1F(histName, histName, nb, array('d', binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
Variables[Type].StatOverflows(kTRUE)
input_tree = makeTrees(Type, "sr_vbf", channel)
scale = float(lumi * physics_processes[Type]['xsec'])
common_weight = build_weights(channel, Type)
makeTrees(Type, 'sr_vbf',
channel).Draw(var + " >> " + histName,
"(" + cut_standard + " )" + common_weight,
"goff")
print Type, scale, common_weight
if var == "mjj":
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(
Variables[Type].GetNbinsX(),
Variables[Type].GetBinContent(nbins) +
Variables[Type].GetBinContent(nbins + 1))
if Type is 'data':
Variables[Type].SetMarkerStyle(20)
Variables[Type].Scale(1, "width")
else:
Variables[Type].SetFillColor(
TColor.GetColor(physics_processes[Type]['color']))
Variables[Type].SetLineColor(
TColor.GetColor(physics_processes[Type]['color']))
Variables[Type].Scale(scale, "width")
if Type is not 'signal_vbf':
stack.Add(Variables[Type], "hist")
added.Add(Variables[Type])
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp(
time.time())
legend = TLegend(.60, .55, .92, .92)
lastAdded = ''
for process in ordered_physics_processes:
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_vbf' and process is not 'signal_ggf':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "f")
if process is 'data':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "p")
c4 = TCanvas("c4", "c4", 600, 700)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.01)
if setLog:
c4.SetLogy()
if "eta" in var:
stack.SetMaximum(Variables['data'].GetMaximum() +
1e8 * Variables['data'].GetMaximum())
else:
stack.SetMaximum(Variables['data'].GetMaximum() +
1e2 * Variables['data'].GetMaximum())
else:
stack.SetMaximum(Variables['data'].GetMaximum() +
0.5 * Variables['data'].GetMaximum())
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
#stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
if channel is 'signal' and blind:
for b in range(Variables['data'].GetNbinsX()):
Variables['data'].SetBinContent(b + 1, 0.0)
Variables['data'].Draw("Esame")
#Variables['signal_vbf'].SetLineWidth(2)
#Variables['signal_ggf'].SetLineWidth(2)
#Variables['signal_vbf'].SetLineColor(1)
#Variables['signal_ggf'].SetLineColor(4)
#Variables['signal_vbf'].Draw("samehist")
#Variables['signal_ggf'].Draw("samehist")
#legend . AddEntry(Variables['signal_vbf'],physics_processes['signal_vbf']['label'] , "l")
#legend . AddEntry(Variables['signal_ggf'],physics_processes['signal_ggf']['label'] , "l")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw("same")
plot_cms(True, lumi_str, c4)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
data = Variables['data'].Clone()
plot_ratio(False, data, added, bin, xlabel, 0.5, 1.5, 5)
f1 = TF1("f1", "1", -5000, 5000)
f1.SetLineColor(4)
f1.SetLineStyle(2)
f1.SetLineWidth(2)
f1.Draw("same")
Pad.Update()
Pad.RedrawAxis()
c4.SaveAs(folder + '/Histo_vbf_' + name + '_' + channel + '.pdf')
c4.SaveAs(folder + '/Histo_vbf_' + name + '_' + channel + '.png')
c4.SaveAs(folder + '/Histo_vbf_' + name + '_' + channel + '.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor(c4)
stack.IsA().Destructor(stack)
def plot_stack(channel, name,var, bin, low, high, ylabel, xlabel, setLog = False):
if var.startswith('met'):
var = update_var(channel,var)
print "Channel: ", channel, ' Var: ', var
folder = 'test'
yields = {}
stack = THStack('a', 'a')
added = TH1D('a', 'a',bin,low,high)
added.Sumw2()
Variables = {}
cut_standard= build_selection(channel,200)
print "Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard,"\n"
reordered_physics_processes = []
if channel == 'Zll': reordered_physics_processes = reversed(ordered_physics_processes)
else: reordered_physics_processes = ordered_physics_processes
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type+name+channel
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
if Type.startswith('QCD') or Type.startswith('Zll') or \
Type.startswith('others') or Type.startswith('Wlv') or \
Type.startswith('Zvv'):
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
makeTrees(Type,channel).Draw(var + " >> " + histName,"(" + cut_standard + ")* weight","goff")
Variables[Type].Scale(float(lumi)*1000)
stack.Add(Variables[Type],"hist")
added.Add(Variables[Type])
#if Type.startswith('signal'):
# Variables[Type].SetLineColor(1)
# Variables[Type].SetLineWidth(3)
# Variables[Type].SetLineStyle(8)
# Trees[Type].Draw(var + " >> " + histName, "(" + cut_standard + " ) *"+weight , "goff")
# Variables[Type].Scale(float(lumi)*1000)
if Type.startswith("data"):
Variables[Type].SetMarkerStyle(20)
makeTrees(Type,channel).Draw(var + " >> " + histName, "(" + cut_standard + " ) * weight" , "goff")
yields[(Type, channel)] = Variables[Type].Integral()
#print "Type is: ",Type, " Channel is: ", channel, " Integral is: ",Variables[Type].Integral()
legend = TLegend(.60,.60,.92,.92)
for process in ordered_physics_processes:
if process is not 'data':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "f")
else:
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "p")
c4 = TCanvas("c4","c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.1)
if setLog:
c4.SetLogy()
stack.SetMaximum( stack.GetMaximum() + 1000*stack.GetMaximum() )
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
#Variables['signal'].Draw("same")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
plot_cms(True,lumi)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False,data,added,bin,xlabel)
f1 = TF1("f1","1",-5000,5000);
f1.SetLineColor(4);
f1.SetLineStyle(2);
f1.SetLineWidth(2);
f1.Draw("same")
c4.SaveAs(folder+'/Histo_' + name + '_'+channel+'.pdf')
if channel is not 'signal':
#and (('Z' in var and 'Z' in channel) or ('W' in var and 'W' in channel) ):
if 'Z' in channel:
print channel," ratios for ",var,' :' , get_ratio('Zll',Variables['Zll'],added,data,bin)
if 'W' in channel:
print channel," ratios for ",var,' :' , get_ratio('Wln',Variables['Wlv'],added,data,bin)
del Variables
del var
c4.IsA().Destructor( c4 )
stack.IsA().Destructor( stack )
def plot_stack(channel,
name,
var,
bin,
low,
high,
ylabel,
xlabel,
setLog=False):
yield_dic = {}
stack = THStack('a', 'a')
if var is 'met':
binLowE = [200, 250, 300, 350, 400, 500, 600, 1000]
# binLowE = [250,300,350,400,500,600,1000]
added = TH1D('added', 'added', len(binLowE) - 1, array('d', binLowE))
else:
added = TH1D('added', 'added', bin, low, high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard = build_selection(channel, metcut)
if var is 'met':
if channel is not 'signal':
xlabel = 'U [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard, "\n"
print 'INFO time is:', datetime.datetime.fromtimestamp(time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
#reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type + '_' + name + '_' + channel
if var is 'met':
#binLowE = [200,250,300,350,400,500,600,900,1500]
binLowE = [200, 250, 300, 350, 400, 500, 600, 1000]
# binLowE = [250,300,350,400,500,600,1000]
Variables[Type] = TH1F(histName, histName,
len(binLowE) - 1, array('d', binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
#print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
if Type is not 'data':
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0], "read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
#total = h1[Type].GetEntries()
f[Type].Close()
else:
total = 1.0
input_tree = makeTrees(Type, "events", channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
w = "((0.0*(npv>-0.5&&npv<=0.5)+3.30418257204*(npv>0.5&&npv<=1.5)+2.59691269521*(npv>1.5&&npv<=2.5)+2.44251087681*(npv>2.5&&npv<=3.5)+2.42846225153*(npv>3.5&&npv<=4.5)+2.40062512591*(npv>4.5&&npv<=5.5)+2.30279811595*(npv>5.5&&npv<=6.5)+2.12054720297*(npv>6.5&&npv<=7.5)+1.9104708827*(npv>7.5&&npv<=8.5)+1.67904936047*(npv>8.5&&npv<=9.5)+1.43348925382*(npv>9.5&&npv<=10.5)+1.17893952713*(npv>10.5&&npv<=11.5)+0.940505177881*(npv>11.5&&npv<=12.5)+0.740901867872*(npv>12.5&&npv<=13.5)+0.56877478036*(npv>13.5&&npv<=14.5)+0.433148655714*(npv>14.5&&npv<=15.5)+0.325343558476*(npv>15.5&&npv<=16.5)+0.241688459349*(npv>16.5&&npv<=17.5)+0.180491032782*(npv>17.5&&npv<=18.5)+0.136993937378*(npv>18.5&&npv<=19.5)+0.104859480066*(npv>19.5&&npv<=20.5)+0.0768271030309*(npv>20.5&&npv<=21.5)+0.0563426184938*(npv>21.5&&npv<=22.5)+0.0454037058117*(npv>22.5&&npv<=23.5)+0.0359945616383*(npv>23.5&&npv<=24.5)+0.0286879205085*(npv>24.5&&npv<=25.5)+0.0208185595478*(npv>25.5&&npv<=26.5)+0.0170977379612*(npv>26.5&&npv<=27.5)+0.0122446391898*(npv>27.5&&npv<=28.5)+0.0148028308301*(npv>28.5&&npv<=29.5)+0.0120527550003*(npv>29.5&&npv<=30.5)+0.00402643194054*(npv>30.5&&npv<=31.5)+0.00981143754301*(npv>31.5&&npv<=32.5)+0.0*(npv>32.5&&npv<=33.5)+0.0155664899019*(npv>33.5&&npv<=34.5)+0.0*(npv>34.5&&npv<=35.5)+0.0*(npv>35.5&&npv<=36.5)+0.0*(npv>36.5&&npv<=37.5)+0.0*(npv>37.5&&npv<=38.5)+0.0*(npv>38.5&&npv<=39.5)))"
if channel is 'signal' or channel is 'Zmm' or channel is 'Wmn':
w_trig = '((met < 250)*0.97 + (met >=250 && met<350)* 0.987 + (met>=350)* 1.0 )'
else:
w_trig = '(1.0)'
anlo1_over_alo = "(1.24087232993*(genBos_pt>100.0&&genBos_pt<=150.0)+1.55807026252*(genBos_pt>150.0&&genBos_pt<=200.0)+1.51043242876*(genBos_pt>200.0&&genBos_pt<=250.0)+1.47333461572*(genBos_pt>250.0&&genBos_pt<=300.0)+1.43497331471*(genBos_pt>300.0&&genBos_pt<=350.0)+1.37846354687*(genBos_pt>350.0&&genBos_pt<=400.0)+1.2920177717*(genBos_pt>400.0&&genBos_pt<=500.0)+1.31414429236*(genBos_pt>500.0&&genBos_pt<=600.0)+1.20453974747*(genBos_pt>600.0))"
a_ewkcorr = "(0.998568444581*(genBos_pt>100.0&&genBos_pt<=150.0)+0.992098286517*(genBos_pt>150.0&&genBos_pt<=200.0)+0.986010290609*(genBos_pt>200.0&&genBos_pt<=250.0)+0.980265498435*(genBos_pt>250.0&&genBos_pt<=300.0)+0.974830448283*(genBos_pt>300.0&&genBos_pt<=350.0)+0.969676202351*(genBos_pt>350.0&&genBos_pt<=400.0)+0.962417128177*(genBos_pt>400.0&&genBos_pt<=500.0)+0.953511139209*(genBos_pt>500.0&&genBos_pt<=600.0)+0.934331895615*(genBos_pt>600.0))"
w_ewkcorr = "(0.980859240872*(genBos_pt>100.0&&genBos_pt<=150.0)+0.962118764182*(genBos_pt>150.0&&genBos_pt<=200.0)+0.944428528597*(genBos_pt>200.0&&genBos_pt<=250.0)+0.927685912907*(genBos_pt>250.0&&genBos_pt<=300.0)+0.911802238928*(genBos_pt>300.0&&genBos_pt<=350.0)+0.896700388113*(genBos_pt>350.0&&genBos_pt<=400.0)+0.875368225896*(genBos_pt>400.0&&genBos_pt<=500.0)+0.849096933047*(genBos_pt>500.0&&genBos_pt<=600.0)+0.792158791839*(genBos_pt>600.0))"
wnlo012_over_wlo = "(1.89123123702*(genBos_pt>100.0&&genBos_pt<=150.0)+1.70414182145*(genBos_pt>150.0&&genBos_pt<=200.0)+1.60726459197*(genBos_pt>200.0&&genBos_pt<=250.0)+1.57205818769*(genBos_pt>250.0&&genBos_pt<=300.0)+1.51688539716*(genBos_pt>300.0&&genBos_pt<=350.0)+1.41090079307*(genBos_pt>350.0&&genBos_pt<=400.0)+1.30757555038*(genBos_pt>400.0&&genBos_pt<=500.0)+1.32046236765*(genBos_pt>500.0&&genBos_pt<=600.0)+1.26852513234*(genBos_pt>600.0))"
z_ewkcorr = "(0.984525344338*(genBos_pt>100.0&&genBos_pt<=150.0)+0.969078612189*(genBos_pt>150.0&&genBos_pt<=200.0)+0.954626582726*(genBos_pt>200.0&&genBos_pt<=250.0)+0.941059330021*(genBos_pt>250.0&&genBos_pt<=300.0)+0.92828367065*(genBos_pt>300.0&&genBos_pt<=350.0)+0.916219976557*(genBos_pt>350.0&&genBos_pt<=400.0)+0.89931198024*(genBos_pt>400.0&&genBos_pt<=500.0)+0.878692669663*(genBos_pt>500.0&&genBos_pt<=600.0)+0.834717745177*(genBos_pt>600.0))"
znlo012_over_zlo = "(1.68500099066*(genBos_pt>100.0&&genBos_pt<=150.0)+1.55256109189*(genBos_pt>150.0&&genBos_pt<=200.0)+1.52259467479*(genBos_pt>200.0&&genBos_pt<=250.0)+1.52062313572*(genBos_pt>250.0&&genBos_pt<=300.0)+1.4322825541*(genBos_pt>300.0&&genBos_pt<=350.0)+1.45741443405*(genBos_pt>350.0&&genBos_pt<=400.0)+1.36849777989*(genBos_pt>400.0&&genBos_pt<=500.0)+1.3580214432*(genBos_pt>500.0&&genBos_pt<=600.0)+1.16484769869*(genBos_pt>600.0))"
wm_postfit = "(1.0)"
zm_postfit = "(1.0)"
g_postfit = "(1.0)"
# this is the scale using the total number of effective events
scale = 1.0
scale = float(lumi) * physics_processes[Type]['xsec'] / total
#print '\n'
# print "type: ", Type, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if Type is not 'data' and Type is not 'signal_dm' and Type is not 'signal_dm_s' and Type is not 'signal_dm_ps' and Type is not 'signal_dm_v' and Type is not 'signal_dm_av_1_2':
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
if Type.startswith('GJets'):
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*0.98*" + str(g_postfit) +
"*" + str(anlo1_over_alo) + "*" + str(a_ewkcorr) + "*" +
str(w) + "*" + str(w_trig), "goff")
elif (Type.startswith('Zvv') or Type.startswith('Zll')):
if channel is 'signal':
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(zm_postfit) +
"*" + str(z_ewkcorr) + "*" + str(znlo012_over_zlo) +
"*" + str(w) + "*" + str(w_trig), "goff")
else:
#SF explicitly written for the leading tight lepton from the root files
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName, "(" + cut_standard +
")*mcWeight*(0.98 *(lep1Eta<2.1)+0.91*(lep1Eta>=2.1))*"
+ str(zm_postfit) + "*" + str(z_ewkcorr) + "*" +
str(znlo012_over_zlo) + "*" + str(w) + "*" +
str(w_trig), "goff")
elif Type.startswith('Wlv'):
if channel is 'signal':
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(wm_postfit) +
"*" + str(wnlo012_over_wlo) + "*" + str(w_ewkcorr) +
"*" + str(w) + "*" + str(w_trig), "goff")
else:
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(wm_postfit) +
"*" + str(wnlo012_over_wlo) + "*" + str(w_ewkcorr) +
"*" + str(w) + "*" + str(w_trig), "goff")
else:
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName, "(" + cut_standard +
") *mcWeight*" + str(w) + "*" + str(w_trig), "goff")
Variables[Type].Scale(scale, "width")
stack.Add(Variables[Type], "hist")
added.Add(Variables[Type])
if Type.startswith('signal_h_ggf'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(1)
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(w), "goff")
Variables[Type].Scale(scale, "width")
if Type.startswith('signal_dm_av_1_2'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(w), "goff")
Variables[Type].Scale(scale, "width")
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
if channel is 'signal' or channel is 'Zmm' or channel is 'Wmn':
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName, "(" + cut_standard +
" && (triggerFired[0]==1 || triggerFired[1]==1 || triggerFired[2]==1) )",
"goff")
else:
makeTrees(Type, "events",
channel).Draw(var + " >> " + histName,
"(" + cut_standard + " )", "goff")
Variables[Type].Scale(1, "width")
yield_dic[physics_processes[Type]['datacard']] += round(
Variables[Type].Integral("width"), 3)
dump_datacard(channel, yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp(
time.time())
# if channel is 'Zmm' or channel is 'Zee':
# legend = TLegend(.60,.65,.92,.92)
# elif channel is 'gjets':
# legend = TLegend(.60,.65,.82,.92)
# else:
# legend = TLegend(.60,.60,.92,.92)
legend = TLegend(.60, .70, .92, .92)
lastAdded = ''
for process in ordered_physics_processes:
#print process
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_dm' and process is not 'signal_dm_av_1_2' and process is not 'signal_h_ggf' and process is not 'signal_h_vbf':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "f")
if process is 'data':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "p")
c4 = TCanvas("c4", "c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.01)
#stack.SetMinimum(10)
if setLog:
c4.SetLogy()
if var is 'fatjet1tau21' or var is 'fatjet1PrunedM':
stack.SetMaximum(stack.GetMaximum() + 1e5 * stack.GetMaximum())
else:
stack.SetMaximum(stack.GetMaximum() + 1e2 * stack.GetMaximum())
else:
stack.SetMaximum(stack.GetMaximum() + 0.5 * stack.GetMaximum())
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw("same")
plot_cms(True, lumi_str)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False, data, added, bin, xlabel, 0, 2.0, 5)
f1 = TF1("f1", "1", -5000, 5000)
f1.SetLineColor(4)
f1.SetLineStyle(2)
f1.SetLineWidth(2)
f1.Draw("same")
outputName = folder + '/mj_Histo_' + name + '_' + channel
c4.SaveAs(outputName + '.pdf')
c4.SaveAs(outputName + '.png')
c4.SaveAs(outputName + '.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor(c4)
stack.IsA().Destructor(stack)
def plot_stack(channel, name,var, bin, low, high, ylabel, xlabel, setLog = False):
folder = 'test'
yield_Zll = {}
yield_dic = {}
yield_Wln = {}
yield_signal = {}
stack = THStack('a', 'a')
added = TH1D('a', 'a',bin,low,high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard= build_selection(channel,200)
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard,"\n"
print 'INFO time is:', datetime.datetime.fromtimestamp( time.time())
reordered_physics_processes = []
if channel == 'Zll': reordered_physics_processes = reversed(ordered_physics_processes)
else: reordered_physics_processes = ordered_physics_processes
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type+'_'+name+'_'+channel
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
print physics_processes[Type]['files'][0]
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0],"read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
f[Type].Close()
input_tree = makeTrees(Type,"events",channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
w = 1.0;
# this is the scale using the total number of effective events
scale = 1.0;
scale = float(lumi)*physics_processes[Type]['xsec']/total
#print "type: ", Type, "weight", w, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if Type.startswith('QCD') or Type.startswith('Zll') or \
Type.startswith('others') or Type.startswith('Wlv') or \
Type.startswith('Zvv'):
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*" +str(w),"goff")
Variables[Type].Scale(scale)
#print "Type: ", Type, "Total Events:", scale* Variables[Type].GetEntries() , "scale", scale, "raw events", Variables[Type].GetEntries()
stack.Add(Variables[Type],"hist")
added.Add(Variables[Type])
if Type.startswith('signal_higgs'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(w),"goff")
Variables[Type].Scale(scale)
if Type.startswith("data"):
Variables[Type].SetMarkerStyle(20)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + " && triggerFired[0]==1)*mcWeight*"+str(w), "goff")
yield_dic[Type] = round(Variables[Type].Integral(),3)
dump_datacard(channel,yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp( time.time())
legend = TLegend(.60,.60,.92,.92)
for process in ordered_physics_processes:
Variables[process].SetTitle(process)
#Variables[process].Write()
if process is not 'data' and process is not 'Zvv_ht200' and process is not 'Zvv_ht400' and process is not 'Zvv_ht600':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "f")
if process is 'data':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "p")
c4 = TCanvas("c4","c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.1)
if setLog:
c4.SetLogy()
stack.SetMaximum( stack.GetMaximum() + 1000*stack.GetMaximum() )
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
Variables['signal_higgs'].Draw("same")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
plot_cms(True,lumi)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False,data,added,bin,xlabel)
f1 = TF1("f1","1",-5000,5000);
f1.SetLineColor(4);
f1.SetLineStyle(2);
f1.SetLineWidth(2);
f1.Draw("same")
c4.SaveAs(folder+'/Histo_' + name + '_'+channel+'.pdf')
del Variables
del var
del f
del h1
c4.IsA().Destructor( c4 )
stack.IsA().Destructor( stack )
def plot_stack(channel, name,var, bin, low, high, ylabel, xlabel, setLog = False):
folder = '/afs/cern.ch/user/z/zdemirag/www/Monojet/frozen/'
if not os.path.exists(folder):
os.mkdir(folder)
yield_dic = {}
stack = THStack('a', 'a')
added = TH1D('a', 'a',bin,low,high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
#cut_standard= build_selection(channel,0) ### Fix this back to 200 ###
cut_standard= build_selection(channel,200) ### Fix this back to 200 ###
if var is 'met':
if channel is not 'signal':
xlabel = 'U [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard,"\n"
print 'INFO time is:', datetime.datetime.fromtimestamp( time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
#reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type+'_'+name+'_'+channel
if var is 'met':
binLowE = [200,250,300,350,400,500,600,1000]
Variables[Type] = TH1F(histName,histName,7,array('d',binLowE))
Variables[Type].Scale(1,"width");
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
#print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
#print physics_processes[Type]['files'][0]
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0],"read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
f[Type].Close()
input_tree = makeTrees(Type,"events",channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
#w = 1.0;
w = '(3.57041 + -1.49846*npv + 0.515829*npv*npv + -0.0839209*npv*npv*npv + 0.00719964*npv*npv*npv*npv + -0.000354548*npv*npv*npv*npv*npv + 1.01544e-05*npv*npv*npv*npv*npv*npv + -1.58019e-07*npv*npv*npv*npv*npv*npv*npv + 1.03663e-09*npv*npv*npv*npv*npv*npv*npv*npv)'
# this is the scale using the total number of effective events
scale = 1.0;
scale = float(lumi)*physics_processes[Type]['xsec']/total
#print '\n'
print "type: ", Type, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if(shapelimits is True):
# WRITING OUT A TREE
file_out = ROOT.TFile("monojet_"+channel+".root", "update")
if Type is 'data':
selectedTree = input_tree.CopyTree( cut_standard );
else:
selectedTree = input_tree.CopyTree( cut_standard );
scale_w = n.zeros(1,dtype=float)
selectedTree.Branch('scale_w',scale_w,'scale_w/D')
for i in range(selectedTree.GetEntries()):
selectedTree.GetEntry(i)
if Type is 'data':
scale_w[0] = 1.0
else:
scale_w[0] = scale
selectedTree.Fill()
selectedTree.Write(Type+"_"+channel)
file_out.Close()
os.system('root -l -q -b "addMCWeight.C(\\"monojet_'+channel+'.root\\",\\"'+Type+'_'+channel+'\\")"')
if Type is not 'data' and Type is not 'signal_dm':
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
if Type.startswith('GJets'):
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*kfactor*" +str(w),"goff")
else:
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*" +str(w),"goff")
Variables[Type].Scale(scale)
stack.Add(Variables[Type],"hist")
added.Add(Variables[Type])
if Type.startswith('signal_dm'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(w),"goff")
Variables[Type].Scale(scale)
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
if channel is 'signal':
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + "&& (triggerFired[1]==1 || triggerFired[0]==1 || triggerFired[2]==1 ))", "goff")
else:
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + ")", "goff")
yield_dic[physics_processes[Type]['datacard']] += round(Variables[Type].Integral(),3)
#print Type, round(Variables[Type].Integral(),3), "total in: ", physics_processes[Type]['datacard'], yield_dic[physics_processes[Type]['datacard']]
dump_datacard(channel,yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp( time.time())
legend = TLegend(.60,.60,.92,.92)
lastAdded = ''
for process in ordered_physics_processes:
#print process
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_dm':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "f")
if process is 'data':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "p")
if process is 'signal_dm':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "l")
c4 = TCanvas("c4","c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.1)
if setLog:
c4.SetLogy()
stack.SetMaximum( stack.GetMaximum() + 1000*stack.GetMaximum() )
else:
stack.SetMaximum( stack.GetMaximum() + stack.GetMaximum() )
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
Variables['signal_dm'].Draw("same")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
plot_cms(True,lumi)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False,data,added,bin,xlabel)
f1 = TF1("f1","1",-5000,5000);
f1.SetLineColor(4);
f1.SetLineStyle(2);
f1.SetLineWidth(2);
f1.Draw("same")
c4.SaveAs(folder+'/'+channel+'/Histo_' + name + '_'+channel+'.pdf')
c4.SaveAs(folder+'/'+channel+'/Histo_' + name + '_'+channel+'.png')
c4.SaveAs(folder+'/'+channel+'/Histo_' + name + '_'+channel+'.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor( c4 )
stack.IsA().Destructor( stack )
def plot_stack(channel,
name,
var,
bin,
low,
high,
ylabel,
xlabel,
setLog=False):
folder = '/afs/cern.ch/user/z/zdemirag/www/monojet/panda/v1'
if not os.path.exists(folder):
os.mkdir(folder)
yield_dic = {}
stack = THStack('a', 'a')
if var.startswith('pfmet') or var.startswith('pfU'):
binLowE = [
250.0, 280.0, 310.0, 340.0, 370.0, 400.0, 430.0, 470.0, 510.0,
550.0, 590.0, 640.0, 690.0, 740.0, 790.0, 840.0, 900.0, 960.0,
1020.0, 1090.0, 1160.0, 1250.0, 1400.0
]
nb = len(binLowE) - 1
added = TH1D('added', 'added', nb, array('d', binLowE))
else:
added = TH1D('added', 'added', bin, low, high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard = build_selection(channel)
if var.startswith('pfU'):
if channel is not 'signal':
xlabel = 'Hadronic Recoil p_{T} [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard, "\n"
print 'INFO time is:', datetime.datetime.fromtimestamp(time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
histName = Type + '_' + name + '_' + channel
if var.startswith('pfmet') or var.startswith('pfU'):
binLowE = [
250.0, 280.0, 310.0, 340.0, 370.0, 400.0, 430.0, 470.0, 510.0,
550.0, 590.0, 640.0, 690.0, 740.0, 790.0, 840.0, 900.0, 960.0,
1020.0, 1090.0, 1160.0, 1250.0, 1400.0
]
n2 = len(binLowE) - 1
Variables[Type] = TH1F(histName, histName, n2, array('d', binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
Variables[Type].StatOverflows(kTRUE)
input_tree = makeTrees(Type, "events", channel)
n_entries = input_tree.GetEntries()
scale = float(lumi) # *physics_processes[Type]['xsec']/total
common_weight = build_weights(channel, Type)
print Type, common_weight, scale
#if Type is not 'data' and Type is not 'signal_ggf' and Type is not 'signal_vbf':
if Type is not 'data' and Type is not 'signal_ggf':
Variables[Type].SetFillColor(
TColor.GetColor(physics_processes[Type]['color']))
Variables[Type].SetLineColor(1)
makeTrees(Type, 'events',
channel).Draw(var + " >> " + histName,
"(" + cut_standard + " )" + common_weight,
"goff")
if var == "pfmet" or var.startswith('pfU'):
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(
Variables[Type].GetNbinsX(),
Variables[Type].GetBinContent(nbins) +
Variables[Type].GetBinContent(nbins + 1))
Variables[Type].Scale(scale, "width")
stack.Add(Variables[Type], "hist")
added.Add(Variables[Type])
if Type.startswith('signal'):
makeTrees(Type, 'events',
channel).Draw(var + " >> " + histName,
"(" + cut_standard + ")" + common_weight,
"goff")
if var == "pfmet" or var.startswith('pfU'):
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(
Variables[Type].GetNbinsX(),
Variables[Type].GetBinContent(nbins) +
Variables[Type].GetBinContent(nbins + 1))
Variables[Type].Scale(scale, "width")
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
if channel in 'signal' or channel in 'Zmm' or channel in 'Wmn':
#met trigger
#makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + "&& (trigger&1)!=0)", "goff")
makeTrees(Type, "events",
channel).Draw(var + " >> " + histName, cut_standard,
"goff")
elif channel in 'GJets':
#photon trigger - no need, the flag is not working properly
makeTrees(Type, "events",
channel).Draw(var + " >> " + histName, cut_standard,
"goff")
else:
#ele trigger
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName,
"(" + cut_standard + "&& (trigger&2)!=0)", "goff")
if var == "pfmet" or var.startswith('pfU'):
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(
Variables[Type].GetNbinsX(),
Variables[Type].GetBinContent(nbins) +
Variables[Type].GetBinContent(nbins + 1))
Variables[Type].Scale(1, "width")
yield_dic[physics_processes[Type]['datacard']] += round(
Variables[Type].Integral("width"), 3)
dump_datacard(channel, yield_dic)
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp(
time.time())
if channel is 'GJets':
legend = TLegend(.60, .65, .82, .92)
else:
legend = TLegend(.60, .55, .92, .92)
lastAdded = ''
for process in ordered_physics_processes:
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
#if process is not 'data' and process is not 'signal_vbf' and process is not 'signal_ggf':
if process is not 'data' and process is not 'signal_ggf':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "f")
if process is 'data':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "p")
c4 = TCanvas("c4", "c4", 600, 700)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.01)
if setLog:
c4.SetLogy()
if "eta" in var:
stack.SetMaximum(Variables['data'].GetMaximum() +
1e8 * Variables['data'].GetMaximum())
else:
stack.SetMaximum(Variables['data'].GetMaximum() +
1e2 * Variables['data'].GetMaximum())
else:
stack.SetMaximum(Variables['data'].GetMaximum() +
0.5 * Variables['data'].GetMaximum())
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
if channel is 'signal' and blind:
for b in range(Variables['data'].GetNbinsX()):
Variables['data'].SetBinContent(b + 1, 0.0)
Variables['data'].Draw("Esame")
#Variables['signal_vbf'].SetLineWidth(2)
Variables['signal_ggf'].SetLineWidth(2)
#Variables['signal_vbf'].SetLineColor(1)
Variables['signal_ggf'].SetLineColor(4)
#Variables['signal_vbf'].Draw("samehist")
#Variables['signal_ggf'].Draw("samehist")
#legend . AddEntry(Variables['signal_vbf'],physics_processes['signal_vbf']['label'] , "l")
#legend . AddEntry(Variables['signal_ggf'],physics_processes['signal_ggf']['label'] , "l")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw("same")
plot_cms(True, lumi_str, c4)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
data = Variables['data'].Clone()
plot_ratio(False, data, added, bin, xlabel, 0.5, 1.5, 5)
f1 = TF1("f1", "1", -5000, 5000)
f1.SetLineColor(4)
f1.SetLineStyle(2)
f1.SetLineWidth(2)
f1.Draw("same")
Pad.Update()
Pad.RedrawAxis()
c4.SaveAs(folder + '/Histo_' + category + '_' + name + '_' + channel +
'.pdf')
c4.SaveAs(folder + '/Histo_' + category + '_' + name + '_' + channel +
'.png')
c4.SaveAs(folder + '/Histo_' + category + '_' + name + '_' + channel +
'.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor(c4)
stack.IsA().Destructor(stack)
def plot_stack(channel, name,var, bin, low, high, ylabel, xlabel, setLog = False):
folder = '/afs/cern.ch/user/d/dabercro/www/plots/' + os.environ.get('CROMBIEDATE') + '_monojet/'
if not os.path.exists(folder):
os.mkdir(folder)
yield_dic = {}
stack = THStack('a', 'a')
if var.startswith('met'):
if vtag or vbf:
binLowE = [250,300,350,400,500,600,750,1000]
else:
binLowE = [200., 230., 260.0, 290.0, 320.0, 350.0, 390.0, 430.0, 470.0, 510.0, 550.0, 590.0, 640.0, 690.0, 740.0, 790.0, 840.0, 900.0, 960.0, 1020.0, 1090.0, 1160.0, 1250.0]
nb = len(binLowE)-1
added = TH1D('added','added',nb,array('d',binLowE))
else:
added = TH1D('added', 'added',bin,low,high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard= build_selection(channel,metcut)
if var.startswith('met'):
if channel is not 'signal':
xlabel = 'U [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard,"\n"
print 'INFO time is:', datetime.datetime.fromtimestamp( time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
if not vbf:
if Type.startswith('EWK') :
continue
#Create the Histograms
histName = Type+'_'+name+'_'+channel
if var.startswith('met'):
if vtag or vbf :
binLowE = [250,300,350,400,500,600,750,1000]
else:
binLowE = [200., 230., 260.0, 290.0, 320.0, 350.0, 390.0, 430.0, 470.0, 510.0, 550.0, 590.0, 640.0, 690.0, 740.0, 790.0, 840.0, 900.0, 960.0, 1020.0, 1090.0, 1160.0, 1250.0]
n2 = len(binLowE)-1
Variables[Type] = TH1F(histName,histName,n2,array('d',binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
Variables[Type].StatOverflows(kTRUE)
if Type is not 'data':
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0],"read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
f[Type].Close()
else:
total = 1.0
input_tree = makeTrees(Type,"events",channel)
n_entries = input_tree.GetEntries()
w_trig = '(1.0)'
# this is the scale using the total number of effective events
scale = 1.0;
scale = float(lumi)*physics_processes[Type]['xsec']/total
#print Type, scale, total
##Common weights used for the analysis
triggercut = "(1.0)"
if channel is 'signal':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[10]==1 || triggerFired[11] == 1 || triggerFired[12] || triggerFired[13] == 1)"
else:
common_weight = "*mcWeight*lepton_SF1*lepton_SF2*METTrigger*puWeight*topPtReweighting"
elif channel is 'Zmm':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[10]==1 || triggerFired[11] == 1 || triggerFired[12] || triggerFired[13] == 1)"
else:
common_weight = "*mcWeight*lepton_SF1*lepton_SF2*METTrigger*puWeight*topPtReweighting*tracking_SF1*tracking_SF2"
elif channel is 'Wmn':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[10]==1 || triggerFired[11] == 1 || triggerFired[12] || triggerFired[13] == 1)"
else:
common_weight = "*mcWeight*lepton_SF1*lepton_SF2*METTrigger*puWeight*topPtReweighting*tracking_SF1"
elif channel is 'Wen':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[0] || triggerFired[1] || triggerFired[2] || triggerFired[3] || triggerFired[4] || triggerFired[5] || triggerFired[26])"
else:
common_weight = "*mcWeight*puWeight*EleTrigger_w1*EleTrigger_w2*lepton_SF1*topPtReweighting*gsfTracking_SF1"
elif channel is 'Zee':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[0] || triggerFired[1] || triggerFired[2] || triggerFired[3] || triggerFired[4] || triggerFired[5] || triggerFired[26])"
else:
common_weight = "*mcWeight*puWeight*lepton_SF1*lepton_SF2*topPtReweighting*gsfTracking_SF1*gsfTracking_SF2"
else:
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[18] || triggerFired[19] || triggerFired[17] || triggerFired[5] || triggerFired[15] || triggerFired[16])"
else:
common_weight = "*mcWeight*PhoTrigger*topPtReweighting*photon_SF*puWeight"
#common_weight = "*mcWeight*lepton_SF1*lepton_SF2*puWeight_true"
if Type is not 'data' and Type is not 'signal_ggf' and Type is not 'signal_vbf':
Variables[Type].SetFillColor(TColor.GetColor(physics_processes[Type]['color']))
Variables[Type].SetLineColor(TColor.GetColor(physics_processes[Type]['color']))
if Type.startswith('GJets') :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_a*akfactor","goff")
#makeTrees(Type,'events',channel).Draw("photonPt*0.98 >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_a*akfactor","goff")
elif (Type.startswith('Zvv')):
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_z*zkfactor","goff")
elif (Type.startswith('Zll')) :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_z*zkfactor","goff")
#makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_z","goff")
elif Type.startswith('Wlv'):
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_w","goff")
elif Type.startswith('QCD') and channel is 'signal':
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight,"goff")
else:
if Type.startswith('QCD') and channel is 'gjets':
# qcd is from the purity study
purity = "((0.04802 * (photonPt >= 175 && photonPt < 200 )) + \
(0.04241 * (photonPt >= 200 && photonPt < 250 )) + \
(0.0364 * (photonPt >= 250 && photonPt < 300 )) + \
(0.0333 * (photonPt >= 300 && photonPt < 350 )) + \
(0.02544 * (photonPt >= 350)))"
#purity = "((0.0320 * (photonPt >= 175 && photonPt < 200 )) + \
# (0.0253 * (photonPt >= 200 && photonPt < 250 )) + \
# (0.0211 * (photonPt >= 250 && photonPt < 300 )) + \
# (0.0223 * (photonPt >= 300 && photonPt < 350 )) + \
# (0.0187 * (photonPt >= 350)))"
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_a*akfactor*"+str(purity),"goff")
#makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*"+str(purity),"goff")
else:
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight,"goff")
if var == "met":
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(Variables[Type].GetNbinsX(),Variables[Type].GetBinContent(nbins)+Variables[Type].GetBinContent(nbins+1))
Variables[Type].Scale(scale,"width")
stack.Add(Variables[Type],"hist")
added.Add(Variables[Type])
if Type.startswith('signal'):
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight,"goff")
if var == "met":
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(Variables[Type].GetNbinsX(),Variables[Type].GetBinContent(nbins)+Variables[Type].GetBinContent(nbins+1))
Variables[Type].Scale(scale,"width")
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
#makeTrees(Type,'events',channel).Draw("photonPt*1.02 >> " + histName,"(" + cut_standard +" && metfilter==1 && filterbadChCandidate && filterbadPFMuon && "+triggercut+")","goff")
#makeTrees(Type,'events',channel).Draw("transverseMass(lep1Pt*1.02,lep1Phi,trueMet,trueMetPhi) >> " + histName,"(" + cut_standard +" && metfilter==1 && filterbadChCandidate && filterbadPFMuon && "+triggercut+")","goff")
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard +" && metfilter==1 && filterbadChCandidate && filterbadPFMuon && "+triggercut+")", "goff")
if var == "met":
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(Variables[Type].GetNbinsX(),Variables[Type].GetBinContent(nbins)+Variables[Type].GetBinContent(nbins+1))
Variables[Type].Scale(1,"width")
yield_dic[physics_processes[Type]['datacard']] += round(Variables[Type].Integral("width"),3)
#yield_dic[physics_processes[Type]['datacard']] += round(Variables[Type].Integral(),3)
#print Type, round(Variables[Type].Integral(),3), "total in: ", physics_processes[Type]['datacard'], yield_dic[physics_processes[Type]['datacard']], round(Variables[Type].Integral("width"),3)
dump_datacard(channel,yield_dic)
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp( time.time())
if channel is 'gjets':
legend = TLegend(.60,.65,.82,.92)
else:
legend = TLegend(.60,.55,.92,.92)
lastAdded = ''
for process in ordered_physics_processes:
#print process
if not vbf:
if process.startswith('EWK'):
continue
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_vbf' and process is not 'signal_ggf':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "f")
if process is 'data':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "p")
#c4 = TCanvas("c4","c4", 900, 1000)
c4 = TCanvas("c4","c4", 600, 700)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
#stack.Add(h_temp)
stack.SetMinimum(0.01)
#stack.SetMinimum(1)
if setLog:
c4.SetLogy()
if var == "ht_cleanedjets":
#stack.SetMaximum( stack.GetMaximum() + 1e1*stack.GetMaximum() )
stack.SetMaximum( Variables['data'].GetMaximum() + 1e3*Variables['data'].GetMaximum() )
else:
stack.SetMaximum( Variables['data'].GetMaximum() + 1e1*Variables['data'].GetMaximum() )
#stack.SetMaximum( Variables['data'].GetMaximum() + 1e4*Variables['data'].GetMaximum() )
else:
#stack.SetMaximum( stack.GetMaximum() + 0.5*stack.GetMaximum() )
#stack.SetMaximum( stack.GetMaximum() + 1*stack.GetMaximum() )
stack.SetMaximum( Variables['data'].GetMaximum() + 0.5*Variables['data'].GetMaximum() )
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
if channel is 'signal' and blind:
for b in range(Variables['data'].GetNbinsX()):
Variables['data'].SetBinContent(b+1,0.0)
Variables['data'].Draw("Esame")
Variables['signal_vbf'].SetLineWidth(2)
#Variables['signal_ggf'].SetLineWidth(2)
Variables['signal_vbf'].SetLineColor(1)
#Variables['signal_ggf'].SetLineColor(4)
Variables['signal_vbf'].Draw("samehist")
#Variables['signal_ggf'].Draw("samehist")
legend . AddEntry(Variables['signal_vbf'],physics_processes['signal_vbf']['label'] , "l")
#legend . AddEntry(Variables['signal_ggf'],physics_processes['signal_ggf']['label'] , "l")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
plot_cms(True,lumi_str,c4)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
data = Variables['data'].Clone()
if vtag is True:
plot_ratio(False,data,added,bin,xlabel,0.25,1.75,5)
else:
if channel is 'Zmm' and var.startswith('met'):
plot_ratio(False,data,added,bin,xlabel,0.5,1.5,5)
else:
plot_ratio(False,data,added,bin,xlabel,0.5,1.5,5)
f1 = TF1("f1","1",-5000,5000);
f1.SetLineColor(4);
f1.SetLineStyle(2);
f1.SetLineWidth(2);
f1.Draw("same")
Pad.Update()
Pad.RedrawAxis()
if vtag is True:
c4.SaveAs(folder+'/Histo_vtag_' + name + '_'+channel+'.pdf')
c4.SaveAs(folder+'/Histo_vtag_' + name + '_'+channel+'.png')
c4.SaveAs(folder+'/Histo_vtag_' + name + '_'+channel+'.C')
elif vbf is True:
c4.SaveAs(folder+'/Histo_vbf_' + name + '_'+channel+'.pdf')
c4.SaveAs(folder+'/Histo_vbf_' + name + '_'+channel+'.png')
c4.SaveAs(folder+'/Histo_vbf_' + name + '_'+channel+'.C')
else:
#c4.SaveAs(folder+'/Histo_notrig_monojet_'+ name + '_'+channel+'.pdf')
#c4.SaveAs(folder+'/Histo_notrig_monojet_'+ name + '_'+channel+'.png')
#c4.SaveAs(folder+'/Histo_notrig_monojet_'+ name + '_'+channel+'.C')
c4.SaveAs(folder+'/Histo_monojet_'+ name + '_'+channel+'.pdf')
c4.SaveAs(folder+'/Histo_monojet_'+ name + '_'+channel+'.png')
c4.SaveAs(folder+'/Histo_monojet_'+ name + '_'+channel+'.C')
#c4.SaveAs(folder+'/Histo_monojet_'+str(lumi_str)+'_' + name + '_'+channel+'.pdf')
#c4.SaveAs(folder+'/Histo_monojet_'+str(lumi_str)+'_' + name + '_'+channel+'.png')
#c4.SaveAs(folder+'/Histo_monojet_'+str(lumi_str)+'_' + name + '_'+channel+'.C')
###### Create a root file and save the histograms individually
#if var == 'photonPt' or var=='dilepPt':
if var=='met':
fhist = ROOT.TFile(channel+"_fullhist.root","recreate")
fhist.cd();
for Type in reordered_physics_processes:
Variables[Type].Write()
fhist.Write()
fhist.Close()
del Variables
del var
del f
del h1
c4.IsA().Destructor( c4 )
stack.IsA().Destructor( stack )
def plot_stack(channel, name,var, bin, low, high, ylabel, xlabel, setLog = False):
yield_dic = {}
stack = THStack('a', 'a')
if var is 'met':
binLowE = [200,250,300,350,400,500,600,1000]
# binLowE = [250,300,350,400,500,600,1000]
added = TH1D('added','added',len(binLowE)-1,array('d',binLowE))
else:
added = TH1D('added', 'added',bin,low,high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard= build_selection(channel,metcut)
if var is 'met':
if channel is not 'signal':
xlabel = 'U [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard,"\n"
print 'INFO time is:', datetime.datetime.fromtimestamp( time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
#reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type+'_'+name+'_'+channel
if var is 'met':
#binLowE = [200,250,300,350,400,500,600,900,1500]
binLowE = [200,250,300,350,400,500,600,1000]
# binLowE = [250,300,350,400,500,600,1000]
Variables[Type] = TH1F(histName,histName,len(binLowE)-1,array('d',binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
#print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
if Type is not 'data':
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0],"read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
#total = h1[Type].GetEntries()
f[Type].Close()
else:
total = 1.0
input_tree = makeTrees(Type,"events",channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
w = "((0.0*(npv>-0.5&&npv<=0.5)+3.30418257204*(npv>0.5&&npv<=1.5)+2.59691269521*(npv>1.5&&npv<=2.5)+2.44251087681*(npv>2.5&&npv<=3.5)+2.42846225153*(npv>3.5&&npv<=4.5)+2.40062512591*(npv>4.5&&npv<=5.5)+2.30279811595*(npv>5.5&&npv<=6.5)+2.12054720297*(npv>6.5&&npv<=7.5)+1.9104708827*(npv>7.5&&npv<=8.5)+1.67904936047*(npv>8.5&&npv<=9.5)+1.43348925382*(npv>9.5&&npv<=10.5)+1.17893952713*(npv>10.5&&npv<=11.5)+0.940505177881*(npv>11.5&&npv<=12.5)+0.740901867872*(npv>12.5&&npv<=13.5)+0.56877478036*(npv>13.5&&npv<=14.5)+0.433148655714*(npv>14.5&&npv<=15.5)+0.325343558476*(npv>15.5&&npv<=16.5)+0.241688459349*(npv>16.5&&npv<=17.5)+0.180491032782*(npv>17.5&&npv<=18.5)+0.136993937378*(npv>18.5&&npv<=19.5)+0.104859480066*(npv>19.5&&npv<=20.5)+0.0768271030309*(npv>20.5&&npv<=21.5)+0.0563426184938*(npv>21.5&&npv<=22.5)+0.0454037058117*(npv>22.5&&npv<=23.5)+0.0359945616383*(npv>23.5&&npv<=24.5)+0.0286879205085*(npv>24.5&&npv<=25.5)+0.0208185595478*(npv>25.5&&npv<=26.5)+0.0170977379612*(npv>26.5&&npv<=27.5)+0.0122446391898*(npv>27.5&&npv<=28.5)+0.0148028308301*(npv>28.5&&npv<=29.5)+0.0120527550003*(npv>29.5&&npv<=30.5)+0.00402643194054*(npv>30.5&&npv<=31.5)+0.00981143754301*(npv>31.5&&npv<=32.5)+0.0*(npv>32.5&&npv<=33.5)+0.0155664899019*(npv>33.5&&npv<=34.5)+0.0*(npv>34.5&&npv<=35.5)+0.0*(npv>35.5&&npv<=36.5)+0.0*(npv>36.5&&npv<=37.5)+0.0*(npv>37.5&&npv<=38.5)+0.0*(npv>38.5&&npv<=39.5)))"
if channel is 'signal' or channel is 'Zmm' or channel is 'Wmn':
w_trig = '((met < 250)*0.97 + (met >=250 && met<350)* 0.987 + (met>=350)* 1.0 )'
else:
w_trig = '(1.0)'
anlo1_over_alo = "(1.24087232993*(genBos_pt>100.0&&genBos_pt<=150.0)+1.55807026252*(genBos_pt>150.0&&genBos_pt<=200.0)+1.51043242876*(genBos_pt>200.0&&genBos_pt<=250.0)+1.47333461572*(genBos_pt>250.0&&genBos_pt<=300.0)+1.43497331471*(genBos_pt>300.0&&genBos_pt<=350.0)+1.37846354687*(genBos_pt>350.0&&genBos_pt<=400.0)+1.2920177717*(genBos_pt>400.0&&genBos_pt<=500.0)+1.31414429236*(genBos_pt>500.0&&genBos_pt<=600.0)+1.20453974747*(genBos_pt>600.0))"
a_ewkcorr = "(0.998568444581*(genBos_pt>100.0&&genBos_pt<=150.0)+0.992098286517*(genBos_pt>150.0&&genBos_pt<=200.0)+0.986010290609*(genBos_pt>200.0&&genBos_pt<=250.0)+0.980265498435*(genBos_pt>250.0&&genBos_pt<=300.0)+0.974830448283*(genBos_pt>300.0&&genBos_pt<=350.0)+0.969676202351*(genBos_pt>350.0&&genBos_pt<=400.0)+0.962417128177*(genBos_pt>400.0&&genBos_pt<=500.0)+0.953511139209*(genBos_pt>500.0&&genBos_pt<=600.0)+0.934331895615*(genBos_pt>600.0))"
w_ewkcorr = "(0.980859240872*(genBos_pt>100.0&&genBos_pt<=150.0)+0.962118764182*(genBos_pt>150.0&&genBos_pt<=200.0)+0.944428528597*(genBos_pt>200.0&&genBos_pt<=250.0)+0.927685912907*(genBos_pt>250.0&&genBos_pt<=300.0)+0.911802238928*(genBos_pt>300.0&&genBos_pt<=350.0)+0.896700388113*(genBos_pt>350.0&&genBos_pt<=400.0)+0.875368225896*(genBos_pt>400.0&&genBos_pt<=500.0)+0.849096933047*(genBos_pt>500.0&&genBos_pt<=600.0)+0.792158791839*(genBos_pt>600.0))"
wnlo012_over_wlo = "(1.89123123702*(genBos_pt>100.0&&genBos_pt<=150.0)+1.70414182145*(genBos_pt>150.0&&genBos_pt<=200.0)+1.60726459197*(genBos_pt>200.0&&genBos_pt<=250.0)+1.57205818769*(genBos_pt>250.0&&genBos_pt<=300.0)+1.51688539716*(genBos_pt>300.0&&genBos_pt<=350.0)+1.41090079307*(genBos_pt>350.0&&genBos_pt<=400.0)+1.30757555038*(genBos_pt>400.0&&genBos_pt<=500.0)+1.32046236765*(genBos_pt>500.0&&genBos_pt<=600.0)+1.26852513234*(genBos_pt>600.0))"
z_ewkcorr = "(0.984525344338*(genBos_pt>100.0&&genBos_pt<=150.0)+0.969078612189*(genBos_pt>150.0&&genBos_pt<=200.0)+0.954626582726*(genBos_pt>200.0&&genBos_pt<=250.0)+0.941059330021*(genBos_pt>250.0&&genBos_pt<=300.0)+0.92828367065*(genBos_pt>300.0&&genBos_pt<=350.0)+0.916219976557*(genBos_pt>350.0&&genBos_pt<=400.0)+0.89931198024*(genBos_pt>400.0&&genBos_pt<=500.0)+0.878692669663*(genBos_pt>500.0&&genBos_pt<=600.0)+0.834717745177*(genBos_pt>600.0))"
znlo012_over_zlo = "(1.68500099066*(genBos_pt>100.0&&genBos_pt<=150.0)+1.55256109189*(genBos_pt>150.0&&genBos_pt<=200.0)+1.52259467479*(genBos_pt>200.0&&genBos_pt<=250.0)+1.52062313572*(genBos_pt>250.0&&genBos_pt<=300.0)+1.4322825541*(genBos_pt>300.0&&genBos_pt<=350.0)+1.45741443405*(genBos_pt>350.0&&genBos_pt<=400.0)+1.36849777989*(genBos_pt>400.0&&genBos_pt<=500.0)+1.3580214432*(genBos_pt>500.0&&genBos_pt<=600.0)+1.16484769869*(genBos_pt>600.0))"
wm_postfit = "(1.0)"
zm_postfit = "(1.0)"
g_postfit = "(1.0)"
# this is the scale using the total number of effective events
scale = 1.0;
scale = float(lumi)*physics_processes[Type]['xsec']/total
#print '\n'
# print "type: ", Type, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if Type is not 'data' and Type is not 'signal_dm' and Type is not 'signal_dm_s' and Type is not 'signal_dm_ps' and Type is not 'signal_dm_v' and Type is not 'signal_dm_av_1_2':
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
if Type.startswith('GJets') :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*0.98*"+str(g_postfit)+"*"+str(anlo1_over_alo)+"*"+str(a_ewkcorr)+"*" +str(w)+"*"+str(w_trig),"goff")
elif (Type.startswith('Zvv') or Type.startswith('Zll')) :
if channel is 'signal' :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(zm_postfit)+"*"+str(z_ewkcorr)+"*"+str(znlo012_over_zlo)+"*"+str(w)+"*"+str(w_trig),"goff")
else:
#SF explicitly written for the leading tight lepton from the root files
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*(0.98 *(lep1Eta<2.1)+0.91*(lep1Eta>=2.1))*"+str(zm_postfit)+"*"+str(z_ewkcorr)+"*"+str(znlo012_over_zlo)+"*"+str(w)+"*"+str(w_trig),"goff")
elif Type.startswith('Wlv'):
if channel is 'signal' :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(wm_postfit)+"*"+str(wnlo012_over_wlo)+"*"+str(w_ewkcorr)+"*" +str(w)+"*"+str(w_trig),"goff")
else:
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(wm_postfit)+"*"+str(wnlo012_over_wlo)+"*"+str(w_ewkcorr)+"*" +str(w)+"*"+str(w_trig),"goff")
else:
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ") *mcWeight*" +str(w)+"*"+str(w_trig),"goff")
Variables[Type].Scale(scale,"width")
stack.Add(Variables[Type],"hist")
added.Add(Variables[Type])
if Type.startswith('signal_h_ggf'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(1)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(w),"goff")
Variables[Type].Scale(scale,"width")
if Type.startswith('signal_dm_av_1_2'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(w),"goff")
Variables[Type].Scale(scale,"width")
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
if channel is 'signal' or channel is 'Zmm' or channel is 'Wmn':
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + " && (triggerFired[0]==1 || triggerFired[1]==1 || triggerFired[2]==1) )", "goff")
else:
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard +" )", "goff")
Variables[Type].Scale(1,"width")
yield_dic[physics_processes[Type]['datacard']] += round(Variables[Type].Integral("width"),3)
dump_datacard(channel,yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp( time.time())
# if channel is 'Zmm' or channel is 'Zee':
# legend = TLegend(.60,.65,.92,.92)
# elif channel is 'gjets':
# legend = TLegend(.60,.65,.82,.92)
# else:
# legend = TLegend(.60,.60,.92,.92)
legend = TLegend(.60,.70,.92,.92)
lastAdded = ''
for process in ordered_physics_processes:
#print process
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_dm' and process is not 'signal_dm_av_1_2' and process is not 'signal_h_ggf' and process is not 'signal_h_vbf':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "f")
if process is 'data':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "p")
c4 = TCanvas("c4","c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.01)
#stack.SetMinimum(10)
if setLog:
c4.SetLogy()
if var is 'fatjet1tau21' or var is 'fatjet1PrunedM':
stack.SetMaximum( stack.GetMaximum() + 1e5*stack.GetMaximum() )
else:
stack.SetMaximum( stack.GetMaximum() + 1e2*stack.GetMaximum() )
else:
stack.SetMaximum( stack.GetMaximum() + 0.5*stack.GetMaximum() )
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
plot_cms(True,lumi_str)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False,data,added,bin,xlabel,0,2.0,5)
f1 = TF1("f1","1",-5000,5000);
f1.SetLineColor(4);
f1.SetLineStyle(2);
f1.SetLineWidth(2);
f1.Draw("same")
outputName = folder+'/mj_Histo_' + name + '_'+channel
c4.SaveAs(outputName+'.pdf')
c4.SaveAs(outputName+'.png')
c4.SaveAs(outputName+'.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor( c4 )
stack.IsA().Destructor( stack )
def plot_stack(channel,
name,
var,
bin,
low,
high,
ylabel,
xlabel,
setLog=False):
folder = 'test'
yield_Zll = {}
yield_dic = {}
yield_Wln = {}
yield_signal = {}
stack = THStack('a', 'a')
added = TH1D('a', 'a', bin, low, high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard = build_selection(channel, 200)
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard, "\n"
print 'INFO time is:', datetime.datetime.fromtimestamp(time.time())
reordered_physics_processes = []
if channel == 'Zll':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type + '_' + name + '_' + channel
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
print physics_processes[Type]['files'][0]
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0], "read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
f[Type].Close()
input_tree = makeTrees(Type, "events", channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
w = 1.0
# this is the scale using the total number of effective events
scale = 1.0
scale = float(lumi) * physics_processes[Type]['xsec'] / total
#print "type: ", Type, "weight", w, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if Type.startswith('QCD') or Type.startswith('Zll') or \
Type.startswith('others') or Type.startswith('Wlv') or \
Type.startswith('Zvv'):
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(w), "goff")
Variables[Type].Scale(scale)
#print "Type: ", Type, "Total Events:", scale* Variables[Type].GetEntries() , "scale", scale, "raw events", Variables[Type].GetEntries()
stack.Add(Variables[Type], "hist")
added.Add(Variables[Type])
if Type.startswith('signal_higgs'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(w), "goff")
Variables[Type].Scale(scale)
if Type.startswith("data"):
Variables[Type].SetMarkerStyle(20)
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName, "(" + cut_standard +
" && triggerFired[0]==1)*mcWeight*" + str(w), "goff")
yield_dic[Type] = round(Variables[Type].Integral(), 3)
dump_datacard(channel, yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp(
time.time())
legend = TLegend(.60, .60, .92, .92)
for process in ordered_physics_processes:
Variables[process].SetTitle(process)
#Variables[process].Write()
if process is not 'data' and process is not 'Zvv_ht200' and process is not 'Zvv_ht400' and process is not 'Zvv_ht600':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "f")
if process is 'data':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "p")
c4 = TCanvas("c4", "c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.1)
if setLog:
c4.SetLogy()
stack.SetMaximum(stack.GetMaximum() + 1000 * stack.GetMaximum())
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
Variables['signal_higgs'].Draw("same")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw("same")
plot_cms(True, lumi)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False, data, added, bin, xlabel)
f1 = TF1("f1", "1", -5000, 5000)
f1.SetLineColor(4)
f1.SetLineStyle(2)
f1.SetLineWidth(2)
f1.Draw("same")
c4.SaveAs(folder + '/Histo_' + name + '_' + channel + '.pdf')
del Variables
del var
del f
del h1
c4.IsA().Destructor(c4)
stack.IsA().Destructor(stack)