def calculate_resolutions(variable,
bin_edges=[],
channel='combined',
res_to_plot=False):
'''
Calculate the resolutions in the bins using the residual method
'''
f = File('unfolding/13TeV/unfolding_TTJets_13TeV.root')
fineBinEdges_path = '{}_{}/responseVis_without_fakes'.format(
variable, channel)
fineBinEdges_hist2d = f.Get(fineBinEdges_path).Clone()
fineBinEdges_hist1d = asrootpy(fineBinEdges_hist2d.ProjectionX())
fineBinEdges = list(fineBinEdges_hist1d.xedges())
nFineBins = len(fineBinEdges) - 1
tmp_residual_path = '{}_{}/residuals/Residuals_Bin_'.format(
variable, channel)
# Absolute lepton eta can have multiple fine bins at the same precision as wide bins. Only taking first.
if variable == 'abs_lepton_eta':
fineBinEdges = [round(entry, 2) for entry in fineBinEdges]
# For N Bin edges find resolutions of bins
resolutions = []
for i in range(len(bin_edges) - 1):
list_of_fine_bins = []
# Find fine bin edges in wide bins
for j, fine_bin_edge in enumerate(fineBinEdges):
if fine_bin_edge >= bin_edges[i] and fine_bin_edge < bin_edges[
i + 1] and j < nFineBins:
list_of_fine_bins.append(j + 1)
# Sum the residuals of the fine bins
for fine_bin in list_of_fine_bins:
if fine_bin == list_of_fine_bins[0]:
fineBin_histRes = f.Get(tmp_residual_path +
str(fine_bin)).Clone()
else:
fineBin_histRes_tmp = f.Get(tmp_residual_path +
str(fine_bin)).Clone()
fineBin_histRes.Add(fineBin_histRes, fineBin_histRes_tmp, 1.0,
1.0)
# Get the quantile at 68% = 1 sigma = Resolution
interval = np.array([0.])
quantile = np.array([0.68])
fineBin_histRes.GetQuantiles(1, interval, quantile)
resolutions.append(round(interval[0], 2))
if res_to_plot:
plotting_resolution(variable, channel, fineBin_histRes,
round(interval[0], 2), i, bin_edges[i],
bin_edges[i + 1])
return resolutions
def parallelTreeWorker(item):
import random, string, os
filename, _tree, value, cut, _hist, weight, f = item
rfile = File(filename)
tree = rfile.Get(_tree)
tree = asrootpy(tree)
_hist = asrootpy(_hist)
try:
if weight is None:
tree.Draw(value, selection=cut, hist=_hist)
else:
#_tree.Draw(value,selection="(%s)*(%s)"%(cut,weight),hist=self.hists[f])
tmpFileName = ''.join(
random.choice(string.ascii_lowercase) for i in range(4))
tmpFile = File("/tmp/%s.root" % tmpFileName, "recreate")
#sel_tree=_tree.copy_tree(selection=cut)
sel_tree = asrootpy(tree.CopyTree(cut))
##print weight
sel_tree.Draw(value, selection=weight, hist=_hist)
tmpFile.Close()
os.remove("/tmp/%s.root" % tmpFileName)
except Exception as e:
print(tree, value, cut, _hist, weight, f)
log_plotlib.info("error:%s" % (e))
log_plotlib.info("file :%s" % (f))
log_plotlib.info("Perhaps try this one:")
for i in tree.glob("*"):
log_plotlib.info(i)
raise RuntimeError("Will stop here!")
rfile.Close()
del (tree)
return (_hist, f)
def get_merged_bin_resolution(res_file, var, low_bin, high_bin):
'''
Return mean value of resolutions in fine bins of the merged bin.
'''
bin_contents = []
f = File(res_file)
res_hist = f.Get('res_r_' + var).Clone()
# change scope from file to memory
res_hist.SetDirectory(0)
f.close()
low_bin_n = res_hist.GetXaxis().FindBin(low_bin)
high_bin_n = res_hist.GetXaxis().FindBin(high_bin)
for bin_i in range(low_bin_n, high_bin_n + 1):
bin_content = res_hist.GetBinContent(bin_i)
# resolution couldnt be reconstructed (High GeV with low stats)
# remove these from list of resolutions
if bin_content == 0: continue
bin_contents.append(bin_content)
# print(bin_contents)
res = np.mean(bin_contents)
return res
def get_response_histogram(responseFileName, variable, channel):
'''
clones the response matrix from file
'''
responseFile = File(responseFileName, 'read')
folder = '{variable}_{channel}'.format(variable=variable, channel=channel)
h_response = responseFile.Get(folder).responseVis_without_fakes.Clone()
return asrootpy(h_response)
def get_electron_normalisation(met_bin, b_tag):
global electron_data_file
input_file = File(electron_data_file)
histogram_for_estimation = 'TTbarPlusMetAnalysis/EPlusJets/QCD e+jets PFRelIso/BinnedMETAnalysis/Electron_patType1CorrectedPFMet_bin_%s/electron_pfIsolation_03_%s' % (
met_bin, b_tag)
input_histogram = input_file.Get(histogram_for_estimation)
result = estimate_with_fit_to_relative_isolation(input_histogram)
value, error = result['value'], result['error']
return value, error
def get_histograms( variable, options ):
config = XSectionConfig( 13 )
path_electron = ''
path_muon = ''
path_combined = ''
histogram_name = ''
if options.visiblePhaseSpace:
histogram_name = 'responseVis_without_fakes'
else :
histogram_name = 'response_without_fakes'
if variable == 'HT':
path_electron = 'unfolding_HT_analyser_electron_channel/%s' % histogram_name
path_muon = 'unfolding_HT_analyser_muon_channel/%s' % histogram_name
path_combined = 'unfolding_HT_analyser_COMBINED_channel/%s' % histogram_name
else :
path_electron = 'unfolding_%s_analyser_electron_channel_patType1CorrectedPFMet/%s' % ( variable, histogram_name )
path_muon = 'unfolding_%s_analyser_muon_channel_patType1CorrectedPFMet/%s' % ( variable, histogram_name )
path_combined = 'unfolding_%s_analyser_COMBINED_channel_patType1CorrectedPFMet/%s' % ( variable, histogram_name )
histogram_information = [
{'file': config.unfolding_central_raw,
'CoM': 13,
'path':path_electron,
'channel':'electron'},
{'file':config.unfolding_central_raw,
'CoM': 13,
'path':path_muon,
'channel':'muon'},
]
if options.combined:
histogram_information = [
{'file': config.unfolding_central_raw,
'CoM': 13,
'path': path_combined,
'channel':'combined'},
]
for histogram in histogram_information:
f = File( histogram['file'] )
# scale to lumi
# nEvents = f.EventFilter.EventCounter.GetBinContent( 1 ) # number of processed events
# config = XSectionConfig( histogram['CoM'] )
# lumiweight = config.ttbar_xsection * config.new_luminosity / nEvents
lumiweight = 1
histogram['hist'] = f.Get( histogram['path'] ).Clone()
histogram['hist'].Scale( lumiweight )
# change scope from file to memory
histogram['hist'].SetDirectory( 0 )
f.close()
return histogram_information
def get_histograms(config, variable, args):
'''
Return a dictionary of the unfolding histogram informations (inc. hist)
'''
path_electron = ''
path_muon = ''
path_combined = ''
histogram_name = 'response_without_fakes'
if args.visiblePhaseSpace:
histogram_name = 'responseVis_without_fakes'
path_electron = '%s_electron/%s' % (variable, histogram_name)
path_muon = '%s_muon/%s' % (variable, histogram_name)
path_combined = '%s_combined/%s' % (variable, histogram_name)
histogram_information = [
{
'file': config.unfolding_central_raw,
'CoM': 13,
'path': path_electron,
'channel': 'electron'
},
{
'file': config.unfolding_central_raw,
'CoM': 13,
'path': path_muon,
'channel': 'muon'
},
]
if args.combined:
histogram_information = [
{
'file': config.unfolding_central_raw,
'CoM': 13,
'path': path_combined,
'channel': 'combined'
},
]
for histogram in histogram_information:
lumiweight = 1
f = File(histogram['file'])
histogram['hist'] = f.Get(histogram['path']).Clone()
# scale to current lumi
lumiweight = config.luminosity_scale
if round(lumiweight, 1) != 1.0:
print("Scaling to {}".format(lumiweight))
histogram['hist'].Scale(lumiweight)
# change scope from file to memory
histogram['hist'].SetDirectory(0)
f.close()
return histogram_information
def root_file_to_hist(input_filename, hist_templates):
hists = copy.deepcopy(hist_templates)
root_file = TFile(input_filename, "read")
for var in hists.keys():
mod_hist = hists[var]
hist = root_file.Get(var)
mod_hist.replace_hist(hist)
return hists
def getStystPlot(hist):
import os
if os.path.exists("syst/"+hist.replace("/","")+"_syst.root"):
systFile=File("syst/"+hist.replace("/","")+"_syst.root","read")
stysthist=systFile.Get(hist)
stysthist.SetDirectory(0)
stysthist.SetLineWidth(0)
stysthist.SetLineColor(0)
else:
raise IOError("no syst for %s"%(hist))
if "Phi" in hist:
stysthist.Smooth()
return stysthist
def root_file_to_hist(input_filename, hist_templates, is_this_data):
hists = copy.deepcopy(hist_templates)
root_file = TFile(input_filename, "read")
for var in hists.keys():
index = 0
# if var in ['hardest_pT', 'uncor_hardest_pT', 'hardest_eta']:
# if var not in ['uncor_hardest_pT']:
if is_this_data:
for mod_hist in hists[var]:
hist_name = "{}#{}".format(var, index)
# Get hist from ROOT file.
hist = root_file.Get(hist_name)
mod_hist.replace_hist(hist)
index += 1
else:
if var != 'uncor_hardest_pT':
for mod_hist in hists[var]:
hist_name = "{}#{}".format(var, index)
# Get hist from ROOT file.
hist = root_file.Get(hist_name)
mod_hist.replace_hist(hist)
index += 1
return hists
def get_muon_normalisation(met_bin, b_tag):
global path_to_files
muon_qcd_file = path_to_files + 'central/QCD_Pt-20_MuEnrichedPt-15_5050pb_PFElectron_PFMuon_PF2PATJets_PFMET.root'
input_file = File(muon_qcd_file)
histogram_for_estimation = 'TTbarPlusMetAnalysis/MuPlusJets/Ref selection/BinnedMETAnalysis/Muon_patType1CorrectedPFMet_bin_%s/muon_AbsEta_%s' % (
met_bin, b_tag)
#if not correctly scaled, rescale here
#
input_histogram = input_file.Get(histogram_for_estimation)
scale_factor = 1.21
value = input_histogram.Integral() * scale_factor
error = sum([
input_histogram.GetBinError(bin_i) * scale_factor
for bin_i in range(1, input_histogram.nbins())
])
return value, error
def get_electron_absolute_eta_templates(b_tag):
global electron_data_file, met_bins
data = File(electron_data_file)
hist_template = 'TTbarPlusMetAnalysis/EPlusJets/QCDConversions/BinnedMETAnalysis/Electron_patType1CorrectedPFMet_bin_%s/electron_AbsEta_0btag'
histograms = []
for met_bin in met_bins:
histogram = data.Get(hist_template % met_bin).Clone(
'qcd_template_absolute_eta_MET_bin_%s_%s' % (met_bin, b_tag))
histogram.Sumw2()
#rebin to 0.2 bin width
current_bin_width = histogram.xwidth(
1) # assume all bins have the same size
rebin = int(0.2 / current_bin_width)
if rebin > 1:
histogram.Rebin(rebin)
n_events = histogram.Integral()
if not n_events == 0:
histogram.Scale(1 / n_events)
histograms.append(histogram)
return histograms
def root_file_to_hist(input_filename, hist_templates):
hists = copy.deepcopy(hist_templates)
root_file = TFile(input_filename, "read")
for var in hists.keys():
index = 0
for mod_hist in hists[var]:
hist_name = "{}#{}".format(var, index)
# Get hist from ROOT file.
hist = root_file.Get(hist_name)
mod_hist.replace_hist(hist)
index += 1
return hists
@author: kreczko
'''
from rootpy.io import File
from rootpy.plotting import Hist
from rootpy import asrootpy
rootpy_hist = Hist(100, 0, 100, type='F')
rootpy_hist.SetName('hist')
test_file = File('test.root', 'RECREATE')
test_file.mkdir("test")
test_file.cd('test')
rootpy_hist.Write()
test_file.Write()
test_file.Close()
read_file = File('test.root')
folder = read_file.Get('test')
hist = folder.hist
print hist.TYPE
read_file.Close()
hist = None
read_file = File('test.root')
hist = read_file.Get('test/hist')
hist1 = hist.empty_clone(type='D')
print hist1.TYPE
read_file.Close()
import glob
from collections import OrderedDict
from ROOT import TFile
data = File("Data.root")
mcfiles = ("DYJetsToLL.root", "W+Jets.root", "tbar{t}.root", "VV.root",
"SingleTop.root")
#mcfiles.remove("Tau.root")
mcfiles = [File(i) for i in mcfiles]
outputHists = OrderedDict()
dirs = set()
for dirpath, dirnames, filenames in data.walk():
for filename in filenames:
print dirpath
h_data = data.Get(path.join(dirpath, filename))
h_mc = h_data.empty_clone()
for f in mcfiles:
h = f.Get(path.join(dirpath, filename))
h_mc += h
h_data -= h_mc
outputHists[dirpath, filename] = h_data
dirs.add(dirpath)
output_file = root_open("nicolas_lowdpTandOS_QCDsubresult_051920.root", "NEW")
for d in dirs:
output_file.cd()
output_file.mkdir(d)
for dirpath, filename in outputHists:
output_file.cd()
output_file.cd(dirpath)
runLumis = None
with open(jsonFile) as j:
runLumis = json.load(j)
fullRunLumi = []
for run in runLumis:
for lumiRange in runLumis[run]:
for lumi in range(lumiRange[0], lumiRange[1] + 1):
fullRunLumi.append([int(run), lumi])
return fullRunLumi
inputFile = File(
'/hdfs/TopQuarkGroup/run2/ntuples/v22/SingleMuonFullDCS/SingleMuon.root')
treeName = 'nTupleTree/tree'
tree = inputFile.Get(treeName)
jsonForFiltering = '/users/ec6821/lumiScripts/lcr2/lcr2/good_list.txt'
json = readJson(jsonForFiltering)
newFile = File('SingleMuonFiltered.root', 'RECREATE')
newFile.mkdir('nTupleTree')
newFile.cd('nTupleTree')
newTree = tree.CloneTree(0)
print 'Number of events in tree : ', tree.GetEntries()
for event in tree:
run = event.__getattr__('Event.Run')
lumi = event.__getattr__('Event.LumiSection')
'QCDStudy/PFIsolation_3jets_WithMETCutAndAsymJetCuts_0btag':
1528. / 0.48,
'QCDStudy/PFIsolation_3jets_WithMETCutAndAsymJetCuts_1orMoreBtag':
491. / 0.67,
'QCDStudy/PFIsolation_WithMETCutAndAsymJetCuts_DR03_0btag':
1398. / 0.61,
'QCDStudy/PFIsolation_WithMETCutAndAsymJetCuts_DR03_1btag':
504. / 0.79,
'QCDStudy/PFIsolation_WithMETCutAndAsymJetCuts_DR03_2orMoreBtags':
210 / 0.73
}
QCD_rate_estimation.relative_isolation_bias = 0.0
results = {}
for histogram_for_estimation in histograms_for_estimation:
input_histogram = input_file.Get(histogram_for_estimation)
result = estimate_with_fit_to_relative_isolation(input_histogram)
results[histogram_for_estimation] = result
#QCD normalised to lumi
input_histogram_qcd = File(histogram_files['QCD']).Get(
histogram_for_estimation.replace('_DR03_', '_'))
input_histogram_qcd.Scale((1.0 * lumi) / 1959.75)
input_histogram_qcd.Rebin(10)
nQCD = input_histogram_qcd.GetBinContent(1)
# print nQCD
# nQCD = qcd_expected[histogram_for_estimation]
fQCD = '---'
if not nQCD == 0:
fQCD = (result['value']) / nQCD
print histogram_for_estimation, ':', result['value'], '+-', result[
'error'], 'QCD: ', nQCD, ',f_QCD:', fQCD
help="set path to save tables")
(options, args) = parser.parse_args()
input_path = options.path
output_folder = options.output_plots_folder
output_pickle_folder = './pickle_files/'
channel = 'electron'
centre_of_mass = 7
make_folder_if_not_exists(output_folder)
make_folder_if_not_exists(output_pickle_folder)
output_formats = ['pdf']
data_histFile = input_path + '/2011/SingleElectron_2011_RunAB_had_leg.root'
data_input_file = File(data_histFile)
data_tree = data_input_file.Get('rootTupleTreeEPlusJets/ePlusJetsTree')
reco_leptons_collection = 'selectedPatElectronsLoosePFlow'
reco_jet_collection = 'cleanedJetsPFlowEPlusJets'
trigger_object_lepton = 'TriggerObjectElectronIsoLeg'
trigger_jet_collection = 'TriggerObjectHadronPFIsoLeg'
print 'Number of events in data tree: ', data_tree.GetEntries()
n_lepton_leg_events = 0
for event in data_tree:
run_number = event.__getattr__('Event.Run')
if run_number >= 160404 and run_number <= 165633:
trigger_name = 'HLT_Ele25_CaloIdVT_TrkIdT_CentralTriJet30'
set_root_defaults()
measurement_config = XSectionConfig(13)
# # caching of variables for shorter access
files_for_pdfs = {
'PDFWeights_%d' % (index - 9):
File(measurement_config.unfolding_pdfweights[index])
for index in range(9, 109)
}
files_central = File(measurement_config.unfolding_powheg_pythia8)
path_to_unfolding = 'abs_lepton_eta_combined'
i = 0
canvas = TCanvas("CanvasRef", "CanvasTitle", 800, 600)
folder = files_central.Get(path_to_unfolding)
h_truth = asrootpy(folder.truthVis.Clone())
h_measured = asrootpy(folder.measuredVis_without_fakes.Clone())
h_measured.Draw()
for k, f in files_for_pdfs.iteritems():
folder = f.Get(path_to_unfolding)
# folder = inputfile.Get( '%s_%s' % ( variable, channel ) )
h_truth = asrootpy(folder.truthVis.Clone())
h_measured = asrootpy(folder.measuredVis_without_fakes.Clone())
h_measured.SetMarkerSize(0)
h_measured.Draw("same")
folder = files_central.Get(path_to_unfolding)
h_truth = asrootpy(folder.truthVis.Clone())
