def plotVar(self,
var,
bins=None,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
reweight=False):
d3t3Weights = None
d3t4Weights = None
ttbarErrorWeights = None
if reweight:
ttbarWeights = -getattr(self.dft3, weightName) * getattr(
self.dft3, FvTName)
# multijetWeights = np.concatenate((self.dfd3.mcPseudoTagWeight * self.dfd3.FvT, -self.dft3.mcPseudoTagWeight * self.dft3.FvT))
multijet = self.dfd3[var]
multijetWeights = getattr(self.dfd3, weightName) * getattr(
self.dfd3, FvTName)
# backgroundWeights = np.concatenate((self.dfd3.mcPseudoTagWeight * self.dfd3.FvT, -self.dft3.mcPseudoTagWeight * self.dft3.FvT, self.dft4.mcPseudoTagWeight))
background = np.concatenate((self.dfd3[var], self.dft4[var]))
backgroundWeights = np.concatenate(
(getattr(self.dfd3, weightName) * getattr(self.dfd3, FvTName),
getattr(self.dft4, weightName)))
# ttbar estimates from reweighted threetag data
d3t3Weights = -1 * multijetWeights * getattr(
self.dfd3, 'FvT_pt3') / getattr(self.dfd3, 'FvT_pd3')
d3t4Weights = getattr(self.dfd3, weightName) * getattr(
self.dfd3, 'FvT_pt4') / getattr(self.dfd3, 'FvT_pd3')
ttbarErrorWeights = np.concatenate(
(getattr(self.dft4, weightName), -d3t4Weights, ttbarWeights,
-d3t3Weights))
ttbarError = np.concatenate((self.dft4[var], self.dfd3[var],
self.dft3[var], self.dfd3[var]))
else:
ttbarWeights = -getattr(self.dft3, weightName)
multijet = np.concatenate((self.dfd3[var], self.dft3[var]))
multijetWeights = np.concatenate(
(getattr(self.dfd3,
weightName), -getattr(self.dft3, weightName)))
# multijetWeights = self.dfd3.mcPseudoTagWeight
# background = np.concatenate((self.dfd3[var], self.dft3[var], self.dft4[var]))
# backgroundWeights = np.concatenate((self.dfd3.mcPseudoTagWeight, -self.dft3.mcPseudoTagWeight, self.dft4.mcPseudoTagWeight))
background = np.concatenate(
(self.dfd3[var], self.dft3[var], self.dft4[var]))
backgroundWeights = np.concatenate(
(getattr(self.dfd3,
weightName), -getattr(self.dft3, weightName),
getattr(self.dft4, weightName)))
# backgroundWeights = np.concatenate((self.dfd3.mcPseudoTagWeight, self.dft4.mcPseudoTagWeight))
self.dsd4 = pltHelper.dataSet(name=d4.name,
points=self.dfd4[var],
weights=getattr(self.dfd4, weightName),
color=d4.color,
alpha=1.0,
linewidth=1)
self.bkgd = pltHelper.dataSet(name='Background Model',
points=background,
weights=backgroundWeights,
color='brown',
alpha=1.0,
linewidth=1)
self.dst4 = pltHelper.dataSet(name=t4.name,
points=self.dft4[var],
weights=getattr(self.dft4, weightName),
color=t4.color,
alpha=1.0,
linewidth=1)
self.dsm3 = pltHelper.dataSet(name='ThreeTag Multijet',
points=multijet,
weights=multijetWeights,
color=d3.color,
alpha=1.0,
linewidth=1)
self.dst3 = pltHelper.dataSet(name=t3.name,
points=self.dft3[var],
weights=ttbarWeights,
color=t3.color,
alpha=1.0,
linewidth=1)
datasets = [self.dsd4, self.bkgd, self.dst4, self.dsm3, self.dst3]
if d3t3Weights is not None:
self.dsd3t3 = pltHelper.dataSet(name=r'ThreeTag $t\bar{t}$ est.',
points=self.dfd3[var],
weights=d3t3Weights,
color=t3.color,
alpha=0.5,
linewidth=2)
datasets += [self.dsd3t3]
if d3t4Weights is not None:
self.dsd3t4 = pltHelper.dataSet(name=r'FourTag $t\bar{t}$ est.',
points=self.dfd3[var],
weights=d3t4Weights,
color=t4.color,
alpha=0.5,
linewidth=2)
datasets += [self.dsd3t4]
if ttbarErrorWeights is not None:
self.dste = pltHelper.dataSet(
name=r'$t\bar{t}$ MC - $t\bar{t}$ est.',
points=ttbarError,
weights=ttbarErrorWeights,
color='black',
alpha=0.5,
linewidth=2)
datasets += [self.dste]
if self.dfzz is not None:
self.dszz = pltHelper.dataSet(
name=zz.name,
points=self.dfzz[var],
weights=getattr(self.dfzz, weightName) * 100,
color=zz.color,
alpha=1.0,
linewidth=1)
datasets += [self.dszz]
if self.dfzh is not None:
self.dszh = pltHelper.dataSet(
name=zh.name,
points=self.dfzh[var],
weights=getattr(self.dfzh, weightName) * 100,
color=zh.color,
alpha=1.0,
linewidth=1)
datasets += [self.dszh]
if type(bins) != list:
if not bins: bins = 50
if type(xmin) == type(None): xmin = self.dfSelected[var].min()
if type(xmax) == type(None): xmax = self.dfSelected[var].max()
width = (xmax - xmin) / bins
bins = [xmin + b * width for b in range(0, bins + 1)]
args = {
'dataSets': datasets,
'ratio': [0, 1],
'ratioRange': [0.9, 1.1] if reweight else [0.5, 1.5],
'ratioTitle': 'Data / Model',
'bins': bins,
'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'xlabel': var.replace('_', ' '),
'ylabel': 'Events / Bin',
}
fig = pltHelper.histPlotter(**args)
figName = outputDir + "/" + var + ('_reweight'
if reweight else '') + '.pdf'
fig.savefig(figName)
print(figName)
def plotCompVar(self,
var,
legName,
bins=None,
xmin=None,
xmax=None,
regName=""):
#ttbarWeights = -getattr(self.dft3,weightName) * getattr(self.dft3,FvTName)
plotVar1 = self.dfd3[var[0]]
plotVar2 = self.dfd3[var[1]]
multijetWeights = getattr(self.dfd3, weightName) * getattr(
self.dfd3, FvTName)
#self.dsd4 = pltHelper.dataSet(name=d4.name,
# points =self.dfd4[var],
# weights=getattr(self.dfd4,weightName),
# color=d4.color, alpha=1.0, linewidth=1)
#self.bkgd = pltHelper.dataSet(name='Background Model',
# points =background,
# weights=backgroundWeights,
# color='brown', alpha=1.0, linewidth=1)
#self.dst4 = pltHelper.dataSet(name=t4.name,
# points =self.dft4[var],
# weights=getattr(self.dft4,weightName),
# color=t4.color, alpha=1.0, linewidth=1)
self.dv1 = pltHelper.dataSet(name=legName[0],
points=plotVar1,
weights=multijetWeights,
color='red',
alpha=1.0,
linewidth=1)
self.dv2 = pltHelper.dataSet(name=legName[1],
points=plotVar2,
weights=multijetWeights,
color='blue',
alpha=1.0,
linewidth=1)
#datasets = [self.dsd4,self.bkgd,self.dst4,self.dsm3,self.dst3]
datasets = [self.dv1, self.dv2]
if not bins: bins = 50
if type(xmin) == type(None): xmin = self.dfSelected[var[0]].min()
if type(xmax) == type(None): xmax = self.dfSelected[var[0]].max()
width = (xmax - xmin) / bins
bins = [xmin + b * width for b in range(-1, bins + 1)]
xlabel = var[0] + "_vs_" + var[1]
args = {
'dataSets': datasets,
'ratio': [0, 1],
'ratioRange': [0.5, 1.5],
'ratioTitle': legName[0] + ' / ' + legName[1],
'bins': bins,
'xlabel': xlabel.replace('_', ' '),
'ylabel': 'Events / Bin',
}
fig = pltHelper.histPlotter(**args)
figName = outputDir + "/" + regName + "_" + xlabel + '.pdf'
fig.savefig(figName)
print(figName)
def plotRWVar(self,
var,
FvT1Name,
FvT2Name,
legName1,
legName2,
bins=None,
xmin=None,
xmax=None,
regName=""):
#ttbarWeights = -getattr(self.dft3,weightName) * getattr(self.dft3,FvTName)
if type(var) == list:
multijet = self.dfd3[var[0]] - self.dfd3[var[1]]
else:
multijet = self.dfd3[var]
multijetWeights_1 = getattr(self.dfd3, weightName) * getattr(
self.dfd3, FvT1Name)
multijetWeights_2 = getattr(self.dfd3, weightName) * getattr(
self.dfd3, FvT2Name)
self.dsm3_1 = pltHelper.dataSet(name=legName1,
points=multijet,
weights=multijetWeights_1,
color='red',
alpha=1.0,
linewidth=1)
self.dsm3_2 = pltHelper.dataSet(name=legName2,
points=multijet,
weights=multijetWeights_2,
color='blue',
alpha=1.0,
linewidth=1)
#datasets = [self.dsd4,self.bkgd,self.dst4,self.dsm3,self.dst3]
datasets = [self.dsm3_1, self.dsm3_2]
if not bins: bins = 50
if type(var) != list:
if type(xmin) == type(None): xmin = self.dfSelected[var].min()
if type(xmax) == type(None): xmax = self.dfSelected[var].max()
else:
xmin = -1
xmax = 1
width = (xmax - xmin) / bins
bins = [xmin + b * width for b in range(-1, bins + 1)]
if type(var) == list:
xlabel = var[0] + "_minus_" + var[1]
else:
xlabel = var
args = {
'dataSets': datasets,
'ratio': [0, 1],
'ratioRange': [0.8, 1.2],
'ratioTitle': legName1 + ' / ' + legName2,
'bins': bins,
'xlabel': xlabel.replace('_', ' '),
'ylabel': 'Events / Bin',
}
fig = pltHelper.histPlotter(**args)
figName = outputDir + "/" + regName + "_" + xlabel + '_rw' + legName1 + '_vs_' + legName2 + '.pdf'
fig.savefig(figName)
print(figName)
def plotVar(self, var, bins=None, xmin=None, xmax=None, regName=""):
#ttbarWeights = -getattr(self.dft3,weightName) * getattr(self.dft3,FvTName)
if type(var) == list:
multijet = self.dfd3[var[0]] - self.dfd3[var[1]]
else:
multijet = self.dfd3[var]
multijetWeights = getattr(self.dfd3, weightName) * getattr(
self.dfd3, FvTName)
#background = np.concatenate((self.dfd3[var], self.dft4[var]))
#backgroundWeights = np.concatenate((getattr(self.dfd3,weightName) * getattr(self.dfd3,FvTName), getattr(self.dft4,weightName)))
#self.dsd4 = pltHelper.dataSet(name=d4.name,
# points =self.dfd4[var],
# weights=getattr(self.dfd4,weightName),
# color=d4.color, alpha=1.0, linewidth=1)
#self.bkgd = pltHelper.dataSet(name='Background Model',
# points =background,
# weights=backgroundWeights,
# color='brown', alpha=1.0, linewidth=1)
#self.dst4 = pltHelper.dataSet(name=t4.name,
# points =self.dft4[var],
# weights=getattr(self.dft4,weightName),
# color=t4.color, alpha=1.0, linewidth=1)
self.dsm3 = pltHelper.dataSet(name='ThreeTag Multijet',
points=multijet,
weights=multijetWeights,
color=d3.color,
alpha=1.0,
linewidth=1)
#self.dst3 = pltHelper.dataSet(name=t3.name,
# points=self.dft3[var],
# weights=ttbarWeights,
# color=t3.color, alpha=1.0, linewidth=1)
#datasets = [self.dsd4,self.bkgd,self.dst4,self.dsm3,self.dst3]
datasets = [self.dsm3]
if not bins: bins = 50
if type(var) != list:
if type(xmin) == type(None): xmin = self.dfSelected[var].min()
if type(xmax) == type(None): xmax = self.dfSelected[var].max()
else:
xmin = -1
xmax = 1
width = (xmax - xmin) / bins
bins = [xmin + b * width for b in range(-1, bins + 1)]
if type(var) == list:
xlabel = var[0] + "_minus_" + var[1]
else:
xlabel = var
args = {
'dataSets': datasets,
#'ratio': [0,1],
#'ratioRange': [0.5,1.5],
#'ratioTitle': 'Data / Model',
'bins': bins,
'xlabel': xlabel.replace('_', ' '),
'ylabel': 'Events / Bin',
}
fig = pltHelper.histPlotter(**args)
figName = outputDir + "/" + regName + "_" + xlabel + '.pdf'
fig.savefig(figName)
print(figName)
def plotVar(self,
var,
bins=None,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
reweight=False,
variance=False,
overflow=False):
d3t3Weights = None
d3t4Weights = None
ttbarErrorWeights = None
if reweight:
ttbarWeights = -getattr(self.dft3, weightName) * getattr(
self.dft3, FvTName)
multijet = self.dfd3[var]
multijetWeights = getattr(self.dfd3, weightName) * getattr(
self.dfd3, FvTName)
background = np.concatenate((self.dfd3[var], self.dft4[var]))
backgroundWeights = np.concatenate(
(getattr(self.dfd3, weightName) * getattr(self.dfd3, FvTName),
getattr(self.dft4, weightName)))
# ttbar estimates from reweighted threetag data
d3t3Weights = -1 * multijetWeights * getattr(
self.dfd3, 'FvT_pt3') / getattr(self.dfd3, 'FvT_pd3')
d3t4Weights = getattr(self.dfd3, weightName) * getattr(
self.dfd3, 'FvT_pt4') / getattr(self.dfd3, 'FvT_pd3')
ttbarErrorWeights = np.concatenate(
(getattr(self.dft4, weightName), -d3t4Weights, ttbarWeights,
-d3t3Weights))
ttbarError = np.concatenate((self.dft4[var], self.dfd3[var],
self.dft3[var], self.dfd3[var]))
else:
ttbarWeights = -getattr(self.dft3, weightName)
multijet = np.concatenate((self.dfd3[var], self.dft3[var]))
multijetWeights = np.concatenate(
(getattr(self.dfd3,
weightName), -getattr(self.dft3, weightName)))
background = np.concatenate(
(self.dfd3[var], self.dft3[var], self.dft4[var]))
backgroundWeights = np.concatenate(
(getattr(self.dfd3,
weightName), -getattr(self.dft3, weightName),
getattr(self.dft4, weightName)))
self.dsd4 = pltHelper.dataSet(name=d4.name,
points=self.dfd4[var],
weights=getattr(self.dfd4, weightName),
color=d4.color,
alpha=1.0,
linewidth=1)
self.bkgd = pltHelper.dataSet(name='Background Model',
points=background,
weights=backgroundWeights,
color='brown',
alpha=1.0,
linewidth=1)
self.dst4 = pltHelper.dataSet(name=t4.name,
points=self.dft4[var],
weights=getattr(self.dft4, weightName),
color=t4.color,
alpha=1.0,
linewidth=1)
self.dsm3 = pltHelper.dataSet(name='ThreeTag Multijet',
points=multijet,
weights=multijetWeights,
color=d3.color,
alpha=1.0,
linewidth=1)
self.dst3 = pltHelper.dataSet(name=t3.name,
points=self.dft3[var],
weights=ttbarWeights,
color=t3.color,
alpha=1.0,
linewidth=1)
datasets = [self.dsd4, self.bkgd, self.dst4, self.dsm3, self.dst3]
if variance:
self.dsm3_variance = pltHelper.dataSet(
name='3b MJ Weight SD',
points=multijet,
weights=multijetWeights * getattr(self.dfd3, FvTName + '_std'),
color=d3.color,
alpha=0.5,
linewidth=1)
datasets += [self.dsm3_variance]
if d3t3Weights is not None:
self.dsd3t3 = pltHelper.dataSet(name=r'ThreeTag $t\bar{t}$ est.',
points=self.dfd3[var],
weights=d3t3Weights,
color=t3.color,
alpha=0.5,
linewidth=2)
datasets += [self.dsd3t3]
if d3t4Weights is not None:
self.dsd3t4 = pltHelper.dataSet(name=r'FourTag $t\bar{t}$ est.',
points=self.dfd3[var],
weights=d3t4Weights,
color=t4.color,
alpha=0.5,
linewidth=2)
datasets += [self.dsd3t4]
if ttbarErrorWeights is not None:
self.dste = pltHelper.dataSet(
name=r'$t\bar{t}$ MC - $t\bar{t}$ est.',
points=ttbarError,
weights=ttbarErrorWeights,
color='black',
alpha=0.5,
linewidth=2)
datasets += [self.dste]
if self.dfzz is not None:
self.dszz = pltHelper.dataSet(
name=zz.name,
points=self.dfzz[var],
weights=getattr(self.dfzz, weightName) * 100,
color=zz.color,
alpha=1.0,
linewidth=1)
datasets += [self.dszz]
if self.dfzh is not None:
self.dszh = pltHelper.dataSet(
name=zh.name,
points=self.dfzh[var],
weights=getattr(self.dfzh, weightName) * 100,
color=zh.color,
alpha=1.0,
linewidth=1)
datasets += [self.dszh]
if type(bins) != list:
if not bins: bins = 50
if type(xmin) == type(None): xmin = self.dfSelected[var].min()
if type(xmax) == type(None): xmax = self.dfSelected[var].max()
width = (xmax - xmin) / bins
bins = [xmin + b * width for b in range(0, bins + 1)]
if reweight:
chisquare = pltHelper.histChisquare(obs=self.dsd4.points,
obs_w=self.dsd4.weights,
exp=self.bkgd.points,
exp_w=self.bkgd.weights,
bins=bins,
overflow=overflow)
args = {
'dataSets': datasets,
'ratio': [0, 1],
'ratioRange': [0.9, 1.1] if reweight else [0.5, 1.5],
'ratioTitle': 'Data / Model',
'bins': bins,
'xmin': xmin,
'xmax': xmax,
'ymin': ymin,
'ymax': ymax,
'xlabel': var.replace('_', ' '),
'ylabel': 'Events / Bin',
'overflow': overflow,
}
fig = pltHelper.histPlotter(**args)
if reweight:
fig.sub1.annotate(
'$\chi^2/$NDF = %1.2f (%1.0f$\%%$)' %
(chisquare.chi2 / chisquare.ndfs, chisquare.prob * 100),
(1.0, 1.02),
horizontalalignment='right',
xycoords='axes fraction')
figName = outputDir + "/" + var + ('_reweight'
if reweight else '') + '.pdf'
fig.savefig(figName)
print(figName)
def plotVar(self,
var,
bins=None,
xmin=None,
xmax=None,
reweight=False,
regName=""):
if reweight:
ttbarWeights = -getattr(self.dft3, weightName) * getattr(
self.dft3, FvTName)
# multijetWeights = np.concatenate((self.dfd3.mcPseudoTagWeight * self.dfd3.FvT, -self.dft3.mcPseudoTagWeight * self.dft3.FvT))
multijet = self.dfd3[var]
multijetWeights = getattr(self.dfd3, weightName) * getattr(
self.dfd3, FvTName)
# backgroundWeights = np.concatenate((self.dfd3.mcPseudoTagWeight * self.dfd3.FvT, -self.dft3.mcPseudoTagWeight * self.dft3.FvT, self.dft4.mcPseudoTagWeight))
background = np.concatenate((self.dfd3[var], self.dft4[var]))
backgroundWeights = np.concatenate(
(getattr(self.dfd3, weightName) * getattr(self.dfd3, FvTName),
getattr(self.dft4, weightName)))
else:
ttbarWeights = -getattr(self.dft3, weightName)
multijet = np.concatenate((self.dfd3[var], self.dft3[var]))
multijetWeights = np.concatenate(
(getattr(self.dfd3,
weightName), -getattr(self.dft3, weightName)))
# multijetWeights = self.dfd3.mcPseudoTagWeight
# background = np.concatenate((self.dfd3[var], self.dft3[var], self.dft4[var]))
# backgroundWeights = np.concatenate((self.dfd3.mcPseudoTagWeight, -self.dft3.mcPseudoTagWeight, self.dft4.mcPseudoTagWeight))
background = np.concatenate(
(self.dfd3[var], self.dft3[var], self.dft4[var]))
backgroundWeights = np.concatenate(
(getattr(self.dfd3,
weightName), -getattr(self.dft3, weightName),
getattr(self.dft4, weightName)))
# backgroundWeights = np.concatenate((self.dfd3.mcPseudoTagWeight, self.dft4.mcPseudoTagWeight))
print(self.bkgd[var])
self.dsd4 = pltHelper.dataSet(name=d4.name,
points=self.dfd4[var],
weights=getattr(self.dfd4, weightName),
color=d4.color,
alpha=1.0,
linewidth=1)
self.bkgd = pltHelper.dataSet(name='Background Model',
points=background,
weights=backgroundWeights,
color='brown',
alpha=1.0,
linewidth=1)
self.dst4 = pltHelper.dataSet(name=t4.name,
points=self.dft4[var],
weights=getattr(self.dft4, weightName),
color=t4.color,
alpha=1.0,
linewidth=1)
self.dsm3 = pltHelper.dataSet(name='ThreeTag Multijet',
points=multijet,
weights=multijetWeights,
color=d3.color,
alpha=1.0,
linewidth=1)
self.dst3 = pltHelper.dataSet(name=t3.name,
points=self.dft3[var],
weights=ttbarWeights,
color=t3.color,
alpha=1.0,
linewidth=1)
datasets = [self.dsd4, self.bkgd, self.dst4, self.dsm3, self.dst3]
if self.dfzz is not None:
self.dszz = pltHelper.dataSet(
name=zz.name,
points=self.dfzz[var],
weights=getattr(self.dfzz, weightName) * 100,
color=zz.color,
alpha=1.0,
linewidth=1)
datasets += [self.dszz]
if self.dfzh is not None:
self.dszh = pltHelper.dataSet(
name=zh.name,
points=self.dfzh[var],
weights=getattr(self.dfzh, weightName) * 100,
color=zh.color,
alpha=1.0,
linewidth=1)
datasets += [self.dszh]
if type(bins) != list:
if not bins: bins = 50
if type(xmin) == type(None): xmin = self.dfSelected[var].min()
if type(xmax) == type(None): xmax = self.dfSelected[var].max()
width = (xmax - xmin) / bins
bins = [xmin + b * width for b in range(-1, bins + 1)]
args = {
'dataSets': datasets,
'ratio': [0, 1],
'ratioRange': [0.5, 1.5],
'ratioTitle': 'Data / Model',
'bins': bins,
'xlabel': var.replace('_', ' '),
'ylabel': 'Events / Bin',
}
fig = pltHelper.histPlotter(**args)
figName = outputDir + "/" + regName + "_" + var + (
'_reweight' if reweight else '') + '.pdf'
fig.savefig(figName)
print(figName)
import matplotlibHelpers as pltHelper
import numpy as np
args={'points': np.random.rand(7000),
'weights': np.random.rand(7000),
'color': 'blue',
'alpha': 1.0,
'linewidth': 1,
'name': 'Output',
}
train=pltHelper.dataSet(**args)
args={'points': np.random.rand(3000),
'weights': np.random.rand(3000),
'color': 'blue',
'alpha': 0.5,
'linewidth': 2,
}
valid=pltHelper.dataSet(**args)
args={'points': np.zeros(0),
'weights': np.zeros(0),
'color': 'black',
'alpha': 1.0,
'linewidth': 1,
'name': 'Training Set',
}
trainLegend=pltHelper.dataSet(**args)
args={'points': np.zeros(0),
'weights': np.zeros(0),
def plotVar(self,
var,
FvTName,
outName,
bins=None,
xmin=None,
xmax=None,
regName=""):
if type(FvTName) == list:
ttbarWeights = -getattr(self.dft3, weightName) * 1. / 3 * (
getattr(self.dft3, FvTName[0]) + getattr(
self.dft3, FvTName[1]) + getattr(self.dft3, FvTName[2]))
multijetWeights = getattr(self.dfd3, weightName) * 1. / 3 * (
getattr(self.dfd3, FvTName[0]) + getattr(
self.dfd3, FvTName[1]) + getattr(self.dfd3, FvTName[2]))
backgroundWeights = np.concatenate(
(getattr(self.dfd3, weightName) * 1. / 3 *
(getattr(self.dfd3, FvTName[0]) + getattr(
self.dfd3, FvTName[1]) + getattr(self.dfd3, FvTName[2])),
getattr(self.dft4, weightName)))
else:
ttbarWeights = -getattr(self.dft3, weightName) * getattr(
self.dft3, FvTName)
multijetWeights = getattr(self.dfd3, weightName) * getattr(
self.dfd3, FvTName)
backgroundWeights = np.concatenate(
(getattr(self.dfd3, weightName) * getattr(self.dfd3, FvTName),
getattr(self.dft4, weightName)))
multijet = self.dfd3[var] if type(
var) != list else self.dfd3[var[0]] - self.dfd3[var[1]]
#dfd3var = self.dfd3[var] if type(var) != list else self.dfd3[var[0]] - self.dfd3[var[1]]
dft4var = self.dft4[var] if type(
var) != list else self.dft4[var[0]] - self.dft4[var[1]]
dfd4var = self.dfd4[var] if type(
var) != list else self.dfd4[var[0]] - self.dfd4[var[1]]
dft3var = self.dft3[var] if type(
var) != list else self.dft3[var[0]] - self.dft3[var[1]]
background = np.concatenate((multijet, dft4var))
self.dsd4 = pltHelper.dataSet(name=d4.name,
points=dfd4var,
weights=getattr(self.dfd4, weightName),
color=d4.color,
alpha=1.0,
linewidth=1)
self.bkgd = pltHelper.dataSet(name='Background Model',
points=background,
weights=backgroundWeights,
color='brown',
alpha=1.0,
linewidth=1)
self.dst4 = pltHelper.dataSet(name=t4.name,
points=dft4var,
weights=getattr(self.dft4, weightName),
color=t4.color,
alpha=1.0,
linewidth=1)
self.dsm3 = pltHelper.dataSet(name='ThreeTag Multijet',
points=multijet,
weights=multijetWeights,
color=d3.color,
alpha=1.0,
linewidth=1)
self.dst3 = pltHelper.dataSet(name=t3.name,
points=dft3var,
weights=ttbarWeights,
color=t3.color,
alpha=1.0,
linewidth=1)
datasets = [self.dsd4, self.bkgd, self.dst4, self.dsm3, self.dst3]
if self.dfzz is not None:
self.dszz = pltHelper.dataSet(
name=zz.name,
points=self.dfzz[var],
weights=getattr(self.dfzz, weightName) * 100,
color=zz.color,
alpha=1.0,
linewidth=1)
datasets += [self.dszz]
if self.dfzh is not None:
self.dszh = pltHelper.dataSet(
name=zh.name,
points=self.dfzh[var],
weights=getattr(self.dfzh, weightName) * 100,
color=zh.color,
alpha=1.0,
linewidth=1)
datasets += [self.dszh]
if type(bins) != list:
if not bins: bins = 50
if type(var) == list:
if type(xmin) == type(None):
xmin = self.dfSelected[var[0]].min()
if type(xmax) == type(None):
xmax = self.dfSelected[var[0]].max()
else:
if type(xmin) == type(None): xmin = self.dfSelected[var].min()
if type(xmax) == type(None): xmax = self.dfSelected[var].max()
width = (xmax - xmin) / bins
bins = [xmin + b * width for b in range(-1, bins + 1)]
if type(var) == list:
xlabel = var[0] + "_minus_" + var[1]
else:
xlabel = var
args = {
'dataSets': datasets,
'ratio': [0, 1],
'ratioRange': [0.5, 1.5],
'ratioTitle': 'Data / Model',
'bins': bins,
'xlabel': xlabel.replace('_', ' '),
'ylabel': 'Events / Bin',
}
fig = pltHelper.histPlotter(**args)
figName = outputDir + "/" + regName + "_" + xlabel + outName + '.pdf'
fig.savefig(figName)
print(figName)