def readFromRootFile(self, filename, TupleMeanStd, weighter):
# this function defines how to convert the root ntuple to the training format
# options are not yet described here
import ROOT
fileTimeOut(filename, 120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get(self.treename)
self.nsamples = tree.GetEntries()
# user code, example works with the example 2D images in root format generated by make_example_data
from DeepJetCore.preprocessing import readListArray
feature_array, n_rechits_cut = readListArray(
filename,
self.treename,
self.feat_branch,
self.nsamples,
list_size=self.max_rechits,
n_feat_per_element=self.n_features,
zeropad=True,
list_size_cut=True)
energy_only = feature_array[:, :, 0:1] #keep dimension
fraction_array, _ = readListArray(
filename,
self.treename,
self.truth_branch,
self.nsamples,
list_size=self.max_rechits,
n_feat_per_element=self.
n_simcluster, #nsimcluster, right now just one, but zero-padded here
zeropad=True,
list_size_cut=True)
print('TrainData_hitlistX: ', filename,
';convert from root: fraction of hits cut ',
100. * float(n_rechits_cut) / float(self.nsamples), '%')
#needs the energy, too to determine weights
fraction_array = numpy.concatenate([fraction_array, energy_only],
axis=-1)
#in case something was removed here
if n_rechits_cut > 0:
feature_array = feature_array[0:self.nsamples - n_rechits_cut]
fraction_array = fraction_array[0:self.nsamples - n_rechits_cut]
self.nsamples = len(feature_array)
self.x = [feature_array]
self.y = [
fraction_array
] # we need the features also in the truth part for weighting
self.w = [] # no event weights
def readFromRootFile(self,filename,TupleMeanStd, weighter):
# this function defines how to convert the root ntuple to the training format
# options are not yet described here
import ROOT
fileTimeOut(filename,120) #give eos a minute to recover
rfile = ROOT.TFile(filename)
tree = rfile.Get(self.treename)
self.nsamples=tree.GetEntries()
max_rechits = 40000
# user code, example works with the example 2D images in root format generated by make_example_data
from DeepJetCore.preprocessing import readListArray
feature_array = readListArray(filename,
self.treename,
"rechit_features",
self.nsamples,
list_size=max_rechits,
n_feat_per_element=7,
zeropad=True)
energy_only = feature_array[:,:,0:1]#keep dimension
fraction_array = readListArray(filename,
self.treename,
"simcluster_fractions",
self.nsamples,
list_size=max_rechits,
n_feat_per_element=7,#nsimcluster, right now just one, but zero-padded here
zeropad=True)
#needs the energy, too to determine weights
fraction_array = numpy.concatenate([fraction_array,energy_only],axis=-1)
#in case something was removed here
self.nsamples=len(feature_array)
self.x=[feature_array]
self.y=[fraction_array] # we need the features also in the truth part for weighting
self.w=[] # no event weights
if plotRechits:
add_to_out = "rh_"
if 'root' == infile[-4:]:
rfile = ROOT.TFile(infile)
tree = rfile.Get("Delphes")
nentries = tree.GetEntries()
if plotRechits:
print('reading features')
features, _ = readListArray(filename=infile,
treename="Delphes",
branchname="rechit_features",
nevents=nentries,
list_size=3500,
n_feat_per_element=10,
zeropad=True,
list_size_cut=True)
print('reading truth')
truth, _ = readListArray(filename=infile,
treename="Delphes",
branchname="rechit_simcluster_fractions",
nevents=nentries,
list_size=3500,
n_feat_per_element=20,
zeropad=True,
list_size_cut=True)