def get_histograms_from_trees(
trees = [],
branch = 'var',
weightBranch = 'EventWeight',
selection = '1',
files = {},
verbose = False,
nBins = 40,
xMin = 0,
xMax = 100,
ignoreUnderflow = True,
):
histograms = {}
nHistograms = 0
# Setup selection and weight string for ttree draw
weightAndSelection = '( %s ) * ( %s )' % ( weightBranch, selection )
for sample, input_file in files.iteritems():
histograms[sample] = {}
for tree in trees:
tempTree = tree
if 'data' in sample and ( 'Up' in tempTree or 'Down' in tempTree ) :
tempTree = tempTree.replace('_'+tempTree.split('_')[-1],'')
chain = None;
if isinstance( input_file, list ):
for f in input_file:
chain.Add(f)
else:
chain = TreeChain(tempTree, [input_file]);
weightAndSelection = '( %s ) * ( %s )' % ( weightBranch, selection )
root_histogram = Hist( nBins, xMin, xMax, type='D')
chain.Draw(branch, weightAndSelection, hist = root_histogram)
if not is_valid_histogram( root_histogram, tree, input_file):
return
# When a tree is filled with a dummy variable, it will end up in the underflow, so ignore it
if ignoreUnderflow:
root_histogram.SetBinContent(0, 0)
root_histogram.SetBinError(0,0)
gcd()
nHistograms += 1
histograms[sample][tree] = root_histogram.Clone()
return histograms
tree = Tree("test")
tree.create_branches(branches)
for i in range(10000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
tree.write()
# Make a histogram of the second tree
hist2 = Hist(100, -10, 10, name='hist2')
tree.Draw('x', 'y > 1', hist=hist2)
hist2.SetDirectory(0) # memory resident
print("The second tree has {0:f} entries where y > 1".format(hist2.Integral()))
f.close()
combined_hist = hist1 + hist2
print("Building TreeChain")
chain = TreeChain('test', ['chaintest2.root', 'chaintest1.root'])
# Make the equivalent of the combined_hist
combined_hist_chain = Hist(100, -10, 10, name='combined')
chain.Draw('x', 'y > 1', hist=combined_hist_chain)
residual = combined_hist_chain - combined_hist
print("The combined histogram (separately) minus "
"the combined from the chain has {0:f} entries".format(
residual.Integral()))
print "Creating test tree in chaintest2.root"
f = open("chaintest2.root", "recreate")
tree = Tree("test")
tree.create_branches([('x', 'F'), ('y', 'F'), ('z', 'F'), ('i', 'I')])
for i in xrange(10000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
tree.write()
# Make a histogram of the second tree
hist2 = tree.Draw('x', 'y > 1', min=-10, max=10, bins=100).Clone()
hist2.SetName('hist2')
hist2.SetDirectory(0) # memory resident
print "The second tree has %f entries where y > 1" % hist2.Integral()
f.close()
combined_hist = hist1 + hist2
print "Building TreeChain"
chain = TreeChain('test', ['chaintest2.root', 'chaintest1.root'])
# Make the equivalent of the combined_hist
combined_hist_chain = chain.Draw('x', 'y > 1', min=-10, max=10, bins=100)
residual = combined_hist_chain - combined_hist
print "The combined histogram (separately) minus the combined from the chain "\
"has %f entries" % residual.Integral()
'SingleTop_tree.root', 'TTJets_PowhegPythia8_tree.root', 'VJets_tree.root',
'QCD_Muon_tree.root', 'QCD_Electron_tree.root'
]
mc_files = glob.glob(base + 'TTJets_PowhegPythia8_tree.root')
data_files_el = glob.glob(base + 'data_electron_tree.root')
data_files_mu = glob.glob(base + 'data_muon_tree.root')
pu_el_mc_reco = Hist(60, 0.5, 60.5, name='pu_el_mc_reco')
pu_mu_mc_reco = Hist(60, 0.5, 60.5, name='pu_mu_mc_reco')
pu_el_reco = Hist(60, 0.5, 60.5, name='pu_el_reco')
pu_mu_reco = Hist(60, 0.5, 60.5, name='pu_mu_reco')
el_mc_chain = TreeChain(
"TTbar_plus_X_analysis/EPlusJets/Ref selection/Pileup/Pileup", mc_files)
el_mc_chain.Draw('NVertices',
'EventWeight * LeptonEfficiencyCorrection',
hist=pu_el_mc_reco)
mu_mc_chain = TreeChain(
"TTbar_plus_X_analysis/MuPlusJets/Ref selection/Pileup/Pileup", mc_files)
mu_mc_chain.Draw('NVertices',
'EventWeight * LeptonEfficiencyCorrection',
hist=pu_mu_mc_reco)
el_chain = TreeChain(
"TTbar_plus_X_analysis/EPlusJets/Ref selection/Pileup/Pileup",
data_files_el)
el_chain.Draw('NVertices', hist=pu_el_reco)
mu_chain = TreeChain(
"TTbar_plus_X_analysis/MuPlusJets/Ref selection/Pileup/Pileup",
data_files_mu)
mu_chain.Draw('NVertices', hist=pu_mu_reco)
