def Add(self,
data,
name='',
weights=None,
sampleType='background',
file=None,
systematics=None):
"""
Add histogram data for this figure.
@param data histogram or list/array of values to plot
@param name name of sample ('ttbar','wjets',etc.)
@param weights weights (if any) for histogram
@param sampleType Kind of sample ('background','signal', or 'data')
@param file The root file of histograms to get systematic uncertainties
@param systematics The total systematic uncertainty for a given sample
(histogram or array of bin content from histogram)
"""
self.names.append(name)
if isinstance(data, ROOT.TH1):
self.hists2plot[name] = hpt.hist2list(data,
name=name,
reBin=self.rebin)
else:
self.hists2plot[name] = hpt.data2list(data)
self.weights[name] = weights
self.sampleTypes[sampleType].append(name)
self.systematics[name] = systematics
return
def initialize(self, nominalHistogram, nominalWeights=None):
"""Setup some initial variables"""
self.total = None # hold the total systematic uncertainty
self.systData = OrderedDict() # hold data for individual systematics
if isinstance(nominalHistogram, ROOT.TH1):
self.systData['nominal'] = hpt.hist2list(nominalHistogram,
name="nominal")
else:
self.systData['nominal'] = hpt.data2list(nominalHistogram,
bins=self.binning,
weights=nominalWeights)
return
def Add(self, data, weights=None, name=''):
"""
Turn data into a numpy array to combine and plot systematic uncertainties.
@param data histogram (also support list/array of values) to plot
@param weights weights for each event for making histogram
@param name name of systematic (JES,JER,etc.)
"""
if not name:
name = data.GetName()
if isinstance(data, ROOT.TH1):
self.systData[name] = hpt.hist2list(data, name=name)
else:
self.systData[name] = hpt.data2list(data,
bins=self.binning,
weights=weights)
return
def Add(self,
data,
name='',
weights=None,
linecolor='k',
linestyle='solid',
color='k',
linewidth=2,
draw='step',
label='',
ratio_den=False,
ratio_num=False,
ratio_partner=None,
yerr=None,
**kwargs):
"""
Add histogram data for this figure.
args = data (1D) || x_data,y_data (2D) array or ROOT hist
kwargs = bins (int or list/np.array): default = 10
weights (None or list/np.array): default = None
uncertainties (None or list/np.array): default = None
linecolor 'black',etc: default = None
fillcolor 'blue',etc.
draw step,stepfilled,errorbar
label "ttbar" or something
"""
self.names.append(name)
normed = False
if 'normed' in kwargs.keys():
normed = kwargs['normed']
if self.typeOfPlot == "histogram" and isinstance(data, ROOT.TH1):
if self.dimensions == 1:
self.hists2plot[name] = hpt.hist2list(data,
name=name,
reBin=self.rebin,
normed=normed)
else:
self.hists2plot[name] = hpt.hist2list2D(data,
name=name,
reBin=self.rebin,
normed=normed)
elif self.typeOfPlot == "efficiency" and isinstance(
data, ROOT.TEfficiency):
self.hists2plot[name] = hpt.TEfficiency2list(data)
elif self.typeOfPlot == "efficiency" and isinstance(data, ROOT.TH1):
total_data = data.Integral()
data.Scale(1. /
total_data) # normalize distribution to show on plot
self.hists2plot[name] = hpt.hist2list(data,
name=name,
reBin=self.rebin,
normed=normed)
self.drawEffDist = True
self.effData.append(name)
else:
if self.dimensions == 1:
self.hists2plot[name] = hpt.data2list(data,
weights=weights,
normed=normed,
binning=self.binning)
if yerr is not None: self.hists2plot[name]['error'] = yerr
else:
self.hists2plot[name] = hpt.data2list2D(data,
weights=weights,
normed=normed,
binning=self.binning)
self.kwargs[name] = kwargs # pass normal matplotlib arguments
self.colors[name] = color
self.yerrors[name] = yerr # method for drawing y-errors
self.draw_types[name] = draw
self.linecolors[name] = linecolor
self.linestyles[name] = linestyle
self.linewidths[name] = linewidth
self.labels[name] = label
self.weights[name] = weights
self.ratio_den[name] = ratio_den # denominator in ratio
self.ratio_num[name] = ratio_num # numerator in ratio
self.ratio_partner[name] = ratio_partner
if draw == 'errorbar':
self.errorbarplot.append(name)
if linestyle == 'solid':
self.linestyles[name] = 'o' # default setting for errorbar
else:
self.linestyles[name] = linestyle # user custom option
return