def draw(self, region, objects, **kwargs):
pad = self.pad(region)
x, y = self.axes(region)
if region == 'ratio' and self.ratio_range is not None:
y = None
draw(objects, pad=pad, xaxis=x, yaxis=y, same=True, **kwargs)
if region == 'ratio':
# update ratio line lengths
for line in self.lines:
line.SetX1(x.GetXmin())
line.SetX2(x.GetXmax())
def draw(self, region, objects, **kwargs):
pad = self.pad(region)
x, y = self.axes(region)
if self.logy:
kwargs['logy'] = True
_, bounds = draw(objects, pad=pad,
xaxis=x, yaxis=y,
same=True, **kwargs)
return bounds
def draw(self, region, objects, **kwargs):
pad = self.pad(region)
x, y = self.axes(region)
if self.logy:
kwargs['logy'] = True
_, bounds = draw(objects, pad=pad,
xaxis=x, yaxis=y,
same=True, **kwargs)
return bounds
def draw(self, region, objects, **kwargs):
pad = self.pad(region)
x, y = self.axes(region)
if region == 'ratio' and self.ratio_limits is not None:
y = None
if region == 'main' and self.logy:
kwargs['logy'] = True
_, bounds = draw(objects, pad=pad,
xaxis=x, yaxis=y,
same=True, **kwargs)
if region == 'ratio':
self.update_lines()
return bounds
def draw(self, region, objects, **kwargs):
pad = self.pad(region)
x, y = self.axes(region)
if region == 'ratio' and self.ratio_limits is not None:
y = None
if region == 'main' and self.logy:
kwargs['logy'] = True
_, bounds = draw(objects, pad=pad,
xaxis=x, yaxis=y,
same=True, **kwargs)
if region == 'ratio':
self.update_lines()
return bounds
def main(args):
fileName = '/data/nawoods/lepsForSIP/lepsForSIP_M2500.root'
checker = MuonCandMatchChecker('Check PF cand matching', [fileName])
checker.processSample()
h = checker.hists['pfCandPt']
h.Sumw2()
h.drawstyle = 'HIST'
h.legendstyle = 'L'
h.linewidth = 2
h.color = 'red'
c = Canvas(1000, 1000)
(xax, yax), lims = draw(
h,
c,
ytitle='PF Cands',
xtitle='PF Cand p_T',
)
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
c.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/matchedCandPt.png')
h2 = checker.hists['pfCandPtVsGenPt']
c2 = Canvas(1000, 1000)
h2.xaxis.SetTitleOffset(1.8)
h2.yaxis.SetTitleOffset(1.7)
h2.xaxis.SetTitleSize(0.5 * h2.xaxis.GetTitleSize())
h2.yaxis.SetTitleSize(0.5 * h2.yaxis.GetTitleSize())
h2.xaxis.SetLabelSize(0.6 * h2.xaxis.GetLabelSize())
h2.yaxis.SetLabelSize(0.6 * h2.yaxis.GetLabelSize())
h2.xaxis.SetTitle("Gen #mu p_{T}")
h2.yaxis.SetTitle("PF cand p_{T}")
h2.Draw("COLZ")
c2.Print(
'/afs/cern.ch/user/n/nawoods/www/ptStudy/matchedCandPtVsGenPt.png')
print "Out of {} good muons, {} failed PF ID ({}%).".format(
checker.nTot, checker.nFailPF,
100. * float(checker.nFailPF) / checker.nTot)
print(
"Out of those failing PF, {} shared a track with a PF charged hadron "
"({}%, {}% of the total)").format(
checker.nPFMatched, 100. * checker.nPFMatched / checker.nFailPF,
100. * checker.nPFMatched / checker.nTot)
def main(args):
fileName = '/data/nawoods/lepsForSIP/lepsForSIP_M2500.root'
checker = MuonCandMatchChecker('Check PF cand matching', [fileName])
checker.processSample()
h = checker.hists['pfCandPt']
h.Sumw2()
h.drawstyle = 'HIST'
h.legendstyle = 'L'
h.linewidth = 2
h.color = 'red'
c = Canvas(1000,1000)
(xax,yax), lims = draw(h, c, ytitle='PF Cands',
xtitle='PF Cand p_T',)
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
c.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/matchedCandPt.png')
h2 = checker.hists['pfCandPtVsGenPt']
c2 = Canvas(1000,1000)
h2.xaxis.SetTitleOffset(1.8)
h2.yaxis.SetTitleOffset(1.7)
h2.xaxis.SetTitleSize(0.5 * h2.xaxis.GetTitleSize())
h2.yaxis.SetTitleSize(0.5 * h2.yaxis.GetTitleSize())
h2.xaxis.SetLabelSize(0.6 * h2.xaxis.GetLabelSize())
h2.yaxis.SetLabelSize(0.6 * h2.yaxis.GetLabelSize())
h2.xaxis.SetTitle("Gen #mu p_{T}")
h2.yaxis.SetTitle("PF cand p_{T}")
h2.Draw("COLZ")
c2.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/matchedCandPtVsGenPt.png')
print "Out of {} good muons, {} failed PF ID ({}%).".format(checker.nTot, checker.nFailPF,
100. * float(checker.nFailPF) / checker.nTot)
print ("Out of those failing PF, {} shared a track with a PF charged hadron "
"({}%, {}% of the total)").format(checker.nPFMatched,
100. * checker.nPFMatched / checker.nFailPF,
100. * checker.nPFMatched / checker.nTot)
def calc_fake_factors(name,
numers_data,
numers_bkg,
denoms_data,
denoms_bkg,
labels,
xtitle,
ytitle,
save=None,
ylimits=None):
assert numers_data, numers_bkg
if not isinstance(numers_data, list):
numers_data = [numers_data]
if not isinstance(numers_bkg, list):
numers_bkg = [numers_bkg]
if not isinstance(denoms_data, list):
denoms_data = [denoms_data]
if not isinstance(denoms_bkg, list):
denoms_bkg = [denoms_bkg]
if save is None:
save = ['pdf', 'png']
stuff = dict()
objects = list()
colors = [
ipyhep.style.red,
ipyhep.style.blue,
ipyhep.style.orange,
ipyhep.style.violet,
]
i_ff = 0
for numer_data, numer_bkg, denom_data, denom_bkg in zip(
numers_data, numers_bkg, denoms_data, denoms_bkg):
for h in (numer_data, numer_bkg, denom_data, denom_bkg):
if h and not h.GetSumw2N():
h.Sumw2()
h_numer = numer_data.Clone()
h_numer.SetDirectory(0)
if numer_bkg:
set_negative_bins_zero(numer_bkg)
h_numer.Add(numer_bkg, -1.0)
h_denom = denom_data.Clone()
h_denom.SetDirectory(0)
if denom_bkg:
set_negative_bins_zero(denom_bkg)
h_denom.Add(denom_bkg, -1.0)
h_ff = h_numer.Clone()
h_ff.SetDirectory(0)
divide_option = ''
h_ff.Divide(h_numer, h_denom, 1.0, 1.0, divide_option)
h_ff.linecolor = colors[i_ff]
h_ff.markercolor = colors[i_ff]
h_ff.markerstyle = 20
h_ff.markersize = 1.2
h_ff.fillstyle = ipyhep.style.fill_hollow
h_ff.drawstyle = 'PE'
h_ff.legendstyle = 'LP'
if labels:
h_ff.title = '%s' % labels[i_ff]
else:
h_ff.title = '%i' % i_ff
h_ff.name = 'h_ff_%s' % h_ff.title
stuff['h_ff_%s' % h_ff.title] = h_ff
objects.append(h_ff)
i_ff += 1
## make canvas
canvas = Canvas(800, 600)
stuff['canvas'] = canvas
xmin, xmax, ymin, ymax = get_limits(objects)
xlimits = (xmin, xmax)
# ylimits = (ymin, ymax)
ylimits = ylimits or (0.0, 1.0)
draw(objects,
pad=canvas,
xtitle=xtitle,
ytitle=ytitle,
xlimits=xlimits,
ylimits=ylimits)
## legend
header = ''
legend = Legend(objects,
pad=canvas,
header=header,
textsize=16,
topmargin=0.02,
leftmargin=0.60,
rightmargin=0.02,
entrysep=0.01,
entryheight=0.04)
legend.Draw()
stuff['legend'] = legend
## save figures
if save:
ipyhep.file.save_figures(canvas, 'h_ff', save)
## save fake factors
timestamp = time.strftime('%Y_%m_%d_%Hh%M')
outfile = 'fakefactors-%s.root' % timestamp
f_out = None
for h_ff in objects:
f_out = ipyhep.file.write(h_ff, outfile)
# f_out.Close()
# ipyhep.file.close_all_files()
return stuff
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)
# set visual attributes
h1.fillstyle = 'solid'
h1.fillcolor = 'green'
h1.linecolor = 'green'
h1.linewidth = 0
h2.fillstyle = 'solid'
h2.fillcolor = 'red'
h2.linecolor = 'red'
h2.linewidth = 0
stack = HistStack([h1, h2], drawstyle='HIST E1 X0')
# plot with ROOT
canvas = Canvas(width=700, height=500)
draw([stack, h3], xtitle='Mass', ytitle='Events', pad=canvas)
# set the number of expected legend entries
legend = Legend([h1, h2, h3], leftmargin=0.45, margin=0.3)
legend.Draw()
label = ROOT.TText(0.3, 0.8, 'ROOT')
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
# plot with matplotlib
set_style('ATLAS', mpl=True)
fig = plt.figure(figsize=(7, 5), dpi=100)
axes = plt.axes()
def main(args):
fileList = glob('/data/nawoods/lepsForSIP/*.root')
files = {int(f.split('_M')[1].split('.')[0]) : f for f in fileList}
checkers = {m : MuonSIPChecker('m={}'.format(m), [f]) for m,f in files.iteritems()}
sipRMS = Graph(len(fileList), type='errors')
sipHists = []
dxyRMS = Graph(len(fileList), type='errors')
dxyHists = []
dzRMS = Graph(len(fileList), type='errors')
dzHists = []
ipHists = []
ipErrHists = []
fracFailing = Graph(len(fileList), type='errors')
for i, m in enumerate(sorted(files.keys())):
checkers[m].processSample()
checkers[m].hists['sip'].Sumw2()
sipRMS.SetPoint(i, float(m), checkers[m].hists['sip'].GetRMS())
sipRMS.SetPointError(i, 0., checkers[m].hists['sip'].GetRMSError())
sipHists.append(checkers[m].hists['sip'])
sipHists[-1].color = getColor(m)
sipHists[-1].title = "m_{{H}} = {}".format(m)
sipHists[-1].drawstyle = 'hist'
sipHists[-1].legendstyle = 'L'
sipHists[-1].linewidth = 2
sipHists[-1].scale(1./sipHists[-1].integral())
ipHists.append(checkers[m].hists['ip'])
ipHists[-1].color = getColor(m)
ipHists[-1].title = "m_{{H}} = {}".format(m)
ipHists[-1].drawstyle = 'hist'
ipHists[-1].legendstyle = 'L'
ipHists[-1].linewidth = 2
ipHists[-1].scale(1./ipHists[-1].integral())
ipErrHists.append(checkers[m].hists['ipErr'])
ipErrHists[-1].color = getColor(m)
ipErrHists[-1].title = "m_{{H}} = {}".format(m)
ipErrHists[-1].drawstyle = 'hist'
ipErrHists[-1].legendstyle = 'L'
ipErrHists[-1].linewidth = 2
ipErrHists[-1].scale(1./ipErrHists[-1].integral())
checkers[m].hists['dxy'].Sumw2()
dxyRMS.SetPoint(i, float(m), checkers[m].hists['dxy'].GetRMS())
dxyRMS.SetPointError(i, 0., checkers[m].hists['dxy'].GetRMSError())
dxyHists.append(checkers[m].hists['dxy'])
dxyHists[-1].color = getColor(m)
dxyHists[-1].title = "m_{{H}} = {}".format(m)
dxyHists[-1].drawstyle = 'hist'
dxyHists[-1].legendstyle = 'L'
dxyHists[-1].linewidth = 2
dxyHists[-1].scale(1./dxyHists[-1].integral())
checkers[m].hists['dz'].Sumw2()
dzRMS.SetPoint(i, float(m), checkers[m].hists['dz'].GetRMS())
dzRMS.SetPointError(i, 0., checkers[m].hists['dz'].GetRMSError())
dzHists.append(checkers[m].hists['dz'])
dzHists[-1].color = getColor(m)
dzHists[-1].title = "m_{{H}} = {}".format(m)
dzHists[-1].drawstyle = 'hist'
dzHists[-1].legendstyle = 'L'
dzHists[-1].linewidth = 2
dzHists[-1].scale(1./dzHists[-1].integral())
fracFailing.SetPoint(i, float(m),
1. - float(checkers[m].nPassSIP) / checkers[m].nTot)
cSIP = Canvas(1000,1000)
draw(sipHists, cSIP, xtitle='#frac{IP_{3D}}{#sigma_{IP_{3D}}}',
ytitle='arb.')
legSIP = Legend(sipHists, cSIP, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legSIP.Draw("same")
cSIP.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/sips.png')
cSIPLog = Canvas(1000,1000)
draw(sipHists, cSIPLog, xtitle='#frac{IP_{3D}}{#sigma_{IP_{3D}}}',
ytitle='arb.', logy=True)
legSIPLog = Legend(sipHists, cSIPLog, textsize=.027, entrysep=0.012, leftmargin=0.6, entryheight=0.03)
legSIPLog.Draw("same")
cSIPLog.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/sipsLog.png')
cSIPRMS = Canvas(1000, 1000)
sipRMS.color = 'b'
sipRMS.drawstyle = 'PE'
sipRMS.legendstyle = 'PE'
draw(sipRMS, cSIPRMS, xtitle="m_{H}", ytitle="RMS(SIP_{3D})", xlimits=(0.,2600.))
cSIPRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/sipRMS.png')
cIP = Canvas(1000,1000)
draw(ipHists, cIP, xtitle='IP_{3D}',
ytitle='arb.')
legIP = Legend(ipHists, cIP, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legIP.Draw("same")
cIP.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ips.png')
cIPLog = Canvas(1000,1000)
draw(ipHists, cIPLog, xtitle='#frac{IP_{3D}}{#sigma_{IP_{3D}}}',
ytitle='arb.', logy=True)
legIPLog = Legend(ipHists, cIPLog, textsize=.027, entrysep=0.012, leftmargin=0.6, entryheight=0.03)
legIPLog.Draw("same")
cIPLog.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ipsLog.png')
cIPErr = Canvas(1000,1000)
draw(ipErrHists, cIPErr, xtitle='#sigma_{IP_{3D}}',
ytitle='arb.')
legIPErr = Legend(ipErrHists, cIPErr, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legIPErr.Draw("same")
cIPErr.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ipErrs.png')
cIPErrLog = Canvas(1000,1000)
draw(ipErrHists, cIPErrLog, xtitle='#sigma_{IP_{3D}}',
ytitle='arb.', logy=True)
legIPErrLog = Legend(ipErrHists, cIPErrLog, textsize=.027, entrysep=0.012, leftmargin=0.6, entryheight=0.03)
legIPErrLog.Draw("same")
cIPErrLog.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/ipErrsLog.png')
cFail = Canvas(1000, 1000)
fracFailing.color = 'b'
fracFailing.drawstyle = 'PE'
fracFailing.legendstyle = 'PE'
draw(fracFailing, cSIPRMS, xtitle="m_{H}", ytitle="Fraction failing SIP", xlimits=(0.,2600.))
cSIPRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/fracFailing.png')
cDXY = Canvas(1000,1000)
draw(dxyHists, cDXY, xtitle='#Delta_{xy}',
ytitle='arb.')
legDXY = Legend(dxyHists, cDXY, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legDXY.Draw("same")
cDXY.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dxys.png')
cDXYRMS = Canvas(1000, 1000)
dxyRMS.color = 'b'
dxyRMS.drawstyle = 'PE'
dxyRMS.legendstyle = 'PE'
draw(dxyRMS, cDXYRMS, xtitle="m_{H}", ytitle="RMS(#Delta_{xy})", xlimits=(0.,2600.))
cDXYRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dxyRMS.png')
cDZ = Canvas(1000,1000)
draw(dzHists, cDZ, xtitle='#Delta_{z}',
ytitle='arb.')
legDZ = Legend(dzHists, cDZ, textsize=.03, entrysep=0.015, leftmargin=0.6, entryheight=0.04)
legDZ.Draw("same")
cDZ.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dzs.png')
cDZRMS = Canvas(1000, 1000)
dzRMS.color = 'b'
dzRMS.drawstyle = 'PE'
dzRMS.legendstyle = 'PE'
draw(dzRMS, cDZRMS, xtitle="m_{H}", ytitle="RMS(#Delta_{z})", xlimits=(0.,2600.))
cDZRMS.Print('/afs/cern.ch/user/n/nawoods/www/sipStudy/dzRMS.png')
def pvalue_plot(poi,
pvalues,
pad=None,
xtitle='X',
ytitle='P_{0}',
linestyle=None,
linecolor=None,
yrange=None,
verbose=False):
"""
Draw a pvalue plot
Parameters
----------
poi : list
List of POI values tested
pvalues : list
List of p-values or list of lists of p-values to overlay
multiple p-value curves
pad : Canvas or Pad, optional (default=None)
Pad to draw onto. Create new pad if None.
xtitle : str, optional (default='X')
The x-axis label (POI name)
ytitle : str, optional (default='P_{0}')
The y-axis label
linestyle : str or list, optional (default=None)
Line style for the p-value graph or a list of linestyles for
multiple p-value graphs.
linecolor : str or list, optional (default=None)
Line color for the p-value graph or a list of linestyles for
multiple p-value graphs.
Returns
-------
pad : Canvas
The pad.
graphs : list of Graph
The p-value graphs
"""
if not pvalues:
raise ValueError("pvalues is empty")
if not poi:
raise ValueError("poi is empty")
# determine if pvalues is list or list of lists
if not isinstance(pvalues[0], (list, tuple)):
pvalues = [pvalues]
if linecolor is not None:
if not isinstance(linecolor, list):
linecolor = [linecolor]
linecolor = cycle(linecolor)
if linestyle is not None:
if not isinstance(linestyle, list):
linestyle = [linestyle]
linestyle = cycle(linestyle)
with preserve_current_canvas():
if pad is None:
pad = Canvas()
pad.cd()
pad.SetLogy()
# create the axis
min_poi, max_poi = min(poi), max(poi)
haxis = Hist(1000, min_poi, max_poi)
xaxis = haxis.xaxis
yaxis = haxis.yaxis
xaxis.SetRangeUser(min_poi, max_poi)
haxis.Draw('AXIS')
min_pvalue = float('inf')
graphs = []
for ipv, pv in enumerate(pvalues):
graph = Graph(len(poi),
linestyle='dashed',
drawstyle='L',
linewidth=2)
for idx, (point, pvalue) in enumerate(zip(poi, pv)):
graph.SetPoint(idx, point, pvalue)
if linestyle is not None:
graph.linestyle = linestyle.next()
if linecolor is not None:
graph.linecolor = linecolor.next()
graphs.append(graph)
curr_min_pvalue = min(pv)
if curr_min_pvalue < min_pvalue:
min_pvalue = curr_min_pvalue
if verbose:
for graph in graphs:
log.info(['{0:1.1f}'.format(xval) for xval in list(graph.x())])
log.info(['{0:0.3f}'.format(yval) for yval in list(graph.y())])
# automatically handles axis limits
axes, bounds = draw(graphs,
pad=pad,
same=True,
logy=True,
xtitle=xtitle,
ytitle=ytitle,
xaxis=xaxis,
yaxis=yaxis,
ypadding=(0.2, 0.1),
logy_crop_value=1E-300)
if yrange is not None:
xaxis, yaxis = axes
yaxis.SetLimits(*yrange)
yaxis.SetRangeUser(*yrange)
min_pvalue = yrange[0]
# draw sigma levels up to minimum of pvalues
line = Line()
line.SetLineStyle(2)
line.SetLineColor(2)
latex = ROOT.TLatex()
latex.SetNDC(False)
latex.SetTextSize(20)
latex.SetTextColor(2)
sigma = 0
while True:
pvalue = gaussian_cdf_c(sigma)
if pvalue < min_pvalue:
break
keepalive(
pad,
latex.DrawLatex(max_poi, pvalue, " {0}#sigma".format(sigma)))
keepalive(pad, line.DrawLine(min_poi, pvalue, max_poi, pvalue))
sigma += 1
pad.RedrawAxis()
pad.Update()
return pad, graphs
stackError = _sumStack.poisson_errors()
stackError.fillstyle = '/'
stackError.fillcolor = 'black'
stackError.drawstyle = '2'
# stack.Draw('HIST')
# stackError.Draw('2 [] same')
objects.append(stackError)
import ROOT
from FireROOT.Analysis.Utils import *
ROOT.gPad.SetLogy(True)
draw(objects,
xtitle='Some Variable [Units]',
ytitle='Events',
ypadding=0.05,
logy=True,
ylimits=(1e-3, 1e3))
print(ROOT.gStyle.GetPadTopMargin())
label = ROOT.TLatex(ROOT.gStyle.GetPadLeftMargin(),
0.9 + ROOT.gStyle.GetPadTopMargin(), 'This is a title')
label.SetTextFont(43)
label.SetTextAlign(11)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
# canvas.Modified()
# canvas.Update()
canvas.Print('test2.pdf')
def addRatio(canvas, num, denom, height=0.23, ratioMin=1., ratioMax=1.4,
bottomMargin=0.34, topMargin=0.06, yTitle="Ratio", mainXAxis=None,
mainYAxis=None, yRange=(.95,1.3), xRange=None):
newCanvas = Canvas(canvas.GetWw(), canvas.GetWh())
newCanvas.Divide(1,2)
pad2 = asrootpy(newCanvas.GetPad(2))
pad2.SetPad(0.,0.,1.,height)
pad2.SetBottomMargin(bottomMargin)
pad2.SetTopMargin(0.)
canvas.SetTopMargin(topMargin)
canvas.SetBottomMargin(0.01)
pad1 = asrootpy(newCanvas.GetPad(1))
pad1.SetPad(0.,height,1.,1.)
pad1.SetTopMargin(topMargin)
pad1.SetBottomMargin(0.01)
hNum = num.clone()
hNum.sumw2()
numerator = Graph(hNum)
hDenom = denom.clone()
hDenom.sumw2()
denominator = Graph(hDenom)
nRemoved = 0
for i in range(numerator.GetN()):
if hDenom[i+1].value <= 0. or hNum[i+1].value <= 0.:
numerator.RemovePoint(i - nRemoved)
denominator.RemovePoint(i - nRemoved)
nRemoved += 1
ratio = numerator / denominator
ratio.drawstyle = 'PE'
ratio.color = 'black'
unity = TLine(hNum.lowerbound(), 1, hNum.upperbound(), 1)
unity.SetLineStyle(7)
if yRange:
ylimits = yRange
else:
ylimits = (1.-((ratio.GetMaximum()-1.)*.15), 1.+((ratio.GetMaximum()-1.)*1.2))
if ylimits[0] >= ylimits[1]:
ylimits = (.95, 1.3)
(xaxis,yaxis),axisRange = draw(ratio, pad2, ytitle=yTitle,
ylimits=ylimits, xlimits=xRange)
pad2.cd()
unity.Draw("SAME")
# correct placement of axes and label/titles
if mainXAxis is not None and mainYAxis is not None:
mainX = mainXAxis
mainY = mainYAxis
ratioX = xaxis
ratioY = yaxis
ratioX.title = mainX.title
ratioX.SetTitleSize(mainX.GetTitleSize() * pad1.height / pad2.height)
ratioX.SetLabelSize(mainX.GetLabelSize() * pad1.height / pad2.height)
ratioY.SetTitleSize(mainY.GetTitleSize() * pad1.height / (2*pad2.height))
ratioY.SetTitleOffset(0.6)
ratioY.SetLabelSize(mainY.GetLabelSize() * pad1.height / (2*pad2.height))
mainX.SetLabelOffset(999)
mainX.SetTitleOffset(999)
pad1.cd()
canvas.DrawClonePad()
return newCanvas
Hist(100, -5, 5, color='salmon',
drawstyle='hist').FillRandom(F1('TMath::Gaus(x, 2, 1)'), 500))
stack.Add(
Hist(100, -5, 5, color='powderblue',
drawstyle='hist').FillRandom(F1('TMath::Gaus(x, 2, 0.6)'), 300))
objects.append(stack)
# create some random histograms
for i, (mu, sigma, n, c,
s) in enumerate(zip(mus, sigmas, events, colors, styles)):
hist = Hist(100,
-5,
5,
color=c,
fillstyle=s,
drawstyle='hist' if i % 2 == 0 else '')
hist.FillRandom(F1('TMath::Gaus(x,{0},{1})'.format(mu, sigma)), n)
objects.append(hist)
# create a graph
graph = Graph(10, drawstyle='P')
for i in range(10):
x = -2 + i * 4 / 10.
graph.SetPoint(i, x, 40 + 10 * sin(x))
objects.append(graph)
draw(objects, xtitle='Some Variable [Units]', ytitle='Events', ypadding=0.05)
# see rootpy.plotting.utils.get_limits for details on what arguments are
# supported for setting the axes ranges.
wait()
def main(args):
fileName = '/data/nawoods/lepsForSIP/lepsForSIP_M2500.root'
checker = MuonPtMatchChecker('Check Pt', [fileName])
checker.processSample()
hs = checker.hists.copy()
for var, h in hs.iteritems():
h.Sumw2()
h.drawstyle = 'HIST'
h.legendstyle = 'L'
h.linewidth = 2
h.color = 'red'
cRes = Canvas(1000,1000)
hs['pfPtRes'].color = 'black'
hs['pfPtRes'].title = 'PF'
hs['tunePPtResUncor'].title = 'Tune P'
(xax,yax), lims = draw([hs['pfPtRes'], hs['tunePPtResUncor']], cRes,
xtitle='#frac{reco p_T - gen p_T}{gen p_T}',
ytitle='Muons')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
legRes = Legend([hs['pfPtRes'], hs['tunePPtResUncor']], cRes)
legRes.Draw("same")
cRes.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptRes.png')
cResComp = Canvas(1000,1000)
(xax,yax), lims = draw(hs['resCompare'], cResComp, ytitle='Muons',
xtitle='#frac{|PF p_T - gen p_T|}{gen p_T} - #frac{|tuneP p_T - gen p_T|}{gen p_T}')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
cResComp.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResComp.png')
cResCor = Canvas(1000,1000)
hs['pfPtRes'].color = 'black'
hs['pfPtRes'].title = 'PF'
hs['tunePPtRes'].title = 'Tune P'
(xax,yax), lims = draw([hs['pfPtRes'], hs['tunePPtRes']], cResCor,
xtitle='#frac{reco p_T - gen p_T}{gen p_T}',
ytitle='Muons')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
legResCor = Legend([hs['pfPtRes'], hs['tunePPtRes']], cResCor)
legResCor.Draw("same")
cResCor.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResCor.png')
cResCompCor = Canvas(1000,1000)
(xax,yax), lims = draw(hs['resCompareBothCor'], cResCompCor, ytitle='Muons',
xtitle='#frac{|PF p_T - gen p_T|}{gen p_T} - #frac{|tuneP p_T - gen p_T|}{gen p_T}')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
cResCompCor.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResCompCor.png')
cResUncor = Canvas(1000,1000)
hs['pfPtResUncor'].color = 'black'
hs['pfPtResUncor'].title = 'PF'
hs['tunePPtResUncor'].title = 'Tune P'
(xax,yax), lims = draw([hs['pfPtResUncor'], hs['tunePPtResUncor']], cResUncor,
xtitle='#frac{reco p_T - gen p_T}{gen p_T}',
ytitle='Muons')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
legResUncor = Legend([hs['pfPtResUncor'], hs['tunePPtResUncor']], cResUncor)
legResUncor.Draw("same")
cResUncor.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResUncor.png')
cResCompUncor = Canvas(1000,1000)
(xax,yax), lims = draw(hs['resCompareUncor'], cResCompUncor, ytitle='Muons',
xtitle='#frac{|PF p_T - gen p_T|}{gen p_T} - #frac{|tuneP p_T - gen p_T|}{gen p_T}')
xax.SetTitleSize(0.4 * xax.GetTitleSize())
xax.SetTitleOffset(1.6)
xax.SetLabelSize(0.7 * xax.GetLabelSize())
cResCompUncor.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/ptResCompUncor.png')
hs2 = checker.hists2D.copy()
hs2['pfPtResVsPt'].yaxis.title = '#frac{PF p_T - gen p_T}{gen p_T}'
hs2['tunePPtResVsPt'].yaxis.title = '#frac{tuneP p_T - gen p_T}{gen p_T}'
hs2['pfPtResVsPt'].yaxis.title = '#frac{|PF p_T - gen p_T|}{gen p_T} - #frac{|tuneP p_T - gen p_T|}{gen p_T}'
for var, h in hs2.iteritems():
c = Canvas(1000,1000)
h.xaxis.title = 'gen p_T'
h.drawstyle = 'COLZ'
h.xaxis.SetTitleSize(0.5 * h.xaxis.GetTitleSize())
h.xaxis.SetLabelSize(0.7 * h.xaxis.GetLabelSize())
h.xaxis.SetTitleOffset(1.4)
h.yaxis.SetTitleSize(0.5 * h.yaxis.GetTitleSize())
h.yaxis.SetLabelSize(0.7 * h.yaxis.GetLabelSize())
h.yaxis.SetTitleOffset(1.4)
h.draw()
c.Print('/afs/cern.ch/user/n/nawoods/www/ptStudy/{}.png'.format(var))
drawstyle='hist',
linewidth=2),
clf,
Category_VBF,
'OS_ISOL',
systematics=True,
mva=True)
hsys = sample.GetHistoSys('QCDscale_ggH3in')
nom = sample.hist.Clone(shallow=True,
drawstyle='hist',
color='red',
fillstyle='hollow',
linewidth=2)
up = hsys.high.Clone(shallow=True,
drawstyle='hist',
color='red',
linestyle='dashed',
fillstyle='hollow',
linewidth=2)
dn = hsys.low.Clone(shallow=True,
drawstyle='hist',
color='red',
linestyle='dotted',
fillstyle='hollow',
linewidth=2)
canvas = Canvas()
draw([nom, up, dn])
canvas.SaveAs('test_ggH3in.png')
# set visual attributes
h1.fillstyle = 'solid'
h1.fillcolor = 'green'
h1.linecolor = 'green'
h1.linewidth = 0
h2.fillstyle = 'solid'
h2.fillcolor = 'red'
h2.linecolor = 'red'
h2.linewidth = 0
stack = HistStack([h1, h2], drawstyle='HIST E1 X0')
# plot with ROOT
canvas = Canvas(width=700, height=500)
draw([stack, h3], xtitle='Mass', ytitle='Events', pad=canvas)
# set the number of expected legend entries
legend = Legend([h1, h2, h3], leftmargin=0.45, margin=0.3)
legend.Draw()
label = ROOT.TText(0.3, 0.8, 'ROOT')
label.SetTextFont(43)
label.SetTextSize(25)
label.SetNDC()
label.Draw()
canvas.Modified()
canvas.Update()
# plot with matplotlib
set_style('ATLAS', mpl=True)
fig = plt.figure(figsize=(7, 5), dpi=100)
axes = plt.axes()
styles = ('\\', '/', '-')
canvas = Canvas()
objects = []
# create a stack
stack = HistStack()
stack.Add(Hist(100, -5, 5, color='salmon', drawstyle='hist').FillRandom(
F1('TMath::Gaus(x, 2, 1)'), 500))
stack.Add(Hist(100, -5, 5, color='powderblue', drawstyle='hist').FillRandom(
F1('TMath::Gaus(x, 2, 0.6)'), 300))
objects.append(stack)
# create some random histograms
for i, (mu, sigma, n, c, s) in enumerate(zip(mus, sigmas, events, colors, styles)):
hist = Hist(100, -5, 5, color=c, fillstyle=s, drawstyle='hist' if i % 2 == 0 else '')
hist.FillRandom(F1('TMath::Gaus(x,{0},{1})'.format(mu, sigma)), n)
objects.append(hist)
# create a graph
graph = Graph(10, drawstyle='P')
for i in range(10):
x = -2 + i * 4 / 10.
graph.SetPoint(i, x, 40 + 10 * sin(x))
objects.append(graph)
draw(objects, xtitle='Some Variable [Units]', ytitle='Events', ypadding=0.05)
# see rootpy.plotting.utils.get_limits for details on what arguments are
# supported for setting the axes ranges.
wait()
from mva.samples import Higgs
from mva.analysis import Analysis
from mva.categories import Category_VBF
from rootpy.plotting import Hist, Canvas
from rootpy.plotting.utils import draw
ana = Analysis(2012)
clf = ana.get_clf(Category_VBF, load=True)
h = Higgs(2012, mode='gg', systematics=True)
sample = h.get_histfactory_sample(
Hist(clf.binning(2012), color='red',
fillstyle='hollow', drawstyle='hist',
linewidth=2),
clf, Category_VBF, 'OS_ISOL',
systematics=True, mva=True)
hsys = sample.GetHistoSys('QCDscale_ggH3in')
nom = sample.hist.Clone(shallow=True, drawstyle='hist', color='red', fillstyle='hollow', linewidth=2)
up = hsys.high.Clone(shallow=True, drawstyle='hist', color='red', linestyle='dashed', fillstyle='hollow', linewidth=2)
dn = hsys.low.Clone(shallow=True, drawstyle='hist', color='red', linestyle='dotted', fillstyle='hollow', linewidth=2)
canvas = Canvas()
draw([nom, up, dn])
canvas.SaveAs('test_ggH3in.png')
# nWithFSR += 1
#print "%s: %d / %d"%(ch, nWithFSR, int(plotter.ntuples['data']['data'][ch].GetEntries()))
#for gr in g.values():
# gr.yaxis.SetTitleOffset(gr.yaxis.GetTitleOffset()*0.8)
# gr.yaxis.SetTitleSize(gr.yaxis.GetTitleSize()*0.9)
#c.Update()
if ana == 'z4l':
xlimits=(40.,90.)
ylimits=(0.,60.)
else:
xlimits=(40.,120.)
ylimits=(0.,120.)
c = Canvas(1000,1000)
(xaxis,yaxis), things = draw(g.values(), c, xtitle='m_{\\text{Z}_1} \\, [\\text{GeV}]',
ytitle='m_{\\text{Z}_2} \\, [\\text{GeV}]',
xlimits=xlimits, ylimits=ylimits)
yaxis.SetTitleSize(yaxis.GetTitleSize()*0.9)
c.Update()
leg = Legend(g.values(), c, leftmargin=0.6, textsize=0.04,
header='\\text{ Data}', entrysep=0.01,
entryheight=0.04)
leg.Draw("same")
plotter.style.setCMSStyle(c, "", True, "", 13, plotter.intLumi)
c.Print('~/www/ZZPlots/mZ2VsmZ1_{}.png'.format(ana))
def stack(x, *args, **kwargs):
## parse arguments
_data = kwargs.pop('data', None)
_bkgs = kwargs.pop('bkgs', None)
_sigs = kwargs.pop('sigs', None)
_treename = kwargs.pop('treename', None)
_datasearchpath = kwargs.pop('datasearchpath', None)
_datadrivensearchpath = kwargs.pop('datadrivensearchpath', None)
_bkgsearchpath = kwargs.pop('bkgsearchpath', None)
_sigsearchpath = kwargs.pop('sigsearchpath', None)
_lumi = kwargs.pop('lumi', None)
global data
global bkgs
global sigs
global treename
global datasearchpath
global datadrivensearchpath
global bkgsearchpath
global sigsearchpath
global lumi
data = _data or data
bkgs = _bkgs or bkgs
sigs = _sigs or sigs
treename = _treename or treename
datasearchpath = _datasearchpath or datasearchpath
datadrivensearchpath = _datadrivensearchpath or datadrivensearchpath
bkgsearchpath = _bkgsearchpath or bkgsearchpath
sigsearchpath = _sigsearchpath or sigsearchpath
if _lumi:
lumi = float(_lumi)
xtitle = kwargs.pop('xtitle', '')
ytitle = kwargs.pop('ytitle', '')
logx = bool(kwargs.pop('logx', False))
logy = bool(kwargs.pop('logy', False))
blind = kwargs.pop('blind', None)
has_blinded_data = False
## save stuff to bookkeep and return
stuff = dict()
stuff['x'] = x
## get data histogram
h_data = None
if data:
sp = datasearchpath # HACK: just data to True!
newx = '%s::%s::%s' % (sp, treename, x)
h_data = ipyhep.tree.project(newx, *args, **kwargs)
if h_data:
stuff['h_data'] = h_data
## blind the data?
if h_data and not blind is None:
if isinstance(blind, tuple):
blind1, blind2 = blind
nbins = h_data.GetNbinsX()
for i_bin in xrange(1, nbins +
2): # skip underflow (but not overflow)
xval1 = h_data.GetXaxis().GetBinLowEdge(i_bin)
xval2 = h_data.GetXaxis().GetBinUpEdge(i_bin)
if xval1 >= blind1 and xval2 <= blind2:
h_data.SetBinContent(i_bin, 0.0)
h_data.SetBinError(i_bin, 0.0)
has_blinded_data = True
else:
nbins = h_data.GetNbinsX()
for i_bin in xrange(1, nbins +
2): # skip underflow (but not overflow)
xval = h_data.GetXaxis().GetBinLowEdge(i_bin)
if xval >= blind:
h_data.SetBinContent(i_bin, 0.0)
h_data.SetBinError(i_bin, 0.0)
has_blinded_data = True
## get background histograms
h_bkgs = list()
n_bkgs = list()
if bkgs:
for bkg in bkgs:
if isinstance(bkg, list):
h_subtotal = None
for dsid in bkg:
assert isinstance(dsid, str)
h_bkg = None
if dsid.isdigit():
## mc backgrounds
sp = bkgsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
else:
## data-driven backgrounds
assert dsid == 'fakes' or dsid == 'efakes'
sp = datadrivensearchpath % dsid
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
if h_bkg:
if h_subtotal:
h_subtotal.Add(h_bkg)
else:
h_subtotal = h_bkg.Clone()
if h_subtotal:
h_bkgs.append(h_subtotal)
dsid = bkg[0]
n_bkgs.append(dsid)
else:
dsid = bkg
assert isinstance(dsid, str)
h_bkg = None
if dsid.isdigit():
## mc backgrounds
sp = bkgsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
else:
## data-driven backgrounds
assert dsid == 'fakes' or dsid == 'efakes'
sp = datadrivensearchpath % dsid
newx = '%s::%s::%s' % (sp, treename, x)
h_bkg = ipyhep.tree.project(newx, *args, **kwargs)
if h_bkg:
h_bkgs.append(h_bkg)
n_bkgs.append(dsid)
if h_bkgs:
stuff['h_bkgs'] = h_bkgs
## get signal histograms
h_sigs = list()
n_sigs = list()
if sigs:
for dsid in sigs:
sp = sigsearchpath % int(dsid)
newx = '%s::%s::%s' % (sp, treename, x)
h_sig = ipyhep.tree.project(newx, *args, **kwargs)
if h_sig:
h_sigs.append(h_sig)
n_sigs.append(dsid)
if h_sigs:
stuff['h_sigs'] = h_sigs
assert h_sigs
## style data
if h_data:
h_data.title = 'Data'
h_data.linecolor = ipyhep.style.black
h_data.linewidth = 2
h_data.markercolor = ipyhep.style.black
h_data.markerstyle = 20
h_data.markersize = 1.2
h_data.fillstyle = ipyhep.style.fill_hollow
h_data.drawstyle = 'PE'
h_data.legendstyle = 'LP'
## scale and style background histograms
if h_bkgs:
assert len(h_bkgs) == len(n_bkgs), '%s\n%s' % (h_bkgs, n_bkgs)
for h, dsid in zip(h_bkgs, n_bkgs):
sf = ipyhep.sampleops.get_sf(dsid)
if dsid.isdigit():
sf *= lumi / __ntuple_lumi
h.Scale(sf)
h.title = ipyhep.sampleops.get_label(dsid)
h.linecolor = ipyhep.style.black
h.linewidth = 1
h.markercolor = ipyhep.sampleops.get_color(dsid)
h.fillcolor = ipyhep.sampleops.get_color(dsid)
h.fillstyle = ipyhep.style.fill_solid
h.legendstyle = 'F'
## calculate stat error on total background
h_bkg_total = None
if h_bkgs:
for h_bkg in h_bkgs:
if h_bkg_total:
h_bkg_total.Add(h_bkg)
else:
h_bkg_total = h_bkg.Clone()
stuff['h_bkg_total'] = h_bkg_total
## style h_bkg_total
if h_bkg_total:
h_bkg_total.title = 'stat. uncert.'
h_bkg_total.linecolor = ipyhep.style.black
h_bkg_total.linewidth = 1
h_bkg_total.markerstyle = 0
h_bkg_total.fillcolor = ipyhep.style.dark_gray
h_bkg_total.fillstyle = ipyhep.style.fill_lines
h_bkg_total.drawstyle = 'E2'
h_bkg_total.legendstyle = 'LF'
## scale and style signal histograms
if h_sigs:
assert len(h_sigs) == len(n_sigs)
for h, dsid in zip(h_sigs, n_sigs):
sf = ipyhep.sampleops.get_sf(dsid)
sf *= lumi / __ntuple_lumi
h.Scale(sf)
h.title = ipyhep.sampleops.get_label(dsid)
h.linecolor = ipyhep.sampleops.get_color(dsid)
h.linewidth = 3
h.fillstyle = ipyhep.style.fill_hollow
h.markerstyle = 0
h.drawstyle = 'HIST'
h.legendstyle = 'L'
## build list of all_hists
all_hists = list()
main_hists = list()
if h_data:
all_hists.append(h_data)
main_hists.append(h_data)
if h_bkgs:
all_hists.extend(h_bkgs)
main_hists.extend(h_bkgs)
if h_bkg_total:
all_hists.append(h_bkg_total)
main_hists.append(h_bkg_total)
if h_sigs:
all_hists.extend(h_sigs)
## get statistics
if all_hists:
stats_list = list()
for h in all_hists:
stats_list.extend(get_stats(h))
html = convert_table_to_html(convert_stats_to_table(stats_list))
stuff['html'] = html
## renormalize for bin widths
bins = kwargs.pop('bins', None)
if bins and isinstance(bins, list):
for h in all_hists:
renormalize_for_bin_widths(h, bins)
## stack background histograms
if h_bkgs:
assert len(h_bkgs) == len(n_bkgs), '%s\n%s' % (h_bkgs, n_bkgs)
h_bkgs.reverse()
n_bkgs.reverse()
hstack = HistStack()
for h in h_bkgs:
hstack.Add(h)
hstack.title = 'stack sum'
hstack.drawstyle = 'HIST'
stuff['stack'] = hstack
h_bkgs.reverse()
n_bkgs.reverse()
# ## convert data to TGraphAsymmErrors
# g_data = None
# if h_data:
# if __use_poissonize:
# g_data = poissonize.GetPoissonizedGraph(h_data)
# else:
# g_data = ROOT.TGraphAsymmErrors()
# i_g = 0
# nbins = h_data.GetNbinsX()
# for i_bin in xrange(1, nbins+1): # skip underflow/overflow
# c = h_data.GetBinContent(i_bin)
# e = h_data.GetBinError(i_bin)
# if c != 0.0:
# g_data.SetPoint(i_g, h_data.GetBinCenter(i_bin), c)
# g_ratio.SetPointError(i_g,
# h_data.GetBinWidth(i_bin)/2.,
# h_data.GetBinWidth(i_bin)/2.,
# e,
# e)
# i_g += 1
## build list of objects to draw
objects = list()
if h_bkgs:
objects.append(stuff['stack'])
objects.append(stuff['h_bkg_total'])
if h_sigs:
objects.extend(h_sigs)
if h_data:
objects.append(h_data)
## set xlimits and ylimits
ypadding = 0.21
logy_crop_value = 7e-3
xmin, xmax, ymin, ymax = 0.0, 1.0, 0.0, 1.0
if objects:
xmin, xmax, ymin, ymax = get_limits(objects,
logx=logx,
logy=logy,
ypadding=ypadding,
logy_crop_value=logy_crop_value)
if logy:
ymin = 7e-3
else:
ymin = 0.0
xlimits = (xmin, xmax)
ylimits = (ymin, ymax)
stuff['xlimits'] = xlimits
stuff['ylimits'] = ylimits
## remove xtitle for do_ratio
_xtitle = xtitle
if h_data and h_bkg_total and kwargs.get('do_ratio'):
_xtitle = ''
## make canvas
canvas = Canvas(800, 600)
stuff['canvas'] = canvas
## draw the objects
if objects:
canvas.cd()
draw(objects,
pad=canvas,
xtitle=_xtitle,
ytitle=ytitle,
xlimits=xlimits,
ylimits=ylimits)
## set log x/y, for some reason doesn't work before draw
if logx or logy:
if logx:
canvas.SetLogx()
if logy:
canvas.SetLogy()
canvas.Update()
## draw blind_line
if has_blinded_data:
if isinstance(blind, tuple):
blind_list = list(blind)
else:
blind_list = [blind]
blind_lines = list()
for bl in blind_list:
line_y1 = ymin
line_y2 = ymax
blind_line = ROOT.TLine(bl, line_y1, bl, line_y2)
blind_line.SetLineColor(ROOT.kGray + 2)
blind_line.SetLineStyle(7)
blind_line.SetLineWidth(2)
blind_line.Draw()
blind_lines.append(blind_line)
stuff['blind_lines'] = blind_lines
canvas.Update()
## legend
lefty = True
if h_bkg_total:
lefty = is_left_sided(h_bkg_total)
elif h_data:
lefty = is_left_sided(h_data)
elif h_sigs:
lefty = is_left_sided(h_sigs[0])
if main_hists:
header = '%.1f fb^{-1}, 13 TeV' % (lumi / 1000.0)
if lefty:
legend = Legend(main_hists,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.60,
rightmargin=0.02,
entrysep=0.01,
entryheight=0.04)
else:
legend = Legend(main_hists,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.03,
rightmargin=0.59,
entrysep=0.01,
entryheight=0.04)
legend.Draw()
stuff['legend'] = legend
if h_sigs:
# header = 'ATLAS Internal'
header = ''
if lefty:
legend2 = Legend(h_sigs,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.37,
rightmargin=0.23,
entrysep=0.01,
entryheight=0.04)
else:
legend2 = Legend(h_sigs,
pad=canvas,
header=header,
textsize=16,
topmargin=0.03,
leftmargin=0.20,
rightmargin=0.40,
entrysep=0.01,
entryheight=0.04)
legend2.Draw()
stuff['legend2'] = legend2
## do_ratio
if h_data and h_bkg_total and kwargs.get('do_ratio'):
## top canvas
top_canvas = stuff.pop('canvas')
stuff['top_canvas'] = top_canvas
## make SM/SM with error band: h_ratio_band
i_sfratio = int(kwargs.get('sfratio', -1))
if i_sfratio < 0: # ratio plot of Data/Model
h_ratio_band = h_bkg_total.Clone()
nbins = h_ratio_band.GetNbinsX()
for i_bin in xrange(nbins + 2):
h_ratio_band.SetBinContent(i_bin, 1.0)
c = h_bkg_total.GetBinContent(i_bin)
e = h_bkg_total.GetBinError(i_bin) / c if c > 0.0 else 0.0
h_ratio_band.SetBinError(i_bin, e)
stuff['h_ratio_band'] = h_ratio_band
else: # ratio plot of Scale Factor for ith background
hi = h_bkgs[i_sfratio]
h_ratio_band = hi.Clone()
nbins = h_ratio_band.GetNbinsX()
for i_bin in xrange(nbins + 2):
h_ratio_band.SetBinContent(i_bin, 1.0)
c = hi.GetBinContent(i_bin)
e = hi.GetBinError(i_bin) / c if c > 0.0 else 0.0
h_ratio_band.SetBinError(i_bin, e)
stuff['h_ratio_band'] = h_ratio_band
## make data/(SM) h_ratio
if i_sfratio < 0:
h_ratio = h_data.Clone()
h_ratio.Divide(h_data, h_bkg_total, 1.0, 1.0)
stuff['h_ratio'] = h_ratio
else:
## SF1 = 1.0 + (data - MCtot) / MC1
sfname = kwargs.get('sfname')
sffile = kwargs.get('sffile')
if not sfname:
sfname = 'h_sf'
hi = h_bkgs[i_sfratio]
h_numer = h_data.Clone()
h_numer.Add(h_bkg_total, -1.0)
## do the division
h_ratio = h_data.Clone(sfname)
h_ratio.Divide(h_numer, hi, 1.0, 1.0)
## add the 1.0
nbins = h_ratio.GetNbinsX()
for i_bin in xrange(nbins + 2):
c = h_ratio.GetBinContent(i_bin)
h_ratio.SetBinContent(i_bin, c + 1.0)
h_ratio_band.SetBinContent(i_bin, c + 1.0)
## ignore bins with no data for SF
for i_bin in xrange(nbins + 2):
c = h_data.GetBinContent(i_bin)
if c <= 0:
h_ratio.SetBinContent(i_bin, 0.0)
h_ratio.SetBinError(i_bin, 0.0)
h_ratio_band.SetBinError(i_bin, 0.0)
stuff['h_ratio'] = h_ratio
if sffile:
f_out = ipyhep.file.write(h_ratio, sffile)
# f_out.Close()
## convert ratio to a TGraphErrors so that Draw('E0')
## shows error bars for points off the pad
g_ratio = ROOT.TGraphErrors()
i_g = 0
for i_bin in xrange(1, nbins + 1): # skip underflow/overflow
ratio_content = h_ratio.GetBinContent(i_bin)
if ratio_content != 0.0:
g_ratio.SetPoint(i_g, h_ratio.GetBinCenter(i_bin),
ratio_content)
g_ratio.SetPointError(i_g,
h_ratio.GetBinWidth(i_bin) / 2.,
h_ratio.GetBinError(i_bin))
i_g += 1
else:
h_ratio.SetBinError(i_bin, 0.0)
stuff['g_ratio'] = g_ratio
## style ratio
h_ratio_band.title = 'bkg uncert.'
if i_sfratio < 0:
h_ratio_band.linecolor = ipyhep.style.yellow
else:
h_ratio_band.linecolor = ipyhep.style.light_gray
h_ratio_band.linewidth = 0
h_ratio_band.markerstyle = 0
if i_sfratio < 0:
h_ratio_band.fillcolor = ipyhep.style.yellow
else:
h_ratio_band.linecolor = ipyhep.style.light_gray
h_ratio_band.fillstyle = ipyhep.style.fill_solid
h_ratio_band.drawstyle = 'E2'
h_ratio_band.legendstyle = 'F'
h_ratio.title = 'ratio'
h_ratio.linecolor = ipyhep.style.black
h_ratio.linewidth = 2
h_ratio.markercolor = ipyhep.style.black
h_ratio.markerstyle = 20
h_ratio.markersize = 1.2
h_ratio.fillstyle = ipyhep.style.fill_hollow
h_ratio.drawstyle = 'PE'
h_ratio.legendstyle = 'LP'
## bottom canvas
bottom_canvas = Canvas(800, 600)
bottom_canvas.cd()
stuff['bottom_canvas'] = bottom_canvas
## set ratio ylimits
ratio_min = kwargs.get('ratio_min', -0.2)
ratio_max = kwargs.get('ratio_max', 2.2)
ratio_ylimits = (ratio_min, ratio_max)
## draw ratio band
if i_sfratio < 0:
_ytitle = 'Data / Model'
else:
hi = h_bkgs[i_sfratio]
_ytitle = 'SF(%s)' % hi.title
draw([h_ratio_band],
pad=bottom_canvas,
xtitle=xtitle,
ytitle=_ytitle,
xlimits=xlimits,
ylimits=ratio_ylimits)
## set log x/y, for some reason doesn't work before draw?
if logx:
bottom_canvas.SetLogx()
bottom_canvas.Update()
### make horiz lines in ratio plot every 0.5:
line_ys = [
y / 10.0
for y in range(10 *
int(round(ratio_min)), 10 * int(round(ratio_max)) +
5, 5)
]
line_x1 = canvas.GetUxmin()
line_x2 = canvas.GetUxmax()
line_xwidth = abs(line_x2 - line_x1)
lines = []
for line_y in line_ys:
line = ROOT.TLine(line_x1 + 0.02 * line_xwidth, line_y,
line_x2 - 0.02 * line_xwidth, line_y)
line.SetLineWidth(1)
line.SetLineStyle(7)
if line_y == 1.0:
line.SetLineColor(ROOT.kGray + 2)
else:
line.SetLineColor(ROOT.kGray + 0)
line.Draw()
lines.append(line)
stuff['lines'] = lines
## draw blind_line
if has_blinded_data:
if isinstance(blind, tuple):
blind_list = list(blind)
else:
blind_list = [blind]
blind_lines = list()
for bl in blind_list:
line_y1 = ymin
line_y2 = ymax
blind_line = ROOT.TLine(bl, line_y1, bl, line_y2)
blind_line.SetLineColor(ROOT.kGray + 2)
blind_line.SetLineStyle(7)
blind_line.SetLineWidth(2)
blind_line.Draw()
blind_lines.append(blind_line)
stuff['blind_lines2'] = blind_lines
canvas.Update()
## draw ratio
g_ratio.Draw('PE0')
# h_ratio.GetYaxis().SetRangeUser(ratio_min, ratio_max)
# h_ratio.Draw('PE,SAME')
## shared canvas
shared_canvas = Canvas(800, 800)
shared_plot = plot_shared_axis(top_canvas,
bottom_canvas,
canvas=shared_canvas,
split=0.35,
axissep=0.01)
stuff['canvas'] = shared_canvas
canvas = shared_canvas
## save figures
save = kwargs.get('save')
if save is None: # NOTE: save can be False to skip saving
save = ['pdf', 'png']
if save:
ipyhep.file.save_figures(canvas, x, save)
global results
results = stuff
return stuff
def pvalue_plot(poi, pvalues, pad=None,
xtitle='X', ytitle='P_{0}',
linestyle=None,
linecolor=None,
yrange=None,
verbose=False):
"""
Draw a pvalue plot
Parameters
----------
poi : list
List of POI values tested
pvalues : list
List of p-values or list of lists of p-values to overlay
multiple p-value curves
pad : Canvas or Pad, optional (default=None)
Pad to draw onto. Create new pad if None.
xtitle : str, optional (default='X')
The x-axis label (POI name)
ytitle : str, optional (default='P_{0}')
The y-axis label
linestyle : str or list, optional (default=None)
Line style for the p-value graph or a list of linestyles for
multiple p-value graphs.
linecolor : str or list, optional (default=None)
Line color for the p-value graph or a list of linestyles for
multiple p-value graphs.
Returns
-------
pad : Canvas
The pad.
graphs : list of Graph
The p-value graphs
"""
if not pvalues:
raise ValueError("pvalues is empty")
if not poi:
raise ValueError("poi is empty")
# determine if pvalues is list or list of lists
if not isinstance(pvalues[0], (list, tuple)):
pvalues = [pvalues]
if linecolor is not None:
if not isinstance(linecolor, list):
linecolor = [linecolor]
linecolor = cycle(linecolor)
if linestyle is not None:
if not isinstance(linestyle, list):
linestyle = [linestyle]
linestyle = cycle(linestyle)
with preserve_current_canvas():
if pad is None:
pad = Canvas()
pad.cd()
pad.SetLogy()
# create the axis
min_poi, max_poi = min(poi), max(poi)
haxis = Hist(1000, min_poi, max_poi)
xaxis = haxis.xaxis
yaxis = haxis.yaxis
xaxis.SetRangeUser(min_poi, max_poi)
haxis.Draw('AXIS')
min_pvalue = float('inf')
graphs = []
for ipv, pv in enumerate(pvalues):
graph = Graph(len(poi), linestyle='dashed',
drawstyle='L', linewidth=2)
for idx, (point, pvalue) in enumerate(zip(poi, pv)):
graph.SetPoint(idx, point, pvalue)
if linestyle is not None:
graph.linestyle = linestyle.next()
if linecolor is not None:
graph.linecolor = linecolor.next()
graphs.append(graph)
curr_min_pvalue = min(pv)
if curr_min_pvalue < min_pvalue:
min_pvalue = curr_min_pvalue
if verbose:
for graph in graphs:
log.info(['{0:1.1f}'.format(xval) for xval in list(graph.x())])
log.info(['{0:0.3f}'.format(yval) for yval in list(graph.y())])
# automatically handles axis limits
axes, bounds = draw(graphs, pad=pad, same=True, logy=True,
xtitle=xtitle, ytitle=ytitle,
xaxis=xaxis, yaxis=yaxis, ypadding=(0.2, 0.1),
logy_crop_value=1E-300)
if yrange is not None:
xaxis, yaxis = axes
yaxis.SetLimits(*yrange)
yaxis.SetRangeUser(*yrange)
min_pvalue = yrange[0]
# draw sigma levels up to minimum of pvalues
line = Line()
line.SetLineStyle(2)
line.SetLineColor(2)
latex = ROOT.TLatex()
latex.SetNDC(False)
latex.SetTextSize(20)
latex.SetTextColor(2)
sigma = 0
while True:
pvalue = gaussian_cdf_c(sigma)
if pvalue < min_pvalue:
break
keepalive(pad, latex.DrawLatex(max_poi, pvalue, " {0}#sigma".format(sigma)))
keepalive(pad, line.DrawLine(min_poi, pvalue, max_poi, pvalue))
sigma += 1
pad.RedrawAxis()
pad.Update()
return pad, graphs
hOr.scale(1./hOr.Integral())
if hNew.Integral():
hNew.scale(1./hNew.Integral())
hOr.color = 'b'
hOr.drawstyle = 'LPE'
hOr.legendstyle = "LPE"
hLeg.color = 'r'
hLeg.drawstyle = 'LPE'
hLeg.legendstyle = "LPE"
hNew.color = 'b'
hNew.drawstyle = 'hist'
cCompare = Canvas(1000,1200)
(xaxis, yaxis), axisRanges = draw([hOr, hLeg], cCompare, ytitle='arb.', xtitle=xTitles[v])
legCompare = Legend([hOr, hLeg], **legPlacement[v])
legCompare.Draw("SAME")
cCompare = addRatio(cCompare, hOr, hLeg, mainXAxis=xaxis,
mainYAxis=yaxis, xRange=axisRanges[:2])
style.setCMSStyle(cCompare, "", True, "Preliminary Simulation", 13, -1)
cCompare.Print(path.join(outdir, 'compare', '{}_m{}_{}.png'.format(v,m,c)))
cNew = Canvas(1000,1000)
draw(hNew, cNew, ytitle='arb', xtitle=xTitles[v])
style.setCMSStyle(cNew, "", True, "Preliminary Simulation", 13, -1)
cNew.Print(path.join(outdir, 'rescued', '{}_m{}_{}.png'.format(v,m,c)))
hOrQQ = Hist(*binning['sm'][v], title='\\text{qq} \\!\\! \\rightarrow \\!\\! \\text{ZZ New ID}')
