diff --git a/PhysicsAnalysis/Algorithms/MuonAnalysisAlgorithms/python/MuonAnalysisSequence.py b/PhysicsAnalysis/Algorithms/MuonAnalysisAlgorithms/python/MuonAnalysisSequence.py
index 6e34250a3e89ed5599489c709bd967165a84feb7..bdd92c37f4c7dc19afce87d42b944abc934f91d7 100644
--- a/PhysicsAnalysis/Algorithms/MuonAnalysisAlgorithms/python/MuonAnalysisSequence.py
+++ b/PhysicsAnalysis/Algorithms/MuonAnalysisAlgorithms/python/MuonAnalysisSequence.py
@@ -80,6 +80,9 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
# Create the analysis algorithm sequence object:
seq = AnaAlgSequence( "MuonAnalysisSequence" + postfix )
+ seq.addMetaConfigDefault ("selectionDecorNames", [])
+ seq.addMetaConfigDefault ("selectionDecorCount", [])
+
# Set up the eta-cut on all muons prior to everything else
alg = createAlgorithm( 'CP::AsgSelectionAlg',
'MuonEtaCutAlg' + postfix )
@@ -89,9 +92,9 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
seq.append( alg, inputPropName = 'particles',
outputPropName = 'particlesOut',
stageName = 'selection',
- selectionDecorNames = [alg.selectionDecoration],
- selectionDecorCount = [2],
- dynConfig = {'preselection' : lambda seq, index : "&&".join (seq.selectionDecorNames (index))})
+ metaConfig = {'selectionDecorNames' : [alg.selectionDecoration],
+ 'selectionDecorCount' : [2]},
+ dynConfig = {'preselection' : lambda meta : "&&".join (meta["selectionDecorNames"])})
# Set up the track selection algorithm:
alg = createAlgorithm( 'CP::AsgLeptonTrackSelectionAlg',
@@ -101,9 +104,9 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
alg.maxDeltaZ0SinTheta = 0.5
seq.append( alg, inputPropName = 'particles',
stageName = 'selection',
- selectionDecorNames = [alg.selectionDecoration],
- selectionDecorCount = [3],
- dynConfig = {'preselection' : lambda seq, index : "&&".join (seq.selectionDecorNames (index))})
+ metaConfig = {'selectionDecorNames' : [alg.selectionDecoration],
+ 'selectionDecorCount' : [3]},
+ dynConfig = {'preselection' : lambda meta : "&&".join (meta["selectionDecorNames"])})
# Set up the muon calibration and smearing algorithm:
alg = createAlgorithm( 'CP::MuonCalibrationAndSmearingAlg',
@@ -113,7 +116,7 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
seq.append( alg, inputPropName = 'muons', outputPropName = 'muonsOut',
affectingSystematics = '(^MUON_ID$)|(^MUON_MS$)|(^MUON_SAGITTA_.*)|(^MUON_SCALE$)',
stageName = 'calibration',
- dynConfig = {'preselection' : lambda seq, index : "&&".join (seq.selectionDecorNames (index))})
+ dynConfig = {'preselection' : lambda meta : "&&".join (meta["selectionDecorNames"])})
# Set up the the pt selection
ptSelectionDecoration = 'selectPt' + postfix + ',as_bits'
@@ -123,9 +126,9 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
alg.selectionTool.minPt = 3e3
seq.append( alg, inputPropName = 'particles',
stageName = 'selection',
- selectionDecorNames = [alg.selectionDecoration],
- selectionDecorCount = [2],
- dynConfig = {'preselection' : lambda seq, index : "&&".join (seq.selectionDecorNames (index))})
+ metaConfig = {'selectionDecorNames' : [alg.selectionDecoration],
+ 'selectionDecorCount' : [2]},
+ dynConfig = {'preselection' : lambda meta : "&&".join (meta["selectionDecorNames"])})
# Setup the muon quality selection
qualitySelectionDecoration = 'good_muon' + postfix + ',as_bits'
@@ -138,9 +141,9 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
alg.badMuonVetoDecoration = badMuonVetoDecoration
seq.append( alg, inputPropName = 'muons',
stageName = 'selection',
- selectionDecorNames = [alg.selectionDecoration],
- selectionDecorCount = [4],
- dynConfig = {'preselection' : lambda seq, index : "&&".join (seq.selectionDecorNames (index))})
+ metaConfig = {'selectionDecorNames' : [alg.selectionDecoration],
+ 'selectionDecorCount' : [4]},
+ dynConfig = {'preselection' : lambda meta : "&&".join (meta["selectionDecorNames"])})
# Set up the isolation calculation algorithm:
if splitWP[1] != 'NonIso' :
@@ -150,16 +153,16 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
alg.isolationDecoration = 'isolated_muon' + postfix + ',as_bits'
seq.append( alg, inputPropName = 'muons', outputPropName = 'muonsOut',
stageName = 'selection',
- selectionDecorNames = [alg.isolationDecoration],
- selectionDecorCount = [1],
- dynConfig = {'preselection' : lambda seq, index : "&&".join (seq.selectionDecorNames (index))})
+ metaConfig = {'selectionDecorNames' : [alg.isolationDecoration],
+ 'selectionDecorCount' : [1]},
+ dynConfig = {'preselection' : lambda meta : "&&".join (meta["selectionDecorNames"])})
pass
# Set up an algorithm used for decorating baseline muon selection:
alg = createAlgorithm( 'CP::AsgSelectionAlg',
'MuonSelectionSummary' + postfix )
addPrivateTool( alg, 'selectionTool', 'CP::AsgFlagSelectionTool' )
- alg.selectionTool.selectionFlags = seq.selectionDecorNames()
+ alg.selectionTool.selectionFlags = seq.getMetaConfig ("selectionDecorNames")
alg.selectionDecoration = 'baselineSelection' + postfix + ',as_char'
seq.append( alg, inputPropName = 'particles',
stageName = 'selection' )
@@ -169,12 +172,12 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
alg = createAlgorithm( 'CP::ObjectCutFlowHistAlg', 'MuonCutFlowDumperAlg' + postfix )
alg.histPattern = 'muon' + postfix + '_cflow_%SYS%'
seq.append( alg, inputPropName = 'input', stageName = 'selection',
- dynConfig = {'selection' : lambda seq, index : seq.selectionDecorNames (index),
- 'selectionNCuts' : lambda seq, index : seq.selectionDecorCount (index)} )
+ dynConfig = {'selection' : lambda meta : meta["selectionDecorNames"],
+ 'selectionNCuts' : lambda meta : meta["selectionDecorCount"]} )
# Set up the output selection
if shallowViewOutput or deepCopyOutput:
- selectionDecorNamesOutput = seq.selectionDecorNames()
+ selectionDecorNamesOutput = seq.getMetaConfig ("selectionDecorNames")
if not ptSelectionOutput:
selectionDecorNamesOutput.remove(ptSelectionDecoration)
if not qualitySelectionOutput:
@@ -185,7 +188,7 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
if shallowViewOutput:
alg = createAlgorithm( 'CP::AsgViewFromSelectionAlg',
'MuonViewFromSelectionAlg' + postfix )
- alg.selection = seq.selectionDecorNamesOutput()
+ alg.selection = selectionDecorNamesOutput
seq.append( alg, inputPropName = 'input', outputPropName = 'output',
stageName = 'selection' )
@@ -203,14 +206,14 @@ def makeMuonAnalysisSequence( dataType, workingPoint,
seq.append( alg, inputPropName = 'muons',
affectingSystematics = '(^MUON_EFF_RECO.*)',
stageName = 'efficiency',
- dynConfig = {'preselection' : lambda seq, index : "&&".join (seq.selectionDecorNames (index))})
+ dynConfig = {'preselection' : lambda meta : "&&".join (meta["selectionDecorNames"])})
# Set up an algorithm dumping the kinematic properties of the muons:
if enableKinematicHistograms:
alg = createAlgorithm( 'CP::KinematicHistAlg', 'MuonKinematicDumperAlg' + postfix )
alg.histPattern = 'muon' + postfix + '_%VAR%_%SYS%'
seq.append( alg, inputPropName = 'input', stageName = 'selection',
- dynConfig = {'preselection' : lambda seq, index : "&&".join (seq.selectionDecorNames (index))})
+ dynConfig = {'preselection' : lambda meta : "&&".join (meta["selectionDecorNames"])})
# Set up a final deep copy making algorithm if requested:
if deepCopyOutput:
diff --git a/PhysicsAnalysis/D3PDTools/AnaAlgorithm/python/AnaAlgSequence.py b/PhysicsAnalysis/D3PDTools/AnaAlgorithm/python/AnaAlgSequence.py
index 35e6ae74813bf593f01868bb8f6d5a1781c80b02..7706b92ef212363a864db879882aca2552349d45 100644
--- a/PhysicsAnalysis/D3PDTools/AnaAlgorithm/python/AnaAlgSequence.py
+++ b/PhysicsAnalysis/D3PDTools/AnaAlgorithm/python/AnaAlgSequence.py
@@ -37,6 +37,7 @@ class AnaAlgSequence( AlgSequence ):
# Set up the sequence's member variables:
self._algorithmMeta = []
self._outputAffectingSystematics = None
+ self._metaConfigDefault = {}
return
@@ -99,12 +100,19 @@ class AnaAlgSequence( AlgSequence ):
'inconsistent state' )
# do the dynamic configuration based on meta-information
- index = 0
+ metaConfig = {}
+ for name, value in self._metaConfigDefault.items() :
+ metaConfig[name] = value[:]
+ pass
for alg, meta in zip( self, self._algorithmMeta ):
for var, func in meta.dynConfig.items() :
- setattr (alg, var, func (self, index))
+ setattr (alg, var, func (metaConfig))
+ pass
+ for name, value in meta.metaConfig.items() :
+ if not name in metaConfig :
+ raise RuntimeError ("metaConfig value " + name + " for algorithm " + alg.name() + " not registered, did you forget to call addMetaConfigDefault?")
+ metaConfig[name] += value[:]
pass
- index += 1
pass
# Make the inputs and outputs dictionaries. Allowing simple sequences to
@@ -260,7 +268,7 @@ class AnaAlgSequence( AlgSequence ):
def append( self, alg, inputPropName, outputPropName = None,
affectingSystematics = None, stageName = 'undefined',
- selectionDecorNames = [], selectionDecorCount = [],
+ metaConfig = {},
dynConfig = {}):
"""Add one analysis algorithm to the sequence
@@ -280,14 +288,14 @@ class AnaAlgSequence( AlgSequence ):
stageName -- name of the current processing stage [optional]
"""
- meta = AnaAlgorithmMeta( stageName=stageName, affectingSystematics=affectingSystematics, inputPropName=inputPropName, outputPropName=outputPropName, selectionDecorNames=selectionDecorNames, selectionDecorCount=selectionDecorCount, dynConfig=dynConfig )
+ meta = AnaAlgorithmMeta( stageName=stageName, affectingSystematics=affectingSystematics, inputPropName=inputPropName, outputPropName=outputPropName, metaConfig=metaConfig, dynConfig=dynConfig )
self += alg
self._algorithmMeta.append( meta )
return self
def insert( self, index, alg, inputPropName, outputPropName = None,
affectingSystematics = None, stageName = 'undefined',
- selectionDecorNames = [], selectionDecorCount = [],
+ metaConfig = {},
dynConfig = {} ):
"""Insert one analysis algorithm into the sequence
@@ -309,7 +317,7 @@ class AnaAlgSequence( AlgSequence ):
stageName -- name of the current processing stage [optional]
"""
- meta = AnaAlgorithmMeta( stageName=stageName, affectingSystematics=affectingSystematics, inputPropName=inputPropName, outputPropName=outputPropName, selectionDecorNames=selectionDecorNames, selectionDecorCount=selectionDecorCount, dynConfig=dynConfig )
+ meta = AnaAlgorithmMeta( stageName=stageName, affectingSystematics=affectingSystematics, inputPropName=inputPropName, outputPropName=outputPropName, metaConfig=metaConfig, dynConfig=dynConfig )
super( AnaAlgSequence, self ).insert( index, alg )
self._algorithmMeta.insert( index, meta )
return self
@@ -406,20 +414,40 @@ class AnaAlgSequence( AlgSequence ):
pass
pass
- def selectionDecorNames (self, index = None) :
- result = []
- for meta in self._algorithmMeta [ 0 : index ] :
- result += meta.selectionDecorNames
+
+
+ def addMetaConfigDefault (self, name, value) :
+ """add a default value for the given meta-configuration entry
+
+ This will both register name as a valid meta-configuration
+ value and set its default value, or add to its default value,
+ if that name is already known."""
+
+ if name in self._metaConfigDefault :
+ self._metaConfigDefault[name] += value
pass
- return result
+ else :
+ self._metaConfigDefault[name] = value
+ pass
+ pass
+
+
- def selectionDecorCount (self, index = None) :
- result = []
- for meta in self._algorithmMeta [ 0 : index ] :
- result += meta.selectionDecorCount
+ def getMetaConfig (self, name) :
+ """get the value for the given meta-configuration entry"""
+
+ if not name in self._metaConfigDefault :
+ raise RuntimeError ("metaConfig value " + name + " not registered, did you forget to call addMetaConfigDefault?")
+ result = self._metaConfigDefault[name][:]
+ for meta in self._algorithmMeta :
+ if name in meta.metaConfig :
+ result += meta.metaConfig[name]
+ pass
pass
return result
+
+
@staticmethod
def allowedStageNames():
return AnaAlgorithmMeta.allowedStageNames ()
diff --git a/PhysicsAnalysis/D3PDTools/AnaAlgorithm/python/AnaAlgorithmMeta.py b/PhysicsAnalysis/D3PDTools/AnaAlgorithm/python/AnaAlgorithmMeta.py
index 5e069b79a4946e6cb1e6657b418ed17a43d04cb1..f10d0a33cd9a8f55de44071d4d8ec0460fe399cc 100644
--- a/PhysicsAnalysis/D3PDTools/AnaAlgorithm/python/AnaAlgorithmMeta.py
+++ b/PhysicsAnalysis/D3PDTools/AnaAlgorithm/python/AnaAlgorithmMeta.py
@@ -13,17 +13,14 @@ class AnaAlgorithmMeta ( object ):
a separate algorithm.
"""
- def __init__( self, stageName, affectingSystematics, inputPropName, outputPropName, selectionDecorNames, selectionDecorCount, dynConfig ):
+ def __init__( self, stageName, affectingSystematics, inputPropName, outputPropName, metaConfig, dynConfig ):
if not stageName in self.allowedStageNames() :
raise ValueError ('unknown stage name ' + stageName + ' allowed stage names are ' + ', '.join(self.allowedStageNames()))
self.stageName = stageName
- if len (selectionDecorNames) != len (selectionDecorCount) :
- raise ValueError ("selectionDecorNames and selectionDecorCount don't have the same length: " + str (len (selectionDecorNames)) + " " + str (len (selectionDecorCount)));
- self.selectionDecorNames = selectionDecorNames
- self.selectionDecorCount = selectionDecorCount
+ self.metaConfig = metaConfig
self.dynConfig = dynConfig
if isinstance( inputPropName, dict ):