def mcimposed(tnp, variable, Lumi, sys='cnt', **keywords):
""".. function:: mcimposed( tnp, variable, sys[, title=thetitle, outputformat=format] )
Function to do the 1-dim plots of the ``variable`` for all datasets which
attribute ``isMC=0`` with its correspondent Monte Carlo partner ``isMC=1``
:param tnp: the pytnp instance
:type tnpdict: pytnp
:param variable: the binned variable to be plotted
:type variable: string
:param Lumi: the luminosity to be put in the title (TO BE DEPRECATED)
:type Lumi: string
:param sys: the efficiency type to be compared, ``fit`` or ``cnt``
:type sys: string
:keyword title: the title of the graph
:type title: string
:keyword outputformat: the output format of the graph (eps, root, png,...)
:type outputformat: string
"""
import ROOT
import rootlogon
from plotfunctions import legend, paveText, plotAsymGraphXY
# Input keywords and defaults
KEYWORDS = {'title': None, 'outputformat': 'eps'}
for key, value in keywords.iteritems():
try:
KEYWORDS[key] = value
except KeyError:
pass
COLOR = [1, 38, 46]
MARKERTYPE = [20, 21, 22]
mcpartner = {}
todelete = []
for dataname, tnpdict in tnp.iteritems():
isnotMC = False
mcpartner[dataname] = []
#-- Find the names for mc partners
try:
dummy = tnpdict['refMC']
isnotMC = True
except KeyError:
pass
#-- Do the plot if it still is not done
try:
dummy = tnp[dataname]['tgraphs']
except KeyError:
#-- Plotting and storing
tnp.plotEff1D(dataname, variable, Lumi)
if isnotMC:
try:
mcpartner[dataname].append(tnpdict['refMC'][sys + '_eff'])
except KeyError:
Message = "No '%s' Monte Carlo dataset for '%s'. Skipping the plot..." % (
sys + '_eff', dataname)
printWarning(mcimposed.__module__ + '.' + mcimposed.__name__,
Message)
#-- Print in the same canvas every mc and data
for dataname, tnpdict in filter(lambda (name, _dict): _dict['isMC'] == 0,
tnp.iteritems()):
#-- Only plot those who find MC partner
if len(mcpartner[dataname]) == 0:
continue
for className, graphDict in tnpdict['tgraphs'].iteritems():
#-- Name of the class for the mc
mcclassName = className.replace(tnp[dataname]['methodUsed'],
sys + '_eff')
if KEYWORDS['title']:
text = paveText(KEYWORDS['title'])
for _graphname, graph in graphDict.iteritems():
i = 0
c = ROOT.TCanvas()
#-- Initializing the frame, cosmethics, ...
#--- FIXME: ONLY TAKE THE FIRST GRAPH AXIS!! Ok if every graph
#---- have the same ranges but no in other case
refFrame = graph.GetHistogram()
_ranges = {'X': None, 'Y': None}
for axisName in _ranges.iterkeys():
axis = eval('refFrame.Get' + axisName + 'axis()')
Nbins = axis.GetNbins()
_min = axis.GetBinLowEdge(1)
_max = axis.GetBinUpEdge(Nbins)
_ranges[axisName] = (_min, _max)
frame = c.DrawFrame(_ranges['X'][0], _ranges['Y'][0],
_ranges['X'][1], _ranges['Y'][1])
xtitle = tnpdict['binnedVar'][variable]['latexName']
unit = tnpdict['binnedVar'][variable]['unit']
if unit != '':
xtitle += ' (' + unit + ') '
frame.GetXaxis().SetTitle(xtitle)
frame.GetYaxis().SetTitle(tnp.effName)
frame.Draw()
leg = legend()
graph.SetLineColor(COLOR[i])
graph.SetMarkerColor(COLOR[i])
graph.SetMarkerStyle(MARKERTYPE[i])
graph.Draw('PSAME')
leg.AddEntry(graph, tnp.resLatex, 'P')
i += 1
#-- Name of the graph for the mc
_graphmcname = _graphname.replace(tnp[dataname]['methodUsed'],
sys + '_eff') + '__mcTrue'
#-- Finding all the mc graphs in the list of partners
for mcdataname in mcpartner[dataname]:
try:
graphmc = tnp[mcdataname]['tgraphs'][mcclassName][
_graphmcname]
graphmc.SetLineColor(COLOR[i])
graphmc.SetMarkerColor(COLOR[i])
graphmc.SetMarkerStyle(MARKERTYPE[i])
graphmc.Draw('PSAME')
leg.AddEntry(graphmc, tnp.resLatex + ' MC ' + sys, 'P')
i += 1
except KeyError:
continue
leg.Draw()
if KEYWORDS['title']:
text.Draw()
c.SaveAs(_graphname + '_SYSMC' '.' + KEYWORDS['outputformat'])
c.Close()
def superImposed( tnpDict, variable, Lumi, **keywords ):
""".. function:: superImposed( tnpdict, variable, Lumi[, title=thetitle, outputformat=format] )
Giving different pytnp instances, the function do the 1-dim plots of the 'variable'
in the same canvas. The pytnp instances must have the same object types (objectType)
(ex: Global muon identification efficiency, ...) with the SAME name.
See the objectType content of a pytnp instance usign the print function for a
pytnp instance.
:param tnpdict: { resName1: tnp1, resName2: tnp2,...}, the keys are strings and the values
pytnp instances
:type tnpdict: dict
:param variable: the binned variable to be plotted
:type variable: string
:param Lumi: the luminosity to be put in the title (TO BE DEPRECATED)
:type Lumi: string
:keyword title: the title of the graph
:type title: string
:keyword outputformat: the output format of the graph (eps, root, png,...)
:type outputformat: string
.. warning::
The user is responsable of having the same binning in every dataset.
"""
import ROOT
import rootlogon
from plotfunctions import legend, paveText, plotAsymGraphXY
# Input keywords and defaults
KEYWORDS = { 'title': None, 'outputformat': 'eps' }
for key,value in keywords.iteritems():
try:
KEYWORDS[key] = value
except KeyError:
pass
COLOR = [ 1, 38, 46, 28, 30 ]
MARKERTYPE = [ 20, 21, 22, 23, 24 ]
#-- Checking we have the same efficiency object for each instance
#-- Going to construct a string identifying totally all the
#-- RooDataSet which have the same effType, objectUse, methodUsed keys
#-- plus variable and bin range information. This define a class
#-- and each class it will be plotted at the same canvas
classesDict = {}
for resName, tnp in tnpDict.iteritems():
for dataname, keys in tnp.RooDataSet.iteritems():
#-- Don't do the plots again if already done
try:
dummy = tnp[dataname]['tgraphs']
except KeyError:
#-- Plotting and storing
tnp.plotEff1D( dataname, variable, Lumi )
#-- Get the info for the tgraphs dict
for className, graphDict in tnp[dataname]['tgraphs'].iteritems():
for _graphname in graphDict.iterkeys():
#-- Normalizing the graph names
pseudoname = _graphname.replace( resName+'_'+className, '' )
try:
classesDict[className].add( pseudoname )
except KeyError:
classesDict[className] = set([ pseudoname ])
#--- All it's done if we have only a file.
if len(tnpDict) == 1:
return
#--- Each key of the classes dict defines a canvas.
#--- Extracting all the graphs from all the datasets
for className, graphpseudonameList in classesDict.iteritems():
#-- FIXME: Queremos que el Montecarlo este activo? Queremos que un mismo rootfile
#--- tenga la posibilidad de plotearse
for pseudoname in graphpseudonameList:
i = 0
frame = None
involvedRes = ''
c = ROOT.TCanvas()
leg = legend()
if KEYWORDS['title']:
text = paveText( KEYWORDS['title'] )
for resName, tnp in tnpDict.iteritems():
involvedRes += resName+'_'
for dataname, dataDict in tnp.iteritems():
try:
#-- FIXME: Necesito algo para evitar que RooDataSets con el mismo patron
#--- y en el mismo fichero se solapen. O quizas marcarlo
#--- como construccion erronea de la instancia (effType, object...)
#--- Seguro ??
graph = dataDict['tgraphs'][className][resName+'_'+className+pseudoname]
except KeyError:
continue
#-- Initializing the frame, cosmethics, ... once
#--- FIXME: ONLY TAKE THE FIRST GRAPH AXIS!! Ok if every graph
#---- have the same ranges but no in other case
if not frame:
refFrame = graph.GetHistogram()
_ranges = { 'X' : None, 'Y': None }
for axisName in _ranges.iterkeys():
axis = eval( 'refFrame.Get'+axisName+'axis()' )
Nbins = axis.GetNbins()
_min = axis.GetBinLowEdge( 1 )
_max = axis.GetBinUpEdge( Nbins )
_ranges[axisName] = (_min,_max)
frame = c.DrawFrame( _ranges['X'][0], _ranges['Y'][0], _ranges['X'][1], _ranges['Y'][1] )
xtitle = dataDict['binnedVar'][variable]['latexName']
unit = dataDict['binnedVar'][variable]['unit']
if unit != '':
xtitle += ' ('+unit+') '
frame.GetXaxis().SetTitle( xtitle )
frame.GetYaxis().SetTitle( tnp.effName )
frame.Draw()
graph.SetLineColor( COLOR[i] )
graph.SetMarkerColor( COLOR[i] )
graph.SetMarkerStyle( MARKERTYPE[i] )
graph.Draw('PSAME')
leg.AddEntry( graph, tnp.resLatex, 'P' )
i += 1
# print resName+'_'+className+pseudoname
leg.Draw()
if KEYWORDS['title']:
text.Draw()
c.SaveAs(involvedRes+className+pseudoname+'.'+KEYWORDS['outputformat'])
c.Close()
def superImposed(tnpDict, variable, Lumi, **keywords):
""".. function:: superImposed( tnpdict, variable, Lumi[, title=thetitle, outputformat=format] )
Giving different pytnp instances, the function do the 1-dim plots of the 'variable'
in the same canvas. The pytnp instances must have the same object types (objectType)
(ex: Global muon identification efficiency, ...) with the SAME name.
See the objectType content of a pytnp instance usign the print function for a
pytnp instance.
:param tnpdict: { resName1: tnp1, resName2: tnp2,...}, the keys are strings and the values
pytnp instances
:type tnpdict: dict
:param variable: the binned variable to be plotted
:type variable: string
:param Lumi: the luminosity to be put in the title (TO BE DEPRECATED)
:type Lumi: string
:keyword title: the title of the graph
:type title: string
:keyword outputformat: the output format of the graph (eps, root, png,...)
:type outputformat: string
.. warning::
The user is responsable of having the same binning in every dataset.
"""
import ROOT
import rootlogon
from plotfunctions import legend, paveText, plotAsymGraphXY
# Input keywords and defaults
KEYWORDS = {'title': None, 'outputformat': 'eps'}
for key, value in keywords.iteritems():
try:
KEYWORDS[key] = value
except KeyError:
pass
COLOR = [1, 38, 46, 28, 30]
MARKERTYPE = [20, 21, 22, 23, 24]
#-- Checking we have the same efficiency object for each instance
#-- Going to construct a string identifying totally all the
#-- RooDataSet which have the same effType, objectUse, methodUsed keys
#-- plus variable and bin range information. This define a class
#-- and each class it will be plotted at the same canvas
classesDict = {}
for resName, tnp in tnpDict.iteritems():
for dataname, keys in tnp.RooDataSet.iteritems():
#-- Don't do the plots again if already done
try:
dummy = tnp[dataname]['tgraphs']
except KeyError:
#-- Plotting and storing
tnp.plotEff1D(dataname, variable, Lumi)
#-- Get the info for the tgraphs dict
for className, graphDict in tnp[dataname]['tgraphs'].iteritems():
for _graphname in graphDict.iterkeys():
#-- Normalizing the graph names
pseudoname = _graphname.replace(resName + '_' + className,
'')
try:
classesDict[className].add(pseudoname)
except KeyError:
classesDict[className] = set([pseudoname])
#--- All it's done if we have only a file.
if len(tnpDict) == 1:
return
#--- Each key of the classes dict defines a canvas.
#--- Extracting all the graphs from all the datasets
for className, graphpseudonameList in classesDict.iteritems():
#-- FIXME: Queremos que el Montecarlo este activo? Queremos que un mismo rootfile
#--- tenga la posibilidad de plotearse
for pseudoname in graphpseudonameList:
i = 0
frame = None
involvedRes = ''
c = ROOT.TCanvas()
leg = legend()
if KEYWORDS['title']:
text = paveText(KEYWORDS['title'])
for resName, tnp in tnpDict.iteritems():
involvedRes += resName + '_'
for dataname, dataDict in tnp.iteritems():
try:
#-- FIXME: Necesito algo para evitar que RooDataSets con el mismo patron
#--- y en el mismo fichero se solapen. O quizas marcarlo
#--- como construccion erronea de la instancia (effType, object...)
#--- Seguro ??
graph = dataDict['tgraphs'][className][resName + '_' +
className +
pseudoname]
except KeyError:
continue
#-- Initializing the frame, cosmethics, ... once
#--- FIXME: ONLY TAKE THE FIRST GRAPH AXIS!! Ok if every graph
#---- have the same ranges but no in other case
if not frame:
refFrame = graph.GetHistogram()
_ranges = {'X': None, 'Y': None}
for axisName in _ranges.iterkeys():
axis = eval('refFrame.Get' + axisName + 'axis()')
Nbins = axis.GetNbins()
_min = axis.GetBinLowEdge(1)
_max = axis.GetBinUpEdge(Nbins)
_ranges[axisName] = (_min, _max)
frame = c.DrawFrame(_ranges['X'][0], _ranges['Y'][0],
_ranges['X'][1], _ranges['Y'][1])
xtitle = dataDict['binnedVar'][variable]['latexName']
unit = dataDict['binnedVar'][variable]['unit']
if unit != '':
xtitle += ' (' + unit + ') '
frame.GetXaxis().SetTitle(xtitle)
frame.GetYaxis().SetTitle(tnp.effName)
frame.Draw()
graph.SetLineColor(COLOR[i])
graph.SetMarkerColor(COLOR[i])
graph.SetMarkerStyle(MARKERTYPE[i])
graph.Draw('PSAME')
leg.AddEntry(graph, tnp.resLatex, 'P')
i += 1
# print resName+'_'+className+pseudoname
leg.Draw()
if KEYWORDS['title']:
text.Draw()
c.SaveAs(involvedRes + className + pseudoname + '.' +
KEYWORDS['outputformat'])
c.Close()
def mcimposed( tnp, variable, Lumi, sys='cnt', **keywords ):
""".. function:: mcimposed( tnp, variable, sys[, title=thetitle, outputformat=format] )
Function to do the 1-dim plots of the ``variable`` for all datasets which
attribute ``isMC=0`` with its correspondent Monte Carlo partner ``isMC=1``
:param tnp: the pytnp instance
:type tnpdict: pytnp
:param variable: the binned variable to be plotted
:type variable: string
:param Lumi: the luminosity to be put in the title (TO BE DEPRECATED)
:type Lumi: string
:param sys: the efficiency type to be compared, ``fit`` or ``cnt``
:type sys: string
:keyword title: the title of the graph
:type title: string
:keyword outputformat: the output format of the graph (eps, root, png,...)
:type outputformat: string
"""
import ROOT
import rootlogon
from plotfunctions import legend, paveText, plotAsymGraphXY
# Input keywords and defaults
KEYWORDS = { 'title': None, 'outputformat': 'eps' }
for key,value in keywords.iteritems():
try:
KEYWORDS[key] = value
except KeyError:
pass
COLOR = [ 1, 38, 46 ]
MARKERTYPE = [ 20, 21, 22 ]
mcpartner = {}
todelete = []
for dataname, tnpdict in tnp.iteritems():
isnotMC = False
mcpartner[dataname] = []
#-- Find the names for mc partners
try:
dummy = tnpdict['refMC']
isnotMC = True
except KeyError:
pass
#-- Do the plot if it still is not done
try:
dummy = tnp[dataname]['tgraphs']
except KeyError:
#-- Plotting and storing
tnp.plotEff1D( dataname, variable, Lumi )
if isnotMC:
try:
mcpartner[dataname].append( tnpdict['refMC'][sys+'_eff'] )
except KeyError:
Message = "No '%s' Monte Carlo dataset for '%s'. Skipping the plot..." % ( sys+'_eff', dataname )
printWarning( mcimposed.__module__+'.'+mcimposed.__name__, Message )
#-- Print in the same canvas every mc and data
for dataname, tnpdict in filter( lambda (name, _dict): _dict['isMC'] == 0, tnp.iteritems() ):
#-- Only plot those who find MC partner
if len(mcpartner[dataname]) == 0:
continue
for className, graphDict in tnpdict['tgraphs'].iteritems():
#-- Name of the class for the mc
mcclassName = className.replace( tnp[dataname]['methodUsed'], sys+'_eff' )
if KEYWORDS['title']:
text = paveText( KEYWORDS['title'] )
for _graphname, graph in graphDict.iteritems():
i = 0
c = ROOT.TCanvas()
#-- Initializing the frame, cosmethics, ...
#--- FIXME: ONLY TAKE THE FIRST GRAPH AXIS!! Ok if every graph
#---- have the same ranges but no in other case
refFrame = graph.GetHistogram()
_ranges = { 'X' : None, 'Y': None }
for axisName in _ranges.iterkeys():
axis = eval( 'refFrame.Get'+axisName+'axis()' )
Nbins = axis.GetNbins()
_min = axis.GetBinLowEdge( 1 )
_max = axis.GetBinUpEdge( Nbins )
_ranges[axisName] = (_min,_max)
frame = c.DrawFrame( _ranges['X'][0], _ranges['Y'][0], _ranges['X'][1], _ranges['Y'][1] )
xtitle = tnpdict['binnedVar'][variable]['latexName']
unit = tnpdict['binnedVar'][variable]['unit']
if unit != '':
xtitle += ' ('+unit+') '
frame.GetXaxis().SetTitle( xtitle )
frame.GetYaxis().SetTitle( tnp.effName )
frame.Draw()
leg = legend()
graph.SetLineColor( COLOR[i] )
graph.SetMarkerColor( COLOR[i] )
graph.SetMarkerStyle( MARKERTYPE[i] )
graph.Draw('PSAME')
leg.AddEntry( graph, tnp.resLatex, 'P' )
i += 1
#-- Name of the graph for the mc
_graphmcname = _graphname.replace( tnp[dataname]['methodUsed'], sys+'_eff' )+'__mcTrue'
#-- Finding all the mc graphs in the list of partners
for mcdataname in mcpartner[dataname]:
try:
graphmc = tnp[mcdataname]['tgraphs'][mcclassName][_graphmcname]
graphmc.SetLineColor( COLOR[i] )
graphmc.SetMarkerColor( COLOR[i] )
graphmc.SetMarkerStyle( MARKERTYPE[i] )
graphmc.Draw('PSAME')
leg.AddEntry( graphmc, tnp.resLatex+' MC '+sys, 'P' )
i += 1
except KeyError:
continue
leg.Draw()
if KEYWORDS['title']:
text.Draw()
c.SaveAs(_graphname+'_SYSMC''.'+KEYWORDS['outputformat'])
c.Close()