def createWeighterObjects(self, allsourcefiles):
#
# Calculates the weights needed for flattening the pt/eta spectrum
from DeepJetCore.Weighter import Weighter
weighter = Weighter()
weighter.undefTruth = self.undefTruth
weighter.class_weights = self.class_weights
branches = [self.weightbranchX, self.weightbranchY]
branches.extend(self.truth_branches)
if self.remove:
weighter.setBinningAndClasses([self.weight_binX, self.weight_binY],
self.weightbranchX,
self.weightbranchY,
self.truth_branches,
self.red_classes,
self.truth_red_fusion,
method=self.referenceclass)
counter = 0
import ROOT
from root_numpy import tree2array, root2array
if self.remove:
for fname in allsourcefiles:
fileTimeOut(fname, 120)
nparray = uproot_root2array(fname,
treename="ttree",
stop=None,
branches=branches)
norm_hist = True
if self.referenceclass == 'flatten':
norm_hist = False
# from IPython import embed;embed()
weighter.addDistributions(nparray, norm_h=norm_hist)
#del nparray
counter = counter + 1
weighter.createRemoveProbabilitiesAndWeights(self.referenceclass)
print("calculate means")
print("debugging this point here!")
from DeepJetCore.preprocessing import meanNormProd
nparray = self.readTreeFromRootToTuple(
allsourcefiles,
branches=self.vtx_branches + self.eta_rel_branches +
self.track_branches + self.global_branches,
limit=500000)
print("read tree from sourcefiles")
for a in (self.vtx_branches + self.eta_rel_branches +
self.track_branches + self.global_branches):
for b in range(len(nparray[a])):
nparray[a][b] = np.where(
np.logical_and(np.isfinite(nparray[a][b]),
np.abs(nparray[a][b]) < 100000.0),
nparray[a][b], 0)
means = np.array([], dtype='float32')
if len(nparray):
means = meanNormProd(nparray)
print("weigheter created")
return {'weigther': weighter, 'means': means}
def createWeighterObjects(self, allsourcefiles):
#
# Calculates the weights needed for flattening the pt/eta spectrum
from DeepJetCore.Weighter import Weighter
weighter = Weighter()
weighter.undefTruth = self.undefTruth
branches = [self.weightbranchX, self.weightbranchY]
branches.extend(self.truth_branches)
if self.remove:
weighter.setBinningAndClasses([self.weight_binX, self.weight_binY],
self.weightbranchX,
self.weightbranchY,
self.truth_branches)
counter = 0
import ROOT
from root_numpy import tree2array, root2array
if self.remove:
for fname in allsourcefiles:
fileTimeOut(fname, 120)
nparray = root2array(fname,
treename="deepntuplizer/tree",
stop=None,
branches=branches)
weighter.addDistributions(nparray)
#del nparray
counter = counter + 1
weighter.createRemoveProbabilitiesAndWeights(self.referenceclass)
return {'weigther': weighter}
def createWeighterObjects(self, allsourcefiles):
# Calculates the weights needed for flattening the pt/eta spectrum
# by initializing a Weighter instance
from DeepJetCore.Weighter import Weighter
weighter = Weighter()
weighter.undefTruth = self.undefTruth
branches = [self.weightbranchX, self.weightbranchY]
branches.extend(self.truth_branches)
# if one uses the Weighter
if self.remove:
weighter.setBinningAndClasses([self.weight_binX, self.weight_binY],
self.weightbranchX,
self.weightbranchY,
self.truth_branches,
method=self.referenceclass)
counter = 0
import ROOT
from root_numpy import tree2array, root2array
if self.remove:
for fname in allsourcefiles:
fileTimeOut(fname, 120)
nparray = root2array(fname,
treename="tree",
stop=None,
branches=branches)
norm_hist = True
if self.referenceclass == 'flatten':
norm_hist = False
weighter.addDistributions(nparray, norm_h=norm_hist)
counter = counter + 1
weighter.createRemoveProbabilitiesAndWeights(self.referenceclass)
return {'weigther': weighter}
def createWeighterObjects(self, allsourcefiles):
#
# Calculates the weights needed for flattening the pt/eta spectrum
from DeepJetCore.Weighter import Weighter
weighter = Weighter()
weighter.undefTruth = self.undefTruth
branches = [self.weightbranchX, self.weightbranchY]
branches.extend(self.truth_branches)
if self.remove:
weighter.setBinningAndClasses([self.weight_binX, self.weight_binY],
self.weightbranchX,
self.weightbranchY,
self.truth_branches)
counter = 0
import ROOT
from root_numpy import tree2array, root2array
if self.remove:
for fname in allsourcefiles:
fileTimeOut(fname, 120)
nparray = root2array(fname,
treename="deepntuplizer/tree",
stop=None,
branches=branches)
weighter.addDistributions(nparray)
#del nparray
counter = counter + 1
weighter.createRemoveProbabilitiesAndWeights(self.referenceclass)
print("calculate means")
from DeepJetCore.preprocessing import meanNormProd
nparray = self.readTreeFromRootToTuple(
allsourcefiles,
branches=self.vtx_branches + self.eta_rel_branches +
self.track_branches + self.global_branches,
limit=500000)
for a in (self.vtx_branches + self.eta_rel_branches +
self.track_branches + self.global_branches):
for b in range(len(nparray[a])):
nparray[a][b] = np.where(nparray[a][b] < 100000.0,
nparray[a][b], 0)
means = np.array([], dtype='float32')
if len(nparray):
means = meanNormProd(nparray)
return {'weigther': weighter, 'means': means}
def createWeighterObjects(self, allsourcefiles):
#
# Calculates the weights needed for flattening the pt/eta spectrum
from DeepJetCore.Weighter import Weighter
weighter = Weighter()
weighter.undefTruth = self.undefTruth
weighter.class_weights = self.class_weights
branches = [self.weightbranchX, self.weightbranchY]
branches.extend(self.truth_branches)
if self.remove:
weighter.setBinningAndClasses([self.weight_binX, self.weight_binY],
self.weightbranchX,
self.weightbranchY,
self.truth_branches,
self.red_classes,
self.truth_red_fusion,
method=self.referenceclass)
counter = 0
import ROOT
from root_numpy import tree2array, root2array
if self.remove:
for fname in allsourcefiles:
fileTimeOut(fname, 120)
nparray = root2array(fname,
treename="deepntuplizer/tree",
stop=None,
branches=branches)
norm_hist = True
if self.referenceclass == 'flatten':
norm_hist = False
weighter.addDistributions(nparray, norm_h=norm_hist)
#del nparray
counter = counter + 1
weighter.createRemoveProbabilitiesAndWeights(self.referenceclass)
#weighter.printHistos('/afs/cern.ch/user/a/ademoor/Flatten/') #If you need to print the 2D histo, choose your output dir
return {'weigther': weighter}