def make_fakes_view(weight_type, scale):
scaled_bare_data = views.ScaleView(all_data_view, scale)
scaled_wz_data = views.ScaleView(all_wz_view, scale)
scaled_data = SubtractionView(scaled_bare_data, scaled_wz_data, restrict_positive=True)
# View of weighted obj1-fails data
obj1_view = views.SubdirectoryView(
scaled_data, 'ss/%s/f1p2f3/%s1' % (tau_charge, weight_type))
# View of weighted obj2-fails data
obj2_view = views.SubdirectoryView(
scaled_data, 'ss/%s/p1f2f3/%s2' % (tau_charge, weight_type))
# View of weighted obj1&2-fails data
obj12_view = views.SubdirectoryView(
scaled_data, 'ss/%s/f1f2f3/%s12' % (tau_charge, weight_type))
# Give the individual object views nice colors
obj1_view = views.TitleView(
views.StyleView(obj1_view, **remove_name_entry(data_styles['TT*'])),
'Reducible bkg. 1')
obj2_view = views.TitleView(
views.StyleView(obj2_view, **remove_name_entry(data_styles['QCD*'])),
'Reducible bkg. 2')
obj12_view = views.TitleView(
views.StyleView(obj12_view, **remove_name_entry(data_styles['WW*'])),
'Reducible bkg. 12')
subtract_obj12_view = views.ScaleView(obj12_view, -1)
return obj1_view, obj2_view, obj12_view, subtract_obj12_view
def correct_for_contrib_in_fakes(x, fudge_factor=1.0):
''' Make a view of MC which corrects for the contribution in the fakes '''
fakes_view = views.PathModifierView(x, get_fakes)
invert_view = views.ScaleView(fakes_view, -1)
output = views.SumView(x, invert_view)
# Fudge factor from Zmumu (from H2TAu inclusive)
fudge = views.ScaleView(output, fudge_factor)
return fudge
def __init__(self, main_view, *to_subtract, **kwargs):
# Make all the subtracted ones negative
negated = [views.ScaleView(x, -1) for x in to_subtract]
# Now make this a sum of the main view + the negated subtractors
# Remember, the base class is a SumView.
super(SubtractionView, self).__init__(main_view, *negated)
self.restrict_positive = kwargs.get('restrict_positive', False)
def make_signal_views(self, rebin, unblinded=True):
''' Make signal views with FR background estimation '''
wz_view = views.SubdirectoryView(
self.rebin_view(self.get_view('WZ*'), rebin), 'os/All_Passed/')
zz_view = views.SubdirectoryView(
self.rebin_view(self.get_view('ZZ*'), rebin), 'os/All_Passed/')
all_data_view = self.rebin_view(self.get_view('data'), rebin)
if unblinded and self.blind:
all_data_view = self.rebin_view(
self.get_view('data', 'unblinded_view'), rebin)
data_view = views.SubdirectoryView(all_data_view, 'os/All_Passed/')
#Categories (to match Abdollah's naming convention)
probes = [p + 'IsoFailed' for p in products_map[self.channel][1]]
cat0 = 'os/' + '_'.join(probes) + '/all_weights_applied/'
cat1 = 'os/' + probes[0] + '/obj1_weight/'
cat2 = 'os/' + probes[1] + '/obj2_weight/'
# View of weighted obj1-fails data
cat1_view = views.SubdirectoryView(all_data_view, cat1)
# View of weighted obj2-fails data
cat2_view = views.SubdirectoryView(all_data_view, cat2)
# View of weighted obj1&2-fails data
cat0_view = views.SubdirectoryView(all_data_view, cat0)
subtract_cat0_view = views.ScaleView(cat0_view, -1)
# Corrected fake view
Zjets_view = views.SumView(cat1_view, cat2_view, subtract_cat0_view)
Zjets_view = views.TitleView(
views.StyleView(Zjets_view, **data_styles['Zjets*']), 'Non-prompt')
charge_fakes = views.TitleView(
views.StyleView(
views.SubdirectoryView(all_data_view, 'os/p1p2p3/c1'),
**data_styles['TT*']), 'Charge mis-id')
output = {
'wz': wz_view,
'zz': zz_view,
'data': data_view,
'cat1': cat1_view,
'cat2': cat2_view,
'Zjets': Zjets_view,
'charge_fakes': charge_fakes,
}
# Add signal
for mass in [110, 120, 130, 140]:
vh_view = views.SubdirectoryView(
self.rebin_view(self.get_view('VH_H2Tau_M-%i' % mass), rebin),
'os/All_Passed/')
output['vh%i' % mass] = vh_view
ww_view = views.SubdirectoryView(
self.rebin_view(self.get_view('VH_%i_HWW' % mass), rebin),
'os/All_Passed/')
output['vh%i_hww' % mass] = ww_view
output['signal%i' % mass] = views.SumView(ww_view, vh_view)
return output
def make_signal_views(self, rebin, unblinded=True):
''' Make signal views with FR background estimation '''
wz_view = views.SubdirectoryView(
self.rebin_view(self.get_view('WZJetsTo3LNu*'), rebin),
'ss/p1p2p3/')
zz_view = views.SubdirectoryView(
self.rebin_view(self.get_view('ZZJetsTo4L*'), rebin), 'ss/p1p2p3/')
all_data_view = self.rebin_view(self.get_view('data'), rebin)
if unblinded:
all_data_view = self.rebin_view(
self.get_view('data', 'unblinded_view'), rebin)
data_view = views.SubdirectoryView(all_data_view, 'ss/p1p2p3/')
# View of weighted obj1-fails data
obj1_view = views.SubdirectoryView(all_data_view, 'ss/f1p2p3/w1')
# View of weighted obj2-fails data
obj2_view = views.SubdirectoryView(all_data_view, 'ss/p1f2p3/w2')
# View of weighted obj1&2-fails data
obj12_view = views.SubdirectoryView(all_data_view, 'ss/f1f2p3/w12')
subtract_obj12_view = views.ScaleView(obj12_view, -1)
# Corrected fake view
fakes_view = views.SumView(obj1_view, obj2_view, subtract_obj12_view)
fakes_view = views.TitleView(
views.StyleView(fakes_view, **data_styles['Zjets*']), 'Non-prompt')
charge_fakes = views.TitleView(
views.StyleView(
views.SubdirectoryView(all_data_view, 'os/p1p2p3/c1'),
**data_styles['TT*']), 'Charge mis-id')
output = {
'wz': wz_view,
'zz': zz_view,
'data': data_view,
'obj1': obj1_view,
'obj2': obj2_view,
'fakes': fakes_view,
'charge_fakes': charge_fakes,
}
# Add signal
for mass in [110, 120, 130, 140]:
vh_view = views.SubdirectoryView(
self.rebin_view(self.get_view('VH_*%i' % mass), rebin),
'ss/p1p2p3/')
output['vh%i' % mass] = vh_view
ww_view = views.SubdirectoryView(
self.rebin_view(self.get_view('WH_%i*' % mass), rebin),
'ss/p1p2p3/')
output['vh%i_hww' % mass] = ww_view
output['signal%i' % mass] = views.SumView(ww_view, vh_view)
return output
def make_fakes_view(sign, weight_type):
# View of weighted obj1-fails data
obj1_view = views.SubdirectoryView(
data_view, '%s/f1p2/%s' % (sign, weight_type))
# View of weighted obj2-fails data
obj2_view = views.SubdirectoryView(
data_view, '%s/p1f2/%s' % (sign, weight_type))
# View of weighted obj1&2-fails data
obj12_view = views.SubdirectoryView(
data_view, '%s/f1f2/%s' % (sign, weight_type))
# Give the individual object views nice colors
subtract_obj12_view = views.ScaleView(obj12_view, -1)
return obj1_view, obj2_view, subtract_obj12_view
def plot_final(self,
variable,
rebin=1,
xaxis='',
maxy=10,
show_error=False,
magnifyHiggs=5):
''' Plot the final output - with bkg. estimation '''
sig_view = self.make_signal_views(rebin)
vh_nx = views.TitleView(
views.StyleView(
views.ScaleView(sig_view['signal120'], magnifyHiggs),
**data_styles['VH*']), "(%s#times) m_{H} = 120" % magnifyHiggs)
stack = views.StackView(
sig_view['wz'],
sig_view['zz'],
sig_view['fakes'],
vh_10x,
)
histo = stack.Get(variable)
histo.Draw()
histo.GetHistogram().GetXaxis().SetTitle(xaxis)
histo.SetMaximum(maxy)
self.keep.append(histo)
# Add legend
legend = self.add_legend(histo, leftside=False, entries=4)
if show_error:
bkg_error_view = BackgroundErrorView(
sig_view['fakes'],
sig_view['wz'],
sig_view['zz'],
)
bkg_error = bkg_error_view.Get(variable)
self.keep.append(bkg_error)
bkg_error.Draw('pe2,same')
legend.AddEntry(bkg_error)
# Use poisson error bars on the data
sig_view['data'] = PoissonView(sig_view['data'], x_err=False)
data = sig_view['data'].Get(variable)
data.Draw('pe,same')
self.keep.append(data)
#legend.AddEntry(data)
legend.Draw()
def make_wz_cr_views(self, rebin):
''' Make WZ control region views with FR background estimation '''
wz_view = views.SubdirectoryView(
self.rebin_view(self.get_view('WZJetsTo3LNu*'), rebin),
'ss/p1p2p3_enhance_wz/')
zz_view = views.SubdirectoryView(
self.rebin_view(self.get_view('ZZJetsTo4L*'), rebin),
'ss/p1p2p3_enhance_wz/')
all_data_view = self.rebin_view(self.get_view('data'), rebin)
data_view = views.SubdirectoryView(all_data_view,
'ss/p1p2p3_enhance_wz/')
# View of weighted obj2-fails data
fakes_view = views.SubdirectoryView(all_data_view,
'ss/p1f2p3_enhance_wz/w2')
fakes_view = views.StyleView(fakes_view, **data_styles['Zjets*'])
# Correct
wz_in_fakes_view = views.SubdirectoryView(
self.rebin_view(self.get_view('WZJetsTo3LNu*'), rebin),
'ss/p1f2p3_enhance_wz/w2')
zz_in_fakes_view = views.SubdirectoryView(
self.rebin_view(self.get_view('ZZJetsTo4L*'), rebin),
'ss/p1f2p3_enhance_wz/w2')
diboson_view = views.SumView(wz_in_fakes_view, zz_in_fakes_view)
inverted_diboson_view = views.ScaleView(diboson_view, -1)
fakes_view = views.SumView(fakes_view, inverted_diboson_view)
fakes_view = views.TitleView(fakes_view, 'Non-prompt')
output = {
'wz': wz_view,
'zz': zz_view,
'data': data_view,
'fakes': fakes_view
}
# Add signal
for mass in [110, 120, 130, 140]:
vh_view = views.SubdirectoryView(
self.rebin_view(self.get_view('VH_*%i' % mass), rebin),
'ss/p1p2p3/')
output['vh%i' % mass] = vh_view
return output
def make_wz_cr_views(self, rebin=1, project=None, project_axis=None):
''' Make WZ control region views with FR background estimation '''
def preprocess(view):
ret = view
if project and project_axis:
ret = ProjectionView(ret, project_axis, project)
return RebinView( ret, rebin )
wz_view_tautau_all = preprocess( self.get_view('WZJetsTo3LNu*ZToTauTau*') )
wz_view_tautau = views.SubdirectoryView(wz_view_tautau_all, 'ss/tau_os/p1p2p3_enhance_wz/')
tomatch = 'WZJetsTo3LNu' if self.sqrts == 7 else 'WZJetsTo3LNu_pythia'
wz_view_3l_all = preprocess( self.get_view(tomatch) )
wz_view_3l = views.SubdirectoryView(wz_view_3l_all, 'ss/tau_os/p1p2p3_enhance_wz/')
wz_view_all = views.SumView(wz_view_tautau_all, wz_view_3l_all)
zz_view_all = preprocess( self.get_view('ZZJetsTo4L*') )
zz_view = views.SubdirectoryView(zz_view_all, 'ss/tau_os/p1p2p3_enhance_wz/')
all_data_view = preprocess( self.get_view('data') )
data_view = views.SubdirectoryView(
all_data_view, 'ss/tau_os/p1p2p3_enhance_wz/')
# View of weighted obj2-fails data
fakes_view = views.SubdirectoryView(
all_data_view, 'ss/tau_os/p1f2p3_enhance_wz/w2')
fakes_view = views.StyleView(fakes_view, **remove_name_entry(data_styles['Zjets*']))
# Correct
wz_in_fakes_view = views.SubdirectoryView(wz_view_all, 'ss/tau_os/p1f2p3_enhance_wz/w2')
zz_in_fakes_view = views.SubdirectoryView(zz_view_all, 'ss/tau_os/p1f2p3_enhance_wz/w2')
diboson_view = views.SumView(wz_in_fakes_view, zz_in_fakes_view)
inverted_diboson_view = views.ScaleView(diboson_view, -1)
fakes_view = views.SumView(fakes_view, inverted_diboson_view)
fakes_view = views.TitleView(fakes_view, 'Reducible bkg.')
output = {
'wz_ztt': wz_view_tautau,
'wz_3l' : wz_view_3l,
'zz' : zz_view,
'data' : data_view,
'fakes' : fakes_view
}
return output
def make_obj3_fail_cr_views(self, rebin):
''' Make views when obj3 fails, estimating the bkg in obj1 pass using
f1p2f3 '''
wz_view = views.SubdirectoryView(
self.rebin_view(self.get_view('WZJetsTo3LNu*'), rebin),
'ss/p1p2f3/')
zz_view = views.SubdirectoryView(
self.rebin_view(self.get_view('ZZJetsTo4L*'), rebin), 'ss/p1p2f3/')
all_data_view = self.rebin_view(self.get_view('data'), rebin)
data_view = views.SubdirectoryView(all_data_view, 'ss/p1p2f3/')
# View of weighted obj1-fails data
obj1_view = views.SubdirectoryView(all_data_view, 'ss/f1p2f3/w1')
# View of weighted obj2-fails data
obj2_view = views.SubdirectoryView(all_data_view, 'ss/p1f2f3/w2')
# View of weighted obj1&2-fails data
obj12_view = views.SubdirectoryView(all_data_view, 'ss/f1f2f3/w12')
subtract_obj12_view = views.ScaleView(obj12_view, -1)
# Corrected fake view
fakes_view = views.SumView(obj1_view, obj2_view, subtract_obj12_view)
fakes_view = views.TitleView(
views.StyleView(fakes_view, **data_styles['Zjets*']), 'Non-prompt')
charge_fakes = views.TitleView(
views.StyleView(
views.SubdirectoryView(all_data_view, 'os/p1p2f3/c1'),
**data_styles['TT*']), 'Charge mis-id')
output = {
'wz': wz_view,
'zz': zz_view,
'data': data_view,
'obj1': obj1_view,
'obj2': obj2_view,
'fakes': fakes_view,
'charge_fakes': charge_fakes,
}
return output
def __init__(self, highv=None, lowv=None, centv=None):
self.highv = highv
self.lowv = lowv
self.centv = views.ScaleView( views.SumView(lowv, self.highv) , 0.5) if not centv else centv
def data_views(files, lumifiles, styles, forceLumi=-1):
''' Builds views of files.
[files] gives an iterator of .root files with histograms to build.
[lumifiles] gives the correspond list of .lumisum files which contain
the effective integrated luminosity of the samples.
The lumi to normalize to is taken as the sum of the data file int. lumis.
'''
files = list(files)
log.info("Creating views from %i files", len(files))
# Map sample_name => root file
histo_files = dict((extract_sample(x), io.open(x)) for x in files)
# Map sample_name => lumi file
lumi_files = dict((extract_sample(x), read_lumi(x)) for x in lumifiles)
# Identify data files
datafiles = set([name for name in histo_files.keys() if 'data' in name])
log.info("Found the following data samples:")
log.info(" ".join(datafiles))
datalumi = 0
for x in datafiles:
if x not in lumi_files:
raise KeyError(
"Can't find a lumi file for %s - I have these ones: " % x +
repr(lumi_files.keys()))
datalumi += lumi_files[x]
log.warning("-> total int. lumi = %0.0fpb-1", datalumi)
if forceLumi > 0:
datalumi = forceLumi
log.warning("-> forcing lumi to = %0.0fpb-1", datalumi)
# Figure out the dataset for each file, and the int lumi.
# Key = dataset name
# Value = {intlumi, rootpy file, weight, weighted view}
output = {}
has_data = False
for sample in histo_files.keys():
raw_file = histo_files[sample]
intlumi = lumi_files[sample]
weight = 1
if intlumi:
weight = datalumi / intlumi
if 'data' in sample:
has_data = True
weight = 1
log.warning(
"Building sample: %s => int lumi: %0.f pb-1. Weight => %0.2E",
sample, intlumi, weight)
view = views.ScaleView(raw_file, weight)
unweighted_view = raw_file
# Find the longest (i.e. most specific) matching style pattern
style = get_best_style(sample, styles)
if style:
log.info("Found style for %s - applying Style View", sample)
# Set style and title
# title = the name of the sample, rootpy Legend uses this.
nicename = copy.copy(style['name'])
log.debug("sample name %s", nicename)
style_dict_no_name = dict(
[i for i in style.iteritems() if i[0] != 'name'])
view = views.TitleView(views.StyleView(view, **style_dict_no_name),
nicename)
unweighted_view = views.TitleView(
views.StyleView(unweighted_view, **style_dict_no_name),
nicename)
else:
log.warning("No matching style found for %s", sample)
output[sample] = {
'intlumi': intlumi,
'file': raw_file,
'weight': weight,
'view': view,
'unweighted_view': unweighted_view
}
if not has_data:
return output
# Merge the data into just 'data'
log.info("Merging data together")
output['data'] = {
'intlumi':
datalumi,
'weight':
1,
'view':
views.SumView(*[output[x]['view'] for x in datafiles]),
'unweighted_view':
views.SumView(*[output[x]['unweighted_view'] for x in datafiles]),
}
return output
def plot_final(self, variable, rebin=1, xaxis='', maxy=24,
show_error=False, qcd_correction=False, stack_higgs=True,
qcd_weight_fraction=0., x_range=None, show_charge_fakes=False,
leftside_legend=False, higgs_xsec_multiplier=1, project=None,
project_axis=None, differential=False, yaxis='Events', tau_charge='tau_os', **kwargs):
''' Plot the final output - with bkg. estimation '''
show_charge_fakes = show_charge_fakes if 'show_charge_fakes' not in self.defaults else self.defaults['show_charge_fakes']
sig_view = self.make_signal_views(unblinded=(not self.blind),
qcd_weight_fraction=qcd_weight_fraction,
rebin=rebin, project=project,
project_axis=project_axis, tau_charge=tau_charge)
if differential:
sig_view = self.apply_to_dict(sig_view, DifferentialView)
vh_10x = views.TitleView(
views.StyleView(
views.ScaleView(sig_view['vh125'], higgs_xsec_multiplier),
**remove_name_entry(data_styles['VH*'])
),
"(%i#times) m_{H} = 125" % higgs_xsec_multiplier
)
charge_fakes_view = MedianView(highv=sig_view['charge_fakes']['sys_up'], centv=sig_view['charge_fakes']['central'])
# Fudge factor to go from 120->125 - change in xsec*BR
#vh_10x = views.ScaleView(vh_10x), .783)
tostack = [sig_view['wz_3l'], sig_view['zz'], sig_view['wz'], sig_view['fakes'], vh_10x] if stack_higgs else \
[sig_view['wz_3l'], sig_view['zz'], sig_view['wz'], sig_view['fakes']]
if show_charge_fakes:
tostack = tostack[:2]+[charge_fakes_view]+tostack[2:]
vh_hww = views.ScaleView(sig_view['vh120_hww'], .783) if 'vh120_hww' in sig_view else None
if vh_hww:
tostack = tostack[:-1] + [vh_hww] + tostack[-1:]
stack = views.StackView( *tostack )
histo = stack.Get(variable)
histo.Draw()
histo.GetHistogram().GetXaxis().SetTitle(xaxis)
histo.GetHistogram().GetYaxis().SetTitle(yaxis)
if x_range:
histo.GetHistogram().GetXaxis().SetRangeUser(x_range[0], x_range[1])
self.keep.append(histo)
# Add legend
entries = len(tostack)+1
if show_error:
entries += 1
legend = self.add_legend(histo, leftside=leftside_legend, entries=entries)
if show_error:
#correct_qcd_view = None
#if qcd_weight_fraction == 0:
# fakes05 = sig_view['weighted_fakes'][1.]
# correct_qcd_view = MedianView(lowv=fakes05, centv=sig_view['fakes'])
#
#elif qcd_weight_fraction == 0.5:
# fakes1 = sig_view['weighted_fakes'][1.]
# correct_qcd_view = MedianView(highv=fakes1, centv=sig_view['fakes'])
#
#elif qcd_weight_fraction == 1:
# fakes05 = sig_view['weighted_fakes'][0.5]
# correct_qcd_view = MedianView(lowv=fakes05, centv=sig_view['fakes'])
bkg_error_view = BackgroundErrorView(
sig_view['fakes'], #correct_qcd_view, #sig_view['fakes'],
views.SumView( sig_view['wz'], sig_view['wz_3l']),
sig_view['zz'],
charge_fakes_view,
fake_error=0.3,
**kwargs
)
bkg_error = bkg_error_view.Get(variable)
self.keep.append(bkg_error)
bkg_error.Draw('pe2,same')
legend.AddEntry(bkg_error)
# Use poisson error bars on the data
sig_view['data'] = PoissonView(sig_view['data'], x_err=False, is_scaled=differential) #PoissonView(, x_err=False)
data = sig_view['data'].Get(variable)
ymax = histo.GetMaximum()
if not self.blind or tau_charge != 'tau_os':
#print "drawing", data.Integral()
data.Draw('pe,same')
legend.AddEntry(data)
ymax = max(ymax, data.GetMaximum())
self.keep.append(data)
if isinstance(maxy, (int, long, float)):
#print "setting maxy to %s" % maxy
histo.SetMaximum(maxy)
self.canvas.Update()
else:
histo.SetMaximum(ymax*1.2)
if not stack_higgs:
higgs_plot = vh_10x.Get(variable)
higgs_plot.Draw('same')
self.keep.append(higgs_plot)
legend.Draw()
def write_shapes(self, variable, rebin, outdir,
qcd_fraction=0., #[1., 0., -1.],
show_charge_fakes=False,
project=None, project_axis=None, different_fakes=False):
''' Write final shapes for [variable] into a TDirectory [outputdir] '''
show_charge_fakes = show_charge_fakes if 'show_charge_fakes' not in self.defaults else self.defaults['show_charge_fakes']
sig_view = self.make_signal_views(unblinded=(not self.blind),
qcd_weight_fraction=qcd_fraction,
rebin=rebin, project=project, project_axis=project_axis)
different_fakes_views = self.make_additional_fakes_view( unblinded=(not self.blind), rebin=rebin,
project=project, project_axis=project_axis)
outdir.cd()
wz_weight = self.get_view('WZJetsTo3LNu*ZToTauTau*', 'weight')
zz_weight = self.get_view('ZZJetsTo4L*', 'weight')
print "wz_weight: %s" % wz_weight
print "zz_weight: %s" % zz_weight
wz = views.FunctorView( views.SumView(sig_view['wz'], sig_view['wz_3l']), make_empty_bin_remover(wz_weight)).Get(variable)
zz = views.FunctorView( sig_view['zz'], make_empty_bin_remover(zz_weight)).Get(variable)
obs = sig_view['data'].Get(variable)
fakes = sig_view['fakes'].Get(variable) if not different_fakes else different_fakes_views['fakes'].Get(variable)
fakes_down = different_fakes_views['fakes'].Get(variable)
fakes_up = PositiveView(
views.SumView( views.ScaleView(sig_view['fakes'], 2.),
views.ScaleView(different_fakes_views['fakes'], -1.)
)
).Get(variable)
wz.SetName('wz')
zz.SetName('zz')
obs.SetName('data_obs')
fakes.SetName('fakes')
fakes_down.SetName('fakes_CMS_vhtt_%s_fakeshape_%sTeVDown' % (outdir.GetName(), self.sqrts))
fakes_up.SetName('fakes_CMS_vhtt_%s_fakeshape_%sTeVUp' % (outdir.GetName(), self.sqrts))
#for mass in [110, 115, 120, 125, 130, 135, 140]:
#set_trace()
for mass in range(90, 165, 5):
try:
vh = None
if mass == 90 and self.sqrts == 8:
vh = views.ScaleView(sig_view['vh100'], 1.3719).Get(variable)
elif mass == 95 and self.sqrts == 8:
vh = views.ScaleView(sig_view['vh100'], 1.1717).Get(variable)
else:
vh = sig_view['vh%i' % mass].Get(variable)
vh.SetName('WH%i' % mass)
vh.SetLineColor(0)
vh.Write()
except KeyError:
#logging.warning('No sample found matching VH_*%i' % mass)
continue
if mass % 10 == 0 and mass < 150:
# Only have 10 GeV steps for WW
if 'vh%i_hww' % mass in sig_view:
ww = sig_view['vh%i_hww' % mass].Get(variable)
ww.SetName('WH_hww%i' % mass)
ww.Write()
wz.Write()
zz.Write()
obs.Write()
fakes.Write()
fakes_down.Write()
fakes_up.Write()
#charge_fakes_CMS_vhtt_emt_chargeFlip_8TeVUpx
if show_charge_fakes:
logging.info('adding charge fakes shape errors')
charge_fakes = sig_view['charge_fakes']['central'].Get(variable)
charge_fakes_sys_up = sig_view['charge_fakes']['sys_up' ].Get(variable) #shift='up')
charge_fakes_sys_down = charge_fakes+charge_fakes - charge_fakes_sys_up
charge_fakes.SetName('charge_fakes')
charge_fakes_sys_up.SetName('charge_fakes_CMS_vhtt_%s_chargeFlip_%sTeVUp' % (self.channel.lower(), self.sqrts))
charge_fakes_sys_down.SetName('charge_fakes_CMS_vhtt_%s_chargeFlip_%sTeVDown' % (self.channel.lower(), self.sqrts))
charge_fakes.Write()
charge_fakes_sys_up.Write()
charge_fakes_sys_down.Write()
def get_ss(x):
return x.replace('em/', 'em/ss/')
mc_view = views.SumView(
*[views.PathModifierView(plotter.get_view(x), get_ss) for x in [
'WZJetsTo3LNu*',
'ZZJetsTo4L*',
'WW*',
'WplusJets_madgraph',
'TTplusJets_madgraph',
'Zjets_M50',
]]
)
#mc_inverted = views.ScaleView(mc_view, -1)
mc_inverted = views.ScaleView(mc_view, -1)
sqrts = 7 if '7TeV' in jobid else 8
qcd_view = views.StyleView(
views.TitleView(
views.ScaleView(
views.SumView(views.PathModifierView(plotter.data, get_ss),
mc_inverted),
1.4 if sqrts == 8 else 1.28 # OS/SS from Valentina
),
'QCD'),
**data_styles['WZ*'])
def get_fakes(x):
def roodatahistizer(hist):
''' Turn a hist into a RooDataHist '''
return ROOT.RooDataHist(hist.GetName(), hist.GetTitle(),
ROOT.RooArgList(x), hist)
# Now a Get() will return a RooDataHist
for type in regions.keys():
# Rebin the histograms. Make this smarter later
regions[type] = views.FunctorView(regions[type], lambda x: x.Rebin(5))
# Make views of the numerator and denominator.
# For RooFit we have to split into Pass & Fail
# So subtract the numerator from the denominator
num_view = regions[type]
all_view = views.PathModifierView(regions[type],
denominator_path_mangler)
negative_num_view = views.ScaleView(num_view, -1)
fail_view = views.SumView(all_view, negative_num_view)
# Now make RooDataHistViews of the numerator & denominator
regions[type] = (
views.FunctorView(num_view, roodatahistizer),
views.FunctorView(fail_view, roodatahistizer),
)
log.info("Making output workspace")
ws = ROOT.RooWorkspace("fit_results")
def ws_import(*args):
getattr(ws, 'import')(*args)
# Fit each region
def data_views(files, lumifiles):
''' Builds views of files.
[files] gives an iterator of .root files with histograms to build.
[lumifiles] gives the correspond list of .lumisum files which contain
the effective integrated luminosity of the samples.
The lumi to normalize to is taken as the sum of the data file int. lumis.
'''
files = list(files)
log.info("Creating views from %i files", len(files))
# Map sample_name => root file
histo_files = dict((extract_sample(x), io.open(x)) for x in files)
# Map sample_name => lumi file
lumi_files = dict((extract_sample(x), read_lumi(x)) for x in lumifiles)
# Identify data files
datafiles = set([name for name in histo_files.keys() if 'data' in name])
log.info("Found the following data samples:")
log.info(" ".join(datafiles))
datalumi = 0
for x in datafiles:
if x not in lumi_files:
raise KeyError(
"Can't find a lumi file for %s - I have these ones: " % x
+ repr(lumi_files.keys()))
datalumi += lumi_files[x]
log.info("-> total int. lumi = %0.0fpb-1", datalumi)
# Figure out the dataset for each file, and the int lumi.
# Key = dataset name
# Value = {intlumi, rootpy file, weight, weighted view}
output = {}
for sample in histo_files.keys():
raw_file = histo_files[sample]
intlumi = lumi_files[sample]
log.info("Building sample: %s => int lumi: %0.f pb-1", sample, intlumi)
weight = 1
if intlumi:
weight = datalumi/intlumi
if 'data' in sample:
weight = 1
log.debug("Weight: %0.2f", weight)
view = views.ScaleView(raw_file, weight)
unweighted_view = raw_file
# Find the longest (i.e. most specific) matching style pattern
best_pattern = ''
for pattern, style_dict in data_styles.iteritems():
log.debug("Checking pattern: %s against %s", pattern, sample)
if fnmatch.fnmatch(sample, pattern):
log.debug("-> it matches!")
if len(pattern) > len(best_pattern):
best_pattern = pattern
log.info("Found new best style for %s: %s",
sample, pattern)
if best_pattern:
style_dict = data_styles[best_pattern]
log.info("Found style for %s - applying Style View", sample)
# Set style and title
# title = the name of the sample, rootpy Legend uses this.
nicename = copy.copy(style_dict['name'])
view = views.TitleView(
views.StyleView(view, **style_dict), nicename
)
unweighted_view = views.TitleView(
views.StyleView(unweighted_view, **style_dict), nicename
)
output[sample] = {
'intlumi': intlumi,
'file' : raw_file,
'weight' : weight,
'view' : view,
'unweighted_view' : unweighted_view
}
# Merge the data into just 'data'
log.info("Merging data together")
output['data'] = {
'intlumi' : datalumi,
'weight' : 1,
'view' : views.SumView(*[output[x]['view'] for x in datafiles]),
'unweighted_view' : views.SumView(*[output[x]['unweighted_view']
for x in datafiles]),
}
return output
''' Make a view where all bins > 0 '''
return views.FunctorView(x, all_bins_positive)
def get_view(sample_pattern):
for sample, sample_info in the_views.iteritems():
if fnmatch.fnmatch(sample, sample_pattern):
return rebin_view(sample_info['view'], args.rebin)
raise KeyError("I can't find a view that matches %s, I have: %s" %
(sample_pattern, " ".join(the_views.keys())))
wz_view = get_view('WZ*')
zz_view = get_view('ZZ*')
data = rebin_view(the_views['data']['view'], args.rebin)
diboson_view = views.SumView(wz_view, zz_view)
inverted_diboson_view = views.ScaleView(diboson_view, -1)
corrected_view = postive_view(views.SumView(data, inverted_diboson_view))
output = io.open(args.outputfile, 'RECREATE')
output.cd()
corr_numerator = corrected_view.Get(args.numerator)
corr_denominator = corrected_view.Get(args.denom)
log.info(
"Corrected: %0.2f/%0.2f = %0.1f%%", corr_numerator.Integral(),
corr_denominator.Integral(), 100 * corr_numerator.Integral() /
corr_denominator.Integral() if corr_denominator.Integral() else 0)
uncorr_numerator = data.Get(args.numerator)
uncorr_denominator = data.Get(args.denom)
