def test_stack():
stack = HistStack()
assert_equal(len(stack), 0)
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='red'))
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='blue'))
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='green'))
assert_equal(len(stack), 3)
stack2 = stack.Clone()
assert_equal(stack2[2].linecolor, 'green')
# test stacked=True
a = Hist(2, 0, 1)
b = Hist(2, 0, 1)
a.Fill(0.2)
b.Fill(0.2)
b.Fill(0.8, 5)
stack = HistStack([a, b])
assert_equal(stack.min(), 2)
assert_equal(stack.max(), 5)
# test stacked=False
a = Hist(2, 0, 20)
b = Hist(2, 10, 20) # binning may be different
a.Fill(0.2)
b.Fill(15, 5)
stack = HistStack([a, b], stacked=False)
assert_equal(stack.min(), 0)
assert_equal(stack.max(), 5)
def test_stack():
stack = HistStack()
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='red'))
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='blue'))
stack.Add(Hist(10, 0, 1, fillstyle='solid', color='green'))
assert_equal(len(stack), 3)
stack2 = stack.Clone()
assert_equal(stack2[2].linecolor, 'green')
def test_hist():
from rootpy.plotting import root2matplotlib as rplt
h = Hist(100, -5, 5)
h.FillRandom('gaus')
rplt.hist(h)
# stack
h1 = h.Clone()
stack = HistStack([h, h1])
rplt.hist(stack)
rplt.hist([h, h1])
def test_bar():
from rootpy.plotting import root2matplotlib as rplt
h = Hist(100, -5, 5)
h.FillRandom('gaus')
rplt.bar(h)
# stack
h1 = h.Clone()
stack = HistStack([h, h1])
rplt.bar(stack)
rplt.bar([h, h1], stacked=True)
rplt.bar([h, h1], stacked=False)
rplt.bar([h, h1], stacked=False, reverse=True)
def make_control_region_data_mc_comparision(
histograms,
histogram_name,
variable,
x_label=r'$m(\mathrm{t}\bar{\mathrm{t}})$ [GeV]',
x_min=300,
x_max=1800,
y_label='Events/50 GeV'):
qcd = histograms['QCD'][histogram_name]
ttjet = histograms['TTJet'][histogram_name]
wjets = histograms['WJets'][histogram_name]
zjets = histograms['ZJets'][histogram_name]
single_top = histograms['SingleTop'][histogram_name]
other = ttjet + wjets + zjets + single_top
data = histograms['data'][histogram_name]
data.SetMarkerSize(3)
qcd.SetTitle('QCD from data')
other.SetTitle('Combined other background')
data.SetTitle('Data')
qcd.fillcolor = 'yellow'
other.fillcolor = 'red'
qcd.fillstyle = 'solid'
other.fillstyle = 'solid'
stack = HistStack()
stack.Add(other)
stack.Add(qcd)
# plot with matplotlib
plt.figure(figsize=(16, 12), dpi=200, facecolor='white')
axes = plt.axes()
rplt.hist(stack, stacked=True, axes=axes)
rplt.errorbar(data, xerr=None, emptybins=False, axes=axes)
plt.xlabel(x_label, CMS.x_axis_title)
plt.ylabel(y_label, CMS.y_axis_title)
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.title(get_title(variable), CMS.title)
plt.legend(numpoints=1, loc='upper right', prop=CMS.legend_properties)
axes.set_xlim(xmin=x_min, xmax=x_max)
axes.set_ylim(ymin=0)
plt.tight_layout()
plt.savefig(variable + '.png')
plt.savefig(variable + '.pdf')
map(h2.Fill, x2)
map(h3.Fill, x1_obs)
map(h3.Fill, x2_obs)
# 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()
stack.Add(h1)
stack.Add(h2)
ratio = h3 / (h1 + h2)
ratio = Hist.divide(h3, h1 + h2)
# plot with matplotlib
plt.figure(figsize=(16, 12), dpi=200)
gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1])
axes = plt.axes()
ax0 = plt.subplot(gs[0])
ax0.minorticks_on()
rplt.bar(stack, stacked=True)
rplt.errorbar(h3, xerr=False, emptybins=False)
# plt.xlabel('Mass', position=(1., 0.), ha='right', fontsize = 24)
def make_data_mc_comparison_plot(
histograms=[],
histogram_lables=[],
histogram_colors=[],
histogram_properties=Histogram_properties(),
data_index=0,
save_folder='plots/',
save_as=['pdf'],
normalise=False,
show_ratio=False,
show_stat_errors_on_mc=False,
draw_vertical_line=0,
systematics_for_ratio=None,
systematics_for_plot=None,
histograms_to_compare=None,
):
'''
systematics_for_plot takes the same input as systematics_for_ratio. There may be some repition with reagrds to
mc_error and mc_relative_errors, but these only deal with a flat error up and down.
'''
save_folder = check_save_folder(save_folder)
# make copies in order not to mess with existing histograms
histograms_ = deepcopy(histograms)
stack = HistStack()
add_mc = stack.Add
for index, histogram in enumerate(histograms_):
label = histogram_lables[index]
color = histogram_colors[index]
histogram.SetTitle(label)
if normalise:
histogram.Sumw2()
if not index == data_index:
histogram.fillstyle = 'solid'
histogram.fillcolor = color
histogram.legendstyle = 'F'
add_mc(histogram)
data = histograms_[data_index]
data.SetMarkerSize(CMS.data_marker_size)
if normalise:
n_events_data = data.Integral()
n_events_mc = stack.Integral()
data.Scale(1 / n_events_data)
stack.Scale(1 / n_events_mc)
# plot with matplotlib
plt.figure(figsize=CMS.figsize, dpi=CMS.dpi, facecolor=CMS.facecolor)
axes = None
if show_ratio:
ratio = data.Clone('ratio')
sumHists = sum(stack.GetHists())
for bin_i in range(1, sumHists.GetNbinsX()):
sumHists.SetBinError(bin_i, 0)
ratio.Divide(sum(stack.GetHists()))
ratio.SetMarkerSize(3)
gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1])
axes = plt.subplot(gs[0])
else:
axes = plt.axes()
if histogram_properties.set_log_y:
axes.set_yscale('log', nonposy="clip")
axes.set_ylim(ymin=1e-2)
if systematics_for_plot != None:
plusErrors = [x + 1 for x in systematics_for_plot]
minusErrors = [1 - x for x in systematics_for_plot]
stack_lower = sum(stack.GetHists())
stack_upper = stack_lower.Clone('upper')
for bin_i in range(1, stack_lower.GetNbinsX() + 1):
central_value = stack_lower.GetBinContent(bin_i)
error_upper_bound = plusErrors[bin_i - 1] * central_value
error_lower_bound = minusErrors[bin_i - 1] * central_value
stack_upper.SetBinContent(bin_i, error_upper_bound)
stack_lower.SetBinContent(bin_i, error_lower_bound)
rplt.fill_between(
stack_lower,
stack_upper,
axes,
hatch='//',
# facecolor = 'Black',
facecolor='None',
edgecolor='Grey',
alpha=1.,
linewidth=0.,
zorder=len(histograms_) + 1)
mc_error = histogram_properties.mc_error
mc_relative_errors = histogram_properties.mc_relative_errors
if mc_relative_errors:
stack_lower = sum(stack.GetHists())
stack_upper = stack_lower.Clone('upper')
for bin_i in range(1, stack_lower.GetNbinsX()):
central_value = stack_lower.GetBinContent(bin_i)
relative_error = mc_relative_errors[bin_i - 1]
error_upper_bound = central_value * (1 + relative_error)
error_lower_bound = central_value * (1 - relative_error)
stack_lower.SetBinContent(bin_i, error_upper_bound)
stack_upper.SetBinContent(bin_i, error_lower_bound)
rplt.fill_between(stack_upper,
stack_lower,
axes,
facecolor='0.75',
alpha=0.5,
hatch='/',
zorder=len(histograms_) + 1)
else:
if mc_error > 0:
stack_lower = sum(stack.GetHists())
stack_upper = stack_lower.Clone('upper')
stack_lower.Scale(1 - mc_error)
stack_upper.Scale(1 + mc_error)
rplt.fill_between(stack_upper,
stack_lower,
axes,
facecolor='0.75',
alpha=0.5,
hatch='/',
zorder=len(histograms_) + 1)
if not mc_error > 0 and show_stat_errors_on_mc:
stack_lower = sum(stack.GetHists())
mc_errors = list(stack_lower.yerravg())
stack_upper = stack_lower.Clone('upper')
for bin_i in range(1, stack_lower.GetNbinsX()):
central_value = stack_lower.GetBinContent(bin_i)
error = mc_errors[bin_i - 1]
error_upper_bound = central_value + error
error_lower_bound = central_value - error
stack_lower.SetBinContent(bin_i, error_lower_bound)
stack_upper.SetBinContent(bin_i, error_upper_bound)
rplt.fill_between(stack_upper,
stack_lower,
axes,
facecolor='0.75',
alpha=0.5,
hatch='/',
zorder=len(histograms_) + 1)
# a comment on zorder: the MC stack should be always at the very back (z = 1),
# then the MC error (z = len(histograms_) + 1) and finally the data
# (z = len(histograms_) + 2)
rplt.hist(stack, stacked=True, axes=axes, zorder=1)
rplt.errorbar(data,
emptybins=histogram_properties.emptybins,
axes=axes,
xerr=histogram_properties.xerr,
elinewidth=2,
capsize=10,
capthick=2,
zorder=len(histograms_) + 2)
if histograms_to_compare:
h_compare = {}
for h, l, c in zip(histograms_to_compare['hists'],
histograms_to_compare['labels'],
histograms_to_compare['colours']):
for histogram in histograms_:
if histogram.GetTitle() not in [
histograms_to_compare['to_replace'], 'data'
]:
h += histogram
h_compare[l] = [h, c]
rplt.step(
h,
axes=axes,
label=l,
color=c,
linewidth=4,
)
# put legend into the correct order (data is always first!)
handles, labels = axes.get_legend_handles_labels()
data_label_index = labels.index('data')
data_handle = handles[data_label_index]
labels.remove('data')
handles.remove(data_handle)
labels.insert(0, 'data')
handles.insert(0, data_handle)
if mc_error > 0 or (not mc_error > 0 and show_stat_errors_on_mc):
p1 = Rectangle((0, 0), 1, 1, fc="0.75", alpha=0.5, hatch='/')
handles.append(p1)
labels.append(histogram_properties.mc_errors_label)
l1 = axes.legend(handles,
labels,
numpoints=1,
frameon=histogram_properties.legend_color,
bbox_to_anchor=histogram_properties.legend_location,
bbox_transform=plt.gcf().transFigure,
prop=CMS.legend_properties,
ncol=histogram_properties.legend_columns)
l1.set_zorder(102)
set_labels(plt,
histogram_properties,
show_x_label=not show_ratio,
axes=axes)
x_limits = histogram_properties.x_limits
y_limits = histogram_properties.y_limits
if len(x_limits) >= 2:
axes.set_xlim(xmin=x_limits[0], xmax=x_limits[-1])
if len(y_limits) >= 2:
axes.set_ylim(ymin=y_limits[0], ymax=y_limits[-1])
else:
y_max = get_best_max_y(
histograms_, x_limits=x_limits) * histogram_properties.y_max_scale
print("Chosen limits : ", 0, y_max)
axes.set_ylim(ymin=0, ymax=y_max)
if histogram_properties.set_log_y:
if not len(y_limits) == 2: # if not user set y-limits, set default
axes.set_ylim(ymin=1e-1)
#draw a red vertical line if needed:
if draw_vertical_line != 0:
plt.axvline(x=draw_vertical_line, color='red', linewidth=3)
if show_ratio:
plt.setp(axes.get_xticklabels(), visible=False)
ax1 = plt.subplot(gs[1])
ax1.minorticks_on()
ax1.grid(True, 'major', linewidth=1)
ax1.axhline(y=1, linewidth=1)
set_labels(plt,
histogram_properties,
show_x_label=True,
show_title=False)
plt.ylabel(r'$\frac{\mathrm{data}}{\mathrm{pred.}}$', CMS.y_axis_title)
ax1.yaxis.set_label_coords(-0.115, 0.8)
rplt.errorbar(ratio,
emptybins=histogram_properties.emptybins,
axes=ax1,
xerr=histogram_properties.xerr,
elinewidth=1.5,
capsize=5,
capthick=1.5)
if histograms_to_compare:
for l, h in h_compare.iteritems():
r = data.Clone(l).Divide(h[0])
rplt.step(
r,
axes=ax1,
label='',
colour=h[1],
linewidth=2,
)
if len(x_limits) >= 2:
ax1.set_xlim(xmin=x_limits[0], xmax=x_limits[-1])
if len(histogram_properties.ratio_y_limits) >= 2:
ax1.set_ylim(ymin=histogram_properties.ratio_y_limits[0],
ymax=histogram_properties.ratio_y_limits[-1])
# dynamic tick placement
adjust_ratio_ticks(ax1.yaxis,
n_ticks=3,
y_limits=histogram_properties.ratio_y_limits)
if histogram_properties.integerXVariable:
ax1.tick_params(axis='x', which='minor', bottom='off', top='off')
if systematics_for_ratio != None:
plusErrors = [x + 1 for x in systematics_for_ratio]
minusErrors = [1 - x for x in systematics_for_ratio]
ratioPlusError = ratio.Clone('plus')
ratioMinusError = ratio.Clone('minus')
for bin_i in range(1, ratioPlusError.GetNbinsX() + 1):
ratioPlusError.SetBinContent(bin_i, plusErrors[bin_i - 1])
ratioMinusError.SetBinContent(bin_i, minusErrors[bin_i - 1])
rplt.fill_between(
ratioPlusError,
ratioMinusError,
axes,
hatch='//',
# facecolor = 'Black',
facecolor='None',
edgecolor='Grey',
alpha=1.,
linewidth=0.,
# zorder = len(histograms_) + 1
zorder=0)
if CMS.tight_layout:
plt.tight_layout()
for save in save_as:
if save == 'root':
saveHistogramsToROOTFile(
data, stack,
save_folder + histogram_properties.name + '.' + save)
else:
plt.savefig(save_folder + histogram_properties.name + '.' + save)
plt.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)
histpad.cd()
histpad.SetFrameBorderSize(2)
histpad.SetFrameLineWidth(2);
canvas.cd()
#ratiopad = Pad(leftmargins[1],0.00,1 - rightmargins[1],bottommargins[1]-0.02)
ratiopad = Pad(leftmargins[1],0.00,1 - rightmargins[1],bottommargins[1]-0.02)
ratiopad.SetBottomMargin(0.33)
ratiopad.SetTopMargin(0.03)
ratiopad.SetFrameLineWidth(2);
if(doRatio):
ratiopad.Draw()
histpad.cd()
stack = HistStack()
datastack = HistStack()
yminimum, ymaximum = RegionYrange(Region)
stack.SetMinimum(yminimum)
stack.SetMaximum(ymaximum)
total = Cut("1")
for cut in Cutlist:
if (variable in cut) and donminus1:continue
total = total & cut
backgroundstacks = OrderedDict()
background = []
for sample in sampleDictionary:
map(h2.Fill, signal)
h3.FillRandom('landau', 1000)
map(h3.Fill, signal_obs)
# 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()
# rebin?
rebin = plots_path.get('rebin', plots_config.get('rebin', None))
if rebin is not None:
hist.rebin(rebin)
# exclusion, so we don't need to plot it
if hist.title in plots_path.get('exclude', []): continue
if group.get('stack it', False):
# overwrite with solid when stacking
hist.fillstyle = 'solid'
stackHists.append(hist)
else:
soloHists.append(hist)
hstack = HistStack(name=h.path)
# for some reason, this causes noticable slowdowns
hstack.drawstyle = 'hist'
map(hstack.Add, stackHists[::-1])
# this is where we would set various parameters of the min, max and so on?
# need to set things like min, max, change to log, etc for hstack and soloHists
normalizeTo = plots_path.get('normalize', plots_config.get('normalize', None))
if normalizeTo is not None:
dataScale = 0.
if "unity" in normalizeTo:
for hist in hstack:
if (hist.integral() != 0):
hist.scale(1.0/hist.integral())
for hist in soloHists:
if (hist.integral() != 0):
samples = []
backgrounds = []
signals = []
data = []
stackyield = 0
errorsum = 0
backgroundstacks = {}
# print "pre sampleDictionary"
for sample in sampleDictionary:
if sample != 'Zjets1516': continue
if Region in BLINDEDLIST and sample is 'Data': continue
LegendEntry = sampleDictionary[sample]['legend']
Type = sampleDictionary[sample]['type']
backgroundstacks[LegendEntry] = HistStack()
# print "post initialisation of backgroundstakcs"
#"pre second sample dictionary"
for (variable2, xmin2, xmax2, nbins2, xtitle2, ytitle2,
variable_bool2) in zip(variable_list, xmin_list, xmax_list,
nbins_list, xtitle_list, ytitle_list,
variable_bool_list):
if variable_bool != 'Plot': continue
canvas = Canvas(width=canvaswidth,
height=int(
(1 - (1 - doRatio) * 0.2) * canvasheight))
#canvas.SetFrameBorderMode(0)
# rebin?
rebin = plots_path.get('rebin',
plots_config.get('rebin', None))
if rebin is not None:
hist.rebin(rebin)
# exclusion, so we don't need to plot it
if hist.title in plots_path.get('exclude', []): continue
if group.get('stack it', False):
# overwrite with solid when stacking
hist.fillstyle = 'solid'
stackHists.append(hist)
else:
soloHists.append(hist)
hstack = HistStack(name=h.path)
# for some reason, this causes noticable slowdowns
hstack.drawstyle = 'hist'
map(hstack.Add, stackHists[::-1])
# this is where we would set various parameters of the min, max and so on?
# need to set things like min, max, change to log, etc for hstack and soloHists
normalizeTo = plots_path.get(
'normalize', plots_config.get('normalize', None))
if normalizeTo is not None:
dataScale = 0.
if normalizeTo not in [hist.title for hist in soloHists]:
raise ValueError(
"Could not find %s as a solo hist for normalizing to."
% normalizeTo)
for hist in soloHists:
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 draw(
name,
category,
data=None,
data_info=None,
model=None,
model_colors=None,
signal=None,
signal_scale=1.,
signal_on_top=False,
signal_linestyles=None,
signal_colors=None,
show_signal_error=False,
fill_signal=False,
stack_signal=True,
units=None,
plot_label=None,
ylabel='Events',
blind=False,
show_ratio=False,
ratio_range=(0, 2),
ratio_height=0.15,
ratio_margin=0.06,
output_formats=None,
systematics=None,
#systematics_components=None,
integer=False,
textsize=22,
logy=False,
logy_min=None,
separate_legends=False,
ypadding=None,
legend_position='right',
range=None,
output_name=None,
output_dir=PLOTS_DIR,
arrow_values=None,
overflow=True,
show_pvalue=False,
top_label=None,
poisson_errors=True):
if model is None and data is None and signal is None:
# insufficient input
raise ValueError("at least one of model, data, "
"or signal must be specified")
if model is not None:
if not isinstance(model, (list, tuple)):
model = [model]
if overflow:
for hist in model:
fold_overflow(hist)
if signal is not None:
if not isinstance(signal, (list, tuple)):
signal = [signal]
if overflow:
for hist in signal:
fold_overflow(hist)
if data is not None and overflow:
fold_overflow(data)
# objects will be populated with all histograms in the main pad
objects = []
legends = []
if show_ratio and (data is None or model is None):
# cannot show the ratio if data or model was not specified
show_ratio = False
if ypadding is None:
# select good defaults for log or linear scales
if logy:
ypadding = (.6, .0)
else:
ypadding = (.35, .0)
template = data or model[0]
xdivisions = min(template.nbins(), 7) if integer else 507
if show_ratio:
fig = RatioPlot(logy=logy,
ratio_title='Data / Model',
ratio_limits=(0, 2),
offset=-72,
ratio_margin=22,
prune_ratio_ticks=True)
else:
fig = SimplePlot(logy=logy)
if signal is not None:
if signal_scale != 1.:
scaled_signal = []
for sig in signal:
scaled_h = sig * signal_scale
scaled_h.SetTitle(r'%g #times %s' %
(signal_scale, sig.GetTitle()))
scaled_signal.append(scaled_h)
else:
scaled_signal = signal
if signal_colors is not None:
set_colors(scaled_signal, signal_colors)
for i, s in enumerate(scaled_signal):
s.drawstyle = 'HIST'
if fill_signal:
s.fillstyle = 'solid'
s.fillcolor = s.linecolor
s.linewidth = 0
s.linestyle = 'solid'
alpha = .75
else:
s.fillstyle = 'hollow'
s.linewidth = 3
if signal_linestyles is not None:
s.linestyle = signal_linestyles[i]
else:
s.linestyle = 'solid'
alpha = 1.
if model is not None:
if model_colors is not None:
set_colors(model, model_colors)
# create the model stack
model_stack = HistStack()
for hist in model:
hist.SetLineWidth(0)
hist.drawstyle = 'hist'
model_stack.Add(hist)
if signal is not None and signal_on_top:
for s in scaled_signal:
model_stack.Add(s)
objects.append(model_stack)
if signal is not None and not signal_on_top:
if stack_signal:
# create the signal stack
signal_stack = HistStack()
for hist in scaled_signal:
signal_stack.Add(hist)
objects.append(signal_stack)
else:
objects.extend(scaled_signal)
if model is not None:
# draw uncertainty band
total_model, high_band_model, low_band_model = uncertainty_band(
model, systematics) #, systematics_components)
high = total_model + high_band_model
low = total_model - low_band_model
error_band_model = rootpy_utils.get_band(low,
high,
middle_hist=total_model)
error_band_model.fillstyle = '/'
error_band_model.fillcolor = 13
error_band_model.linecolor = 10
error_band_model.markersize = 0
error_band_model.markercolor = 10
error_band_model.drawstyle = 'e2'
objects.append(error_band_model)
if signal is not None and show_signal_error:
total_signal, high_band_signal, low_band_signal = uncertainty_band(
signal, systematics) #, systematics_components)
high = (total_signal + high_band_signal) * signal_scale
low = (total_signal - low_band_signal) * signal_scale
if signal_on_top:
high += total_model
low += total_model
error_band_signal = rootpy_utils.get_band(low,
high,
middle_hist=total_signal *
signal_scale)
error_band_signal.fillstyle = '\\'
error_band_signal.fillcolor = 13
error_band_signal.linecolor = 10
error_band_signal.markersize = 0
error_band_signal.markercolor = 10
error_band_signal.drawstyle = 'e2'
objects.append(error_band_signal)
if data is not None and blind is not True:
# create the data histogram
if isinstance(blind, tuple):
low, high = blind
# zero out bins in blind region
for bin in data.bins():
if (low < bin.x.high <= high or low <= bin.x.low < high):
data[bin.idx] = (0., 0.)
if poisson_errors:
# convert data to TGraphAsymmErrors with Poisson errors
data_poisson = data.poisson_errors()
data_poisson.markersize = 1.2
data_poisson.drawstyle = 'PZ'
objects.append(data_poisson)
else:
# Gaussian errors
data.drawstyle = 'E0'
objects.append(data)
# draw ratio plot
if model is not None and show_ratio:
fig.cd('ratio')
total_model = sum(model)
ratio_hist = Hist.divide(data, total_model)
# remove bins where data is zero
max_dev = 0
for bin in data.bins():
if bin.value <= 0:
ratio_hist[bin.idx] = (-100, 0)
else:
ratio_value = ratio_hist[bin.idx].value
dev = abs(ratio_value - 1)
if dev > max_dev:
max_dev = dev
if max_dev < 0.2:
ratio_range = (0.8, 1.2)
elif max_dev < 0.4:
ratio_range = (0.6, 1.4)
ruler_high = (ratio_range[1] + 1.) / 2.
ruler_low = (ratio_range[0] + 1.) / 2.
ratio_hist.linecolor = 'black'
ratio_hist.linewidth = 2
ratio_hist.fillstyle = 'hollow'
ratio_hist.drawstyle = 'E0'
"""
# draw empty copy of ratio_hist first so lines will show
ratio_hist_tmp = ratio_hist.Clone()
ratio_hist_tmp.Reset()
ratio_hist_tmp.Draw()
ratio_hist_tmp.yaxis.SetLimits(*ratio_range)
ratio_hist_tmp.yaxis.SetRangeUser(*ratio_range)
ratio_hist_tmp.yaxis.SetTitle('Data / Model')
ratio_hist_tmp.yaxis.SetNdivisions(4)
# not certain why the following is needed
ratio_hist_tmp.yaxis.SetTitleOffset(style.GetTitleYOffset())
ratio_xrange = range or ratio_hist.bounds()
ratio_hist_tmp.xaxis.SetLimits(*ratio_xrange)
#ratio_hist_tmp.xaxis.SetRangeUser(*ratio_xrange)
ratio_hist_tmp.xaxis.SetTickLength(
ratio_hist_tmp.xaxis.GetTickLength() * 2)
# draw ratio=1 line
line = Line(ratio_xrange[0], 1,
ratio_xrange[1], 1)
line.linestyle = 'dashed'
line.linewidth = 2
line.Draw()
# draw high ratio line
line_up = Line(ratio_xrange[0], ruler_high,
ratio_xrange[1], ruler_high)
line_up.linestyle = 'dashed'
line_up.linewidth = 2
line_up.Draw()
# draw low ratio line
line_dn = Line(ratio_xrange[0], ruler_low,
ratio_xrange[1], ruler_low)
line_dn.linestyle = 'dashed'
line_dn.linewidth = 2
line_dn.Draw()
"""
# draw band below points on ratio plot
ratio_hist_high = Hist.divide(total_model + high_band_model,
total_model)
ratio_hist_low = Hist.divide(total_model - low_band_model,
total_model)
fig.cd('ratio')
error_band = rootpy_utils.get_band(ratio_hist_high, ratio_hist_low)
error_band.fillstyle = '/'
error_band.fillcolor = '#858585'
error_band.drawstyle = 'E2'
fig.draw('ratio', [error_band, ratio_hist], xdivisions=xdivisions)
if separate_legends:
fig.cd('main')
right_legend = Legend(len(signal) + 1 if signal is not None else 1,
pad=fig.pad('main'),
**legend_params('right', textsize))
right_legend.AddEntry(data, style='lep')
if signal is not None:
for s in reversed(scaled_signal):
right_legend.AddEntry(s, style='F' if fill_signal else 'L')
legends.append(right_legend)
if model is not None:
n_entries = len(model)
if systematics:
n_entries += 1
model_legend = Legend(n_entries,
pad=fig.pad('main'),
**legend_params('left', textsize))
for hist in reversed(model):
model_legend.AddEntry(hist, style='F')
if systematics:
model_err_band = error_band_model.Clone()
model_err_band.linewidth = 0
model_err_band.linecolor = 'white'
model_err_band.fillcolor = '#858585'
model_err_band.title = 'Uncert.'
model_legend.AddEntry(model_err_band, style='F')
legends.append(model_legend)
else:
n_entries = 1
if signal is not None:
n_entries += len(scaled_signal)
if model is not None:
n_entries += len(model)
if systematics:
n_entries += 1
fig.cd('main')
legend = Legend(n_entries,
pad=fig.pad('main'),
**legend_params(legend_position, textsize))
if data is not None:
legend.AddEntry(data, style='lep')
if signal is not None:
for s in reversed(scaled_signal):
legend.AddEntry(s, style='F' if fill_signal else 'L')
if model:
for hist in reversed(model):
legend.AddEntry(hist, style='F')
model_err_band = error_band_model.Clone()
model_err_band.linewidth = 0
model_err_band.linecolor = 'white'
model_err_band.fillcolor = '#858585'
model_err_band.title = 'Uncert.'
legend.AddEntry(model_err_band, style='F')
legends.append(legend)
# draw the objects
bounds = fig.draw('main',
objects,
ypadding=ypadding,
logy_crop_value=1E-1,
xdivisions=xdivisions)
xaxis, yaxis = fig.axes('main')
base_xaxis = xaxis
base_xaxis.range_user = template.bounds()
base_xaxis.limits = template.bounds()
xmin, xmax, ymin, ymax = bounds
if show_ratio:
base_xaxis = fig.axes('ratio')[0]
base_xaxis.range_user = template.bounds()
base_xaxis.limits = template.bounds()
# draw the legends
fig.cd('main')
for legend in legends:
legend.Draw()
label_plot(
fig.pad('main'),
template=template,
xaxis=base_xaxis,
yaxis=yaxis,
xlabel=name,
ylabel=ylabel,
units=units,
category_label=category.label,
extra_label=plot_label,
extra_label_position='right' if legend_position == 'left' else 'left',
data_info=data_info)
if logy and logy_min is not None:
yaxis.min = logy_min
ymin = logy_min
# draw arrows
if arrow_values is not None:
arrow_top = ymin + (ymax - ymin) / 2.
fig.cd('main')
for value in arrow_values:
arrow = Arrow(value, arrow_top, value, ymin, 0.05, '|>')
arrow.SetAngle(30)
arrow.SetLineWidth(2)
arrow.Draw()
if show_pvalue and data is not None and model:
fig.cd('main')
total_model = sum(model)
# show p-value and chi^2
pvalue = total_model.Chi2Test(data, 'WW')
pvalue_label = ROOT.TLatex(0.6, 0.97, "p-value={0:.2f}".format(pvalue))
pvalue_label.SetNDC(True)
pvalue_label.SetTextFont(43)
pvalue_label.SetTextSize(16)
pvalue_label.Draw()
chi2 = total_model.Chi2Test(data, 'WW CHI2/NDF')
chi2_label = ROOT.TLatex(0.78, 0.97,
"#chi^{{2}}/ndf={0:.2f}".format(chi2))
chi2_label.SetNDC(True)
chi2_label.SetTextFont(43)
chi2_label.SetTextSize(16)
chi2_label.Draw()
if top_label is not None:
fig.cd('main')
label = ROOT.TLatex(
fig.pad('main').GetLeftMargin() + 0.08, 0.97, top_label)
label.SetNDC(True)
label.SetTextFont(43)
label.SetTextSize(16)
label.Draw()
if output_name is not None:
# create the output filename
filename = 'var_{0}_{1}'.format(category.name,
output_name.lower().replace(' ', '_'))
if logy:
filename += '_logy'
filename += '_root'
if output_formats is None:
output_formats = ('png', )
# save the figure
save_canvas(fig, output_dir, filename, formats=output_formats)
return fig
def plotHistos(histos, text="", option="", statbox=True):
"""
Plots a list of histograms
"""
log = logging.getLogger('pyroplot')
import rootpy
from rootpy.plotting import Hist, HistStack
from ROOT import kRed, gPad, TPaveStats
# only for 1D and 2D type histograms:
# determine and set maximum for all histograms
stack = HistStack()
for hist in histos:
if not hist.__class__.__name__ == "Hist3D":
stack.Add(hist)
# determine maximum value and set it for all histograms
if stack.GetHists():
maxplot = stack.GetMaximum()
minplot = stack.GetMinimum()
for hist in stack.GetHists():
hist.SetMaximum(maxplot)
# special treatment for log scale Y
if gPad.GetLogy():
hist.SetMinimum(1.)
else:
# if histogram minimum is positive, set to 0.
if minplot > 0:
hist.SetMinimum(0.)
for idx, hist in enumerate(histos):
try:
thisopt = option
# if not first histo, add "same" to options so previous ones are not overwritten
if idx:
if statbox:
thisopt += "sames"
else:
thisopt += "same"
histcopy = hist.DrawCopy(thisopt)
# on first go: print identifying text on pad
if not idx and text:
markPad(text=text, x=.14, y=.85, size=0.041)
if statbox:
thisopt += "sames"
else:
thisopt += "same"
histcopy.Draw(thisopt)
gPad.Update()
if statbox:
try:
statBox = histcopy.GetListOfFunctions().FindObject("stats")
# offset from last statbox
offset = .18
# needs to be larger for Profile & 2D histos
if (hist.__class__.__name__ == "Profile"
or hist.__class__.__name__ == "Hist2D"
or hist.__class__.__name__ == "Profile2D"
or hist.__class__.__name__ == "Hist3D"):
offset = .26
statBox.SetY1NDC(statBox.GetY1NDC() - offset * (idx))
statBox.SetY2NDC(statBox.GetY2NDC() - offset * (idx))
statBox.SetTextColor(hist.GetLineColor())
statBox.SetBorderSize(2)
except AttributeError:
log.debug("Could not get statbox for histogram " +
hist.GetName())
except rootpy.ROOTError, e:
log.error(
"Drawing histogram %s caused ROOTError exception ('%s')" %
(hist.GetName(), e.msg))
gPad.Clear() # otherwise this happens again when drawing..
markPad(text="Could not draw %s ('%s')" % (hist.GetName(), e.msg),
x=0.14,
y=0.4,
size=0.023,
color=kRed)
return # give up!