def readFromRootFile(self,filename,TupleMeanStd, weighter):
from preprocessing import MeanNormZeroPad
import numpy
from stopwatch import stopwatch
import c_meanNormZeroPad
c_meanNormZeroPad.zeroPad()
sw=stopwatch()
swall=stopwatch()
import ROOT
fileTimeOut(filename,120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples=tree.GetEntries()
print('took ', sw.getAndReset(), ' seconds for getting tree entries')
# split for convolutional network
x_global = MeanNormZeroPad(filename,TupleMeanStd,
[self.branches[0]],
[self.branchcutoffs[0]],self.nsamples)
print('took ', sw.getAndReset(), ' seconds for mean norm and zero padding (C module)')
nparray = self.readTreeFromRootToTuple(filename)
if self.remove:
notremoves=weighter.createNotRemoveIndices(nparray)
undef=nparray['isUndefined']
notremoves-=undef
print('took ', sw.getAndReset(), ' to create remove indices')
if self.weight:
weights=weighter.getJetWeights(nparray)
elif self.remove:
weights=notremoves
else:
print('neither remove nor weight')
weights=numpy.ones(self.nsamples)
truthtuple = nparray[self.truthclasses]
alltruth=self.reduceTruth(truthtuple)
if self.remove:
print('remove')
weights=weights[notremoves > 0]
x_global=x_global[notremoves > 0]
alltruth=alltruth[notremoves > 0]
newnsamp=x_global.shape[0]
print('reduced content to ', int(float(newnsamp)/float(self.nsamples)*100),'%')
self.nsamples = newnsamp
self.w=[weights]
self.x=[x_global]
self.y=[alltruth]
def getFlavourClassificationData(self,filename,TupleMeanStd, weighter):
from stopwatch import stopwatch
sw=stopwatch()
swall=stopwatch()
import ROOT
fileTimeOut(filename,120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get(self.treename)
self.nsamples=tree.GetEntries()
#print('took ', sw.getAndReset(), ' seconds for getting tree entries')
Tuple = self.readTreeFromRootToTuple(filename)
x_all = MeanNormZeroPad(filename,TupleMeanStd,self.branches,self.branchcutoffs,self.nsamples)
#print('took ', sw.getAndReset(), ' seconds for mean norm and zero padding (C module)')
notremoves=numpy.array([])
weights=numpy.array([])
if self.remove:
notremoves=weighter.createNotRemoveIndices(Tuple)
weights=notremoves
#print('took ', sw.getAndReset(), ' to create remove indices')
elif self.weight:
#print('creating weights')
weights= weighter.getJetWeights(Tuple)
else:
print('neither remove nor weight')
weights=numpy.empty(self.nsamples)
weights.fill(1.)
truthtuple = Tuple[self.truthclasses]
#print(self.truthclasses)
alltruth=self.reduceTruth(truthtuple)
#print(alltruth.shape)
if self.remove:
#print('remove')
weights=weights[notremoves > 0]
x_all=x_all[notremoves > 0]
alltruth=alltruth[notremoves > 0]
newnsamp=x_all.shape[0]
#print('reduced content to ', int(float(newnsamp)/float(self.nsamples)*100),'%')
self.nsamples = newnsamp
#print('took in total ', swall.getAndReset(),' seconds for conversion')
return weights,x_all,alltruth, notremoves
def readFromRootFile(self,filename,TupleMeanStd, weighter):
from preprocessing import MeanNormApply, MeanNormZeroPad, MeanNormZeroPadParticles
import numpy
from stopwatch import stopwatch
sw=stopwatch()
swall=stopwatch()
import ROOT
fileTimeOut(filename,120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples=tree.GetEntries()
print('took ', sw.getAndReset(), ' seconds for getting tree entries')
# split for convolutional network
x_global = MeanNormZeroPad(
filename,None,
[self.branches[0]],
[self.branchcutoffs[0]],self.nsamples
)
x_cpf = MeanNormZeroPadParticles(
filename,None,
self.branches[1],
self.branchcutoffs[1],self.nsamples
)
x_sv = MeanNormZeroPadParticles(
filename,None,
self.branches[2],
self.branchcutoffs[2],self.nsamples
)
print('took ', sw.getAndReset(), ' seconds for mean norm and zero padding (C module)')
npy_array = self.readTreeFromRootToTuple(filename)
reg_truth=npy_array['gen_pt_WithNu'].view(numpy.ndarray)
reco_pt=npy_array['jet_corr_pt'].view(numpy.ndarray)
correctionfactor=numpy.zeros(self.nsamples)
for i in range(self.nsamples):
correctionfactor[i]=reg_truth[i]/reco_pt[i]
truthtuple = npy_array[self.truthclasses]
alltruth=self.reduceTruth(truthtuple)
self.x=[x_global, x_cpf, x_sv, reco_pt]
self.y=[alltruth,correctionfactor]
self._normalize_input_(weighter, npy_array)
def readFromRootFile(self,filename,TupleMeanStd, weighter):
from preprocessing import MeanNormApply, MeanNormZeroPad, MeanNormZeroPadParticles
import numpy
from stopwatch import stopwatch
import c_meanNormZeroPad
c_meanNormZeroPad.zeroPad()
sw=stopwatch()
swall=stopwatch()
import ROOT
fileTimeOut(filename,120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples=tree.GetEntries()
print('took ', sw.getAndReset(), ' seconds for getting tree entries')
# split for convolutional network
x_global = MeanNormZeroPad(filename,TupleMeanStd,
[self.branches[0]],
[self.branchcutoffs[0]],self.nsamples)
x_cpf = MeanNormZeroPadParticles(filename,TupleMeanStd,
self.branches[1],
self.branchcutoffs[1],self.nsamples)
x_npf = MeanNormZeroPadParticles(filename,TupleMeanStd,
self.branches[2],
self.branchcutoffs[2],self.nsamples)
print('took ', sw.getAndReset(), ' seconds for mean norm and zero padding (C module)')
nparray = self.readTreeFromRootToTuple(filename)
if self.remove:
notremoves=weighter.createNotRemoveIndices(nparray)
undef=nparray['isUndefined']
hf = np_slice.any(axis=1)
notremoves -= undef
print('took ', sw.getAndReset(), ' to create remove indices')
if self.weight:
weights=weighter.getJetWeights(nparray)
elif self.remove:
weights=notremoves
else:
print('neither remove nor weight')
weights=numpy.ones(self.nsamples)
pttruth = nparray[self.regtruth]
ptreco = nparray[self.regreco]
truthtuple = nparray[self.truthclasses]
#print(self.truthclasses)
alltruth=self.reduceTruth(truthtuple)
#
# sort vectors (according to pt at the moment)
#
idxs = x_cpf[:,:,0].argsort() #0 is pt ratio
xshape = x_cpf.shape
static_idxs = numpy.indices(xshape)
idxs = idxs.reshape((xshape[0], xshape[1], 1))
idxs = numpy.repeat(idxs, xshape[2], axis=2)
x_cpf = x_cpf[static_idxs[0], idxs, static_idxs[2]]
idxs = x_npf[:,:,0].argsort() #0 is pt ratio
xshape = x_npf.shape
static_idxs = numpy.indices(xshape)
idxs = idxs.reshape((xshape[0], xshape[1], 1))
idxs = numpy.repeat(idxs, xshape[2], axis=2)
x_npf = x_npf[static_idxs[0], idxs, static_idxs[2]]
#print(alltruth.shape)
if self.remove:
print('remove')
weights=weights[notremoves > 0]
x_global=x_global[notremoves > 0]
x_cpf = x_cpf[notremoves > 0]
x_npf = x_npf[notremoves > 0]
# x_npf=x_npf[notremoves > 0]
alltruth=alltruth[notremoves > 0]
pttruth=pttruth[notremoves > 0]
ptreco=ptreco[notremoves > 0]
newnsamp=x_global.shape[0]
print('reduced content to ', int(float(newnsamp)/float(self.nsamples)*100),'%')
self.nsamples = newnsamp
self.w=[weights]
self.x=[x_global,x_cpf,x_npf,ptreco]
self.y=[alltruth,pttruth]
def readFromRootFile(self,filename,TupleMeanStd, weighter):
from preprocessing import MeanNormZeroPad, MeanNormZeroPadParticles
import numpy
from stopwatch import stopwatch
sw=stopwatch()
swall=stopwatch()
import ROOT
fileTimeOut(filename,120) #give eos two minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get(self.treename)
self.nsamples=tree.GetEntries()
Tuple = self.readTreeFromRootToTuple(filename)
###########################################################################################
############ this is where you define how to read in the branches and what to do with them
###########################################################################################
############ MeanNormZeroPad means that all branches are just put into a serial list
############ such as: jet1_pt, jet1_eta, jet2_pt, jet2_eta, ...
############ if there are not suffiecient jets, the rest of the list is filled
############ with zero (zero padding)
############ In addition, the variables are transformed such that they are centred around
############ zero and the width of the distribution is about 1.
############ This is only a technica trick that makes it easier for the DNN to converge
reco_global = MeanNormZeroPad(filename,TupleMeanStd,
self.branches,
self.branchcutoffs,self.nsamples)
############ Another choice for the preprocessing that will be important for you is
############ MeanNormZeroPadParticles. It does the same rescaling as MeanNormZeroPad,
############ but organises the array as a 2D array per event. Such that e.g. each
############ jet has its own list. This can be important when e.g. using more
############ evolved neural networks than just dense layers. We will come to this later,
############ however, I put an example already here (but commented)
#reco_jetslist = MeanNormZeroPadParticles(filename,TupleMeanStd,
# self.branches[3], # the jet branches (see function above)
# self.branchcutoffs[3], # the jet branch cut-offs (maximum six) as defined above
# self.nsamples)
############ Here we read the branch that contains the truth information
truth = Tuple['gen_mttbar']
oldlength=self.nsamples
if self.remove:
notremoves=weighter.createNotRemoveIndices(Tuple)
# this has do be done for each array produced before
# don't forget!
# it selects only the entries from the array that should not be removed,
# (where the notremoves array as an entry above 0)
reco_global=reco_global[notremoves > 0]
truth=truth[notremoves > 0]
print("kept "+str(int(float(self.nsamples)/float(oldlength))*100)+"%" )
# we don't use weights for now, so we fill the weight array with 1
weights=numpy.empty(self.nsamples)
weights.fill(1.)
self.nsamples=truth.shape[0]
# any array that shoul dbe used by the DNN needs to be added here
# w: these are the weights (you don't have to change this)
# x: this is the reconstructed information to fill
# y: the true information
self.w=[weights]
self.x=[reco_global]
self.y=[truth]
def readFromRootFile(self,filename,TupleMeanStd, weighter):
from preprocessing import MeanNormApply, MeanNormZeroPad, MeanNormZeroPadParticles
import numpy
from stopwatch import stopwatch
sw=stopwatch()
swall=stopwatch()
import ROOT
fileTimeOut(filename,120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples=tree.GetEntries()
print('took ', sw.getAndReset(), ' seconds for getting tree entries')
# split for convolutional network
x_global = MeanNormZeroPad(filename,TupleMeanStd,
[self.branches[0]],
[self.branchcutoffs[0]],self.nsamples)
x_a = MeanNormZeroPadParticles(filename,TupleMeanStd,
self.branches[1],
self.branchcutoffs[1],self.nsamples)
x_b = MeanNormZeroPadParticles(filename,TupleMeanStd,
self.branches[2],
self.branchcutoffs[2],self.nsamples)
print('took ', sw.getAndReset(), ' seconds for mean norm and zero padding (C module)')
Tuple = self.readTreeFromRootToTuple(filename)
if self.remove:
notremoves=weighter.createNotRemoveIndices(Tuple)
print('took ', sw.getAndReset(), ' to create remove indices')
if self.weight:
weights=weighter.getJetWeights(Tuple)
elif self.remove:
weights=notremoves
else:
print('neither remove nor weight')
weights=numpy.empty(self.nsamples)
weights.fill(1.)
truthtuple = Tuple[self.truthclasses]
#print(self.truthclasses)
alltruth=self.reduceTruth(truthtuple)
#print(alltruth.shape)
if self.remove:
print('remove')
weights=weights[notremoves > 0]
x_global=x_global[notremoves > 0]
x_a=x_a[notremoves > 0]
x_b=x_b[notremoves > 0]
alltruth=alltruth[notremoves > 0]
newnsamp=x_global.shape[0]
print('reduced content to ', int(float(newnsamp)/float(self.nsamples)*100),'%')
self.nsamples = newnsamp
print(x_global.shape,self.nsamples)
self.w=[weights]
self.x=[x_global,x_a,x_b]
self.y=[alltruth]
def readFromRootFile(self, filename, TupleMeanStd, weighter):
#the first part is standard, no changes needed
from preprocessing import MeanNormApply, MeanNormZeroPad, MeanNormZeroPadParticles
import numpy
import ROOT
fileTimeOut(filename, 120) #give eos 2 minutes to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples = tree.GetEntries()
#the definition of what to do with the branches
# those are the global branches (jet pt etc)
# they should be just glued to each other in one vector
# and zero padded (and mean subtracted and normalised)
x_global = MeanNormZeroPad(filename, TupleMeanStd, [self.branches[0]],
[self.branchcutoffs[0]], self.nsamples)
# the second part (the pf candidates) should be treated particle wise
# an array with (njets, nparticles, nproperties) is created
x_cpf = MeanNormZeroPadParticles(filename, TupleMeanStd,
self.branches[1],
self.branchcutoffs[1], self.nsamples)
# maybe also an image of the energy density of charged particles
# should be added
x_chmap = createDensityMap(
filename,
TupleMeanStd,
'Cpfcan_erel', #use the energy to create the image
self.nsamples,
# 7 bins in eta with a total width of 2*0.9
['Cpfcan_eta', 'jet_eta', 7, 0.9],
# 7 bins in phi with a total width of 2*0.9
['Cpfcan_phi', 'jet_phi', 7, 0.9],
'nCpfcand',
# the last is an offset because the relative energy as
# can be found in the ntuples is shifted by 1
-1)
# now, some jets are removed to avoid pt and eta biases
Tuple = self.readTreeFromRootToTuple(filename)
if self.remove:
# jets are removed until the shapes in eta and pt are the same as
# the truth class 'isQCD'
notremoves = weighter.createNotRemoveIndices(Tuple)
undef = Tuple[self.undefTruth]
notremoves -= undef
if self.weight:
weights = weighter.getJetWeights(Tuple)
elif self.remove:
weights = notremoves
else:
print('neither remove nor weight')
weights = numpy.empty(self.nsamples)
weights.fill(1.)
# create all collections:
truthtuple = Tuple[self.truthclasses]
alltruth = self.reduceTruth(truthtuple)
# remove the entries to get same jet shapes
if self.remove:
print('remove')
weights = weights[notremoves > 0]
x_global = x_global[notremoves > 0]
x_cpf = x_cpf[notremoves > 0]
x_chmap = x_chmap[notremoves > 0]
alltruth = alltruth[notremoves > 0]
newnsamp = x_global.shape[0]
print('reduced content to ',
int(float(newnsamp) / float(self.nsamples) * 100), '%')
self.nsamples = newnsamp
# fill everything
self.w = [weights]
self.x = [x_global, x_cpf, x_chmap]
self.y = [alltruth]
def readFromRootFile(self, filename, TupleMeanStd, weighter):
from preprocessing import MeanNormApply, MeanNormZeroPad, createDensityMap, createCountMap, MeanNormZeroPadParticles
import numpy
from stopwatch import stopwatch
sw = stopwatch()
swall = stopwatch()
import ROOT
fileTimeOut(filename, 120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples = tree.GetEntries()
print('took ', sw.getAndReset(), ' seconds for getting tree entries')
# split for convolutional network
x_global = MeanNormZeroPad(filename, TupleMeanStd, [self.branches[0]],
[self.branchcutoffs[0]], self.nsamples)
#here the difference starts
x_chmap = createDensityMap(filename,
TupleMeanStd,
'Cpfcan_ptrel',
self.nsamples,
['Cpfcan_eta', 'jet_eta', 20, 0.5],
['Cpfcan_phi', 'jet_phi', 20, 0.5],
'nCpfcand',
-1,
weightbranch='Cpfcan_puppiw')
x_chcount = createCountMap(filename, TupleMeanStd, self.nsamples,
['Cpfcan_eta', 'jet_eta', 20, 0.5],
['Cpfcan_phi', 'jet_phi', 20, 0.5],
'nCpfcand')
x_neumap = createDensityMap(filename,
TupleMeanStd,
'Npfcan_ptrel',
self.nsamples,
['Npfcan_eta', 'jet_eta', 20, 0.5],
['Npfcan_phi', 'jet_phi', 20, 0.5],
'nNpfcand',
-1,
weightbranch='Npfcan_puppiw')
x_neucount = createCountMap(filename, TupleMeanStd, self.nsamples,
['Npfcan_eta', 'jet_eta', 20, 0.5],
['Npfcan_phi', 'jet_phi', 20, 0.5],
'nNpfcand')
print('took ', sw.getAndReset(),
' seconds for mean norm and zero padding (C module)')
Tuple = self.readTreeFromRootToTuple(filename)
if self.remove:
notremoves = weighter.createNotRemoveIndices(Tuple)
undef = Tuple['isUndefined']
notremoves -= undef
print('took ', sw.getAndReset(), ' to create remove indices')
if self.weight:
weights = weighter.getJetWeights(Tuple)
elif self.remove:
weights = notremoves
else:
print('neither remove nor weight')
weights = numpy.ones(self.nsamples)
pttruth = Tuple[self.regtruth]
ptreco = Tuple[self.regreco]
truthtuple = Tuple[self.truthclasses]
#print(self.truthclasses)
alltruth = self.reduceTruth(truthtuple)
x_map = numpy.concatenate((x_chmap, x_chcount, x_neumap, x_neucount),
axis=3)
#print(alltruth.shape)
if self.remove:
print('remove')
weights = weights[notremoves > 0]
x_global = x_global[notremoves > 0]
x_map = x_map[notremoves > 0]
alltruth = alltruth[notremoves > 0]
pttruth = pttruth[notremoves > 0]
ptreco = ptreco[notremoves > 0]
newnsamp = x_global.shape[0]
print('reduced content to ',
int(float(newnsamp) / float(self.nsamples) * 100), '%')
self.nsamples = newnsamp
print(x_global.shape, self.nsamples)
self.w = [weights]
self.x = [x_global, x_map, ptreco]
self.y = [alltruth, pttruth]
def readFromRootFile(self, filename, TupleMeanStd, weighter):
from preprocessing import MeanNormApply, createCountMap, createDensity, MeanNormZeroPad, createDensityMap, MeanNormZeroPadParticles
import numpy
from stopwatch import stopwatch
sw = stopwatch()
swall = stopwatch()
import ROOT
fileTimeOut(filename, 120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples = tree.GetEntries()
print('took ', sw.getAndReset(), ' seconds for getting tree entries')
# split for convolutional network
x_global = MeanNormZeroPad(filename, TupleMeanStd, [self.branches[0]],
[self.branchcutoffs[0]], self.nsamples)
x_cpf = MeanNormZeroPadParticles(filename, TupleMeanStd,
self.branches[1],
self.branchcutoffs[1], self.nsamples)
x_npf = MeanNormZeroPadParticles(filename, TupleMeanStd,
self.branches[2],
self.branchcutoffs[2], self.nsamples)
x_sv = MeanNormZeroPadParticles(filename, TupleMeanStd,
self.branches[3],
self.branchcutoffs[3], self.nsamples)
#here the difference starts
nbins = 8
x_chmap = createDensity(
filename,
inbranches=['Cpfcan_ptrel', 'Cpfcan_etarel', 'Cpfcan_phirel'],
modes=['sum', 'average', 'average'],
nevents=self.nsamples,
dimension1=['Cpfcan_eta', 'jet_eta', nbins, 0.45],
dimension2=['Cpfcan_phi', 'jet_phi', nbins, 0.45],
counterbranch='nCpfcand',
offsets=[-1, -0.5, -0.5])
x_neumap = createDensity(
filename,
inbranches=['Npfcan_ptrel', 'Npfcan_etarel', 'Npfcan_phirel'],
modes=['sum', 'average', 'average'],
nevents=self.nsamples,
dimension1=['Npfcan_eta', 'jet_eta', nbins, 0.45],
dimension2=['Npfcan_phi', 'jet_phi', nbins, 0.45],
counterbranch='nCpfcand',
offsets=[-1, -0.5, -0.5])
x_chcount = createCountMap(filename, TupleMeanStd, self.nsamples,
['Cpfcan_eta', 'jet_eta', nbins, 0.45],
['Cpfcan_phi', 'jet_phi', nbins, 0.45],
'nCpfcand')
x_neucount = createCountMap(filename, TupleMeanStd, self.nsamples,
['Npfcan_eta', 'jet_eta', nbins, 0.45],
['Npfcan_phi', 'jet_phi', nbins, 0.45],
'nNpfcand')
print('took ', sw.getAndReset(),
' seconds for mean norm and zero padding (C module)')
Tuple = self.readTreeFromRootToTuple(filename)
if self.remove:
notremoves = weighter.createNotRemoveIndices(Tuple)
undef = Tuple['isUndefined']
notremoves -= undef
print('took ', sw.getAndReset(), ' to create remove indices')
if self.weight:
weights = weighter.getJetWeights(Tuple)
elif self.remove:
weights = notremoves
else:
print('neither remove nor weight')
weights = numpy.empty(self.nsamples)
weights.fill(1.)
truthtuple = Tuple[self.truthclasses]
#print(self.truthclasses)
alltruth = self.reduceTruth(truthtuple)
regtruth = Tuple['gen_pt_WithNu']
regreco = Tuple['jet_corr_pt']
#print(alltruth.shape)
if self.remove:
print('remove')
weights = weights[notremoves > 0]
x_global = x_global[notremoves > 0]
x_cpf = x_cpf[notremoves > 0]
x_npf = x_npf[notremoves > 0]
x_sv = x_sv[notremoves > 0]
x_chmap = x_chmap[notremoves > 0]
x_neumap = x_neumap[notremoves > 0]
x_chcount = x_chcount[notremoves > 0]
x_neucount = x_neucount[notremoves > 0]
alltruth = alltruth[notremoves > 0]
regreco = regreco[notremoves > 0]
regtruth = regtruth[notremoves > 0]
newnsamp = x_global.shape[0]
print('reduced content to ',
int(float(newnsamp) / float(self.nsamples) * 100), '%')
self.nsamples = newnsamp
x_map = numpy.concatenate((x_chmap, x_neumap, x_chcount, x_neucount),
axis=3)
self.w = [weights, weights]
self.x = [x_global, x_cpf, x_npf, x_sv, x_map, regreco]
self.y = [alltruth, regtruth]
def readFromRootFile(self, filename, TupleMeanStd, weighter):
from preprocessing import MeanNormApply, MeanNormZeroPad, MeanNormZeroPadParticles
import numpy
from stopwatch import stopwatch
sw = stopwatch()
swall = stopwatch()
import ROOT
fileTimeOut(filename, 120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples = tree.GetEntries()
print('took ', sw.getAndReset(), ' seconds for getting tree entries')
# split for convolutional network
x_global = MeanNormZeroPad(filename, TupleMeanStd, [self.branches[0]],
[self.branchcutoffs[0]], self.nsamples)
x_cpf = MeanNormZeroPadParticles(filename, TupleMeanStd,
self.branches[1],
self.branchcutoffs[1], self.nsamples)
x_npf = MeanNormZeroPadParticles(filename, TupleMeanStd,
self.branches[2],
self.branchcutoffs[2], self.nsamples)
x_sv = MeanNormZeroPadParticles(filename, TupleMeanStd,
self.branches[3],
self.branchcutoffs[3], self.nsamples)
x_reg = MeanNormZeroPad(filename, TupleMeanStd, [self.branches[4]],
[self.branchcutoffs[4]], self.nsamples)
print('took ', sw.getAndReset(),
' seconds for mean norm and zero padding (C module)')
Tuple = self.readTreeFromRootToTuple(filename)
reg_truth = Tuple['gen_pt_WithNu'].view(numpy.ndarray)
reco_pt = Tuple['jet_corr_pt'].view(numpy.ndarray)
correctionfactor = numpy.zeros(self.nsamples)
for i in range(self.nsamples):
correctionfactor[i] = reg_truth[i] / reco_pt[i]
if self.remove:
notremoves = weighter.createNotRemoveIndices(Tuple)
undef = Tuple['isUndefined']
notremoves -= undef
print('took ', sw.getAndReset(), ' to create remove indices')
if self.weight:
weights = weighter.getJetWeights(Tuple)
elif self.remove:
weights = notremoves
else:
print('neither remove nor weight')
weights = numpy.empty(self.nsamples)
weights.fill(1.)
truthtuple = Tuple[self.truthclasses]
#print(self.truthclasses)
alltruth = self.reduceTruth(truthtuple)
#print(alltruth.shape)
if self.remove:
print('remove')
weights = weights[notremoves > 0]
x_global = x_global[notremoves > 0]
x_cpf = x_cpf[notremoves > 0]
x_npf = x_npf[notremoves > 0]
x_sv = x_sv[notremoves > 0]
alltruth = alltruth[notremoves > 0]
x_reg = x_reg[notremoves > 0]
correctionfactor = correctionfactor[notremoves > 0]
newnsamp = x_global.shape[0]
print('reduced content to ',
int(float(newnsamp) / float(self.nsamples) * 100), '%')
self.nsamples = newnsamp
print(x_global.shape, self.nsamples)
self.w = [weights, weights]
self.x = [x_global, x_cpf, x_npf, x_sv, x_reg]
self.y = [alltruth, correctionfactor]
def readFromRootFile(self,filename,TupleMeanStd, weighter):
from preprocessing import MeanNormApply, MeanNormZeroPad, MeanNormZeroPadParticles, MeanNormZeroPadBinned
import numpy
from stopwatch import stopwatch
sw=stopwatch()
swall=stopwatch()
import ROOT
fileTimeOut(filename,120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples=tree.GetEntries()
#self.nsamples = 10 #TESTING
print('took ', sw.getAndReset(), ' seconds for getting tree entries')
# split for convolutional network
x_global = MeanNormZeroPad(filename,TupleMeanStd,
[self.branches[0]],
[self.branchcutoffs[0]],self.nsamples)
# needed
# (dimension #1, center #1, nbins 1, half width 1)
# (dimension #2, center #2, nbins 2, half width 2)
# sum o stack -- max to stack/zero pad
x_cpf, sum_cpf = MeanNormZeroPadBinned(
filename, 'nCpfcand', self.nsamples,
('Cpfcan_eta', 'jet_eta', self.nbins, self.jet_radius), #X axis
('Cpfcan_phi', 'jet_phi', self.nbins, self.jet_radius), #Y axis
(TupleMeanStd, self.branches[1], self.branchcutoffs[1]), #means/std, branches to use, #per-bin # of particles to be kept
(self.sums_scaling['charged'], self.binned_sums['charged']), #variables to be summed (no zero padding yet)
)
x_npf, sum_npf = MeanNormZeroPadBinned(
filename, 'nNpfcand', self.nsamples,
('Npfcan_eta', 'jet_eta', self.nbins, self.jet_radius),
('Npfcan_phi', 'jet_phi', self.nbins, self.jet_radius),
(TupleMeanStd, self.branches[2], self.branchcutoffs[2]),
(self.sums_scaling['neutral'], self.binned_sums['neutral']),
)
x_sv, sum_sv = MeanNormZeroPadBinned(
filename, 'nsv', self.nsamples,
('sv_eta', 'jet_eta', self.nbins, self.jet_radius),
('sv_phi', 'jet_phi', self.nbins, self.jet_radius),
(TupleMeanStd, self.branches[3], self.branchcutoffs[3]),
(self.sums_scaling['svs'], self.binned_sums['svs']),
)
#merging sum variables together
x_sum = numpy.concatenate((sum_cpf, sum_npf, sum_sv), axis=3)
print('took ', sw.getAndReset(), ' seconds for mean norm and zero padding (C module)')
Tuple = self.readTreeFromRootToTuple(filename)
if self.remove:
notremoves=weighter.createNotRemoveIndices(Tuple)
undef=Tuple['isUndefined']
notremoves-=undef
print('took ', sw.getAndReset(), ' to create remove indices')
if self.weight:
weights=weighter.getJetWeights(Tuple)
elif self.remove:
weights=notremoves
else:
print('neither remove nor weight')
weights=numpy.empty(self.nsamples)
weights.fill(1.)
truthtuple = Tuple[self.truthclasses]
#print(self.truthclasses)
alltruth=self.reduceTruth(truthtuple)
pt_truth = Tuple[self.regtruth]
#print(alltruth.shape)
if self.remove:
print('remove')
weights = weights[notremoves > 0]
x_global = x_global[notremoves > 0]
x_cpf = x_cpf[notremoves > 0]
x_npf = x_npf[notremoves > 0]
x_sv = x_sv[notremoves > 0]
x_sum = x_sum[notremoves > 0]
alltruth = alltruth[notremoves > 0]
pt_truth = pt_truth[notremoves > 0]
newnsamp=x_global.shape[0]
print('reduced content to ', int(float(newnsamp)/float(self.nsamples)*100),'%')
self.nsamples = newnsamp
self.w = [weights]
self.x = [x_global, x_cpf, x_npf, x_sv, x_sum]
self.y = [alltruth, pt_truth]
def readFromRootFile(self,filename,TupleMeanStd, weighter):
from preprocessing import MeanNormApply, MeanNormZeroPad, MeanNormZeroPadParticles
import numpy
from stopwatch import stopwatch
sw=stopwatch()
swall=stopwatch()
import ROOT
fileTimeOut(filename,120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get("deepntuplizer/tree")
self.nsamples=tree.GetEntries()
print('took ', sw.getAndReset(), ' seconds for getting tree entries')
# split for convolutional network
x_global = MeanNormZeroPad(filename,TupleMeanStd,
[self.branches[0]],
[self.branchcutoffs[0]],self.nsamples)
x_cpf = MeanNormZeroPadParticles(filename,TupleMeanStd,
self.branches[1],
self.branchcutoffs[1],self.nsamples)
x_npf = MeanNormZeroPadParticles(filename,TupleMeanStd,
self.branches[2],
self.branchcutoffs[2],self.nsamples)
x_sv = MeanNormZeroPadParticles(filename,TupleMeanStd,
self.branches[3],
self.branchcutoffs[3],self.nsamples)
print('took ', sw.getAndReset(), ' seconds for mean norm and zero padding (C module)')
Tuple = self.readTreeFromRootToTuple(filename)
if self.remove:
notremoves=weighter.createNotRemoveIndices(Tuple)
undef=Tuple['isUndefined']
notremoves-=undef
print('took ', sw.getAndReset(), ' to create remove indices')
if self.weight:
weights=weighter.getJetWeights(Tuple)
elif self.remove:
weights=notremoves
else:
print('neither remove nor weight')
weights=numpy.empty(self.nsamples)
weights.fill(1.)
truthtuple = Tuple[self.truthclasses]
#print(self.truthclasses)
alltruth=self.reduceTruth(truthtuple)
mask = Tuple[['nCpfcand','nNpfcand','nsv']]
maskListNpf = []
maskListCpf = []
maskListSv = []
for i in range(0,Tuple.shape[0]):
nMax = int(mask[i][0])
if(nMax>25): nMax=25
list0 = [[1.]*nMax+[0.]*(25-nMax)]*8
nMax = int(mask[i][1])
if(nMax>25): nMax=25
maskListNpf.append(list0)
nMax = int(mask[i][1])
if(nMax>25): nMax=25
list1 = [[1.]*nMax+[0.]*(25-nMax)]*4
maskListCpf.append(list1)
nMax = int(mask[i][2])
if(nMax>4): nMax=4
list2 = [[1.]*nMax+[0.]*(4-nMax)]*8
maskListSv.append(list2)
maskListNpf = numpy.asarray(maskListNpf,dtype=float)
maskListCpf = numpy.asarray(maskListCpf,dtype=float)
maskListSv = numpy.asarray(maskListSv,dtype=float)
print ('zero shapes ', maskListNpf.shape, ' ' ,maskListCpf.shape , ' ' ,maskListSv.shape )
#print(alltruth.shape)
if self.remove:
print('remove')
weights=weights[notremoves > 0]
x_global=x_global[notremoves > 0]
x_cpf=x_cpf[notremoves > 0]
x_npf=x_npf[notremoves > 0]
x_sv=x_sv[notremoves > 0]
maskListNpf = maskListNpf[notremoves > 0]
maskListCpf = maskListCpf[notremoves > 0]
maskListSv = maskListSv[notremoves > 0]
alltruth=alltruth[notremoves > 0]
newnsamp=x_global.shape[0]
print('reduced content to ', int(float(newnsamp)/float(self.nsamples)*100),'%')
self.nsamples = newnsamp
print(x_global.shape,self.nsamples)
self.w=[weights]
print (' types ', type (x_cpf) , type (maskListNpf), ' ' ,type(maskListCpf) , ' ' , type(maskListSv) )
self.x=[x_global,x_cpf,x_npf,x_sv,maskListNpf,maskListCpf,maskListSv]
self.y=[alltruth]
