fit_srh_dir = '%s/bkgonly_srh' % fits_dir
cmd_srl = 'run_bkgonly.py -i %s -o %s --sr SRiL -c %s --val --data data --log %s/bkgonly_fit_srl.log' % (histograms_srl_path, fit_srl_dir, configfile, log_dir)
cmd_srh = 'run_bkgonly.py -i %s -o %s --sr SRiH -c %s --val --data data --log %s/bkgonly_fit_srh.log' % (histograms_srh_path, fit_srh_dir, configfile, log_dir)
if do_syst:
cmd_srl += ' --syst'
cmd_srh += ' --syst'
run_cmd(cmd_srl)
run_cmd(cmd_srh)
ws_srl = fit_srl_dir + "/BkgOnlyFit_combined_BasicMeasurement_model_afterFit.root"
ws_srh = fit_srh_dir + "/BkgOnlyFit_combined_BasicMeasurement_model_afterFit.root"
mus_srl = get_normalization_factors(ws_srl)
mus_srh = get_normalization_factors(ws_srh)
#-------
# Tables
#-------
backgrounds_str = 'photonjet,wgamma,[zllgamma,znunugamma],ttbarg,efake,jfake,[diphoton,vgammagamma]'
# Yields tables
## CR
yieldstable(ws_srl, backgrounds_str, 'CRQ,CRW,CRT', tables_dir+'/table_cr_srl.tex', 'CR for SRL', is_cr=True)
yieldstable(ws_srh, backgrounds_str, 'CRQ,CRW,CRT', tables_dir+'/table_cr_srh.tex', 'CR for SRH', is_cr=True)
## VR
def prepare_histograms_for_plots(input_path,
output_path,
regions,
backgrounds,
variables,
ws=None,
merge_dict={},
norm_dict={}):
syst = 'Nom' # only nominal for now
file_ = ROOT.TFile.Open(input_path)
h_data = []
h_bkg = {name: [] for name in backgrounds}
for region in regions:
for variable in variables:
## data
h_data.append(
get_histogram_from_file(file_, 'data', variable, region, ''))
## backgrounds
for name in backgrounds:
h_bkg[name].append(
get_histogram_from_file(file_, name, variable, region,
syst))
# Scale background with scale factors, if workspace not None
if ws is not None and norm_dict:
mus = get_normalization_factors(ws)
for cr, bkg in norm_dict.items():
if cr in mus:
mu = mus[cr]
for hist in h_bkg[bkg]:
hist.Scale(mu[0])
# Merge backgrounds to plot
if merge_dict:
for merge_name, merge_list in merge_dict.items():
h_bkg[merge_name] = [
hist.Clone(hist.GetName().replace(merge_list[0], merge_name))
for hist in h_bkg[merge_list[0]]
]
for name in merge_list[1:]:
for h1, h2 in zip(h_bkg[merge_name], h_bkg[name]):
h1.Add(h2, 1)
for name in merge_list:
del h_bkg[name]
fin = ROOT.TFile(output_path, 'recreate')
for hist in h_data:
hist.Write(hist.GetName())
for hlist in h_bkg.itervalues():
for hist in hlist:
hist.Write(hist.GetName())
fin.Close()
def main():
parser = argparse.ArgumentParser(description='')
# histograms file / output directory
parser.add_argument('-i', dest='input_file')
parser.add_argument('-o', dest='output', default='.')
parser.add_argument('--save', help='Save histograms in this rootfile')
# samples, regions, variables
parser.add_argument('-v', '--variable', dest='variables', required=True)
parser.add_argument('-r',
'--region',
dest='regions',
help='regions separated by ,')
# Backgrounds
parser.add_argument('--mc',
action='store_true',
help='use all backgrounds from MC')
# normalization
parser.add_argument('--muq', help='mu value for photon+jet')
parser.add_argument(
'--ws', help='Bkg-only fit workspace to extract normalization factors')
# other
parser.add_argument('-l', dest='lumi')
parser.add_argument('--data', help='data15|data16|data')
parser.add_argument('--opt', action='store_true', help='Optimization plot')
parser.add_argument('--sel',
dest='selection',
default='',
help='Custom selection')
parser.add_argument('--outname',
help='If custom selection use this output_name')
parser.add_argument('--n1', action='store_true', help='N-1 plot')
parser.add_argument('--signal',
action='store_true',
help='Add signal samples (separated with ,)')
parser.add_argument('--blind', action='store_true')
parser.add_argument('--pl', action='store_true', help='publink')
parser.add_argument('--www', action='store_true', help='create webpage')
parser.add_argument('--ext', dest='extensions', default='pdf', help='')
parser.add_argument('--ratio',
default='default',
help='ratio type: none,default')
parser.add_argument('--sepfakes',
action='store_true',
help='separete efakes/jfakes')
global args
args = parser.parse_args()
do_scale = True
if args.lumi == '0':
do_scale = False
get_histogram = partial(miniutils.get_histogram,
remove_var=args.n1,
lumi=args.lumi,
scale=do_scale)
# regions
if args.regions is not None:
regions = args.regions.split(',')
else:
regions = [
'',
]
# variables
variables = args.variables.split(',')
# systematics
syst = 'Nom' # only nominal for now
## plots style
set_atlas_style()
# Backgrounds
if args.mc:
backgrounds = [
'photonjet',
'multijet',
'zgamma',
'wgamma',
'wjets',
'zjets',
'ttbar',
'ttbarg',
]
else:
backgrounds = [
'photonjet',
'zgamma',
'wgamma',
'ttbarg',
'jfake',
'efake',
'diphoton',
# 'vgammagamma',
]
# Standard DATA/Backgrounds plot
for region in regions:
for variable in variables:
print 'plotting %s in region %s ...' % (variable, region)
if args.input_file:
selection = region
else:
if not args.selection:
selection = getattr(regions_, region)
else:
selection = args.selection
if args.selection:
region_name = region
else:
region_name = region #[:-2]
## backgrounds
h_bkg = OrderedDict()
for name in backgrounds:
h_bkg[name] = get_histogram(name,
variable=variable,
region=region_name,
selection=selection,
syst=syst)
# If fit workspace given -> scale backgrounds according to normalization factos
if args.ws is not None and os.path.isfile(args.ws):
mus = get_normalization_factors(args.ws)
if 'CRQ' in mus:
mu = mus['CRQ']
#histogram_scale(h_bkg['photonjet'], *mu)
h_bkg['photonjet'].Scale(mu[0])
if 'CRW' in mus:
mu = mus['CRW']
#histogram_scale(h_bkg['wgamma'], *mu)
h_bkg['wgamma'].Scale(mu[0])
if 'CRT' in mus:
mu = mus['CRT']
#histogram_scale(h_bkg['ttbarg'], *mu)
h_bkg['ttbarg'].Scale(mu[0])
else:
if args.muq is not None:
#histogram_scale(h_bkg['photonjet'], float(args.muq))
h_bkg['photonjet'].Scale(args.muq)
# Merge backgrounds to plot
## V + jets
if args.mc:
h_bkg['vjets'] = h_bkg['wjets'].Clone()
h_bkg['vjets'].Add(h_bkg['zjets'], 1)
del h_bkg['wjets']
del h_bkg['zjets']
## V + gamma
# h_bkg['vgamma'] = h_bkg['wgamma'].Clone()
# h_bkg['vgamma'].Add(h_bkg['zgamma'], 1)
# del h_bkg['wgamma']
# del h_bkg['zgamma']
# if 'vqqgamma' in h_bkg:
# h_bkg['vgamma'].Add(h_bkg['vqqgamma'], 1)
# del h_bkg['vqqgamma']
# h_bkg['vgamma'].SetName(h_bkg['vgamma'].GetName().replace('wgamma', 'vgamma'))
## tt + gamma
if args.mc:
h_bkg['tgamma'] = h_bkg['ttbarg'].Clone()
h_bkg['tgamma'].Add(h_bkg['ttbar'], 1)
del h_bkg['ttbar']
else:
h_bkg['tgamma'] = h_bkg['ttbarg'].Clone()
del h_bkg['ttbarg']
h_bkg['tgamma'].SetName(h_bkg['tgamma'].GetName().replace(
'ttbarg', 'tgamma'))
## diphoton
# if 'diphoton' in h_bkg:
# h_bkg['diphoton'].Add(h_bkg['vgammagamma'], 1)
# del h_bkg['vgammagamma']
## fakes
if not args.sepfakes:
h_bkg['fakes'] = h_bkg['efake'].Clone()
h_bkg['fakes'].Add(h_bkg['jfake'], 1)
h_bkg['fakes'].SetName(h_bkg['efake'].GetName().replace(
'efake', 'fakes'))
del h_bkg['efake']
del h_bkg['jfake']
## data
h_data = None
if args.data:
h_data = get_histogram(args.data,
variable=variable,
region=region_name,
selection=selection,
syst=syst,
revert_cut=args.blind)
## signal
h_signal = None
if args.signal:
h_signal = OrderedDict()
if 'SRL' in region:
signal1 = 'GGM_GG_bhmix_1900_450'
signal2 = 'GGM_GG_bhmix_1900_650'
elif 'SRH' in region:
signal1 = 'GGM_GG_bhmix_1900_1810'
signal2 = 'GGM_GG_bhmix_1900_1860'
else:
signal1 = 'GGM_GG_bhmix_1900_650'
signal2 = 'GGM_GG_bhmix_1900_1650'
h_signal[signal1] = get_histogram(signal1,
variable=variable,
region=region_name,
selection=selection,
syst=syst)
h_signal[signal2] = get_histogram(signal2,
variable=variable,
region=region_name,
selection=selection,
syst=syst)
varname = variable.replace('[',
'').replace(']',
'').replace('/', '_over_')
if args.selection and args.outname:
tag = args.outname
else:
tag = region
if args.ws is not None:
outname = os.path.join(
args.output, 'can_{}_{}_afterFit'.format(tag, varname))
else:
outname = os.path.join(
args.output, 'can_{}_{}_beforeFit'.format(tag, varname))
do_plot(outname,
variable,
data=h_data,
bkg=h_bkg,
signal=h_signal,
region_name=region,
extensions=args.extensions.split(','),
do_ratio=(args.ratio != 'none'))
if args.pl:
os.system('pl %s.pdf' % outname)
# save
if args.save is not None:
file_name = args.save
with RootFile(file_name, 'update') as f:
f.write(h_data)
for hist in h_bkg.itervalues():
f.write(hist)
if h_signal is not None:
for hist in h_signal.itervalues():
f.write(hist)
def yieldstable(workspace, samples, channels, output_name, table_name, is_cr=False, show_before_fit=False, unblind=True):
if is_cr:
show_before_fit=True
normalization_factors = get_normalization_factors(workspace)
#sample_str = samples.replace(",","_")
from cmdLineUtils import cmdStringToListOfLists
samples_list = cmdStringToListOfLists(samples)
regions_list = [ '%s_cuts' % r for r in channels.split(",") ]
#samples_list = samples.split(",")
# call the function to calculate the numbers, or take numbers from pickle file
if workspace.endswith(".pickle"):
print "READING PICKLE FILE"
f = open(workspace, 'r')
m = pickle.load(f)
f.close()
else:
#m = YieldsTable.latexfitresults(workspace, regions_list, samples_list, 'obsData')
m = latexfitresults(workspace, regions_list, samples_list)
with open(output_name.replace('.tex', '.pickle'), 'w') as f:
pickle.dump(m, f)
regions_names = [ region.replace("_cuts", "").replace('_','\_') for region in m['names'] ]
field_names = [table_name,] + regions_names
align = ['l',] + [ 'r' for i in regions_names ]
samples_list_decoded = []
for isam, sample in enumerate(samples_list):
sampleName = getName(sample)
samples_list_decoded.append(sampleName)
samples_list = samples_list_decoded
tablel = LatexTable(field_names, align=align, env=True)
tablep = PrettyTable(field_names, align=align)
# number of observed events
if unblind:
row = ['Observed events',] + [ '%d' % n for n in m['nobs'] ]
else:
row = ['Observed events',] + [ '-' for n in m['nobs'] ]
tablel.add_row(row)
tablep.add_row(row)
tablel.add_line()
tablep.add_line()
#print the total fitted (after fit) number of events
# if the N_fit - N_error extends below 0, make the error physical , meaning extend to 0
rowl = ['Expected SM events', ]
rowp = ['Expected SM events', ]
for index, n in enumerate(m['TOTAL_FITTED_bkg_events']):
if (n - m['TOTAL_FITTED_bkg_events_err'][index]) > 0. :
rowl.append('$%.2f \pm %.2f$' % (n, m['TOTAL_FITTED_bkg_events_err'][index]))
rowp.append('%.2f ± %.2f' % (n, m['TOTAL_FITTED_bkg_events_err'][index]))
else:
#print "WARNING: negative symmetric error after fit extends below 0. for total bkg pdf: will print asymmetric error w/ truncated negative error reaching to 0."
rowl.append('$%.2f_{-%.2f}^{+%.2f}$' % (n, n, m['TOTAL_FITTED_bkg_events_err'][index]))
rowp.append('%.2f -%.2f +%.2f' % (n, n, m['TOTAL_FITTED_bkg_events_err'][index]))
tablel.add_row(rowl)
tablel.add_line()
tablep.add_row(rowp)
tablep.add_line()
map_listofkeys = m.keys()
# print fitted number of events per sample
# if the N_fit - N_error extends below 0, make the error physical , meaning extend to 0
for sample in samples_list:
for name in map_listofkeys:
rowl = []
rowp = []
if not "Fitted_events_" in name:
continue
sample_name = name.replace("Fitted_events_", "")
if sample_name != sample:
continue
rowl.append('%s' % labels_latex_dict.get(sample_name, sample_name).replace('_', '\_'))
rowp.append('%s' % labels_html_dict.get(sample_name, sample_name))
for index, n in enumerate(m[name]):
if ((n - m['Fitted_err_'+sample][index]) > 0.) or not abs(n) > 0.00001:
rowl.append('$%.2f \\pm %.2f$' % (n, m['Fitted_err_'+sample][index]))
rowp.append('%.2f ± %.2f' % (n, m['Fitted_err_'+sample][index]))
else:
#print "WARNING: negative symmetric error after fit extends below 0. for sample", sample, " will print asymmetric error w/ truncated negative error reaching to 0."
rowl.append('$%.2f_{-%.2f}^{+%.2f}$' % (n, n, m['Fitted_err_'+sample][index]))
rowp.append('%.2f -%.2f +%.2f' % (n, n, m['Fitted_err_'+sample][index]))
tablel.add_row(rowl)
tablep.add_row(rowp)
tablel.add_line()
tablep.add_line()
# print the total expected (before fit) number of events
if show_before_fit:
# if the N_fit - N_error extends below 0, make the error physical , meaning extend to 0
rowl = ['Before SM events',]
rowp = ['(before fit) SM events',]
total_before = []
purity_before = []
for index, n in enumerate(m['TOTAL_MC_EXP_BKG_events']):
if regions_names[index].startswith('CR'):
total_before.append(n)
rowl.append('$%.2f$' % n)
rowp.append('%.2f' % n)
tablel.add_row(rowl)
tablel.add_line()
tablep.add_row(rowp)
tablep.add_line()
map_listofkeys = m.keys()
# print expected number of events per sample
# if the N_fit - N_error extends below 0, make the error physical , meaning extend to 0
for sample in samples_list:
for name in map_listofkeys:
rowl = []
rowp = []
if "MC_exp_events_" in name and sample in name:
sample_name = name.replace("MC_exp_events_","")
if sample_name != sample:
continue
rowl.append('(before fit) %s' % labels_latex_dict.get(sample_name, sample_name).replace('_', '\_'))
rowp.append('(before fit) %s' % labels_html_dict.get(sample_name, sample_name))
for index, n in enumerate(m[name]):
if regions_names[index] == 'CRQ' and sample == 'photonjet':
purity_before.append(n)
if regions_names[index] == 'CRW' and sample == 'wgamma':
purity_before.append(n)
if regions_names[index] == 'CRT' and sample == 'ttbarg':
purity_before.append(n)
rowl.append('$%.2f$' % n)
rowp.append('%.2f' % n)
tablel.add_row(rowl)
tablep.add_row(rowp)
tablel.add_line()
tablep.add_line()
if show_before_fit and all([r.startswith('CR') for r in regions_names]) and normalization_factors is not None:
tablel.add_row(['', '', '', ''])
tablel.add_line()
tablep.add_row(['', '', '', ''])
tablep.add_line()
# purity
rowl = ['Background purity',]
rowp = ['Background purity',]
for index, region in enumerate(regions_names):
purity = int(purity_before[index]/total_before[index] * 100.)
rowl.append('$%i\%%$' % purity)
rowp.append('%i%%' % purity)
tablel.add_row(rowl)
tablel.add_line()
tablep.add_row(rowp)
tablep.add_line()
# normalization
rowl = ['Normalization factor ($\mu$)',]
rowp = ['Normalization factor (mu)',]
for region in regions_names:
rowl.append('$%.2f \pm %.2f$' % normalization_factors[region])
rowp.append('%.2f ± %.2f' % normalization_factors[region])
tablel.add_row(rowl)
tablel.add_line()
tablep.add_row(rowp)
tablep.add_line()
tablel.save_tex(output_name)
with open(output_name.replace('.tex', '.html'), 'w+') as f:
f.write(tablep.get_html_string())
def yieldstable(workspace,
samples,
channels,
output_name,
table_name='',
show_before_fit=False,
unblind=True,
show_cr_info=False,
cr_dict={}):
if show_cr_info:
show_before_fit = True
normalization_factors = get_normalization_factors(workspace)
samples_list = cmdStringToListOfLists(samples)
regions_list = ['%s_cuts' % r for r in channels.split(",")]
# call the function to calculate the numbers, or take numbers from pickle file
if workspace.endswith(".pickle"):
print "Reading from pickle file"
f = open(workspace, 'r')
m = pickle.load(f)
f.close()
else:
#m = YieldsTable.latexfitresults(workspace, regions_list, samples_list, 'obsData')
m = latexfitresults(workspace, regions_list, samples_list)
with open(output_name.replace('.tex', '.pickle'), 'w') as f:
pickle.dump(m, f)
regions_names = [
region.replace("_cuts", "").replace('_', '\_') for region in m['names']
]
field_names = [
table_name,
] + regions_names
align = [
'l',
] + ['r' for i in regions_names]
samples_list_decoded = []
for isam, sample in enumerate(samples_list):
sampleName = getName(sample)
samples_list_decoded.append(sampleName)
samples_list = samples_list_decoded
tablel = LatexTable(field_names, align=align, env=True)
# number of observed events
if unblind:
row = [
'Observed events',
] + ['%d' % n for n in m['nobs']]
else:
row = [
'Observed events',
] + ['-' for n in m['nobs']]
tablel.add_row(row)
tablel.add_line()
# Total fitted (after fit) number of events
# if the N_fit - N_error extends below 0, make the error physical, meaning extend to 0
rowl = [
'Expected SM events',
]
for index, n in enumerate(m['TOTAL_FITTED_bkg_events']):
if (n - m['TOTAL_FITTED_bkg_events_err'][index]) > 0.:
rowl.append('$%.2f \pm %.2f$' %
(n, m['TOTAL_FITTED_bkg_events_err'][index]))
else:
rowl.append('$%.2f_{-%.2f}^{+%.2f}$' %
(n, n, m['TOTAL_FITTED_bkg_events_err'][index]))
tablel.add_row(rowl)
tablel.add_line()
map_listofkeys = m.keys()
# After fit number of events per sample (if the N_fit-N_error extends below 0, make the error physical, meaning extend to 0)
for sample in samples_list:
for name in map_listofkeys:
rowl = []
if not "Fitted_events_" in name:
continue
sample_name = name.replace("Fitted_events_", "")
if sample_name != sample:
continue
rowl.append('%s' % labels_latex_dict.get(
sample_name, sample_name).replace('_', '\_'))
for index, n in enumerate(m[name]):
if ((n - m['Fitted_err_' + sample][index]) >
0.) or not abs(n) > 0.00001:
rowl.append('$%.2f \\pm %.2f$' %
(n, m['Fitted_err_' + sample][index]))
else:
rowl.append('$%.2f_{-%.2f}^{+%.2f}$' %
(n, n, m['Fitted_err_' + sample][index]))
tablel.add_row(rowl)
tablel.add_line()
# Total expected (before fit) number of events
if show_before_fit:
# if the N_fit - N_error extends below 0, make the error physical, meaning extend to 0
rowl = [
'Before fit SM events',
]
total_before = {}
purity_before = {}
for index, n in enumerate(m['TOTAL_MC_EXP_BKG_events']):
reg_name = regions_names[index]
if cr_dict and reg_name in cr_dict:
total_before[reg_name] = n
rowl.append('$%.2f$' % n)
tablel.add_row(rowl)
tablel.add_line()
map_listofkeys = m.keys()
# Expected number of events per sample (if the N_fit - N_error extends below 0, make the error physical, meaning extend to 0)
for sample in samples_list:
for name in map_listofkeys:
rowl = []
if "MC_exp_events_" in name and sample in name:
sample_name = name.replace("MC_exp_events_", "")
if sample_name != sample:
continue
rowl.append('Before fit %s' % labels_latex_dict.get(
sample_name, sample_name).replace('_', '\_'))
for index, n in enumerate(m[name]):
reg_name = regions_names[index]
if cr_dict and reg_name in cr_dict and sample == cr_dict[
reg_name]:
purity_before[reg_name] = n
rowl.append('$%.2f$' % n)
tablel.add_row(rowl)
tablel.add_line()
if show_cr_info and normalization_factors is not None:
tablel.add_row(['' for i in range(len(regions_names) + 1)])
tablel.add_line()
# purity
rowl = [
'Background purity',
]
for region in regions_names:
try:
purity = int(
round(purity_before[region] / total_before[region] * 100.))
rowl.append('$%i\%%$' % purity)
except:
rowl.append('-')
tablel.add_row(rowl)
tablel.add_line()
# normalization
rowl = [
'Normalization factor ($\mu$)',
]
for region in regions_names:
try:
rowl.append('$%.2f \pm %.2f$' % normalization_factors[region])
except:
rowl.append('-')
tablel.add_row(rowl)
tablel.add_line()
tablel.save_tex(output_name)
def main():
parser = argparse.ArgumentParser(description='')
# histograms file / output directory
parser.add_argument('-i', dest='input_file')
parser.add_argument('-o', dest='output', default='.')
parser.add_argument('--save', help='Save histograms in this rootfile')
# samples, regions, variables
parser.add_argument('-v', '--variable', dest='variables', required=True)
parser.add_argument('-r', '--region', dest='regions', help='regions separated by ,')
# Backgrounds
parser.add_argument('--mc', action='store_true', help='use all backgrounds from MC')
# normalization
parser.add_argument('--ws', help='Bkg-only fit workspace to extract normalization factors')
# other
parser.add_argument('-l', dest='lumi')
parser.add_argument('--data', help='data15|data16|data')
parser.add_argument('--opt', action='store_true', help='Optimization plot')
parser.add_argument('--sel', dest='selection', default='', help='Custom selection')
parser.add_argument('--outname', help='If custom selection use this output_name')
parser.add_argument('--n1', action='store_true', help='N-1 plot')
parser.add_argument('--signal', action='store_true', help='Add signal samples (separated with ,)')
parser.add_argument('--blind', action='store_true')
parser.add_argument('--pl', action='store_true', help='publink')
parser.add_argument('--www', action='store_true', help='create webpage')
parser.add_argument('--ext', dest='extensions', default='pdf', help='')
parser.add_argument('--ratio', default='default', help='ratio type: none,default')
global args
args = parser.parse_args()
do_scale = True
if args.lumi == '0':
do_scale = False
get_histogram = partial(miniutils.get_histogram, remove_var=args.n1, lumi=args.lumi, scale=do_scale)
# regions
if args.regions is not None:
regions = args.regions.split(',')
else:
regions = ['',]
# variables
variables = args.variables.split(',')
# systematics
syst = 'Nom' # only nominal for now
## plots style
set_atlas_style()
# Backgrounds
if args.mc:
backgrounds = [
'photonjet',
'multijet',
'zgamma',
'wgamma',
'wjets',
'zjets',
'ttbar',
'ttbarg',
]
else:
backgrounds = [
'photonjet',
'zgamma',
'wgamma',
'ttbarg',
'jfake',
'efake',
'diphoton',
'vgammagamma',
]
# Plot from histograms file
if args.input_file:
ifile = ROOT.TFile.Open(args.input_file)
for region in regions:
region_name = region #.split('_')[0]
for variable in variables:
print 'plotting %s in region %s ...' % (variable, region)
## backgrounds
h_bkg = OrderedDict()
backgrounds = ['photonjet', 'vgamma', 'tgamma', 'diphoton', 'efake', 'jfake']
for name in backgrounds:
h_bkg[name] = get_histogram_from_file(ifile, name, variable, region_name, syst=syst)
## data
h_data = get_histogram_from_file(ifile, 'data', variable, region_name, syst=syst)
## signal
h_signal = OrderedDict()
if region.endswith('_L'):
h_signal['GGM_M3_mu_1600_250'] = get_histogram_from_file(ifile, 'GGM_M3_mu_1600_250', variable, region_name, syst=syst)
h_signal['GGM_M3_mu_1600_650'] = get_histogram_from_file(ifile, 'GGM_M3_mu_1600_650', variable, region_name, syst=syst)
elif region.endswith('_H'):
h_signal['GGM_M3_mu_1600_1250'] = get_histogram_from_file(ifile, 'GGM_M3_mu_1600_1250', variable, region_name, syst)
h_signal['GGM_M3_mu_1600_1450'] = get_histogram_from_file(ifile, 'GGM_M3_mu_1600_1450', variable, region_name, syst)
variable = variable.replace('/', '_over_')
outname = os.path.join(args.output, 'can_{}_{}_afterFit'.format(region, variable))
do_plot(outname, variable, data=h_data, bkg=h_bkg, signal=h_signal, region_name=region)
ifile.Close()
sys.exit(0)
# Standard DATA/Backgrounds plot
for region in regions:
for variable in variables:
print 'plotting %s in region %s ...' % (variable, region)
if args.input_file:
selection = region
else:
if not args.selection:
selection = getattr(regions_, region)
else:
selection = args.selection
if args.selection:
region_name = region
else:
region_name = region #[:-2]
## backgrounds
h_bkg = OrderedDict()
for name in backgrounds:
h_bkg[name] = get_histogram(name, variable=variable, region=region_name, selection=selection, syst=syst)
# If fit workspace given -> scale backgrounds according to normalization factos
if args.ws is not None and os.path.isfile(args.ws):
mus = get_normalization_factors(args.ws)
if 'CRQ' in mus:
mu = mus['CRQ']
histogram_scale(h_bkg['photonjet'], *mu)
if 'CRW' in mus:
mu = mus['CRW']
histogram_scale(h_bkg['wgamma'], *mu)
if 'vqqgamma' in h_bkg:
histogram_scale(h_bkg['vqqgamma'], *mu)
if 'CRT' in mus:
mu = mus['CRT']
histogram_scale(h_bkg['ttbarg'], *mu)
# Merge backgrounds to plot
## V + jets
if args.mc:
h_bkg['vjets'] = h_bkg['wjets'].Clone()
h_bkg['vjets'].Add(h_bkg['zjets'], 1)
del h_bkg['wjets']
del h_bkg['zjets']
## V + gamma
# h_bkg['vgamma'] = h_bkg['wgamma'].Clone()
# h_bkg['vgamma'].Add(h_bkg['zgamma'], 1)
# del h_bkg['wgamma']
# del h_bkg['zgamma']
# if 'vqqgamma' in h_bkg:
# h_bkg['vgamma'].Add(h_bkg['vqqgamma'], 1)
# del h_bkg['vqqgamma']
# h_bkg['vgamma'].SetName(h_bkg['vgamma'].GetName().replace('wgamma', 'vgamma'))
## tt + gamma
if args.mc:
h_bkg['tgamma'] = h_bkg['ttbarg'].Clone()
h_bkg['tgamma'].Add(h_bkg['ttbar'], 1)
del h_bkg['ttbar']
else:
h_bkg['tgamma'] = h_bkg['ttbarg'].Clone()
del h_bkg['ttbarg']
h_bkg['tgamma'].SetName(h_bkg['tgamma'].GetName().replace('ttbarg', 'tgamma'))
## diphoton
if 'diphoton' in h_bkg:
h_bkg['diphoton'].Add(h_bkg['vgammagamma'], 1)
del h_bkg['vgammagamma']
## fakes
h_bkg['fakes'] = h_bkg['efake'].Clone()
h_bkg['fakes'].Add(h_bkg['jfake'], 1)
h_bkg['fakes'].SetName(h_bkg['efake'].GetName().replace('efake', 'fakes'))
del h_bkg['efake']
del h_bkg['jfake']
## data
h_data = None
if args.data:
h_data = get_histogram(args.data, variable=variable, region=region_name, selection=selection, syst=syst, revert_cut=args.blind)
## add overflow bins to the last bin
for hist in h_bkg.itervalues():
histogram_add_overflow_bin(hist)
if h_data is not None:
histogram_add_overflow_bin(h_data)
## signal
h_signal = None
if args.signal:
h_signal = OrderedDict()
if region.endswith('L'):
signal1 = 'GGM_M3_mu_1900_250'
signal2 = 'GGM_M3_mu_1900_650'
elif region.endswith('H'):
signal1 = 'GGM_M3_mu_1900_1650'
signal2 = 'GGM_M3_mu_1900_1850'
h_signal[signal1] = get_histogram(signal1, variable=variable, region=region_name, selection=selection, syst=syst)
h_signal[signal2] = get_histogram(signal2, variable=variable, region=region_name, selection=selection, syst=syst)
histogram_add_overflow_bin(h_signal[signal1])
histogram_add_overflow_bin(h_signal[signal2])
varname = variable.replace('[', '').replace(']', '').replace('/', '_over_')
if args.selection and args.outname:
tag = args.outname
else:
tag = region
if args.ws is not None:
outname = os.path.join(args.output, 'can_{}_{}_afterFit'.format(tag, varname))
else:
outname = os.path.join(args.output, 'can_{}_{}_beforeFit'.format(tag, varname))
do_plot(outname, variable, data=h_data, bkg=h_bkg, signal=h_signal, region_name=region, extensions=args.extensions.split(','), do_ratio=(args.ratio!='none'))
if args.pl:
os.system('pl %s.pdf' % outname)
# save
if args.save is not None:
file_name = args.save
with RootFile(file_name, 'update') as f:
f.write(h_data)
for hist in h_bkg.itervalues():
f.write(hist)
if h_signal is not None:
for hist in h_signal.itervalues():
f.write(hist)
