'& abs(antiMuon_genEta) < 2.4 ' \
'& muon_recoMatches == 1' \
'& antiMuon_recoMatches == 1' \
'& decayMode == 13'
# specify which branches to load
branches = [
'nPU', 'muon_genEta', 'antiMuon_genEta', 'muon_genPhi', 'antiMuon_genPhi',
'muon_dxy', 'antiMuon_dxy', 'muon_dz', 'antiMuon_dz', 'muon_tkIso',
'antiMuon_tkIso', 'muon_pfIso', 'antiMuon_pfIso', 'muon_isFromPV',
'antiMuon_isFromPV'
]
print(">>> Load Events in gen acceptance")
df = [
tree_to_df(
root2array(i, treeName[0], selection=selection, branches=branches), 5)
for i in inputs
]
df = pd.concat(df)
# print(">>> add new columns")
# df['delRLL'] = np.sqrt(
# (df['muon_genEta'] - df['antiMuon_genEta']) ** 2 + (df['muon_genPhi'] - df['antiMuon_genPhi']) ** 2)
#
#
# df = df.query('delRLL > 0.4')
# dfFromPV = df.query('muon_isFromPV == 1 & antiMuon_isFromPV == 1')
# dfNotFromPV = df.query('muon_isFromPV == 0 & antiMuon_isFromPV == 0')
#
# plot_scatter(dfFromPV['muon_dxy'], dfFromPV['antiMuon_dxy'], '$\mu^{-} d_{xy}$', '$\mu^{+} d_{xy}$',
branches_Reco = [
'nPV', 'muon_Category', 'antiMuon_Category', 'muon_recoEta',
'antiMuon_recoEta', 'muon_recoPhi', 'antiMuon_recoPhi', 'muon_recoPt',
'antiMuon_recoPt'
]
branches_Gen = [
'nPV', 'muon_genEta', 'antiMuon_genEta', 'muon_genPhi', 'antiMuon_genPhi',
'muon_genPt', 'antiMuon_genPt'
]
print(">>> Load Events in reco acceptance")
dfReco = [
tree_to_df(
root2array(i,
treeName[0],
selection=selection_Reco,
branches=branches_Reco), 5) for i in inputs
]
dfReco = pd.concat(dfReco)
dfReco = dfReco.rename(
columns={
"muon_recoEta": "LepEta",
"muon_recoPhi": "LepPhi",
"muon_recoPt": "LepPt",
"antiMuon_recoEta": "AntiLepEta",
"antiMuon_recoPhi": "AntiLepPhi",
"antiMuon_recoPt": "AntiLepPt"
})
print(">>> Load Events in gen acceptance")
os.mkdir(output)
if isinstance(inputs, (list, )):
treeName = list_trees(inputs[0])
else:
treeName = list_trees(inputs)
inputs = [
inputs,
]
if (len(treeName) > 1):
print("more then one tree in file ... specify, which tree to use")
exit()
dfPV = [
tree_to_df(root2array(i, treeName[0], branches=['nPV']), 5) for i in inputs
]
dfPV = pd.concat(dfPV)
hPV = ROOT.TH1D("hPV", "th1d number of primary vertices shape", 75, -0.5, 74.5)
for nPV in dfPV['nPV']:
hPV.Fill(nPV)
hPV.Scale(1. / hPV.Integral())
# acceptance selection
selection = 'muon_genRecoMatches == 1 ' \
'& antiMuon_genRecoMatches == 1 ' \
# '& (decayMode == 13 | decayMode == 151313)'
for era, input in inputs.iteritems():
print("##### era {0}".format(era))
treeName = rn.list_trees(input[0])[0]
print(">>> Load Events from {0} files".format(len(input)))
_df = []
for i in input:
print("> file " + i)
tfile = ROOT.TFile.Open(i)
if (tfile.Get(treeName).GetEntries(selection) == 0):
print("> no events in this file found! continue with next file")
continue
_df.append(
tree_to_df(
rn.root2array(i,
treeName,
selection=selection,
branches=branches)))
print(">>> Concatenate")
df = pd.concat(_df)
df['q'] = (df['q1'] + df['q2']) / 2.
# ### additional requirement at Sel:
# # case for is2HLT and only tag fails additional cut: tag and probe has to be chanched
# selectedOr = df['is2HLT']
#
# if tkIsoCut is not None:
# selectedOr = selectedOr | (df['tkIso1'] >= tkIsoCut) & (df['tkIso2'] < tkIsoCut)
# if pfIsoCut is not None:
# selectedOr = selectedOr | ((df['pfIso1'] >= pfIsoCut) & (df['pfIso2'] < pfIsoCut))
input = glob.glob(
"/pnfs/desy.de/cms/tier2/store/user/dwalter/SingleMuon/MuonTrees_V06/200610_143117/0000/output_0_*"
)
treeName = rn.list_trees(input[0])[0]
print(">>> Load Events from {0} files".format(len(input)))
_df = []
for i, ifile in enumerate(input):
print("> file {0}/{1}".format(i, len(input)))
sys.stdout.flush()
tfile = ROOT.TFile(ifile)
ttree = tfile.Get(treeName)
if ttree.GetEntries() == 0:
print("tree has no entries, continue")
continue
_df.append(tree_to_df(rn.root2array(ifile, treeName)))
print(">>> Concatenate")
df = pd.concat(_df)
###############################################
# computation of difficiencies and acceptances #
###############################################
def diffacc(selection):
"""
selection:
lumbda function that returns a bool like lambda x: df['pt'] > x
return:
function for difficiency depending on selection as defined in lambda function
os.mkdir(output)
if isinstance(inputs, (list,)):
treeName = list_trees(inputs[0])
else:
treeName = list_trees(inputs)
inputs = [inputs, ]
if (len(treeName) > 1):
print("more then one tree in file ... specify, which tree to use")
exit()
print(">>> Load Events in gen acceptance")
dfGen = [tree_to_df(root2array(i, treeName[0], selection=selection, branches=branches), 1) for i in inputs]
dfGen = pd.concat(dfGen)
dfGen = dfGen.rename(columns={
'Muon_ID[muon_genRecoObj]_0': 'muon_ID',
'Muon_ID[antiMuon_genRecoObj]_0': 'antiMuon_ID',
'Muon_triggerBits[muon_genRecoObj]_0': 'muon_triggerBits',
'Muon_triggerBits[antiMuon_genRecoObj]_0': 'antiMuon_triggerBits',
'Muon_tkRelIso[muon_genRecoObj]_0': 'muon_tkIso',
'Muon_tkRelIso[antiMuon_genRecoObj]_0': 'antiMuon_tkIso',
'Muon_pfRelIso04_all[muon_genRecoObj]_0': 'muon_pfIso',
'Muon_pfRelIso04_all[antiMuon_genRecoObj]_0': 'antiMuon_pfIso',
'Muon_eta[muon_genRecoObj]_0': 'muon_eta',
'Muon_eta[antiMuon_genRecoObj]_0': 'antiMuon_eta',
'Muon_pt[muon_genRecoObj]_0': 'muon_pt',
'Muon_pt[antiMuon_genRecoObj]_0': 'antiMuon_pt',