possibility to store variables with half-precision floats (16bits)

PhysicsTools/Heppy/python/analyzers/core/autovars.py

@@ -11,7 +11,7 @@ class NTupleVariable:
        - name, type, help, default: obvious 
        - function: a function that taken an object computes the value to fill (e.g. lambda event : len(event.goodVertices))
     """
-    def __init__(self, name, function, type=float, help="", default=-99, mcOnly=False, filler=None, nillable=False):
+    def __init__(self, name, function, type=float, help="", default=-99, mcOnly=False, filler=None, nillable=False, storageType="default"):
         self.name = name
         self.function = function
         self.type = type
@@ -20,15 +20,16 @@ def __init__(self, name, function, type=float, help="", default=-99, mcOnly=Fals
         self.mcOnly  = mcOnly
         self.filler  = filler
         self.nillable = nillable
+        self.storageType = storageType
     def __call__(self,object):
         ret = self.function(object)
         return ret
     def makeBranch(self,treeNumpy,isMC):
         if self.mcOnly and not isMC: return
-        treeNumpy.var(self.name, type=self.type, default=self.default, title=self.help, filler=self.filler)
+        treeNumpy.var(self.name, type=self.type, default=self.default, title=self.help, storageType=self.storageType, filler=self.filler)
     def fillBranch(self,treeNumpy,object,isMC):
         if self.mcOnly and not isMC: return
-        treeNumpy.fill(self.name, self(object))
+        treeNumpy.fill(self.name, self(object), storageType=self.storageType)
     def __repr__(self):
         return "<NTupleVariable[%s]>" % self.name
 
@@ -62,7 +63,7 @@ def ownVars(self,isMC):
                                   # ^-- lambda object : subvar(so(object)) doesn't work due to scoping, see
                                   #     http://stackoverflow.com/questions/2295290/what-do-lambda-function-closures-capture-in-python/2295372#2295372
                                   type = subvar.type, help = subvar.help, default = subvar.default, mcOnly = subvar.mcOnly,
-                                  filler = subvar.filler))
+                                  filler = subvar.filler, storageType = subvar.storageType))
                 self._subObjectVars[isMC] = subvars
             vars += self._subObjectVars[isMC]
         return vars
@@ -151,15 +152,15 @@ def makeBranches(self,treeNumpy,isMC):
         for v in allvars:
             h = v.help
             if self.help: h = "%s for %s" % ( h if h else v.name, self.help )
-            treeNumpy.var("%s_%s" % (self.name, v.name), type=v.type, default=v.default, title=h, filler=v.filler)
+            treeNumpy.var("%s_%s" % (self.name, v.name), type=v.type, default=v.default, title=h, filler=v.filler, storageType=v.storageType)
     def fillBranches(self,treeNumpy,object,isMC):
         if self.mcOnly and not isMC: return
         if object is None:
             if self.nillable: return
             raise RuntimeError, "Error, object not found or None when filling branch %s" % self.name
         allvars = self.objectType.allVars(isMC)
         for v in allvars:
-            treeNumpy.fill("%s_%s" % (self.name, v.name), v(object))
+            treeNumpy.fill("%s_%s" % (self.name, v.name), v(object), storageType=v.storageType)
     def __repr__(self):
         return "<NTupleObject[%s]>" % self.name
 
@@ -188,7 +189,7 @@ def makeBranchesScalar(self,treeNumpy,isMC):
             for i in xrange(1,self.maxlen+1):
                 h = v.help
                 if self.help: h = "%s for %s [%d]" % ( h if h else v.name, self.help, i-1 )
-                treeNumpy.var("%s%d_%s" % (self.name, i, v.name), type=v.type, default=v.default, title=h, filler=v.filler)
+                treeNumpy.var("%s%d_%s" % (self.name, i, v.name), type=v.type, default=v.default, title=h, storageType=v.storageType, filler=v.filler)
     def makeBranchesVector(self,treeNumpy,isMC):
         if not isMC and self.objectType.mcOnly: return
         treeNumpy.var("n"+self.name, int)
@@ -197,7 +198,7 @@ def makeBranchesVector(self,treeNumpy,isMC):
             h = v.help
             if self.help: h = "%s for %s" % ( h if h else v.name, self.help )
             name="%s_%s" % (self.name, v.name) if v.name != "" else self.name
-            treeNumpy.vector(name, "n"+self.name, self.maxlen, type=v.type, default=v.default, title=h, filler=v.filler)
+            treeNumpy.vector(name, "n"+self.name, self.maxlen, type=v.type, default=v.default, title=h, storageType=v.storageType, filler=v.filler)
     def fillBranchesScalar(self,treeNumpy,collection,isMC):
         if not isMC and self.objectType.mcOnly: return
         if self.filter != None: collection = [ o for o in collection if self.filter(o) ]
@@ -209,7 +210,7 @@ def fillBranchesScalar(self,treeNumpy,collection,isMC):
         for i in xrange(num): 
             o = collection[i]
             for v in allvars:
-                treeNumpy.fill("%s%d_%s" % (self.name, i+1, v.name), v(o))
+                treeNumpy.fill("%s%d_%s" % (self.name, i+1, v.name), v(o), storageType=v.storageType)
     def fillBranchesVector(self,treeNumpy,collection,isMC):
         if not isMC and self.objectType.mcOnly: return
         if self.filter != None: collection = [ o for o in collection if self.filter(o) ]
@@ -220,7 +221,7 @@ def fillBranchesVector(self,treeNumpy,collection,isMC):
         allvars = self.objectType.allVars(isMC)
         for v in allvars:
             name="%s_%s" % (self.name, v.name) if v.name != "" else self.name
-            treeNumpy.vfill(name, [ v(collection[i]) for i in xrange(num) ])
+            treeNumpy.vfill(name, [ v(collection[i]) for i in xrange(num) ], storageType=v.storageType)
     def __repr__(self):
         return "<NTupleCollection[%s]>" % self.name
 

PhysicsTools/Heppy/python/analyzers/gen/LHEWeightAnalyzer.py

@@ -18,7 +18,7 @@ class LHEWeightAnalyzer( Analyzer ):
     """
     def __init__(self, cfg_ana, cfg_comp, looperName ):
         super(LHEWeightAnalyzer,self).__init__(cfg_ana,cfg_comp,looperName)
-
+        self.normLHEWeights = getattr(cfg_ana, "normLHEWeights", False)
         self.LHEWeightsNames=[]
 
     def declareHandles(self):
@@ -66,6 +66,7 @@ def process(self, event):
                 # Check if id is string or int and convert to int if it's a string
                 try:
                     int(w.id)
+                    if self.normLHEWeights: w.wgt = w.wgt/event.LHE_originalWeight
                     event.LHE_weights.append(w)
                 except ValueError:
                     if not type(w.id) == str:

PhysicsTools/HeppyCore/python/statistics/tree.py

@@ -35,7 +35,7 @@ def branch_(self, selfmap, varName, type, len, postfix="", storageType="default"
         if storageType == "default": 
             storageType = self.defaultIntType if type is int else self.defaultFloatType
         if type is float  :
-            if storageType == "F": 
+            if storageType in ["F","H"]: 
                 selfmap[varName]=numpy.zeros(len,numpy.float32)
                 self.tree.Branch(varName,selfmap[varName],varName+postfix+'/F')
             elif storageType == "D":
@@ -113,20 +113,21 @@ def reset(self):
                 if isinstance(value, ROOT.TObject) and value.ClassName() == "TClonesArray":
                     value.ExpandCreateFast(0)
 
-    def fill(self, varName, value ):
+    def fill(self, varName, value, storageType="default" ):
+        if isinstance(value,float) and storageType=="H": value=np.float16(value)
         if isinstance(self.vars[varName], numpy.ndarray):
             self.vars[varName][0]=value
         else:
             self.fillers[varName](self.vars[varName],value)
 
-    def vfill(self, varName, values ):
+    def vfill(self, varName, values, storageType="default" ):
         a = self.vecvars[varName]
         if isinstance(a, numpy.ndarray):
             for (i,v) in enumerate(values):
-                a[i]=v
+                a[i]=numpy.float16(v) if storageType=="H" else v
         else:
             if isinstance(a, ROOT.TObject) and a.ClassName() == "TClonesArray":
                 a.ExpandCreateFast(len(values))
             fillit = self.fillers[varName]
             for (i,v) in enumerate(values):
-                fillit(a[i],v)
+                fillit(a[i],numpy.float16(v) if storageType=="H" else v)