def plotEffMap( self, name, x, y, Lumi, **keywords ):
""".. method:: plotEff2D( name, varX, varY, Lumi )
Giving a RooDataSet name in directory-like format,
the function will do a bi-dimensional plot of
efficiency with ``varX`` and ``varY`` variables. Also, it
will stores the graph within the dataset dictionary
:param name: name of the dataset
:type name: string
:param varX: binned variable to be used in the x-axis
:type varX: string
:param varY: binned variable to be used in the y-axis
:type varY: string
:param Lumi: luminosity
:type Lumi: string
:raise KeyError: the dataset is not in the root file (to be changed to NameError)
:raise KeyError: some binned variables is not in the dataset
"""
import ROOT
ROOT.gROOT.SetBatch(1)
import pytnp.steerplots.rootlogon
from tnputils import getBinning,listTableEff,getEff
#FIXME: Meter los errores en la misma linea (ahora te salta
# de linea (TEXT option)
try:
dataSet = self.RooDataSet[name]
except KeyError:
message = """There is no RooDataSet with name '%s'""" % name
printError( self.__module__+'.plotEfMap', message, AttributeError )
#-- Are the variables in the RooDataSet?
varNotInDatasetList = filter( lambda var: not var in self[name]['binnedVar'].keys(), [x, y] )
if len(varNotInDatasetList) != 0:
message = 'No binned variable: '
for var in varNotInDatasetList:
message += "'%s' " % var
message += "in the RooDataSet '%s'. Skipping plot generation..." % name
printWarning( self.__module__+'.plotEffMap', message )
# --- Skipping plot generation
return
#--- Name for the histo and for the plot file to be saved
histoName = 'TH2F_'+name
#--- Checking if the histo is already stored and plotted
if self[name].has_key('TH2'): #### FIXME: has_key o has_attribute ???
#-- Skipping, work it's done!
return None
#---- Preparing all the stuff
title = self.resLatex+', '+self[name]['objectType']+' '+self[name]['effType']+' '+dataSet.GetTitle()
yNbins = self[name]['variables'][y]['binN']
#arrayBinsY = self[name]['variables'][y]['arrayBins']---> # I need the PyDoubleBuffer
#------------------------------------------
__argSet__ = dataSet.get()
dum, arrayBinsY = getBinning( __argSet__[y] )
#------------------------------------------
xNbins = self[name]['variables'][x]['binN']
#arrayBinsX = self[name]['variables'][x]['arrayBins']---> # I need the PyDoubleBuffer
#------------------------------------------
dum, arrayBinsX = getBinning( __argSet__[x] )
#------------------------------------------
hTitleOfHist = name.replace('/','_')
h = ROOT.TH2F( hTitleOfHist, '', xNbins, arrayBinsX, yNbins, arrayBinsY )
hlo = h.Clone("eff_lo")
hlo.SetName( 'low_'+hTitleOfHist )
hhi = h.Clone("eff_hi")
hhi.SetName( 'high_'+hTitleOfHist )
#-- Getting the efficiencies
_listTableEffList = listTableEff(self.RooDataSet[name])
#skipPoints = False
for binDict in _listTableEffList:
#-- Extract error values
#if abs(binDict[self[name]['eff']][0]-self.badPoint[0]) < 1e-9:
# skipPoints = True
# continue
b = h.FindBin( binDict[x][0] , binDict[y][0] )
h.SetBinContent(b, binDict[self[name]['eff']][0])
h.SetBinError(b, (binDict[self[name]['eff']][2]-binDict[self[name]['eff']][1])/2.0 ) # WATCH: Error 'Simetrized'
hlo.SetBinContent(b, binDict[self[name]['eff']][1])
hhi.SetBinContent(b, binDict[self[name]['eff']][2])
c = ROOT.TCanvas()
#c.SetLogy(isLog[1])
h.GetYaxis().SetTitle(self[name]['variables'][y]['latexName'])
h.GetXaxis().SetTitle(self[name]['variables'][x]['latexName'])
h.GetZaxis().SetTitle(self[name]['variables'][self[name]['eff']]['latexName'])
#h.SetTitle( title ) --> Out titles
h.SetTitle( ' CMS Preliminary,'+Lumi+' #sqrt{s}=7 TeV ' )
#h.SetTitle('' )
h.Draw('COLZ')
htext = h.Clone('htext')
htext.SetMarkerSize(1.0)
htext.SetMarkerColor(1)
#if isLog[1]:
# ROOT.gStyle.SetPaintTextFormat("1.2f")
# htext.Draw('ESAMETEXT0')
#else:
ROOT.gStyle.SetPaintTextFormat("1.3f")
#htext.SetMarkerSize(2.2)
htext.Draw('SAMETEXTE0')
#plotName = self.resonance+'_'+name.replace('/','_')+isLog[0]+'.eps'
plotName = self.resonance+'_'+name.replace('/','_')+'.eps'
c.SaveAs(plotName)
# if skipPoints:
# message = '\033[1;33mplotEff2D Warning: Some efficiencies points are failed in the fit, the last plot will skip '\
# 'values with %.4f\033[1;m' % self.badPoint[0]
# print message
#FIXME: attribute o llave del diccionario del rootdataset??
try:
self[name]['TH2'][histoName] = (h, hlo, hhi)
except KeyError:
self[name]['TH2F'] = {}
self[name]['TH2F'][histoName] = (h, hlo, hhi)
def plotEff1D( self, name, inputVarName, Lumi ):
""".. method:: plotEff1D( dataname, variable, Lumi )
Given a name directory-like for a ROOT.RooDataSet object,
the function creates a 1-dim plot of 'variable_name' extracted
from the object and it will save it in a eps file. Also
it will store the graph object creating a new key in the
dataset dictionary::
self[nameRooDataSet]['tgraphs'] = { 'class_graph_name': TGraphAsymmErrors, ... }
:param name: name of the dataset
:type name: string
:param variable: binned variable to use
:type variable: string
:param Lumi: luminosity
:type Lumi: string
:raise KeyError: the dataset is not in the root file (to be changed to NameError)
:raise KeyError: the binned variable is not in the dataset
"""
#import rootlogon
from tnputils import listTableEff,getEff, graphclassname
from pytnp.steerplots.plotfunctions import plotAsymGraphXY
dataset = None
#-- Getting the class graph name
_graphclassname = graphclassname( self, name )
#-- Checking if the object exists
try:
dataset = self.RooDataSet[name]
except KeyError:
message = """you must introduce a valid name, '%s' is not a RooDataSet in the root file""" % name
printError( self.__module__, message, KeyError )
#--- Empty dataset
if self.RooDataSet[name].numEntries() == 0:
message = """Empty RooDataSet '%s'. Skipping...""" % name
printWarning( self.__module__+'.plotEff1D',message)
return None
#--- Checking variable
if not inputVarName in self[name]['binnedVar'].keys():
message = """you must introduce a valid binned variable name, '%s' is not in the '%s' RooDataSet\n""" % (inputVarName,name )
message += """The list of binned variables are '%s'""" % str(self[name]['binnedVar'].keys())
printError( self.__module__, message, KeyError )
self[name]['tgraphs'] = { _graphclassname: {} }
# Special case: we have only one variable
if len(self[name]['binnedVar']) == 1:
graphName = self.resonance+'_'+self[name]['effType']+'_'+self[name]['objectType']+'_'+\
self[name]['methodUsed']+'__'+inputVarName
if self[name]['isMC'] == 1:
graphName += '__mcTrue'
#--- Extracting the efficiency values per bin
plotList = listTableEff( dataset )
_min = 0
_max = 0
#--- Setting the points and extracting the min and max value
#--- in order to build the frame to plot
XPoints = []
YPoints = []
for varDict in plotList:
(eff,effLo,effHi) = varDict[self.effName]
(var,varLo,varHi) = varDict[inputVarName]
XPoints.append( (var, varLo, varHi) )
YPoints.append( (eff, effLo, effHi) )
_min = min( _min, varLo )
_max = max( _max, varHi )
#--- Cosmethics to storing the plot
#title = self[name]['objectType']+' category'
xtitle = self[name]['variables'][inputVarName]['latexName']+' '+self[name]['variables'][inputVarName]['unit']
ytitle = 'efficiency'
title = ' CMS Preliminary,'+Lumi+' #sqrt{s}=7 TeV '
self[name]['tgraphs'][_graphclassname][graphName] = plotAsymGraphXY( XPoints, YPoints, xtitle, ytitle,\
returnGraph=True, rangeFrame = (_min,0,_max,1.05), title=title, graphname=graphName )
return
#-- More than one binned variable
for varName in filter( lambda x: x != inputVarName, self[name]['binnedVar'].keys()):
_otherVarList_ = filter( lambda x: x != varName, self[name]['binnedVar'].keys() )
_otherVar_ = ''
for i in _otherVarList_:
_otherVar_ += i+', '
_otherVar_ = _otherVar_[:-2]
if len(_otherVar_ ) != 0:
_otherVar_ = ' in all the range of \'' +_otherVar_+'\''
print '\033[1;34m'+name+': \''+varName+'\' bins'+_otherVar_+'\033[1;m'
#--- Extracting bins and array of bins
binsN = self[name]['variables'][varName]['binN']
arrayBins = self[name]['variables'][varName]['arrayBins']
for bin in xrange(binsN):
arrayBins = self[name]['variables'][varName]['arrayBins']
Lo = arrayBins[bin]
Hi = arrayBins[bin+1]
Central = (Hi+Lo)/2.0
graphName = self.resonance+'_'+self[name]['effType']+'_'+self[name]['objectType']+'_'+\
self[name]['methodUsed']+'__'+varName+'_bin'+str(bin)+'_'
#graphName = self.resonance+'_'+self[name]['methodUsed']+'_'+self[name]['effType']+'_'+\
# self[name]['objectType']+'__'+varName+'_bin'+str(bin)+'_'
#Getting list of efficiency values plus variables
_plotList = eval('getEff(dataset,self.effName,'+varName+'='+str(Central)+')')
#print _plotList
#Extracting info to plot
#try:
(eff,effErrorLo,effErrorHi),otherVarDict = _plotList[0]
if len(filter( lambda (eff,__dic): eff[0] == 0.0, _plotList )) == len(_plotList):
printWarning( self.__module__+'.plotEff1D',"Skipping... Efficiencies in the dataset not calculated")
continue
_max = {}
_min = {}
varPoints = {}
effPoints = {}
for otherVarName, val in otherVarDict.iteritems():
varPoints[otherVarName] = []
effPoints[otherVarName] = []
_max[otherVarName] = 0.0
_min[otherVarName] = 0.0
for (eff,effLo,effHi),otherVarDict in _plotList:
for otherVarName, (central, low, high) in otherVarDict.iteritems():
_min[otherVarName] = min( _min[otherVarName], low )
_max[otherVarName] = max( _max[otherVarName], high )
varPoints[otherVarName].append( (central,low,high) )
effPoints[otherVarName].append( (eff, effLo, effHi) )
title = self.resLatex+', ('+str(Lo)+','+str(Hi)+') '+self[name]['variables'][varName]['latexName']+' range, '
title += self[name]['objectType']+' category'
for otherVarName, (central, low, high) in otherVarDict.iteritems():
graphname = graphName+otherVarName
if self[name]['isMC'] == 1:
graphname += '__mcTrue'
xtitle = self[name]['variables'][otherVarName]['latexName']+\
' '+self[name]['variables'][otherVarName]['unit']
ytitle = 'efficiency'
title = 'CMS Preliminary,'+Lumi+' #sqrt{s}=7 TeV'
ranges = (_min[otherVarName],0,_max[otherVarName],1.05)
self[name]['tgraphs'][_graphclassname][graphname] = plotAsymGraphXY( varPoints[otherVarName], effPoints[otherVarName],\
xtitle, ytitle, returnGraph=True, rangeFrame=ranges, title=title, graphname=graphname)
