def runFill(opts) :
batchMode = opts.batch
inputFakeDir = opts.input_fake
inputGenDir = opts.input_other
outputDir = opts.output_dir
verbose = opts.verbose
debug = opts.debug
blinded = not opts.unblind
tightight = opts.require_tight_tight
if debug : dataset.Dataset.verbose_parsing = True
groups = dataset.DatasetGroup.build_groups_from_files_in_dir(opts.samples_dir)
if not skip_charge_flip : groups.append(dataset.DatasetGroup.build_qflip_from_simulated_samples(groups))
groups.append(first([g for g in groups if g.is_data]).clone_data_as_fake())
groups = parse_group_option(opts, groups)
if verbose : print '\n'.join("group {0} : {1} samples".format(g.name, len(g.datasets)) for g in groups)
if debug :
print '\n'.join("group {0} : {1} samples: {2}".format(g.name,
len(g.datasets),
'\n\t'+'\n\t'.join(d.name for d in g.datasets))
for g in groups)
if verbose : print "filling histos"
# eval will take care of aborting on typos
onthefly_tight_def = eval(opts.tight_def) if opts.tight_def else None
mkdirIfNeeded(outputDir)
systematics = get_list_of_syst_to_fill(opts)
regions = regions_to_plot(opts.include_regions, opts.exclude_regions, opts.regions)
if verbose : print "about to loop over these systematics:\n %s"%str(systematics)
if verbose : print "about to loop over these regions:\n %s"%str(regions)
if batchMode:
for group in groups:
for systematic in systematics:
if systUtils.Group(group.name).isNeededForSys(systematic):
opts.syst = systematic
for selection in regions:
submit_batch_fill_job_per_group_per_selection(group=group, selection=selection, opts=opts)
else:
for group in groups:
systematics = [s for s in systematics if systUtils.Group(group.name).isNeededForSys(s)]
if not systematics : print "warning, empty syst list. You should have at least the nominal"
for systematic in systematics:
# note to self: here you will want to use a modified Sample.setHftInputDir
# for now we just have the fake syst that are in the nominal tree
tree_name = 'hlfv_tuple'
chain = IndexedChain(tree_name)
input_dir = opts.input_fake if group.name=='fake' else opts.input_other
for ds in group.datasets:
chain.Add(os.path.join(input_dir, systUtils.Sample(ds.name, group.name).setSyst(systematic).filename))
if opts.verbose:
print "{0} : {1} entries from {2} samples".format(group.name,
chain.GetEntries(),
len(group.datasets))
chain.cache_directory = os.path.abspath('./selection_cache/'+group.name+'/')
tcuts = [r.TCut(reg, selection_formulas()[reg]) for reg in regions]
chain.retrieve_entrylists(tcuts)
counters_pre, histos_pre = dict(), dict()
counters_npre, histos_npre = dict(), dict()
cached_tcuts = [] if opts.disable_cache else chain.tcuts_with_existing_list()
uncached_tcuts = tcuts if opts.disable_cache else chain.tcuts_without_existing_list()
if verbose : print 'filling cached cuts: ',' '.join([c.GetName() for c in cached_tcuts])
for cut in cached_tcuts:
chain.preselect(cut)
c_pre, h_pre = count_and_fill(chain=chain, sample=group.name,
syst=systematic, verbose=verbose,
debug=debug, blinded=blinded,
onthefly_tight_def=onthefly_tight_def,
tightight=tightight, quicktest=opts.quick_test,
cached_cut=cut)
out_filename = (systUtils.Group(group.name)
.setSyst(systematic)
.setHistosDir(outputDir)
.setCurrentSelection(cut.GetName())).filenameHisto
writeObjectsToFile(out_filename, h_pre, verbose)
counters_pre = dictSum(counters_pre, c_pre)
histos_pre = dictSum(histos_pre, h_pre)
if uncached_tcuts:
if verbose : print 'filling uncached cuts: ',' '.join([c.GetName() for c in uncached_tcuts])
counters_npre, histos_npre = count_and_fill(chain=chain, sample=group.name,
syst=systematic, verbose=verbose,
debug=debug, blinded=blinded,
onthefly_tight_def=onthefly_tight_def,
tightight=tightight,
quicktest=opts.quick_test,
noncached_cuts=uncached_tcuts)
for sel, histos in histos_npre.iteritems():
out_filename = (systUtils.Group(group.name)
.setSyst(systematic)
.setHistosDir(outputDir)
.setCurrentSelection(sel)).filenameHisto
writeObjectsToFile(out_filename, histos, verbose)
chain.save_lists()
def runFill(opts):
lepton = opts.lepton
batchMode = opts.batch
inputDir = opts.input_dir
outputDir = opts.output_dir
verbose = opts.verbose
debug = opts.debug
dataset.Dataset.verbose_parsing = True if debug else False
groups = dataset.DatasetGroup.build_groups_from_files_in_dir(
opts.samples_dir)
if opts.group: groups = [g for g in groups if g.name == opts.group]
if verbose:
print '\n'.join(
"group {0} : {1} samples".format(g.name, len(g.datasets))
for g in groups)
if debug:
print '\n'.join("group {0} : {1} samples: {2}".format(
g.name, len(g.datasets), '\n\t' + '\n\t'.join(d.name
for d in g.datasets))
for g in groups)
if verbose: print "filling histos"
outputDir = outputDir + '/' + lepton + '/histos'
mkdirIfNeeded(outputDir)
if batchMode:
for group in groups:
submit_batch_fill_job_per_group(group, opts)
else:
for group in groups:
tree_name = 'ss3l_tuple'
chain = IndexedChain(tree_name)
for ds in group.datasets:
chain.Add(os.path.join(inputDir, ds.name + '.root'))
if opts.verbose:
print "{0} : {1} entries from {2} samples".format(
group.name, chain.GetEntries(), len(group.datasets))
chain.cache_directory = os.path.abspath('./selection_cache/' +
group.name + '/')
tcuts = [
r.TCut(reg,
selection_formulas()[reg])
for reg in regions_to_plot(opts.include_regions,
opts.exclude_regions, opts.regions)
]
chain.retrieve_entrylists(tcuts)
counters_pre, histos_pre = dict(), dict()
counters_npre, histos_npre = dict(), dict()
cached_tcuts = [] if opts.disable_cache else chain.tcuts_with_existing_list(
)
uncached_tcuts = tcuts if opts.disable_cache else chain.tcuts_without_existing_list(
)
print 'todo: skip cuts for which the histo files are there'
if verbose:
print 'filling cached cuts: ', ' '.join(
[c.GetName() for c in cached_tcuts])
for cut in cached_tcuts:
chain.preselect(cut)
c_pre, h_pre = count_and_fill(chain=chain,
opts=opts,
group=group,
cached_cut=cut)
counters_pre = dictSum(counters_pre, c_pre)
histos_pre = dictSum(histos_pre, h_pre)
if verbose:
print 'filling uncached cuts: ', ' '.join(
[c.GetName() for c in uncached_tcuts])
if uncached_tcuts:
counters_npre, histos_npre = count_and_fill(
chain=chain,
opts=opts,
group=group,
noncached_cuts=uncached_tcuts)
chain.save_lists()
all_histos = dictSum(histos_pre, histos_npre)
for sel, histos in all_histos.iteritems():
# write histos for each sel to a separate file (finer granularity, better caching)
out_filename = os.path.join(outputDir,
group.name + '_' + sel + '.root')
if verbose: print 'saving to ', out_filename
writeObjectsToFile(out_filename, histos, verbose)
def runFill(opts):
batchMode = opts.batch
inputFakeDir = opts.input_fake
inputGenDir = opts.input_other
outputDir = opts.output_dir
verbose = opts.verbose
debug = opts.debug
blinded = not opts.unblind
tightight = opts.require_tight_tight
if debug: dataset.Dataset.verbose_parsing = True
groups = dataset.DatasetGroup.build_groups_from_files_in_dir(
opts.samples_dir)
if not skip_charge_flip:
groups.append(
dataset.DatasetGroup.build_qflip_from_simulated_samples(groups))
groups.append(first([g for g in groups if g.is_data]).clone_data_as_fake())
groups = parse_group_option(opts, groups)
if verbose:
print '\n'.join(
"group {0} : {1} samples".format(g.name, len(g.datasets))
for g in groups)
if debug:
print '\n'.join("group {0} : {1} samples: {2}".format(
g.name, len(g.datasets), '\n\t' + '\n\t'.join(d.name
for d in g.datasets))
for g in groups)
if verbose: print "filling histos"
# eval will take care of aborting on typos
onthefly_tight_def = eval(opts.tight_def) if opts.tight_def else None
mkdirIfNeeded(outputDir)
systematics = get_list_of_syst_to_fill(opts)
regions = regions_to_plot(opts.include_regions, opts.exclude_regions,
opts.regions)
if verbose:
print "about to loop over these systematics:\n %s" % str(systematics)
if verbose: print "about to loop over these regions:\n %s" % str(regions)
if batchMode:
for group in groups:
for systematic in systematics:
if systUtils.Group(group.name).isNeededForSys(systematic):
opts.syst = systematic
for selection in regions:
submit_batch_fill_job_per_group_per_selection(
group=group, selection=selection, opts=opts)
else:
for group in groups:
systematics = [
s for s in systematics
if systUtils.Group(group.name).isNeededForSys(s)
]
if not systematics:
print "warning, empty syst list. You should have at least the nominal"
for systematic in systematics:
# note to self: here you will want to use a modified Sample.setHftInputDir
# for now we just have the fake syst that are in the nominal tree
tree_name = 'hlfv_tuple'
chain = IndexedChain(tree_name)
input_dir = opts.input_fake if group.name == 'fake' else opts.input_other
for ds in group.datasets:
chain.Add(
os.path.join(
input_dir,
systUtils.Sample(
ds.name,
group.name).setSyst(systematic).filename))
if opts.verbose:
print "{0} : {1} entries from {2} samples".format(
group.name, chain.GetEntries(), len(group.datasets))
chain.cache_directory = os.path.abspath('./selection_cache/' +
group.name + '/')
tcuts = [
r.TCut(reg,
selection_formulas()[reg]) for reg in regions
]
chain.retrieve_entrylists(tcuts)
counters_pre, histos_pre = dict(), dict()
counters_npre, histos_npre = dict(), dict()
cached_tcuts = [] if opts.disable_cache else chain.tcuts_with_existing_list(
)
uncached_tcuts = tcuts if opts.disable_cache else chain.tcuts_without_existing_list(
)
if verbose:
print 'filling cached cuts: ', ' '.join(
[c.GetName() for c in cached_tcuts])
for cut in cached_tcuts:
chain.preselect(cut)
c_pre, h_pre = count_and_fill(
chain=chain,
sample=group.name,
syst=systematic,
verbose=verbose,
debug=debug,
blinded=blinded,
onthefly_tight_def=onthefly_tight_def,
tightight=tightight,
quicktest=opts.quick_test,
cached_cut=cut)
out_filename = (systUtils.Group(
group.name).setSyst(systematic).setHistosDir(
outputDir).setCurrentSelection(
cut.GetName())).filenameHisto
writeObjectsToFile(out_filename, h_pre, verbose)
counters_pre = dictSum(counters_pre, c_pre)
histos_pre = dictSum(histos_pre, h_pre)
if uncached_tcuts:
if verbose:
print 'filling uncached cuts: ', ' '.join(
[c.GetName() for c in uncached_tcuts])
counters_npre, histos_npre = count_and_fill(
chain=chain,
sample=group.name,
syst=systematic,
verbose=verbose,
debug=debug,
blinded=blinded,
onthefly_tight_def=onthefly_tight_def,
tightight=tightight,
quicktest=opts.quick_test,
noncached_cuts=uncached_tcuts)
for sel, histos in histos_npre.iteritems():
out_filename = (systUtils.Group(
group.name).setSyst(systematic).setHistosDir(
outputDir).setCurrentSelection(sel)
).filenameHisto
writeObjectsToFile(out_filename, histos, verbose)
chain.save_lists()
def main():
parser = optparse.OptionParser(usage=usage)
parser.add_option('-g', '--group', help='group to be processed (used only in fill mode)')
parser.add_option('-i', '--input-dir', default='./out/fakerate')
parser.add_option('-o', '--output-dir', default='./out/fake_scale_factor')
parser.add_option('-l', '--lepton', default='el', help='either el or mu')
parser.add_option('-r', '--region', help='one of the regions for which we saved the fake ntuples')
parser.add_option('--samples-dir', default='samples/', help='directory with the list of samples; default ./samples/')
parser.add_option('-T', '--tight-def', help='on-the-fly tight def, one of defs in fakeUtils.py: fakeu.lepIsTight_std, etc.')
parser.add_option('-f', '--fill-histos', action='store_true', default=False, help='force fill (default only if needed)')
parser.add_option('--keep-real', action='store_true', default=False, help='do not subtract real (to get real lep efficiency)')
parser.add_option('--debug', action='store_true')
parser.add_option('--verbose', action='store_true')
parser.add_option('--disable-cache', action='store_true', help='disable the entry cache')
(options, args) = parser.parse_args()
inputDir = options.input_dir
outputDir = options.output_dir
lepton = options.lepton
region = options.region
keepreal = options.keep_real
debug = options.debug
verbose = options.verbose
if lepton not in ['el', 'mu'] : parser.error("invalid lepton '%s'"%lepton)
regions = kin.selection_formulas().keys()
assert region in regions,"invalid region '%s', must be one of %s"%(region, str(sorted(regions)))
regions = [region]
dataset.Dataset.verbose_parsing = True if debug else False
groups = dataset.DatasetGroup.build_groups_from_files_in_dir(options.samples_dir)
if options.group : groups = [g for g in groups if g.name==options.group]
group_names = [g.name for g in groups]
outputDir = outputDir+'/'+region+'/'+lepton # split the output in subdirectories, so we don't overwrite things
mkdirIfNeeded(outputDir)
templateOutputFilename = "scale_factor_{0}.root".format(lepton)
outputFileName = os.path.join(outputDir, templateOutputFilename)
cacheFileName = outputFileName.replace('.root', '_cache.root')
doFillHistograms = options.fill_histos or not os.path.exists(cacheFileName)
onthefly_tight_def = eval(options.tight_def) if options.tight_def else None # eval will take care of aborting on typos
if verbose : utils.print_running_conditions(parser, options)
vars = ['mt0', 'mt1', 'pt0', 'pt1', 'eta1', 'pt1_eta1']
#fill histos
if doFillHistograms :
start_time = time.clock()
num_processed_entries = 0
histosPerGroup = bookHistos(vars, group_names, region=region)
histosPerSource = bookHistosPerSource(vars, leptonSources, region=region)
histosPerGroupPerSource = bookHistosPerSamplePerSource(vars, group_names, leptonSources, region=region)
for group in groups:
tree_name = 'hlfv_tuple'
chain = IndexedChain(tree_name)
for ds in group.datasets:
fname = os.path.join(inputDir, ds.name+'.root')
if os.path.exists(fname):
chain.Add(fname)
if verbose:
print "{0} : {1} entries from {2} samples".format(group.name, chain.GetEntries(), len(group.datasets))
chain.cache_directory = os.path.abspath('./selection_cache/'+group.name+'/')
tcuts = [r.TCut(reg, selection_formulas()[reg]) for reg in regions]
print 'tcuts ',[c.GetName() for c in tcuts]
chain.retrieve_entrylists(tcuts)
counters_pre, histos_pre = dict(), dict()
counters_npre, histos_npre = dict(), dict()
print 'tcuts_with_existing_list ',str([c.GetName() for c in chain.tcuts_with_existing_list()])
print 'tcuts_without_existing_list ',str([c.GetName() for c in chain.tcuts_without_existing_list()])
cached_tcuts = [] if options.disable_cache else chain.tcuts_with_existing_list()
print 'cached_tcuts ',[c.GetName() for c in cached_tcuts]
uncached_tcuts = tcuts if options.disable_cache else chain.tcuts_without_existing_list()
print 'todo: skip cuts for which the histo files are there'
if verbose:
print " --- group : {0} ---".format(group.name)
print '\n\t'.join(chain.filenames)
if verbose : print 'filling cached cuts: ',' '.join([c.GetName() for c in cached_tcuts])
if verbose: print "%s : %d entries"%(group.name, chain.GetEntries())
histosThisGroup = histosPerGroup[group.name]
histosThisGroupPerSource = dict((v, histosPerGroupPerSource[v][group.name]) for v in histosPerGroupPerSource.keys())
for cut in cached_tcuts:
print 'cached_tcut ',cut
chain.preselect(cut)
num_processed_entries += fillHistos(chain, histosThisGroup, histosPerSource,
histosThisGroupPerSource,
lepton, group,
cut, cut_is_cached=True,
onthefly_tight_def=onthefly_tight_def,
verbose=verbose)
if verbose : print 'filling uncached cuts: ',' '.join([c.GetName() for c in uncached_tcuts])
if uncached_tcuts:
assert len(uncached_tcuts)==1, "expecting only one cut, got {}".format(len(uncached_tcuts))
cut = uncached_tcuts[0]
chain.preselect(None)
num_processed_entries += fillHistos(chain, histosThisGroup, histosPerSource,
histosThisGroupPerSource,
lepton, group,
cut, cut_is_cached=False,
onthefly_tight_def=onthefly_tight_def,
verbose=verbose)
chain.save_lists()
writeHistos(cacheFileName, histosPerGroup, histosPerSource, histosPerGroupPerSource, verbose)
end_time = time.clock()
delta_time = end_time - start_time
if verbose:
print ("processed {0:d} entries ".format(num_processed_entries)
+"in "+("{0:d} min ".format(int(delta_time/60)) if delta_time>60 else
"{0:.1f} s ".format(delta_time))
+"({0:.1f} kHz)".format(num_processed_entries/delta_time))
# return
# compute scale factors
histosPerGroup = fetchHistos(cacheFileName, histoNames(vars, group_names, region), verbose)
histosPerSource = fetchHistos(cacheFileName, histoNamesPerSource(vars, leptonSources, region), verbose)
histosPerSamplePerSource = fetchHistos(cacheFileName, histoNamesPerSamplePerSource(vars, group_names, leptonSources, region), verbose)
plotStackedHistos(histosPerGroup, outputDir+'/by_group', region, verbose)
plotStackedHistosSources(histosPerSource, outputDir+'/by_source', region, verbose)
plotPerSourceEff(histosPerVar=histosPerSource, outputDir=outputDir+'/by_source', lepton=lepton, region=region, verbose=verbose)
for g in group_names:
hps = dict((v, histosPerSamplePerSource[v][g])for v in vars)
plotPerSourceEff(histosPerVar=hps, outputDir=outputDir, lepton=lepton, region=region, sample=g, verbose=verbose)
hn_sf_eta = histoname_sf_vs_eta (lepton)
hn_sf_pt = histoname_sf_vs_pt (lepton)
hn_da_eta = histoname_data_fake_eff_vs_eta(lepton)
hn_da_pt = histoname_data_fake_eff_vs_pt (lepton)
subtractReal = not keepreal
objs_eta = subtractRealAndComputeScaleFactor(histosPerGroup, 'eta1', hn_sf_eta, hn_da_eta, outputDir, region, subtractReal, verbose)
objs_pt = subtractRealAndComputeScaleFactor(histosPerGroup, 'pt1', hn_sf_pt, hn_da_pt, outputDir, region, subtractReal, verbose)
objs_pt_eta = subtractRealAndComputeScaleFactor(histosPerGroup, 'pt1_eta1',
histoname_sf_vs_pt_eta(lepton),
histoname_data_fake_eff_vs_pt_eta(lepton),
outputDir, region, subtractReal, verbose)
rootUtils.writeObjectsToFile(outputFileName, dictSum(dictSum(objs_eta, objs_pt), objs_pt_eta), verbose)
if verbose : print "saved scale factors to %s" % outputFileName
def runFill(opts):
lepton = opts.lepton
batchMode = opts.batch
inputDir = opts.input_dir
outputDir = opts.output_dir
verbose = opts.verbose
debug = opts.debug
dataset.Dataset.verbose_parsing = True if debug else False
groups = dataset.DatasetGroup.build_groups_from_files_in_dir(opts.samples_dir)
if opts.group : groups = [g for g in groups if g.name==opts.group]
if verbose : print '\n'.join("group {0} : {1} samples".format(g.name, len(g.datasets)) for g in groups)
if debug :
print '\n'.join("group {0} : {1} samples: {2}".format(g.name,
len(g.datasets),
'\n\t'+'\n\t'.join(d.name for d in g.datasets))
for g in groups)
if verbose : print "filling histos"
outputDir = outputDir+'/'+lepton+'/histos'
mkdirIfNeeded(outputDir)
if batchMode:
for group in groups:
submit_batch_fill_job_per_group(group, opts)
else:
for group in groups:
tree_name = 'hlfv_tuple'
chain = IndexedChain(tree_name)
for ds in group.datasets:
chain.Add(os.path.join(inputDir, ds.name+'.root'))
if opts.verbose:
print "{0} : {1} entries from {2} samples".format(group.name,
chain.GetEntries(),
len(group.datasets))
chain.cache_directory = os.path.abspath('./selection_cache/'+group.name+'/')
tcuts = [r.TCut(reg, selection_formulas()[reg])
for reg in regions_to_plot(opts.include_regions, opts.exclude_regions, opts.regions)]
chain.retrieve_entrylists(tcuts)
counters_pre, histos_pre = dict(), dict()
counters_npre, histos_npre = dict(), dict()
cached_tcuts = [] if opts.disable_cache else chain.tcuts_with_existing_list()
uncached_tcuts = tcuts if opts.disable_cache else chain.tcuts_without_existing_list()
print 'todo: skip cuts for which the histo files are there'
if verbose : print 'filling cached cuts: ',' '.join([c.GetName() for c in cached_tcuts])
for cut in cached_tcuts:
chain.preselect(cut)
c_pre, h_pre = count_and_fill(chain=chain, opts=opts,
group=group,
cached_cut=cut)
counters_pre = dictSum(counters_pre, c_pre)
histos_pre = dictSum(histos_pre, h_pre)
if verbose : print 'filling uncached cuts: ',' '.join([c.GetName() for c in uncached_tcuts])
if uncached_tcuts:
counters_npre, histos_npre = count_and_fill(chain=chain, opts=opts,
group=group,
noncached_cuts=uncached_tcuts)
chain.save_lists()
all_histos = dictSum(histos_pre, histos_npre)
for sel, histos in all_histos.iteritems():
# write histos for each sel to a separate file (finer granularity, better caching)
out_filename = os.path.join(outputDir, group.name+'_'+sel+'.root')
if verbose : print 'saving to ',out_filename
writeObjectsToFile(out_filename, histos, verbose)
def main():
parser = optparse.OptionParser(usage=usage)
parser.add_option('-g',
'--group',
help='group to be processed (used only in fill mode)')
parser.add_option('-i', '--input-dir', default='./out/fakerate')
parser.add_option('-o', '--output-dir', default='./out/fake_scale_factor')
parser.add_option('-l', '--lepton', default='el', help='either el or mu')
parser.add_option(
'-r',
'--region',
help='one of the regions for which we saved the fake ntuples')
parser.add_option(
'--samples-dir',
default='samples/',
help='directory with the list of samples; default ./samples/')
parser.add_option(
'-T',
'--tight-def',
help=
'on-the-fly tight def, one of defs in fakeUtils.py: fakeu.lepIsTight_std, etc.'
)
parser.add_option('-f',
'--fill-histos',
action='store_true',
default=False,
help='force fill (default only if needed)')
parser.add_option('--keep-real',
action='store_true',
default=False,
help='do not subtract real (to get real lep efficiency)')
parser.add_option('--debug', action='store_true')
parser.add_option('--verbose', action='store_true')
parser.add_option('--disable-cache',
action='store_true',
help='disable the entry cache')
(options, args) = parser.parse_args()
inputDir = options.input_dir
outputDir = options.output_dir
lepton = options.lepton
region = options.region
keepreal = options.keep_real
debug = options.debug
verbose = options.verbose
if lepton not in ['el', 'mu']: parser.error("invalid lepton '%s'" % lepton)
regions = kin.selection_formulas().keys()
assert region in regions, "invalid region '%s', must be one of %s" % (
region, str(sorted(regions)))
regions = [region]
dataset.Dataset.verbose_parsing = True if debug else False
groups = dataset.DatasetGroup.build_groups_from_files_in_dir(
options.samples_dir)
if options.group: groups = [g for g in groups if g.name == options.group]
group_names = [g.name for g in groups]
outputDir = outputDir + '/' + region + '/' + lepton # split the output in subdirectories, so we don't overwrite things
mkdirIfNeeded(outputDir)
templateOutputFilename = "scale_factor_{0}.root".format(lepton)
outputFileName = os.path.join(outputDir, templateOutputFilename)
cacheFileName = outputFileName.replace('.root', '_cache.root')
doFillHistograms = options.fill_histos or not os.path.exists(cacheFileName)
onthefly_tight_def = eval(
options.tight_def
) if options.tight_def else None # eval will take care of aborting on typos
if verbose: utils.print_running_conditions(parser, options)
vars = ['mt0', 'mt1', 'pt0', 'pt1', 'eta1', 'pt1_eta1']
#fill histos
if doFillHistograms:
start_time = time.clock()
num_processed_entries = 0
histosPerGroup = bookHistos(vars, group_names, region=region)
histosPerSource = bookHistosPerSource(vars,
leptonSources,
region=region)
histosPerGroupPerSource = bookHistosPerSamplePerSource(vars,
group_names,
leptonSources,
region=region)
for group in groups:
tree_name = 'hlfv_tuple'
chain = IndexedChain(tree_name)
for ds in group.datasets:
fname = os.path.join(inputDir, ds.name + '.root')
if os.path.exists(fname):
chain.Add(fname)
if verbose:
print "{0} : {1} entries from {2} samples".format(
group.name, chain.GetEntries(), len(group.datasets))
chain.cache_directory = os.path.abspath('./selection_cache/' +
group.name + '/')
tcuts = [r.TCut(reg, selection_formulas()[reg]) for reg in regions]
print 'tcuts ', [c.GetName() for c in tcuts]
chain.retrieve_entrylists(tcuts)
counters_pre, histos_pre = dict(), dict()
counters_npre, histos_npre = dict(), dict()
print 'tcuts_with_existing_list ', str(
[c.GetName() for c in chain.tcuts_with_existing_list()])
print 'tcuts_without_existing_list ', str(
[c.GetName() for c in chain.tcuts_without_existing_list()])
cached_tcuts = [] if options.disable_cache else chain.tcuts_with_existing_list(
)
print 'cached_tcuts ', [c.GetName() for c in cached_tcuts]
uncached_tcuts = tcuts if options.disable_cache else chain.tcuts_without_existing_list(
)
print 'todo: skip cuts for which the histo files are there'
if verbose:
print " --- group : {0} ---".format(group.name)
print '\n\t'.join(chain.filenames)
if verbose:
print 'filling cached cuts: ', ' '.join(
[c.GetName() for c in cached_tcuts])
if verbose:
print "%s : %d entries" % (group.name, chain.GetEntries())
histosThisGroup = histosPerGroup[group.name]
histosThisGroupPerSource = dict(
(v, histosPerGroupPerSource[v][group.name])
for v in histosPerGroupPerSource.keys())
for cut in cached_tcuts:
print 'cached_tcut ', cut
chain.preselect(cut)
num_processed_entries += fillHistos(
chain,
histosThisGroup,
histosPerSource,
histosThisGroupPerSource,
lepton,
group,
cut,
cut_is_cached=True,
onthefly_tight_def=onthefly_tight_def,
verbose=verbose)
if verbose:
print 'filling uncached cuts: ', ' '.join(
[c.GetName() for c in uncached_tcuts])
if uncached_tcuts:
assert len(uncached_tcuts
) == 1, "expecting only one cut, got {}".format(
len(uncached_tcuts))
cut = uncached_tcuts[0]
chain.preselect(None)
num_processed_entries += fillHistos(
chain,
histosThisGroup,
histosPerSource,
histosThisGroupPerSource,
lepton,
group,
cut,
cut_is_cached=False,
onthefly_tight_def=onthefly_tight_def,
verbose=verbose)
chain.save_lists()
writeHistos(cacheFileName, histosPerGroup, histosPerSource,
histosPerGroupPerSource, verbose)
end_time = time.clock()
delta_time = end_time - start_time
if verbose:
print("processed {0:d} entries ".format(num_processed_entries) +
"in " +
("{0:d} min ".format(int(delta_time / 60))
if delta_time > 60 else "{0:.1f} s ".format(delta_time)) +
"({0:.1f} kHz)".format(num_processed_entries / delta_time))
# return
# compute scale factors
histosPerGroup = fetchHistos(cacheFileName,
histoNames(vars, group_names, region),
verbose)
histosPerSource = fetchHistos(
cacheFileName, histoNamesPerSource(vars, leptonSources, region),
verbose)
histosPerSamplePerSource = fetchHistos(
cacheFileName,
histoNamesPerSamplePerSource(vars, group_names, leptonSources, region),
verbose)
plotStackedHistos(histosPerGroup, outputDir + '/by_group', region, verbose)
plotStackedHistosSources(histosPerSource, outputDir + '/by_source', region,
verbose)
plotPerSourceEff(histosPerVar=histosPerSource,
outputDir=outputDir + '/by_source',
lepton=lepton,
region=region,
verbose=verbose)
for g in group_names:
hps = dict((v, histosPerSamplePerSource[v][g]) for v in vars)
plotPerSourceEff(histosPerVar=hps,
outputDir=outputDir,
lepton=lepton,
region=region,
sample=g,
verbose=verbose)
hn_sf_eta = histoname_sf_vs_eta(lepton)
hn_sf_pt = histoname_sf_vs_pt(lepton)
hn_da_eta = histoname_data_fake_eff_vs_eta(lepton)
hn_da_pt = histoname_data_fake_eff_vs_pt(lepton)
subtractReal = not keepreal
objs_eta = subtractRealAndComputeScaleFactor(histosPerGroup, 'eta1',
hn_sf_eta, hn_da_eta,
outputDir, region,
subtractReal, verbose)
objs_pt = subtractRealAndComputeScaleFactor(histosPerGroup, 'pt1',
hn_sf_pt, hn_da_pt, outputDir,
region, subtractReal, verbose)
objs_pt_eta = subtractRealAndComputeScaleFactor(
histosPerGroup, 'pt1_eta1', histoname_sf_vs_pt_eta(lepton),
histoname_data_fake_eff_vs_pt_eta(lepton), outputDir, region,
subtractReal, verbose)
rootUtils.writeObjectsToFile(
outputFileName, dictSum(dictSum(objs_eta, objs_pt), objs_pt_eta),
verbose)
if verbose: print "saved scale factors to %s" % outputFileName
