def __max__(plotable, include_error = True):
if plotable is None:
return 0
if plotable.__class__.__name__ == 'Hist':
return plotable.max(include_error = include_error)
if plotable.__class__.__name__ == 'Graph':
ve = graph_to_value_errors_tuplelist(plotable)
if not include_error:
return max([v for v,_,_ in ve])
return max( [v + err_high for v, _, err_high in ve] )
def __max__(plotable, include_error=True):
if plotable is None:
return 0
if plotable.__class__.__name__ == 'Hist':
return plotable.max(include_error=include_error)
if plotable.__class__.__name__ == 'Graph':
ve = graph_to_value_errors_tuplelist(plotable)
if not include_error:
return max([v for v, _, _ in ve])
return max([v + err_high for v, _, err_high in ve])
def make_plots( histograms, category, output_folder, histname, show_ratio = True, show_before_unfolding = False ):
global variable, k_values
channel = 'electron'
if 'electron' in histname:
channel = 'electron'
elif 'muon' in histname:
channel = 'muon'
else:
channel = 'combined'
# plot with matplotlib
hist_data = histograms['unfolded']
if category == 'central':
hist_data_with_systematics = histograms['unfolded_with_systematics']
hist_measured = histograms['measured']
hist_data.markersize = 2
hist_data.marker = 'o'
if category == 'central':
hist_data_with_systematics.markersize = 2
hist_data_with_systematics.marker = 'o'
hist_measured.markersize = 2
hist_measured.marker = 'o'
hist_measured.color = 'red'
plt.figure( figsize = CMS.figsize, dpi = CMS.dpi, facecolor = CMS.facecolor )
if show_ratio:
gs = gridspec.GridSpec( 2, 1, height_ratios = [5, 1] )
axes = plt.subplot( gs[0] )
else:
axes = plt.axes()
plt.xlabel( '$%s$ [GeV]' % variables_latex[variable], CMS.x_axis_title )
axes.minorticks_on()
plt.ylabel( r'$\frac{1}{\sigma} \frac{d\sigma}{d' + variables_latex[variable] + '} \left[\mathrm{GeV}^{-1}\\right]$', CMS.y_axis_title )
plt.tick_params( **CMS.axis_label_major )
plt.tick_params( **CMS.axis_label_minor )
hist_data.visible = True
if category == 'central':
hist_data_with_systematics.visible = True
rplt.errorbar( hist_data_with_systematics, axes = axes, label = 'do_not_show', xerr = None, capsize = 0, elinewidth = 2, zorder = len( histograms ) + 1 )
rplt.errorbar( hist_data, axes = axes, label = 'do_not_show', xerr = None, capsize = 15, capthick = 3, elinewidth = 2, zorder = len( histograms ) + 2 )
rplt.errorbar( hist_data, axes = axes, label = 'data', xerr = None, yerr = False, zorder = len( histograms ) + 3 ) # this makes a nicer legend entry
if show_before_unfolding:
rplt.errorbar( hist_measured, axes = axes, label = 'data (before unfolding)', xerr = None, zorder = len( histograms ) )
for key, hist in sorted( histograms.iteritems() ):
if not 'unfolded' in key and not 'measured' in key:
hist.linewidth = 4
# setting colours
if 'POWHEG_PYTHIA' in key or 'matchingdown' in key:
hist.linestyle = 'longdashdot'
hist.SetLineColor( kBlue )
elif 'POWHEG_HERWIG' in key or 'scaledown' in key:
hist.linestyle = 'dashed'
hist.SetLineColor( kGreen )
elif 'MADGRAPH_ptreweight' in key:
hist.linestyle = 'dashed'
hist.SetLineColor( kBlack )
elif 'MADGRAPH' in key:
hist.linestyle = 'solid'
hist.SetLineColor( kRed + 1 )
elif 'matchingup' in key:
hist.linestyle = 'verylongdashdot'
hist.linecolor = 'orange'
elif 'MCATNLO' in key or 'scaleup' in key:
hist.linestyle = 'dotted'
hist.SetLineColor( kMagenta + 3 )
rplt.hist( hist, axes = axes, label = measurements_latex[key], zorder = sorted( histograms, reverse = True ).index( key ) )
handles, labels = axes.get_legend_handles_labels()
# making data first in the list
data_label_index = labels.index( 'data' )
data_handle = handles[data_label_index]
labels.remove( 'data' )
handles.remove( data_handle )
labels.insert( 0, 'unfolded data' )
handles.insert( 0, data_handle )
new_handles, new_labels = [], []
for handle, label in zip( handles, labels ):
if not label == 'do_not_show':
new_handles.append( handle )
new_labels.append( label )
legend_location = (0.98, 0.88)
if variable == 'MT':
legend_location = (0.05, 0.88)
elif variable == 'ST':
legend_location = (0.95, 0.88)
plt.legend( new_handles, new_labels, numpoints = 1, prop = CMS.legend_properties, frameon = False, bbox_to_anchor=legend_location,
bbox_transform=plt.gcf().transFigure )
label, channel_label = get_cms_labels( channel )
# title
plt.title( label,loc='right', **CMS.title )
# CMS text
# note: fontweight/weight does not change anything as we use Latex text!!!
plt.text(0.05, 0.98, r"\textbf{CMS}", transform=axes.transAxes, fontsize=42,
verticalalignment='top',horizontalalignment='left')
# channel text
axes.text(0.95, 0.98, r"\emph{%s}" %channel_label, transform=axes.transAxes, fontsize=40,
verticalalignment='top',horizontalalignment='right')
if show_ratio:
plt.setp( axes.get_xticklabels(), visible = False )
ax1 = plt.subplot( gs[1] )
ax1.minorticks_on()
#ax1.grid( True, 'major', linewidth = 1 )
# setting the x_limits identical to the main plot
x_limits = axes.get_xlim()
ax1.set_xlim(x_limits)
ax1.yaxis.set_major_locator( MultipleLocator( 0.5 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
plt.xlabel( '$%s$ [GeV]' % variables_latex[variable], CMS.x_axis_title )
plt.tick_params( **CMS.axis_label_major )
plt.tick_params( **CMS.axis_label_minor )
plt.ylabel( '$\\frac{\\textrm{pred.}}{\\textrm{data}}$', CMS.y_axis_title_small )
ax1.yaxis.set_label_coords(-0.115, 0.8)
#draw a horizontal line at y=1 for data
plt.axhline(y = 1, color = 'black', linewidth = 2)
for key, hist in sorted( histograms.iteritems() ):
if not 'unfolded' in key and not 'measured' in key:
ratio = hist.Clone()
ratio.Divide( hist_data ) #divide by data
rplt.hist( ratio, axes = ax1, label = 'do_not_show' )
stat_lower = hist_data.Clone()
stat_upper = hist_data.Clone()
syst_lower = hist_data.Clone()
syst_upper = hist_data.Clone()
# plot error bands on data in the ratio plot
for bin_i in range( 1, hist_data.GetNbinsX() + 1 ):
stat_errors = graph_to_value_errors_tuplelist(hist_data)
stat_lower.SetBinContent( bin_i, 1 - stat_errors[bin_i-1][1]/stat_errors[bin_i-1][0] )
stat_upper.SetBinContent( bin_i, 1 + stat_errors[bin_i-1][2]/stat_errors[bin_i-1][0] )
if category == 'central':
syst_errors = graph_to_value_errors_tuplelist(hist_data_with_systematics)
syst_lower.SetBinContent( bin_i, 1 - syst_errors[bin_i-1][1]/syst_errors[bin_i-1][0] )
syst_upper.SetBinContent( bin_i, 1 + syst_errors[bin_i-1][2]/syst_errors[bin_i-1][0] )
if category == 'central':
rplt.fill_between( syst_lower, syst_upper, ax1,
color = 'yellow', alpha = 0.5 )
rplt.fill_between( stat_upper, stat_lower, ax1, color = '0.75',
alpha = 0.5 )
# legend for ratio plot
p_stat = mpatches.Patch(facecolor='0.75', label='Stat.',alpha = 0.5, edgecolor='black' )
p_stat_and_syst = mpatches.Patch(facecolor='yellow', label=r'Stat. $\oplus$ Syst.', alpha = 0.5, edgecolor='black' )
l1 = ax1.legend(handles = [p_stat], loc = 'upper left',
frameon = False, prop = {'size':26})
ax1.legend(handles = [p_stat_and_syst], loc = 'lower left',
frameon = False, prop = {'size':26})
ax1.add_artist(l1)
if variable == 'MET':
ax1.set_ylim( ymin = 0.7, ymax = 1.3 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.2 ) )
# ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
if variable == 'MT':
ax1.set_ylim( ymin = 0.8, ymax = 1.2 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.2 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
elif variable == 'HT' or variable == 'ST':
ax1.set_ylim( ymin = 0.5, ymax = 1.5 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.5 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
elif variable == 'WPT':
ax1.set_ylim( ymin = 0.75, ymax = 1.5 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.25 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.05 ) )
if CMS.tight_layout:
plt.tight_layout()
path = output_folder + str( measurement_config.centre_of_mass_energy ) + 'TeV/' + variable + '/' + category
make_folder_if_not_exists( path )
for output_format in output_formats:
filename = path + '/' + histname + '_kv' + str( k_values[channel] ) + '.' + output_format
if channel == 'combined':
filename = filename.replace( '_kv' + str( k_values[channel] ), '' )
plt.savefig( filename )
del hist_data, hist_measured
plt.close()
gc.collect()
def make_plots( histograms, category, output_folder, histname, show_ratio = True, show_before_unfolding = False ):
global variable, phase_space
channel = 'electron'
if 'electron' in histname:
channel = 'electron'
elif 'muon' in histname:
channel = 'muon'
else:
channel = 'combined'
# plot with matplotlib
hist_data = histograms['unfolded']
if category == 'central':
hist_data_with_systematics = histograms['unfolded_with_systematics']
hist_measured = histograms['measured']
hist_data.markersize = 2
hist_data.marker = 'o'
if category == 'central':
hist_data_with_systematics.markersize = 2
hist_data_with_systematics.marker = 'o'
hist_measured.markersize = 2
hist_measured.marker = 'o'
hist_measured.color = 'red'
plt.figure( figsize = CMS.figsize, dpi = CMS.dpi, facecolor = CMS.facecolor )
if show_ratio:
gs = gridspec.GridSpec( 2, 1, height_ratios = [5, 1] )
axes = plt.subplot( gs[0] )
else:
axes = plt.axes()
if variable in ['NJets', 'abs_lepton_eta', 'lepton_eta']:
plt.xlabel( '$%s$' % variables_latex[variable], CMS.x_axis_title )
else:
plt.xlabel( '$%s$ [GeV]' % variables_latex[variable], CMS.x_axis_title )
if not variable in ['NJets']:
axes.minorticks_on()
if variable in ['NJets', 'abs_lepton_eta', 'lepton_eta']:
plt.ylabel( r'$\frac{1}{\sigma} \frac{d\sigma}{d' + variables_latex[variable] + '}$', CMS.y_axis_title )
else:
plt.ylabel( r'$\frac{1}{\sigma} \frac{d\sigma}{d' + variables_latex[variable] + '} \left[\mathrm{GeV}^{-1}\\right]$', CMS.y_axis_title )
plt.tick_params( **CMS.axis_label_major )
if not variable in ['NJets']:
plt.tick_params( **CMS.axis_label_minor )
hist_data.visible = True
if category == 'central':
hist_data_with_systematics.visible = True
rplt.errorbar( hist_data_with_systematics, axes = axes, label = 'do_not_show', xerr = None, capsize = 0, elinewidth = 2, zorder = len( histograms ) + 1 )
rplt.errorbar( hist_data, axes = axes, label = 'do_not_show', xerr = None, capsize = 15, capthick = 3, elinewidth = 2, zorder = len( histograms ) + 2 )
rplt.errorbar( hist_data, axes = axes, label = 'data', xerr = None, yerr = False, zorder = len( histograms ) + 3 ) # this makes a nicer legend entry
if show_before_unfolding:
rplt.errorbar( hist_measured, axes = axes, label = 'data (before unfolding)', xerr = None, zorder = len( histograms ) )
dashes = {}
for key, hist in sorted( histograms.items() ):
zorder = sorted( histograms, reverse = False ).index( key )
if key == 'powhegPythia8' and zorder != len(histograms) - 3:
zorder = len(histograms) - 3
elif key != 'powhegPythia8' and not 'unfolded' in key:
while zorder >= len(histograms) - 3:
zorder = zorder - 1
if not 'unfolded' in key and not 'measured' in key:
hist.linewidth = 4
# setting colours
linestyle = None
if 'amcatnlo_HERWIG' in key or 'massdown' in key:
hist.SetLineColor( kBlue )
dashes[key] = [25,5,5,5,5,5,5,5]
elif 'madgraphMLM' in key or 'scaledown' in key:
hist.SetLineColor( 417 )
dashes[key] = [5,5]
elif 'MADGRAPH_ptreweight' in key:
hist.SetLineColor( kBlack )
elif 'powhegPythia8' in key:
linestyle = 'solid'
dashes[key] = None
hist.SetLineColor( 633 )
elif 'massup' in key or 'amcatnlo' in key:
hist.SetLineColor( 807 )
dashes[key] = [20,5]
elif 'MCATNLO' in key or 'scaleup' in key:
hist.SetLineColor( 619 )
dashes[key] = [5,5,10,5]
if linestyle != None:
hist.linestyle = linestyle
line, h = rplt.hist( hist, axes = axes, label = measurements_latex[key], zorder = zorder )
if dashes[key] != None:
line.set_dashes(dashes[key])
h.set_dashes(dashes[key])
handles, labels = axes.get_legend_handles_labels()
# making data first in the list
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 )
new_handles, new_labels = [], []
zipped = dict( zip( labels, handles ) )
labelOrder = ['data',
measurements_latex['powhegPythia8'],
measurements_latex['amcatnlo'],
measurements_latex['amcatnlo_HERWIG'],
measurements_latex['madgraphMLM'],
measurements_latex['scaleup'],
measurements_latex['scaledown'],
measurements_latex['massup'],
measurements_latex['massdown']
]
for label in labelOrder:
if label in labels:
new_handles.append(zipped[label])
new_labels.append(label)
legend_location = (0.97, 0.82)
if variable == 'MT':
legend_location = (0.05, 0.82)
elif variable == 'ST':
legend_location = (0.97, 0.82)
elif variable == 'WPT':
legend_location = (1.0, 0.84)
elif variable == 'abs_lepton_eta':
legend_location = (1.0, 0.94)
plt.legend( new_handles, new_labels, numpoints = 1, prop = CMS.legend_properties, frameon = False, bbox_to_anchor=legend_location,
bbox_transform=plt.gcf().transFigure )
label, channel_label = get_cms_labels( channel )
# title
plt.title( label,loc='right', **CMS.title )
# CMS text
# note: fontweight/weight does not change anything as we use Latex text!!!
logo_location = (0.05, 0.98)
prelim_location = (0.05, 0.92)
channel_location = ( 0.05, 0.86)
if variable == 'WPT':
logo_location = (0.03, 0.98)
prelim_location = (0.03, 0.92)
channel_location = (0.03, 0.86)
elif variable == 'abs_lepton_eta':
logo_location = (0.03, 0.98)
prelim_location = (0.03, 0.92)
channel_location = (0.03, 0.86)
plt.text(logo_location[0], logo_location[1], r"\textbf{CMS}", transform=axes.transAxes, fontsize=42,
verticalalignment='top',horizontalalignment='left')
# preliminary
plt.text(prelim_location[0], prelim_location[1], r"\emph{Preliminary}",
transform=axes.transAxes, fontsize=42,
verticalalignment='top',horizontalalignment='left')
# channel text
plt.text(channel_location[0], channel_location[1], r"\emph{%s}" %channel_label, transform=axes.transAxes, fontsize=40,
verticalalignment='top',horizontalalignment='left')
ylim = axes.get_ylim()
if ylim[0] < 0:
axes.set_ylim( ymin = 0.)
if variable == 'WPT':
axes.set_ylim(ymax = ylim[1]*1.3)
elif variable == 'abs_lepton_eta':
axes.set_ylim(ymax = ylim[1]*1.3)
else :
axes.set_ylim(ymax = ylim[1]*1.2)
if show_ratio:
plt.setp( axes.get_xticklabels(), visible = False )
ax1 = plt.subplot( gs[1] )
if not variable in ['NJets']:
ax1.minorticks_on()
#ax1.grid( True, 'major', linewidth = 1 )
# setting the x_limits identical to the main plot
x_limits = axes.get_xlim()
ax1.set_xlim(x_limits)
ax1.yaxis.set_major_locator( MultipleLocator( 0.5 ) )
if not variable in ['NJets']:
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
if variable in ['NJets', 'abs_lepton_eta', 'lepton_eta']:
plt.xlabel('$%s$' % variables_latex[variable], CMS.x_axis_title )
else:
plt.xlabel( '$%s$ [GeV]' % variables_latex[variable], CMS.x_axis_title )
plt.tick_params( **CMS.axis_label_major )
if not variable in ['NJets']:
plt.tick_params( **CMS.axis_label_minor )
plt.ylabel( '$\\frac{\\textrm{pred.}}{\\textrm{data}}$', CMS.y_axis_title )
ax1.yaxis.set_label_coords(-0.115, 0.8)
#draw a horizontal line at y=1 for data
plt.axhline(y = 1, color = 'black', linewidth = 2)
for key, hist in sorted( histograms.iteritems() ):
if not 'unfolded' in key and not 'measured' in key:
ratio = hist.Clone()
ratio.Divide( hist_data ) #divide by data
line, h = rplt.hist( ratio, axes = ax1, label = 'do_not_show' )
if dashes[key] != None:
h.set_dashes(dashes[key])
stat_lower = hist_data.Clone()
stat_upper = hist_data.Clone()
syst_lower = hist_data.Clone()
syst_upper = hist_data.Clone()
# plot error bands on data in the ratio plot
stat_errors = graph_to_value_errors_tuplelist(hist_data)
if category == 'central':
syst_errors = graph_to_value_errors_tuplelist(hist_data_with_systematics)
for bin_i in range( 1, hist_data.GetNbinsX() + 1 ):
stat_value, stat_error, _ = stat_errors[bin_i-1]
stat_rel_error = stat_error/stat_value
stat_lower.SetBinContent( bin_i, 1 - stat_rel_error )
stat_upper.SetBinContent( bin_i, 1 + stat_rel_error )
if category == 'central':
syst_value, syst_error_down, syst_error_up = syst_errors[bin_i-1]
syst_rel_error_down = syst_error_down/syst_value
syst_rel_error_up = syst_error_up/syst_value
syst_lower.SetBinContent( bin_i, 1 - syst_rel_error_down )
syst_upper.SetBinContent( bin_i, 1 + syst_rel_error_up )
if category == 'central':
rplt.fill_between( syst_lower, syst_upper, ax1,
color = 'yellow' )
rplt.fill_between( stat_upper, stat_lower, ax1, color = '0.75',
)
loc = 'upper left'
# if variable in ['ST']:
# loc = 'upper right'
# legend for ratio plot
p_stat = mpatches.Patch(facecolor='0.75', label='Stat.', edgecolor='black' )
p_stat_and_syst = mpatches.Patch(facecolor='yellow', label=r'Stat. $\oplus$ Syst.', edgecolor='black' )
l1 = ax1.legend(handles = [p_stat, p_stat_and_syst], loc = loc,
frameon = False, prop = {'size':26}, ncol = 2)
# ax1.legend(handles = [p_stat_and_syst], loc = 'lower left',
# frameon = False, prop = {'size':30})
ax1.add_artist(l1)
if variable == 'MET':
ax1.set_ylim( ymin = 0.8, ymax = 1.2 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.5 ) )
# ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
if variable == 'MT':
ax1.set_ylim( ymin = 0.8, ymax = 1.2 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.2 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
elif variable == 'HT':
ax1.set_ylim( ymin = 0.8, ymax = 1.37 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.2 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
elif variable == 'ST':
ax1.set_ylim( ymin = 0.7, ymax = 1.5 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.5 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
elif variable == 'WPT':
ax1.set_ylim( ymin = 0.8, ymax = 1.2 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.5 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
elif variable == 'NJets':
ax1.set_ylim( ymin = 0.7, ymax = 1.5 )
elif variable == 'abs_lepton_eta':
ax1.set_ylim( ymin = 0.8, ymax = 1.2 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.2 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
elif variable == 'lepton_pt':
ax1.set_ylim( ymin = 0.8, ymax = 1.3 )
ax1.yaxis.set_major_locator( MultipleLocator( 0.2 ) )
ax1.yaxis.set_minor_locator( MultipleLocator( 0.1 ) )
if CMS.tight_layout:
plt.tight_layout()
path = '{output_folder}/{centre_of_mass_energy}TeV/{phaseSpace}/{variable}/'
path = path.format(
output_folder = output_folder,
centre_of_mass_energy = measurement_config.centre_of_mass_energy,
phaseSpace = phase_space,
variable = variable
)
make_folder_if_not_exists( path )
for output_format in output_formats:
filename = path + '/' + histname + '.' + output_format
plt.savefig( filename )
del hist_data, hist_measured
plt.close()
gc.collect()
