def plot_distribution(self, output_file):
f = r.TFile.Open(os.path.join(self.input_directory, output_file),
'read')
intuple = f.Get('pythia8-Geant4')
h = Hist2D(100, 0, 350, 100, 0, 6)
for muon in intuple:
px = muon.px
py = muon.py
pz = muon.pz
p = np.sqrt(px**2 + py**2 + pz**2)
pt = np.sqrt(px**2 + py**2)
h.Fill(p, pt)
c = Canvas()
r.gStyle.SetOptStat(11111111)
h.Draw("colz")
c.Draw()
c.SaveAs(os.path.join(self.input_directory, "p_dist.png"))
x_max = gr.GetXaxis().GetXmax()
y_min = gr.GetXaxis().GetXmin()
y_max = gr.GetXaxis().GetXmax()
c.Clear()
gr.Draw('a3')
gr.Draw('Xp same')
# a straight line y=x to be a reference
f_dia = ROOT.TF1("f_dia", "x",
h1.GetXaxis().GetXmin(),
h1.GetXaxis().GetXmax())
f_dia.SetLineColor(9)
f_dia.SetLineWidth(2)
f_dia.SetLineStyle(2)
f_dia.Draw("same")
leg = ROOT.TLegend(0.52, 0.15, 0.87, 0.35)
leg.SetFillColor(0)
leg.SetShadowColor(17)
leg.SetBorderSize(3)
leg.AddEntry(gr, "QQ points", "p")
leg.AddEntry(gr, "68% CL band", "f")
leg.AddEntry(f_dia, "Diagonal line", "l")
leg.Draw()
c.Modified()
c.Update()
c.OwnMembers()
c.Draw()
raw_input()
print variable + " " + min + " " + max + " " + nbins + " " + title + " " + ytitle
for (xvariable, xmin, xmax, xnbins, xtitle, xytitle,
xplot) in zip(xvariable_list, xmin_list, xmax_list,
xnbins_list, xtitle_list, xytitle_list,
xplot_list):
if xplot == "Plot":
if xvariable == variable:
continue
# Canvas Variables and declaration
print xvariable + " " + xmin + " " + xmax + " " + xnbins + " " + xtitle + " " + xytitle
canvas = Canvas(width=700, height=500)
#canvas.SetLeftMargin(0.1)
canvas.SetRightMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.Draw()
#canvas.SetLogy()
canvas.SetGrid()
histname = "hist" + xvariable + "_" + variable + background_name
print histname
histogram = histname + "(" + xnbins + "," + xmin + "," + xmax + "," + nbins + "," + min + "," + max + ")"
print histogram
histname = compressed.Draw(xvariable + ":" + variable +
">>" + histogram,
selection=weight * total,
drawstyle='COLZ')
histname.set_title(signalprocess + " " +
background_name)
histname.GetZaxis().SetTitle("Entries/" + xytitle +
class Plotter(object):
def __init__(self,
channel,
year,
plot_dir,
base_dir,
post_fix,
selection_data,
selection_mc,
selection_tight,
pandas_selection,
lumi,
model,
transformation,
features,
process_signals,
plot_signals,
blinded,
datacards=[],
mini_signals=False,
do_ratio=True,
mc_subtraction=True,
dir_suffix='',
relaxed_mc_scaling=1.,
data_driven=True):
self.channel = channel.split('_')[0]
self.year = year
self.full_channel = channel
self.plt_dir = '/'.join(
[plot_dir, channel, '_'.join([dir_suffix,
get_time_str()])])
self.base_dir = base_dir
self.post_fix = post_fix
self.selection_data = ' & '.join(selection_data)
self.selection_mc = ' & '.join(selection_mc)
self.selection_tight = selection_tight
self.pandas_selection = pandas_selection
self.lumi = lumi
self.model = model
self.transformation = transformation
self.features = features
self.process_signals = process_signals
self.plot_signals = plot_signals if self.process_signals else []
self.blinded = blinded
self.selection_lnt = 'not (%s)' % self.selection_tight
self.do_ratio = do_ratio
self.mini_signals = mini_signals
self.datacards = datacards
self.mc_subtraction = mc_subtraction
self.relaxed_mc_scaling = relaxed_mc_scaling
self.data_driven = data_driven
if self.year == 2018:
from plotter.samples.samples_2018 import get_data_samples, get_mc_samples, get_signal_samples
if self.year == 2017:
from plotter.samples.samples_2017 import get_data_samples, get_mc_samples, get_signal_samples
if self.year == 2016:
from plotter.samples.samples_2016 import get_data_samples, get_mc_samples, get_signal_samples
self.get_data_samples = get_data_samples
self.get_mc_samples = get_mc_samples
self.get_signal_samples = get_signal_samples
def total_weight_calculator(self, df, weight_list, scalar_weights=[]):
total_weight = df[weight_list[0]].to_numpy().astype(np.float)
for iw in weight_list[1:]:
total_weight *= df[iw].to_numpy().astype(np.float)
for iw in scalar_weights:
total_weight *= iw
return total_weight
def create_canvas(self, ratio=True):
if ratio:
self.canvas = Canvas(width=700, height=700)
self.canvas.Draw()
self.canvas.cd()
self.main_pad = Pad(0., 0.25, 1., 1.)
self.main_pad.Draw()
self.canvas.cd()
self.ratio_pad = Pad(0., 0., 1., 0.25)
self.ratio_pad.Draw()
self.main_pad.SetTicks(True)
self.main_pad.SetBottomMargin(0.)
self.main_pad.SetLeftMargin(0.15)
self.main_pad.SetRightMargin(0.15)
self.ratio_pad.SetLeftMargin(0.15)
self.ratio_pad.SetRightMargin(0.15)
self.ratio_pad.SetTopMargin(0.)
self.ratio_pad.SetGridy()
self.ratio_pad.SetBottomMargin(0.3)
else:
self.canvas = Canvas(width=700, height=700)
self.canvas.Draw()
self.canvas.cd()
self.main_pad = Pad(0., 0., 1., 1.)
self.main_pad.Draw()
self.canvas.cd()
self.ratio_pad = Pad(-1., -1., -.9, -.9)
self.ratio_pad.Draw() # put it outside the canvas
self.main_pad.SetTicks(True)
self.main_pad.SetTopMargin(0.15)
self.main_pad.SetBottomMargin(0.15)
self.main_pad.SetLeftMargin(0.15)
self.main_pad.SetRightMargin(0.15)
def create_datacards(self,
data,
bkgs,
signals,
label,
protect_empty_bins=['nonprompt']):
'''
FIXME! For now this is specific to the data-driven case
'''
# save a ROOT file with histograms, aka datacard
datacard_dir = '/'.join([self.plt_dir, 'datacards'])
makedirs(datacard_dir, exist_ok=True)
outfile = ROOT.TFile.Open(
'/'.join([datacard_dir, 'datacard_%s.root' % label]), 'recreate')
outfile.cd()
# data in tight
data.name = 'data_obs'
data.Write()
# reads off a dictionary
for bkg_name, bkg in bkgs.items():
bkg.name = bkg_name.split('#')[0]
bkg.drawstyle = 'HIST E'
bkg.color = 'black'
bkg.linewidth = 2
# manual protection against empty bins, that would make combine crash
if bkg_name in protect_empty_bins:
for ibin in bkg.bins_range():
if bkg.GetBinContent(ibin) <= 0.:
bkg.SetBinContent(ibin, 1e-2)
bkg.SetBinError(ibin, np.sqrt(1e-2))
bkg.Write()
# signals
for isig in signals:
isig.name = isig.name.split('#')[0]
isig.drawstyle = 'HIST E'
isig.color = 'black'
isig.Write()
# print out the txt datacard
with open(
'/'.join([
datacard_dir,
'datacard_%s_%s.txt' % (label, isig.name)
]), 'w') as card:
card.write('''
imax 1 number of bins
jmax * number of processes minus 1
kmax * number of nuisance parameters
--------------------------------------------------------------------------------------------------------------------------------------------
shapes * {cat} datacard_{cat}.root $PROCESS $PROCESS_$SYSTEMATIC
--------------------------------------------------------------------------------------------------------------------------------------------
bin {cat}
observation {obs:d}
--------------------------------------------------------------------------------------------------------------------------------------------
bin {cat} {cat} {cat}
process {signal_name} nonprompt prompt
process 0 1 2
rate {signal:.4f} {nonprompt:.4f} {prompt:.4f}
--------------------------------------------------------------------------------------------------------------------------------------------
lumi lnN 1.025 - -
norm_prompt_{ch}_{y}_{cat} lnN - - 1.15
norm_nonprompt_{ch}_{y}_{cat} lnN - 1.20 -
norm_sig_{ch}_{y}_{cat} lnN 1.2 - -
--------------------------------------------------------------------------------------------------------------------------------------------
{cat} autoMCStats 0 0 1
'''.format(
cat=label,
obs=int(data.integral()) if self.blinded == False else -1,
signal_name=isig.name,
signal=isig.integral(),
ch=self.full_channel,
y=self.year,
prompt=bkgs['prompt'].integral(),
nonprompt=bkgs['nonprompt'].integral(),
))
outfile.Close()
def plot(self):
evaluator = Evaluator(self.model, self.transformation, self.features)
makedirs(self.plt_dir, exist_ok=True)
makedirs('/'.join([self.plt_dir, 'lin']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'log']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'lin', 'png']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'lin', 'root']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'log', 'png']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'log', 'root']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'lnt_region', 'lin']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'lnt_region', 'log']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'lnt_region', 'lin', 'png']),
exist_ok=True)
makedirs('/'.join([self.plt_dir, 'lnt_region', 'lin', 'root']),
exist_ok=True)
makedirs('/'.join([self.plt_dir, 'lnt_region', 'log', 'png']),
exist_ok=True)
makedirs('/'.join([self.plt_dir, 'lnt_region', 'log', 'root']),
exist_ok=True)
makedirs('/'.join([self.plt_dir, 'shapes', 'lin']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'shapes', 'log']), exist_ok=True)
makedirs('/'.join([self.plt_dir, 'shapes', 'lin', 'png']),
exist_ok=True)
makedirs('/'.join([self.plt_dir, 'shapes', 'lin', 'root']),
exist_ok=True)
makedirs('/'.join([self.plt_dir, 'shapes', 'log', 'png']),
exist_ok=True)
makedirs('/'.join([self.plt_dir, 'shapes', 'log', 'root']),
exist_ok=True)
# NN evaluator
print('============> starting reading the trees')
print('Plots will be stored in: ', self.plt_dir)
now = time()
signal = []
if self.process_signals:
signal = self.get_signal_samples(self.channel,
self.base_dir,
self.post_fix,
self.selection_data,
mini=self.mini_signals)
else:
signal = []
data = self.get_data_samples(self.channel, self.base_dir,
self.post_fix, self.selection_data)
mc = self.get_mc_samples(self.channel, self.base_dir, self.post_fix,
self.selection_mc)
print('============> it took %.2f seconds' % (time() - now))
# evaluate FR
for isample in (mc + data): #+signal):
isample.df['fr'] = evaluator.evaluate(isample.df)
# already corrected, ready to be applied in lnt-not-tight
isample.df['fr_corr'] = isample.df['fr'] / (1. - isample.df['fr'])
# apply an extra selection to the pandas dataframes
if len(self.pandas_selection):
for isample in (mc + data + signal):
isample.df = isample.df.query(self.pandas_selection)
# split the dataframe in tight and lnt-not-tight (called simply lnt for short)
print('============> splitting dataframe in tight and loose not tight')
for isample in (mc + data + signal):
isample.df_tight = isample.df.query(self.selection_tight)
if isample not in signal:
isample.df_lnt = isample.df.query(self.selection_lnt)
# free some mem
del isample.df
gc.collect()
print('============> ... done')
# sort depending on their position in the stack
mc.sort(key=lambda x: x.position_in_stack)
# now we plot
self.create_canvas(self.do_ratio)
for ivar in variables:
variable, bins, label, xlabel, ylabel, extra_sel = ivar.var, ivar.bins, ivar.label, ivar.xlabel, ivar.ylabel, ivar.extra_selection
print('plotting', label)
######################################################################################
# plot MC stacks, in tight and lnt
######################################################################################
stack_prompt = []
stack_nonprompt = []
stack_nonprompt_control = []
for imc in mc:
if extra_sel:
mc_df_tight = imc.df_tight.query(extra_sel)
mc_df_lnt = imc.df_lnt.query(extra_sel)
else:
mc_df_tight = imc.df_tight
mc_df_lnt = imc.df_lnt
histo_tight = Hist(bins,
title=imc.label,
markersize=0,
legendstyle='F',
name=imc.datacard_name + '#' + label + '#t')
weights = self.total_weight_calculator(
mc_df_tight, ['weight'] + imc.extra_signal_weights,
[self.lumi, imc.lumi_scaling])
histo_tight.fill_array(mc_df_tight[variable],
weights=weights *
self.relaxed_mc_scaling)
histo_tight.fillstyle = 'solid'
histo_tight.fillcolor = 'steelblue' if self.data_driven else imc.colour
histo_tight.linewidth = 0
stack_prompt.append(histo_tight)
# optionally remove the MC subtraction in loose-not-tight
# may help if MC stats is terrible (and it often is)
if self.data_driven:
if self.mc_subtraction:
histo_lnt = Hist(bins,
title=imc.label,
markersize=0,
legendstyle='F',
name=imc.datacard_name + '#' + label +
'#lnt')
weights = self.total_weight_calculator(
mc_df_lnt,
['weight', 'fr_corr'] + imc.extra_signal_weights,
[-1., self.lumi, imc.lumi_scaling])
histo_lnt.fill_array(mc_df_lnt[variable],
weights=weights *
self.relaxed_mc_scaling)
histo_lnt.fillstyle = 'solid'
histo_lnt.fillcolor = 'skyblue' if self.data_driven else imc.colour
histo_lnt.linewidth = 0
stack_nonprompt.append(histo_lnt)
histo_lnt_control = Hist(bins,
title=imc.label,
markersize=0,
legendstyle='F',
name=imc.datacard_name + '#' +
label + '#lntcontrol')
weights_control = self.total_weight_calculator(
mc_df_lnt, ['weight'] + imc.extra_signal_weights,
[self.lumi, imc.lumi_scaling])
histo_lnt_control.fill_array(mc_df_lnt[variable],
weights=weights_control *
self.relaxed_mc_scaling)
histo_lnt_control.fillstyle = 'solid'
histo_lnt_control.fillcolor = imc.colour
histo_lnt_control.linewidth = 0
# print(histo_lnt_control)
# print(histo_lnt_control.fillcolor)
# print(imc.name, imc.colour)
# print(histo_lnt_control.integral())
stack_nonprompt_control.append(histo_lnt_control)
# merge different samples together (add the histograms)
# prepare two temporary containers for the post-grouping histograms
stack_prompt_tmp = []
stack_nonprompt_tmp = []
stack_nonprompt_control_tmp = []
for ini, fin in [(stack_prompt, stack_prompt_tmp),
(stack_nonprompt, stack_nonprompt_tmp),
(stack_nonprompt_control,
stack_nonprompt_control_tmp)]:
for k, v in groups.items():
grouped = []
for ihist in ini:
if ihist.name.split('#')[0] in v:
grouped.append(ihist)
elif ihist.name.split('#')[0] not in togroup:
fin.append(ihist)
if len(grouped):
group = sum(grouped)
group.title = k
group.name = '#'.join([k] + ihist.name.split('#')[1:])
group.fillstyle = grouped[0].fillstyle
group.fillcolor = grouped[0].fillcolor
group.linewidth = grouped[0].linewidth
fin.append(group)
stack_prompt = stack_prompt_tmp
stack_nonprompt = stack_nonprompt_tmp
stack_nonprompt_control = stack_nonprompt_control_tmp
######################################################################################
# plot the signals
######################################################################################
all_signals = []
signals_to_plot = []
for isig in signal:
if variable not in self.datacards:
if not isig.toplot:
continue
if variable == 'fr' or variable == 'fr_corr':
continue
if extra_sel:
isig_df_tight = isig.df_tight.query(extra_sel)
else:
isig_df_tight = isig.df_tight
histo_tight = Hist(
bins,
title=isig.label,
markersize=0,
legendstyle='L',
name=isig.datacard_name + '#' + label
) # the "#" thing is a trick to give hists unique name, else ROOT complains
weights = self.total_weight_calculator(
isig_df_tight, ['weight'] + isig.extra_signal_weights,
[self.lumi, isig.lumi_scaling])
histo_tight.fill_array(isig_df_tight[variable],
weights=weights)
histo_tight.color = isig.colour
histo_tight.fillstyle = 'hollow'
histo_tight.linewidth = 2
histo_tight.linestyle = 'dashed'
histo_tight.drawstyle = 'HIST'
all_signals.append(histo_tight)
if isig.toplot: signals_to_plot.append(histo_tight)
######################################################################################
# plot the data
######################################################################################
data_prompt = []
data_nonprompt = []
data_nonprompt_control = []
for idata in data:
if extra_sel:
idata_df_tight = idata.df_tight.query(extra_sel)
idata_df_lnt = idata.df_lnt.query(extra_sel)
else:
idata_df_tight = idata.df_tight
idata_df_lnt = idata.df_lnt
histo_tight = Hist(bins,
title=idata.label,
markersize=1,
legendstyle='LEP')
histo_tight.fill_array(idata_df_tight[variable])
data_prompt.append(histo_tight)
if self.data_driven:
histo_lnt = Hist(bins,
title=idata.label,
markersize=0,
legendstyle='F')
histo_lnt.fill_array(idata_df_lnt[variable],
weights=idata_df_lnt.fr_corr)
histo_lnt.fillstyle = 'solid'
histo_lnt.fillcolor = 'skyblue'
histo_lnt.linewidth = 0
histo_lnt_control = Hist(bins,
title=idata.label,
markersize=1,
legendstyle='LEP')
histo_lnt_control.fill_array(idata_df_lnt[variable])
data_nonprompt.append(histo_lnt)
data_nonprompt_control.append(histo_lnt_control)
if self.data_driven:
# put the prompt backgrounds together
all_exp_prompt = sum(stack_prompt)
all_exp_prompt.title = 'prompt'
# put the nonprompt backgrounds together
all_exp_nonprompt = sum(stack_nonprompt + data_nonprompt)
all_exp_nonprompt.fillstyle = 'solid'
all_exp_nonprompt.fillcolor = 'skyblue'
all_exp_nonprompt.linewidth = 0
all_exp_nonprompt.title = 'nonprompt'
# create the stacks
stack = HistStack([all_exp_prompt, all_exp_nonprompt],
drawstyle='HIST',
title='')
stack_control = HistStack(stack_nonprompt_control,
drawstyle='HIST',
title='')
else:
stack = HistStack(stack_prompt, drawstyle='HIST', title='')
# stat uncertainty
hist_error = stack.sum #sum([all_exp_prompt, all_exp_nonprompt])
hist_error.drawstyle = 'E2'
hist_error.fillstyle = '/'
hist_error.color = 'gray'
hist_error.title = 'stat. unc.'
hist_error.legendstyle = 'F'
if self.data_driven:
hist_error_control = stack_control.sum
hist_error_control.drawstyle = 'E2'
hist_error_control.fillstyle = '/'
hist_error_control.color = 'gray'
hist_error_control.title = 'stat. unc.'
hist_error_control.legendstyle = 'F'
# put the data together
all_obs_prompt = sum(data_prompt)
all_obs_prompt.title = 'observed'
if self.data_driven:
all_obs_nonprompt_control = sum(data_nonprompt_control)
all_obs_nonprompt_control.title = 'observed'
all_obs_nonprompt_control.drawstyle = 'EP'
# prepare the legend
print(signals_to_plot)
for jj in signals_to_plot:
print(jj.name, jj.integral())
if len(signals_to_plot):
legend = Legend([all_obs_prompt, stack, hist_error],
pad=self.main_pad,
leftmargin=0.,
rightmargin=0.,
topmargin=0.,
textfont=42,
textsize=0.025,
entrysep=0.01,
entryheight=0.04)
legend_signals = Legend(signals_to_plot,
pad=self.main_pad,
leftmargin=0.,
rightmargin=0.,
topmargin=0.,
textfont=42,
textsize=0.025,
entrysep=0.01,
entryheight=0.04)
legend_signals.SetBorderSize(0)
legend_signals.x1 = 0.42
legend_signals.y1 = 0.74
legend_signals.x2 = 0.88
legend_signals.y2 = 0.90
legend_signals.SetFillColor(0)
legend.SetBorderSize(0)
legend.x1 = 0.2
legend.y1 = 0.74
legend.x2 = 0.45
legend.y2 = 0.90
legend.SetFillColor(0)
else:
legend = Legend([all_obs_prompt, stack, hist_error],
pad=self.main_pad,
leftmargin=0.,
rightmargin=0.,
topmargin=0.,
textfont=42,
textsize=0.03,
entrysep=0.01,
entryheight=0.04)
legend.SetBorderSize(0)
legend.x1 = 0.55
legend.y1 = 0.74
legend.x2 = 0.88
legend.y2 = 0.90
legend.SetFillColor(0)
# plot with ROOT, linear and log scale
for islogy in [False, True]:
things_to_plot = [stack, hist_error]
if not self.blinded:
things_to_plot.append(all_obs_prompt)
# plot signals, as an option
if self.plot_signals:
things_to_plot += signals_to_plot
# set the y axis range
# FIXME! setting it by hand to each object as it doesn't work if passed to draw
if islogy:
yaxis_max = 40. * max(
[ithing.max() for ithing in things_to_plot])
else:
yaxis_max = 1.65 * max(
[ithing.max() for ithing in things_to_plot])
if islogy: yaxis_min = 0.01
else: yaxis_min = 0.
for ithing in things_to_plot:
ithing.SetMaximum(yaxis_max)
draw(things_to_plot,
xtitle=xlabel,
ytitle=ylabel,
pad=self.main_pad,
logy=islogy)
# expectation uncertainty in the ratio pad
ratio_exp_error = Hist(bins)
ratio_data = Hist(bins)
for ibin in hist_error.bins_range():
ratio_exp_error.set_bin_content(ibin, 1.)
ratio_exp_error.set_bin_error(
ibin,
hist_error.get_bin_error(ibin) /
hist_error.get_bin_content(ibin)
if hist_error.get_bin_content(ibin) != 0. else 0.)
ratio_data.set_bin_content(
ibin,
all_obs_prompt.get_bin_content(ibin) /
hist_error.get_bin_content(ibin)
if hist_error.get_bin_content(ibin) != 0. else 0.)
ratio_data.set_bin_error(
ibin,
all_obs_prompt.get_bin_error(ibin) /
hist_error.get_bin_content(ibin)
if hist_error.get_bin_content(ibin) != 0. else 0.)
ratio_data.drawstyle = 'EP'
ratio_data.title = ''
ratio_exp_error.drawstyle = 'E2'
ratio_exp_error.markersize = 0
ratio_exp_error.title = ''
ratio_exp_error.fillstyle = '/'
ratio_exp_error.color = 'gray'
for ithing in [ratio_data, ratio_exp_error]:
ithing.xaxis.set_label_size(
ithing.xaxis.get_label_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
ithing.yaxis.set_label_size(
ithing.yaxis.get_label_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
ithing.xaxis.set_title_size(
ithing.xaxis.get_title_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
ithing.yaxis.set_title_size(
ithing.yaxis.get_title_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
ithing.yaxis.set_ndivisions(405)
ithing.yaxis.set_title_offset(0.4)
things_to_plot = [ratio_exp_error]
if not self.blinded:
things_to_plot.append(ratio_data)
draw(things_to_plot,
xtitle=xlabel,
ytitle='obs/exp',
pad=self.ratio_pad,
logy=False,
ylimits=(0.5, 1.5))
line = ROOT.TLine(min(bins), 1., max(bins), 1.)
line.SetLineColor(ROOT.kBlack)
line.SetLineWidth(1)
self.ratio_pad.cd()
line.Draw('same')
# chi2_score_text = '\chi^{2}/NDF = %.1f' %(all_obs_prompt.Chi2Test(hist_error, 'UW CHI2/NDF'))
chi2_score_text = 'p-value = %.2f' % (all_obs_prompt.Chi2Test(
hist_error, 'UW'))
chi2_score = ROOT.TLatex(0.7, 0.81, chi2_score_text)
chi2_score.SetTextFont(43)
chi2_score.SetTextSize(15)
chi2_score.SetNDC()
chi2_score.Draw('same')
self.canvas.cd()
# FIXME! add SS and OS channels
if self.full_channel == 'mmm': channel = '\mu\mu\mu'
elif self.full_channel == 'eee': channel = 'eee'
elif self.full_channel == 'mem_os':
channel = '\mu^{\pm}\mu^{\mp}e'
elif self.full_channel == 'mem_ss':
channel = '\mu^{\pm}\mu^{\pm}e'
elif self.full_channel == 'eem_os':
channel = 'e^{\pm}e^{\mp}\mu'
elif self.full_channel == 'eem_ss':
channel = 'e^{\pm}e^{\pm}\mu'
else:
assert False, 'ERROR: Channel not valid.'
finalstate = ROOT.TLatex(0.68, 0.68, channel)
finalstate.SetTextFont(43)
finalstate.SetTextSize(25)
finalstate.SetNDC()
finalstate.Draw('same')
self.canvas.cd()
# remove old legend
for iprim in self.canvas.primitives:
if isinstance(iprim, Legend):
self.canvas.primitives.remove(iprim)
legend.Draw('same')
if self.plot_signals:
legend_signals.Draw('same')
CMS_lumi(self.main_pad,
4,
0,
lumi_13TeV="%d, L = %.1f fb^{-1}" %
(self.year, self.lumi / 1000.))
self.canvas.Modified()
self.canvas.Update()
for iformat in ['pdf', 'png', 'root']:
self.canvas.SaveAs('/'.join([
self.plt_dir, 'log' if islogy else 'lin',
iformat if iformat != 'pdf' else '',
'%s%s.%s' %
(label, '_log' if islogy else '_lin', iformat)
]))
# plot distributions in loose not tight
# check MC contamination there
if self.data_driven and variable not in ['fr', 'fr_corr']:
things_to_plot = [
stack_control, hist_error_control,
all_obs_nonprompt_control
]
# set the y axis range
# FIXME! setting it by hand to each object as it doesn't work if passed to draw
if islogy:
yaxis_max = 40. * max(
[ithing.max() for ithing in things_to_plot])
else:
yaxis_max = 1.65 * max(
[ithing.max() for ithing in things_to_plot])
if islogy: yaxis_min = 0.01
else: yaxis_min = 0.
for ithing in things_to_plot:
ithing.SetMaximum(yaxis_max)
ithing.SetMinimum(yaxis_min)
draw(things_to_plot,
xtitle=xlabel,
ytitle=ylabel,
pad=self.main_pad,
logy=islogy,
ylimits=(yaxis_min, yaxis_max))
new_legend = Legend(
stack_control.hists +
[hist_error_control, all_obs_nonprompt_control],
pad=self.main_pad,
leftmargin=0.,
rightmargin=0.,
topmargin=0.,
textfont=42,
textsize=0.03,
entrysep=0.01,
entryheight=0.04)
new_legend.SetBorderSize(0)
new_legend.x1 = 0.55
new_legend.y1 = 0.71
new_legend.x2 = 0.88
new_legend.y2 = 0.90
new_legend.SetFillColor(0)
# divide MC to subtract by data
stack_nonprompt_control_scaled_list = []
for ihist in stack_control.hists:
new_hist = copy(ihist)
for ibin in new_hist.bins_range():
new_hist.SetBinContent(
ibin,
np.nan_to_num(
np.divide(
new_hist.GetBinContent(ibin),
all_obs_nonprompt_control.
GetBinContent(ibin))))
new_hist.SetBinError(
ibin,
np.nan_to_num(
np.divide(
new_hist.GetBinError(ibin),
all_obs_nonprompt_control.
GetBinContent(ibin))))
stack_nonprompt_control_scaled_list.append(new_hist)
stack_control_scaled = HistStack(
stack_nonprompt_control_scaled_list,
drawstyle='HIST',
title='')
stack_control_scaled_err = stack_control_scaled.sum
stack_control_scaled_err.drawstyle = 'E2'
stack_control_scaled_err.fillstyle = '/'
stack_control_scaled_err.color = 'gray'
stack_control_scaled_err.title = 'stat. unc.'
stack_control_scaled_err.legendstyle = 'F'
draw([stack_control_scaled, stack_control_scaled_err],
xtitle=xlabel,
ytitle='MC/data',
pad=self.ratio_pad,
logy=False)
stack_control_scaled.xaxis.set_label_size(
stack_control_scaled.xaxis.get_label_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
stack_control_scaled.yaxis.set_label_size(
stack_control_scaled.yaxis.get_label_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
stack_control_scaled.xaxis.set_title_size(
stack_control_scaled.xaxis.get_title_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
stack_control_scaled.yaxis.set_title_size(
stack_control_scaled.yaxis.get_title_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
stack_control_scaled.yaxis.set_ndivisions(405)
stack_control_scaled.yaxis.set_title_offset(0.4)
stack_control_scaled.SetMinimum(0.)
stack_control_scaled.SetMaximum(1.5)
CMS_lumi(self.main_pad,
4,
0,
lumi_13TeV="%d, L = %.1f fb^{-1}" %
(self.year, self.lumi / 1000.))
self.canvas.cd()
# remove old legend
for iprim in self.canvas.primitives:
if isinstance(iprim, Legend):
self.canvas.primitives.remove(iprim)
# draw new legend
new_legend.Draw('same')
self.canvas.Modified()
self.canvas.Update()
for iformat in ['pdf', 'png', 'root']:
self.canvas.SaveAs('/'.join([
self.plt_dir, 'lnt_region',
'log' if islogy else 'lin',
iformat if iformat != 'pdf' else '',
'%s%s.%s' %
(label, '_log' if islogy else '_lin', iformat)
]))
# compare shapes in tight and loose not tight
# data in tight
all_obs_prompt_norm = copy(all_obs_prompt)
if all_obs_prompt_norm.integral() != 0:
all_obs_prompt_norm.Scale(
np.nan_to_num(
np.divide(1., all_obs_prompt_norm.integral())))
#import pdb; pdb.set_trace()
all_obs_prompt_norm.drawstyle = 'hist e'
all_obs_prompt_norm.linecolor = 'black'
all_obs_prompt_norm.markersize = 0
all_obs_prompt_norm.legendstyle = 'LE'
all_obs_prompt_norm.title = ''
all_obs_prompt_norm.label = 'data - tight'
# data MC subtracted in loose
all_obs_prompt_mc_sub_norm = copy(all_obs_prompt)
all_obs_prompt_mc_sub_norm.add(all_exp_prompt, -1)
all_obs_prompt_mc_sub_norm.Scale(
np.nan_to_num(
np.divide(1.,
all_obs_prompt_mc_sub_norm.integral())))
all_obs_prompt_mc_sub_norm.drawstyle = 'hist e'
all_obs_prompt_mc_sub_norm.linecolor = 'green'
all_obs_prompt_mc_sub_norm.markersize = 0
all_obs_prompt_mc_sub_norm.legendstyle = 'LE'
all_obs_prompt_mc_sub_norm.title = ''
all_obs_prompt_mc_sub_norm.label = '(data-MC) - tight'
# data in loose
all_obs_nonprompt_control_norm = copy(
all_obs_nonprompt_control)
all_obs_nonprompt_control_norm.Scale(
np.nan_to_num(
np.divide(
1.,
all_obs_nonprompt_control_norm.integral())))
all_obs_nonprompt_control_norm.drawstyle = 'hist e'
all_obs_nonprompt_control_norm.linecolor = 'red'
all_obs_nonprompt_control_norm.markersize = 0
all_obs_nonprompt_control_norm.legendstyle = 'LE'
all_obs_nonprompt_control_norm.title = ''
all_obs_nonprompt_control_norm.label = 'data - l-n-t'
# data MC subtracted in loose
all_obs_nonprompt_control_mc_sub_norm = copy(
all_obs_nonprompt_control)
all_obs_nonprompt_control_mc_sub_norm.add(
stack_control.sum, -1)
all_obs_nonprompt_control_mc_sub_norm.Scale(
np.nan_to_num(
np.divide(
1.,
all_obs_nonprompt_control_mc_sub_norm.integral(
))))
all_obs_nonprompt_control_mc_sub_norm.drawstyle = 'hist e'
all_obs_nonprompt_control_mc_sub_norm.linecolor = 'blue'
all_obs_nonprompt_control_mc_sub_norm.markersize = 0
all_obs_nonprompt_control_mc_sub_norm.legendstyle = 'LE'
all_obs_nonprompt_control_mc_sub_norm.title = ''
all_obs_nonprompt_control_mc_sub_norm.label = '(data-MC) - l-n-t'
things_to_plot = [
all_obs_prompt_norm,
all_obs_prompt_mc_sub_norm,
all_obs_nonprompt_control_norm,
all_obs_nonprompt_control_mc_sub_norm,
]
yaxis_max = max([ii.GetMaximum() for ii in things_to_plot])
draw(things_to_plot,
xtitle=xlabel,
ytitle=ylabel,
pad=self.main_pad,
logy=islogy,
ylimits=(yaxis_min, 1.55 * yaxis_max))
self.canvas.cd()
# remove old legend
for iprim in self.canvas.primitives:
if isinstance(iprim, Legend):
self.canvas.primitives.remove(iprim)
shape_legend = Legend([],
pad=self.main_pad,
leftmargin=0.,
rightmargin=0.,
topmargin=0.,
textfont=42,
textsize=0.03,
entrysep=0.01,
entryheight=0.04)
shape_legend.AddEntry(all_obs_prompt_norm,
all_obs_prompt_norm.label,
all_obs_prompt_norm.legendstyle)
shape_legend.AddEntry(
all_obs_prompt_mc_sub_norm,
all_obs_prompt_mc_sub_norm.label,
all_obs_prompt_mc_sub_norm.legendstyle)
shape_legend.AddEntry(
all_obs_nonprompt_control_norm,
all_obs_nonprompt_control_norm.label,
all_obs_nonprompt_control_norm.legendstyle)
shape_legend.AddEntry(
all_obs_nonprompt_control_mc_sub_norm,
all_obs_nonprompt_control_mc_sub_norm.label,
all_obs_nonprompt_control_mc_sub_norm.legendstyle)
shape_legend.SetBorderSize(0)
shape_legend.x1 = 0.50
shape_legend.y1 = 0.71
shape_legend.x2 = 0.88
shape_legend.y2 = 0.90
shape_legend.SetFillColor(0)
shape_legend.Draw('same')
# plot ratios
all_obs_prompt_norm_ratio = copy(all_obs_prompt_norm)
all_obs_prompt_mc_sub_norm_ratio = copy(
all_obs_prompt_mc_sub_norm)
all_obs_nonprompt_control_norm_ratio = copy(
all_obs_nonprompt_control_norm)
all_obs_nonprompt_control_mc_sub_norm_ratio = copy(
all_obs_nonprompt_control_mc_sub_norm)
all_obs_prompt_norm_ratio.Divide(
all_obs_prompt_mc_sub_norm_ratio)
all_obs_nonprompt_control_norm_ratio.Divide(
all_obs_prompt_mc_sub_norm_ratio)
all_obs_nonprompt_control_mc_sub_norm_ratio.Divide(
all_obs_prompt_mc_sub_norm_ratio)
things_to_plot_ratio = [
all_obs_prompt_norm_ratio,
all_obs_nonprompt_control_norm_ratio,
all_obs_nonprompt_control_mc_sub_norm_ratio,
]
for ithing in things_to_plot_ratio:
ithing.xaxis.set_label_size(
ithing.xaxis.get_label_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
ithing.yaxis.set_label_size(
ithing.yaxis.get_label_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
ithing.xaxis.set_title_size(
ithing.xaxis.get_title_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
ithing.yaxis.set_title_size(
ithing.yaxis.get_title_size() * 3.
) # the scale should match that of the main/ratio pad size ratio
ithing.yaxis.set_ndivisions(405)
ithing.yaxis.set_title_offset(0.4)
ithing.SetMinimum(0.)
ithing.SetMaximum(2.)
draw(things_to_plot_ratio,
xtitle=xlabel,
ytitle='1/(data-MC)_{tight}',
pad=self.ratio_pad,
logy=False,
ylimits=(0., 2.))
self.ratio_pad.cd()
line.Draw('same')
CMS_lumi(self.main_pad,
4,
0,
lumi_13TeV="%d, L = %.1f fb^{-1}" %
(self.year, self.lumi / 1000.))
self.canvas.Modified()
self.canvas.Update()
for iformat in ['pdf', 'png', 'root']:
self.canvas.SaveAs('/'.join([
self.plt_dir, 'shapes', 'log' if islogy else 'lin',
iformat if iformat != 'pdf' else '',
'%s%s.%s' %
(label, '_log' if islogy else '_lin', iformat)
]))
# save only the datacards you want, don't flood everything
if len(self.datacards) and label not in self.datacards:
continue
# FIXME! allow it to save datacards even for non data driven bkgs
if self.data_driven:
self.create_datacards(data=all_obs_prompt,
bkgs={
'prompt': all_exp_prompt,
'nonprompt': all_exp_nonprompt
},
signals=all_signals,
label=label)
