names=["histo1", "histo2"]
# set drawing options
legends=["Test Histo 1", "Test Histo 2"]
options=["LPE", "HIST"]
opt_legends=["LPE", "L"]
marker_types=[22, 0]
marker_sizes=line_sizes=[2, 2]
marker_colors=line_colors=[2,4]
binning=[0,10,20,30,40,50,60,70,80,90,100]
# set fig name
figname="examplev1"
# draw the histograms
theone = oneplot()
theone.initialize(file=file,names=names,legends=legends, opt_legends=opt_legends,
ratio=0.2,xtitle="Arbitrary",ytitle="Arbitrary",
figname=figname, binning=binning,
ratiotitle="Histo2/Histo1",
marker_types=marker_types, marker_sizes=marker_sizes, marker_colors=marker_colors,
line_sizes=line_sizes, line_colors=line_colors)
theone.plot1DHistogram()
theone.finish()
# end
def plot_histograms(process, variable_name, normalization_option):
# read histograms from the signal files
if process == 'signal':
root_file_1 = TFile(FILE_PATH + "/BDT/BDT_Signal_histogram_3.root")
root_file_2 = TFile(FILE_PATH +
"/210_eeee_fid_preselected_histograms.root")
signals = root_file_1.Get(variable_name)
backgrounds = root_file_2.Get(variable_name)
# specify the output png file name in different cases
# no normalization
if normalization_option == 0:
figname = "signal_" + FIGURE_NAME[variable_name]
# normalize the histograms to 40fb-1 with their respective cross-section
elif normalization_option == 1:
signals.Scale(0.18 * 40 * EFF_SIGNAL_PRE_SELECTION /
signals.Integral(0, -1))
backgrounds.Scale(1.85 * 40 * EFF_BACKGROUND_PRE_SELECTION /
backgrounds.Integral(0, -1))
figname = "signal_" + FIGURE_NAME[
variable_name] + "_normalized_to_40fb-1"
# normalize the histograms to unity area
elif normalization_option == 2:
signals.Scale(1. / signals.Integral(0, -1))
backgrounds.Scale(1. / backgrounds.Integral(0, -1))
figname = "signal_" + FIGURE_NAME[
variable_name] + "_normalized_to_unity_area"
elif process == 'background':
root_file_1 = TFile(FILE_PATH + "/BDT/BDT_Background_histogram_3.root")
root_file_2 = TFile(FILE_PATH +
"/3210_eeee_fid_preselected_histograms.root")
signals = root_file_1.Get(variable_name)
backgrounds = root_file_2.Get(variable_name)
print "signal events:", signals.Integral(0, -1)
print "background events", backgrounds.Integral(0, -1)
# if there is no signal or backround event, do not normalize the histograms
if (signals.Integral(0, -1) == 0 or backgrounds.Integral(0, -1) == 0):
normalization_option = 0
# specify the output png file name in different cases
# no normalization
if normalization_option == 0:
figname = "background_" + FIGURE_NAME[variable_name]
# normalize the histograms to 40fb-1 with their respective cross-section
elif normalization_option == 1:
signals.Scale(0.18 * 40 * EFF_SIGNAL_PRE_SELECTION /
signals.Integral(0, -1))
backgrounds.Scale(1.85 * 40 * EFF_BACKGROUND_PRE_SELECTION /
backgrounds.Integral(0, -1))
figname = "background_" + FIGURE_NAME[
variable_name] + "_normalized_to_40fb-1"
# normalize the histograms to unity area
elif normalization_option == 2:
signals.Scale(1. / signals.Integral(0, -1))
backgrounds.Scale(1. / backgrounds.Integral(0, -1))
figname = "background_" + FIGURE_NAME[
variable_name] + "_normalized_to_unity_area"
histograms = []
histograms.append(signals)
histograms.append(backgrounds)
names = []
names.append("final selection")
names.append("pre-selection")
xtitle = AXES_TITLES[variable_name][0]
ytitle = AXES_TITLES[variable_name][1]
legends = LEGENDS[variable_name]
options = ["HIST", "HIST"]
opt_legends = ["L", "L"]
marker_types = [26, 32]
marker_sizes = [1, 3]
line_sizes = [2, 2]
marker_colors = line_colors = fill_colors = [30, 38]
fill_types = [0, 0]
# call oneplot.py for plotting in ATLAS style
theone = oneplot()
theone._figtype = ["png"]
theone.initialize(
list_histo=histograms,
names=names,
legends=legends,
opt_legends=opt_legends,
xtitle=xtitle,
ytitle=ytitle,
figname=figname,
# binning= [i*0.25 for i in range(40)],
binning=BINNING[variable_name],
options=options,
marker_types=marker_types,
marker_colors=marker_colors,
marker_sizes=marker_sizes,
line_colors=line_colors,
line_sizes=line_sizes,
fill_colors=fill_colors,
fill_types=fill_types)
theone.plot1DHistogram()
theone.finish()
# close the read files
root_file_1.Close()
root_file_2.Close()
# Draw histograms
xtitle = TITLES[var_name][0]
ytitle = TITLES[var_name][1]
legends = ["leading_{l}", "subleading_{l}", "leading_{v}", "subleading_{v}"]
options = ["HIST", "HIST", "HIST", "HIST"]
opt_legends = ["L", "L", "L", "L"]
marker_types = [26, 26, 32, 32]
marker_sizes = [1, 1, 1, 1]
line_sizes = [2, 2, 2, 2]
marker_colors=line_colors=fill_colors=[4, 6, 2, 12]
fill_types = [0, 0, 0, 0]
figname = cut + "_" + var_name
# call one plot
theone = oneplot()
theone._figtype=["png"]
theone.initialize(list_histo=histos,names=names,legends=legends, opt_legends=opt_legends,
xtitle=xtitle,ytitle=ytitle,
figname=figname,
options=options, marker_types=marker_types, marker_colors=marker_colors, marker_sizes=marker_sizes,
line_colors=line_colors, line_sizes=line_sizes,
fill_colors=fill_colors, fill_types=fill_types)
theone.plot1DHistogram()
theone.finish()
for entry in VAR_2:
for par in entry[0]:
for var in entry[1]:
# load histograms
def plot_two_histograms(normalization_option, file_name_portion):
histograms = []
# no noramlization
if normalization_option == 0:
pass
# normalize the histograms to 40fb-1 with their respective cross-section
if normalization_option == 1:
# print "'signals' has type: ", type(signals)
signals.Scale(0.18 * 40 * EFF_SIGNAL_PRE_SELECTION /
signals.Integral(0, -1))
backgrounds.Scale(1.85 * 40 * EFF_BACKGROUND_PRE_SELECTION /
backgrounds.Integral(0, -1))
# normalize the histograms to unity area
if normalization_option == 2:
signals.Scale(1. / signals.Integral(0, -1))
backgrounds.Scale(1. / backgrounds.Integral(0, -1))
histograms.append(signals)
histograms.append(backgrounds)
names = []
names.append("VBS signal")
names.append("QCD background")
xtitle = "BDT Score"
ytitle = "events scaled"
legends = ["VBS signal", "QCD background"]
options = ["HIST", "HIST"]
opt_legends = ["L", "L"]
marker_types = [26, 32]
marker_sizes = [1, 3]
line_sizes = [2, 2]
marker_colors = line_colors = fill_colors = [30, 38]
fill_types = [0, 0]
if normalization_option == 0:
figname = "BDT_classification_cut_" + file_name_portion + "_rebinned"
elif normalization_option == 1:
figname = "BDT_classification_cut_" + file_name_portion + "_normalized_to_40fb-1_rebinned"
elif normalization_option == 2:
figname = "BDT_classification_cut_" + file_name_portion + "_normalized_to_unity_area_rebinned"
# call oneplot.py for plotting in ATLAS style
theone = oneplot()
theone._figtype = ["png"]
theone.initialize(
list_histo=histograms,
names=names,
legends=legends,
opt_legends=opt_legends,
xtitle=xtitle,
ytitle=ytitle,
figname=figname,
# binning=np.arange(-1, 1.03, 0.03).tolist(),
options=options,
marker_types=marker_types,
marker_colors=marker_colors,
marker_sizes=marker_sizes,
line_colors=line_colors,
line_sizes=line_sizes,
fill_colors=fill_colors,
fill_types=fill_types)
theone.plot1DHistogram()
theone.finish()
def plot_histograms(normalization_option, file_name):
# read the root file in list
root_file_1 = TFile(file_name)
root_file_2 = TFile(
file_name.split("_")[0] + "_Background_histogram_" +
file_name.split(".")[0].split("_")[-1] + ".root")
# read signal and background histograms from the file
signals = root_file_1.Get("h1")
backgrounds = root_file_2.Get("h1")
# calculate figure of merits
calculate_figure_of_merit(signals, backgrounds)
# read suffix of the file
suffix = file_name.split(".")[0].split("_")[-1]
# no normalization
if normalization_option == 0:
# specify the output png file name
figname = "BDT_signal_background_distribution_" + suffix
# normalize the histograms to 40fb-1 with their respective cross-sections
if normalization_option == 1:
signals.Scale(0.18 * 40 * EFF_SIGNAL_PRE_SELECTION /
signals.Integral(0, -1))
backgrounds.Scale(1.85 * 40 * EFF_BACKGROUND_PRE_SELECTION /
backgrounds.Integral(0, -1))
# specify the output png file name
figname = "BDT_signal_background_distribution_normalized_to_unity_area_" + suffix
# normalize the histograms to unity area
elif normalization_option == 2:
print "total numebr of signal events", signals.Integral(0, -1)
print "total numebr of background events", backgrounds.Integral(0, -1)
signals.Scale(1. / signals.Integral(0, -1))
backgrounds.Scale(1. / backgrounds.Integral(0, -1))
# specify the output png file name
figname = "BDT_signal_background_distribution_normalized_to_40fb-1_" + suffix
histograms = []
histograms.append(signals)
histograms.append(backgrounds)
names = []
names.append("signal")
names.append("background")
xtitle = "BDT Score"
if normalization_option == 0:
ytitle = "events"
elif normalization_option == 1:
ytitle = "events normalized to 40 fb-1"
elif normalization_option == 2:
ytitle = "events normalized to unity area"
legends = ["VBS Singal", "QCD Background"]
options = ["HIST", "HIST"]
opt_legends = ["L", "L"]
marker_types = [26, 32]
marker_sizes = [1, 3]
line_sizes = [2, 2]
marker_colors = line_colors = fill_colors = [30, 38]
fill_types = [0, 0]
# call oneplot.py for plotting in ATLAS style
theone = oneplot()
theone._figtype = ["png"]
theone.initialize(list_histo=histograms,
names=names,
legends=legends,
opt_legends=opt_legends,
xtitle=xtitle,
ytitle=ytitle,
figname=figname,
options=options,
marker_types=marker_types,
marker_colors=marker_colors,
marker_sizes=marker_sizes,
line_colors=line_colors,
line_sizes=line_sizes,
fill_colors=fill_colors,
fill_types=fill_types)
theone.plot1DHistogram()
theone.finish()
# close the read files
root_file_1.Close()
root_file_2.Close()