def makeDataFrame(self):
sample_dict = {}
samples_all, samples_singlefake, samples_doublefake = createSampleLists(analysis_dir=self.analysis_dir, server = self.server, channel=self.channel)
working_samples = samples_doublefake
working_samples = setSumWeights(working_samples)
print('###########################################################')
print('# measuring doublefakerake...')
print('# %d samples to be used:'%(len(working_samples)))
print('###########################################################')
for w in working_samples: print('{:<20}{:<20}'.format(*[w.name,('path: '+w.ana_dir)]))
chain = TChain('tree') #TChain'ing all data samples together
for i,s in enumerate(working_samples):
sample = working_samples[0]
file_name = '/'.join([sample.ana_dir, sample.dir_name, sample.tree_prod_name, 'tree.root'])
chain.Add(file_name)
dataframe = RDataFrame(chain)
weight = 'weight * lhe_weight'
dataframe = dataframe.Define('w',weight)\
.Define('ptCone',self.ptCone())\
.Define('abs_hnl_hn_vis_eta','abs(hnl_hn_vis_eta)')\
.Define('abs_hnl_hn_eta','abs(hnl_hn_eta)')\
.Define('abs_l1_eta','abs(l1_eta)')\
.Define('abs_l2_eta','abs(l2_eta)')\
.Define('abs_l1_jet_flavour_parton','abs(l1_jet_flavour_parton)')\
.Define('abs_l2_jet_flavour_parton','abs(l2_jet_flavour_parton)')\
return dataframe
def get_frame(tree_name, file_name):
frame = RDataFrame(tree_name, file_name)
if verbose:
colNames = frame.GetColumnNames()
for j in colNames:
print(j)
return frame
def get_frame(tree_name, file_name):
"""
Getter of the frame from the file
"""
frame = RDataFrame(tree_name, file_name)
if verbose:
colNames = frame.GetColumnNames()
for j in colNames:
print(j)
return frame
def split(file_name): #without.root
tfile = rt.TFile(file_name+'.root')
tree = tfile.Get('tree')
df = RDF(tree)
n = tree.GetEntries()
df1 = df.Range(0,int(n/2))
df2 = df.Range(int(n/2),0)
df1.Snapshot('tree', '%s_training_half.root'%file_name)
df2.Snapshot('tree', '%s_untouched_half.root'%file_name)
def __rdf_from_dataset(self, dataset):
t_names = [ntuple.directory for ntuple in \
dataset.ntuples]
if len(set(t_names)) == 1:
tree_name = t_names.pop()
else:
raise NameError(
'Impossible to create RDataFrame with different tree names')
chain = TChain()
ftag_fchain = {}
for ntuple in dataset.ntuples:
chain.Add('{}/{}'.format(ntuple.path, ntuple.directory))
for friend in ntuple.friends:
if friend.tag not in ftag_fchain.keys():
ftag_fchain[friend.tag] = TChain()
ftag_fchain[friend.tag].Add('{}/{}'.format(
friend.path, friend.directory))
for ch in ftag_fchain.values():
chain.AddFriend(ch)
# Keep friend chains alive
self.friend_tchains.append(ch)
if self.nthreads != 1:
EnableImplicitMT(self.nthreads)
# Keep main chain alive
self.tchains.append(chain)
rdf = RDataFrame(chain)
rcw = RDataFrameCutWeight(rdf)
return rcw
def merge_friend(output_ntp_name, friends, tree_branch_dict, config):
# Here we don't drop any branch. We do only keep specified trees.
opts = RDF.RSnapshotOptions()
opts.fMode = 'UPDATE'
for full_path, tree in friends.items():
if config[full_path]['keep']:
rd1 = RDataFrame(tree)
cut = concat_selections(config[full_path]['selection'])
if cut:
rd2 = rd1.Filter(cut)
else:
rd2 = rd1
output_br = make_output_vec(tree_branch_dict[full_path])
rd2.Snapshot(full_path, output_ntp_name, output_br, opts)
def getRDF(pattern, treeName=None, keepObj=None):
if treeName is None: treeName = 'tuple0/DecayTree'
patterns = [pattern] if isinstance(pattern, str) else pattern
from ROOT import TChain, RDataFrame
import glob
ch1 = TChain(treeName)
for pn in patterns:
for f in glob.glob(pn):
ch1.Add(f)
if keepObj is not None:
keepObj.append(ch1) # to keep this object in the memory
return RDataFrame(ch1)
## if no container is provided, return both objects
return RDataFrame(ch1), ch1
def __rdf_from_dataset(self, dataset):
chain, self.friend_tchains = rdf_from_dataset_helper(dataset)
if self.nthreads != 1:
EnableImplicitMT(self.nthreads)
# Keep main chain alive
self.tchains.append(chain)
rdf = RDataFrame(chain)
rcw = RDataFrameCutWeight(rdf)
return rcw
def get_frame(file_name, df_index=0, tree_name="O2mcparticle_001"):
"""
Getter of the frame from the file
"""
if not path.isfile(file_name):
raise ValueError("Did not find AOD file", file_name)
sub_names = run_cmd(f"rootls {file_name}").strip().split()
df_name = []
for i in sub_names:
if not i.startswith("DF_") and not i.startswith("TF_"):
continue
df_name.append(i)
df_name = df_name[df_index]
print(df_name)
frame = RDataFrame(f"{df_name}/{tree_name}", file_name)
if verbose:
colNames = frame.GetColumnNames()
for j in enumerate(colNames):
print(j, frame.GetColumnType(j[1]))
return frame
def produceLightTree(sample='DY',ch='mmm'):
if ch == 'mmm':
d17B = data_B_mmm+suffix; d17C = data_C_mmm+suffix; d17D = data_D_mmm+suffix; d17E = data_E_mmm+suffix; d17F = data_F_mmm+suffix;
SFR_012_L = SFR_MMM_012_L
l2_tight = l2_m_tight
if ch == 'eem':
d17B = data_B_eem+suffix; d17C = data_C_eem+suffix; d17D = data_D_eem+suffix; d17E = data_E_eem+suffix; d17F = data_F_eem+suffix;
t = rt.TChain('tree')
if sample == 'DY':
t.Add(DY)
t.Add(DY_ext)
if sample == 'data':
t.Add(d17B)
#t.Add(d17C)
#t.Add(d17D)
#t.Add(d17E)
#t.Add(d17F)
print '\n\ttotal entries:', t.GetEntries()
df = RDF(t)
df1 = df.Define('LOOSE', '1 * (' + SFR_012_L + ' && hnl_dr_12 > 0.3 && hnl_dr_02 > 0.3 && abs(hnl_m_01 - 91.19) < 10 && hnl_q_01 == 0 )' )
df2 = df1.Define('TIGHT', '1 * (' + SFR_012_L + ' && hnl_dr_12 > 0.3 && hnl_dr_02 > 0.3 && abs(hnl_m_01 - 91.19) < 10 && hnl_q_01 == 0 && ' + l2_tight + ')' )
num_L = df2.Filter('LOOSE == 1').Count().GetValue()
print '\n\tloose entries in MR:', num_L
num_T = df2.Filter('TIGHT == 1').Count().GetValue()
print '\n\ttight entries in MR:', num_T
df2 = df2.Define('ptcone', PTCONEL2)
branchList = rt.vector('string')()
for br in ['event', 'lumi', 'run', 'LOOSE', 'TIGHT', 'l2_reliso_rho_03', 'l2_Medium', 'l2_eta', 'l2_pt', 'l2_dxy', 'l2_dz', 'ptcone']:
branchList.push_back(br)
df2.Snapshot('tree', saveDir+'/%s_%s_6_24B_Lcut_29_4.root'%(sample,ch), branchList)
def __init__(self, name, label, selection, datacard_name, colour,
position_in_stack, basedir, postfix, isdata, ismc, issignal,
weight, xs):
self.name = name
print 'loading', self.name
self.label = label
self.selection = selection
self.datacard_name = datacard_name
self.colour = colour
self.position_in_stack = position_in_stack
self.basedir = basedir
self.postfix = postfix
self.isdata = isdata
self.ismc = ismc
self.issignal = issignal
self.weight = weight
self.xs = xs
self.nevents = 1.
self.file = '/'.join([basedir, self.name, postfix])
if not self.isdata:
nevents_file = '/'.join(
[basedir, self.name, 'SkimAnalyzerCount/SkimReport.txt'])
with open(nevents_file) as ff:
lines = ff.readlines()
for line in lines:
if 'Sum Norm Weights' in line:
self.nevents = float(re.findall(r'\d+', lines[2])[0])
break
tree_file = '/'.join([self.basedir, self.name, self.postfix])
rdf = RDF('tree', tree_file)
rdf = rdf.Filter(self.selection)
# set_trace()
df = rdf.AsNumpy()
self.df = pd.DataFrame(df)
# scale to 1/pb
self.lumi_scaling = 1. if self.isdata else (self.xs / self.nevents)
def makeRootDataFrameFromTree(self, tree_file_name, tree_name='tree', verbose=False, friend_name='ML', friend_file_name=None):
'''Cache files/trees'''
ttree = self.readTree(tree_file_name, tree_name, verbose)
if verbose:
print ('read dataframe', dataframe, 'from file', tree_file_name)
if friend_file_name:
ttree.AddFriend(friend_name + '=tree',friend_file_name)
#VALIDATE#
validate1 = ttree.GetEntries('l2_pt - %s.l2_pt'%friend_name)
validate2 = ttree.GetEntries('event - %s.event'%friend_name)
if not validate1+validate2 == 0: print ('\n\tERROR: FRIEND TREE NOT ALIGNED, FAKERATE USELESS', m, n)
gROOT.cd()
# dataframe = RDataFrame(tree_name,tree_file_name)
dataframe = RDataFrame(ttree)
return dataframe
parser.add_argument('--do',
nargs='+',
help="list of collections to make plots of")
args = parser.parse_args()
models = {
'm': (';m_{{{0}}} [GeV]; Events', 50, 0, 1000),
'pt': (';p_{{T,{0}}} [GeV]; Events', 50, 0, 1000),
'eta': (';#eta_{{{0}}}; Events', 15, -3., 3.),
'phi': (';#phi_{{{0}}}; Events', 20, -4., 4.)
}
outdir = 'plots'
if args.output:
outdir = args.output
rdf = RDataFrame("CollectionTree", args.infile)
rdf = rdf.Define('wgt', 'EventInfoAuxDyn.mcEventWeights[0]')
canv = TCanvas('c', '', 800, 600)
for coll in args.do:
coll = coll.split(':')
if len(coll) != 1 and len(coll) != 4:
print('collection should either be "name" or "name:nbins:xmin:xmax"')
continue
varsuff = ''
if 'fatjet' in coll[0]:
cname = 'AntiKt10TruthTrimmedPtFrac5SmallR20JetsAux'
elif 'jet' in coll[0]:
cname = 'AntiKt4TruthDressedWZJetsAux'
gInterpreter.Declare('''
Int_t getBin(Double_t x, Double_t y, TH2D* histo) {
return histo->FindFixBin(x, y);
}
auto getWeight(Double_t x, Double_t y, TH2D* histo) {
auto binIdx = getBin(x, y, histo);
return histo->GetBinContent(binIdx);
}
''')
gInterpreter.Declare(f'auto histoNtp = new TFile("{histoNtpN}", "read");')
gInterpreter.Declare(f'auto histo = dynamic_cast<TH2D*>(histoNtp->Get("{histoN}"));')
dfInit = RDataFrame(mcTreeN, mcNtpN)
df = dfInit.Define('wjk_alt', 'getWeight(b_ownpv_ndof, ntracks, histo)').Define('wt', 'wpid*wtrk*wjk_alt')
# NOTE: This comes from the existing ntuple
mcRootBrs = df.AsNumpy(columns=['wjk_occ', 'wjk_alt'])
wtJkOccRoot = mcRootBrs['wjk_occ']
wtJkOccAltRoot = mcRootBrs['wjk_alt']
histoRootMdl = TH2DModel(
'histoRoot', 'histoRoot',
20, 1, 200, 20, 0, 450
)
histoRoot = df.Histo2D(histoRootMdl, 'b_ownpv_ndof', 'ntracks', 'wt')
##################
"Triggered_HLT_PFMETNoMu120_PFMHTNoMu120_IDTight_vX",
"Triggered_HLT_IsoMu27_vX",
]
branch_vec = ROOT.vector("string")()
[branch_vec.push_back(branch) for branch in branches]
if not options.is_dataframe:
print(
"No dataframe was given. Handling the arguments as trees and adding them to chain."
)
input_files = args
input_chain = ROOT.TChain("MVATree")
for input_file in input_files:
input_chain.Add(input_file)
print("Finished loading chain with ", input_chain.GetEntries(), " entries")
data_frame = RDF(input_chain, branch_vec)
else:
print("Dataframe flag was set. Handling argument as dataframe.")
input_file = args[0]
data_frame = RDF("tree", input_file)
print("Finished converting the chain to RDataFrame")
if not options.is_dataframe and options.save:
print("saving dataframe to disk as ",
data_mc_string + "_" + options.name + "_dataframe.root")
data_frame.Snapshot(
"tree", data_mc_string + "_" + options.name + "_dataframe.root",
branch_vec)
print("saved dataframe to disk ...")
"DeltaR_AK4Jet_LooseElectron",
"DeltaR_AK4Jet_LooseMuon"
]
branch_vec = ROOT.vector("string")()
[branch_vec.push_back(branch) for branch in branches]
if not options.is_dataframe:
print(
"No dataframe was given. Handling the arguments as trees and adding them to chain."
)
input_files = args
input_chain = ROOT.TChain("MVATree")
for input_file in input_files:
input_chain.Add(input_file)
print("Finished loading chain with ", input_chain.GetEntries(), " entries")
data_frame = RDF(input_chain, branch_vec)
else:
print("Dataframe flag was set. Handling argument as dataframe.")
input_file = args[0]
data_frame = RDF("tree", input_file)
print("Finished converting the chain to RDataFrame")
if not options.is_dataframe and options.save:
print("saving dataframe to disk as ",
data_mc_string + "_" + options.name + "_dataframe.root")
data_frame.Snapshot(
"tree", data_mc_string + "_" + options.name + "_dataframe.root",
branch_vec)
print("saved dataframe to disk ...")
'{lb} <= {br} && {br} <= {ub}'.format(br=br,
lb=train + validation,
ub=100)
}
########
# Main #
########
if __name__ == '__main__':
args = parse_input()
gInterpreter.Declare('auto rand_gen = TRandom3({});'.format(args.seed))
init_frame = RDataFrame(args.tree, args.ntp)
rand_frame = init_frame.Define('rand_split', 'rand_gen.Uniform(0, 100)')
if args.debug:
print('loaded {} with {} entries'.format(
args.ntp,
rand_frame.Count().GetValue()))
cuts = get_cuts(args.train_ratio, args.validation_ratio)
for sample, cut in cuts.items():
subsample_frame = rand_frame.Filter(cut)
output_ntp = join(args.output_dir,
'{}_{}.root'.format(get_filename(args.ntp), sample))
subsample_frame.Snapshot(args.tree, output_ntp)
if args.debug:
from ROOT import (ROOT, RDataFrame, TCanvas, TH1D)
import numpy as np
ROOT.EnableImplicitMT()
#get file and tree from directory
input_ntuple = "/data/bfys/valukash/forAli/DaVinci_jpsiphi_MC_upgrade.root"
input_tree_name = "Bs2jpsiphi/DecayTree"
dataframe = RDataFrame(input_tree_name, input_ntuple)
#dataframe_with_truep = dataframe.Define('Bs_momentum', 'pow( Bs_TRUEP_X*Bs_TRUEP_X + Bs_TRUEP_Y*Bs_TRUEP_Y + Bs_TRUEP_Z*Bs_TRUEP_Z , 0.5)')
df_bkg = dataframe.Filter("Bs_BKGCAT == 0 || Bs_BKGCAT == 50")
#df_cuts1 = dataframe_with_truep.Filter("(Bs_TAU > 0.0015) && (Bs_M > 5150) && (Bs_M < 5550) && (Jpsi_M > 3020) && (Jpsi_M < 3170) && (Phi_M > 980) && (Phi_M < 1050) && (muplus_PT > 500) && (mumin_PT > 500) && ((Bs_ENDVERTEX_CHI2/Bs_ENDVERTEX_NDOF) < 20) && (Jpsi_ENDVERTEX_CHI2/Jpsi_ENDVERTEX_NDOF < 16) && (Phi_ENDVERTEX_CHI2/Phi_ENDVERTEX_NDOF < 25)", "trigger_cuts")
cut1 = df_bkg.Filter("(Bs_TAU > 0.0015)", "tau_cut")
cut2 = cut1.Filter("(Bs_M > 5150) && (Bs_M < 5550)", "b_mass_cut")
cut3 = cut2.Filter("(Jpsi_M > 3020) && (Jpsi_M < 3170)", "jpsi_mass_cut")
cut4 = cut3.Filter("(Phi_M > 980) && (Phi_M < 1050)", "phi_mass_cut")
cut5 = cut4.Filter("(muplus_PT > 500) && (mumin_PT > 500)", "mu_pt_cut")
cut6 = cut5.Filter("(Bs_ENDVERTEX_CHI2/Bs_ENDVERTEX_NDOF) < 20", "b_vtx_cut")
cut7 = cut6.Filter("(Jpsi_ENDVERTEX_CHI2/Jpsi_ENDVERTEX_NDOF < 16)",
"jpsi_vtx_cut")
cut8 = cut7.Filter("(Phi_ENDVERTEX_CHI2/Phi_ENDVERTEX_NDOF < 25)",
"phi_vtx_cut")
cut9 = cut8.Filter("mumin_PIDmu > 0 && muplus_PIDmu > 0", "mu_id_cut")
cut10 = cut9.Filter("Kmin_PIDK > 0 && Kplus_PIDK > 0", "k_id_cut")
#print("Mumin eff: ")
#mumincut = cut9.Report()
#mumincut.Print()
print("All stats: ")
cutsreport = dataframe.Report()
# initialize RDataFrame
data_frame = None
# either create the RDataFrame from a ROOT tree in a file or from a ROOT chain made up of several files
# or create it directly from an existing RDataFrame including a ROOT tree
if not options.is_dataframe:
print(
"No dataframe was given. Handling the arguments as trees and adding them to chain."
)
input_files = args
input_chain = ROOT.TChain(options.treename)
for input_file in input_files:
input_chain.Add(input_file)
print("Finished loading chain with ", input_chain.GetEntries(), " entries")
data_frame = RDF(input_chain, branch_vec)
else:
print("Dataframe flag was set. Handling argument as dataframe.")
input_file = args[0]
data_frame = RDF(options.treename, input_file)
print("Finished creating the RDataFrame")
# possibly save the created RDataFrame to disk
if not options.is_dataframe and options.save:
print("saving dataframe to disk as ",
data_mc_string + "_" + names + "_dataframe.root")
data_frame.Snapshot("tree",
data_mc_string + "_" + names + "_dataframe.root",
branch_vec)
print("saved dataframe to disk ...")
parser.add_argument("config", help="Path to the YAML configuration file")
args = parser.parse_args()
ROOT.ROOT.EnableImplicitMT()
gROOT.SetBatch()
with open(os.path.expandvars(args.config), 'r') as stream:
try:
params = yaml.full_load(stream)
except yaml.YAMLError as exc:
print(exc)
# define paths for loading data and storing results
mc_path = os.path.expandvars(params['MC_PATH'])
data_path = os.path.expandvars(params['DATA_PATH'])
dataDF = RDF('DataTable', data_path)
mcDF = RDF('SignalTable', mc_path)
genDF = RDF('GenTable', mc_path)
results_dir = os.environ['HYPERML_RESULTS_{}'.format(params['NBODY'])]
file_name = results_dir + '/' + params['FILE_PREFIX'] + '_std.root'
results_file = TFile(file_name, 'recreate')
for cclass in params['CENTRALITY_CLASS']:
cent_dir = results_file.mkdir('{}-{}'.format(cclass[0], cclass[1]))
dataCentDF = dataDF.Filter('centrality >= {} && centrality < {}'.format(
cclass[0], cclass[1]))
mcCentDF = mcDF.Filter('centrality >= {} && centrality < {}'.format(
cclass[0], cclass[1]))
genCentDF = genDF.Filter('centrality >= {} && centrality < {}'.format(
cclass[0], cclass[1]))
frames.append(new_frm)
apply_skim_cuts(new_frm, skim_cuts, ref)
########
# Main #
########
if __name__ == '__main__':
ntp_dst = '../../ntuples/ref-rdx-run1/Dst-mix/Dst--21_10_21--mix--all--2011-2012--md-mu--phoebe.root'
ntp_d0 = '../../ntuples/ref-rdx-run1/D0-mix/D0--21_10_21--mix--all--2011-2012--md-mu--phoebe.root'
if len(sys.argv) == 1 or sys.argv[1].lower() == 'dst':
print('Working on Dst...')
frame_dst = RDataFrame('ntp1', ntp_dst)
apply_cuts(frame_dst, DST_CUTS, DST_SKIM_CUTS, DST_REF_NUMS)
elif sys.argv[1].lower() == 'd0':
print('Working on D0...')
frame_d0 = RDataFrame('ntp1', ntp_d0)
apply_cuts(frame_d0, D0_CUTS, D0_SKIM_CUTS, D0_REF_NUMS)
elif sys.argv[1].lower() == 'dstwsmu':
print('Working on Dst wrong-sign Mu...')
frame_dst = RDataFrame('ntp1', ntp_dst)
apply_cuts(frame_dst, DST_WS_MU_CUTS, DST_SKIM_CUTS,
DST_WS_MU_REF_NUMS)
elif sys.argv[1].lower() == 'dstwspi':
print('Working on Dst wrong-sign slow Pi...')
frame_dst = RDataFrame('ntp1', ntp_dst)
apply_cuts(frame_dst, DST_WS_PI_CUTS, DST_SKIM_CUTS,
DST_WS_PI_REF_NUMS)
from ROOT import (
ROOT,
RDataFrame,
)
ROOT.EnableImplicitMT()
#get file and tree from directory
input_ntuple = "/data/bfys/valukash/forAli/DaVinci_jpsiphi_MC_upgrade.root"
input_tree_name = "Bs2jpsiphi/DecayTree"
dataframe = RDataFrame(input_tree_name, input_ntuple)
trigger_cuts = "(Bs_TAU > 0.0015) && (Bs_M > 5150) && (Bs_M < 5550) && (Jpsi_M > 3020) && (Jpsi_M < 3170) && (Phi_M > 980) && (Phi_M < 1050) && (muplus_PT > 500) && (mumin_PT > 500) && ((Bs_ENDVERTEX_CHI2/Bs_ENDVERTEX_NDOF) < 20) && (Jpsi_ENDVERTEX_CHI2/Jpsi_ENDVERTEX_NDOF < 16) && (Phi_ENDVERTEX_CHI2/Phi_ENDVERTEX_NDOF < 25)"
muon_cuts = "!eventmuons_BPVIPCHI2[eventmuons_BPVIPCHI2 < 8].empty() && Bs_len > 0"
df_cuts1 = dataframe.Filter(trigger_cuts)
df_cuts2 = df_cuts1.Filter(muon_cuts)
nentries = dataframe.Count().GetValue()
ntriggered = df_cuts1.Count().GetValue()
ncut = df_cuts2.Count().GetValue()
print("entries: " + str(nentries))
print("triggered: " + str(ntriggered))
print("cut: " + str(ncut))
TrueBs = df_cuts2.Filter("Bs_TRUEID == 531").Count().GetValue()
TrueBsbar = df_cuts2.Filter("Bs_TRUEID == -531").Count().GetValue()
#define right tags, wrong tags and untagged; tagging efficiency, mistag probability and tagging performance
R = df_cuts2.Filter(
"(Bs_OSMuon_TAGDEC == 1 && Bs_TRUEID == 531) || (Bs_OSMuon_TAGDEC == -1 && Bs_TRUEID == -531)"
).Count().GetValue()
def measureSFR(self, drawPlot = False):
sample_dict = {}
samples_all, samples_singlefake, samples_doublefake = createSampleLists(analysis_dir=self.analysis_dir, server = self.server, channel=self.channel)
working_samples = samples_singlefake
working_samples = setSumWeights(working_samples)
print('###########################################################')
print('# measuring singlefakerake...')
print('# %d samples to be used:'%(len(working_samples)))
print('###########################################################')
for w in working_samples: print('{:<20}{:<20}'.format(*[w.name,('path: '+w.ana_dir)]))
chain = TChain('tree') #TChain'ing all data samples together
for i,s in enumerate(working_samples):
sample = working_samples[0]
file_name = '/'.join([sample.ana_dir, sample.dir_name, sample.tree_prod_name, 'tree.root'])
chain.Add(file_name)
dataframe = RDataFrame(chain)
weight = 'weight * lhe_weight'
dataframe = dataframe.Define('w',weight)\
.Define('ptCone',self.ptCone())\
.Define('abs_hnl_hn_vis_eta','abs(hnl_hn_vis_eta)')\
.Define('abs_hnl_hn_eta','abs(hnl_hn_eta)')\
.Define('abs_l1_eta','abs(l1_eta)')\
.Define('abs_l2_eta','abs(l2_eta)')\
.Define('abs_l1_jet_flavour_parton','abs(l1_jet_flavour_parton)')\
.Define('abs_l2_jet_flavour_parton','abs(l2_jet_flavour_parton)')\
# bins_ptCone = np.array([5.,10., 20., 30., 40.,70., 2000])
# bins_eta = np.array([0., 0.8, 1.2, 2.4])
bins_ptCone = np.array([5.,10., 20., 30., 40.,70.])
bins_eta = np.array([0., 0.8, 1.2, 2.4])
selection_baseline = getSelection(self.channel,'MR_SF')
selection_LL_uncorrelated = '(' + ' & '\
.join([\
selection_baseline,\
getSelection(self.channel,'L_L_uncorrelated')\
]) + ')'
selection_TT_uncorrelated = '(' + ' & '\
.join([\
selection_baseline,\
getSelection(self.channel,'L_L_uncorrelated'),\
getSelection(self.channel,'T_T')\
]) + ')'
h_LL_uncorrelated = dataframe\
.Filter(selection_LL_uncorrelated)\
.Histo2D(('h_LL_uncorrelated','h_LL_uncorrelated',len(bins_ptCone)-1,bins_ptCone, len(bins_eta)-1, bins_eta),'ptCone','abs_hnl_hn_vis_eta','w')
#name the axis, also initiate the dataframe call
h_LL_uncorrelated.SetTitle(';ptCone [GeV]; dimuon #eta')
h_TT_uncorrelated = dataframe\
.Filter(selection_TT_uncorrelated)\
.Histo2D(('h_TT_uncorrelated','h_TT_uncorrelated',len(bins_ptCone)-1,bins_ptCone, len(bins_eta)-1, bins_eta),'ptCone','abs_hnl_hn_vis_eta','w')
#name the axis, also initiate the dataframe call
h_TT_uncorrelated.SetTitle(';ptCone [GeV]; dimuon #eta')
# preparing the histo and save it into a .root file
sfr_TH2_dir = '/home/dehuazhu/HNL/CMSSW_9_4_6_patch1/src/PlotFactory/DataBkgPlots/modules/DDE_singlefake.root'
sfr_hist = h_TT_uncorrelated.Clone()
# sfr_hist = h_LL_uncorrelated.Clone()
# sfrhist = h_baseline.Clone()
# sfr_hist.Divide(h_LL_uncorrelated.Clone())
# dfr_hist.SaveAs(sfr_TH2_dir) #uncomment this to save the TH2
# draw the histo if required
if drawPlot == True:
can = TCanvas('can', '')
# sfr_hist.Draw('colzTextE')
# sfr_hist.Draw('colz')
sfr_hist.Draw()
pf.showlumi('%d entries'%(sfr_hist.GetEntries()))
# pf.showlogopreliminary()
can.Update()
set_trace()
from ROOT import (ROOT, RDataFrame, TCanvas)
ROOT.EnableImplicitMT()
#get file and tree from directory
minbias_ntuple = "/user/egovorko/work/public/minbias_JpsiPhi.root"
signal_ntuple = "/data/bfys/valukash/forAli/DaVinci_jpsiphi_MC_upgrade.root"
input_tree_name = "Bs2jpsiphi/DecayTree"
minbias_df = RDataFrame(input_tree_name, minbias_ntuple)
signal_df = RDataFrame(input_tree_name, signal_ntuple)
PIDmu_cut = "eventmuons_PIDmu[eventmuons_PIDmu > 0].size() >= 1"
#pt_cut = "eventmuons_PT[eventmuons_PT > 500].size() >= 1"
minbias_pidmu = minbias_df.Filter(PIDmu_cut)
#minbias_pt = minbias_pidmu.Filter(pt_cut)
signal_pidmu = signal_df.Filter(PIDmu_cut)
#signal_pt = signal_pidmu.Filter(pt_cut)
c1 = TCanvas()
c1.Divide(2, 2)
c1.cd(1)
pidmu1 = minbias_df.Histo1D("eventmuons_PIDmu")
pidmu1.SetTitle("Event muon PIDmu distribution (min bias)")
pidmu1.GetXaxis().SetTitle("PIDmu")
pidmu1.Draw()
c1.cd(2)
pidmu2 = signal_df.Histo1D("eventmuons_PIDmu")
pidmu2.SetTitle("Event muon PIDmu distribution (signal)")
pidmu2.GetXaxis().SetTitle("PIDmu")
pidmu2.SetLineColor(2)
description='find retention rates for various trigger paths.')
parser.add_argument('ntp', help='specify ntuple path.')
parser.add_argument('tree', help='specify tree name.')
parser.add_argument('-t',
'--trigger-paths',
nargs='+',
help='specify trigger paths.')
return parser.parse_args()
########
# Main #
########
if __name__ == '__main__':
args = parse_input()
frame = RDataFrame(args.tree, args.ntp)
cuts = []
for tp in args.trigger_paths:
c = frame.Filter(
tp, tp) # This is to avoid garbage collector to delete our pointer
cuts.append(c)
report = frame.Report()
report.Print()
########
# Main #
########
if __name__ == '__main__':
args = parse_input()
histos = glob_histos(args.histo_folder)
config = parse_config(args.config)
loaded_histos = dict()
output_opts = RSnapshotOptions()
output_opts.fMode = 'UPDATE'
first_write = True
for idx, tree in enumerate(config['trees']):
print('Processing tree {}...'.format(tree))
init_frame = RDataFrame(tree, args.input_ntp)
frames = [init_frame]
output_brs = vector('string')(['runNumber', 'eventNumber'])
for br, directive in config['config'].items():
if tree in directive['skip_tree']:
continue
print(' Processing {}...'.format(br))
params = ', '.join(resolve_params(directive['vars'], idx))
histo_name = directive['histo_name']
histo_dim = len(directive['vars'])
debug_br = 'debug_{}_bin_idx'.format(br)
wt_histo = load_histo(
def get_dataframe(dataset):
tchain, friend_tchains = rdf_from_dataset_helper(dataset)
rdf = RDataFrame(tchain)
setattr(rdf, 'tchain', tchain)
setattr(rdf, 'friend_tchains', friend_tchains)
return rdf
from ROOT import (
ROOT,
RDataFrame,
)
ROOT.EnableImplicitMT()
#get file and tree from directory
input_ntuple = "/data/bfys/valukash/forAli/DaVinci_jpsiphi_MC_upgrade.root"
input_tree_name = "Bs2jpsiphi/DecayTree"
dataframe = RDataFrame(input_tree_name, input_ntuple)
nentries = dataframe.Count().GetValue()
bs_tau = "(Bs_TAU > 0.002)"
bs_m = "(Bs_M > 5150) && (Bs_M < 5550)"
jpsi_m = "(Jpsi_M > 3020) && (Jpsi_M < 3170)"
phi_m = "(Phi_M > 980) && (Phi_M < 1050)"
bs_vtx = "(Bs_ENDVERTEX_CHI2/Bs_ENDVERTEX_NDOF < 20)"
jpsi_vtx = "(Jpsi_ENDVERTEX_CHI2/Jpsi_ENDVERTEX_NDOF < 16)"
phi_vtx = "(Phi_ENDVERTEX_CHI2/Phi_ENDVERTEX_NDOF < 25)"
mu_pt = "(muplus_PT > 500) && (mumin_PT > 500)"
trigger_cut_list = [
bs_tau, bs_m, jpsi_m, phi_m, bs_vtx, jpsi_vtx, phi_vtx, mu_pt
]
efficiency_list = []
cum_efficiency_list = []
cum_eff_df = dataframe
for i in range(len(trigger_cut_list)):
eff_df = dataframe.Filter(trigger_cut_list[i])
eff = (eff_df.Count().GetValue()) / nentries
efficiency_list.append(eff)
"AK15Jet_DeepAK15_TvsQCD",
]
branch_vec = ROOT.vector("string")()
[branch_vec.push_back(branch) for branch in branches]
if not options.is_dataframe:
print(
"No dataframe was given. Handling the arguments as trees and adding them to chain."
)
input_files = args
input_chain = ROOT.TChain("MVATree")
for input_file in input_files:
input_chain.Add(input_file)
print("Finished loading chain with ", input_chain.GetEntries(), " entries")
data_frame = RDF(input_chain, branch_vec)
else:
print("Dataframe flag was set. Handling argument as dataframe.")
input_file = args[0]
data_frame = RDF("tree", input_file)
print("Finished converting the chain to RDataFrame")
if not options.is_dataframe and options.save:
print("saving dataframe to disk as ",
data_mc_string + "_" + options.name + "_dataframe.root")
data_frame.Snapshot(
"tree", data_mc_string + "_" + options.name + "_dataframe.root",
branch_vec)
print("saved dataframe to disk ...")
"pt_pfmet_raw_div_pt_genmet" : "pt_pfmet_raw/pt_genmet",
"pt_pfmet_t1_div_pt_genmet" : "pt_pfmet_t1/pt_genmet",
"pt_pfmet_t1smear_div_pt_genmet" : "pt_pfmet_t1smear/pt_genmet"
}
branch_vec = ROOT.vector("string")()
[branch_vec.push_back(branch) for branch in branches]
if not options.is_dataframe:
print ("No dataframe was given. Handling the arguments as trees and adding them to chain.")
input_files = args
input_chain = ROOT.TChain("METAnalyzer/MET_tree")
for input_file in input_files:
input_chain.Add(input_file)
print ("Finished loading chain with ", input_chain.GetEntries(), " entries")
data_frame = RDF(input_chain, branch_vec)
else:
print ("Dataframe flag was set. Handling argument as dataframe.")
input_file = args[0]
data_frame = RDF("tree", input_file)
print ("Finished converting the chain to RDataFrame")
if not options.is_dataframe and options.save:
print ("saving dataframe to disk as ", data_mc_string + "_" + names + "_dataframe.root")
data_frame.Snapshot("tree", data_mc_string + "_" + names + "_dataframe.root", branch_vec)
print ("saved dataframe to disk ...")
name = options.name
selection = options.selection