def process_efficiency(self):
print("Doing efficiencies", self.mcordata, self.period)
print("Using run selection for eff histo", \
self.runlistrigger[self.triggerbit], "for period", self.period)
if self.doml is True:
print("Doing ml analysis")
else:
print("No extra selection needed since we are doing std analysis")
for ibin2 in range(len(self.lvar2_binmin)):
if self.corr_eff_mult[ibin2] is True:
print("Reweighting efficiencies for bin", ibin2)
else:
print("Not reweighting efficiencies for bin", ibin2)
create_folder_struc(self.d_results, self.l_path)
arguments = [(i,) for i in range(len(self.l_root))]
self.parallelizer(self.process_efficiency_single, arguments, self.p_chunksizeunp)
tmp_merged = f"/data/tmp/hadd/{self.case}_{self.typean}/histoeff_{self.period}/{get_timestamp_string()}/"
mergerootfiles(self.l_histoeff, self.n_fileeff, tmp_merged)
def process_histomass(self):
print("Doing masshisto", self.mcordata, self.period)
print("Using run selection for mass histo", \
self.runlistrigger, "for period", self.period)
if self.doml is True:
print("Doing ml analysis")
else:
print("No extra selection needed since we are doing std analysis")
# Load potential custom cuts
self.load_cuts()
create_folder_struc(self.d_results, self.l_path)
arguments = [(i, ) for i in range(len(self.l_root))]
self.parallelizer(self.process_histomass_single, arguments,
self.p_chunksizeunp) # pylint: disable=no-member
tmp_merged = \
f"/data/tmp/hadd/{self.case}_{self.typean}/mass_{self.period}/{get_timestamp_string()}/"
mergerootfiles(self.l_histomass, self.n_filemass, tmp_merged)
def multi_valevents(self):
for indexp in range(self.prodnumber):
self.process_listsample[indexp].process_valevents_par()
tmp_merged = f"/data/tmp/hadd/{self.case}_{self.typean}/val_all/{get_timestamp_string()}/"
mergerootfiles(self.lper_evtvalroot, self.f_evtvalroot_mergedallp,
tmp_merged)
def multi_preparenorm(self):
tmp_merged = \
f"/data/tmp/hadd/{self.case}_{self.typean}/norm_processer/{get_timestamp_string()}/"
mergerootfiles(self.lper_normfiles, self.f_evtvalroot_mergedallp,
tmp_merged)
def multi_histomass(self):
for indexp in range(self.prodnumber):
if self.p_useperiod[indexp] == 1:
self.process_listsample[indexp].process_histomass()
tmp_merged = f"/data/tmp/hadd/{self.case}_{self.typean}/mass/{get_timestamp_string()}/"
mergerootfiles(self.lper_filemass, self.filemass_mergedall, tmp_merged)
def multi_cutvariation(self, domass, doeff, dofit, docross):
if domass is True or doeff is True or dofit is True:
for indexp in range(self.prodnumber):
if self.p_useperiodforlimits[indexp] == 1:
print("Processing systematics period: ", indexp)
min_cv_cut, max_cv_cut = \
self.process_listsample[indexp].define_cutvariation_limits()
if domass is True:
for indexp in range(self.prodnumber):
if self.p_useperiod[indexp] == 1:
self.process_listsample[indexp].cutvariation_masshistos(
min_cv_cut, max_cv_cut)
tmp_merged = f"/data/tmp/hadd/{self.case}_{self.typean}/cutvar_mass/" \
f"{get_timestamp_string()}/"
mergerootfiles(self.lper_filemass_cutvar,
self.filemass_cutvar_mergedall, tmp_merged)
if doeff is True:
for indexp in range(self.prodnumber):
if self.p_useperiod[indexp] == 1:
self.process_listsample[indexp].cutvariation_efficiencies(min_cv_cut, \
max_cv_cut)
tmp_merged = f"/data/tmp/hadd/{self.case}_{self.typean}/cutvar_eff/" \
f"{get_timestamp_string()}/"
mergerootfiles(self.lper_fileeff_cutvar,
self.fileeff_cutvar_mergedall, tmp_merged)
if dofit is True:
for indexp in range(self.prodnumber):
if self.p_useperiod[indexp] == 1:
self.process_listsample[indexp].cutvariation_fitter(
min_cv_cut, max_cv_cut)
self.process_listsample[indexp].cutvariation_efficiency()
self.myprocesstot.cutvariation_fitter(min_cv_cut, max_cv_cut)
self.myprocesstot.cutvariation_efficiency()
if docross is True:
for indexp in range(self.prodnumber):
if self.p_useperiod[indexp] == 1:
self.process_listsample[
indexp].cutvariation_makenormyields()
self.myprocesstot.cutvariation_makenormyields()
histname = [
"histoSigmaCorr", "hDirectEffpt", "hFeedDownEffpt", "hRECpt"
]
for name in histname:
for indexp in range(self.prodnumber):
if self.p_useperiod[indexp] == 1:
if domass is True or doeff is True or dofit is True:
self.process_listsample[indexp].cutvariation_makeplots(name, \
min_cv_cut, \
max_cv_cut)
else:
self.process_listsample[
indexp].cutvariation_makeplots(
name, None, None)
if domass is True or doeff is True or dofit is True:
self.myprocesstot.cutvariation_makeplots(
name, min_cv_cut, max_cv_cut)
else:
self.myprocesstot.cutvariation_makeplots(name, None, None)
def multi_efficiency(self):
for indexp in range(self.prodnumber):
self.process_listsample[indexp].process_efficiency()
mergerootfiles(self.lper_fileeff, self.fileeff_mergedall)
def multi_histomass(self):
for indexp in range(self.prodnumber):
self.process_listsample[indexp].process_histomass()
mergerootfiles(self.lper_filemass, self.filemass_mergedall)
def multi_valevents(self):
for indexp in range(self.prodnumber):
self.process_listsample[indexp].process_valevents_par()
mergerootfiles(self.lper_evtvalroot, self.f_evtvalroot_mergedallp)
def process_valevents_par(self):
print("doing event validation", self.mcordata, self.period)
create_folder_struc(self.d_val, self.l_path)
arguments = [(i,) for i in range(len(self.l_evtorig))]
self.parallelizer(self.process_valevents, arguments, self.p_chunksizeskim)
mergerootfiles(self.l_evtvalroot, self.f_totevtvalroot)
def multi_preparenorm(database, case, typean, doperiodbyperiod):
logger = get_logger()
lper_normfilesorig = []
lper_normfiles = []
dlper_valevtroot = database["validation"]["data"]["dir"]
resultsdata = database["analysis"][typean]["data"]["results"]
for res_path, lper_val in zip(resultsdata, dlper_valevtroot):
lper_normfilesorig.append(join(lper_val, "correctionsweights.root"))
lper_normfiles.append(join(res_path, "correctionsweights.root"))
f_normmerged = join(database["analysis"][typean]["data"]["resultsallp"],
"correctionsweights.root")
listempty = []
tmp_merged = f"/data/tmp/hadd/{case}_{typean}/norm_analyzer/{get_timestamp_string()}/"
useperiod = database["analysis"][typean]["useperiod"]
for indexp in range(len(resultsdata)):
logger.info("Origin path: %s, target path: %s",
lper_normfilesorig[indexp], lper_normfiles[indexp])
mergerootfiles([lper_normfilesorig[indexp]], lper_normfiles[indexp],
tmp_merged)
if doperiodbyperiod and useperiod[indexp]:
listempty.append(lper_normfiles[indexp])
mergerootfiles(listempty, f_normmerged, tmp_merged)
