def __init__(self, outputDir, executable_analyze, samples, lepton_selections, central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
histograms_to_fit = [], select_rle_output = False, executable_prep_dcard="prepareDatacard"):
analyzeConfig.__init__(self, outputDir, executable_analyze, "charge_flip", central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
histograms_to_fit)
self.samples = samples
self.prep_dcard_processesToCopy = ["data_obs", "DY", "DY_fake", "WJets", "TTbar", "Singletop", "Diboson"]
self.prep_dcard_signals = [ "DY" ]
self.lepton_selections = lepton_selections
#self.hadTau_selection = hadTau_selection
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
for lepton_selection in self.lepton_selections:
key_dir = getKey(sample_name, lepton_selection)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_RLES ]:
initDict(self.dirs, [ key_dir, dir_type ])
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_selection ]), process_name)
##print "self.dirs = ", self.dirs
self.cfgFile_analyze_original = os.path.join(self.workingDir, "analyze_charge_flip_cfg.py")
self.cfgFile_prep_dcard_original = os.path.join(self.workingDir, "prepareDatacards_cfg.py")
#self.histogramDir_prep_dcard = "charge_flip_SS_Tight"
self.select_rle_output = select_rle_output
def __init__(self, treeName, outputDir, executable_addMEM, samples, era, debug, running_method,
max_files_per_job, mem_integrations_per_job, max_mem_integrations, num_parallel_jobs):
self.treeName = treeName
self.outputDir = outputDir
self.executable_addMEM = executable_addMEM
self.channel = "2lss_1tau"
self.mem_integrations_per_job = mem_integrations_per_job
self.max_files_per_job = max_files_per_job
self.max_mem_integrations = max_mem_integrations
self.samples = samples
self.era = era
self.debug = debug
assert(running_method.lower() in [
"sbatch", "makefile"]), "Invalid running method: %s" % running_method
self.running_method = running_method
self.is_sbatch = False
self.is_makefile = False
if self.running_method.lower() == "sbatch":
self.is_sbatch = True
else:
self.is_makefile = True
self.makefile = os.path.join(
self.outputDir, "Makefile_%s" % self.channel)
self.num_parallel_jobs = num_parallel_jobs
self.workingDir = os.getcwd()
print "Working directory is: " + self.workingDir
create_if_not_exists(self.outputDir)
self.stdout_file = codecs.open(os.path.join(
self.outputDir, "stdout_%s.log" % self.channel), 'w', 'utf-8')
self.stderr_file = codecs.open(os.path.join(
self.outputDir, "stderr_%s.log" % self.channel), 'w', 'utf-8')
self.dirs = {}
self.samples = samples
self.cfgFile_addMEM_original = os.path.join(self.workingDir, "addMEM_2lss_1tau_cfg.py")
self.cfgFiles_addMEM_modified = {}
self.logFiles_addMEM = {}
self.sbatchFile_addMEM = os.path.join(
self.outputDir, "sbatch_addMEM_%s.py" % self.channel)
self.inputFiles = {}
self.outputFiles = {}
self.hadd_records = {}
self.filesToClean = []
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(sample_name)
for dir_type in [ DKEY_CFGS, DKEY_NTUPLES, DKEY_FINAL_NTUPLES, DKEY_LOGS, DKEY_HADD ]:
initDict(self.dirs, [ key_dir, dir_type ])
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, process_name)
self.cvmfs_error_log = {}
def __init__(self, configDir, outputDir, executable_analyze, samples, lepton_selections, central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
histograms_to_fit = [], select_rle_output = False, executable_prep_dcard="prepareDatacard"):
analyzeConfig.__init__(self, configDir, outputDir, executable_analyze, "charge_flip", central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
histograms_to_fit)
self.samples = samples
self.prep_dcard_processesToCopy = ["data_obs", "DY", "DY_fake", "WJets", "TTbar", "Singletop", "Diboson"]
self.prep_dcard_signals = [ "DY" ]
self.lepton_selections = lepton_selections
#self.hadTau_selection = hadTau_selection
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
# Edit Siddh ~~~~~~~~~~~~~
run_process = False
for sprocess_run in sample_process_run_s:
if sprocess_run == process_name:
run_process = True
#print "Run process: ", sprocess_run
if run_process == False:
continue
#print "run_process:",process_name
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
for lepton_selection in self.lepton_selections:
key_dir = getKey(sample_name, lepton_selection)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_RLES ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
"_".join([ lepton_selection ]), process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_selection ]), process_name)
##print "self.dirs = ", self.dirs
self.cfgFile_analyze_original = os.path.join(self.workingDir, "analyze_charge_flip_cfg.py")
self.cfgFile_prep_dcard_original = os.path.join(self.workingDir, "prepareDatacards_cfg.py")
#self.histogramDir_prep_dcard = "charge_flip_SS_Tight"
self.select_rle_output = select_rle_output
def __init__(self, outputDir, executable_analyze, samples, charge_selections,
jet_minPt, jet_maxPt, jet_minAbsEta, jet_maxAbsEta, hadTau_selections, absEtaBins, ptBins, central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
executable_comp_jetToTauFakeRate):
analyzeConfig.__init__(self, outputDir, executable_analyze, "jetToTauFakeRate", central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
[])
self.samples = samples
self.charge_selections = charge_selections
self.jet_minPt = jet_minPt
self.jet_maxPt = jet_maxPt
self.jet_minAbsEta = jet_minAbsEta
self.jet_maxAbsEta = jet_maxAbsEta
self.hadTau_selections = hadTau_selections
self.absEtaBins = absEtaBins
self.ptBins = ptBins
self.executable_comp_jetToTauFakeRate = executable_comp_jetToTauFakeRate
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
for charge_selection in self.charge_selections:
key_dir = getKey(sample_name, charge_selection)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD ]:
initDict(self.dirs, [ key_dir, dir_type ])
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ charge_selection ]), process_name)
##print "self.dirs = ", self.dirs
self.cfgFile_analyze_original = os.path.join(self.workingDir, "analyze_jetToTauFakeRate_cfg.py")
self.histogramFile_comp_jetToTauFakeRate = {}
self.cfgFile_comp_jetToTauFakeRate_original = os.path.join(self.workingDir, "comp_jetToTauFakeRate_cfg.py")
self.cfgFile_comp_jetToTauFakeRate_modified = {}
self.histogramDir_numerator = {}
self.histogramDir_denominator = {}
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
for lepton_selection in self.lepton_selections:
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
central_or_shifts_extended = [ "" ]
central_or_shifts_extended.extend(self.central_or_shifts)
central_or_shifts_extended.extend([ "hadd", "addBackgrounds" ])
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [ process_name, "hadd" ]
for process_name_or_dummy in process_name_extended:
key_dir = getKey(process_name_or_dummy, lepton_selection_and_frWeight, central_or_shift_or_dummy)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_ROOT, DKEY_RLES, DKEY_SYNC ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight ]), process_name_or_dummy, central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight ]), process_name_or_dummy, central_or_shift_or_dummy)
for subdirectory in [ "addBackgrounds", "addBackgroundLeptonFakes", "prepareDatacards", "addSystFakeRates", "makePlots" ]:
key_dir = getKey(subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_ROOT, DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_SYNC ]:
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" % numDirectories)
numDirectories_created = 0;
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100*numDirectories_created >= frac*numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
mcClosure_regex = re.compile('Fakeable_mcClosure_(?P<type>m|e)_wFakeRateWeights')
for lepton_selection in self.lepton_selections:
electron_selection = lepton_selection
muon_selection = lepton_selection
hadTauVeto_selection = "Tight"
hadTauVeto_selection = "|".join([ hadTauVeto_selection, self.hadTauVeto_selection_part2 ])
if lepton_selection == "Fakeable_mcClosure_e":
electron_selection = "Fakeable"
muon_selection = "Tight"
elif lepton_selection == "Fakeable_mcClosure_m":
electron_selection = "Tight"
muon_selection = "Fakeable"
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
if lepton_frWeight == "disabled" and not lepton_selection in [ "Tight" ]:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
is_signal = (sample_category == "signal")
for central_or_shift in self.central_or_shifts:
inputFileList = inputFileLists[sample_name]
for jobId in inputFileList.keys():
if central_or_shift != "central":
isFR_shape_shift = (central_or_shift in systematics.FR_all)
if not ((lepton_selection == "Fakeable" and isFR_shape_shift) or lepton_selection == "Tight"):
continue
if not is_mc and not isFR_shape_shift:
continue
if central_or_shift in systematics.LHE().ttH and sample_category != "signal":
continue
if central_or_shift in systematics.LHE().ttW and sample_category != "TTW":
continue
if central_or_shift in systematics.LHE().ttZ and sample_category != "TTZ":
continue
if central_or_shift in systematics.DYMCReweighting and not is_dymc_reweighting(sample_name):
continue
logging.info(" ... for '%s' and systematic uncertainty option '%s'" % (lepton_selection_and_frWeight, central_or_shift))
# build config files for executing analysis code
key_analyze_dir = getKey(process_name, lepton_selection_and_frWeight, central_or_shift)
analyze_job_tuple = (process_name, lepton_selection_and_frWeight, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
syncOutput = ''
syncTree = ''
syncRequireGenMatching = True
if self.do_sync:
mcClosure_match = mcClosure_regex.match(lepton_selection_and_frWeight)
if lepton_selection_and_frWeight == 'Tight':
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_SR.root' % (self.channel, central_or_shift))
syncTree = 'syncTree_%s_SR' % self.channel
syncRequireGenMatching = True
elif lepton_selection_and_frWeight == 'Fakeable_wFakeRateWeights':
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_Fake.root' % (self.channel, central_or_shift))
syncTree = 'syncTree_%s_Fake' % self.channel
elif mcClosure_match:
mcClosure_type = mcClosure_match.group('type')
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_mcClosure_%s.root' % (self.channel, central_or_shift, mcClosure_type))
syncTree = 'syncTree_%s_mcClosure_%s' % (self.channel, mcClosure_type)
else:
continue
if syncTree and central_or_shift != "central":
syncTree = os.path.join(central_or_shift, syncTree)
syncRLE = ''
if self.do_sync and self.rle_select:
syncRLE = self.rle_select % syncTree
if not os.path.isfile(syncRLE):
logging.warning("Input RLE file for the sync is missing: %s; skipping the job" % syncRLE)
continue
if syncOutput:
self.inputFiles_sync['sync'].append(syncOutput)
cfgFile_modified_path = os.path.join(self.dirs[key_analyze_dir][DKEY_CFGS], "analyze_%s_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_LOGS], "analyze_%s_%s_%s_%i.log" % analyze_job_tuple)
rleOutputFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_RLES], "rle_%s_%s_%s_%i.txt" % analyze_job_tuple) \
if self.select_rle_output else ""
histogramFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_HIST], "analyze_%s_%s_%s_%i.root" % analyze_job_tuple)
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : cfgFile_modified_path,
'histogramFile' : histogramFile_path,
'logFile' : logFile_path,
'selEventsFileName_output' : rleOutputFile_path,
'electronSelection' : electron_selection,
'muonSelection' : muon_selection,
'apply_leptonGenMatching' : self.apply_leptonGenMatching,
'hadTauSelection_veto' : hadTauVeto_selection,
'applyFakeRateWeights' : self.applyFakeRateWeights if not lepton_selection == "Tight" else "disabled",
'central_or_shift' : central_or_shift,
'syncOutput' : syncOutput,
'syncTree' : syncTree,
'syncRLE' : syncRLE,
'syncRequireGenMatching' : syncRequireGenMatching,
'useNonNominal' : self.use_nonnominal,
'apply_hlt_filter' : self.hlt_filter,
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job], sample_info, lepton_selection)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name, lepton_selection_and_frWeight)
hadd_stage1_job_tuple = (process_name, lepton_selection_and_frWeight)
key_hadd_stage1_job = getKey(*hadd_stage1_job_tuple)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1_job] = os.path.join(self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s_%s.root" % hadd_stage1_job_tuple)
if self.do_sync: continue
if is_mc:
logging.info("Creating configuration files to run 'addBackgrounds' for sample %s" % process_name)
sample_categories = [ sample_category ]
if is_signal:
sample_categories = [ "signal", "ttH", "ttH_htt", "ttH_hww", "ttH_hzz", "ttH_hmm", "ttH_hzg" ]
for sample_category in sample_categories:
# sum non-fake and fake contributions for each MC sample separately
genMatch_categories = [ "nonfake", "conversions", "fake" ]
for genMatch_category in genMatch_categories:
key_hadd_stage1_job = getKey(process_name, lepton_selection_and_frWeight)
key_addBackgrounds_dir = getKey(process_name, lepton_selection_and_frWeight, "addBackgrounds")
addBackgrounds_job_tuple = None
processes_input = None
process_output = None
if genMatch_category == "nonfake":
# sum non-fake contributions for each MC sample separately
# input processes: TT3l0g0j,...
# output processes: TT; ...
if sample_category in [ "signal" ]:
lepton_genMatches = []
lepton_genMatches.extend(self.lepton_genMatches_nonfakes)
lepton_genMatches.extend(self.lepton_genMatches_conversions)
lepton_genMatches.extend(self.lepton_genMatches_fakes)
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in lepton_genMatches ]
elif sample_category in [ "ttH" ]:
lepton_genMatches = []
lepton_genMatches.extend(self.lepton_genMatches_nonfakes)
lepton_genMatches.extend(self.lepton_genMatches_conversions)
processes_input = []
processes_input.extend([ "%s%s" % ("ttH_htt", genMatch) for genMatch in lepton_genMatches ])
processes_input.extend([ "%s%s" % ("ttH_hww", genMatch) for genMatch in lepton_genMatches ])
processes_input.extend([ "%s%s" % ("ttH_hzz", genMatch) for genMatch in lepton_genMatches ])
processes_input.extend([ "%s%s" % ("ttH_hzg", genMatch) for genMatch in lepton_genMatches ])
processes_input.extend([ "%s%s" % ("ttH_hmm", genMatch) for genMatch in lepton_genMatches ])
else:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_genMatches_nonfakes ]
process_output = sample_category
addBackgrounds_job_tuple = (process_name, sample_category, lepton_selection_and_frWeight)
elif genMatch_category == "conversions":
# sum fake contributions for each MC sample separately
# input processes: TT2l1g0j, TT1l2g0j, TT0l3g0j; ...
# output processes: TT_conversion; ...
if sample_category in [ "signal" ]:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_genMatches_conversions ]
elif sample_category in [ "ttH" ]:
processes_input = []
processes_input.extend([ "%s%s" % ("ttH_htt", genMatch) for genMatch in self.lepton_genMatches_conversions ])
processes_input.extend([ "%s%s" % ("ttH_hww", genMatch) for genMatch in self.lepton_genMatches_conversions ])
processes_input.extend([ "%s%s" % ("ttH_hzz", genMatch) for genMatch in self.lepton_genMatches_conversions ])
processes_input.extend([ "%s%s" % ("ttH_hzg", genMatch) for genMatch in self.lepton_genMatches_conversions ])
processes_input.extend([ "%s%s" % ("ttH_hmm", genMatch) for genMatch in self.lepton_genMatches_conversions ])
else:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_genMatches_conversions ]
process_output = "%s_conversion" % sample_category
addBackgrounds_job_tuple = (process_name, "%s_conversion" % sample_category, lepton_selection_and_frWeight)
elif genMatch_category == "fake":
# sum fake contributions for each MC sample separately
# input processes: TT2l0g1j, TT1l1g1j, TT1l0g2j, TT0l2g1j, TT0l1g2j, TT0l0g3j; ...
# output processes: TT_fake; ...
if sample_category in [ "signal" ]:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_genMatches_fakes ]
elif sample_category in [ "ttH" ]:
processes_input = []
processes_input.extend([ "%s%s" % ("ttH_htt", genMatch) for genMatch in self.lepton_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hww", genMatch) for genMatch in self.lepton_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hzz", genMatch) for genMatch in self.lepton_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hzg", genMatch) for genMatch in self.lepton_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hmm", genMatch) for genMatch in self.lepton_genMatches_fakes ])
else:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_genMatches_fakes ]
process_output = "%s_fake" % sample_category
addBackgrounds_job_tuple = (process_name, "%s_fake" % sample_category, lepton_selection_and_frWeight)
if processes_input:
logging.info(" ...for genMatch option = '%s'" % genMatch_category)
key_addBackgrounds_job = getKey(*addBackgrounds_job_tuple)
cfgFile_modified = os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_tuple)
outputFile = os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_tuple)
self.jobOptions_addBackgrounds[key_addBackgrounds_job] = {
'inputFile' : self.outputFile_hadd_stage1[key_hadd_stage1_job],
'cfgFile_modified' : cfgFile_modified,
'outputFile' : outputFile,
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], os.path.basename(cfgFile_modified).replace("_cfg.py", ".log")),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight) ],
'processes_input' : processes_input,
'process_output' : process_output
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds[key_addBackgrounds_job])
# initialize input and output file names for hadd_stage1_5
key_hadd_stage1_5_dir = getKey("hadd", lepton_selection_and_frWeight)
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight)
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.jobOptions_addBackgrounds[key_addBackgrounds_job]['outputFile'])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job] = os.path.join(self.dirs[key_hadd_stage1_5_dir][DKEY_HIST],
"hadd_stage1_5_%s.root" % lepton_selection_and_frWeight)
# add output files of hadd_stage1 for data to list of input files for hadd_stage1_5
if not is_mc:
key_hadd_stage1_job = getKey(process_name, lepton_selection_and_frWeight)
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight)
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.outputFile_hadd_stage1[key_hadd_stage1_job])
if self.do_sync: continue
# sum fake background contributions for the total of all MC sample
# input processes: TT2l0g1j, TT1l1g1j, TT1l0g2j, TT0l3j, TT0l3j, TT0l3j, TT0l3j; ...
# output process: fakes_mc
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight)
key_addBackgrounds_dir = getKey("addBackgrounds")
addBackgrounds_job_fakes_tuple = ("fakes_mc", lepton_selection_and_frWeight)
key_addBackgrounds_job_fakes = getKey(*addBackgrounds_job_fakes_tuple)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
sample_categories.extend([ "signal" ])
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_fake" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_cfg.py" % addBackgrounds_job_fakes_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s.root" % addBackgrounds_job_fakes_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s.log" % addBackgrounds_job_fakes_tuple),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight) ],
'processes_input' : processes_input,
'process_output' : "fakes_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes])
# sum conversion background contributions for the total of all MC sample
# input processes: TT2l0g1j, TT1l1g1j, TT1l0g2j, TT0l3j, TT0l3j, TT0l3j, TT0l3j; ...
# output process: conversions
addBackgrounds_job_conversions_tuple = ("conversions", lepton_selection_and_frWeight)
key_addBackgrounds_job_conversions = getKey(*addBackgrounds_job_conversions_tuple)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
sample_categories.extend([ "signal" ])
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_conversion" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_conversions] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_cfg.py" % addBackgrounds_job_conversions_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s.root" % addBackgrounds_job_conversions_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s.log" % addBackgrounds_job_conversions_tuple),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight) ],
'processes_input' : processes_input,
'process_output' : "conversions"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_conversions])
# initialize input and output file names for hadd_stage2
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight)
key_hadd_stage2_dir = getKey("hadd", lepton_selection_and_frWeight)
key_hadd_stage2_job = getKey(lepton_selection_and_frWeight)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_conversions]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job])
self.outputFile_hadd_stage2[key_hadd_stage2_job] = os.path.join(self.dirs[key_hadd_stage2_dir][DKEY_HIST],
"hadd_stage2_%s.root" % lepton_selection_and_frWeight)
if self.do_sync:
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_syncNtuple(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_syncNtuple(lines_makefile)
outputFile_sync_path = os.path.join(self.outputDir, DKEY_SYNC, '%s.root' % self.channel)
self.outputFile_sync['sync'] = outputFile_sync_path
self.targets.append(outputFile_sync_path)
self.addToMakefile_hadd_sync(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
logging.info("Creating configuration files to run 'addBackgroundFakes'")
key_hadd_stage1_5_job = getKey(get_lepton_selection_and_frWeight("Fakeable", "enabled"))
key_addFakes_job = getKey("fakes_data")
category_sideband = "ttZctrl_Fakeable_wFakeRateWeights"
self.jobOptions_addFakes[key_addFakes_job] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "addBackgroundLeptonFakes_cfg.py"),
'outputFile' : os.path.join(self.dirs[DKEY_HIST], "addBackgroundLeptonFakes.root"),
'logFile' : os.path.join(self.dirs[DKEY_LOGS], "addBackgroundLeptonFakes.log"),
'category_signal' : "ttZctrl_Tight",
'category_sideband' : category_sideband
}
self.createCfg_addFakes(self.jobOptions_addFakes[key_addFakes_job])
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"))
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
logging.info("Creating configuration files to run 'prepareDatacards'")
for histogramToFit in self.histograms_to_fit:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"))
key_prep_dcard_dir = getKey("prepareDatacards")
prep_dcard_job_tuple = (self.channel, histogramToFit)
key_prep_dcard_job = getKey(histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : self.histogramDir_prep_dcard,
'histogramToFit' : histogramToFit,
'label' : None
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
# add shape templates for the following systematic uncertainties:
# - 'CMS_ttHl_Clos_norm_e'
# - 'CMS_ttHl_Clos_shape_e'
# - 'CMS_ttHl_Clos_norm_m'
# - 'CMS_ttHl_Clos_shape_m'
key_prep_dcard_job = getKey(histogramToFit)
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"))
key_add_syst_fakerate_dir = getKey("addSystFakeRates")
add_syst_fakerate_job_tuple = (self.channel, histogramToFit)
key_add_syst_fakerate_job = getKey(histogramToFit)
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job] = {
'inputFile' : self.jobOptions_prep_dcard[key_prep_dcard_job]['datacardFile'],
'cfgFile_modified' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_CFGS], "addSystFakeRates_%s_%s_cfg.py" % add_syst_fakerate_job_tuple),
'outputFile' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_DCRD], "addSystFakeRates_%s_%s.root" % add_syst_fakerate_job_tuple),
'category' : self.channel,
'histogramToFit' : histogramToFit,
'plots_outputFileName' : os.path.join(self.dirs[DKEY_PLOT], "addSystFakeRates.png")
}
histogramDir_nominal = self.histogramDir_prep_dcard
for lepton_type in [ 'e', 'm' ]:
lepton_mcClosure = "Fakeable_mcClosure_%s" % lepton_type
if lepton_mcClosure not in self.lepton_selections:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_mcClosure, "enabled")
key_addBackgrounds_job_fakes = getKey("fakes_mc", lepton_selection_and_frWeight)
histogramDir_mcClosure = self.mcClosure_dir[lepton_mcClosure]
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job].update({
'add_Clos_%s' % lepton_type : ("Fakeable_mcClosure_%s" % lepton_type) in self.lepton_selections,
'inputFile_nominal_%s' % lepton_type : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'histogramName_nominal_%s' % lepton_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_nominal, histogramToFit),
'inputFile_mcClosure_%s' % lepton_type : self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'],
'histogramName_mcClosure_%s' % lepton_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_mcClosure, histogramToFit)
})
self.createCfg_add_syst_fakerate(self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job])
logging.info("Creating configuration files to run 'makePlots'")
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"))
key_makePlots_dir = getKey("makePlots")
key_makePlots_job = getKey('')
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard,
'label' : "t#bar{t}Z control region",
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addBackgrounds)
self.sbatchFile_addBackgrounds = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds, self.jobOptions_addBackgrounds)
self.sbatchFile_addBackgrounds_sum = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_sum_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds_sum, self.jobOptions_addBackgrounds_sum)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFakes)
self.sbatchFile_addFakes = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFakes_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addFakes, self.sbatchFile_addFakes, self.jobOptions_addFakes)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_backgrounds_from_data(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_add_syst_fakerate(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def __init__(self,
configDir,
outputDir,
executable_prodNtuple,
cfgFile_prodNtuple,
samples,
max_files_per_job,
era,
preselection_cuts,
leptonSelection,
hadTauSelection,
debug,
running_method,
version,
num_parallel_jobs,
pool_id=''):
self.configDir = configDir
self.outputDir = outputDir
self.executable_prodNtuple = executable_prodNtuple
self.max_num_jobs = 200000
self.samples = samples
self.max_files_per_job = max_files_per_job
self.era = era
self.preselection_cuts = preselection_cuts
self.leptonSelection = leptonSelection
self.hadTauSelection = hadTauSelection
self.debug = debug
assert (running_method.lower()
in ["sbatch",
"makefile"]), "Invalid running method: %s" % running_method
self.running_method = running_method
self.is_sbatch = False
self.is_makefile = False
if self.running_method.lower() == "sbatch":
self.is_sbatch = True
else:
self.is_makefile = True
self.makefile = os.path.join(self.configDir, "Makefile_prodNtuple")
self.num_parallel_jobs = num_parallel_jobs
self.pool_id = pool_id if pool_id else uuid.uuid4()
self.workingDir = os.getcwd()
print "Working directory is: " + self.workingDir
self.version = version
create_if_not_exists(self.configDir)
create_if_not_exists(self.outputDir)
self.stdout_file = codecs.open(
os.path.join(self.configDir, "stdout_prodNtuple.log"), 'w',
'utf-8')
self.stderr_file = codecs.open(
os.path.join(self.configDir, "stderr_prodNtuple.log"), 'w',
'utf-8')
self.dirs = {}
self.samples = samples
self.cfgFile_prodNtuple_original = os.path.join(
self.workingDir, cfgFile_prodNtuple)
self.cfgFiles_prodNtuple_modified = {}
self.logFiles_prodNtuple = {}
self.sbatchFile_prodNtuple = os.path.join(self.configDir,
"sbatch_prodNtuple.py")
self.inputFiles = {}
self.outputFiles = {}
self.filesToClean = []
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(sample_name)
for dir_type in [DKEY_CFGS, DKEY_NTUPLES, DKEY_LOGS]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, process_name)
for dir_type in [DKEY_CFGS, DKEY_LOGS]:
initDict(self.dirs, [dir_type])
if dir_type in [DKEY_CFGS, DKEY_NTUPLES, DKEY_LOGS]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type)
##print "self.dirs = ", self.dirs
self.cvmfs_error_log = {}
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(process_name)
for dir_type in [DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel, process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel, process_name)
for dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD,
DKEY_PLOT, DKEY_HADD_RT
]:
initDict(self.dirs, [dir_type])
if dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT,
DKEY_HADD_RT
]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type,
self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type,
self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" %
numDirectories)
numDirectories_created = 0
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100 * numDirectories_created >= frac * numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" %
sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(
sample_info, self.max_files_per_job)
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info(
"Creating configuration files to run '%s' for sample %s" %
(self.executable_analyze, process_name))
inputFileList = inputFileLists[sample_name]
for jobId in inputFileList.keys():
##print "processing sample %s: jobId = %i" % (process_name, jobId)
# build config files for executing analysis code
key_analyze_dir = getKey(process_name)
analyze_job_tuple = (process_name, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning(
"No input ntuples for %s --> skipping job !!" %
(key_analyze_job))
continue
cfgFile_modified_path = os.path.join(
self.dirs[key_analyze_dir][DKEY_CFGS],
"analyze_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(
self.dirs[key_analyze_dir][DKEY_LOGS],
"analyze_%s_%i.log" % analyze_job_tuple)
histogramFile_path = os.path.join(
self.dirs[key_analyze_dir][DKEY_HIST],
"analyze_%s_%i.root" % analyze_job_tuple)
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles': ntupleFiles,
'cfgFile_modified': cfgFile_modified_path,
'histogramFile': histogramFile_path,
'histogramDir': 'analyze_hadTopTagger',
'logFile': logFile_path,
'hadTauSelection': self.hadTau_selection,
'lumiScale': 1.,
'selectBDT': True,
}
self.createCfg_analyze(
self.jobOptions_analyze[key_analyze_job], sample_info)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name,
lepton_selection_and_frWeight)
key_hadd_stage1_job = getKey(process_name)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(
self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[
key_hadd_stage1_job] = os.path.join(
self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s.root" % process_name)
self.targets.append(
self.outputFile_hadd_stage1[key_hadd_stage1_job])
self.sbatchFile_analyze = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
if self.is_sbatch:
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_analyze)
self.createScript_sbatch_analyze(self.executable_analyze,
self.sbatchFile_analyze,
self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
process_name = sample_info["process_name_specific"]
logging.info("Building dictionaries for sample %s..." % process_name)
for lepton_selection in self.lepton_selections:
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
if lepton_frWeight == "disabled" and not lepton_selection in ["Tight", "forBDTtraining"]:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
for leptonChargeSelection in self.leptonChargeSelections:
central_or_shift_extensions = ["", "hadd", "addBackgrounds"]
central_or_shift_dedicated = self.central_or_shifts if self.runTHweights(sample_info) else self.central_or_shifts_external
central_or_shifts_extended = central_or_shift_extensions + central_or_shift_dedicated
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [ process_name, "hadd" ]
for process_name_or_dummy in process_name_extended:
if central_or_shift_or_dummy in [ "hadd", "addBackgrounds" ] and process_name_or_dummy in [ "hadd" ]:
continue
if central_or_shift_or_dummy not in central_or_shift_extensions and not self.accept_systematics(
central_or_shift_or_dummy, is_mc, lepton_selection, leptonChargeSelection, sample_info
):
continue
key_dir = getKey(process_name_or_dummy, lepton_selection_and_frWeight, leptonChargeSelection, central_or_shift_or_dummy)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight, leptonChargeSelection ]), process_name_or_dummy, central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight, leptonChargeSelection ]), process_name_or_dummy)
for subdirectory in [ "addBackgrounds", "addBackgroundLeptonFakes", "prepareDatacards", "addSystFakeRates", "makePlots" ]:
key_dir = getKey(subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" % numDirectories)
numDirectories_created = 0;
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100*numDirectories_created >= frac*numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
for lepton_selection in self.lepton_selections:
hadTau_selection = "Tight"
hadTau_selection = "|".join([hadTau_selection, self.hadTau_selection_part2])
electron_selection = lepton_selection
muon_selection = lepton_selection
if lepton_selection == "forBDTtraining":
electron_selection = "Loose"
muon_selection = "Loose"
elif lepton_selection == "Fakeable_mcClosure_e":
electron_selection = "Fakeable"
muon_selection = "Tight"
elif lepton_selection == "Fakeable_mcClosure_m":
electron_selection = "Tight"
muon_selection = "Fakeable"
if "forBDTtraining" in lepton_selection:
electron_selection = "Loose"
muon_selection = "Loose"
elif lepton_selection == "Fakeable_mcClosure_e":
electron_selection = "Fakeable"
muon_selection = "Tight"
elif lepton_selection == "Fakeable_mcClosure_m":
electron_selection = "Tight"
muon_selection = "Fakeable"
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
if lepton_frWeight == "disabled" and not lepton_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
for leptonChargeSelection in self.leptonChargeSelections:
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
inputFileList = inputFileLists[sample_name]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
use_th_weights = self.runTHweights(sample_info)
central_or_shift_dedicated = self.central_or_shifts if use_th_weights else self.central_or_shifts_external
for central_or_shift in central_or_shift_dedicated:
if not self.accept_systematics(
central_or_shift, is_mc, lepton_selection, leptonChargeSelection, sample_info
):
continue
central_or_shifts_local = []
if central_or_shift == "central" and not use_th_weights:
for central_or_shift_local in self.central_or_shifts_internal:
if self.accept_systematics(
central_or_shift_local, is_mc, lepton_selection, leptonChargeSelection, sample_info
):
central_or_shifts_local.append(central_or_shift_local)
logging.info(" ... for '%s' and systematic uncertainty option '%s'" % (lepton_selection_and_frWeight, central_or_shift))
# build config files for executing analysis code
key_analyze_dir = getKey(process_name, lepton_selection_and_frWeight, leptonChargeSelection, central_or_shift)
for jobId in inputFileList.keys():
analyze_job_tuple = (process_name, lepton_selection_and_frWeight, leptonChargeSelection, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
cfgFile_modified_path = os.path.join(self.dirs[key_analyze_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % analyze_job_tuple)
rleOutputFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_RLES], "rle_%s_%s_%s_%s_%i.txt" % analyze_job_tuple) \
if self.select_rle_output else ""
histogramFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_HIST], "analyze_%s_%s_%s_%s_%i.root" % analyze_job_tuple)
applyFakeRateWeights = self.applyFakeRateWeights \
if self.isBDTtraining or lepton_selection.find("Tight") == -1 \
else "disabled"
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : cfgFile_modified_path,
'histogramFile' : histogramFile_path,
'logFile' : logFile_path,
'selEventsFileName_output' : rleOutputFile_path,
'electronSelection' : electron_selection,
'muonSelection' : muon_selection,
'apply_leptonGenMatching' : self.apply_leptonGenMatching,
'leptonChargeSelection' : leptonChargeSelection,
'applyFakeRateWeights' : applyFakeRateWeights,
'hadTauSelection' : hadTau_selection,
'central_or_shift' : central_or_shift,
'central_or_shifts_local' : central_or_shifts_local,
'fillGenEvtHistograms' : True,
'selectBDT' : self.isBDTtraining,
'apply_hlt_filter' : self.hlt_filter,
'selectBDT' : self.isBDTtraining,
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job], sample_info, lepton_selection)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name, lepton_selection_and_frWeight, leptonChargeSelection)
hadd_stage1_job_tuple = (process_name, lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage1_job = getKey(*hadd_stage1_job_tuple)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1_job] = os.path.join(self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s_%s_%s.root" % hadd_stage1_job_tuple)
if self.isBDTtraining:
continue
# add output files of hadd_stage1 to list of input files for hadd_stage1_5
key_hadd_stage1_job = getKey(process_name, lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage1_5_dir = getKey("hadd", lepton_selection_and_frWeight, leptonChargeSelection)
hadd_stage1_5_job_tuple = (lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage1_5_job = getKey(*hadd_stage1_5_job_tuple)
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.outputFile_hadd_stage1[key_hadd_stage1_job])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job] = os.path.join(self.dirs[key_hadd_stage1_5_dir][DKEY_HIST],
"hadd_stage1_5_%s_%s.root" % hadd_stage1_5_job_tuple)
if self.isBDTtraining:
continue
# sum fake background contributions for the total of all MC sample
# input processes: TT_fake, TTW_fake, TTWW_fake, ...
# output process: fakes_mc
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight, leptonChargeSelection)
key_addBackgrounds_dir = getKey("addBackgrounds")
addBackgrounds_job_fakes_tuple = ("fakes_mc", lepton_selection_and_frWeight, leptonChargeSelection)
key_addBackgrounds_job_fakes = getKey(*addBackgrounds_job_fakes_tuple)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_fake" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_fakes_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_fakes_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_fakes_tuple),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight, leptonChargeSelection) ],
'processes_input' : processes_input,
'process_output' : "fakes_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes])
# sum conversion background contributions for the total of all MC sample
# input processes: TT_Convs, TTW_Convs, TTWW_Convs, ...
# output process: Convs
addBackgrounds_job_Convs_tuple = ("Convs", lepton_selection_and_frWeight, leptonChargeSelection)
key_addBackgrounds_job_Convs = getKey(*addBackgrounds_job_Convs_tuple)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
processes_input = []
for sample_category in self.convs_backgrounds:
processes_input.append("%s_Convs" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_Convs_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_Convs_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_Convs_tuple),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight, leptonChargeSelection) ],
'processes_input' : processes_input,
'process_output' : "Convs"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs])
# sum signal contributions from HH->4tau ("tttt"), HH->2W2tau ("wwtt"), and HH->4W ("wwww"),
# separately for "nonfake" and "fake" contributions
genMatch_categories = [ "nonfake", "fake" ]
for genMatch_category in genMatch_categories:
for signal_base, signal_input in self.signal_io.items():
addBackgrounds_job_signal_tuple = (lepton_selection_and_frWeight, leptonChargeSelection, signal_base, genMatch_category)
key_addBackgrounds_job_signal = getKey(*addBackgrounds_job_signal_tuple)
if key_addBackgrounds_job_signal in self.jobOptions_addBackgrounds_sum.keys():
continue
processes_input = signal_input
process_output = signal_base
if genMatch_category == "fake":
processes_input = [ process_input + "_fake" for process_input in processes_input ]
process_output += "_fake"
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_signal] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_%s_cfg.py" % addBackgrounds_job_signal_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s_%s.root" % addBackgrounds_job_signal_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s_%s.log" % addBackgrounds_job_signal_tuple),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight, leptonChargeSelection) ],
'processes_input' : processes_input,
'process_output' : process_output
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_signal])
key_hadd_stage2_job = getKey(lepton_selection_and_frWeight, leptonChargeSelection)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_signal]['outputFile'])
# initialize input and output file names for hadd_stage2
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage2_dir = getKey("hadd", lepton_selection_and_frWeight, leptonChargeSelection)
hadd_stage2_job_tuple = (lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage2_job = getKey(*hadd_stage2_job_tuple)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job])
self.outputFile_hadd_stage2[key_hadd_stage2_job] = os.path.join(self.dirs[key_hadd_stage2_dir][DKEY_HIST],
"hadd_stage2_%s_%s.root" % hadd_stage2_job_tuple)
if self.isBDTtraining:
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.targets.extend(self.phoniesToAdd)
self.addToMakefile_validate(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
logging.info("Creating configuration files to run 'addBackgroundFakes'")
for leptonChargeSelection in self.leptonChargeSelections:
key_hadd_stage1_5_job = getKey(get_lepton_selection_and_frWeight("Fakeable", "enabled"), leptonChargeSelection)
key_addFakes_dir = getKey("addBackgroundLeptonFakes")
key_addFakes_job = getKey("data_fakes", leptonChargeSelection)
category_sideband = "hh_4l_%s_Fakeable_wFakeRateWeights" % leptonChargeSelection
self.jobOptions_addFakes[key_addFakes_job] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addFakes_dir][DKEY_CFGS], "addBackgroundLeptonFakes_%s_cfg.py" % leptonChargeSelection),
'outputFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_HIST], "addBackgroundLeptonFakes_%s.root" % leptonChargeSelection),
'logFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_LOGS], "addBackgroundLeptonFakes_%s.log" % leptonChargeSelection),
'category_signal' : "hh_4l_%s_Tight" % leptonChargeSelection,
'category_sideband' : category_sideband
}
self.createCfg_addFakes(self.jobOptions_addFakes[key_addFakes_job])
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), leptonChargeSelection)
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
logging.info("Creating configuration files to run 'prepareDatacards'")
for histogramToFit in self.histograms_to_fit:
key_prep_dcard_dir = getKey("prepareDatacards")
if "OS" in self.leptonChargeSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
prep_dcard_job_tuple = (self.channel, "OS", histogramToFit)
key_prep_dcard_job = getKey("OS", histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : self.histogramDir_prep_dcard,
'histogramToFit' : histogramToFit,
'label' : '4l',
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
if "SS" in self.leptonChargeSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
prep_dcard_job_tuple = (self.channel, "SS", histogramToFit)
key_prep_dcard_job = getKey("SS", histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : self.histogramDir_prep_dcard_SS,
'histogramToFit' : histogramToFit,
'label' : '4l SS',
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
# add shape templates for the following systematic uncertainties:
# - 'CMS_ttHl_Clos_norm_e'
# - 'CMS_ttHl_Clos_shape_e'
# - 'CMS_ttHl_Clos_norm_m'
# - 'CMS_ttHl_Clos_shape_m'
for leptonChargeSelection in self.leptonChargeSelections:
key_prep_dcard_job = getKey(leptonChargeSelection, histogramToFit)
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), leptonChargeSelection)
key_add_syst_fakerate_dir = getKey("addSystFakeRates")
add_syst_fakerate_job_tuple = (self.channel, leptonChargeSelection, histogramToFit)
key_add_syst_fakerate_job = getKey(leptonChargeSelection, histogramToFit)
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job] = {
'inputFile' : self.jobOptions_prep_dcard[key_prep_dcard_job]['datacardFile'],
'cfgFile_modified' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_CFGS], "addSystFakeRates_%s_%s_%s_cfg.py" % add_syst_fakerate_job_tuple),
'outputFile' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_DCRD], "addSystFakeRates_%s_%s_%s.root" % add_syst_fakerate_job_tuple),
'category' : self.channel,
'histogramToFit' : histogramToFit,
'plots_outputFileName' : os.path.join(self.dirs[DKEY_PLOT], "addSystFakeRates.png")
}
histogramDir_nominal = None
if leptonChargeSelection == "OS":
histogramDir_nominal = self.histogramDir_prep_dcard
elif leptonChargeSelection == "SS":
histogramDir_nominal = self.histogramDir_prep_dcard_SS
else:
raise ValueError("Invalid parameter 'leptonChargeSelection' = %s !!" % leptonChargeSelection)
for lepton_type in [ 'e', 'm' ]:
lepton_mcClosure = "Fakeable_mcClosure_%s" % lepton_type
if lepton_mcClosure not in self.lepton_selections:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_mcClosure, "enabled")
key_addBackgrounds_job_fakes = getKey("fakes_mc", lepton_selection_and_frWeight, leptonChargeSelection)
histogramDir_mcClosure = self.mcClosure_dir['%s_%s' % (lepton_mcClosure, leptonChargeSelection)]
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job].update({
'add_Clos_%s' % lepton_type : ("Fakeable_mcClosure_%s" % lepton_type) in self.lepton_selections,
'inputFile_nominal_%s' % lepton_type : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'histogramName_nominal_%s' % lepton_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_nominal, histogramToFit),
'inputFile_mcClosure_%s' % lepton_type : self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'],
'histogramName_mcClosure_%s' % lepton_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_mcClosure, histogramToFit)
})
self.createCfg_add_syst_fakerate(self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job])
logging.info("Creating configuration files to run 'makePlots'")
key_makePlots_dir = getKey("makePlots")
if "OS" in self.leptonChargeSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_makePlots_job = getKey("OS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard,
'label' : '4l',
'make_plots_backgrounds' : self.make_plots_backgrounds,
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "SS" in self.leptonChargeSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
key_makePlots_job = getKey("SS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_SS_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s_SS.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard_SS,
'label' : "4l SS",
'make_plots_backgrounds' : self.make_plots_backgrounds,
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "Fakeable_mcClosure" in self.lepton_selections: #TODO
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_makePlots_job = getKey("Fakeable_mcClosure", "OS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots_mcClosure,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_mcClosure_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_mcClosure_%s.png" % self.channel)
}
self.createCfg_makePlots_mcClosure(self.jobOptions_make_plots[key_makePlots_job])
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addBackgrounds)
self.sbatchFile_addBackgrounds = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds, self.jobOptions_addBackgrounds)
self.sbatchFile_addBackgrounds_sum = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_sum_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds_sum, self.jobOptions_addBackgrounds_sum)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFakes)
self.sbatchFile_addFakes = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFakes_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addFakes, self.sbatchFile_addFakes, self.jobOptions_addFakes)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_backgrounds_from_data(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_add_syst_fakerate(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.addToMakefile_validate(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def __init__(self, outputDir, executable_analyze, cfgFile_analyze_original, samples, hadTau_selection, hadTau_charge_selections, applyFakeRateWeights, central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
executable_addBackgrounds, executable_addBackgroundJetToTauFakes, histograms_to_fit, select_rle_output = False, executable_prep_dcard="prepareDatacard"):
analyzeConfig.__init__(self, outputDir, executable_analyze, "1l_2tau", central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
histograms_to_fit)
self.samples = samples
self.lepton_and_hadTau_selections = [ "Tight", "Fakeable", "Fakeable_mcClosure" ]
self.lepton_and_hadTau_frWeights = [ "enabled", "disabled" ]
self.hadTau_selection_part2 = hadTau_selection
self.hadTau_charge_selections = hadTau_charge_selections
self.applyFakeRateWeights = applyFakeRateWeights
self.lepton_genMatches = [ "1l0j", "0l1j" ]
self.hadTau_genMatches = [ "2t0e0m0j", "1t1e0m0j", "1t0e1m0j", "1t0e0m1j", "0t2e0m0j", "0t1e1m0j", "0t1e0m1j", "0t0e2m0j", "0t0e1m1j", "0t0e0m2j" ]
self.apply_leptonGenMatching = None
self.apply_hadTauGenMatching = None
self.lepton_and_hadTau_genMatches_nonfakes = []
self.lepton_and_hadTau_genMatches_fakes = []
if self.applyFakeRateWeights == "3L":
self.apply_leptonGenMatching = True
self.apply_hadTauGenMatching = True
for lepton_genMatch in self.lepton_genMatches:
for hadTau_genMatch in self.hadTau_genMatches:
lepton_and_hadTau_genMatch = "&".join([ lepton_genMatch, hadTau_genMatch ])
if lepton_genMatch.endswith("0j") and hadTau_genMatch.endswith("0j"):
self.lepton_and_hadTau_genMatches_nonfakes.append(lepton_and_hadTau_genMatch)
else:
self.lepton_and_hadTau_genMatches_fakes.append(lepton_and_hadTau_genMatch)
elif applyFakeRateWeights == "2tau":
self.apply_leptonGenMatching = False
self.apply_hadTauGenMatching = True
for hadTau_genMatch in self.hadTau_genMatches:
if hadTau_genMatch.endswith("0j"):
self.lepton_and_hadTau_genMatches_nonfakes.append(hadTau_genMatch)
else:
self.lepton_and_hadTau_genMatches_fakes.append(hadTau_genMatch)
else:
raise ValueError("Invalid Configuration parameter 'applyFakeRateWeights' = %s !!" % applyFakeRateWeights)
self.executable_addBackgrounds = executable_addBackgrounds
self.executable_addFakes = executable_addBackgroundJetToTauFakes
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
for lepton_and_hadTau_selection in self.lepton_and_hadTau_selections:
for lepton_and_hadTau_frWeight in self.lepton_and_hadTau_frWeights:
if lepton_and_hadTau_frWeight == "enabled" and not lepton_and_hadTau_selection.startswith("Fakeable"):
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight)
for hadTau_charge_selection in self.hadTau_charge_selections:
key_dir = getKey(sample_name, lepton_and_hadTau_selection, lepton_and_hadTau_frWeight, hadTau_charge_selection)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES ]:
initDict(self.dirs, [ key_dir, dir_type ])
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection ]), process_name)
for dir_type in [ DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ dir_type ])
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
##print "self.dirs = ", self.dirs
self.nonfake_backgrounds = [ "TT", "TTW", "TTZ", "EWK", "Rares" ]
self.cfgFile_analyze_original = os.path.join(self.workingDir, cfgFile_analyze_original)
self.cfgFile_addBackgrounds_original = os.path.join(self.workingDir, "addBackgrounds_cfg.py")
self.cfgFile_addBackgrounds_modified = {}
self.histogramFile_addBackgrounds = {}
self.histogramDir_addBackgrounds = {}
self.process_output_addBackgrounds = {}
self.histogramFile_hadd_stage1_5 = os.path.join(self.outputDir, DKEY_HIST, "histograms_harvested_stage1_5_%s.root" % self.channel)
self.cfgFile_addFakes_original = os.path.join(self.workingDir, "addBackgroundJetToTauFakes_cfg.py")
self.cfgFile_addFakes_modified = {}
self.histogramFile_addFakes = {}
self.prep_dcard_processesToCopy = [ "data_obs" ] + self.nonfake_backgrounds + [ "fakes_data", "fakes_mc" ]
self.histogramDir_prep_dcard = "1l_2tau_OS_Tight"
self.histogramDir_prep_dcard_SS = "1l_2tau_SS_Tight"
self.make_plots_backgrounds = self.nonfake_backgrounds + [ "fakes_data" ]
self.cfgFile_make_plots_mcClosure_original = os.path.join(self.workingDir, "makePlots_mcClosure_cfg.py")
self.cfgFiles_make_plots_mcClosure_modified = []
self.select_rle_output = select_rle_output
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
process_name = sample_info["process_name_specific"]
logging.info("Building dictionaries for sample %s..." % process_name)
for lepton_selection in self.lepton_selections:
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
if lepton_frWeight == "disabled" and not lepton_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
for leptonChargeSelection in self.leptonChargeSelections:
central_or_shift_extensions = ["", "hadd", "addBackgrounds"]
central_or_shift_dedicated = self.central_or_shifts if self.runTHweights(sample_info) else self.central_or_shifts_external
central_or_shifts_extended = central_or_shift_extensions + central_or_shift_dedicated
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [ process_name, "hadd" ]
for process_name_or_dummy in process_name_extended:
if central_or_shift_or_dummy in [ "hadd", "addBackgrounds" ] and process_name_or_dummy in [ "hadd" ]:
continue
if central_or_shift_or_dummy not in central_or_shift_extensions and not self.accept_systematics(
central_or_shift_or_dummy, is_mc, lepton_selection, leptonChargeSelection, sample_info
):
continue
key_dir = getKey(process_name_or_dummy, lepton_selection_and_frWeight, leptonChargeSelection, central_or_shift_or_dummy)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.get_dir_type(dir_type), dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight, leptonChargeSelection ]), process_name_or_dummy, central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight, leptonChargeSelection ]), process_name_or_dummy)
for subdirectory in [ "addBackgrounds", "addBackgroundLeptonFakes", "addBackgroundLeptonFlips", "prepareDatacards", "addSystFakeRates", "makePlots" ]:
key_dir = getKey(subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
self.dirs[key_dir][dir_type] = os.path.join(self.get_dir_type(dir_type), dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.get_dir_type(dir_type), dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" % numDirectories)
numDirectories_created = 0;
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100*numDirectories_created >= frac*numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
for lepton_selection in self.lepton_selections:
electron_selection = lepton_selection
muon_selection = lepton_selection
hadTauVeto_selection = "Tight"
hadTauVeto_selection = "|".join([ hadTauVeto_selection, self.hadTauVeto_selection_part2 ])
if lepton_selection == "forBDTtraining":
electron_selection = "Loose"
muon_selection = "Loose"
elif lepton_selection == "Fakeable_mcClosure_e":
electron_selection = "Fakeable"
muon_selection = "Tight"
elif lepton_selection == "Fakeable_mcClosure_m":
electron_selection = "Tight"
muon_selection = "Fakeable"
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
if lepton_frWeight == "disabled" and not lepton_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
for leptonChargeSelection in self.leptonChargeSelections:
if 'mcClosure' in lepton_selection and leptonChargeSelection != 'SS':
continue
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
inputFileList = inputFileLists[sample_name]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
use_th_weights = self.runTHweights(sample_info)
central_or_shift_dedicated = self.central_or_shifts if use_th_weights else self.central_or_shifts_external
for central_or_shift in central_or_shift_dedicated:
if not self.accept_systematics(
central_or_shift, is_mc, lepton_selection, leptonChargeSelection, sample_info
):
continue
central_or_shifts_local = []
if central_or_shift == "central" and not use_th_weights:
for central_or_shift_local in self.central_or_shifts_internal:
if self.accept_systematics(
central_or_shift_local, is_mc, lepton_selection, leptonChargeSelection, sample_info
):
central_or_shifts_local.append(central_or_shift_local)
logging.info(" ... for '%s' and systematic uncertainty option '%s'" % (lepton_selection_and_frWeight, central_or_shift))
# build config files for executing analysis code
key_analyze_dir = getKey(process_name, lepton_selection_and_frWeight, leptonChargeSelection, central_or_shift)
for jobId in inputFileList.keys():
analyze_job_tuple = (process_name, lepton_selection_and_frWeight, leptonChargeSelection, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
cfgFile_modified_path = os.path.join(self.dirs[key_analyze_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % analyze_job_tuple)
rleOutputFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_RLES], "rle_%s_%s_%s_%s_%i.txt" % analyze_job_tuple) \
if self.select_rle_output else ""
histogramFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_HIST], "analyze_%s_%s_%s_%s_%i.root" % analyze_job_tuple)
applyFakeRateWeights = self.applyFakeRateWeights \
if lepton_selection.find("Tight") == -1 \
else "disabled"
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : cfgFile_modified_path,
'histogramFile' : histogramFile_path,
'logFile' : logFile_path,
'selEventsFileName_output' : rleOutputFile_path,
'electronSelection' : electron_selection,
'muonSelection' : muon_selection,
'apply_leptonGenMatching' : self.apply_leptonGenMatching,
'hadTauSelection' : hadTauVeto_selection,
'leptonChargeSelection' : leptonChargeSelection,
'applyFakeRateWeights' : applyFakeRateWeights,
'central_or_shift' : central_or_shift,
'central_or_shifts_local' : central_or_shifts_local,
'selectBDT' : self.isBDTtraining,
'apply_hlt_filter' : self.hlt_filter,
'useNonNominal' : self.use_nonnominal,
'fillGenEvtHistograms' : True,
'gen_mHH' : self.gen_mHH,
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job], sample_info, lepton_selection)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name, lepton_selection_and_frWeight, leptonChargeSelection)
hadd_stage1_job_tuple = (process_name, lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage1_job = getKey(*hadd_stage1_job_tuple)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1_job] = os.path.join(self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s_%s_%s.root" % hadd_stage1_job_tuple)
if self.isBDTtraining or self.do_sync:
continue
# add output files of hadd_stage1 to list of input files for hadd_stage1_5
key_hadd_stage1_job = getKey(process_name, lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage1_5_dir = getKey("hadd", lepton_selection_and_frWeight, leptonChargeSelection)
hadd_stage1_5_job_tuple = (lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage1_5_job = getKey(*hadd_stage1_5_job_tuple)
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.outputFile_hadd_stage1[key_hadd_stage1_job])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job] = os.path.join(self.dirs[key_hadd_stage1_5_dir][DKEY_HIST],
"hadd_stage1_5_%s_%s.root" % hadd_stage1_5_job_tuple)
if self.isBDTtraining or self.do_sync:
continue
# sum fake background contributions for the total of all MC sample
# input processes: TT_fake, TTW_fake, TTWW_fake, ...
# output process: fakes_mc
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight, leptonChargeSelection)
key_addBackgrounds_dir = getKey("addBackgrounds")
addBackgrounds_job_fakes_tuple = ("fakes_mc", lepton_selection_and_frWeight, leptonChargeSelection)
key_addBackgrounds_job_fakes = getKey(*addBackgrounds_job_fakes_tuple)
sample_categories = self.get_sample_categories()
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_fake" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_fakes_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_fakes_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_fakes_tuple),
'categories' : [ getHistogramDir(category, lepton_selection, lepton_frWeight, leptonChargeSelection) for category in self.categories ],
'processes_input' : processes_input,
'process_output' : "fakes_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes])
# sum fake background contributions for the total of all MC sample
# input processes: TT_flip, TTW_flip, TTWW_flip, ...
# output process: flips_mc
addBackgrounds_job_flips_tuple = ("flips_mc", lepton_selection_and_frWeight, leptonChargeSelection)
key_addBackgrounds_job_flips = getKey(*addBackgrounds_job_flips_tuple)
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_flip" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_flips] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_flips_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_flips_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_flips_tuple),
'categories' : [ getHistogramDir(category, lepton_selection, lepton_frWeight, leptonChargeSelection) for category in self.categories ],
'processes_input' : processes_input,
'process_output' : "flips_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_flips])
# sum conversion background contributions for the total of all MC sample
# input processes: TT_Convs, TTW_Convs, TTWW_Convs, ...
# output process: Convs
addBackgrounds_job_Convs_tuple = ("Convs", lepton_selection_and_frWeight, leptonChargeSelection)
key_addBackgrounds_job_Convs = getKey(*addBackgrounds_job_Convs_tuple)
processes_input = []
for sample_category in self.convs_backgrounds:
processes_input.append("%s_Convs" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_Convs_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_Convs_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_Convs_tuple),
'categories' : [ getHistogramDir(category, lepton_selection, lepton_frWeight, leptonChargeSelection) for category in self.categories ],
'processes_input' : processes_input,
'process_output' : "Convs"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs])
# sum signal contributions from HH->4tau ("tttt"), HH->2W2tau ("wwtt"), and HH->4W ("wwww"),
# separately for "nonfake" and "fake" contributions
genMatch_categories = [ "nonfake", "fake" ]
for genMatch_category in genMatch_categories:
for signal_base, signal_input in self.signal_io.items():
addBackgrounds_job_signal_tuple = (lepton_selection_and_frWeight, leptonChargeSelection, signal_base, genMatch_category)
key_addBackgrounds_job_signal = getKey(*addBackgrounds_job_signal_tuple)
if key_addBackgrounds_job_signal in self.jobOptions_addBackgrounds_sum.keys():
continue
processes_input = signal_input
process_output = signal_base
if genMatch_category == "fake":
processes_input = [ process_input + "_fake" for process_input in processes_input ]
process_output += "_fake"
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_signal] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_%s_cfg.py" % addBackgrounds_job_signal_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s_%s.root" % addBackgrounds_job_signal_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s_%s.log" % addBackgrounds_job_signal_tuple),
'categories' : [ getHistogramDir(category, lepton_selection, lepton_frWeight, leptonChargeSelection) for category in self.categories ],
'processes_input' : processes_input,
'process_output' : process_output
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_signal])
key_hadd_stage2_job = getKey(lepton_selection_and_frWeight, leptonChargeSelection)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_signal]['outputFile'])
# initialize input and output file names for hadd_stage2
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage2_dir = getKey("hadd", lepton_selection_and_frWeight, leptonChargeSelection)
hadd_stage2_job_tuple = (lepton_selection_and_frWeight, leptonChargeSelection)
key_hadd_stage2_job = getKey(*hadd_stage2_job_tuple)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_flips]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job])
self.outputFile_hadd_stage2[key_hadd_stage2_job] = os.path.join(self.dirs[key_hadd_stage2_dir][DKEY_HIST],
"hadd_stage2_%s_%s.root" % hadd_stage2_job_tuple)
if self.isBDTtraining or self.do_sync:
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
if self.isBDTtraining:
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
elif self.do_sync:
self.createScript_sbatch_syncNtuple(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
if self.isBDTtraining:
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
elif self.do_sync:
self.addToMakefile_syncNtuple(lines_makefile)
outputFile_sync_path = os.path.join(self.outputDir, DKEY_SYNC, '%s.root' % self.channel)
self.outputFile_sync['sync'] = outputFile_sync_path
self.targets.append(outputFile_sync_path)
self.addToMakefile_hadd_sync(lines_makefile)
else:
raise ValueError("Internal logic error")
self.targets.extend(self.phoniesToAdd)
self.addToMakefile_validate(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
logging.info("Creating configuration files to run 'addBackgroundFakes'")
for category in self.categories:
for leptonChargeSelection in self.leptonChargeSelections:
key_hadd_stage1_5_job = getKey(get_lepton_selection_and_frWeight("Fakeable", "enabled"), leptonChargeSelection)
key_addFakes_dir = getKey("addBackgroundLeptonFakes")
addFakes_job_tuple = (category, leptonChargeSelection)
key_addFakes_job = getKey("data_fakes", *addFakes_job_tuple)
self.jobOptions_addFakes[key_addFakes_job] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addFakes_dir][DKEY_CFGS], "addBackgroundLeptonFakes_%s_%s_cfg.py" % addFakes_job_tuple),
'outputFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_HIST], "addBackgroundLeptonFakes_%s_%s.root" % addFakes_job_tuple),
'logFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_LOGS], "addBackgroundLeptonFakes_%s_%s.log" % addFakes_job_tuple),
'category_signal' : getHistogramDir(category, "Tight", "disabled", leptonChargeSelection),
'category_sideband' : getHistogramDir(category, "Fakeable", "enabled", leptonChargeSelection)
}
self.createCfg_addFakes(self.jobOptions_addFakes[key_addFakes_job])
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), leptonChargeSelection)
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
#--------------------------------------------------------------------------
# add histograms in OS and SS regions,
# so that "data_fakes" background can be subtracted from OS control region used to estimate charge flip background
key_hadd_stage1_5_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_hadd_stage1_6_dir = getKey("hadd", get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_hadd_stage1_6_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
if key_hadd_stage1_6_job not in self.inputFiles_hadd_stage1_6:
self.inputFiles_hadd_stage1_6[key_hadd_stage1_6_job] = []
for category in self.categories:
key_addFakes_job = getKey("data_fakes", category, leptonChargeSelection)
self.inputFiles_hadd_stage1_6[key_hadd_stage1_6_job].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
self.inputFiles_hadd_stage1_6[key_hadd_stage1_6_job].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job])
self.outputFile_hadd_stage1_6[key_hadd_stage1_6_job] = os.path.join(self.dirs[key_hadd_stage1_6_dir][DKEY_HIST],
"hadd_stage1_6_Tight_OS.root")
#--------------------------------------------------------------------------
logging.info("Creating configuration files to run 'addBackgroundFlips'")
for category in self.categories:
key_hadd_stage1_6_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_addFlips_dir = getKey("addBackgroundLeptonFlips")
key_addFlips_job = getKey("data_flips", category)
self.jobOptions_addFlips[key_addFlips_job] = {
'inputFile' : self.outputFile_hadd_stage1_6[key_hadd_stage1_6_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addFlips_dir][DKEY_CFGS], "addBackgroundLeptonFlips_%s_cfg.py" % category),
'outputFile' : os.path.join(self.dirs[key_addFlips_dir][DKEY_HIST], "addBackgroundLeptonFlips_%s.root" % category),
'logFile' : os.path.join(self.dirs[key_addFlips_dir][DKEY_LOGS], "addBackgroundLeptonFlips_%s.log" % category),
'category_signal' : getHistogramDir(category, "Tight", "disabled", "SS" ),
'category_sideband' : getHistogramDir(category, "Tight", "disabled", "OS" )
}
self.createCfg_addFlips(self.jobOptions_addFlips[key_addFlips_job])
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addFlips[key_addFlips_job]['outputFile'])
logging.info("Creating configuration files to run 'prepareDatacards'...")
for category in self.categories:
for histogramToFit in self.histograms_to_fit:
logging.info(" ... for category %s, histogram %s" % (category, histogramToFit))
prep_dcard_HH = set()
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
sample_category = sample_info["sample_category"]
masses_to_exclude = ["3000", "2500", "2000", "1750", "1500", "1250"]
if sample_category.startswith("signal"):
sample_category = sample_info["sample_category_hh"]
doAdd = False
if "BDTOutput" in histogramToFit or "MVAOutput" in histogramToFit:
if ("SM" in histogramToFit or any(nonresPoint in histogramToFit for nonresPoint in NONRESONANT_KEYS)) and 'nonresonant' in sample_category:
doAdd = True
if "spin0" in histogramToFit and "spin0" in sample_category and histogramToFit[9:13] in sample_category:
doAdd = True
if "spin2" in histogramToFit and "spin2" in sample_category and histogramToFit[9:13] in sample_category:
doAdd = True
for mass in masses_to_exclude:
if mass in sample_category: doAdd = False
else:
doAdd = True
if doAdd:
if "wwww" in sample_category:
prep_dcard_HH.add(sample_category.replace("wwww", "zzzz"))
prep_dcard_HH.add(sample_category.replace("wwww", "wwww"))
prep_dcard_HH.add(sample_category.replace("wwww", "zzww"))
elif "wwtt" in sample_category:
prep_dcard_HH.add(sample_category.replace("wwtt", "ttzz"))
prep_dcard_HH.add(sample_category.replace("wwtt", "ttww"))
elif "tttt" in sample_category:
prep_dcard_HH.add(sample_category)
else:
raise ValueError("Failed to identify relevant HH decay mode(s) for 'sample_category' = %s !!" % sample_category)
prep_dcard_HH = list(prep_dcard_HH)
prep_dcard_H = []
prep_dcard_other_nonfake_backgrounds = []
for process in self.nonfake_backgrounds:
if process in [ "VH", "WH", "ZH", "TH", "tHq", "tHW", "TTH", "TTWH", "TTZH", "ggH", "qqH" ]:
prep_dcard_H.append("%s_hww" % process)
prep_dcard_H.append("%s_hzz" % process)
prep_dcard_H.append("%s_htt" % process)
prep_dcard_H.append("%s_hbb" % process)
else:
prep_dcard_other_nonfake_backgrounds.append(process)
self.prep_dcard_processesToCopy = [ "data_obs" ] + prep_dcard_HH + prep_dcard_H + prep_dcard_other_nonfake_backgrounds + [ "Convs", "data_fakes", "data_flips", "fakes_mc", "flips_mc" ]
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
key_prep_dcard_dir = getKey("prepareDatacards")
prep_dcard_job_tuple = (self.channel, category, "SS", histogramToFit)
key_prep_dcard_job = getKey(category, "SS", histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : getHistogramDir(category, "Tight", "disabled", "SS"),
'histogramToFit' : histogramToFit,
'label' : "2lSS"
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
if "OS" in self.leptonChargeSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
prep_dcard_job_tuple = (self.channel, category, "OS", histogramToFit)
key_prep_dcard_job = getKey(category, "OS", histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : getHistogramDir(category, "Tight", "disabled", "OS"),
'histogramToFit' : histogramToFit,
'label' : "2lOS",
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
# add shape templates for the following systematic uncertainties:
# - 'CMS_ttHl_Clos_norm_e'
# - 'CMS_ttHl_Clos_shape_e'
# - 'CMS_ttHl_Clos_norm_m'
# - 'CMS_ttHl_Clos_shape_m'
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
key_add_syst_fakerate_dir = getKey("addSystFakeRates")
add_syst_fakerate_job_tuple = (self.channel, category, "SS", histogramToFit)
key_add_syst_fakerate_job = getKey(category, "SS", histogramToFit)
key_prep_dcard_job = getKey(category, "SS", histogramToFit)
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job] = {
'inputFile' : self.jobOptions_prep_dcard[key_prep_dcard_job]['datacardFile'],
'cfgFile_modified' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_CFGS], "addSystFakeRates_%s_%s_%s_%s_cfg.py" % add_syst_fakerate_job_tuple),
'outputFile' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_DCRD], "addSystFakeRates_%s_%s_%s_%s.root" % add_syst_fakerate_job_tuple),
'category' : category,
'histogramToFit' : histogramToFit,
'plots_outputFileName' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_PLOT], "addSystFakeRates.png")
}
histogramDir_nominal = "%s/sel/evt/fakes_mc" % getHistogramDir(category, "Tight", "disabled", "SS")
for lepton_type in [ 'e', 'm' ]:
lepton_mcClosure = "Fakeable_mcClosure_%s" % lepton_type
if lepton_mcClosure not in self.lepton_selections:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_mcClosure, "enabled")
key_addBackgrounds_job_fakes = getKey("fakes_mc", lepton_selection_and_frWeight, "SS")
histogramDir_mcClosure = "%s/sel/evt/fakes_mc" % self.mcClosure_dir['%s_%s' % (lepton_mcClosure, "SS")]
if "BDTOutput" in histogramToFit or "MVAOutput" in histogramToFit:
histogramDir_nominal = histogramDir_nominal.replace("/sel/evt", "/sel/datacard")
histogramDir_mcClosure = histogramDir_mcClosure.replace("/sel/evt", "/sel/datacard")
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job].update({
'add_Clos_%s' % lepton_type : ("Fakeable_mcClosure_%s" % lepton_type) in self.lepton_selections,
'inputFile_nominal_%s' % lepton_type : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'histogramName_nominal_%s' % lepton_type : "%s/%s" % (histogramDir_nominal, histogramToFit),
'inputFile_mcClosure_%s' % lepton_type : self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'],
'histogramName_mcClosure_%s' % lepton_type : "%s/%s" % (histogramDir_mcClosure, histogramToFit)
})
self.createCfg_add_syst_fakerate(self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job])
logging.info("Creating configuration files to run 'makePlots'")
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
key_makePlots_dir = getKey("makePlots")
key_makePlots_job = getKey("SS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard,
'label' : "2lSS",
'make_plots_backgrounds' : self.make_plots_backgrounds,
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "OS" in self.leptonChargeSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_makePlots_job = getKey("OS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_OS_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s_OS.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard_OS,
'label' : "2lOS",
'make_plots_backgrounds' : self.make_plots_backgrounds_OS,
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "Fakeable_mcClosure" in self.lepton_selections: #TODO
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
key_makePlots_job = getKey("Fakeable_mcClosure", "SS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots_mcClosure,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_mcClosure_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_mcClosure_%s.png" % self.channel),
}
self.createCfg_makePlots_mcClosure(self.jobOptions_make_plots[key_makePlots_job])
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addBackgrounds)
self.sbatchFile_addBackgrounds = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds, self.jobOptions_addBackgrounds)
self.sbatchFile_addBackgrounds_sum = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_sum_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds_sum, self.jobOptions_addBackgrounds_sum)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFakes)
self.sbatchFile_addFakes = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFakes_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addFakes, self.sbatchFile_addFakes, self.jobOptions_addFakes)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFlips)
self.sbatchFile_addFlips = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFlips_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addFlips, self.sbatchFile_addFlips, self.jobOptions_addFlips)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_backgrounds_from_data_withFlips(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_add_syst_fakerate(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.addToMakefile_validate(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
process_name = sample_info["process_name_specific"]
logging.info("Building dictionaries for sample %s..." % process_name)
for lepton_and_hadTau_selection in self.lepton_and_hadTau_selections:
for lepton_and_hadTau_frWeight in self.lepton_and_hadTau_frWeights:
if lepton_and_hadTau_frWeight == "enabled" and not lepton_and_hadTau_selection.startswith("Fakeable"):
continue
if lepton_and_hadTau_frWeight == "disabled" and not lepton_and_hadTau_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight)
for chargeSumSelection in self.chargeSumSelections:
central_or_shift_extensions = ["", "hadd", "addBackgrounds"]
central_or_shift_dedicated = self.central_or_shifts if self.runTHweights(sample_info) else self.central_or_shifts_external
central_or_shifts_extended = central_or_shift_extensions + central_or_shift_dedicated
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [ process_name, "hadd" ]
for process_name_or_dummy in process_name_extended:
if central_or_shift_or_dummy in [ "hadd", "addBackgrounds" ] and process_name_or_dummy in [ "hadd" ]:
continue
if central_or_shift_or_dummy not in central_or_shift_extensions and not self.accept_systematics(
central_or_shift_or_dummy, is_mc, lepton_and_hadTau_selection, chargeSumSelection, sample_info
):
continue
key_dir = getKey(process_name_or_dummy, lepton_and_hadTau_selection_and_frWeight, chargeSumSelection, central_or_shift_or_dummy)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.get_dir_type(dir_type), dir_type, self.channel,
"_".join([ lepton_and_hadTau_selection_and_frWeight, chargeSumSelection ]), process_name_or_dummy, central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_and_hadTau_selection_and_frWeight, chargeSumSelection ]), process_name_or_dummy)
for subdirectory in [ "addBackgrounds", "addBackgroundLeptonFakes", "prepareDatacards", "addSystFakeRates", "makePlots" ]:
key_dir = getKey(subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
self.dirs[key_dir][dir_type] = os.path.join(self.get_dir_type(dir_type), dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.get_dir_type(dir_type), dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" % numDirectories)
numDirectories_created = 0;
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100*numDirectories_created >= frac*numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
mcClosure_regex = re.compile('Fakeable_mcClosure_(?P<type>m|e|t)_wFakeRateWeights')
for lepton_and_hadTau_selection in self.lepton_and_hadTau_selections:
lepton_selection = lepton_and_hadTau_selection
hadTau_selection = lepton_and_hadTau_selection
electron_selection = lepton_selection
muon_selection = lepton_selection
if self.applyFakeRateWeights == "3tau":
lepton_selection = "Tight"
hadTau_selection = "|".join([ hadTau_selection, self.hadTau_selection_part2 ])
if lepton_and_hadTau_selection == "forBDTtraining":
lepton_selection = "Loose"
electron_selection = lepton_selection
muon_selection = lepton_selection
hadTau_selection = "Tight|%s" % self.hadTau_selection_relaxed
elif lepton_and_hadTau_selection == "Fakeable_mcClosure_e":
electron_selection = "Fakeable"
muon_selection = "Tight"
hadTau_selection = "Tight"
hadTau_selection = "|".join([hadTau_selection, self.hadTau_selection_part2])
elif lepton_and_hadTau_selection == "Fakeable_mcClosure_m":
electron_selection = "Tight"
muon_selection = "Fakeable"
hadTau_selection = "Tight"
hadTau_selection = "|".join([hadTau_selection, self.hadTau_selection_part2])
elif lepton_and_hadTau_selection == "Fakeable_mcClosure_t":
electron_selection = "Tight"
muon_selection = "Tight"
hadTau_selection = "Fakeable"
hadTau_selection = "|".join([hadTau_selection, self.hadTau_selection_part2])
for lepton_and_hadTau_frWeight in self.lepton_and_hadTau_frWeights:
if lepton_and_hadTau_frWeight == "enabled" and not lepton_and_hadTau_selection.startswith("Fakeable"):
continue
if lepton_and_hadTau_frWeight == "disabled" and not lepton_and_hadTau_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight)
for chargeSumSelection in self.chargeSumSelections:
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
inputFileList = inputFileLists[sample_name]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
use_th_weights = self.runTHweights(sample_info)
central_or_shift_dedicated = self.central_or_shifts if use_th_weights else self.central_or_shifts_external
for central_or_shift in central_or_shift_dedicated:
if not self.accept_systematics(
central_or_shift, is_mc, lepton_and_hadTau_selection, chargeSumSelection, sample_info
):
continue
central_or_shifts_local = []
if central_or_shift == "central" and not use_th_weights:
for central_or_shift_local in self.central_or_shifts_internal:
if self.accept_systematics(
central_or_shift_local, is_mc, lepton_and_hadTau_selection, chargeSumSelection, sample_info
):
central_or_shifts_local.append(central_or_shift_local)
logging.info(" ... for '%s' and systematic uncertainty option '%s'" % (lepton_and_hadTau_selection_and_frWeight, central_or_shift))
# build config files for executing analysis code
key_analyze_dir = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, chargeSumSelection, central_or_shift)
for jobId in inputFileList.keys():
analyze_job_tuple = (process_name, lepton_and_hadTau_selection_and_frWeight, chargeSumSelection, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
cfgFile_modified_path = os.path.join(self.dirs[key_analyze_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % analyze_job_tuple)
rleOutputFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_RLES], "rle_%s_%s_%s_%s_%i.txt" % analyze_job_tuple) \
if self.select_rle_output else ""
histogramFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_HIST], "analyze_%s_%s_%s_%s_%i.root" % analyze_job_tuple)
applyFakeRateWeights = self.applyFakeRateWeights \
if not (lepton_selection == "Tight" and hadTau_selection.find("Tight") != -1) \
else "disabled"
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : cfgFile_modified_path,
'histogramFile' : histogramFile_path,
'logFile' : logFile_path,
'selEventsFileName_output' : rleOutputFile_path,
'electronSelection' : electron_selection,
'muonSelection' : muon_selection,
'apply_leptonGenMatching' : self.apply_leptonGenMatching,
'hadTauSelection' : hadTau_selection,
'apply_hadTauGenMatching' : self.apply_hadTauGenMatching,
'chargeSumSelection' : chargeSumSelection,
'applyFakeRateWeights' : applyFakeRateWeights,
'central_or_shift' : central_or_shift,
'central_or_shifts_local' : central_or_shifts_local,
'apply_hlt_filter' : self.hlt_filter,
'useNonNominal' : self.use_nonnominal,
'fillGenEvtHistograms' : True,
'selectBDT' : self.isBDTtraining,
'gen_mHH' : self.gen_mHH,
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job], sample_info, lepton_and_hadTau_selection)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
hadd_stage1_job_tuple = (process_name, lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
key_hadd_stage1_job = getKey(*hadd_stage1_job_tuple)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1_job] = os.path.join(self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s_%s_%s.root" % hadd_stage1_job_tuple)
if self.isBDTtraining:
continue
# add output files of hadd_stage1 to list of input files for hadd_stage1_5
key_hadd_stage1_job = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
key_hadd_stage1_5_dir = getKey("hadd", lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
hadd_stage1_5_job_tuple = (lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
key_hadd_stage1_5_job = getKey(*hadd_stage1_5_job_tuple)
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.outputFile_hadd_stage1[key_hadd_stage1_job])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job] = os.path.join(self.dirs[key_hadd_stage1_5_dir][DKEY_HIST],
"hadd_stage1_5_%s_%s.root" % hadd_stage1_5_job_tuple)
if self.isBDTtraining:
continue
# sum fake background contributions for the total of all MC sample
# input processes: TT_fake, TTW_fake, TTWW_fake, ...
# output process: fakes_mc
key_hadd_stage1_5_job = getKey(lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
key_addBackgrounds_dir = getKey("addBackgrounds")
addBackgrounds_job_fakes_tuple = ("fakes_mc", lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
key_addBackgrounds_job_fakes = getKey(*addBackgrounds_job_fakes_tuple)
sample_categories = self.get_sample_categories()
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_fake" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_fakes_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_fakes_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_fakes_tuple),
'categories' : [ getHistogramDir(lepton_selection, hadTau_selection, lepton_and_hadTau_frWeight, chargeSumSelection) ],
'processes_input' : processes_input,
'process_output' : "fakes_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes])
# sum conversion background contributions for the total of all MC sample
# input processes: TT_Convs, TTW_Convs, TTWW_Convs, ...
# output process: Convs
addBackgrounds_job_Convs_tuple = ("Convs", lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
key_addBackgrounds_job_Convs = getKey(*addBackgrounds_job_Convs_tuple)
processes_input = []
for sample_category in self.convs_backgrounds:
processes_input.append("%s_Convs" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_Convs_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_Convs_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_Convs_tuple),
'categories' : [ getHistogramDir(lepton_selection, hadTau_selection, lepton_and_hadTau_frWeight, chargeSumSelection) ],
'processes_input' : processes_input,
'process_output' : "Convs"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs])
if self.isBDTtraining:
continue
# initialize input and output file names for hadd_stage2
key_hadd_stage1_5_job = getKey(lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
key_hadd_stage2_dir = getKey("hadd", lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
hadd_stage2_job_tuple = (lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
key_hadd_stage2_job = getKey(*hadd_stage2_job_tuple)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_and_hadTau_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job])
self.outputFile_hadd_stage2[key_hadd_stage2_job] = os.path.join(self.dirs[key_hadd_stage2_dir][DKEY_HIST],
"hadd_stage2_%s_%s.root" % hadd_stage2_job_tuple)
if self.isBDTtraining:
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.targets.extend(self.phoniesToAdd)
self.addToMakefile_validate(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
logging.info("Creating configuration files to run 'addBackgroundFakes'")
for chargeSumSelection in self.chargeSumSelections:
key_hadd_stage1_5_job = getKey(get_lepton_and_hadTau_selection_and_frWeight("Fakeable", "enabled"), chargeSumSelection)
key_addFakes_dir = getKey("addBackgroundLeptonFakes")
key_addFakes_job = getKey("data_fakes", chargeSumSelection)
category_sideband = None
if self.applyFakeRateWeights == "4L":
category_sideband = "hh_1l_3tau_%s_Fakeable_wFakeRateWeights" % chargeSumSelection
elif self.applyFakeRateWeights == "3tau":
category_sideband = "hh_1l_3tau_%s_Fakeable_wFakeRateWeights" % chargeSumSelection
else:
raise ValueError("Invalid Configuration parameter 'applyFakeRateWeights' = %s !!" % self.applyFakeRateWeights)
self.jobOptions_addFakes[key_addFakes_job] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addFakes_dir][DKEY_CFGS], "addBackgroundLeptonFakes_%s_cfg.py" % chargeSumSelection),
'outputFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_HIST], "addBackgroundLeptonFakes_%s.root" % chargeSumSelection),
'logFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_LOGS], "addBackgroundLeptonFakes_%s.log" % chargeSumSelection),
'category_signal' : "hh_1l_3tau_%s_Tight" % chargeSumSelection,
'category_sideband' : category_sideband
}
self.createCfg_addFakes(self.jobOptions_addFakes[key_addFakes_job])
key_hadd_stage2_job = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), chargeSumSelection)
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
logging.info("Creating configuration files to run 'prepareDatacards'...")
for histogramToFit in self.histograms_to_fit:
logging.info(" ... for histogram %s" % histogramToFit)
prep_dcard_HH = set()
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
sample_category = sample_info["sample_category"]
if sample_category.startswith("signal"):
sample_category = sample_info["sample_category_hh"]
doAdd = False
if "BDTOutput" in histogramToFit or "MVAOutput" in histogramToFit:
if ("SM" in histogramToFit or any(nonresPoint in histogramToFit for nonresPoint in NONRESONANT_KEYS)) and 'nonresonant' in sample_category:
doAdd = True
if ("spin0" in histogramToFit and "spin0" in sample_category) or ("spin2" in histogramToFit and "spin2" in sample_category):
startpos = None
for pattern in [ "MVAOutput", "BDTOutput" ]:
if pattern in histogramToFit:
startpos = histogramToFit.find(pattern) + len(pattern) + 1 # CV: increment startpos by 1 to account for trailing "_"
if not startpos:
raise ValueError("Failed to parse histogram name = '%s' !!" % histogramToFit)
endpos = histogramToFit.find("_", startpos)
masspoint = histogramToFit[startpos:endpos]
if ("_%s_" % masspoint) in sample_category:
doAdd = True
else:
doAdd = True
if doAdd:
if "_wwww" in sample_category:
prep_dcard_HH.add(sample_category.replace("_wwww", "_zzzz"))
prep_dcard_HH.add(sample_category.replace("_wwww", "_wwww"))
prep_dcard_HH.add(sample_category.replace("_wwww", "_zzww"))
if not ("BDTOutput" in histogramToFit or "MVAOutput" in histogramToFit):
prep_dcard_HH.add(sample_category.replace("_wwww", ""))
elif "_wwtt" in sample_category:
prep_dcard_HH.add(sample_category.replace("_wwtt", "_ttzz"))
prep_dcard_HH.add(sample_category.replace("_wwtt", "_ttww"))
if not ("BDTOutput" in histogramToFit or "MVAOutput" in histogramToFit):
prep_dcard_HH.add(sample_category.replace("_wwtt", ""))
elif "_tttt" in sample_category:
prep_dcard_HH.add(sample_category)
if not ("BDTOutput" in histogramToFit or "MVAOutput" in histogramToFit):
prep_dcard_HH.add(sample_category.replace("_tttt", ""))
else:
raise ValueError("Failed to identify relevant HH decay mode(s) for 'sample_category' = %s !!" % sample_category)
prep_dcard_HH = list(prep_dcard_HH)
prep_dcard_H = []
prep_dcard_other_nonfake_backgrounds = []
for process in self.nonfake_backgrounds:
if process in [ "VH", "WH", "ZH", "TH", "tHq", "tHW", "TTH", "TTWH", "TTZH", "ggH", "qqH" ]:
prep_dcard_H.append("%s_hww" % process)
prep_dcard_H.append("%s_hzz" % process)
prep_dcard_H.append("%s_htt" % process)
prep_dcard_H.append("%s_hbb" % process)
else:
prep_dcard_other_nonfake_backgrounds.append(process)
self.prep_dcard_processesToCopy = [ "data_obs" ] + prep_dcard_HH + prep_dcard_H + prep_dcard_other_nonfake_backgrounds + [ "Convs", "data_fakes", "fakes_mc" ]
key_prep_dcard_dir = getKey("prepareDatacards")
if "OS" in self.chargeSumSelections:
key_hadd_stage2_job = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "OS")
prep_dcard_job_tuple = (self.channel, "OS", histogramToFit)
key_prep_dcard_job = getKey("OS", histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : self.histogramDir_prep_dcard,
'histogramToFit' : histogramToFit,
'label' : None
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
if "SS" in self.chargeSumSelections:
key_hadd_stage2_job = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "SS")
prep_dcard_job_tuple = (self.channel, "SS", histogramToFit)
key_prep_dcard_job = getKey("SS", histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : self.histogramDir_prep_dcard_SS,
'histogramToFit' : histogramToFit,
'label' : 'SS'
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
# add shape templates for the following systematic uncertainties:
# - 'CMS_ttHl_Clos_norm_e'
# - 'CMS_ttHl_Clos_shape_e'
# - 'CMS_ttHl_Clos_norm_m'
# - 'CMS_ttHl_Clos_shape_m'
# - 'CMS_ttHl_Clos_norm_t'
# - 'CMS_ttHl_Clos_shape_t'
for chargeSumSelection in self.chargeSumSelections:
key_prep_dcard_job = getKey(chargeSumSelection, histogramToFit)
key_hadd_stage2_job = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), chargeSumSelection)
key_add_syst_fakerate_dir = getKey("addSystFakeRates")
add_syst_fakerate_job_tuple = (self.channel, chargeSumSelection, histogramToFit)
key_add_syst_fakerate_job = getKey(chargeSumSelection, histogramToFit)
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job] = {
'inputFile' : self.jobOptions_prep_dcard[key_prep_dcard_job]['datacardFile'],
'cfgFile_modified' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_CFGS], "addSystFakeRates_%s_%s_%s_cfg.py" % add_syst_fakerate_job_tuple),
'outputFile' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_DCRD], "addSystFakeRates_%s_%s_%s.root" % add_syst_fakerate_job_tuple),
'category' : self.channel,
'histogramToFit' : histogramToFit,
'plots_outputFileName' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_PLOT], "addSystFakeRates.png")
}
histogramDir_nominal = None
if chargeSumSelection == "OS":
histogramDir_nominal = "%s/sel/evt/fakes_mc" % self.histogramDir_prep_dcard
elif chargeSumSelection == "SS":
histogramDir_nominal = "%s/sel/evt/fakes_mc" % self.histogramDir_prep_dcard_SS
else:
raise ValueError("Invalid parameter 'chargeSumSelection' = %s !!" % chargeSumSelection)
for lepton_and_hadTau_type in [ 'e', 'm', 't' ]:
lepton_and_hadTau_mcClosure = "Fakeable_mcClosure_%s" % lepton_and_hadTau_type
if lepton_and_hadTau_mcClosure not in self.lepton_and_hadTau_selections:
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_mcClosure, "enabled")
key_addBackgrounds_job_fakes = getKey("fakes_mc", lepton_and_hadTau_selection_and_frWeight, chargeSumSelection)
histogramDir_mcClosure = "%s/sel/evt/fakes_mc" % self.mcClosure_dir['%s_%s' % (lepton_and_hadTau_mcClosure, chargeSumSelection)]
if "BDTOutput" in histogramToFit or "MVAOutput" in histogramToFit:
histogramDir_nominal = histogramDir_nominal.replace("/sel/evt", "/sel/datacard")
histogramDir_mcClosure = histogramDir_mcClosure.replace("/sel/evt", "/sel/datacard")
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job].update({
'add_Clos_%s' % lepton_and_hadTau_type : ("Fakeable_mcClosure_%s" % lepton_and_hadTau_type) in self.lepton_and_hadTau_selections,
'inputFile_nominal_%s' % lepton_and_hadTau_type : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'histogramName_nominal_%s' % lepton_and_hadTau_type : "%s/%s" % (histogramDir_nominal, histogramToFit),
'inputFile_mcClosure_%s' % lepton_and_hadTau_type : self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'],
'histogramName_mcClosure_%s' % lepton_and_hadTau_type : "%s/%s" % (histogramDir_mcClosure, histogramToFit)
})
self.createCfg_add_syst_fakerate(self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job])
logging.info("Creating configuration files to run 'makePlots'")
key_makePlots_dir = getKey("makePlots")
if "OS" in self.chargeSumSelections:
key_hadd_stage2_job = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "OS")
key_makePlots_job = getKey("OS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard,
'label' : '1l+3#tau_{h}',
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "SS" in self.chargeSumSelections:
key_hadd_stage2_job = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "SS")
key_makePlots_job = getKey("SS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_SS_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s_SS.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard_SS,
'label' : "1l+3#tau_{h} SS",
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "Fakeable_mcClosure" in self.lepton_and_hadTau_selections: #TODO
key_hadd_stage2_job = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "OS")
key_makePlots_job = getKey("Fakeable_mcClosure", "OS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots_mcClosure,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_mcClosure_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_mcClosure_%s.png" % self.channel)
}
self.createCfg_makePlots_mcClosure(self.jobOptions_make_plots[key_makePlots_job])
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addBackgrounds)
self.sbatchFile_addBackgrounds = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds, self.jobOptions_addBackgrounds)
self.sbatchFile_addBackgrounds_sum = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_sum_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds_sum, self.jobOptions_addBackgrounds_sum)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFakes)
self.sbatchFile_addFakes = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFakes_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addFakes, self.sbatchFile_addFakes, self.jobOptions_addFakes)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_backgrounds_from_data(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_add_syst_fakerate(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.addToMakefile_validate(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
process_name = sample_info["process_name_specific"]
logging.info("Building dictionaries for sample %s..." % process_name)
for chargeSumSelection in self.chargeSumSelections:
for lepton_and_hadTau_selection in self.lepton_and_hadTau_selections:
for lepton_and_hadTau_frWeight in self.lepton_and_hadTau_frWeights:
if lepton_and_hadTau_frWeight == "enabled" and not lepton_and_hadTau_selection.startswith("Fakeable"):
continue
if lepton_and_hadTau_frWeight == "disabled" and not lepton_and_hadTau_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight)
central_or_shift_extensions = ["", "hadd", "copyHistograms", "addBackgrounds"]
central_or_shift_dedicated = self.central_or_shifts if self.runTHweights(sample_info) else self.central_or_shifts_external
central_or_shifts_extended = central_or_shift_extensions + central_or_shift_dedicated
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [ process_name, "hadd" ]
for process_name_or_dummy in process_name_extended:
if process_name_or_dummy in [ "hadd" ] and central_or_shift_or_dummy != "":
continue
evtcategories_extended = [""]
evtcategories_extended.extend(self.evtCategories)
if central_or_shift_or_dummy in [ "hadd", "copyHistograms", "addBackgrounds" ] and process_name_or_dummy in [ "hadd" ]:
continue
if central_or_shift_or_dummy not in central_or_shift_extensions and not self.accept_systematics(
central_or_shift_or_dummy, is_mc, lepton_and_hadTau_selection, chargeSumSelection, sample_category, sample_name, sample_info
):
continue
key_dir = getKey(process_name_or_dummy, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight, central_or_shift_or_dummy)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_ROOT, DKEY_RLES, DKEY_SYNC ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
"_".join([ lepton_and_hadTau_selection_and_frWeight, chargeSumSelection ]), process_name_or_dummy, central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_and_hadTau_selection_and_frWeight, chargeSumSelection ]), process_name_or_dummy, central_or_shift_or_dummy)
for subdirectory in [ "addBackgrounds", "addBackgroundLeptonFakes", "prepareDatacards", "addSystFakeRates", "makePlots" ]:
key_dir = getKey(subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_ROOT, DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_SYNC ]:
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" % numDirectories)
numDirectories_created = 0;
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100*numDirectories_created >= frac*numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
for chargeSumSelection in self.chargeSumSelections:
for lepton_and_hadTau_selection in self.lepton_and_hadTau_selections:
lepton_selection = lepton_and_hadTau_selection
##hadTau_selection = None
##if lepton_and_hadTau_selection == "Tight":
## hadTau_selection = "%s|%s" % (lepton_and_hadTau_selection, self.hadTau_mva_wp)
##else:
## hadTau_selection = lepton_and_hadTau_selection
hadTau_selection = "%s|%s" % (lepton_and_hadTau_selection, self.hadTau_mva_wp)
electron_selection = lepton_selection
muon_selection = lepton_selection
if lepton_and_hadTau_selection == "Fakeable_mcClosure_e":
electron_selection = "Fakeable"
muon_selection = "Tight"
hadTau_selection = "Tight|%s" % self.hadTau_mva_wp
elif lepton_and_hadTau_selection == "Fakeable_mcClosure_m":
electron_selection = "Tight"
muon_selection = "Fakeable"
hadTau_selection = "Tight|%s" % self.hadTau_mva_wp
elif lepton_and_hadTau_selection == "Fakeable_mcClosure_t":
electron_selection = "Tight"
muon_selection = "Tight"
hadTau_selection = "Fakeable"
for lepton_and_hadTau_frWeight in self.lepton_and_hadTau_frWeights:
if lepton_and_hadTau_frWeight == "enabled" and not lepton_and_hadTau_selection.startswith("Fakeable"):
continue
if lepton_and_hadTau_frWeight == "disabled" and not lepton_and_hadTau_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight)
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
inputFileList = inputFileLists[sample_name]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
use_th_weights = self.runTHweights(sample_info)
central_or_shift_dedicated = self.central_or_shifts if use_th_weights else self.central_or_shifts_external
for central_or_shift in central_or_shift_dedicated:
if not self.accept_systematics(
central_or_shift, is_mc, lepton_and_hadTau_selection, chargeSumSelection, sample_category, sample_name, sample_info
):
continue
central_or_shifts_local = []
if central_or_shift == "central" and not use_th_weights:
for central_or_shift_local in self.central_or_shifts_internal:
if self.accept_systematics(
central_or_shift_local, is_mc, lepton_and_hadTau_selection, chargeSumSelection, sample_category, sample_name, sample_info
):
central_or_shifts_local.append(central_or_shift_local)
logging.info(" ... for '%s' and systematic uncertainty option '%s'" % (lepton_and_hadTau_selection_and_frWeight, central_or_shift))
# build config files for executing analysis code
key_analyze_dir = getKey(process_name, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight, central_or_shift)
for jobId in inputFileList.keys():
analyze_job_tuple = (process_name, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
cfgFile_modified_path = os.path.join(self.dirs[key_analyze_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % analyze_job_tuple)
histogramFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_HIST], "analyze_%s_%s_%s_%s_%i.root" % analyze_job_tuple)
logFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % analyze_job_tuple)
rleOutputFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_RLES], "rle_%s_%s_%s_%s_%i.txt" % analyze_job_tuple) \
if self.select_rle_output else ""
applyFakeRateWeights = self.applyFakeRateWeights \
if self.isBDTtraining or not lepton_and_hadTau_selection == "Tight" \
else "disabled"
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : cfgFile_modified_path,
'histogramFile' : histogramFile_path,
'logFile' : logFile_path,
'selEventsFileName_output' : rleOutputFile_path,
'electronSelection' : electron_selection,
'muonSelection' : muon_selection,
'apply_leptonGenMatching' : self.apply_leptonGenMatching,
'hadTauSelection' : hadTau_selection,
'apply_hadTauGenMatching' : self.apply_hadTauGenMatching,
'chargeSumSelection' : chargeSumSelection,
'applyFakeRateWeights' : applyFakeRateWeights,
'central_or_shift' : central_or_shift,
'central_or_shifts_local' : central_or_shifts_local,
'selectBDT' : self.isBDTtraining,
'apply_hlt_filter' : self.hlt_filter,
'useNonNominal' : self.use_nonnominal,
'fillGenEvtHistograms' : True,
'useObjectMultiplicity' : True,
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job], sample_info, lepton_and_hadTau_selection)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight, "hadd")
hadd_stage1_job_tuple = (process_name, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
key_hadd_stage1_job = getKey(*hadd_stage1_job_tuple)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1_job] = os.path.join(self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s_%s_%s.root" % hadd_stage1_job_tuple)
if self.isBDTtraining:
self.targets.append(self.outputFile_hadd_stage1[key_hadd_stage1_job])
if self.isBDTtraining:
continue
#----------------------------------------------------------------------------
# split hadd_stage1 files into separate files, one for each event category
for category in self.evtCategories:
key_hadd_stage1_job = getKey(process_name, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
key_copyHistograms_dir = getKey(process_name, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight, "copyHistograms")
copyHistograms_job_tuple = (category, process_name, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
key_copyHistograms_job = getKey(*copyHistograms_job_tuple)
cfgFile_modified = os.path.join(self.dirs[key_copyHistograms_dir][DKEY_CFGS], "copyHistograms_%s_%s_%s_%s_cfg.py" % copyHistograms_job_tuple)
outputFile = os.path.join(self.dirs[key_copyHistograms_dir][DKEY_HIST], "copyHistograms_%s_%s_%s_%s.root" % copyHistograms_job_tuple)
self.jobOptions_copyHistograms[key_copyHistograms_job] = {
'inputFile' : self.outputFile_hadd_stage1[key_hadd_stage1_job],
'cfgFile_modified' : cfgFile_modified,
'outputFile' : outputFile,
'logFile' : os.path.join(self.dirs[key_copyHistograms_dir][DKEY_LOGS], os.path.basename(cfgFile_modified).replace("_cfg.py", ".log")),
'categories' : [ category ],
}
self.createCfg_copyHistograms(self.jobOptions_copyHistograms[key_copyHistograms_job])
#----------------------------------------------------------------------------
# add output files of copyHistograms jobs to list of input files for hadd_stage1_5
for category in self.evtCategories:
key_copyHistograms_job = getKey(category, process_name, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
key_hadd_stage1_5_dir = getKey("hadd", chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
hadd_stage1_5_job_tuple = (category, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
key_hadd_stage1_5_job = getKey(*hadd_stage1_5_job_tuple)
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.jobOptions_copyHistograms[key_copyHistograms_job]['outputFile'])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job] = os.path.join(self.dirs[key_hadd_stage1_5_dir][DKEY_HIST],
"hadd_stage1_5_%s_%s_%s.root" % hadd_stage1_5_job_tuple)
if self.isBDTtraining:
continue
for category in self.evtCategories:
# sum fake background contributions for the total of all MC sample
# input processes: TT_fake, TTW_fake, TTWW_fake, ...
# output process: fakes_mc
key_hadd_stage1_5_job = getKey(category, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
key_addBackgrounds_dir = getKey("addBackgrounds")
addBackgrounds_job_fakes_tuple = ("fakes_mc", category, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
key_addBackgrounds_job_fakes = getKey(*addBackgrounds_job_fakes_tuple)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_fake" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_%s_cfg.py" % addBackgrounds_job_fakes_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s_%s.root" % addBackgrounds_job_fakes_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s_%s.log" % addBackgrounds_job_fakes_tuple),
'categories' : [ getHistogramDir(category, lepton_and_hadTau_selection, lepton_and_hadTau_frWeight, chargeSumSelection) ],
'processes_input' : processes_input,
'process_output' : "fakes_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes])
# sum conversion background contributions for the total of all MC sample
# input processes: TT_Convs, TTW_Convs, TTWW_Convs, ...
# output process: Convs
addBackgrounds_job_Convs_tuple = ("Convs", category, lepton_and_hadTau_selection, lepton_and_hadTau_frWeight, chargeSumSelection)
key_addBackgrounds_job_Convs = getKey(*addBackgrounds_job_Convs_tuple)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_Convs" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_%s_%s_cfg.py" % addBackgrounds_job_Convs_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s_%s_%s.root" % addBackgrounds_job_Convs_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s_%s_%s.log" % addBackgrounds_job_Convs_tuple),
'categories' : [ getHistogramDir(category, lepton_and_hadTau_selection, lepton_and_hadTau_frWeight, chargeSumSelection) ],
'processes_input' : processes_input,
'process_output' : "Convs"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs])
# sum signal contributions from gluon fusion and VBF HH production,
# separately for "nonfake" and "fake" contributions
genMatch_categories = [ "nonfake", "fake" ]
for genMatch_category in genMatch_categories:
for signal_base, signal_input in self.signal_io.items():
addBackgrounds_job_signal_tuple = (category, chargeSumSelection, lepton_and_hadTau_selection, signal_base, genMatch_category)
key_addBackgrounds_job_signal = getKey(*addBackgrounds_job_signal_tuple)
if key_addBackgrounds_job_signal in self.jobOptions_addBackgrounds_sum.keys():
continue
processes_input = signal_input
process_output = signal_base
if genMatch_category == "fake":
processes_input = [ process_input + "_fake" for process_input in processes_input ]
process_output += "_fake"
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_signal] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_%s_%s_cfg.py" % addBackgrounds_job_signal_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s_%s_%s.root" % addBackgrounds_job_signal_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s_%s_%s.log" % addBackgrounds_job_signal_tuple),
'categories' : [ getHistogramDir(category, lepton_and_hadTau_selection, lepton_and_hadTau_frWeight, chargeSumSelection) ],
'processes_input' : processes_input,
'process_output' : process_output
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_signal])
key_hadd_stage2_job = getKey(category, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_signal]['outputFile'])
# initialize input and output file names for hadd_stage2
key_hadd_stage1_5_job = getKey(category, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
key_hadd_stage2_dir = getKey("hadd", chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
hadd_stage2_job_tuple = (category, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
key_hadd_stage2_job = getKey(*hadd_stage2_job_tuple)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job])
self.outputFile_hadd_stage2[key_hadd_stage2_job] = os.path.join(self.dirs[key_hadd_stage2_dir][DKEY_HIST],
"hadd_stage2_%s_%s_%s.root" % hadd_stage2_job_tuple)
if self.isBDTtraining:
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done")
return self.num_jobs
logging.info("Creating configuration files to run 'addBackgroundFakes'")
for chargeSumSelection in self.chargeSumSelections:
for category in self.evtCategories:
key_hadd_stage1_5_job = getKey(category, chargeSumSelection, get_lepton_and_hadTau_selection_and_frWeight("Fakeable", "enabled"))
key_addFakes_dir = getKey("addBackgroundLeptonFakes")
addFakes_job_tuple = (category, chargeSumSelection)
key_addFakes_job = getKey("data_fakes", *addFakes_job_tuple)
self.jobOptions_addFakes[key_addFakes_job] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addFakes_dir][DKEY_CFGS], "addBackgroundLeptonFakes_%s_%s_cfg.py" % addFakes_job_tuple),
'outputFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_HIST], "addBackgroundLeptonFakes_%s_%s.root" % addFakes_job_tuple),
'logFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_LOGS], "addBackgroundLeptonFakes_%s_%s.log" % addFakes_job_tuple),
'category_signal' : getHistogramDir(category, "Tight", "disabled", chargeSumSelection),
'category_sideband' : getHistogramDir(category, "Fakeable", "enabled", chargeSumSelection)
}
self.createCfg_addFakes(self.jobOptions_addFakes[key_addFakes_job])
key_hadd_stage2_job = getKey(category, chargeSumSelection, get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"))
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
logging.info("Creating configuration files to run 'prepareDatacards'")
for chargeSumSelection in self.chargeSumSelections:
for category in self.evtCategories:
for histogramToFit in self.histograms_to_fit:
key_hadd_stage2_job = getKey(category, chargeSumSelection, get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"))
key_prep_dcard_dir = getKey("prepareDatacards")
prep_dcard_job_tuple = (self.channel, category, chargeSumSelection, histogramToFit)
key_prep_dcard_job = getKey(category, chargeSumSelection, histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : getHistogramDir(category, "Tight", "disabled", chargeSumSelection),
'histogramToFit' : histogramToFit
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
# add shape templates for the following systematic uncertainties:
# - 'CMS_ttHl_Clos_norm_e'
# - 'CMS_ttHl_Clos_shape_e'
# - 'CMS_ttHl_Clos_norm_m'
# - 'CMS_ttHl_Clos_shape_m'
key_add_syst_fakerate_dir = getKey("addSystFakeRates")
add_syst_fakerate_job_tuple = (self.channel, category, chargeSumSelection, histogramToFit)
key_add_syst_fakerate_job = getKey(category, chargeSumSelection, histogramToFit)
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job] = {
'inputFile' : self.jobOptions_prep_dcard[key_prep_dcard_job]['datacardFile'],
'cfgFile_modified' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_CFGS], "addSystFakeRates_%s_%s_%s_%s_cfg.py" % add_syst_fakerate_job_tuple),
'outputFile' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_DCRD], "addSystFakeRates_%s_%s_%s_%s.root" % add_syst_fakerate_job_tuple),
'category' : category,
'histogramToFit' : histogramToFit,
'plots_outputFileName' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_PLOT], "addSystFakeRates.png")
}
histogramDir_nominal = getHistogramDir(category, "Tight", "disabled", chargeSumSelection)
for lepton_and_hadTau_type in [ 'e', 'm', 't' ]:
lepton_and_hadTau_mcClosure = "Fakeable_mcClosure_%s" % lepton_and_hadTau_type
if lepton_and_hadTau_mcClosure not in self.lepton_and_hadTau_selections:
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_mcClosure, "enabled")
key_addBackgrounds_job_fakes = getKey("fakes_mc", category, chargeSumSelection, lepton_and_hadTau_selection_and_frWeight)
histogramDir_mcClosure = self.mcClosure_dir[lepton_and_hadTau_mcClosure+'_%s' %chargeSumSelection]
histogramDir_mcClosure = histogramDir_mcClosure.replace(self.evtCategory_inclusive, category)
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job].update({
'add_Clos_%s' % lepton_and_hadTau_type : ("Fakeable_mcClosure_%s" % lepton_and_hadTau_type) in self.lepton_and_hadTau_selections,
'inputFile_nominal_%s' % lepton_and_hadTau_type : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'histogramName_nominal_%s' % lepton_and_hadTau_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_nominal, histogramToFit),
'inputFile_mcClosure_%s' % lepton_and_hadTau_type : self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'],
'histogramName_mcClosure_%s' % lepton_and_hadTau_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_mcClosure, histogramToFit)
})
self.createCfg_add_syst_fakerate(self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job])
logging.info("Creating configuration files to run 'makePlots'")
for chargeSumSelection in self.chargeSumSelections:
key_hadd_stage2_job = getKey(self.evtCategory_inclusive, chargeSumSelection, get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"))
key_makePlots_dir = getKey("makePlots")
key_makePlots_job = getKey(chargeSumSelection)
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_%s_cfg.py" % (self.channel, chargeSumSelection)),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s_%s.png" % (self.channel, chargeSumSelection)),
'histogramDir' : getHistogramDir(self.evtCategory_inclusive, "Tight", "disabled", chargeSumSelection),
'label' : '1l1tau',
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "Fakeable_mcClosure" in self.lepton_and_hadTau_selections: #TODO
key_makePlots_job = getKey(chargeSumSelection)
key_hadd_stage2 = getKey(chargeSumSelection, get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"))
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots_mcClosure,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_mcClosure_%s_%s_cfg.py" % (self.channel, chargeSumSelection)),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_mcClosure_%s_%s.png" % (self.channel, chargeSumSelection))
}
self.createCfg_makePlots_mcClosure(self.jobOptions_make_plots[key_makePlots_job])
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_copyHistograms)
self.sbatchFile_copyHistograms = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_copyHistograms_%s.py" % self.channel)
self.createScript_sbatch_copyHistograms(self.executable_copyHistograms, self.sbatchFile_copyHistograms, self.jobOptions_copyHistograms)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addBackgrounds)
self.sbatchFile_addBackgrounds = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_%s.py" % self.channel)
self.createScript_sbatch_addBackgrounds(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds, self.jobOptions_addBackgrounds)
self.sbatchFile_addBackgrounds_sum = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_sum_%s.py" % self.channel)
self.createScript_sbatch_addBackgrounds(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds_sum, self.jobOptions_addBackgrounds_sum)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFakes)
self.sbatchFile_addFakes = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFakes_%s.py" % self.channel)
self.createScript_sbatch_addFakes(self.executable_addFakes, self.sbatchFile_addFakes, self.jobOptions_addFakes)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_copyHistograms(lines_makefile, make_target = "phony_copyHistograms", make_dependency = "phony_hadd_stage1")
self.addToMakefile_backgrounds_from_data(lines_makefile, make_dependency = "phony_copyHistograms")
#----------------------------------------------------------------------------
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_add_syst_fakerate(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
for lepton_selection in self.lepton_selections:
#lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight)
key_dir = getKey(process_name, lepton_selection)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
"_".join([ lepton_selection ]), process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_selection ]), process_name)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
##print "self.dirs = ", self.dirs
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
else:
create_if_not_exists(self.dirs[key])
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_name, sample_info, self.max_files_per_job, self.debug)
for lepton_selection in self.lepton_selections:
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
# Edit Siddh ~~~~~~~~~~~~~
run_process = False
for sprocess_run in sample_process_run_s:
if sprocess_run == process_name:
run_process = True
#print "Run process: ", sprocess_run
if run_process == False:
continue
#print "run_process:",process_name
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
#if not ("DY" in process_name or "Muon" in process_name): continue
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
is_signal = (sample_category == "signal")
inputFileList = inputFileLists[sample_name]
for central_or_shift in self.central_or_shifts:
for jobId in inputFileList.keys():
#if central_or_shift != "central" and not (lepton_and_hadTau_selection.startswith("Tight") and lepton_charge_selection == "SS"):
# continue
if central_or_shift != "central" and not is_mc:
continue
# build config files for executing analysis code
key_dir = getKey(process_name, lepton_selection)
key_analyze_job = getKey(process_name, lepton_selection, central_or_shift, jobId)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
print "Warning: ntupleFiles['%s'] = %s --> skipping job !!" % (key_job, ntupleFiles)
continue
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : os.path.join(self.dirs[key_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % \
(self.channel, process_name, lepton_selection, central_or_shift, jobId)),
'histogramFile' : os.path.join(self.dirs[key_dir][DKEY_HIST], "%s_%s_%s_%i.root" % \
(process_name, lepton_selection, central_or_shift, jobId)),
'logFile' : os.path.join(self.dirs[key_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % \
(self.channel, process_name, lepton_selection, central_or_shift, jobId)),
'rleOutputFile' : os.path.join(self.dirs[key_dir][DKEY_RLES], "rle_%s_%s_%s_%s_%i.txt" % \
(self.channel, process_name, lepton_selection, central_or_shift, jobId)) if self.select_rle_output else "",
'sample_category' : sample_category,
'triggers' : sample_info["triggers"],
'lepton_selection' : lepton_selection,
#'apply_leptonGenMatching' : self.apply_leptonGenMatching,
#'apply_hadTauGenMatching' : self.apply_hadTauGenMatching,
#'applyFakeRateWeights' : self.applyFakeRateWeights if not (lepton_selection == "Tight" and hadTau_selection.find("Tight") != -1) else "disabled",
'applyFakeRateWeights' : "disabled",
'use_HIP_mitigation_mediumMuonId' : True,
'is_mc' : is_mc,
'central_or_shift' : central_or_shift,
'lumi_scale' : 1. if not (self.use_lumi and is_mc) else sample_info["xsection"] * self.lumi / sample_info["nof_events"],
'apply_genWeight' : sample_info["genWeight"] if (is_mc and "genWeight" in sample_info.keys()) else False,
'apply_trigger_bits' : (is_mc and (self.era == "2015" or (self.era == "2016" and sample_info["reHLT"]))) or not is_mc
}
#applyFakeRateWeights = self.applyFakeRateWeights
#if lepton_and_hadTau_frWeight == "disabled":
# applyFakeRateWeights = "disabled"
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job])
# initialize input and output file names for hadd_stage1
key_hadd_stage1 = getKey(process_name, lepton_selection)
if not key_hadd_stage1 in self.inputFiles_hadd_stage1.keys():
self.inputFiles_hadd_stage1[key_hadd_stage1] = []
self.inputFiles_hadd_stage1[key_hadd_stage1].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_%s_%s_%s.root" % \
(self.channel, process_name, lepton_selection))
print key_hadd_stage1, self.channel, process_name, lepton_selection, self.outputFile_hadd_stage1[key_hadd_stage1]
#key_hadd_stage1 = getKey(process_name, lepton_selection)
#key_hadd_stage1_5 = getKey(lepton_selection)
#print self.inputFiles_hadd_stage1_5
#self.inputFiles_hadd_stage1_5[key_hadd_stage1_5].append(self.outputFile_hadd_stage1[key_hadd_stage1])
key_addBackgrounds_job = getKey(lepton_selection)
sample_categories = []
sample_categories.extend([ "signal" ])
processes_input = []
# initialize input and output file names for hadd_stage2
key_hadd_stage2 = getKey(lepton_selection)
if not key_hadd_stage2 in self.inputFiles_hadd_stage2.keys():
self.inputFiles_hadd_stage2[key_hadd_stage2] = []
#if lepton_selection == "Tight":
# self.inputFiles_hadd_stage2[key_hadd_stage2].append(self.jobOptions_addBackgrounds[key_addBackgrounds_job]['outputFile'])
#key_hadd_stage1_5 = getKey(lepton_and_hadTau_selection_and_frWeight)
self.inputFiles_hadd_stage2[key_hadd_stage2].append(self.outputFile_hadd_stage1[key_hadd_stage1])
self.outputFile_hadd_stage2[key_hadd_stage2] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage2_%s_%s.root" % \
(self.channel, lepton_selection))
key_hadd_stage2 = getKey(lepton_selection)
#self.inputFiles_hadd_stage2[key_hadd_stage2].append(self.jobOptions_addFlips[key_addFlips_job]['outputFile'])
logging.info("Creating configuration files to run 'prepareDatacards'")
processesToCopy = []
for process in self.prep_dcard_processesToCopy:
processesToCopy.append(process)
self.prep_dcard_processesToCopy = processesToCopy
processesToCopy = []
for process in self.prep_dcard_signals:
processesToCopy.append(process)
self.prep_dcard_signals = processesToCopy
for histogramToFit in self.histograms_to_fit:
key_prep_dcard_job = getKey(histogramToFit)
key_hadd_stage2 = getKey(lepton_selection)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "prepareDatacards_%s_%s_cfg.py" % (self.channel, histogramToFit)),
'datacardFile' : os.path.join(self.dirs[DKEY_DCRD], "prepareDatacards_%s_%s.root" % (self.channel, histogramToFit)),
'histogramDir' : self.histogramDir_prep_dcard,
'histogramToFit' : histogramToFit,
'label' : None
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
#self.addToMakefile_backgrounds_from_data(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
#self.addToMakefile_add_syst_dcard(lines_makefile)
#self.addToMakefile_make_plots(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done")
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
logging.info("Building dictionaries for sample %s..." % process_name)
central_or_shift_extensions = ["", "hadd", "addBackgrounds"]
central_or_shifts_extended = central_or_shift_extensions + self.central_or_shifts
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [ process_name, "hadd" ]
for process_name_or_dummy in process_name_extended:
if central_or_shift_or_dummy in [ "hadd", "addBackgrounds" ] and process_name_or_dummy in [ "hadd" ]:
if not is_mc:
continue
if not self.accept_central_or_shift(central_or_shift_or_dummy, sample_info):
continue
key_dir = getKey(process_name_or_dummy, central_or_shift_or_dummy)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel, process_name_or_dummy, central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, process_name_or_dummy)
for subdirectory in [ "addBackgrounds", "prepareDatacards" ]:
key_dir = getKey(subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_HIST, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_COMBINE_OUTPUT ]:
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_HADD_RT, DKEY_PLOT, DKEY_COMBINE_OUTPUT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" % numDirectories)
numDirectories_created = 0;
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100*numDirectories_created >= frac*numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
self.inputFileIds = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
inputFileList = inputFileLists[sample_name]
is_mc = (sample_info["type"] == "mc")
sample_category = sample_info["sample_category"]
for central_or_shift in self.central_or_shifts:
if central_or_shift != "central" and not is_mc:
continue
if not self.accept_central_or_shift(central_or_shift, sample_info):
continue
key_analyze_dir = getKey(process_name, central_or_shift)
for jobId in inputFileList.keys():
analyze_job_tuple = (process_name, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
rleOutputFile = os.path.join(
self.dirs[key_analyze_dir][DKEY_RLES],
"rle_{channel}_{process_name}_{central_or_shift}_{jobId}_%s_%s.txt".format(
channel = self.channel,
process_name = process_name,
central_or_shift = central_or_shift,
jobId = jobId,
)) if self.select_rle_output else ""
cfgFile_modified_path = os.path.join(self.dirs[key_analyze_dir][DKEY_CFGS], "analyze_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_LOGS], "analyze_%s_%s_%i.log" % analyze_job_tuple)
histogramFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_HIST], "analyze_%s_%s_%i.root" % analyze_job_tuple)
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : cfgFile_modified_path,
'histogramFile' : histogramFile_path,
'selEventsFileName_output' : rleOutputFile,
'logFile' : logFile_path,
'absEtaBins_e' : self.absEtaBins_e,
'ptBins_e' : self.ptBins_e,
'absEtaBins_mu' : self.absEtaBins_mu,
'ptBins_mu' : self.ptBins_mu,
'central_or_shift' : central_or_shift,
'fillGenEvtHistograms' : self.fillGenEvtHistograms,
'triggers_mu_cfg' : "leptonFR_triggers['{}']['{}']".format(self.era, 'mu'),
'triggers_e_cfg' : "leptonFR_triggers['{}']['{}']".format(self.era, 'e'),
'lep_mva_cut_e' : float(self.lep_mva_cut_e),
'lep_mva_cut_mu' : float(self.lep_mva_cut_mu),
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job], sample_info)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name)
key_hadd_stage1_job = getKey(process_name)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1_job] = os.path.join(self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s.root" % process_name)
# initialize input and output file names for hadd_stage1_5
key_hadd_stage1_5_dir = getKey("hadd")
key_hadd_stage1_5_job = getKey('')
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
for key_hadd_stage1_job in self.outputFile_hadd_stage1.keys():
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.outputFile_hadd_stage1[key_hadd_stage1_job])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job] = os.path.join(self.dirs[key_hadd_stage1_5_dir][DKEY_HIST], "hadd_stage1_5.root" )
# sum fake contributions for the total of all MC samples
# input processes: TTj,... ## HERE !!
# output process: fakes_mc
key_hadd_stage1_5_job = getKey('')
key_addBackgrounds_dir = getKey("addBackgrounds")
key_addBackgrounds_job_sum = getKey("fakes_mc")
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
sample_categories.extend(self.ttHProcs)
processes_input = []
for sample_category in sample_categories:
processes_input.append("%sj" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_sum] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_cfg.py" % "fakes_mc"),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s.root" % "fakes_mc"),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s.log" % "fakes_mc"),
'categories' : [
"LeptonFakeRate/numerator/electrons_tight",
"LeptonFakeRate/denominator/electrons_fakeable",
"LeptonFakeRate/numerator/muons_tight",
"LeptonFakeRate/denominator/muons_fakeable"
],
'processes_input' : processes_input,
'process_output' : "fakes_mc",
'histogramsToCopy' : list(self.histograms_to_fit.keys()),
'sysShifts' : []
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_sum])
# create configuration files to run 'addBackgrounds_LeptonFakeRate'
key_addBackgrounds_job_leptonFR = getKey('')
self.jobOptions_addBackgrounds_LeptonFakeRate[key_addBackgrounds_job_leptonFR] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], os.path.basename(self.cfgFile_addBackgrounds_LeptonFakeRate)),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackground_LeptonFakeRate.root"),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], os.path.basename(self.cfgFile_addBackgrounds_LeptonFakeRate.replace("_cfg.py", ".log")) ),
}
self.createCfg_addBackgrounds_LeptonFakeRate(self.jobOptions_addBackgrounds_LeptonFakeRate[key_addBackgrounds_job_leptonFR])
# create configuration files to run 'addBackgrounds_Convs_LeptonFakeRate'
key_addBackgrounds_job_conv = getKey('')
self.jobOptions_addBackgrounds_Convs_LeptonFakeRate[key_addBackgrounds_job_conv] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], os.path.basename(self.cfgFile_addBackgrounds_Convs_LeptonFakeRate)),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackground_Convs_LeptonFakeRate.root"),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], os.path.basename(self.cfgFile_addBackgrounds_Convs_LeptonFakeRate.replace("_cfg.py", ".log")) ),
}
self.createCfg_addBackgrounds_Convs_LeptonFakeRate(self.jobOptions_addBackgrounds_Convs_LeptonFakeRate[key_addBackgrounds_job_conv])
# initialize input and output file names for hadd_stage2
key_hadd_stage2_dir = getKey("hadd")
key_hadd_stage2_job = getKey('')
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
# CV: hadd_stage_1_5 output file does not need to be added as input for hadd_stage_2,
# as addBackgrounds_LeptonFakeRate output file contains all histograms except fakes_mc
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_sum]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_LeptonFakeRate[key_addBackgrounds_job_leptonFR]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_Convs_LeptonFakeRate[key_addBackgrounds_job_conv]['outputFile'])
self.outputFile_hadd_stage2[key_hadd_stage2_job] = os.path.join(self.dirs[key_hadd_stage2_dir][DKEY_HIST], "hadd_stage2.root")
# We need to generate the eta and pt bins for electrons and muons
lepton_bins = {}
categories = []
for lepton in ['electron', 'muon']:
if lepton not in lepton_bins:
lepton_bins[lepton] = {}
absEtaBins = None
ptBins = None
lepton_short = None
if lepton == 'electron':
absEtaBins = self.absEtaBins_e
ptBins = self.ptBins_e
lepton_short = 'e'
elif lepton == 'muon':
absEtaBins = self.absEtaBins_mu
ptBins = self.ptBins_mu
lepton_short = 'mu'
else:
raise ValueError('Invalid lepton type: %s' % lepton)
for selection in ['tight', 'fakeable']:
if selection not in lepton_bins[lepton]:
lepton_bins[lepton][selection] = []
num_or_den = None
if selection == 'tight':
num_or_den = 'numerator'
elif selection == 'fakeable':
num_or_den = 'denominator'
else:
raise ValueError('Invalid lepton selection: %s' % selection)
for absEtaBin_idx in range(0, len(absEtaBins) - 1):
absEtaBinLowerEdge = absEtaBins[absEtaBin_idx]
absEtaBinUpperEdge = absEtaBins[absEtaBin_idx + 1]
absEtaBinString = getEtaBin(absEtaBinLowerEdge, absEtaBinUpperEdge)
for ptBin_idx in range(0, len(ptBins) - 1):
ptBinsLowerEdge = ptBins[ptBin_idx]
ptBinsUpperEdge = ptBins[ptBin_idx + 1]
ptBinString = getPtBin(ptBinsLowerEdge, ptBinsUpperEdge)
absEta_and_ptBinString = '%s_%s' % (absEtaBinString, ptBinString)
lepton_bins[lepton][selection].append(
construct_lepton_params(
lepton, lepton_short, selection, absEta_and_ptBinString,
error_msg = "No fit parameter range specified for abs(eta) range = (%.3f, %.3f) and "
"pT range = (%.3f, %.3f) for lepton type '%s' !!" % \
(absEtaBinLowerEdge, absEtaBinUpperEdge, ptBinsLowerEdge, ptBinsUpperEdge, lepton)
) + (absEtaBinLowerEdge, absEtaBinUpperEdge, ptBinsLowerEdge, ptBinsUpperEdge, 0)
)
categories.append(
(
"LeptonFakeRate/%s/%ss_%s/%s/%s" % (num_or_den, lepton, selection, absEtaBinString, ptBinString),
"%ss_%s_%s_shapes" % (lepton, selection, absEta_and_ptBinString),
)
)
# Let's also add inclusive category
lepton_bins[lepton][selection].append(
construct_lepton_params(
lepton, lepton_short, selection, 'incl',
error_msg = "No fit parameter range specified for lepton type %s" % lepton
) + (-1., -1., -1., -1., 1)
)
categories.append(
(
"LeptonFakeRate/%s/%ss_%s/incl" % (num_or_den, lepton, selection),
"%ss_%s_incl_shapes" % (lepton, selection),
)
)
lepton_bins_merged = []
for lepton_type in lepton_bins:
for lepton_selection in lepton_bins[lepton_type]:
lepton_bins_merged.extend(lepton_bins[lepton_type][lepton_selection])
if self.prep_dcard:
logging.info("Creating configuration files to run 'prepareDatacards_LeptonFakeRate'")
datacards = []
for histogramToFit in self.histograms_to_fit:
key_prep_dcard_dir = getKey("prepareDatacards")
key_prep_dcard_job = getKey(histogramToFit)
datacard = os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s.root" % (histogramToFit))
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_LeptonFakeRate_%s_cfg.py" % histogramToFit),
'datacardFile' : datacard,
'histogramDir' : (self.histogramDir_prep_dcard),
'histogramToFit' : histogramToFit,
'label' : None,
'categories' : categories,
}
datacards.append(datacard)
self.createCfg_prep_dcard_LeptonFakeRate(self.jobOptions_prep_dcard[key_prep_dcard_job])
# Create setupDatacards_LeptonFakeRate.py script from the template
systematics_leptonFR = []
for systematic in self.central_or_shifts:
if systematic == 'central':
continue
systematic_name = systematic.replace('Up', '').replace('Down', '')
if systematic_name not in systematics_leptonFR:
systematics_leptonFR.append(systematic_name)
setup_dcards_template_file = os.path.join(jinja_template_dir, 'setupDatacards_LeptonFakeRate.py.template')
with open(setup_dcards_template_file, 'r') as setup_dcards_template_file_ptr:
setup_dcards_template = setup_dcards_template_file_ptr.read()
setup_dcards_script = jinja2.Template(setup_dcards_template).render(
leptons = lepton_bins_merged,
central_or_shifts = systematics_leptonFR,
signal_process = "QCD" if self.use_QCD_fromMC else "data_fakes",
)
setup_dcards_script_path = os.path.join(self.dirs[DKEY_SCRIPTS], 'setupDatacards_LeptonFakeRate.py')
logging.debug("writing setupDatacards_LeptonFakeRate script file = '%s'" % setup_dcards_script_path)
with codecs.open(setup_dcards_script_path, "w", "utf-8") as setup_dcards_script_file:
setup_dcards_script_file.write(setup_dcards_script)
setup_dcards_script_file.flush()
os.fsync(setup_dcards_script_file.fileno())
add_chmodX(setup_dcards_script_path)
if self.use_QCD_fromMC:
postfit_plot_script_path = os.path.join(os.environ['CMSSW_BASE'], 'src/tthAnalysis/HiggsToTauTau/data/leptonFR/scripts/postFitPlot_fakes_from_mc.py')
yieldtable_script_path = os.path.join(os.environ['CMSSW_BASE'], 'src/tthAnalysis/HiggsToTauTau/data/leptonFR/scripts/yieldTable_fakes_from_mc.py')
else:
postfit_plot_script_path = os.path.join(os.environ['CMSSW_BASE'], 'src/tthAnalysis/HiggsToTauTau/data/leptonFR/scripts/postFitPlot_fakes_from_data.py')
yieldtable_script_path = os.path.join(os.environ['CMSSW_BASE'], 'src/tthAnalysis/HiggsToTauTau/data/leptonFR/scripts/yieldTable_fakes_from_data.py')
# Create run_postFit.sh script from the template
combine_output_dir = os.path.join(self.dirs[DKEY_COMBINE_OUTPUT], 'output')
postfit_template_file = os.path.join(jinja_template_dir, 'run_postFit.sh.template')
with open(postfit_template_file, 'r') as postfit_template_file_ptr:
postfit_template = postfit_template_file_ptr.read()
for lepton in ['electron', 'muon']:
for selection in ['fakeable', 'tight']:
is_num = selection == 'tight'
for params in lepton_bins[lepton][selection]:
l_array, l_range, l_sub_dir, l_eta_low, l_eta_high, l_pt_low, l_pt_high, l_is_inclusive = params
postfit_script = jinja2.Template(postfit_template).render(
new_cmssw_base = self.cmssw_base_dir_combine,
setup_dcards_script = setup_dcards_script_path,
postfit_plot_script = postfit_plot_script_path,
int_lumi_data = self.lumi,
yieldtable_script = yieldtable_script_path,
output_dir = combine_output_dir,
numerator_plotLabel = self.numerator_plotLabel,
denominator_plotLabel = self.denominator_plotLabel,
l_array = l_array,
l_range = l_range,
l_sub_dir = l_sub_dir,
l_eta_low = l_eta_low,
l_eta_high = l_eta_high,
l_pt_low = l_pt_low,
l_pt_high = l_pt_high,
l_is_inclusive = l_is_inclusive,
is_num = is_num,
numerator_output_dir = os.path.join(combine_output_dir, 'mlfit_LeptonFakeRate_%s' % self.numerator_histogram),
denominator_output_dir = os.path.join(combine_output_dir, 'mlfit_LeptonFakeRate_%s' % self.denominator_histogram),
selection = selection,
lepton_letter = 'e' if lepton == 'electron' else 'mu',
grep_value = "QCD" if self.use_QCD_fromMC else "data_fakes",
)
postfit_script_path = os.path.join(
self.dirs[DKEY_SCRIPTS],
'mlfit_%s_%s.sh' % (self.numerator_histogram if is_num else self.denominator_histogram, l_array)
)
logging.debug("Writing run_postFit script file = '%s'" % postfit_script_path)
with codecs.open(postfit_script_path, "w", "utf-8") as postfit_script_file:
postfit_script_file.write(postfit_script)
postfit_script_file.flush()
os.fsync(postfit_script_file.fileno())
add_chmodX(postfit_script_path)
key_prep_dcard_dir = getKey("prepareDatacards")
fit_value_file = os.path.join(combine_output_dir, 'fit_values.txt')
makefile_template_file = os.path.join(jinja_template_dir, 'Makefile_postFit.template')
makefile_template = open(makefile_template_file, 'r').read()
makefile_templatized = jinja2.Template(makefile_template).render(
new_cmssw_base = self.cmssw_base_dir_combine,
setup_dcards_script = setup_dcards_script_path,
numerator_histogram = self.numerator_histogram,
denominator_histogram = self.denominator_histogram,
scripts_dir = self.dirs[DKEY_SCRIPTS],
numerator_datacard = os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s.root" % self.numerator_histogram),
denominator_datacard = os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s.root" % self.denominator_histogram),
output_dir = combine_output_dir,
numerator_output_dir = os.path.join(combine_output_dir, 'mlfit_LeptonFakeRate_%s' % self.numerator_histogram),
denominator_output_dir = os.path.join(combine_output_dir, 'mlfit_LeptonFakeRate_%s' % self.denominator_histogram),
lepton_bins = lepton_bins,
fit_values = fit_value_file,
)
makefile_path = os.path.join(self.dirs[DKEY_SCRIPTS], 'Makefile_postFit')
logging.debug("Writing run_postFit script file = '%s'" % makefile_path)
with codecs.open(makefile_path, "w", "utf-8") as makefile_path_file:
makefile_path_file.write(makefile_templatized)
makefile_path_file.flush()
os.fsync(makefile_path_file.fileno())
self.jobOptions_combine = {
'inputFile' : ' '.join(datacards),
'outputFile' : fit_value_file,
'makefile_path' : makefile_path,
'logFile' : os.path.join(self.dirs[DKEY_LOGS], 'postFit.log'),
}
key_comp_LeptonFakeRate = getKey('')
leptonFR_final_output = os.path.join(combine_output_dir, 'leptonFakeRates.root')
self.jobOptions_comp_LeptonFakeRate[key_comp_LeptonFakeRate] = {
'inputFile' : [ fit_value_file, self.outputFile_hadd_stage2[key_hadd_stage2_job] ],
'outputFile' : leptonFR_final_output,
'absEtaBins_e' : self.absEtaBins_e,
'ptBins_e' : self.ptBins_e,
'absEtaBins_mu' : self.absEtaBins_mu,
'ptBins_mu' : self.ptBins_mu,
'logFile' : os.path.join(self.dirs[DKEY_LOGS], os.path.basename(self.cfgFile_comp_LeptonFakeRate).replace('_cfg.py', '.log')),
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], os.path.basename(self.cfgFile_comp_LeptonFakeRate)),
'plots_outputFileName' : os.path.join(self.dirs[DKEY_PLOT], "comp_LeptonFakeRate.png")
}
self.createCfg_comp_LeptonFakeRate(self.jobOptions_comp_LeptonFakeRate[key_comp_LeptonFakeRate])
self.targets.append(self.jobOptions_comp_LeptonFakeRate[key_comp_LeptonFakeRate]['outputFile'])
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_LeptonFakeRate.py")
self.sbatchFile_addBackgrounds_sum = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_sum_LeptonFakeRate.py")
self.sbatchFile_addBackgrounds_LeptonFakeRate = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_LeptonFakeRate.py")
self.sbatchFile_addBackgrounds_Convs_LeptonFakeRate = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_Convs_LeptonFakeRate.py")
self.sbatchFile_comp_LeptonFakeRate = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_comp_LeptonFakeRate.py")
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
self.createScript_sbatch(self.executable_addBackgrounds_recursively, self.sbatchFile_addBackgrounds_sum, self.jobOptions_addBackgrounds_sum)
self.createScript_sbatch(self.executable_addBackgrounds_LeptonFakeRate, self.sbatchFile_addBackgrounds_LeptonFakeRate, self.jobOptions_addBackgrounds_LeptonFakeRate)
self.createScript_sbatch(self.executable_addBackgrounds_LeptonFakeRate, self.sbatchFile_addBackgrounds_Convs_LeptonFakeRate, self.jobOptions_addBackgrounds_Convs_LeptonFakeRate)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_comp_LeptonFakeRate)
self.createScript_sbatch(self.executable_comp_LeptonFakeRate, self.sbatchFile_comp_LeptonFakeRate, self.jobOptions_comp_LeptonFakeRate)
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_backgrounds_from_data(lines_makefile) ## this step now does both e Conv, data_fakes and fakes_mc computation
# self.addToMakefile_backgrounds_from_MC(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile, make_dependency = " ".join([
"phony_addBackgrounds_LeptonFakeRate", "phony_addBackgrounds_Convs_LeptonFakeRate", "phony_addBackgrounds_sum"
]))
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_combine(lines_makefile)
self.addToMakefile_comp_LeptonFakeRate(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def __init__(
self,
configDir,
outputDir,
cfgFile_prodNtuple,
samples,
max_files_per_job,
era,
preselection_cuts,
leptonSelection,
hadTauWP,
check_output_files,
running_method,
version,
num_parallel_jobs,
pileup,
golden_json,
dry_run,
isDebug,
gen_matching_by_index,
use_nonnominal,
use_home,
skip_tools_step,
verbose=False,
pool_id='',
):
self.configDir = configDir
self.outputDir = outputDir
self.max_num_jobs = 200000
self.samples = samples
self.max_files_per_job = max_files_per_job
self.era = era
self.preselection_cuts = preselection_cuts
self.leptonSelection = leptonSelection
self.hadTauWP = hadTauWP
self.check_output_files = check_output_files
self.verbose = verbose
self.dry_run = dry_run
self.isDebug = isDebug
self.gen_matching_by_index = gen_matching_by_index
self.use_nonnominal = use_nonnominal
self.use_home = use_home
self.pileup = pileup
self.golden_json = golden_json
if running_method.lower() not in ["sbatch", "makefile"]:
raise ValueError("Invalid running method: %s" % running_method)
if not os.path.isfile(self.pileup):
raise ValueError('No such file: %s' % self.pileup)
self.pileup_histograms = get_pileup_histograms(self.pileup)
if not os.path.isfile(self.golden_json):
raise ValueError('No such file: %s' % self.golden_json)
self.running_method = running_method
self.is_sbatch = self.running_method.lower() == "sbatch"
self.is_makefile = not self.is_sbatch
self.makefile = os.path.join(self.configDir, "Makefile_prodNtuple")
self.num_parallel_jobs = num_parallel_jobs
self.skip_tools_step = skip_tools_step
self.pool_id = pool_id if pool_id else uuid.uuid4()
self.workingDir = os.getcwd()
logging.info("Working directory is: %s" % self.workingDir)
self.template_dir = os.path.join(os.getenv('CMSSW_BASE'), 'src',
'tthAnalysis', 'HiggsToTauTau',
'test', 'templates')
logging.info("Templates directory is: %s" % self.template_dir)
self.version = version
self.samples = samples
create_if_not_exists(self.configDir)
create_if_not_exists(self.outputDir)
self.stdout_file_path = os.path.join(self.configDir,
"stdout_prodNtuple.log")
self.stderr_file_path = os.path.join(self.configDir,
"stderr_prodNtuple.log")
self.sw_ver_file_cfg = os.path.join(self.configDir,
"VERSION_prodNtuple.log")
self.sw_ver_file_out = os.path.join(self.outputDir,
"VERSION_prodNtuple.log")
self.stdout_file_path, self.stderr_file_path, self.sw_ver_file_cfg, self.sw_ver_file_out = get_log_version(
(self.stdout_file_path, self.stderr_file_path,
self.sw_ver_file_cfg, self.sw_ver_file_out))
self.cfgFile_prodNtuple_original = os.path.join(
self.template_dir, cfgFile_prodNtuple)
self.sbatchFile_prodNtuple = os.path.join(self.configDir,
"sbatch_prodNtuple.py")
self.cfgFiles_prodNtuple_modified = {}
self.logFiles_prodNtuple = {}
self.inputFiles = {}
self.outputFiles = {}
self.filesToClean = []
self.dirs = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(sample_name)
for dir_type in [DKEY_CFGS, DKEY_NTUPLES, DKEY_LOGS]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, process_name)
for dir_type in [DKEY_CFGS, DKEY_LOGS]:
initDict(self.dirs, [dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type)
self.cvmfs_error_log = {}
self.executable = "produceNtuple.sh"
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
for lepton_and_hadTau_selection in self.lepton_and_hadTau_selections:
for lepton_and_hadTau_frWeight in self.lepton_and_hadTau_frWeights:
if lepton_and_hadTau_frWeight == "enabled" and not lepton_and_hadTau_selection.startswith("Fakeable"):
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight)
for hadTau_charge_selection in self.hadTau_charge_selections:
key_dir = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
"_".join([ lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection ]), process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection ]), process_name)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
##print "self.dirs = ", self.dirs
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
else:
create_if_not_exists(self.dirs[key])
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_name, sample_info, self.max_files_per_job, self.debug)
for lepton_and_hadTau_selection in self.lepton_and_hadTau_selections:
lepton_selection = lepton_and_hadTau_selection
if self.applyFakeRateWeights == "2tau":
lepton_selection = "Tight"
hadTau_selection = "|".join([ lepton_and_hadTau_selection, self.hadTau_selection_part2 ])
if lepton_and_hadTau_selection == "forBDTtraining":
lepton_selection = "Loose"
hadTau_selection = "Tight|%s" % self.hadTau_selection_relaxed
for lepton_and_hadTau_frWeight in self.lepton_and_hadTau_frWeights:
if lepton_and_hadTau_frWeight == "enabled" and not lepton_and_hadTau_selection.startswith("Fakeable"):
continue
if lepton_and_hadTau_frWeight == "disabled" and not lepton_and_hadTau_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight)
for hadTau_charge_selection in self.hadTau_charge_selections:
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
is_signal = (sample_category == "signal")
for central_or_shift in self.central_or_shifts:
inputFileList = inputFileLists[sample_name]
for jobId in inputFileList.keys():
if central_or_shift != "central":
isFR_shape_shift = False
for FR_shape_shift in [
"CMS_ttHl_FRe_shape",
"CMS_ttHl_FRm_shape",
"CMS_ttHl_FRjt_norm",
"CMS_ttHl_FRjt_shape" ]:
if central_or_shift.find(FR_shape_shift) != -1:
isFR_shape_shift = True
if not ((lepton_and_hadTau_selection == "Fakeable" and hadTau_charge_selection == "OS" and isFR_shape_shift) or
(lepton_and_hadTau_selection == "Tight" and hadTau_charge_selection == "OS")):
continue
if not is_mc and not isFR_shape_shift:
continue
if central_or_shift.startswith("CMS_ttHl_thu_shape_ttH") and sample_category != "signal":
continue
if central_or_shift.startswith("CMS_ttHl_thu_shape_ttW") and sample_category != "TTW":
continue
if central_or_shift.startswith("CMS_ttHl_thu_shape_ttZ") and sample_category != "TTZ":
continue
##print "processing sample %s: jobId = %i, central_or_shift = '%s'" % (process_name, jobId, central_or_shift)
# build config files for executing analysis code
key_dir = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
key_analyze_job = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection, central_or_shift, jobId)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
print "Warning: ntupleFiles['%s'] = %s --> skipping job !!" % (key_file, ntupleFiles)
continue
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : os.path.join(self.dirs[key_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%s_%i_cfg.py" % \
(self.channel, process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection, central_or_shift, jobId)),
'histogramFile' : os.path.join(self.dirs[key_dir][DKEY_HIST], "%s_%s_%s_%s_%i.root" % \
(process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection, central_or_shift, jobId)),
'logFile' : os.path.join(self.dirs[key_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%s_%i.log" % \
(self.channel, process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection, central_or_shift, jobId)),
'rleOutputFile' : os.path.join(self.dirs[key_dir][DKEY_RLES], "rle_%s_%s_%s_%s_%s_%i.txt" % \
(self.channel, process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection, central_or_shift, jobId)) if self.select_rle_output else "",
'sample_category' : sample_category,
'process_name_specific' : sample_info["process_name_specific"],
'triggers' : sample_info["triggers"],
'lepton_selection' : lepton_selection,
'apply_leptonGenMatching' : self.apply_leptonGenMatching,
'hadTau_selection' : hadTau_selection,
'apply_hadTauGenMatching' : self.apply_hadTauGenMatching,
'hadTau_charge_selection' : hadTau_charge_selection,
'applyFakeRateWeights' : self.applyFakeRateWeights if not (lepton_selection == "Tight" and hadTau_selection.find("Tight") != -1) else "disabled",
##'use_HIP_mitigation_bTag' : sample_info["use_HIP_mitigation_bTag"],
##'use_HIP_mitigation_mediumMuonId' : sample_info["use_HIP_mitigation_mediumMuonId"],
'use_HIP_mitigation_bTag' : True,
'use_HIP_mitigation_mediumMuonId' : True,
'is_mc' : is_mc,
'central_or_shift' : central_or_shift,
'lumi_scale' : 1. if not (self.use_lumi and is_mc) else sample_info["xsection"] * self.lumi / sample_info["nof_events"],
'apply_genWeight' : sample_info["genWeight"] if (is_mc and "genWeight" in sample_info) else False,
'apply_trigger_bits' : (is_mc and (self.era == "2015" or (self.era == "2016" and sample_info["reHLT"]))) or not is_mc,
'selectBDT': self.isBDTtraining,
'changeBranchNames' : self.changeBranchNames
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job])
# initialize input and output file names for hadd_stage1
key_hadd_stage1 = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
if not key_hadd_stage1 in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1] = []
self.inputFiles_hadd_stage1[key_hadd_stage1].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_%s_%s_%s_%s.root" % \
(self.channel, process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection))
if self.isBDTtraining:
continue
if is_mc:
logging.info("Creating configuration files to run 'addBackgrounds' for sample %s" % process_name)
sample_categories = [ sample_category ]
if is_signal:
sample_categories = [ "signal", "ttH", "ttH_htt", "ttH_hww", "ttH_hzz" ]
for sample_category in sample_categories:
# sum non-fake contributions for each MC sample separately
# input processes: TT2t0e0m0j, TT1t1e0m0j, TT1t0e1m0j", TT0t2e0m0j, TT0t1e1m0j, TT0t0e2m0j; TTW2t0e0m0j,...
# output processes: TT; ...
key_hadd_stage1 = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
key_addBackgrounds_job = getKey(process_name, sample_category, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
processes_input = None
if sample_category in [ "signal" ]:
lepton_and_hadTau_genMatches = []
lepton_and_hadTau_genMatches.extend(self.lepton_and_hadTau_genMatches_nonfakes)
lepton_and_hadTau_genMatches.extend(self.lepton_and_hadTau_genMatches_fakes)
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in lepton_and_hadTau_genMatches ]
elif sample_category in [ "ttH" ]:
processes_input = []
processes_input.extend([ "%s%s" % ("ttH_htt", genMatch) for genMatch in self.lepton_and_hadTau_genMatches_nonfakes ])
processes_input.extend([ "%s%s" % ("ttH_hww", genMatch) for genMatch in self.lepton_and_hadTau_genMatches_nonfakes ])
processes_input.extend([ "%s%s" % ("ttH_hzz", genMatch) for genMatch in self.lepton_and_hadTau_genMatches_nonfakes ])
else:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_and_hadTau_genMatches_nonfakes ]
if processes_input:
self.jobOptions_addBackgrounds[key_addBackgrounds_job] = {
'inputFile' : self.outputFile_hadd_stage1[key_hadd_stage1],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "addBackgrounds_%s_%s_%s_%s_%s_cfg.py" % \
(self.channel, process_name, sample_category, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)),
'outputFile' : os.path.join(self.dirs[DKEY_HIST], "addBackgrounds_%s_%s_%s_%s_%s.root" % \
(self.channel, process_name, sample_category, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)),
'logFile' : os.path.join(self.dirs[DKEY_LOGS], "addBackgrounds_%s_%s_%s_%s_%s.log" % \
(self.channel, process_name, sample_category, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)),
'categories' : [ getHistogramDir(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight, hadTau_charge_selection) ],
'processes_input' : processes_input,
'process_output' : sample_category
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds[key_addBackgrounds_job])
# initialize input and output file names for hadd_stage1_5
key_hadd_stage1_5 = getKey(lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
if not key_hadd_stage1_5 in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5].append(self.jobOptions_addBackgrounds[key_addBackgrounds_job]['outputFile'])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_5_%s_%s_%s.root" % \
(self.channel, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection))
# sum fake contributions for each MC sample separately
# input processes: TT1t0e0m1j, TT0t1e0m1j, TT0t0e1m1j, TT0t0e0m2j; TTW1t0e0m1j,...
# output processes: TT_fake; ...
key_hadd_stage1 = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
key_addBackgrounds_job = getKey(process_name, "%s_fake" % sample_category, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
processes_input = None
if sample_category in [ "signal" ]:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_and_hadTau_genMatches_fakes ]
elif sample_category in [ "ttH" ]:
processes_input = []
processes_input.extend([ "%s%s" % ("ttH_htt", genMatch) for genMatch in self.lepton_and_hadTau_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hww", genMatch) for genMatch in self.lepton_and_hadTau_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hzz", genMatch) for genMatch in self.lepton_and_hadTau_genMatches_fakes ])
else:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_and_hadTau_genMatches_fakes ]
if processes_input:
self.jobOptions_addBackgrounds[key_addBackgrounds_job] = {
'inputFile' : self.outputFile_hadd_stage1[key_hadd_stage1],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "addBackgrounds_%s_fakes_%s_%s_%s_%s_cfg.py" % \
(self.channel, process_name, sample_category, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)),
'outputFile' : os.path.join(self.dirs[DKEY_HIST], "addBackgrounds_%s_fakes_%s_%s_%s_%s.root" % \
(self.channel, process_name, sample_category, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)),
'logFile' : os.path.join(self.dirs[DKEY_LOGS], "addBackgrounds_%s_fakes_%s_%s_%s_%s.log" % \
(self.channel, process_name, sample_category, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)),
'categories' : [ getHistogramDir(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight, hadTau_charge_selection) ],
'processes_input' : processes_input,
'process_output' : "%s_fake" % sample_category
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds[key_addBackgrounds_job])
# initialize input and output file names for hadd_stage1_5
key_hadd_stage1_5 = getKey(lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
if not key_hadd_stage1_5 in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5].append(self.jobOptions_addBackgrounds[key_addBackgrounds_job]['outputFile'])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_5_%s_%s_%s.root" % \
(self.channel, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection))
if self.isBDTtraining:
continue
# add output files of hadd_stage1 for data to list of input files for hadd_stage1_5
if not is_mc:
key_hadd_stage1 = getKey(process_name, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
key_hadd_stage1_5 = getKey(lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
if not key_hadd_stage1_5 in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5].append(self.outputFile_hadd_stage1[key_hadd_stage1])
if self.isBDTtraining:
continue
# sum fake contributions for the total of all MC sample
# input processes: TT1t0e0m1j, TT0t1e0m1j, TT0t0e1m1j, TT0t0e0m2j; TTW1t0e0m1j,...
# output process: fakes_mc
key_addBackgrounds_job = getKey(lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
sample_categories.extend([ "signal" ])
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_fake" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "addBackgrounds_%s_fakes_mc_%s_%s_cfg.py" % \
(self.channel, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)),
'outputFile' : os.path.join(self.dirs[DKEY_HIST], "addBackgrounds_%s_fakes_mc_%s_%s.root" % \
(self.channel, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)),
'logFile' : os.path.join(self.dirs[DKEY_LOGS], "addBackgrounds_%s_fakes_mc_%s_%s.log" % \
(self.channel, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)),
'categories' : [ getHistogramDir(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight, hadTau_charge_selection) ],
'processes_input' : processes_input,
'process_output' : "fakes_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job])
# initialize input and output file names for hadd_stage2
key_hadd_stage2 = getKey(lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
if not key_hadd_stage2 in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2] = []
if lepton_and_hadTau_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job]['outputFile'])
key_hadd_stage1_5 = getKey(lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection)
self.inputFiles_hadd_stage2[key_hadd_stage2].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5])
self.outputFile_hadd_stage2[key_hadd_stage2] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage2_%s_%s_%s.root" % \
(self.channel, lepton_and_hadTau_selection_and_frWeight, hadTau_charge_selection))
if self.isBDTtraining:
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done")
return self.num_jobs
logging.info("Creating configuration files to run 'addBackgroundFakes'")
for hadTau_charge_selection in self.hadTau_charge_selections:
key_addFakes_job = getKey("fakes_data", hadTau_charge_selection)
key_hadd_stage1_5 = getKey(get_lepton_and_hadTau_selection_and_frWeight("Fakeable", "enabled"), hadTau_charge_selection)
category_sideband = None
if self.applyFakeRateWeights == "3L":
category_sideband = "1l_2tau_%s_Fakeable_wFakeRateWeights" % hadTau_charge_selection
elif self.applyFakeRateWeights == "2tau":
category_sideband = "1l_2tau_%s_Fakeable_wFakeRateWeights" % hadTau_charge_selection
else:
raise ValueError("Invalid Configuration parameter 'applyFakeRateWeights' = %s !!" % applyFakeRateWeights)
self.jobOptions_addFakes[key_addFakes_job] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "addBackgroundLeptonFakes_%s_%s_cfg.py" % \
(self.channel, hadTau_charge_selection)),
'outputFile' : os.path.join(self.dirs[DKEY_HIST], "addBackgroundLeptonFakes_%s_%s.root" % \
(self.channel, hadTau_charge_selection)),
'logFile' : os.path.join(self.dirs[DKEY_LOGS], "addBackgroundLeptonFakes_%s_%s.log" % \
(self.channel, hadTau_charge_selection)),
'category_signal' : "1l_2tau_%s_Tight" % hadTau_charge_selection,
'category_sideband' : category_sideband
}
self.createCfg_addFakes(self.jobOptions_addFakes[key_addFakes_job])
key_hadd_stage2 = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), hadTau_charge_selection)
self.inputFiles_hadd_stage2[key_hadd_stage2].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
logging.info("Creating configuration files to run 'prepareDatacards'")
for histogramToFit in self.histograms_to_fit:
key_prep_dcard_job = getKey(histogramToFit)
key_hadd_stage2 = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "OS")
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "prepareDatacards_%s_%s_cfg.py" % (self.channel, histogramToFit)),
'datacardFile' : os.path.join(self.dirs[DKEY_DCRD], "prepareDatacards_%s_%s.root" % (self.channel, histogramToFit)),
'histogramDir' : self.histogramDir_prep_dcard,
'histogramToFit' : histogramToFit,
'label' : None
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
if "SS" in self.hadTau_charge_selections:
key_prep_dcard_job = getKey(histogramToFit, "SS")
key_hadd_stage2 = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "SS")
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "prepareDatacards_%s_SS_%s_cfg.py" % (self.channel, histogramToFit)),
'datacardFile' : os.path.join(self.dirs[DKEY_DCRD], "prepareDatacards_%s_SS_%s.root" % (self.channel, histogramToFit)),
'histogramDir' : self.histogramDir_prep_dcard_SS,
'histogramToFit' : histogramToFit,
'label' : 'SS'
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
logging.info("Creating configuration files to run 'makePlots'")
key_makePlots_job = getKey("OS")
key_hadd_stage2 = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "OS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "makePlots_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[DKEY_PLOT], "makePlots_%s.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard,
'label' : None,
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "SS" in self.hadTau_charge_selections:
key_makePlots_job = getKey("SS")
key_hadd_stage2 = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "SS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "makePlots_%s_SS_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[DKEY_PLOT], "makePlots_%s_SS.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard_SS,
'label' : "SS",
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "Fakeable_mcClosure" in self.lepton_and_hadTau_selections:
key_makePlots_job = getKey("OS")
key_hadd_stage2 = getKey(get_lepton_and_hadTau_selection_and_frWeight("Tight", "disabled"), "OS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots_mcClosure,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified' : os.path.join(self.dirs[DKEY_CFGS], "makePlots_mcClosure_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[DKEY_PLOT], "makePlots_mcClosure_%s.png" % self.channel)
}
self.createCfg_makePlots_mcClosure(self.jobOptions_make_plots[key_makePlots_job])
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addBackgrounds)
self.sbatchFile_addBackgrounds = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds, self.jobOptions_addBackgrounds)
self.sbatchFile_addBackgrounds_sum = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_sum_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds_sum, self.jobOptions_addBackgrounds_sum)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFakes)
self.sbatchFile_addFakes = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFakes_%s.py" % self.channel)
self.createScript_sbatch(self.executable_addFakes, self.sbatchFile_addFakes, self.jobOptions_addFakes)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_backgrounds_from_data(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done")
return self.num_jobs
def __init__(
self,
configDir,
localDir,
outputDir,
output_file,
executable,
samples,
max_files_per_job,
era,
binning,
use_gen_weight,
check_output_files,
running_method,
num_parallel_jobs,
pool_id='',
verbose=False,
dry_run=False,
use_home=False,
keep_logs=False,
submission_cmd=None,
):
self.configDir = configDir
self.localDir = localDir
self.outputDir = outputDir
self.executable = executable
self.max_num_jobs = 200000
self.samples = samples
self.max_files_per_job = max_files_per_job
self.era = era
self.binning = binning
self.use_gen_weight = use_gen_weight
self.check_output_files = check_output_files
self.verbose = verbose
self.dry_run = dry_run
self.use_home = use_home
self.keep_logs = keep_logs
if running_method.lower() not in ["sbatch", "makefile"]:
raise ValueError("Invalid running method: %s" % running_method)
self.running_method = running_method
self.is_sbatch = self.running_method.lower() == "sbatch"
self.is_makefile = not self.is_sbatch
self.makefile = os.path.join(self.localDir, "Makefile_nonResDenom")
self.num_parallel_jobs = num_parallel_jobs
self.pool_id = pool_id if pool_id else uuid.uuid4()
self.workingDir = os.getcwd()
logging.info("Working directory is: %s" % self.workingDir)
self.template_dir = os.path.join(os.getenv('CMSSW_BASE'), 'src',
'tthAnalysis', 'HiggsToTauTau',
'test', 'templates')
logging.info("Templates directory is: %s" % self.template_dir)
create_if_not_exists(self.configDir)
create_if_not_exists(self.localDir)
create_if_not_exists(self.outputDir)
self.output_file = os.path.join(self.outputDir, output_file)
self.stdout_file_path = os.path.join(self.localDir,
"stdout_nonResDenom.log")
self.stderr_file_path = os.path.join(self.localDir,
"stderr_nonResDenom.log")
self.sw_ver_file_cfg = os.path.join(self.localDir,
"VERSION_nonResDenom.log")
self.sw_ver_file_out = os.path.join(self.outputDir,
"VERSION_nonResDenom.log")
self.submission_out = os.path.join(self.localDir,
"SUBMISSION_nonResDenom.log")
self.stdout_file_path, self.stderr_file_path, self.sw_ver_file_cfg, self.sw_ver_file_out, self.submission_out = get_log_version(
(self.stdout_file_path, self.stderr_file_path,
self.sw_ver_file_cfg, self.sw_ver_file_out, self.submission_out))
check_submission_cmd(self.submission_out, submission_cmd)
self.sbatchFile_nonResDenom = os.path.join(self.localDir,
"sbatch_nonResDenom.py")
self.cfgFiles_nonResDenom = {}
self.logFiles_nonResDenom = {}
self.scriptFiles_nonResDenom = {}
self.jobOptions_sbatch = {}
self.inputFiles = {}
self.outputFiles_tmp = {}
self.outputFiles = {}
self.phoniesToAdd = []
self.filesToClean = [self.configDir]
self.targets = []
self.dirs = {}
all_dirs = [
DKEY_CFGS, DKEY_HISTO_TMP, DKEY_HISTO, DKEY_PLOTS, DKEY_LOGS,
DKEY_SCRIPTS, DKEY_HADD_RT
]
cfg_dirs = [
DKEY_CFGS, DKEY_LOGS, DKEY_PLOTS, DKEY_SCRIPTS, DKEY_HADD_RT
]
self.gen_weights = {}
if self.use_gen_weight:
ref_genweights = os.path.join(os.environ['CMSSW_BASE'], 'src',
'tthAnalysis', 'HiggsToTauTau',
'data',
'refGenWeight_{}.txt'.format(era))
with open(ref_genweights, 'r') as f:
for line in f:
line_split = line.strip().split()
assert (len(line_split) == 2)
sample_name = line_split[0]
ref_genweight = float(line_split[1])
assert (sample_name not in self.gen_weights)
self.gen_weights[sample_name] = ref_genweight
for sample_name, sample_info in self.samples.items():
if not sample_info['use_it']:
continue
process_name = sample_info["process_name_specific"]
if self.use_gen_weight:
assert (re.sub('_duplicate$', '', process_name)
in self.gen_weights)
key_dir = getKey(process_name)
for dir_type in all_dirs:
if dir_type == DKEY_PLOTS:
continue
initDict(self.dirs, [key_dir, dir_type])
if dir_type in cfg_dirs:
dir_choice = self.configDir if dir_type == DKEY_CFGS else self.localDir
self.dirs[key_dir][dir_type] = os.path.join(
dir_choice, dir_type, process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, process_name)
for dir_type in cfg_dirs:
initDict(self.dirs, [dir_type])
dir_choice = self.configDir if dir_type == DKEY_CFGS else self.localDir
self.dirs[dir_type] = os.path.join(dir_choice, dir_type)
if dir_choice != self.configDir:
self.filesToClean.append(self.dirs[dir_type])
self.cvmfs_error_log = {}
self.num_jobs = {
'hadd': 0,
'nonResDenom': 0,
'plot': 0,
}
def __init__(self,
treeName,
outputDir,
cfgDir,
executable_addMEM,
samples,
era,
debug,
running_method,
max_files_per_job,
mem_integrations_per_job,
max_mem_integrations,
num_parallel_jobs,
leptonSelection,
hadTauSelection,
isForBDTtraining,
channel,
pool_id=''):
self.treeName = treeName
self.outputDir = outputDir
self.cfgDir = cfgDir
self.executable_addMEM = executable_addMEM
self.mem_integrations_per_job = mem_integrations_per_job
self.max_files_per_job = max_files_per_job
self.max_mem_integrations = max_mem_integrations
self.samples = samples
self.era = era
self.debug = debug
self.channel = channel
self.leptonSelection = leptonSelection
self.hadTauSelection = hadTauSelection
self.hadTauDefinition = self.hadTauSelection.split('|')[0]
self.hadTauWorkingPoint = self.hadTauSelection.split('|')[1]
self.maxPermutations_branchName = "maxPermutations_addMEM_%s_lep%s_tau%s_%s" % (
self.channel,
self.leptonSelection,
self.hadTauDefinition,
self.hadTauWorkingPoint,
)
self.isForBDTtraining = isForBDTtraining
if running_method.lower() not in ["sbatch", "makefile"]:
raise ValueError("Invalid running method: %s" % running_method)
self.running_method = running_method
self.is_sbatch = False
self.is_makefile = False
if self.running_method.lower() == "sbatch":
self.is_sbatch = True
else:
self.is_makefile = True
self.makefile = os.path.join(self.cfgDir, "Makefile_%s" % self.channel)
self.num_parallel_jobs = num_parallel_jobs
self.pool_id = pool_id if pool_id else uuid.uuid4()
self.workingDir = os.getcwd()
logging.info("Working directory is: {workingDir}".format(
workingDir=self.workingDir))
for dirPath in [self.outputDir, self.cfgDir]:
create_if_not_exists(dirPath)
self.stdout_file = codecs.open(
os.path.join(self.cfgDir, "stdout_%s.log" % self.channel), 'w',
'utf-8')
self.stderr_file = codecs.open(
os.path.join(self.cfgDir, "stderr_%s.log" % self.channel), 'w',
'utf-8')
self.dirs = {}
self.samples = samples
self.cfgFiles_addMEM_modified = {}
self.shFiles_addMEM_modified = {}
self.logFiles_addMEM = {}
self.sbatchFile_addMEM = os.path.join(
self.cfgDir, "sbatch_addMEM_%s.py" % self.channel)
self.inputFiles = {}
self.outputFiles = {}
self.hadd_records = {}
self.filesToClean = []
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or \
sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(sample_name)
for dir_type in [DKEY_NTUPLES, DKEY_FINAL_NTUPLES]:
initDict(self.dirs, [key_dir, dir_type])
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel, process_name)
for dir_type in [DKEY_CFGS, DKEY_LOGS, DKEY_HADD, DKEY_HADD_RT]:
initDict(self.dirs, [key_dir, dir_type])
self.dirs[key_dir][dir_type] = os.path.join(
self.cfgDir, dir_type, self.channel, process_name)
self.cvmfs_error_log = {}
def __init__(self, outputDir, executable_analyze, cfgFile_analyze_original, samples, lepton_charge_selections, hadTau_selection, applyFakeRateWeights, central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
executable_addBackgrounds, executable_addFakes, executable_addFlips, histograms_to_fit, select_rle_output = False, executable_prep_dcard="prepareDatacard"):
analyzeConfig.__init__(self, outputDir, executable_analyze, "2lss_1tau", central_or_shifts,
max_files_per_job, era, use_lumi, lumi, debug, running_method, num_parallel_jobs,
histograms_to_fit)
self.samples = samples
self.lepton_and_hadTau_selections = [ "Tight", "Fakeable", "Fakeable_mcClosure" ]
self.lepton_and_hadTau_frWeights = [ "enabled", "disabled" ]
self.lepton_charge_selections = lepton_charge_selections
self.hadTau_selection_part2 = hadTau_selection
self.applyFakeRateWeights = applyFakeRateWeights
self.lepton_genMatches = [ "2l0j", "1l1j", "0l2j" ]
self.hadTau_genMatches = [ "1t0e0m0j", "0t1e0m0j", "0t0e1m0j", "0t0e0m1j" ]
self.apply_leptonGenMatching = None
self.apply_hadTauGenMatching = None
self.lepton_and_hadTau_genMatches_nonfakes = []
self.lepton_and_hadTau_genMatches_fakes = []
if self.applyFakeRateWeights == "3L":
self.apply_leptonGenMatching = True
self.apply_hadTauGenMatching = True
for lepton_genMatch in self.lepton_genMatches:
for hadTau_genMatch in self.hadTau_genMatches:
lepton_and_hadTau_genMatch = "&".join([ lepton_genMatch, hadTau_genMatch ])
if lepton_genMatch.endswith("0j") and hadTau_genMatch.endswith("0j"):
self.lepton_and_hadTau_genMatches_nonfakes.append(lepton_and_hadTau_genMatch)
else:
self.lepton_and_hadTau_genMatches_fakes.append(lepton_and_hadTau_genMatch)
elif applyFakeRateWeights == "2lepton":
self.apply_leptonGenMatching = True
self.apply_hadTauGenMatching = False
for lepton_genMatch in self.lepton_genMatches:
if lepton_genMatch.endswith("0j"):
self.lepton_and_hadTau_genMatches_nonfakes.append(lepton_genMatch)
else:
self.lepton_and_hadTau_genMatches_fakes.append(lepton_genMatch)
else:
raise ValueError("Invalid Configuration parameter 'applyFakeRateWeights' = %s !!" % applyFakeRateWeights)
self.executable_addBackgrounds = executable_addBackgrounds
self.executable_addFakes = executable_addFakes
self.executable_addFlips = executable_addFlips
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [ "additional_signal_overlap", "background_data_estimate" ]:
continue
process_name = sample_info["process_name_specific"]
for lepton_and_hadTau_selection in self.lepton_and_hadTau_selections:
for lepton_and_hadTau_frWeight in self.lepton_and_hadTau_frWeights:
if lepton_and_hadTau_frWeight == "enabled" and not lepton_and_hadTau_selection.startswith("Fakeable"):
continue
lepton_and_hadTau_selection_and_frWeight = get_lepton_and_hadTau_selection_and_frWeight(lepton_and_hadTau_selection, lepton_and_hadTau_frWeight)
for lepton_charge_selection in self.lepton_charge_selections:
key_dir = getKey(sample_name, lepton_and_hadTau_selection, lepton_and_hadTau_frWeight, lepton_charge_selection)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES ]:
initDict(self.dirs, [ key_dir, dir_type ])
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_and_hadTau_selection_and_frWeight, lepton_charge_selection ]), process_name)
for dir_type in [ DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ dir_type ])
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
##print "self.dirs = ", self.dirs
if self.applyFakeRateWeights == "3L":
self.nonfake_backgrounds = [ "TT", "TTW", "TTZ", "EWK", "Rares" ]
self.prep_dcard_processesToCopy = [ "data_obs", "TT", "TTW", "TTZ", "EWK", "Rares", "fakes_data", "fakes_mc", "flips_data" ]
self.make_plots_backgrounds = [ "TT", "TTW", "TTZ", "EWK", "Rares", "fakes_data", "flips_data" ]
elif applyFakeRateWeights == "2lepton":
if era == '2015':
for sample_name in [
"/TTJets_SingleLeptFromTbar_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v2/MINIAODSIM",
"/TTJets_SingleLeptFromT_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM",
"/TTJets_DiLept_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM",
"/DYJetsToLL_M-10to50_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM",
"/DYJetsToLL_M-50_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM",
"/WJetsToLNu_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM",
"/ST_tW_top_5f_inclusiveDecays_13TeV-powheg-pythia8_TuneCUETP8M1/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM",
"/ST_tW_antitop_5f_inclusiveDecays_13TeV-powheg-pythia8_TuneCUETP8M1/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM",
"/ST_t-channel_4f_leptonDecays_13TeV-amcatnlo-pythia8_TuneCUETP8M1/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM",
"/ST_t-channel_4f_leptonDecays_13TeV-amcatnlo-pythia8_TuneCUETP8M1/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12_ext1-v1/MINIAODSIM",
"/ST_s-channel_4f_leptonDecays_13TeV-amcatnlo-pythia8_TuneCUETP8M1/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v2/MINIAODSIM",
"/WWTo2L2Nu_13TeV-powheg/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM",
"/ZZTo4L_13TeV_powheg_pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM" ]:
self.samples[sample_name]["sample_category"] = "background_data_estimate"
self.samples["/WZTo3LNu_TuneCUETP8M1_13TeV-powheg-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM"]["sample_category"] = "WZ"
elif era == '2016':
for sample_name in [
'/TTJets_SingleLeptFromT_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM',
'/TTJets_SingleLeptFromT_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM',
'/TTJets_SingleLeptFromTbar_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM',
'/TTJets_SingleLeptFromTbar_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM',
'/TTJets_DiLept_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v4/MINIAODSIM',
'/TTJets_DiLept_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0_ext1-v1/MINIAODSIM',
'/DYJetsToLL_M-10to50_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM',
'/DYJetsToLL_M-50_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_reHLT_80X_mcRun2_asymptotic_v14_ext1-v1/MINIAODSIM',
'/WJetsToLNu_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_reHLT_80X_mcRun2_asymptotic_v14_ext1-v1/MINIAODSIM',
'/ST_tW_antitop_5f_inclusiveDecays_13TeV-powheg-pythia8_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM',
'/ST_tW_top_5f_NoFullyHadronicDecays_13TeV-powheg_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM',
'/ST_t-channel_antitop_4f_inclusiveDecays_13TeV-powhegV2-madspin-pythia8_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM',
'/ST_t-channel_top_4f_inclusiveDecays_13TeV-powhegV2-madspin-pythia8_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM',
'/ST_s-channel_4f_leptonDecays_13TeV-amcatnlo-pythia8_TuneCUETP8M1/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM',
'/WWTo2L2Nu_13TeV-powheg/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM',
'/ZZTo4L_13TeV-amcatnloFXFX-pythia8/RunIISpring16MiniAODv2-PUSpring16RAWAODSIM_reHLT_80X_mcRun2_asymptotic_v14-v1/MINIAODSIM' ]:
self.samples[sample_name]["sample_category"] = "background_data_estimate"
self.samples["/WZTo3LNu_TuneCUETP8M1_13TeV-powheg-pythia8/RunIISpring16MiniAODv2-PUSpring16_80X_mcRun2_asymptotic_2016_miniAODv2_v0-v1/MINIAODSIM"]["sample_category"] = "WZ"
else:
raise ValueError("Invalid Configuration parameter 'era' = %s !!" % era)
self.nonfake_backgrounds = [ "TTW", "TTZ", "WZ", "Rares" ]
self.prep_dcard_processesToCopy = [ "data_obs", "TTW", "TTZ", "WZ", "Rares", "fakes_data", "fakes_mc", "flips_data" ]
self.make_plots_backgrounds = [ "TTW", "TTZ", "WZ", "Rares", "fakes_data", "flips_data" ]
else:
raise ValueError("Invalid Configuration parameter 'applyFakeRateWeights' = %s !!" % applyFakeRateWeights)
self.cfgFile_analyze_original = os.path.join(self.workingDir, cfgFile_analyze_original)
self.cfgFile_addBackgrounds_original = os.path.join(self.workingDir, "addBackgrounds_cfg.py")
self.cfgFile_addBackgrounds_modified = {}
self.histogramFile_addBackgrounds = {}
self.histogramDir_addBackgrounds = {}
self.process_output_addBackgrounds = {}
self.histogramFile_hadd_stage1_5 = os.path.join(self.outputDir, DKEY_HIST, "histograms_harvested_stage1_5_%s.root" % self.channel)
self.cfgFile_addFakes_original = os.path.join(self.workingDir, "addBackgroundLeptonFakes_cfg.py")
self.cfgFile_addFakes_modified = {}
self.histogramFile_addFakes = {}
self.histogramFile_addFlips = os.path.join(self.outputDir, DKEY_HIST, "addBackgroundLeptonFlips_%s.root" % self.channel)
self.cfgFile_addFlips_original = os.path.join(self.workingDir, "addBackgroundLeptonFlips_cfg.py")
self.cfgFile_addFlips_modified = os.path.join(self.outputDir, DKEY_CFGS, "addBackgroundLeptonFlips_%s_cfg.py" % self.channel)
self.histogramDir_prep_dcard = "2lss_1tau_SS_Tight"
self.histogramDir_prep_dcard_OS = "2lss_1tau_OS_Tight"
self.cfgFile_make_plots_original = os.path.join(self.workingDir, "makePlots_2lss_1tau_cfg.py")
self.cfgFile_make_plots_mcClosure_original = os.path.join(self.workingDir, "makePlots_mcClosure_cfg.py")
self.cfgFiles_make_plots_mcClosure_modified = []
self.select_rle_output = select_rle_output
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(process_name)
for dir_type in [DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel, process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel, process_name)
for dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD,
DKEY_PLOT, DKEY_HADD_RT
]:
initDict(self.dirs, [dir_type])
if dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT,
DKEY_HADD_RT
]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type,
self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type,
self.channel)
##print "self.dirs = ", self.dirs
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
else:
create_if_not_exists(self.dirs[key])
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
logging.info("Checking input files for sample %s" %
sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(
sample_name, sample_info, self.max_files_per_job, self.debug)
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
process_name = sample_info["process_name_specific"]
logging.info(
"Creating configuration files to run '%s' for sample %s" %
(self.executable_analyze, process_name))
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
inputFileList = inputFileLists[sample_name]
for jobId in inputFileList.keys():
##print "processing sample %s: jobId = %i" % (process_name, jobId)
# build config files for executing analysis code
key_dir = getKey(process_name)
key_analyze_job = getKey(process_name, jobId)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
print "Warning: ntupleFiles['%s'] = %s --> skipping job !!" % (
key_file, ntupleFiles)
continue
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : os.path.join(self.dirs[key_dir][DKEY_CFGS], "analyze_%s_%s_%i_cfg.py" % \
(self.channel, process_name, jobId)),
'histogramFile' : os.path.join(self.dirs[key_dir][DKEY_HIST], "%s_%i.root" % \
(process_name, jobId)),
'logFile' : os.path.join(self.dirs[key_dir][DKEY_LOGS], "analyze_%s_%s_%i.log" % \
(self.channel, process_name, jobId)),
'sample_category' : sample_category,
'hadTau_selection' : self.hadTau_selection,
'use_HIP_mitigation_mediumMuonId' : True,
'is_mc' : is_mc,
'lumi_scale' : 1. if not (self.use_lumi and is_mc) else sample_info["xsection"] * self.lumi / sample_info["nof_events"],
'apply_genWeight' : sample_info["genWeight"] if (is_mc and "genWeight" in sample_info) else False,
'selectBDT' : True,
'changeBranchNames' : self.changeBranchNames
}
self.createCfg_analyze(
self.jobOptions_analyze[key_analyze_job])
# initialize input and output file names for hadd_stage1
key_hadd_stage1 = getKey(process_name)
if not key_hadd_stage1 in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1] = []
self.inputFiles_hadd_stage1[key_hadd_stage1].append(
self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_%s_%s.root" % \
(self.channel, process_name))
self.targets.append(
self.outputFile_hadd_stage1[key_hadd_stage1])
if self.is_sbatch:
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_analyze)
self.sbatchFile_analyze = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze,
self.sbatchFile_analyze,
self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
logging.info("Building dictionaries for sample %s..." % process_name)
for lepton_selection in self.lepton_selections:
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
if lepton_frWeight == "disabled" and not lepton_selection in ["Tight"]:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
for lepton_charge_selection in self.lepton_charge_selections:
if 'mcClosure' in lepton_selection and lepton_charge_selection != 'SS':
# Run MC closure only for the region that complements the SR
continue
central_or_shift_extensions = ["", "hadd", "addBackgrounds"]
central_or_shift_dedicated = self.central_or_shifts if self.runTHweights(sample_info) else self.central_or_shifts_external
central_or_shifts_extended = central_or_shift_extensions + central_or_shift_dedicated
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [ process_name, "hadd" ]
for process_name_or_dummy in process_name_extended:
if central_or_shift_or_dummy in [ "hadd", "addBackgrounds" ] and process_name_or_dummy in [ "hadd" ]:
continue
if central_or_shift_or_dummy not in central_or_shift_extensions and not self.accept_systematics(
central_or_shift_or_dummy, is_mc, lepton_selection, lepton_charge_selection, sample_info
):
continue
key_dir = getKey(process_name_or_dummy, lepton_selection_and_frWeight, lepton_charge_selection, central_or_shift_or_dummy)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight, lepton_charge_selection ]), process_name_or_dummy, central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight, lepton_charge_selection ]), process_name_or_dummy)
for subdirectory in [ "addBackgrounds", "addBackgroundLeptonFakes", "addBackgroundLeptonFlips", "prepareDatacards", "addSystFakeRates", "makePlots" ]:
key_dir = getKey(subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" % numDirectories)
numDirectories_created = 0;
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100*numDirectories_created >= frac*numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
mcClosure_regex = re.compile('Fakeable_mcClosure_(?P<type>m|e)_wFakeRateWeights')
for lepton_selection in self.lepton_selections:
electron_selection = lepton_selection
muon_selection = lepton_selection
hadTauVeto_selection = "Tight"
hadTauVeto_selection = "|".join([ hadTauVeto_selection, self.hadTauVeto_selection_part2 ])
if lepton_selection == "Fakeable_mcClosure_e":
electron_selection = "Fakeable"
muon_selection = "Tight"
elif lepton_selection == "Fakeable_mcClosure_m":
electron_selection = "Tight"
muon_selection = "Fakeable"
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
if lepton_frWeight == "disabled" and not lepton_selection in [ "Tight" ]:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
for lepton_charge_selection in self.lepton_charge_selections:
if 'mcClosure' in lepton_selection and lepton_charge_selection != 'SS':
# Run MC closure only for the region that complements the SR
continue
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
inputFileList = inputFileLists[sample_name]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
use_th_weights = self.runTHweights(sample_info)
central_or_shift_dedicated = self.central_or_shifts if use_th_weights else self.central_or_shifts_external
for central_or_shift in central_or_shift_dedicated:
if not self.accept_systematics(
central_or_shift, is_mc, lepton_selection, lepton_charge_selection, sample_info
):
continue
central_or_shifts_local = []
if central_or_shift == "central" and not use_th_weights:
for central_or_shift_local in self.central_or_shifts_internal:
if self.accept_systematics(
central_or_shift_local, is_mc, lepton_selection, lepton_charge_selection, sample_info
):
central_or_shifts_local.append(central_or_shift_local)
logging.info(" ... for '%s' and systematic uncertainty option '%s'" % (lepton_selection_and_frWeight, central_or_shift))
# build config files for executing analysis code
key_analyze_dir = getKey(process_name, lepton_selection_and_frWeight, lepton_charge_selection, central_or_shift)
for jobId in inputFileList.keys():
analyze_job_tuple = (process_name, lepton_selection_and_frWeight, lepton_charge_selection, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
syncOutput = ''
syncTree = ''
syncGenMatch = self.lepton_genMatches_nonfakes
if self.do_sync:
mcClosure_match = mcClosure_regex.match(lepton_selection_and_frWeight)
if lepton_selection_and_frWeight == 'Tight':
if lepton_charge_selection == 'SS':
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_SR.root' % (self.channel, central_or_shift))
syncTree = 'syncTree_%s_SR' % self.channel
elif lepton_charge_selection == 'OS':
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_Flip.root' % (self.channel, central_or_shift))
syncTree = 'syncTree_%s_Flip' % self.channel
else:
continue
elif lepton_selection_and_frWeight == 'Fakeable_wFakeRateWeights' and lepton_charge_selection == 'SS':
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_Fake.root' % (self.channel, central_or_shift))
syncTree = 'syncTree_%s_Fake' % self.channel
elif mcClosure_match and lepton_charge_selection == 'SS':
mcClosure_type = mcClosure_match.group('type')
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_mcClosure_%s.root' % (self.channel, central_or_shift, mcClosure_type))
syncTree = 'syncTree_%s_mcClosure_%s' % (self.channel, mcClosure_type)
else:
continue
if syncTree and central_or_shift != "central":
syncTree = os.path.join(central_or_shift, syncTree)
syncRLE = ''
if self.do_sync and self.rle_select:
syncRLE = self.rle_select % syncTree
if not os.path.isfile(syncRLE):
logging.warning("Input RLE file for the sync is missing: %s; skipping the job" % syncRLE)
continue
if syncOutput:
self.inputFiles_sync['sync'].append(syncOutput)
cfgFile_modified_path = os.path.join(self.dirs[key_analyze_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % analyze_job_tuple)
rleOutputFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_RLES], "rle_%s_%s_%s_%s_%i.txt" % analyze_job_tuple) \
if self.select_rle_output else ""
histogramFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_HIST], "analyze_%s_%s_%s_%s_%i.root" % analyze_job_tuple)
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : cfgFile_modified_path,
'histogramFile' : histogramFile_path,
'logFile' : logFile_path,
'selEventsFileName_output' : rleOutputFile_path,
'electronSelection' : electron_selection,
'muonSelection' : muon_selection,
'apply_leptonGenMatching' : self.apply_leptonGenMatching,
'leptonChargeSelection' : lepton_charge_selection,
'hadTauSelection_veto' : hadTauVeto_selection,
'applyFakeRateWeights' : self.applyFakeRateWeights if not lepton_selection == "Tight" else "disabled",
'central_or_shift' : central_or_shift,
'central_or_shifts_local' : central_or_shifts_local,
'syncOutput' : syncOutput,
'syncTree' : syncTree,
'syncRLE' : syncRLE,
'useNonNominal' : self.use_nonnominal,
'apply_hlt_filter' : self.hlt_filter,
'syncGenMatch' : syncGenMatch,
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job], sample_info, lepton_selection)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name, lepton_selection_and_frWeight, lepton_charge_selection)
hadd_stage1_job_tuple = (process_name, lepton_selection_and_frWeight, lepton_charge_selection)
key_hadd_stage1_job = getKey(*hadd_stage1_job_tuple)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1_job] = os.path.join(self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s_%s_%s.root" % hadd_stage1_job_tuple)
if self.do_sync: continue
if is_mc:
logging.info("Creating configuration files to run 'addBackgrounds' for sample %s" % process_name)
sample_categories = [ sample_category ]
for sample_category in sample_categories:
# sum non-fake and fake contributions for each MC sample separately
genMatch_categories = [ "nonfake", "Convs", "fake", "flip" ]
for genMatch_category in genMatch_categories:
key_hadd_stage1_job = getKey(process_name, lepton_selection_and_frWeight, lepton_charge_selection)
key_addBackgrounds_dir = getKey(process_name, lepton_selection_and_frWeight, lepton_charge_selection, "addBackgrounds")
addBackgrounds_job_tuple = None
processes_input = None
process_output = None
if genMatch_category == "nonfake":
# sum non-fake contributions for each MC sample separately
# input processes: TT2l0g0j; ...
# output processes: TT; ...
if sample_category in self.ttHProcs:
lepton_genMatches = []
lepton_genMatches.extend(self.lepton_genMatches_nonfakes)
lepton_genMatches.extend(self.lepton_genMatches_Convs)
processes_input = []
processes_input.extend([ "%s%s" % ("ttH_htt", genMatch) for genMatch in lepton_genMatches ])
processes_input.extend([ "%s%s" % ("ttH_hww", genMatch) for genMatch in lepton_genMatches ])
processes_input.extend([ "%s%s" % ("ttH_hzz", genMatch) for genMatch in lepton_genMatches ])
processes_input.extend([ "%s%s" % ("ttH_hzg", genMatch) for genMatch in lepton_genMatches ])
processes_input.extend([ "%s%s" % ("ttH_hmm", genMatch) for genMatch in lepton_genMatches ])
else:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_genMatches_nonfakes ]
process_output = sample_category
addBackgrounds_job_tuple = (process_name, sample_category, lepton_selection_and_frWeight, lepton_charge_selection)
elif genMatch_category == "Convs":
# sum conversion background contributions for each MC sample separately
# input processes: TT1l1g0j, TT0l2g0j; ...
# output processes: TT_Convs; ...
if sample_category in self.ttHProcs:
processes_input = []
processes_input.extend([ "%s%s" % ("ttH_htt", genMatch) for genMatch in self.lepton_genMatches_Convs ])
processes_input.extend([ "%s%s" % ("ttH_hww", genMatch) for genMatch in self.lepton_genMatches_Convs ])
processes_input.extend([ "%s%s" % ("ttH_hzz", genMatch) for genMatch in self.lepton_genMatches_Convs ])
processes_input.extend([ "%s%s" % ("ttH_hzg", genMatch) for genMatch in self.lepton_genMatches_Convs ])
processes_input.extend([ "%s%s" % ("ttH_hmm", genMatch) for genMatch in self.lepton_genMatches_Convs ])
else:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_genMatches_Convs ]
process_output = "%s_Convs" % sample_category
addBackgrounds_job_tuple = (process_name, "%s_Convs" % sample_category, lepton_selection_and_frWeight, lepton_charge_selection)
elif genMatch_category == "fake":
# sum fake contributions for each MC sample separately
# input processes: TT1l0g1j, TT0l1g1j, TT0l0g2j; ...
# output processes: TT_fake; ...
if sample_category in self.ttHProcs:
processes_input = []
processes_input.extend([ "%s%s" % ("ttH_htt", genMatch) for genMatch in self.lepton_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hww", genMatch) for genMatch in self.lepton_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hzz", genMatch) for genMatch in self.lepton_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hzg", genMatch) for genMatch in self.lepton_genMatches_fakes ])
processes_input.extend([ "%s%s" % ("ttH_hmm", genMatch) for genMatch in self.lepton_genMatches_fakes ])
else:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_genMatches_fakes ]
process_output = "%s_fake" % sample_category
addBackgrounds_job_tuple = (process_name, "%s_fake" % sample_category, lepton_selection_and_frWeight, lepton_charge_selection)
elif genMatch_category == "flip":
# sum flip contributions for each MC sample separately
# input processes: TT2l2f0g0j&2t0e0m0j, TT2l1f0g0j&2t0e0m0j; ...
# output processes: TT_flip; ...
if sample_category in self.ttHProcs:
processes_input = []
processes_input.extend([ "%s%s" % ("ttH_htt", genMatch) for genMatch in self.lepton_genMatches_flips ])
processes_input.extend([ "%s%s" % ("ttH_hww", genMatch) for genMatch in self.lepton_genMatches_flips ])
processes_input.extend([ "%s%s" % ("ttH_hzz", genMatch) for genMatch in self.lepton_genMatches_flips ])
processes_input.extend([ "%s%s" % ("ttH_hzg", genMatch) for genMatch in self.lepton_genMatches_flips ])
processes_input.extend([ "%s%s" % ("ttH_hmm", genMatch) for genMatch in self.lepton_genMatches_flips ])
else:
processes_input = [ "%s%s" % (sample_category, genMatch) for genMatch in self.lepton_genMatches_flips ]
process_output = "%s_flip" % sample_category
addBackgrounds_job_tuple = (process_name, "%s_flip" % sample_category, lepton_selection_and_frWeight, lepton_charge_selection)
if processes_input:
logging.info(" ...for genMatch option = '%s'" % genMatch_category)
key_addBackgrounds_job = getKey(*addBackgrounds_job_tuple)
cfgFile_modified = os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_%s_cfg.py" % addBackgrounds_job_tuple)
outputFile = os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s_%s.root" % addBackgrounds_job_tuple)
self.jobOptions_addBackgrounds[key_addBackgrounds_job] = {
'inputFile' : self.outputFile_hadd_stage1[key_hadd_stage1_job],
'cfgFile_modified' : cfgFile_modified,
'outputFile' : outputFile,
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], os.path.basename(cfgFile_modified).replace("_cfg.py", ".log")),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight, lepton_charge_selection) ],
'processes_input' : processes_input,
'process_output' : process_output
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds[key_addBackgrounds_job])
# initialize input and output file names for hadd_stage1_5
key_hadd_stage1_5_dir = getKey("hadd", lepton_selection_and_frWeight, lepton_charge_selection)
hadd_stage1_5_job_tuple = (lepton_selection_and_frWeight, lepton_charge_selection)
key_hadd_stage1_5_job = getKey(*hadd_stage1_5_job_tuple)
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.jobOptions_addBackgrounds[key_addBackgrounds_job]['outputFile'])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job] = os.path.join(self.dirs[key_hadd_stage1_5_dir][DKEY_HIST],
"hadd_stage1_5_%s_%s.root" % hadd_stage1_5_job_tuple)
# add output files of hadd_stage1 for data to list of input files for hadd_stage1_5
if not is_mc:
key_hadd_stage1_job = getKey(process_name, lepton_selection_and_frWeight, lepton_charge_selection)
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight, lepton_charge_selection)
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.outputFile_hadd_stage1[key_hadd_stage1_job])
if self.do_sync: continue
# sum fake background contributions for the total of all MC samples
# input processes: TT1l0g1j, TT0l1g1j, TT0l0g2j; ...
# output process: fakes_mc
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight, lepton_charge_selection)
key_addBackgrounds_dir = getKey("addBackgrounds")
addBackgrounds_job_fakes_tuple = ("fakes_mc", lepton_selection_and_frWeight, lepton_charge_selection)
key_addBackgrounds_job_fakes = getKey(*addBackgrounds_job_fakes_tuple)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
sample_categories.extend(self.ttHProcs)
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_fake" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_fakes_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_fakes_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_fakes_tuple),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight, lepton_charge_selection) ],
'processes_input' : processes_input,
'process_output' : "fakes_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes])
# sum flip background contributions for the total of all MC sample
# input processes: TT2l1f0g0j,TT2l2f0g0j; ...
# output process: flips_mc
addBackgrounds_job_flips_tuple = ("flips_mc", lepton_selection_and_frWeight, lepton_charge_selection)
key_addBackgrounds_job_flips = getKey(*addBackgrounds_job_flips_tuple)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
sample_categories.extend(self.ttHProcs)
processes_input = []
for sample_category in sample_categories:
processes_input.append("%s_flip" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_flips] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_flips_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_flips_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_flips_tuple),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight, lepton_charge_selection) ],
'processes_input' : processes_input,
'process_output' : "flips_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_flips])
# sum conversion background contributions for the total of all MC samples
# input processes: TT1l1g0j, TT0l2g0j; ...
# output process: Convs
addBackgrounds_job_Convs_tuple = ("Convs", lepton_selection_and_frWeight, lepton_charge_selection)
key_addBackgrounds_job_Convs = getKey(*addBackgrounds_job_Convs_tuple)
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
sample_categories.extend(self.ttHProcs)
processes_input = []
for sample_category in self.convs_backgrounds:
processes_input.append("%s_Convs" % sample_category)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_Convs_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_Convs_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_Convs_tuple),
'categories' : [ getHistogramDir(lepton_selection, lepton_frWeight, lepton_charge_selection) ],
'processes_input' : processes_input,
'process_output' : "Convs"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs])
# initialize input and output file names for hadd_stage2
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight, lepton_charge_selection)
key_hadd_stage2_dir = getKey("hadd", lepton_selection_and_frWeight, lepton_charge_selection)
hadd_stage2_job_tuple = (lepton_selection_and_frWeight, lepton_charge_selection)
key_hadd_stage2_job = getKey(*hadd_stage2_job_tuple)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_flips]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job])
self.outputFile_hadd_stage2[key_hadd_stage2_job] = os.path.join(self.dirs[key_hadd_stage2_dir][DKEY_HIST],
"hadd_stage2_%s_%s.root" % hadd_stage2_job_tuple)
if self.do_sync:
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_syncNtuple(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_syncNtuple(lines_makefile)
outputFile_sync_path = os.path.join(self.outputDir, DKEY_SYNC, '%s.root' % self.channel)
self.outputFile_sync['sync'] = outputFile_sync_path
self.addToMakefile_hadd_sync(lines_makefile)
self.addToMakefile_validate(lines_makefile)
self.targets.extend(self.phoniesToAdd)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
logging.info("Creating configuration files to run 'addBackgroundFakes'")
for lepton_charge_selection in self.lepton_charge_selections:
key_hadd_stage1_5_job = getKey(get_lepton_selection_and_frWeight("Fakeable", "enabled"), lepton_charge_selection)
key_addFakes_dir = getKey("addBackgroundLeptonFakes")
addFakes_job_tuple = (lepton_charge_selection)
key_addFakes_job = getKey("data_fakes", lepton_charge_selection)
category_sideband = None
if self.applyFakeRateWeights == "2lepton":
category_sideband = "ttWctrl_%s_Fakeable_wFakeRateWeights" % lepton_charge_selection
else:
raise ValueError("Invalid Configuration parameter 'applyFakeRateWeights' = %s !!" % self.applyFakeRateWeights)
self.jobOptions_addFakes[key_addFakes_job] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addFakes_dir][DKEY_CFGS], "addBackgroundLeptonFakes_%s_cfg.py" % addFakes_job_tuple),
'outputFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_HIST], "addBackgroundLeptonFakes_%s.root" % addFakes_job_tuple),
'logFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_LOGS], "addBackgroundLeptonFakes_%s.log" % addFakes_job_tuple),
'category_signal' : "ttWctrl_%s_Tight" % lepton_charge_selection,
'category_sideband' : category_sideband
}
self.createCfg_addFakes(self.jobOptions_addFakes[key_addFakes_job])
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), lepton_charge_selection)
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
#--------------------------------------------------------------------------
# CV: add histograms in OS and SS regions,
# so that "data_fakes" background can be subtracted from OS control region used to estimate charge flip background
key_hadd_stage1_5_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_addFakes_job = getKey("data_fakes", "OS")
key_hadd_stage1_6_dir = getKey("hadd", get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_hadd_stage1_6_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
if key_hadd_stage1_6_job not in self.inputFiles_hadd_stage1_6:
self.inputFiles_hadd_stage1_6[key_hadd_stage1_6_job] = []
self.inputFiles_hadd_stage1_6[key_hadd_stage1_6_job].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
self.inputFiles_hadd_stage1_6[key_hadd_stage1_6_job].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job])
self.outputFile_hadd_stage1_6[key_hadd_stage1_6_job] = os.path.join(self.dirs[key_hadd_stage1_6_dir][DKEY_HIST], "hadd_stage1_6_Tight_OS.root")
#--------------------------------------------------------------------------
logging.info("Creating configuration files to run 'addBackgroundFlips'")
key_addFlips_dir = getKey("addBackgroundLeptonFlips")
key_addFlips_job = getKey("data_flips")
self.jobOptions_addFlips[key_addFlips_job] = {
'inputFile' : self.outputFile_hadd_stage1_6,
'cfgFile_modified' : os.path.join(self.dirs[key_addFlips_dir][DKEY_CFGS], "addBackgroundLeptonFlips_cfg.py"),
'outputFile' : os.path.join(self.dirs[key_addFlips_dir][DKEY_HIST], "addBackgroundLeptonFlips.root"),
'logFile' : os.path.join(self.dirs[key_addFlips_dir][DKEY_LOGS], "addBackgroundLeptonFlips.log"),
'category_signal' : "ttWctrl_SS_Tight",
'category_sideband' : "ttWctrl_OS_Tight"
}
self.createCfg_addFlips(self.jobOptions_addFlips[key_addFlips_job])
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addFlips[key_addFlips_job]['outputFile'])
logging.info("Creating configuration files to run 'prepareDatacards'")
for histogramToFit in self.histograms_to_fit:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
key_prep_dcard_dir = getKey("prepareDatacards")
prep_dcard_job_tuple = (self.channel, histogramToFit)
key_prep_dcard_job = getKey(histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : self.histogramDir_prep_dcard,
'histogramToFit' : histogramToFit,
'label' : None
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
# add shape templates for the following systematic uncertainties:
# - 'CMS_ttHl_Clos_norm_e'
# - 'CMS_ttHl_Clos_shape_e'
# - 'CMS_ttHl_Clos_norm_m'
# - 'CMS_ttHl_Clos_shape_m'
key_add_syst_fakerate_dir = getKey("addSystFakeRates")
add_syst_fakerate_job_tuple = (self.channel, histogramToFit)
key_add_syst_fakerate_job = getKey(histogramToFit)
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job] = {
'inputFile' : self.jobOptions_prep_dcard[key_prep_dcard_job]['datacardFile'],
'cfgFile_modified' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_CFGS], "addSystFakeRates_%s_%s_cfg.py" % add_syst_fakerate_job_tuple),
'outputFile' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_DCRD], "addSystFakeRates_%s_%s.root" % add_syst_fakerate_job_tuple),
'category' : self.channel,
'histogramToFit' : histogramToFit,
'plots_outputFileName' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_PLOT], "addSystFakeRates.png")
}
histogramDir_nominal = self.histogramDir_prep_dcard
for lepton_type in [ 'e', 'm' ]:
lepton_mcClosure = "Fakeable_mcClosure_%s" % lepton_type
if lepton_mcClosure not in self.lepton_selections:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_mcClosure, "enabled")
key_addBackgrounds_job_fakes = getKey("fakes_mc", lepton_selection_and_frWeight, 'SS')
histogramDir_mcClosure = self.mcClosure_dir['%s_%s' % (lepton_mcClosure, 'SS')]
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job].update({
'add_Clos_%s' % lepton_type : ("Fakeable_mcClosure_%s" % lepton_type) in self.lepton_selections,
'inputFile_nominal_%s' % lepton_type : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'histogramName_nominal_%s' % lepton_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_nominal, histogramToFit),
'inputFile_mcClosure_%s' % lepton_type : self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'],
'histogramName_mcClosure_%s' % lepton_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_mcClosure, histogramToFit)
})
self.createCfg_add_syst_fakerate(self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job])
logging.info("Creating configuration files to run 'makePlots'")
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
key_makePlots_dir = getKey("makePlots")
key_makePlots_job = getKey("SS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard,
'label' : "t#bar{t}W control region",
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.sbatchFile_addBackgrounds = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_%s.py" % self.channel)
self.sbatchFile_addBackgrounds_sum = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_sum_%s.py" % self.channel)
self.sbatchFile_addFakes = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFakes_%s.py" % self.channel)
self.sbatchFile_addFlips = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFlips_%s.py" % self.channel)
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addBackgrounds)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds, self.jobOptions_addBackgrounds)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds_sum, self.jobOptions_addBackgrounds_sum)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFakes)
self.createScript_sbatch(self.executable_addFakes, self.sbatchFile_addFakes, self.jobOptions_addFakes)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFlips)
self.createScript_sbatch(self.executable_addFlips, self.sbatchFile_addFlips, self.jobOptions_addFlips)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_backgrounds_from_data_withFlips(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_add_syst_fakerate(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.addToMakefile_validate(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
logging.info("Building dictionaries for sample %s..." %
process_name)
for charge_selection in self.charge_selections:
central_or_shift_extensions = ["", "hadd", "addBackgrounds"]
central_or_shifts_extended = central_or_shift_extensions + self.central_or_shifts
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [process_name, "hadd"]
for process_name_or_dummy in process_name_extended:
if central_or_shift_or_dummy in [
"hadd"
] and process_name_or_dummy in ["hadd"]:
continue
if central_or_shift_or_dummy != "central" and central_or_shift_or_dummy not in central_or_shift_extensions:
if not is_mc:
continue
if not self.accept_central_or_shift(
central_or_shift_or_dummy, sample_info):
continue
key_dir = getKey(process_name_or_dummy,
charge_selection,
central_or_shift_or_dummy)
for dir_type in [
DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES
]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel,
"_".join([charge_selection]),
process_name_or_dummy,
central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel,
"_".join([charge_selection]),
process_name_or_dummy)
for subdirectory in ["comp_jetToTauFakeRate", "makePlots"]:
key_dir = getKey(subdirectory)
for dir_type in [
DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT
]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD,
DKEY_PLOT, DKEY_HADD_RT
]:
initDict(self.dirs, [dir_type])
if dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT,
DKEY_HADD_RT
]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type,
self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type,
self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" %
numDirectories)
numDirectories_created = 0
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100 * numDirectories_created >= frac * numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" %
sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(
sample_info, self.max_files_per_job)
self.inputFileIds = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
inputFileList = inputFileLists[sample_name]
logging.info(
"Creating configuration files to run '%s' for sample %s" %
(self.executable_analyze, process_name))
is_mc = (sample_info["type"] == "mc")
sample_category = sample_info["sample_category"]
for charge_selection in self.charge_selections:
for central_or_shift in self.central_or_shifts:
if central_or_shift != "central" and not is_mc:
continue
if not self.accept_central_or_shift(
central_or_shift, sample_info):
continue
# build config files for executing analysis code
key_analyze_dir = getKey(process_name, charge_selection,
central_or_shift)
for jobId in inputFileList.keys():
analyze_job_tuple = (process_name, charge_selection,
central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning(
"No input ntuples for %s --> skipping job !!" %
(key_analyze_job))
continue
cfgFile_modified_path = os.path.join(
self.dirs[key_analyze_dir][DKEY_CFGS],
"analyze_%s_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(
self.dirs[key_analyze_dir][DKEY_LOGS],
"analyze_%s_%s_%s_%i.log" % analyze_job_tuple)
histogramFile_path = os.path.join(
self.dirs[key_analyze_dir][DKEY_HIST],
"analyze_%s_%s_%s_%i.root" % analyze_job_tuple)
rleOutputFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_RLES], "rle_%s_%s_%s_%i.txt" % analyze_job_tuple) \
if self.select_rle_output else ""
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles': ntupleFiles,
'cfgFile_modified': cfgFile_modified_path,
'histogramFile': histogramFile_path,
'logFile': logFile_path,
'chargeSelection': charge_selection,
'jet_minPt': self.jet_minPt,
'jet_maxPt': self.jet_maxPt,
'jet_minAbsEta': self.jet_minAbsEta,
'jet_maxAbsEta': self.jet_maxAbsEta,
'hadTau_selection_tight':
self.hadTau_selection_tight,
'hadTauSelection_denominator':
self.hadTau_selection_denominator,
'hadTauSelections_numerator':
self.hadTau_selections_numerator,
'trigMatchingOptions': self.trigMatchingOptions,
'selEventsFileName_output': rleOutputFile_path,
'absEtaBins': self.absEtaBins,
'decayModes': self.decayModes,
'central_or_shift': central_or_shift,
'central_or_shifts_local': [],
'apply_hlt_filter': self.hlt_filter,
}
self.createCfg_analyze(
self.jobOptions_analyze[key_analyze_job],
sample_info)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name,
charge_selection)
hadd_stage1_job_tuple = (process_name,
charge_selection)
key_hadd_stage1_job = getKey(*hadd_stage1_job_tuple)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[
key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[
key_hadd_stage1_job].append(
self.jobOptions_analyze[key_analyze_job]
['histogramFile'])
self.outputFile_hadd_stage1[
key_hadd_stage1_job] = os.path.join(
self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s_%s.root" %
hadd_stage1_job_tuple)
# initialize input and output file names for hadd_stage2
key_hadd_stage1_job = getKey(process_name, charge_selection)
key_hadd_stage2_dir = getKey("hadd", charge_selection)
key_hadd_stage2_job = getKey(charge_selection)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(
self.outputFile_hadd_stage1[key_hadd_stage1_job])
self.outputFile_hadd_stage2[
key_hadd_stage2_job] = os.path.join(
self.dirs[key_hadd_stage2_dir][DKEY_HIST],
"hadd_stage2_%s.root" % charge_selection)
logging.info(
"Creating configuration files for executing 'comp_jetToTauFakeRate'"
)
for charge_selection in self.charge_selections:
charge_key = "comp_%s" % charge_selection
self.comp_input_files[charge_key] = []
for trigMatchingOption in self.trigMatchingOptions:
key_hadd_stage2_job = getKey(charge_selection)
key_comp_jetToTauFakeRate_dir = getKey("comp_jetToTauFakeRate")
key_comp_jetToTauFakeRate_job = getKey(charge_selection,
trigMatchingOption)
self.jobOptions_comp_jetToTauFakeRate[
key_comp_jetToTauFakeRate_job] = {
'inputFile':
self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified':
os.path.join(
self.dirs[DKEY_CFGS],
"comp_jetToTauFakeRate_%s_%s_cfg.py" %
(charge_selection, trigMatchingOption)),
'outputFile':
os.path.join(
self.dirs[DKEY_HIST],
"comp_jetToTauFakeRate_%s_%s.root" %
(charge_selection, trigMatchingOption)),
'logFile':
os.path.join(
self.dirs[DKEY_LOGS],
"comp_jetToTauFakeRate_%s_%s.log" %
(charge_selection, trigMatchingOption)),
'looseRegion':
"jetToTauFakeRate_%s_%s/denominator/" %
(charge_selection, trigMatchingOption),
'tightRegion':
"jetToTauFakeRate_%s_%s/numerator/" %
(charge_selection, trigMatchingOption),
'absEtaBins':
self.absEtaBins,
'ptBins':
self.ptBins,
'decayModes':
self.decayModes,
'hadTauSelections':
self.hadTau_selections_numerator,
'trigMatchingOption':
trigMatchingOption,
'plots_outputFileName':
os.path.join(
self.dirs[key_comp_jetToTauFakeRate_dir]
[DKEY_PLOT], "comp_jetToTauFakeRate_%s.png" %
trigMatchingOption)
}
self.createCfg_comp_jetToTauFakeRate(
self.jobOptions_comp_jetToTauFakeRate[
key_comp_jetToTauFakeRate_job])
comp_output = self.jobOptions_comp_jetToTauFakeRate[
key_comp_jetToTauFakeRate_job]['outputFile']
self.targets.append(comp_output)
self.comp_input_files[charge_key].append(comp_output)
self.comp_output_files[charge_key] = os.path.join(
self.dirs[DKEY_HIST],
"comp_jetToTauFakeRate_%s.root" % charge_selection)
logging.info("Creating configuration files to run 'makePlots'")
for charge_selection in self.charge_selections:
key_hadd_stage2_job = getKey(charge_selection)
key_makePlots_dir = getKey("makePlots")
key_makePlots_job = getKey(charge_selection)
self.jobOptions_make_plots[key_makePlots_job] = {
'executable':
self.executable_make_plots,
'inputFile':
self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified':
os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS],
"makePlots_%s_cfg.py" % self.channel),
'outputFile':
os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT],
"makePlots_%s.png" % self.channel),
'histogramDir':
"jetToTauFakeRate_%s" % charge_selection,
'label':
None,
'make_plots_backgrounds':
self.make_plots_backgrounds
}
self.createCfg_makePlots(
self.jobOptions_make_plots[key_makePlots_job])
for trigMatchingOption in self.trigMatchingOptions:
self.cfgFile_make_plots = self.cfgFile_make_plots_denominator
for absEtaBin in ["absEtaLt1_5", "absEta1_5to9_9"]:
key_hadd_stage2_job = getKey(charge_selection)
key_makePlots_job = getKey(charge_selection,
trigMatchingOption, absEtaBin,
"denominator")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(
self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_%s_%s_denominator_%s_cfg.py" % \
(self.channel, charge_selection, trigMatchingOption, absEtaBin)),
'outputFile' : os.path.join(
self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s_%s_%s_denominator_%s.png" % (self.channel, charge_selection, trigMatchingOption, absEtaBin)),
'histogramDir' : "jetToTauFakeRate_%s_%s/denominator/%s" % (charge_selection, trigMatchingOption, absEtaBin),
'label' : None,
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(
self.jobOptions_make_plots[key_makePlots_job])
for hadTau_selection_numerator in self.hadTau_selections_numerator:
key_hadd_stage2_job = getKey(charge_selection)
key_makePlots_job = getKey(charge_selection,
trigMatchingOption,
absEtaBin, "numerator",
hadTau_selection_numerator)
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(
self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_%s_%s_numerator_%s_%s_cfg.py" % \
(self.channel, charge_selection, trigMatchingOption, hadTau_selection_numerator, absEtaBin)),
'outputFile' : os.path.join(
self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s_%s_%s_numerator_%s_%s.png" % \
(self.channel, charge_selection, trigMatchingOption, hadTau_selection_numerator, absEtaBin)),
'histogramDir' : "jetToTauFakeRate_%s_%s/numerator/%s/%s" % (charge_selection, trigMatchingOption, hadTau_selection_numerator, absEtaBin),
'label' : None,
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(
self.jobOptions_make_plots[key_makePlots_job])
self.sbatchFile_analyze = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.sbatchFile_comp_jetToTauFakeRate = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_comp_jetToTauFakeRate.py")
if self.is_sbatch:
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_analyze)
self.createScript_sbatch_analyze(self.executable_analyze,
self.sbatchFile_analyze,
self.jobOptions_analyze)
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_comp_jetToTauFakeRate)
self.createScript_sbatch(self.executable_comp_jetToTauFakeRate,
self.sbatchFile_comp_jetToTauFakeRate,
self.jobOptions_comp_jetToTauFakeRate)
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile,
make_dependency="phony_hadd_stage1",
max_mem='4096M')
self.addToMakefile_comp_jetToTauFakeRate(lines_makefile)
self.addToMakefile_comp_hadd(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
logging.info("Building dictionaries for sample %s..." % process_name)
for lepton_selection in self.lepton_selections:
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
if lepton_frWeight == "disabled" and not lepton_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
for chargeSumSelection in self.chargeSumSelections:
central_or_shift_extensions = ["", "hadd", "addBackgrounds"]
central_or_shift_dedicated = self.central_or_shifts if self.runTHweights(sample_info) else self.central_or_shifts_external
central_or_shifts_extended = central_or_shift_extensions + central_or_shift_dedicated
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [ process_name, "hadd" ]
for process_name_or_dummy in process_name_extended:
if central_or_shift_or_dummy in [ "hadd", "addBackgrounds" ] and process_name_or_dummy in [ "hadd" ]:
continue
if central_or_shift_or_dummy not in central_or_shift_extensions and not self.accept_systematics(
central_or_shift_or_dummy, is_mc, lepton_selection, chargeSumSelection, sample_info
):
continue
key_dir = getKey(process_name_or_dummy, lepton_selection_and_frWeight, chargeSumSelection, central_or_shift_or_dummy)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight, chargeSumSelection ]), process_name_or_dummy, central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_selection_and_frWeight, chargeSumSelection ]), process_name_or_dummy)
for subdirectory in [ "addBackgrounds", "addBackgroundLeptonFakes", "prepareDatacards", "addSystFakeRates", "makePlots" ]:
key_dir = getKey(subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" % numDirectories)
numDirectories_created = 0;
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100*numDirectories_created >= frac*numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
mcClosure_regex = re.compile('Fakeable_mcClosure_(?P<type>m|e)_wFakeRateWeights')
for lepton_selection in self.lepton_selections:
electron_selection = lepton_selection
muon_selection = lepton_selection
hadTauVeto_selection = "Tight"
hadTauVeto_selection = "|".join([ hadTauVeto_selection, self.hadTauVeto_selection_part2 ])
if lepton_selection == "forBDTtraining":
electron_selection = "Loose"
muon_selection = "Loose"
elif lepton_selection == "Fakeable_mcClosure_e":
electron_selection = "Fakeable"
muon_selection = "Tight"
elif lepton_selection == "Fakeable_mcClosure_m":
electron_selection = "Tight"
muon_selection = "Fakeable"
for lepton_frWeight in self.lepton_frWeights:
if lepton_frWeight == "enabled" and not lepton_selection.startswith("Fakeable"):
continue
if lepton_frWeight == "disabled" and not lepton_selection in [ "Tight", "forBDTtraining" ]:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_selection, lepton_frWeight)
for chargeSumSelection in self.chargeSumSelections:
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
inputFileList = inputFileLists[sample_name]
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
use_th_weights = self.runTHweights(sample_info)
central_or_shift_dedicated = self.central_or_shifts if use_th_weights else self.central_or_shifts_external
for central_or_shift in central_or_shift_dedicated:
if not self.accept_systematics(
central_or_shift, is_mc, lepton_selection, chargeSumSelection, sample_info
):
continue
central_or_shifts_local = []
if central_or_shift == "central" and not use_th_weights:
for central_or_shift_local in self.central_or_shifts_internal:
if self.accept_systematics(
central_or_shift_local, is_mc, lepton_selection, chargeSumSelection, sample_info
):
central_or_shifts_local.append(central_or_shift_local)
logging.info(" ... for '%s' and systematic uncertainty option '%s'" % (lepton_selection_and_frWeight, central_or_shift))
# build config files for executing analysis code
key_analyze_dir = getKey(process_name, lepton_selection_and_frWeight, chargeSumSelection, central_or_shift)
for jobId in inputFileList.keys():
analyze_job_tuple = (process_name, lepton_selection_and_frWeight, chargeSumSelection, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
syncOutput = ''
syncTree = ''
if self.do_sync:
if chargeSumSelection != 'OS':
continue
mcClosure_match = mcClosure_regex.match(lepton_selection_and_frWeight)
if lepton_selection_and_frWeight == 'Tight':
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_SR.root' % (self.channel, central_or_shift))
syncTree = 'syncTree_%s_SR' % self.channel.replace('_', '')
elif lepton_selection_and_frWeight == 'Fakeable_wFakeRateWeights':
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_Fake.root' % (self.channel, central_or_shift))
syncTree = 'syncTree_%s_Fake' % self.channel.replace('_', '')
elif mcClosure_match:
mcClosure_type = mcClosure_match.group('type')
syncOutput = os.path.join(self.dirs[key_analyze_dir][DKEY_SYNC], '%s_%s_mcClosure_%s.root' % (self.channel, central_or_shift, mcClosure_type))
syncTree = 'syncTree_%s_mcClosure_%s' % (self.channel.replace('_', ''), mcClosure_type)
else:
continue
if syncTree and central_or_shift != "central":
syncTree = os.path.join(central_or_shift, syncTree)
syncRLE = ''
if self.do_sync and self.rle_select:
syncRLE = self.rle_select % syncTree
if not os.path.isfile(syncRLE):
logging.warning("Input RLE file for the sync is missing: %s; skipping the job" % syncRLE)
continue
if syncOutput:
self.inputFiles_sync['sync'].append(syncOutput)
cfgFile_modified_path = os.path.join(self.dirs[key_analyze_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % analyze_job_tuple)
rleOutputFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_RLES], "rle_%s_%s_%s_%s_%i.txt" % analyze_job_tuple) \
if self.select_rle_output else ""
histogramFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_HIST], "analyze_%s_%s_%s_%s_%i.root" % analyze_job_tuple)
branchName_memOutput = '%s_%s' % (self.MEMbranch, self.get_addMEM_systematics(central_or_shift)) \
if self.MEMbranch else ''
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : cfgFile_modified_path,
'histogramFile' : histogramFile_path,
'logFile' : logFile_path,
'selEventsFileName_output' : rleOutputFile_path,
'electronSelection' : electron_selection,
'muonSelection' : muon_selection,
'apply_leptonGenMatching' : self.apply_leptonGenMatching,
'hadTauSelection' : hadTauVeto_selection,
'chargeSumSelection' : chargeSumSelection,
'applyFakeRateWeights' : self.applyFakeRateWeights if not lepton_selection == "Tight" else "disabled",
'central_or_shift' : central_or_shift,
'central_or_shifts_local' : central_or_shifts_local,
'selectBDT' : self.isBDTtraining,
'branchName_memOutput' : branchName_memOutput,
'syncOutput' : syncOutput,
'syncTree' : syncTree,
'syncRLE' : syncRLE,
'apply_hlt_filter' : self.hlt_filter,
'useNonNominal' : self.use_nonnominal,
'fillGenEvtHistograms' : True,
'isControlRegion' : self.isControlRegion,
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job], sample_info, lepton_selection)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name, lepton_selection_and_frWeight, chargeSumSelection)
hadd_stage1_job_tuple = (process_name, lepton_selection_and_frWeight, chargeSumSelection)
key_hadd_stage1_job = getKey(*hadd_stage1_job_tuple)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1_job] = os.path.join(self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s_%s_%s.root" % hadd_stage1_job_tuple)
if self.isBDTtraining or self.do_sync:
continue
# add output files of hadd_stage1 for data to list of input files for hadd_stage1_5
key_hadd_stage1_job = getKey(process_name, lepton_selection_and_frWeight, chargeSumSelection)
key_hadd_stage1_5_dir = getKey("hadd", lepton_selection_and_frWeight, chargeSumSelection)
hadd_stage1_5_job_tuple = (lepton_selection_and_frWeight, chargeSumSelection)
key_hadd_stage1_5_job = getKey(*hadd_stage1_5_job_tuple)
if not key_hadd_stage1_5_job in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job] = []
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5_job].append(self.outputFile_hadd_stage1[key_hadd_stage1_job])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job] = os.path.join(self.dirs[key_hadd_stage1_5_dir][DKEY_HIST],
"hadd_stage1_5_%s_%s.root" % hadd_stage1_5_job_tuple)
if self.isBDTtraining or self.do_sync:
continue
## doing list of processes to make the hadd in _Convs and _fake
## we could remove the tH ones with althernative couplings
sample_categories = []
sample_categories.extend(self.nonfake_backgrounds)
sample_categories.extend(self.ttHProcs)
processes_input_base = self.get_processes_input_base(sample_categories)
# sum fake background contributions for the total of all MC sample
# input processes: TT_fake, TTW_fake, TTWW_fake, ...
# output process: fakes_mc
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight, chargeSumSelection)
key_addBackgrounds_dir = getKey("addBackgrounds")
addBackgrounds_job_fakes_tuple = ("fakes_mc", lepton_selection_and_frWeight, chargeSumSelection)
key_addBackgrounds_job_fakes = getKey(*addBackgrounds_job_fakes_tuple)
processes_input = []
for process_input_base in processes_input_base:
if "HH" in process_input_base:
continue
processes_input.append("%s_fake" % process_input_base)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_fakes_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_fakes_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_fakes_tuple),
'categories' : [ getHistogramDir(self.channel, lepton_selection, lepton_frWeight, chargeSumSelection) ],
'processes_input' : processes_input,
'process_output' : "fakes_mc"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes])
# sum conversion background contributions for the total of all MC sample
# input processes: TT_Convs, TTW_Convs, TTWW_Convs, ...
# output process: Convs
addBackgrounds_job_Convs_tuple = ("Convs", lepton_selection_and_frWeight, chargeSumSelection)
key_addBackgrounds_job_Convs = getKey(*addBackgrounds_job_Convs_tuple)
processes_input = []
for process_input_base in self.convs_backgrounds:
if "HH" in process_input_base:
continue
processes_input.append("%s_Convs" % process_input_base)
self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_CFGS], "addBackgrounds_%s_%s_%s_cfg.py" % addBackgrounds_job_Convs_tuple),
'outputFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_HIST], "addBackgrounds_%s_%s_%s.root" % addBackgrounds_job_Convs_tuple),
'logFile' : os.path.join(self.dirs[key_addBackgrounds_dir][DKEY_LOGS], "addBackgrounds_%s_%s_%s.log" % addBackgrounds_job_Convs_tuple),
'categories' : [ getHistogramDir(self.channel, lepton_selection, lepton_frWeight, chargeSumSelection) ],
'processes_input' : processes_input,
'process_output' : "Convs"
}
self.createCfg_addBackgrounds(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs])
# initialize input and output file names for hadd_stage2
key_hadd_stage1_5_job = getKey(lepton_selection_and_frWeight, chargeSumSelection)
key_hadd_stage2_dir = getKey("hadd", lepton_selection_and_frWeight, chargeSumSelection)
hadd_stage2_job_tuple = (lepton_selection_and_frWeight, chargeSumSelection)
key_hadd_stage2_job = getKey(*hadd_stage2_job_tuple)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
if lepton_selection == "Tight":
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_Convs]['outputFile'])
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job])
self.outputFile_hadd_stage2[key_hadd_stage2_job] = os.path.join(self.dirs[key_hadd_stage2_dir][DKEY_HIST],
"hadd_stage2_%s_%s.root" % hadd_stage2_job_tuple)
if self.isBDTtraining or self.do_sync:
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
if self.isBDTtraining:
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
elif self.do_sync:
self.createScript_sbatch_syncNtuple(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
if self.isBDTtraining:
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
elif self.do_sync:
self.addToMakefile_syncNtuple(lines_makefile)
outputFile_sync_path = os.path.join(self.outputDir, DKEY_SYNC, '%s.root' % self.channel)
self.outputFile_sync['sync'] = outputFile_sync_path
self.addToMakefile_hadd_sync(lines_makefile)
else:
raise ValueError("Internal logic error")
self.addToMakefile_validate(lines_makefile)
self.targets.extend(self.phoniesToAdd)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
logging.info("Creating configuration files to run 'addBackgroundFakes'")
for chargeSumSelection in self.chargeSumSelections:
key_hadd_stage1_5_job = getKey(get_lepton_selection_and_frWeight("Fakeable", "enabled"), chargeSumSelection)
key_addFakes_dir = getKey("addBackgroundLeptonFakes")
key_addFakes_job = getKey("data_fakes", chargeSumSelection)
category_sideband = "{}_{}_Fakeable_wFakeRateWeights".format(self.channel, chargeSumSelection)
self.jobOptions_addFakes[key_addFakes_job] = {
'inputFile' : self.outputFile_hadd_stage1_5[key_hadd_stage1_5_job],
'cfgFile_modified' : os.path.join(self.dirs[key_addFakes_dir][DKEY_CFGS], "addBackgroundLeptonFakes_%s_cfg.py" % chargeSumSelection),
'outputFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_HIST], "addBackgroundLeptonFakes_%s.root" % chargeSumSelection),
'logFile' : os.path.join(self.dirs[key_addFakes_dir][DKEY_LOGS], "addBackgroundLeptonFakes_%s.log" % chargeSumSelection),
'category_signal' : "{}_{}_Tight".format(self.channel, chargeSumSelection),
'category_sideband' : category_sideband
}
self.createCfg_addFakes(self.jobOptions_addFakes[key_addFakes_job])
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), chargeSumSelection)
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.jobOptions_addFakes[key_addFakes_job]['outputFile'])
logging.info("Creating configuration files to run 'prepareDatacards'")
for histogramToFit in self.histograms_to_fit:
key_prep_dcard_dir = getKey("prepareDatacards")
if "OS" in self.chargeSumSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
prep_dcard_job_tuple = (self.channel, "OS", histogramToFit)
key_prep_dcard_job = getKey("OS", histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : self.histogramDir_prep_dcard,
'histogramToFit' : histogramToFit,
'label' : None
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
if "SS" in self.chargeSumSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
prep_dcard_job_tuple = (self.channel, "SS", histogramToFit)
key_prep_dcard_job = getKey("SS", histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s_%s.root" % prep_dcard_job_tuple),
'histogramDir' : self.histogramDir_prep_dcard_SS,
'histogramToFit' : histogramToFit,
'label' : 'SS'
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
# add shape templates for the following systematic uncertainties:
# - 'CMS_ttHl_Clos_norm_e'
# - 'CMS_ttHl_Clos_shape_e'
# - 'CMS_ttHl_Clos_norm_m'
# - 'CMS_ttHl_Clos_shape_m'
for chargeSumSelection in self.chargeSumSelections:
key_prep_dcard_job = getKey(chargeSumSelection, histogramToFit)
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), chargeSumSelection)
key_add_syst_fakerate_dir = getKey("addSystFakeRates")
add_syst_fakerate_job_tuple = (self.channel, chargeSumSelection, histogramToFit)
key_add_syst_fakerate_job = getKey(chargeSumSelection, histogramToFit)
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job] = {
'inputFile' : self.jobOptions_prep_dcard[key_prep_dcard_job]['datacardFile'],
'cfgFile_modified' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_CFGS], "addSystFakeRates_%s_%s_%s_cfg.py" % add_syst_fakerate_job_tuple),
'outputFile' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_DCRD], "addSystFakeRates_%s_%s_%s.root" % add_syst_fakerate_job_tuple),
'category' : self.channel,
'histogramToFit' : histogramToFit,
'plots_outputFileName' : os.path.join(self.dirs[key_add_syst_fakerate_dir][DKEY_PLOT], "addSystFakeRates.png")
}
histogramDir_nominal = None
if chargeSumSelection == "OS":
histogramDir_nominal = self.histogramDir_prep_dcard
elif chargeSumSelection == "SS":
histogramDir_nominal = self.histogramDir_prep_dcard_SS
else:
raise ValueError("Invalid parameter 'chargeSumSelection' = %s !!" % chargeSumSelection)
for lepton_type in [ 'e', 'm' ]:
lepton_mcClosure = "Fakeable_mcClosure_%s" % lepton_type
if lepton_mcClosure not in self.lepton_selections:
continue
lepton_selection_and_frWeight = get_lepton_selection_and_frWeight(lepton_mcClosure, "enabled")
key_addBackgrounds_job_fakes = getKey("fakes_mc", lepton_selection_and_frWeight, chargeSumSelection)
histogramDir_mcClosure = self.mcClosure_dir['%s_%s' % (lepton_mcClosure, chargeSumSelection)]
self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job].update({
'add_Clos_%s' % lepton_type : ("Fakeable_mcClosure_%s" % lepton_type) in self.lepton_selections,
'inputFile_nominal_%s' % lepton_type : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'histogramName_nominal_%s' % lepton_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_nominal, histogramToFit),
'inputFile_mcClosure_%s' % lepton_type : self.jobOptions_addBackgrounds_sum[key_addBackgrounds_job_fakes]['outputFile'],
'histogramName_mcClosure_%s' % lepton_type : "%s/sel/evt/fakes_mc/%s" % (histogramDir_mcClosure, histogramToFit)
})
self.createCfg_add_syst_fakerate(self.jobOptions_add_syst_fakerate[key_add_syst_fakerate_job])
logging.info("Creating configuration files to run 'makePlots'")
key_makePlots_dir = getKey("makePlots")
if "OS" in self.chargeSumSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_makePlots_job = getKey("OS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard,
'label' : self.channel,
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "SS" in self.chargeSumSelections:
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "SS")
key_makePlots_job = getKey("SS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_%s_SS_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_%s_SS.png" % self.channel),
'histogramDir' : self.histogramDir_prep_dcard_SS,
'label' : "{} SS".format(self.channel),
'make_plots_backgrounds' : self.make_plots_backgrounds
}
self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
if "Fakeable_mcClosure" in self.lepton_selections: #TODO
key_hadd_stage2_job = getKey(get_lepton_selection_and_frWeight("Tight", "disabled"), "OS")
key_makePlots_job = getKey("OS")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable' : self.executable_make_plots_mcClosure,
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_makePlots_dir][DKEY_CFGS], "makePlots_mcClosure_%s_cfg.py" % self.channel),
'outputFile' : os.path.join(self.dirs[key_makePlots_dir][DKEY_PLOT], "makePlots_mcClosure_%s.png" % self.channel)
}
self.createCfg_makePlots_mcClosure(self.jobOptions_make_plots[key_makePlots_job])
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.sbatchFile_addBackgrounds = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_%s.py" % self.channel)
self.sbatchFile_addBackgrounds_sum = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addBackgrounds_sum_%s.py" % self.channel)
self.sbatchFile_addFakes = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_addFakes_%s.py" % self.channel)
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addBackgrounds)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds, self.jobOptions_addBackgrounds)
self.createScript_sbatch(self.executable_addBackgrounds, self.sbatchFile_addBackgrounds_sum, self.jobOptions_addBackgrounds_sum)
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_addFakes)
self.createScript_sbatch(self.executable_addFakes, self.sbatchFile_addFakes, self.jobOptions_addFakes)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_backgrounds_from_data(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_add_syst_fakerate(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.addToMakefile_validate(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(process_name)
for dir_type in [DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel, "",
process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel, "",
process_name)
for dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_DCRD, DKEY_PLOT,
DKEY_HADD_RT
]:
initDict(self.dirs, [dir_type])
if dir_type in [DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_HADD_RT]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type,
self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type,
self.channel)
##print "self.dirs = ", self.dirs
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
else:
create_if_not_exists(self.dirs[key])
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
logging.info("Checking input files for sample %s" %
sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(
sample_name, sample_info, self.max_files_per_job, self.debug)
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
process_name = sample_info["process_name_specific"]
logging.info(
"Creating configuration files to run '%s' for sample %s" %
(self.executable_analyze, process_name))
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
is_signal = (sample_category == "signal")
for central_or_shift in self.central_or_shifts:
inputFileList = inputFileLists[sample_name]
for jobId in inputFileList.keys():
if central_or_shift != "central" and not is_mc:
continue
if central_or_shift.startswith(
"CMS_ttHl_thu_shape_ttH"
) and sample_category != "signal":
continue
if central_or_shift.startswith(
"CMS_ttHl_thu_shape_ttW"
) and sample_category != "TTW":
continue
if central_or_shift.startswith(
"CMS_ttHl_thu_shape_ttZ"
) and sample_category != "TTZ":
continue
# build config files for executing analysis code
key_dir = getKey(process_name)
key_analyze_job = getKey(process_name, central_or_shift,
jobId)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
print "Warning: ntupleFiles['%s'] = %s --> skipping job !!" % (
key_job, ntupleFiles)
continue
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : os.path.join(self.dirs[key_dir][DKEY_CFGS], "analyze_%s_%s_%s_%i_cfg.py" % \
(self.channel, process_name, central_or_shift, jobId)),
'histogramFile' : os.path.join(self.dirs[key_dir][DKEY_HIST], "%s_%s_%i.root" % \
(process_name, central_or_shift, jobId)),
'logFile' : os.path.join(self.dirs[key_dir][DKEY_LOGS], "analyze_%s_%s_%s_%i.log" % \
(self.channel, process_name, central_or_shift, jobId)),
'rleOutputFile' : os.path.join(self.dirs[key_dir][DKEY_RLES], "rle_%s_%s_%s_%i.txt" % \
(self.channel, process_name, central_or_shift, jobId)) if self.select_rle_output else "",
'sample_category' : sample_category,
'triggers' : sample_info["triggers"],
'hadTau_selection' : self.hadTau_selection_part2,
##'use_HIP_mitigation_mediumMuonId' : sample_info["use_HIP_mitigation_mediumMuonId"],
'use_HIP_mitigation_mediumMuonId' : True,
'is_mc' : is_mc,
'central_or_shift' : central_or_shift,
'lumi_scale' : 1. if not (self.use_lumi and is_mc) else sample_info["xsection"] * self.lumi / sample_info["nof_events"],
'apply_genWeight' : sample_info["genWeight"] if (is_mc and "genWeight" in sample_info.keys()) else False,
'apply_trigger_bits' : (is_mc and (self.era == "2015" or (self.era == "2016" and sample_info["reHLT"]))) or not is_mc
}
self.createCfg_analyze(
self.jobOptions_analyze[key_analyze_job])
# initialize input and output file names for hadd_stage1
key_hadd_stage1 = getKey(process_name)
if not key_hadd_stage1 in self.inputFiles_hadd_stage1.keys(
):
self.inputFiles_hadd_stage1[key_hadd_stage1] = []
self.inputFiles_hadd_stage1[key_hadd_stage1].append(
self.jobOptions_analyze[key_analyze_job]
['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_%s_%s.root" % \
(self.channel, process_name))
# initialize input and output file names for hadd_stage2
key_hadd_stage1 = getKey(process_name)
key_hadd_stage2 = getKey("all")
if not key_hadd_stage2 in self.inputFiles_hadd_stage2.keys():
self.inputFiles_hadd_stage2[key_hadd_stage2] = []
self.inputFiles_hadd_stage2[key_hadd_stage2].append(
self.outputFile_hadd_stage1[key_hadd_stage1])
self.outputFile_hadd_stage2[key_hadd_stage2] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage2_%s.root" % \
(self.channel))
logging.info("Creating configuration files to run 'prepareDatacards'")
for evtSelection in self.evtSelections:
for histogramToFit in self.histograms_to_fit:
key_prep_dcard_job = getKey(evtSelection, histogramToFit)
key_hadd_stage2 = getKey("all")
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile':
self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified':
os.path.join(
self.dirs[DKEY_CFGS],
"prepareDatacards_%s_%s_%s_cfg.py" %
(self.channel, evtSelection, histogramToFit)),
'datacardFile':
os.path.join(
self.dirs[DKEY_DCRD],
"prepareDatacards_%s_%s_%s.root" %
(self.channel, evtSelection, histogramToFit)),
'histogramDir':
"_".join([self.histogramDir_prep_dcard, evtSelection]),
'histogramToFit':
histogramToFit,
'label':
None
}
self.createCfg_prep_dcard(
self.jobOptions_prep_dcard[key_prep_dcard_job])
logging.info("Creating configuration files to run 'makePlots'")
for evtSelection in self.evtSelections:
key_makePlots_job = getKey(evtSelection)
key_hadd_stage2 = getKey("all")
self.jobOptions_make_plots[key_makePlots_job] = {
'executable':
self.executable_make_plots,
'inputFile':
self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified':
os.path.join(
self.dirs[DKEY_CFGS],
"makePlots_%s_%s_cfg.py" % (self.channel, evtSelection)),
'outputFile':
os.path.join(
self.dirs[DKEY_PLOT],
"makePlots_%s_%s.png" % (self.channel, evtSelection)),
'histogramDir':
"_".join([self.histogramDir_prep_dcard, evtSelection]),
'label':
evtSelection,
'make_plots_backgrounds':
self.make_plots_backgrounds
}
self.createCfg_makePlots(
self.jobOptions_make_plots[key_makePlots_job])
if self.is_sbatch:
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_analyze)
self.sbatchFile_analyze = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch()
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done")
def __init__(self,
treeName,
outputDir,
cfgDir,
executable_addMEM,
samples,
era,
check_output_files,
running_method,
max_files_per_job,
mem_integrations_per_job,
max_mem_integrations,
num_parallel_jobs,
leptonSelection,
hadTauSelection,
integration_choice,
jet_cleaning_by_index,
dry_run,
use_nonnominal,
use_home,
channel,
rle_filter_file = '',
submission_cmd = None,
pool_id = '',
max_jobs_per_sample = -1,
):
self.treeName = treeName
self.outputDir = outputDir
self.cfgDir = cfgDir
self.executable_addMEM = executable_addMEM
self.mem_integrations_per_job = mem_integrations_per_job
self.max_files_per_job = max_files_per_job
self.max_mem_integrations = max_mem_integrations
self.max_jobs_per_sample = max_jobs_per_sample
self.samples = samples
self.era = era
self.check_output_files = check_output_files
self.channel = channel
self.rle_filter_file = rle_filter_file
self.leptonSelection = leptonSelection
self.hadTauSelection = hadTauSelection
if self.hadTauSelection:
self.hadTauDefinition = self.hadTauSelection.split('|')[0]
self.hadTauWorkingPoint = self.hadTauSelection.split('|')[1]
else:
self.hadTauDefinition = None
self.hadTauWorkingPoint = None
self.maxPermutations_branchName = None
self.integration_choice = integration_choice
self.jet_cleaning_by_index = jet_cleaning_by_index
logging.info(
"Number of integration points: %s" % self.integration_choice
)
if running_method.lower() not in ["sbatch", "makefile"]:
raise ValueError("Invalid running method: %s" % running_method)
self.running_method = running_method
self.is_sbatch = False
self.is_makefile = False
if self.running_method.lower() == "sbatch":
self.is_sbatch = True
else:
self.is_makefile = True
self.makefile = os.path.join(
self.cfgDir, "Makefile_%s" % self.channel)
self.num_parallel_jobs = num_parallel_jobs
self.dry_run = dry_run
self.use_nonnominal = use_nonnominal
self.use_home = use_home
self.pool_id = pool_id if pool_id else uuid.uuid4()
self.workingDir = os.getcwd()
logging.info("Working directory is: {workingDir}".format(workingDir = self.workingDir))
for dirPath in [self.outputDir, self.cfgDir]:
create_if_not_exists(dirPath)
self.stdout_file_path = os.path.join(self.cfgDir, "stdout_%s.log" % self.channel)
self.stderr_file_path = os.path.join(self.cfgDir, "stderr_%s.log" % self.channel)
self.sw_ver_file_cfg = os.path.join(self.cfgDir, "VERSION_%s.log" % self.channel)
self.sw_ver_file_out = os.path.join(self.outputDir, "VERSION_%s.log" % self.channel)
self.submission_out = os.path.join(self.cfgDir, "SUBMISSION_%s.log" % self.channel)
self.stdout_file_path, self.stderr_file_path, self.sw_ver_file_cfg, self.sw_ver_file_out, self.submission_out = get_log_version((
self.stdout_file_path, self.stderr_file_path, self.sw_ver_file_cfg, self.sw_ver_file_out, self.submission_out
))
check_submission_cmd(self.submission_out, submission_cmd)
self.dirs = {}
self.samples = samples
self.cfgFiles_addMEM_modified = {}
self.shFiles_addMEM_modified = {}
self.logFiles_addMEM = {}
self.sbatchFile_addMEM = os.path.join(self.cfgDir, "sbatch_addMEM_%s.py" % self.channel)
self.inputFiles = {}
self.outputFiles = {}
self.hadd_records = {}
self.filesToClean = []
del self.samples['sum_events']
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(sample_name)
for dir_type in [DKEY_NTUPLES, DKEY_FINAL_NTUPLES]:
initDict(self.dirs, [key_dir, dir_type])
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, process_name)
for dir_type in [DKEY_CFGS, DKEY_LOGS, DKEY_HADD, DKEY_HADD_RT]:
initDict(self.dirs, [key_dir, dir_type])
self.dirs[key_dir][dir_type] = os.path.join(self.cfgDir, dir_type, self.channel, process_name)
self.cvmfs_error_log = {}
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(process_name)
for dir_type in [
DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES, DKEY_SYNC
]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel, process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel, process_name)
for dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD,
DKEY_PLOT, DKEY_HADD_RT, DKEY_SYNC
]:
initDict(self.dirs, [dir_type])
if dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT,
DKEY_HADD_RT
]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type,
self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type,
self.channel)
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
else:
create_if_not_exists(self.dirs[key])
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" %
sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(
sample_info, self.max_files_per_job)
for apply_jetSmearing in self.apply_jetSmearing_options:
jetSmearingLabel = None
if apply_jetSmearing:
jetSmearingLabel = "jetSmearingEnabled"
else:
jetSmearingLabel = "jetSmearingDisabled"
for apply_metSmearing in self.apply_metSmearing_options:
metSmearingLabel = None
if apply_metSmearing:
metSmearingLabel = "metSmearingEnabled"
else:
metSmearingLabel = "metSmearingDisabled"
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
isSignal = True if process_name.find(
"signal") != -1 else False
logging.info(
"Creating configuration files to run '%s' for sample %s"
% (self.executable_analyze, process_name))
sample_category = sample_info["sample_category"]
inputFileList = inputFileLists[sample_name]
numJobsPerFile = None
if sample_info[
"process_name_specific"] == "signal_ggf_nonresonant_node_sm_hh_2b2v":
numJobsPerFile = 500
elif sample_info[
"process_name_specific"] == "signal_ggf_nonresonant_cHHH1_hh_2b2v":
numJobsPerFile = 100
elif sample_info[
"process_name_specific"] == "TTJets_DiLept":
numJobsPerFile = 50
elif sample_info[
"process_name_specific"] == "TTJets_DiLept_ext1":
numJobsPerFile = 50
elif sample_info["process_name_specific"] == "TTTo2L2Nu":
numJobsPerFile = 10
else:
raise ValueError("Invalid sample: %s" %
sample_info["process_name_specific"])
numJobs = numJobsPerFile * len(inputFileList.keys())
for jobId in range(1, numJobs + 1):
ntupleId = ((jobId - 1) / numJobsPerFile) + 1
maxSelEvents = 500
skipSelEvents = maxSelEvents * (
(jobId - 1) % numJobsPerFile)
# build config files for executing analysis code
key_dir = getKey(process_name)
key_analyze_job = getKey(process_name,
jetSmearingLabel,
metSmearingLabel, jobId)
ntupleFiles = inputFileList[ntupleId]
if len(ntupleFiles) == 0:
logging.warning(
"No input ntuples for %s --> skipping job !!" %
(key_analyze_job))
continue
cfgFile_modified_path = os.path.join(
self.dirs[key_dir][DKEY_CFGS],
"analyze_%s_%s_%s_%s_%i_cfg.py" %
(self.channel, process_name, jetSmearingLabel,
metSmearingLabel, jobId))
histogramFile_path = os.path.join(
self.dirs[key_dir][DKEY_HIST],
"analyze_%s_%s_%s_%s_%i.root" %
(self.channel, process_name, jetSmearingLabel,
metSmearingLabel, jobId))
logFile_path = os.path.join(
self.dirs[key_dir][DKEY_LOGS],
"analyze_%s_%s_%s_%s_%i.log" %
(self.channel, process_name, jetSmearingLabel,
metSmearingLabel, jobId))
rleOutputFile_path = os.path.join(self.dirs[key_dir][DKEY_RLES], "rle_%s_%s_%s_%s_%i.txt" % (self.channel, process_name, jetSmearingLabel, metSmearingLabel, jobId)) \
if self.select_rle_output else ""
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles': ntupleFiles,
'cfgFile_modified': cfgFile_modified_path,
'histogramFile': histogramFile_path,
'logFile': logFile_path,
'selEventsFileName_output': rleOutputFile_path,
'apply_jetSmearing': apply_jetSmearing,
'apply_metSmearing': apply_metSmearing,
'maxSelEvents': maxSelEvents,
'skipSelEvents': skipSelEvents
}
self.createCfg_analyze(
self.jobOptions_analyze[key_analyze_job],
sample_info)
# initialize input and output file names for hadd_stage1
key_hadd_stage1 = getKey(process_name,
jetSmearingLabel,
metSmearingLabel)
if not key_hadd_stage1 in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1] = []
self.inputFiles_hadd_stage1[key_hadd_stage1].append(
self.jobOptions_analyze[key_analyze_job]
['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_%s_%s_%s_%s.root" % \
(self.channel, process_name, jetSmearingLabel, metSmearingLabel))
# add output files of hadd_stage1 to list of input files for hadd_stage2
key_hadd_stage1 = getKey(process_name, jetSmearingLabel,
metSmearingLabel)
key_hadd_stage2 = getKey("")
if not key_hadd_stage2 in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2] = []
self.inputFiles_hadd_stage2[key_hadd_stage2].append(
self.outputFile_hadd_stage1[key_hadd_stage1])
self.outputFile_hadd_stage2[
key_hadd_stage2] = os.path.join(
self.dirs[DKEY_HIST],
"histograms_harvested_stage2_%s.root" %
self.channel)
if self.is_sbatch:
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_analyze)
self.sbatchFile_analyze = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze,
self.sbatchFile_analyze,
self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.targets.extend(self.outputFile_hadd_stage2.values())
self.createMakefile(lines_makefile)
logging.info("Done")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
process_name = sample_info["process_name_specific"]
for charge_selection in self.charge_selections:
key_dir = getKey(process_name, charge_selection)
for dir_type in [DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel,
"_".join([charge_selection]), process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel,
"_".join([charge_selection]), process_name)
for dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD,
DKEY_PLOT, DKEY_HADD_RT
]:
initDict(self.dirs, [dir_type])
if dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT,
DKEY_HADD_RT
]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type,
self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type,
self.channel)
##print "self.dirs = ", self.dirs
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
else:
create_if_not_exists(self.dirs[key])
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
logging.info("Checking input files for sample %s" %
sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(
sample_name, sample_info, self.max_files_per_job, self.debug)
self.inputFileIds = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
process_name = sample_info["process_name_specific"]
logging.info(
"Creating configuration files to run '%s' for sample %s" %
(self.executable_analyze, process_name))
is_mc = (sample_info["type"] == "mc")
lumi_scale = 1. if not (self.use_lumi and is_mc) else sample_info[
"xsection"] * self.lumi / sample_info["nof_events"]
apply_genWeight = sample_info["apply_genWeight"] if (
is_mc and "apply_genWeight" in sample_info.keys()) else False
sample_category = sample_info["sample_category"]
triggers = sample_info["triggers"]
apply_trigger_bits = (
is_mc and
(self.era == "2015" or
(self.era == "2016" and sample_info["reHLT"]))) or not is_mc
for charge_selection in self.charge_selections:
for central_or_shift in self.central_or_shifts:
inputFileList = inputFileLists[sample_name]
for jobId in inputFileList.keys():
if central_or_shift != "central" and not is_mc:
continue
if central_or_shift.startswith(
"CMS_ttHl_thu_shape_ttH"
) and sample_category != "signal":
continue
if central_or_shift.startswith(
"CMS_ttHl_thu_shape_ttW"
) and sample_category != "TTW":
continue
if central_or_shift.startswith(
"CMS_ttHl_thu_shape_ttZ"
) and sample_category != "TTZ":
continue
# build config files for executing analysis code
key_dir = getKey(process_name, charge_selection)
key_analyze_job = getKey(process_name,
charge_selection,
central_or_shift, jobId)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
print "Warning: ntupleFiles['%s'] = %s --> skipping job !!" % (
key_job, ntupleFiles)
continue
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : os.path.join(self.dirs[key_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % \
(self.channel, process_name, charge_selection, central_or_shift, jobId)),
'histogramFile' : os.path.join(self.dirs[key_dir][DKEY_HIST], "%s_%s_%s_%i.root" % \
(process_name, charge_selection, central_or_shift, jobId)),
'logFile' : os.path.join(self.dirs[key_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % \
(self.channel, process_name, charge_selection, central_or_shift, jobId)),
'sample_category' : sample_category,
'triggers' : sample_info["triggers"],
'charge_selection' : charge_selection,
'jet_minPt' : self.jet_minPt,
'jet_maxPt' : self.jet_maxPt,
'jet_minAbsEta' : self.jet_minAbsEta,
'jet_maxAbsEta' : self.jet_maxAbsEta,
'hadTau_selection_denominator' : self.hadTau_selection_denominator,
'hadTau_selections_numerator' : self.hadTau_selections_numerator,
'absEtaBins' : self.absEtaBins,
##'use_HIP_mitigation_mediumMuonId' : sample_info["use_HIP_mitigation_mediumMuonId"],
'use_HIP_mitigation_mediumMuonId' : True,
'is_mc' : is_mc,
'central_or_shift' : central_or_shift,
'lumi_scale' : 1. if not (self.use_lumi and is_mc) else sample_info["xsection"] * self.lumi / sample_info["nof_events"],
'apply_genWeight' : sample_info["genWeight"] if (is_mc and "genWeight" in sample_info.keys()) else False,
'apply_trigger_bits' : (is_mc and (self.era == "2015" or (self.era == "2016" and sample_info["reHLT"]))) or not is_mc,
}
self.createCfg_analyze(
self.jobOptions_analyze[key_analyze_job])
# initialize input and output file names for hadd_stage1
key_hadd_stage1 = getKey(process_name,
charge_selection)
if not key_hadd_stage1 in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1] = []
self.inputFiles_hadd_stage1[key_hadd_stage1].append(
self.jobOptions_analyze[key_analyze_job]
['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_%s_%s_%s.root" % \
(self.channel, process_name, charge_selection))
# initialize input and output file names for hadd_stage2
key_hadd_stage1 = getKey(process_name, charge_selection)
key_hadd_stage2 = getKey(charge_selection)
if not key_hadd_stage2 in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2] = []
self.inputFiles_hadd_stage2[key_hadd_stage2].append(
self.outputFile_hadd_stage1[key_hadd_stage1])
self.outputFile_hadd_stage2[key_hadd_stage2] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage2_%s_%s.root" % \
(self.channel, charge_selection))
logging.info(
"Creating configuration files for executing 'comp_jetToTauFakeRate'"
)
for charge_selection in self.charge_selections:
key_comp_jetToTauFakeRate_job = getKey(charge_selection)
key_hadd_stage2 = getKey(charge_selection)
self.jobOptions_comp_jetToTauFakeRate[
key_comp_jetToTauFakeRate_job] = {
'inputFile':
self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified':
os.path.join(
self.dirs[DKEY_CFGS],
"comp_jetToTauFakeRate_%s_cfg.py" % charge_selection),
'outputFile':
os.path.join(
self.dirs[DKEY_HIST],
"comp_jetToTauFakeRate_%s.root" % charge_selection),
'logFile':
os.path.join(
self.dirs[DKEY_LOGS],
"comp_jetToTauFakeRate_%s.log" % charge_selection),
'looseRegion':
"jetToTauFakeRate_%s/denominator/" % charge_selection,
'tightRegion':
"jetToTauFakeRate_%s/numerator/" % charge_selection,
'absEtaBins':
self.absEtaBins,
'ptBins':
self.ptBins
}
self.createCfg_comp_jetToTauFakeRate(
self.jobOptions_comp_jetToTauFakeRate[
key_comp_jetToTauFakeRate_job])
self.targets.append(self.jobOptions_comp_jetToTauFakeRate[
key_comp_jetToTauFakeRate_job]['outputFile'])
logging.info("Creating configuration files to run 'makePlots'")
for charge_selection in self.charge_selections:
key_makePlots_job = getKey(charge_selection)
key_hadd_stage2 = getKey(charge_selection)
self.jobOptions_make_plots[key_makePlots_job] = {
'executable':
self.executable_make_plots,
'inputFile':
self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified':
os.path.join(self.dirs[DKEY_CFGS],
"makePlots_%s_cfg.py" % self.channel),
'outputFile':
os.path.join(self.dirs[DKEY_PLOT],
"makePlots_%s.png" % self.channel),
'histogramDir':
"jetToTauFakeRate_%s" % charge_selection,
'label':
None,
'make_plots_backgrounds': ["TT", "TTW", "TTZ", "EWK", "Rares"],
}
self.createCfg_makePlots(
self.jobOptions_make_plots[key_makePlots_job])
self.cfgFile_make_plots = self.cfgFile_make_plots_denominator
for absEtaBin in ["absEtaLt1_5", "absEta1_5to9_9"]:
key_makePlots_job = getKey(charge_selection, absEtaBin,
"denominator")
key_hadd_stage2 = getKey(charge_selection)
self.jobOptions_make_plots[key_makePlots_job] = {
'executable':
self.executable_make_plots,
'inputFile':
self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified':
os.path.join(
self.dirs[DKEY_CFGS],
"makePlots_%s_%s_denominator_%s_cfg.py" %
(self.channel, charge_selection, absEtaBin)),
'outputFile':
os.path.join(
self.dirs[DKEY_PLOT],
"makePlots_%s_%s_denominator_%s.png" %
(self.channel, charge_selection, absEtaBin)),
'histogramDir':
"jetToTauFakeRate_%s/denominator/%s" %
(charge_selection, absEtaBin),
'label':
None,
'make_plots_backgrounds':
["TT", "TTW", "TTZ", "EWK", "Rares"],
}
self.createCfg_makePlots(
self.jobOptions_make_plots[key_makePlots_job])
for hadTau_selection_numerator in self.hadTau_selections_numerator:
key_makePlots_job = getKey(charge_selection, absEtaBin,
"numerator",
hadTau_selection_numerator)
key_hadd_stage2 = getKey(charge_selection)
self.jobOptions_make_plots[key_makePlots_job] = {
'executable':
self.executable_make_plots,
'inputFile':
self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified':
os.path.join(
self.dirs[DKEY_CFGS],
"makePlots_%s_%s_numerator_%s_%s_cfg.py" %
(self.channel, charge_selection,
hadTau_selection_numerator, absEtaBin)),
'outputFile':
os.path.join(
self.dirs[DKEY_PLOT],
"makePlots_%s_%s_numerator_%s_%s.png" %
(self.channel, charge_selection,
hadTau_selection_numerator, absEtaBin)),
'histogramDir':
"jetToTauFakeRate_%s/numerator/%s/%s" %
(charge_selection, hadTau_selection_numerator,
absEtaBin),
'label':
None,
'make_plots_backgrounds':
["TT", "TTW", "TTZ", "EWK", "Rares"],
}
self.createCfg_makePlots(
self.jobOptions_make_plots[key_makePlots_job])
if self.is_sbatch:
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_analyze)
self.sbatchFile_analyze = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze,
self.sbatchFile_analyze,
self.jobOptions_analyze)
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_comp_jetToTauFakeRate)
self.sbatchFile_comp_jetToTauFakeRate = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_comp_jetToTauFakeRate.py")
self.createScript_sbatch(self.executable_comp_jetToTauFakeRate,
self.sbatchFile_comp_jetToTauFakeRate,
self.jobOptions_comp_jetToTauFakeRate)
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_comp_jetToTauFakeRate(lines_makefile)
self.addToMakefile_make_plots(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done")
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
for mode in self.modes:
key_dir = getKey(process_name, mode)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel, "_".join([ mode ]), process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, "_".join([ mode ]), process_name)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_SYNC ]:
if dir_type == DKEY_SYNC and not self.do_sync:
continue
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
##print "self.dirs = ", self.dirs
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
else:
create_if_not_exists(self.dirs[key])
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
for mode in self.modes:
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
sample_category = sample_info["sample_category"]
is_mc = (sample_info["type"] == "mc")
for central_or_shift in self.central_or_shifts:
inputFileList = inputFileLists[sample_name]
for jobId in inputFileList.keys():
# build config files for executing analysis code
key_dir = getKey(process_name, mode)
key_analyze_job = getKey(process_name, mode, central_or_shift, jobId)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : os.path.join(self.dirs[key_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % \
(self.channel, process_name, mode, central_or_shift, jobId)),
'histogramFile' : os.path.join(self.dirs[key_dir][DKEY_HIST], "%s_%s_%s_%i.root" % \
(process_name, mode, central_or_shift, jobId)),
'logFile' : os.path.join(self.dirs[key_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % \
(self.channel, process_name, mode, central_or_shift, jobId)),
'sample_category' : sample_category,
'mode' : mode,
'lepton_selection' : self.lepton_selection,
'hadTau_selection' : self.hadTau_selection,
'SVfit4tau_logM_wMassConstraint_MarkovChain' : self.SVfit4tau_logM_wMassConstraint_MarkovChain,
'SVfit4tau_logM_woMassConstraint_MarkovChain' : self.SVfit4tau_logM_woMassConstraint_MarkovChain,
'SVfit4tau_logM_wMassConstraint_VAMP' : self.SVfit4tau_logM_wMassConstraint_VAMP,
'use_HIP_mitigation_mediumMuonId' : False,
'is_mc' : is_mc,
'central_or_shift' : central_or_shift,
'lumi_scale' : 1.,
'apply_genWeight' : sample_info["genWeight"] if (is_mc and "genWeight" in sample_info) else False,
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job])
# initialize input and output file names for hadd_stage1
key_hadd_stage1 = getKey(process_name, mode)
if not key_hadd_stage1 in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1] = []
self.inputFiles_hadd_stage1[key_hadd_stage1].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_%s_%s_%s.root" % \
(self.channel, process_name, mode))
# initialize input and output file names for hadd_stage2
key_hadd_stage2 = getKey()
if not key_hadd_stage2 in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2] = []
self.inputFiles_hadd_stage2[key_hadd_stage2].append(self.outputFile_hadd_stage1[key_hadd_stage1])
self.outputFile_hadd_stage2[key_hadd_stage2] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage2_%s.root" % \
(self.channel))
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done")
return self.num_jobs
def create(self):
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
central_or_shifts_extended = [""]
central_or_shifts_extended.extend(self.central_or_shifts)
central_or_shifts_extended.extend(
["hadd", "copyHistograms", "addBackgrounds"])
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [process_name, "hadd"]
for process_name_or_dummy in process_name_extended:
if process_name_or_dummy in [
"hadd"
] and central_or_shift_or_dummy != "":
continue
if central_or_shift_or_dummy in [
"hadd", "copyHistograms", "addBackgrounds"
] and process_name_or_dummy in ["hadd"]:
continue
key_dir = getKey(process_name_or_dummy,
central_or_shift_or_dummy)
for dir_type in [
DKEY_CFGS, DKEY_LOGS, DKEY_RLES, DKEY_SYNC
]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel,
process_name_or_dummy,
central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel,
process_name_or_dummy,
central_or_shift_or_dummy)
for subdirectory in [
"addBackgrounds", "addBackgroundLeptonFakes",
"prepareDatacards", "addSystFakeRates", "makePlots"
]:
key_dir = getKey(subdirectory)
for dir_type in [
DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_ROOT, DKEY_DCRD,
DKEY_PLOT
]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_SYNC, DKEY_HADD_RT
]:
initDict(self.dirs, [dir_type])
if dir_type in [DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_HADD_RT]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type,
self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type,
self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" %
numDirectories)
numDirectories_created = 0
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100 * numDirectories_created >= frac * numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" %
sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(
sample_info, self.max_files_per_job)
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info(
"Creating configuration files to run '%s' for sample %s" %
(self.executable_analyze, process_name))
inputFileList = inputFileLists[sample_name]
for jobId in inputFileList.keys():
for central_or_shift in self.central_or_shifts:
logging.info(" ... for systematic uncertainty %s" %
central_or_shift)
key_analyze_dir = getKey(process_name, central_or_shift)
analyze_job_tuple = (process_name, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
print("Warning: no ntupleFiles --> skipping job !!")
continue
syncOutput = os.path.join(
self.dirs[key_analyze_dir][DKEY_SYNC],
'%s_%s.root' % (self.channel, central_or_shift))
syncOutputTree = self.output_tree if central_or_shift == "central" else os.path.join(
central_or_shift, self.output_tree)
self.inputFiles_sync['sync'].append(syncOutput)
cfgFile_modified_path = os.path.join(
self.dirs[key_analyze_dir][DKEY_CFGS],
"analyze_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(
self.dirs[key_analyze_dir][DKEY_LOGS],
"analyze_%s_%s_%i.log" % analyze_job_tuple)
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles':
ntupleFiles,
'cfgFile_modified':
cfgFile_modified_path,
'histogramFile':
'',
'logFile':
logFile_path,
'syncTree':
syncOutputTree,
'syncOutput':
syncOutput,
'syncRLE':
self.rle_select if self.rle_select
and '%s' not in self.rle_select else '',
'useNonNominal':
self.use_nonnominal,
}
self.createCfg_analyze(
self.jobOptions_analyze[key_analyze_job], sample_info)
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_analyze)
self.sbatchFile_analyze = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
self.createScript_sbatch_syncNtuple(self.executable_analyze,
self.sbatchFile_analyze,
self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_syncNtuple(lines_makefile)
outputFile_sync_path = os.path.join(self.outputDir, DKEY_SYNC,
'%s.root' % self.channel)
self.outputFile_sync['sync'] = outputFile_sync_path
self.addToMakefile_hadd_sync(lines_makefile)
self.targets.extend(self.phoniesToAdd)
self.createMakefile(lines_makefile)
logging.info("Done")
return self.num_jobs
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
process_name = sample_info["process_name_specific"]
# for charge_selection in self.charge_selections: ## NO CHARGE SELECTION NEEDED HERE
# key_dir = getKey(process_name, charge_selection) ## NO CHARGE SELECTION NEEDED HERE
key_dir = getKey(process_name)
for dir_type in [DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS, DKEY_LOGS]:
# self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
# "_".join([ charge_selection ]), process_name) ## NO CHARGE SELECTION NEEDED HERE
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel, process_name)
else:
# self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
# "_".join([ charge_selection ]), process_name) ## NO CHARGE SELECTION NEEDED HERE
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel, process_name)
for dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_HIST, DKEY_DCRD,
DKEY_PLOT, DKEY_HADD_RT
]:
initDict(self.dirs, [dir_type])
if dir_type in [
DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_HADD_RT
]: ## DKEY_PLOT TO BE ADDED LATER
self.dirs[dir_type] = os.path.join(self.configDir, dir_type,
self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type,
self.channel)
print "self.dirs = ", self.dirs
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
else:
create_if_not_exists(self.dirs[key])
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
logging.info("Checking input files for sample %s" %
sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(
sample_name, sample_info, self.max_files_per_job, self.debug)
self.inputFileIds = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
process_name = sample_info["process_name_specific"]
logging.info(
"Creating configuration files to run '%s' for sample %s" %
(self.executable_analyze, process_name))
is_mc = (sample_info["type"] == "mc")
lumi_scale = 1. if not (self.use_lumi and is_mc) else sample_info[
"xsection"] * self.lumi / sample_info["nof_events"]
apply_genWeight = sample_info["apply_genWeight"] if (
is_mc and "apply_genWeight" in sample_info.keys()) else False
sample_category = sample_info["sample_category"]
triggers = sample_info["triggers"]
apply_trigger_bits = (
is_mc and
(self.era == "2015" or
(self.era == "2016" and sample_info["reHLT"]))) or not is_mc
# for charge_selection in self.charge_selections: ## NO CHARGE SELECTION NEEDED HERE
for central_or_shift in self.central_or_shifts:
inputFileList = inputFileLists[sample_name]
for jobId in inputFileList.keys():
if central_or_shift != "central" and not is_mc:
continue
if central_or_shift.startswith(
"CMS_ttHl_thu_shape_ttH"
) and sample_category != "signal":
continue
if central_or_shift.startswith(
"CMS_ttHl_thu_shape_ttW"
) and sample_category != "TTW":
continue
if central_or_shift.startswith(
"CMS_ttHl_thu_shape_ttZ"
) and sample_category != "TTZ":
continue
# build config files for executing analysis code
# key_dir = getKey(process_name, charge_selection) ## NO CHARGE SELECTION NEEDED HERE
key_dir = getKey(process_name)
# key_analyze_job = getKey(process_name, charge_selection, central_or_shift, jobId) ## NO CHARGE SELECTION NEEDED HERE
key_analyze_job = getKey(process_name, central_or_shift,
jobId)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
print "Warning: ntupleFiles['%s'] = %s --> skipping job !!" % (
key_job, ntupleFiles)
continue
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
# 'cfgFile_modified' : os.path.join(self.dirs[key_dir][DKEY_CFGS], "analyze_%s_%s_%s_%s_%i_cfg.py" % \
# (self.channel, process_name, charge_selection, central_or_shift, jobId)), ## NO CHARGE SELECTION NEEDED HERE
# 'histogramFile' : os.path.join(self.dirs[key_dir][DKEY_HIST], "%s_%s_%s_%i.root" % \
# (process_name, charge_selection, central_or_shift, jobId)), ## NO CHARGE SELECTION NEEDED HERE
# 'logFile' : os.path.join(self.dirs[key_dir][DKEY_LOGS], "analyze_%s_%s_%s_%s_%i.log" % \
# (self.channel, process_name, charge_selection, central_or_shift, jobId)), ## NO CHARGE SELECTION NEEDED HERE
'cfgFile_modified' : os.path.join(self.dirs[key_dir][DKEY_CFGS], "analyze_%s_%s_%s_%i_cfg.py" % \
(self.channel, process_name, central_or_shift, jobId)),
'histogramFile' : os.path.join(self.dirs[key_dir][DKEY_HIST], "%s_%s_%i.root" % \
(process_name, central_or_shift, jobId)),
'logFile' : os.path.join(self.dirs[key_dir][DKEY_LOGS], "analyze_%s_%s_%s_%i.log" % \
(self.channel, process_name, central_or_shift, jobId)),
'sample_category' : sample_category,
'triggers' : sample_info["triggers"],
# 'charge_selection' : charge_selection, ## NO CHARGE SELECTION NEEDED HERE
# 'jet_minPt' : self.jet_minPt,
# 'jet_maxPt' : self.jet_maxPt,
# 'jet_minAbsEta' : self.jet_minAbsEta,
# 'jet_maxAbsEta' : self.jet_maxAbsEta,
# 'hadTau_selections' : self.hadTau_selections,
'absEtaBins_e' : self.absEtaBins_e,
'absEtaBins_mu' : self.absEtaBins_mu,
'absPtBins_e' : self.absPtBins_e,
'absPtBins_mu' : self.absPtBins_mu,
##'use_HIP_mitigation_mediumMuonId' : sample_info["use_HIP_mitigation_mediumMuonId"],
'use_HIP_mitigation_mediumMuonId' : True,
'is_mc' : is_mc,
'central_or_shift' : central_or_shift,
'lumi_scale' : 1. if not (self.use_lumi and is_mc) else sample_info["xsection"] * self.lumi / sample_info["nof_events"],
'apply_genWeight' : sample_info["genWeight"] if (is_mc and "genWeight" in sample_info.keys()) else False,
'apply_trigger_bits' : (is_mc and (self.era == "2015" or (self.era == "2016" and sample_info["reHLT"]))) or not is_mc,
}
self.createCfg_analyze(
self.jobOptions_analyze[key_analyze_job])
# initialize input and output file names for hadd_stage1
# key_hadd_stage1 = getKey(process_name, charge_selection)
key_hadd_stage1 = getKey(
process_name) ## NO CHARGE SELECTION NEEDED HERE
if not key_hadd_stage1 in self.inputFiles_hadd_stage1:
self.inputFiles_hadd_stage1[key_hadd_stage1] = []
self.inputFiles_hadd_stage1[key_hadd_stage1].append(
self.jobOptions_analyze[key_analyze_job]
['histogramFile'])
# self.outputFile_hadd_stage1[key_hadd_stage1] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_%s_%s_%s.root" % \
# (self.channel, process_name, charge_selection)) ## NO CHARGE SELECTION NEEDED HERE
self.outputFile_hadd_stage1[key_hadd_stage1] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage1_%s_%s.root" % \
(self.channel, process_name))
# initialize input and output file names for hadd_stage1_5
key_hadd_stage1_5 = getKey('')
if not key_hadd_stage1_5 in self.inputFiles_hadd_stage1_5:
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5] = []
for key_hadd_stage1 in self.outputFile_hadd_stage1.keys():
self.inputFiles_hadd_stage1_5[key_hadd_stage1_5].append(
self.outputFile_hadd_stage1[key_hadd_stage1])
self.outputFile_hadd_stage1_5[key_hadd_stage1_5] = os.path.join(
self.dirs[DKEY_HIST], "histograms_harvested_stage1_5.root")
## Creating configuration files to run 'addBackgrounds_LeptonFakeRate' [stage 1.5]
key_addBackgrounds_job = getKey('')
self.jobOptions_addBackgrounds_LeptonFakeRate[
key_addBackgrounds_job] = {
'inputFile':
self.outputFile_hadd_stage1_5[key_hadd_stage1_5],
'cfgFile_modified':
os.path.join(
self.dirs[DKEY_CFGS],
os.path.basename(
self.cfgFile_addBackgrounds_LeptonFakeRate)),
'outputFile':
os.path.join(self.dirs[DKEY_HIST],
"addBackground_LeptonFakeRate.root"),
'logFile':
os.path.join(
self.dirs[DKEY_LOGS],
os.path.basename(
self.cfgFile_addBackgrounds_LeptonFakeRate.replace(
"_cfg.py", ".log"))),
}
self.createCfg_addBackgrounds_LeptonFakeRate(
self.
jobOptions_addBackgrounds_LeptonFakeRate[key_addBackgrounds_job])
# initialize input and output file names for hadd_stage2
# key_hadd_stage2 = getKey(charge_selection) ## NO CHARGE SELECTION NEEDED HERE
# if not key_hadd_stage2 in self.inputFiles_hadd_stage2: ## NO CHARGE SELECTION NEEDED HERE
# self.inputFiles_hadd_stage2[key_hadd_stage2] = [] ## NO CHARGE SELECTION NEEDED HERE
# self.inputFiles_hadd_stage2[key_hadd_stage2].append(self.outputFile_hadd_stage1[key_hadd_stage1]) ## NO CHARGE SELECTION NEEDED HERE
# self.outputFile_hadd_stage2[key_hadd_stage2] = os.path.join(self.dirs[DKEY_HIST], "histograms_harvested_stage2_%s_%s.root" % \ ## NO CHARGE SELECTION NEEDED HERE
# (self.channel, charge_selection)) ## NO CHARGE SELECTION NEEDED HERE
key_hadd_stage2 = getKey('')
if not key_hadd_stage2 in self.inputFiles_hadd_stage2:
self.inputFiles_hadd_stage2[key_hadd_stage2] = []
for key_hadd_stage1_5 in self.outputFile_hadd_stage1_5.keys():
self.inputFiles_hadd_stage2[key_hadd_stage2].append(
self.outputFile_hadd_stage1_5[key_hadd_stage1_5])
self.inputFiles_hadd_stage2[key_hadd_stage2].append(
self.jobOptions_addBackgrounds_LeptonFakeRate[
key_addBackgrounds_job]['outputFile'])
self.outputFile_hadd_stage2[key_hadd_stage2] = os.path.join(
self.dirs[DKEY_HIST], "histograms_harvested_stage2.root")
if self.prep_dcard:
processesToCopy = []
signals = []
logging.info(
"Creating configuration files to run 'prepareDatacards_LeptonFakeRate'"
)
for process in self.prep_dcard_signals:
signals.append(process)
self.prep_dcard_signals = signals
for process in self.prep_dcard_processesToCopy:
processesToCopy.append(process)
self.prep_dcard_processesToCopy = processesToCopy
for histogramToFit in self.histograms_to_fit:
key_prep_dcard_job = getKey(histogramToFit)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile':
self.outputFile_hadd_stage2[key_hadd_stage2],
'cfgFile_modified':
os.path.join(
self.dirs[DKEY_CFGS],
"prepareDatacards_LeptonFakeRate_%s_cfg.py" %
(histogramToFit)),
'datacardFile':
os.path.join(self.dirs[DKEY_DCRD],
"prepareDatacards_%s.root" %
(histogramToFit)),
'histogramDir': (self.histogramDir_prep_dcard),
'histogramToFit':
histogramToFit,
'label':
None
}
# self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job]) ## DEF LINE
self.createCfg_prep_dcard_LeptonFakeRate(
self.jobOptions_prep_dcard[key_prep_dcard_job])
if self.is_sbatch:
logging.info(
"Creating script for submitting '%s' jobs to batch system" %
self.executable_analyze)
self.sbatchFile_analyze = os.path.join(
self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
# self.createScript_sbatch()
self.createScript_sbatch_analyze(self.executable_analyze,
self.sbatchFile_analyze,
self.jobOptions_analyze)
self.sbatchFile_addBackgrounds_LeptonFakeRate = os.path.join(
self.dirs[DKEY_SCRIPTS],
"sbatch_addBackgrounds_LeptonFakeRate_%s.py" % self.channel)
self.createScript_sbatch(
self.executable_addBackgrounds_LeptonFakeRate,
self.sbatchFile_addBackgrounds_LeptonFakeRate,
self.jobOptions_addBackgrounds_LeptonFakeRate)
# logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_comp_jetToTauFakeRate)
# self.sbatchFile_comp_jetToTauFakeRate = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_comp_jetToTauFakeRate.py")
# self.createScript_sbatch(self.executable_comp_jetToTauFakeRate, self.sbatchFile_comp_jetToTauFakeRate, self.jobOptions_comp_jetToTauFakeRate)
#### FAKE RATE COMP BLOCK COMMENTED OUT ########################
# logging.info("Creating configuration files for executing 'comp_jetToTauFakeRate'")
# for charge_selection in self.charge_selections:
# key_comp_jetToTauFakeRate_job = getKey(charge_selection)
# key_hadd_stage2 = getKey(charge_selection)
# self.jobOptions_comp_jetToTauFakeRate[key_comp_jetToTauFakeRate_job] = {
# 'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
# 'cfgFile_modified' : os.path.join(
# self.dirs[DKEY_CFGS], "comp_jetToTauFakeRate_%s_cfg.py" % charge_selection),
# 'outputFile' : os.path.join(
# self.dirs[DKEY_HIST], "comp_jetToTauFakeRate_%s.root" % charge_selection),
# 'looseRegion' : "jetToTauFakeRate_%s/denominator/" % charge_selection,
# 'tightRegion' : "jetToTauFakeRate_%s/numerator/" % charge_selection,
# 'absEtaBins' : self.absEtaBins,
# 'ptBins' : self.ptBins
# }
# self.createCfg_comp_jetToTauFakeRate(self.jobOptions_comp_jetToTauFakeRate[key_comp_jetToTauFakeRate_job])
# self.targets.append(self.jobOptions_comp_jetToTauFakeRate[key_comp_jetToTauFakeRate_job]['outputFile'])
# logging.info("Creating configuration files to run 'makePlots'")
# for charge_selection in self.charge_selections:
# key_makePlots_job = getKey(charge_selection)
# key_hadd_stage2 = getKey(charge_selection)
# self.jobOptions_make_plots[key_makePlots_job] = {
# 'executable' : self.executable_make_plots,
# 'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
# 'cfgFile_modified' : os.path.join(
# self.dirs[DKEY_CFGS], "makePlots_%s_cfg.py" % self.channel),
# 'outputFile' : os.path.join(
# self.dirs[DKEY_PLOT], "makePlots_%s.png" % self.channel),
# 'histogramDir' : "jetToTauFakeRate_%s" % charge_selection,
# 'label' : None,
# 'make_plots_backgrounds' : [ "TT", "TTW", "TTZ", "EWK", "Rares" ],
# }
# self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
# self.cfgFile_make_plots = self.cfgFile_make_plots_denominator
# for absEtaBin in [ "absEtaLt1_5", "absEta1_5to9_9" ]:
# key_makePlots_job = getKey(charge_selection, absEtaBin, "denominator")
# key_hadd_stage2 = getKey(charge_selection)
# self.jobOptions_make_plots[key_makePlots_job] = {
# 'executable' : self.executable_make_plots,
# 'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
# 'cfgFile_modified' : os.path.join(
# self.dirs[DKEY_CFGS], "makePlots_%s_%s_denominator_%s_cfg.py" % (self.channel, charge_selection, absEtaBin)),
# 'outputFile' : os.path.join(
# self.dirs[DKEY_PLOT], "makePlots_%s_%s_denominator_%s.png" % (self.channel, charge_selection, absEtaBin)),
# 'histogramDir' : "jetToTauFakeRate_%s/denominator/%s" % (charge_selection, absEtaBin),
# 'label' : None,
# 'make_plots_backgrounds' : [ "TT", "TTW", "TTZ", "EWK", "Rares" ],
# }
# self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
# for hadTau_selection in self.hadTau_selections:
# key_makePlots_job = getKey(charge_selection, absEtaBin, "numerator", hadTau_selection)
# key_hadd_stage2 = getKey(charge_selection)
# self.jobOptions_make_plots[key_makePlots_job] = {
# 'executable' : self.executable_make_plots,
# 'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2],
# 'cfgFile_modified' : os.path.join(
# self.dirs[DKEY_CFGS], "makePlots_%s_%s_numerator_%s_%s_cfg.py" % (self.channel, charge_selection, hadTau_selection, absEtaBin)),
# 'outputFile' : os.path.join(
# self.dirs[DKEY_PLOT], "makePlots_%s_%s_numerator_%s_%s.png" % (self.channel, charge_selection, hadTau_selection, absEtaBin)),
# 'histogramDir' : "jetToTauFakeRate_%s/numerator/%s/%s" % (charge_selection, hadTau_selection, absEtaBin),
# 'label' : None,
# 'make_plots_backgrounds' : [ "TT", "TTW", "TTZ", "EWK", "Rares" ],
# }
# self.createCfg_makePlots(self.jobOptions_make_plots[key_makePlots_job])
#########################################################
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
# self.addToMakefile_hadd_stage1_5(lines_makefile)
self.addToMakefile_backgrounds_from_data(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile)
self.addToMakefile_prep_dcard(lines_makefile)
# self.addToMakefile_comp_jetToTauFakeRate(lines_makefile) ## TO BE IMPLEMENTED LATER
# self.addToMakefile_make_plots(lines_makefile) ## TO BE IMPLEMENTED LATER
self.targets = [
outputFile for outputFile in self.outputFile_hadd_stage2.values()
]
self.createMakefile(lines_makefile)
logging.info("Done")
def create(self):
"""Creates all necessary config files and runs the complete analysis workfow -- either locally or on the batch system
"""
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
is_mc = (sample_info["type"] == "mc")
logging.info("Building dictionaries for sample %s..." % process_name)
for lepton_selection in self.lepton_selections:
central_or_shift_extensions = ["", "hadd", "addBackgrounds"]
central_or_shifts_extended = central_or_shift_extensions + self.central_or_shifts
for central_or_shift_or_dummy in central_or_shifts_extended:
process_name_extended = [ process_name, "hadd" ]
for process_name_or_dummy in process_name_extended:
if central_or_shift_or_dummy in [ "hadd", "addBackgrounds" ] and process_name_or_dummy in [ "hadd" ]:
continue
if central_or_shift_or_dummy != "central" and central_or_shift_or_dummy not in central_or_shift_extensions:
if not is_mc:
continue
if not self.accept_central_or_shift(central_or_shift_or_dummy, sample_info):
continue
key_dir = getKey(process_name_or_dummy, lepton_selection, central_or_shift_or_dummy)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_RLES ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel,
"_".join([ lepton_selection ]), process_name_or_dummy, central_or_shift_or_dummy)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel,
"_".join([ lepton_selection ]), process_name_or_dummy)
for subdirectory in [ "prepareDatacards" ]:
key_dir = getKey(subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
initDict(self.dirs, [ key_dir, dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT ]:
self.dirs[key_dir][dir_type] = os.path.join(self.configDir, dir_type, self.channel, subdirectory)
else:
self.dirs[key_dir][dir_type] = os.path.join(self.outputDir, dir_type, self.channel, subdirectory)
for dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_HIST, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_COMBINE_OUTPUT ]:
initDict(self.dirs, [ dir_type ])
if dir_type in [ DKEY_CFGS, DKEY_SCRIPTS, DKEY_LOGS, DKEY_DCRD, DKEY_PLOT, DKEY_HADD_RT, DKEY_COMBINE_OUTPUT ]:
self.dirs[dir_type] = os.path.join(self.configDir, dir_type, self.channel)
else:
self.dirs[dir_type] = os.path.join(self.outputDir, dir_type, self.channel)
numDirectories = 0
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
numDirectories += len(self.dirs[key])
else:
numDirectories += 1
logging.info("Creating directory structure (numDirectories = %i)" % numDirectories)
numDirectories_created = 0;
frac = 1
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
numDirectories_created += len(self.dirs[key])
else:
create_if_not_exists(self.dirs[key])
numDirectories_created = numDirectories_created + 1
while 100*numDirectories_created >= frac*numDirectories:
logging.info(" %i%% completed" % frac)
frac = frac + 1
logging.info("Done.")
inputFileLists = {}
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
logging.info("Checking input files for sample %s" % sample_info["process_name_specific"])
inputFileLists[sample_name] = generateInputFileList(sample_info, self.max_files_per_job)
for lepton_selection in self.lepton_selections:
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"]:
continue
process_name = sample_info["process_name_specific"]
logging.info("Creating configuration files to run '%s' for sample %s" % (self.executable_analyze, process_name))
is_mc = (sample_info["type"] == "mc")
inputFileList = inputFileLists[sample_name]
for central_or_shift in self.central_or_shifts:
if central_or_shift != "central" and not is_mc:
continue
# build config files for executing analysis code
key_analyze_dir = getKey(process_name, lepton_selection, central_or_shift)
for jobId in inputFileList.keys():
analyze_job_tuple = (process_name, lepton_selection, central_or_shift, jobId)
key_analyze_job = getKey(*analyze_job_tuple)
ntupleFiles = inputFileList[jobId]
if len(ntupleFiles) == 0:
logging.warning("No input ntuples for %s --> skipping job !!" % (key_analyze_job))
continue
cfgFile_modified_path = os.path.join(self.dirs[key_analyze_dir][DKEY_CFGS], "analyze_%s_%s_%s_%i_cfg.py" % analyze_job_tuple)
logFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_LOGS], "analyze_%s_%s_%s_%i.log" % analyze_job_tuple)
rleOutputFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_RLES], "rle_%s_%s_%s_%i.txt" % analyze_job_tuple) \
if self.select_rle_output else ""
histogramFile_path = os.path.join(self.dirs[key_analyze_dir][DKEY_HIST], "analyze_%s_%s_%s_%i.root" % analyze_job_tuple)
self.jobOptions_analyze[key_analyze_job] = {
'ntupleFiles' : ntupleFiles,
'cfgFile_modified' : cfgFile_modified_path,
'histogramFile' : histogramFile_path,
'logFile' : logFile_path,
'selEventsFileName_output' : rleOutputFile_path,
'leptonSelection' : lepton_selection,
'applyFakeRateWeights' : "disabled",
'central_or_shift' : central_or_shift,
}
self.createCfg_analyze(self.jobOptions_analyze[key_analyze_job], sample_info)
# initialize input and output file names for hadd_stage1
key_hadd_stage1_dir = getKey(process_name, lepton_selection)
hadd_stage1_job_tuple = (process_name, lepton_selection)
key_hadd_stage1_job = getKey(*hadd_stage1_job_tuple)
if not key_hadd_stage1_job in self.inputFiles_hadd_stage1.keys():
self.inputFiles_hadd_stage1[key_hadd_stage1_job] = []
self.inputFiles_hadd_stage1[key_hadd_stage1_job].append(self.jobOptions_analyze[key_analyze_job]['histogramFile'])
self.outputFile_hadd_stage1[key_hadd_stage1_job] = os.path.join(self.dirs[key_hadd_stage1_dir][DKEY_HIST],
"hadd_stage1_%s_%s.root" % hadd_stage1_job_tuple)
# initialize input and output file names for hadd_stage2
key_hadd_stage1_job = getKey(process_name, lepton_selection)
key_hadd_stage2_dir = getKey("hadd", lepton_selection)
key_hadd_stage2_job = getKey(lepton_selection)
if not key_hadd_stage2_job in self.inputFiles_hadd_stage2.keys():
self.inputFiles_hadd_stage2[key_hadd_stage2_job] = []
self.inputFiles_hadd_stage2[key_hadd_stage2_job].append(self.outputFile_hadd_stage1[key_hadd_stage1_job])
self.outputFile_hadd_stage2[key_hadd_stage2_job] = os.path.join(self.dirs[key_hadd_stage2_dir][DKEY_HIST],
"hadd_stage2_%s.root" % lepton_selection)
logging.info("Creating configuration files to run 'prepareDatacards'")
processesToCopy = []
for process in self.prep_dcard_processesToCopy:
processesToCopy.append(process)
self.prep_dcard_processesToCopy = processesToCopy
processesToCopy = []
for process in self.prep_dcard_signals:
processesToCopy.append(process)
self.prep_dcard_signals = processesToCopy
for histogramToFit in self.histograms_to_fit:
key_hadd_stage2_job = getKey("Tight")
key_prep_dcard_dir = getKey("prepareDatacards")
prep_dcard_job_tuple = (self.channel, histogramToFit)
key_prep_dcard_job = getKey(histogramToFit)
datacardFile = os.path.join(self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_%s_%s.root" % prep_dcard_job_tuple)
self.jobOptions_prep_dcard[key_prep_dcard_job] = {
'inputFile' : self.outputFile_hadd_stage2[key_hadd_stage2_job],
'cfgFile_modified' : os.path.join(self.dirs[key_prep_dcard_dir][DKEY_CFGS], "prepareDatacards_%s_%s_cfg.py" % prep_dcard_job_tuple),
'datacardFile' : datacardFile,
'histogramDir' : self.histogramDir_prep_dcard,
'histogramToFit' : histogramToFit,
'label' : None
}
self.createCfg_prep_dcard(self.jobOptions_prep_dcard[key_prep_dcard_job])
jobOptions_makefile = copy.deepcopy(self.jobOptions_postFit)
jobOptions_makefile['fit_result'] = os.path.join(
self.dirs[DKEY_COMBINE_OUTPUT], 'fit_{}'.format(histogramToFit), jobOptions_makefile['target']
)
jobOptions_makefile['hadd_stage2'] = self.outputFile_hadd_stage2[key_hadd_stage2_job]
jobOptions_makefile['prepare_datacard'] = datacardFile
jobOptions_makefile['data_datacard'] = os.path.join(
self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_data_%s_%s.root" % prep_dcard_job_tuple
)
jobOptions_makefile['pseudodata_datacard'] = os.path.join(
self.dirs[key_prep_dcard_dir][DKEY_DCRD], "prepareDatacards_pseudodata_%s_%s.root" % prep_dcard_job_tuple
)
jobOptions_makefile['makefile'] = os.path.join(
self.dirs[DKEY_COMBINE_OUTPUT], 'Makefile_{}'.format(histogramToFit)
)
jobOptions_makefile['stdout'] = os.path.join(
self.dirs[DKEY_COMBINE_OUTPUT], 'stdout_{}.log'.format(histogramToFit)
)
self.createCfg_postFit(jobOptions_makefile)
self.sbatchFile_analyze = os.path.join(self.dirs[DKEY_SCRIPTS], "sbatch_analyze_%s.py" % self.channel)
if self.is_sbatch:
logging.info("Creating script for submitting '%s' jobs to batch system" % self.executable_analyze)
self.createScript_sbatch_analyze(self.executable_analyze, self.sbatchFile_analyze, self.jobOptions_analyze)
logging.info("Creating Makefile")
lines_makefile = []
self.addToMakefile_analyze(lines_makefile)
self.addToMakefile_hadd_stage1(lines_makefile)
self.addToMakefile_hadd_stage2(lines_makefile, make_dependency = "phony_hadd_stage1")
self.addToMakefile_prep_dcard(lines_makefile)
self.addToMakefile_postFit(lines_makefile)
self.createMakefile(lines_makefile)
logging.info("Done.")
return self.num_jobs
def __init__(
self,
configDir,
outputDir,
output_file,
executable,
projection_module,
samples,
max_files_per_job,
era,
plot,
check_output_files,
running_method,
num_parallel_jobs,
pool_id='',
verbose=False,
dry_run=False,
use_home=False,
submission_cmd=None,
):
self.configDir = configDir
self.outputDir = outputDir
self.executable = executable
self.projection_module = projection_module
self.max_num_jobs = 200000
self.samples = samples
self.max_files_per_job = max_files_per_job
self.era = era
self.plot = plot
self.check_output_files = check_output_files
self.verbose = verbose
self.dry_run = dry_run
self.use_home = use_home
if running_method.lower() not in ["sbatch", "makefile"]:
raise ValueError("Invalid running method: %s" % running_method)
self.running_method = running_method
self.is_sbatch = self.running_method.lower() == "sbatch"
self.is_makefile = not self.is_sbatch
self.makefile = os.path.join(
self.configDir, "Makefile_{}".format(self.projection_module))
self.num_parallel_jobs = num_parallel_jobs
self.pool_id = pool_id if pool_id else uuid.uuid4()
self.workingDir = os.getcwd()
logging.info("Working directory is: %s" % self.workingDir)
self.template_dir = os.path.join(os.getenv('CMSSW_BASE'), 'src',
'tthAnalysis', 'HiggsToTauTau',
'test', 'templates')
logging.info("Templates directory is: %s" % self.template_dir)
create_if_not_exists(self.configDir)
create_if_not_exists(self.outputDir)
self.output_file = os.path.join(self.outputDir, output_file)
self.stdout_file_path = os.path.join(
self.configDir, "stdout_{}.log".format(self.projection_module))
self.stderr_file_path = os.path.join(
self.configDir, "stderr_{}.log".format(self.projection_module))
self.sw_ver_file_cfg = os.path.join(
self.configDir, "VERSION_{}.log".format(self.projection_module))
self.sw_ver_file_out = os.path.join(
self.outputDir, "VERSION_{}.log".format(self.projection_module))
self.submission_out = os.path.join(self.configDir, "SUBMISSION.log")
self.stdout_file_path, self.stderr_file_path, self.sw_ver_file_cfg, self.sw_ver_file_out, self.submission_out = get_log_version(
(self.stdout_file_path, self.stderr_file_path,
self.sw_ver_file_cfg, self.sw_ver_file_out, self.submission_out))
check_submission_cmd(self.submission_out, submission_cmd)
self.sbatchFile_projection = os.path.join(
self.configDir, "sbatch_{}.py".format(self.projection_module))
self.cfgFiles_projection = {}
self.logFiles_projection = {}
self.scriptFiles_projection = {}
self.jobOptions_sbatch = {}
self.inputFiles = {}
self.outputFiles_tmp = {}
self.outputFiles = {}
self.phoniesToAdd = []
self.filesToClean = []
self.targets = []
self.makefile_target = "sbatch_{}".format(self.projection_module)
self.dirs = {}
all_dirs = [
DKEY_CFGS, DKEY_HISTO_TMP, DKEY_HISTO, DKEY_PLOTS, DKEY_LOGS,
DKEY_SCRIPTS, DKEY_HADD_RT
]
cfg_dirs = [
DKEY_CFGS, DKEY_LOGS, DKEY_PLOTS, DKEY_SCRIPTS, DKEY_HADD_RT
]
ref_genWeightsFile = os.path.join(
os.environ['CMSSW_BASE'], 'src', 'tthAnalysis', 'HiggsToTauTau',
'data', 'refGenWeight_{}.txt'.format(self.era))
self.ref_genWeights = load_refGenWeightsFromFile(
ref_genWeightsFile) if projection_module != 'puHist' else {}
for sample_name, sample_info in self.samples.items():
if not sample_info['use_it']:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(process_name)
for dir_type in all_dirs:
if dir_type == DKEY_PLOTS:
continue
initDict(self.dirs, [key_dir, dir_type])
if dir_type in cfg_dirs:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, process_name)
for dir_type in cfg_dirs:
initDict(self.dirs, [dir_type])
self.dirs[dir_type] = os.path.join(self.configDir, dir_type)
self.cvmfs_error_log = {}
self.num_jobs = {
'hadd': 0,
'project': 0,
'plot': 0,
}
def __init__(self,
configDir,
outputDir,
executable_preselNtuple,
channel,
samples,
era,
debug,
running_method,
rle_directory,
version,
num_parallel_jobs,
pool_id=''):
self.configDir = configDir
self.outputDir = outputDir
self.executable_preselNtuple = executable_preselNtuple
self.channel = channel
self.max_files_per_job = 1
self.max_num_jobs = 200000
self.samples = samples
self.era = era
self.debug = debug
assert (running_method.lower()
in ["sbatch",
"makefile"]), "Invalid running method: %s" % running_method
self.running_method = running_method
self.is_sbatch = False
self.is_makefile = False
if self.running_method.lower() == "sbatch":
self.is_sbatch = True
else:
self.is_makefile = True
self.makefile = os.path.join(self.configDir,
"Makefile_%s" % self.channel)
self.num_parallel_jobs = num_parallel_jobs
self.pool_id = pool_id if pool_id else uuid.uuid4()
self.workingDir = os.getcwd()
print "Working directory is: " + self.workingDir
self.rle_directory = rle_directory
self.version = version
if self.rle_directory == 'default':
self.rle_directory = os.path.join(
##os.path.join("/hdfs/local/ttH_2tau", getpass.getuser(), "ttHNtupleProduction", ERA, version),
'/home',
getpass.getuser(),
'ttHAnalysis',
self.era,
self.version,
'rles',
self.channel)
elif self.rle_directory:
if not os.path.isdir(self.rle_directory):
logging.error("No such directory: '{directory_name}'".format(
directory_name=self.rle_directory))
sys.exit(1)
create_if_not_exists(self.configDir)
create_if_not_exists(self.outputDir)
self.stdout_file = codecs.open(
os.path.join(self.outputDir, "stdout_%s.log" % self.channel), 'w',
'utf-8')
self.stderr_file = codecs.open(
os.path.join(self.outputDir, "stderr_%s.log" % self.channel), 'w',
'utf-8')
self.dirs = {}
self.samples = samples
self.cfgFiles_preselNtuple_modified = {}
self.logFiles_preselNtuple = {}
self.sbatchFile_preselNtuple = os.path.join(
self.configDir, "sbatch_preselNtuple_%s.py" % self.channel)
self.inputFiles = {}
self.outputFiles = {}
self.filesToClean = []
for sample_name, sample_info in self.samples.items():
if not sample_info["use_it"] or sample_info["sample_category"] in [
"additional_signal_overlap", "background_data_estimate"
]:
continue
process_name = sample_info["process_name_specific"]
key_dir = getKey(sample_name)
for dir_type in [DKEY_CFGS, DKEY_NTUPLES, DKEY_LOGS]:
initDict(self.dirs, [key_dir, dir_type])
if dir_type in [DKEY_CFGS]:
self.dirs[key_dir][dir_type] = os.path.join(
self.configDir, dir_type, self.channel, process_name)
else:
self.dirs[key_dir][dir_type] = os.path.join(
self.outputDir, dir_type, self.channel, process_name)
##print "self.dirs = ", self.dirs
self.cvmfs_error_log = {}
