def make_signal_fits( sampMan, sel_base, eta_cuts, plot_var, xvar, binning, workspace, suffix ) :
sampMan.clear_hists()
for samp in sampMan.get_samples(isSignal=True ) :
print 'Sample = ', samp.name
res = re.match('(MadGraph|Pythia)ResonanceMass(\d+)_.*', samp.name )
if res is None :
print 'Could not interpret path ', samp.name
else :
mass = float(res.group(2))
if samp.name.count( 'width0p01' ) :
width = 0.0001
else :
res2 = re.match('(MadGraph|Pythia)ResonanceMass(\d+)_width(\d)', samp.name )
width = float(res2.group(3))/100.
if mass != 450 :
continue
ph_selection_sr = '%s==1' %defs.get_phid_selection('all')
ph_idx_sr = defs.get_phid_idx( 'all' )
print binning[mass]
addtl_cuts_sr = 'ph_pt[%s] > 50 && %s > %f && %s < %f ' %(ph_idx_sr, plot_var, binning[mass][1], plot_var, binning[mass][2] )
xvar.setBins(10000,'cache')
xvar.setMin('cache',-100)
xvar.setMax('cache',1500)
for ieta in eta_cuts :
full_suffix = '%s_%s_%s' %(samp.name, suffix, ieta)
eta_str_sr = 'ph_Is%s[%s]' %( ieta, ph_idx_sr )
full_sel_sr = ' && '.join( [sel_base, ph_selection_sr, eta_str_sr, addtl_cuts_sr] )
#hist_sr = clone_sample_and_draw( sampMan, sampname, plot_var, full_sel_sr, binning )
sampMan.create_hist( samp, plot_var, full_sel_sr, binning[mass] )
hist_sr = samp.hist
integral = hist_sr.Integral()
#hist_sr.Scale( 1.0/integral )
#datahist = ROOT.RooDataHist( 'srhist_%s' %full_suffix, 'srhist', ROOT.RooArgList(xvar), hist_sr)
#histpdf = ROOT.RooHistPdf( 'srhistpdf_%s' %(full_suffix), 'srhistpdf' , ROOT.RooArgSet( xvar), datahist, 3 )
fit_max = mass*1.1
fit_min = mass/1.8
if mass >= 1000 :
fit_max = mass*1.05
fit_min = mass*0.7
xvar.setMin( fit_min )
xvar.setMax( fit_max )
fitManager = FitManager( 'bwxcb', 0, hist_sr, plot_var, ieta, xvar, full_suffix, True,
sample_params={'mass' : mass, 'width' : width}, )
fit_distribution( fitManager)
#save_fit( fitManager, sampMan, workspace, stats_pos='left' )
iter_managers = []
iter_managers.append( fitManager )
saved_result = False
for i in range( 0, 4 ) :
#for i in [0.5, 0.3, 0.2, 0.1, 0.05] :
print 'GOTHERE1'
iter_managers.append(FitManager( 'bwxcb', 0, hist_sr, plot_var, ieta, xvar, full_suffix, True,
sample_params={'mass' : mass, 'width' : width}, ))
print 'GOTHERE2'
cv_sigma = iter_managers[-2].cb_sigma.getValV()
lo_sigma = iter_managers[-2].cb_sigma.getErrorLo()
hi_sigma = iter_managers[-2].cb_sigma.getErrorHi()
cv_power = iter_managers[-2].cb_power.getValV()
lo_power = iter_managers[-2].cb_power.getErrorLo()
hi_power = iter_managers[-2].cb_power.getErrorHi()
cv_mass = iter_managers[-2].cb_mass.getValV()
lo_mass = iter_managers[-2].cb_mass.getErrorLo()
hi_mass = iter_managers[-2].cb_mass.getErrorHi()
err_sigma = hi_sigma/cv_sigma
err_power = hi_power/cv_power
err_mass = hi_mass/cv_mass
#new_lim_sigma = ( cv_sigma, cv_sigma*(1-i), cv_sigma*(1+i))
#new_lim_power = ( cv_power, cv_power*(1-i), cv_power*(1+i) )
#new_lim_mass = ( cv_mass, cv_mass*(1-i), cv_mass*(1+i) )
new_lim_sigma = ( cv_sigma, cv_sigma+lo_sigma, cv_sigma+hi_sigma)
new_lim_power = ( cv_power, cv_power+lo_power, cv_power+hi_power )
new_lim_mass = ( cv_mass, cv_mass+lo_mass, cv_mass+hi_mass )
#if cv_sigma < 0 :
# new_lim_sigma = ( new_lim_sigma[0], new_lim_sigma[2], new_lim_sigma[1])
#if cv_power < 0 :
# new_lim_power = ( new_lim_power[0], new_lim_power[2], new_lim_power[1])
#if cv_mass < 0 :
# new_lim_mass = ( new_lim_mass[0], new_lim_mass[2], new_lim_mass[1] )
print 'NEW DEFAULTS'
print new_lim_sigma
print new_lim_power
print new_lim_mass
iter_managers[-1].set_vals('cb_sigma', mass, new_lim_sigma )
iter_managers[-1].set_vals('cb_power', mass, new_lim_power )
iter_managers[-1].set_vals('cb_mass', mass, new_lim_mass )
print 'GOTHERE3'
fit_distribution( iter_managers[-1] )
print 'GOTHERE4'
cv_sigma_new = iter_managers[-1].cb_sigma.getValV()
lo_sigma_new = iter_managers[-1].cb_sigma.getErrorLo()
hi_sigma_new = iter_managers[-1].cb_sigma.getErrorHi()
cv_power_new = iter_managers[-1].cb_power.getValV()
lo_power_new = iter_managers[-1].cb_power.getErrorLo()
hi_power_new = iter_managers[-1].cb_power.getErrorHi()
cv_mass_new = iter_managers[-1].cb_mass.getValV()
lo_mass_new = iter_managers[-1].cb_mass.getErrorLo()
hi_mass_new = iter_managers[-1].cb_mass.getErrorHi()
err_sigma_new = hi_sigma_new/cv_sigma_new
err_power_new = hi_power_new/cv_power_new
err_mass_new = hi_mass_new/cv_mass_new
print 'Sigma : Previous error = %f, new error = %f' %( err_sigma, err_sigma_new )
print 'Power : Previous error = %f, new error = %f' %( err_power, err_power_new )
print 'Mass : Previous error = %f, new error = %f' %( err_mass, err_mass_new )
print 'GOTHERE5'
# if we get worse results with the new fit, then use the previous one
if math.fabs(err_sigma_new) > math.fabs(err_sigma) or math.fabs(err_power_new) > math.fabs(err_power) or math.fabs(err_mass_new) > math.fabs(err_mass) :
print 'GOTHERE6'
save_fit( iter_managers[-2], sampMan, workspace, stats_pos='left' )
print 'GOTHERE7'
saved_result = True
break
# if we haven't saved the fit yet, then the best
# version is the latest
if not saved_result :
print 'GOTHERE8'
save_fit( iter_managers[-1], sampMan, workspace, stats_pos='left' )
print 'GOTHERE9'
def make_wjets_fit( sampMan, sample, sel_base, eta_cut, plot_var, shape_var, num_var, binning, xvar, suffix='', closure=False, workspace=None) :
ph_selection_sr = '%s==1' %defs.get_phid_selection('medium')
ph_selection_den = '%s==1'%defs.get_phid_selection( num_var, shape_var )
ph_selection_num = '%s==1' %defs.get_phid_selection( num_var )
ph_selection_shape = '%s==1' %defs.get_phid_selection( shape_var )
ph_idx_sr = defs.get_phid_idx( 'medium' )
ph_idx_den = defs.get_phid_idx( num_var, shape_var )
ph_idx_num = defs.get_phid_idx( num_var )
ph_idx_shape = defs.get_phid_idx( shape_var )
xmin = xvar.getMin()
xmax = xvar.getMax()
addtl_cuts_sr = 'ph_pt[%s] > 50 && %s > %d && %s < %d ' %( ph_idx_sr, plot_var, xmin, plot_var, xmax )
addtl_cuts_den = 'ph_pt[%s] > 50 && %s > %d && %s < %d ' %( ph_idx_den, plot_var, xmin, plot_var, xmax )
addtl_cuts_num = 'ph_pt[%s] > 50 && %s > %d && %s < %d ' %( ph_idx_num, plot_var, xmin, plot_var, xmax )
addtl_cuts_shape = 'ph_pt[%s] > 50 && %s > %d && %s < %d ' %( ph_idx_shape, plot_var, xmin, plot_var, xmax )
cut_var_shape = defs.get_phid_cut_var( shape_var )
cut_var_num = defs.get_phid_cut_var( num_var )
eta_str_shape = 'ph_Is%s[%s]' %( eta_cut, ph_idx_shape )
eta_str_den = 'ph_Is%s[%s]' %( eta_cut, ph_idx_den)
eta_str_num = 'ph_Is%s[%s]' %( eta_cut, ph_idx_num )
eta_str_sr = 'ph_Is%s[%s]' %( eta_cut, ph_idx_sr )
cut_vals_shape = get_cut_defaults( shape_var, eta_cut )
cut_vals_num = get_cut_defaults( num_var, eta_cut )
cut_str_base = ' {var}[{idx}] > {val_low} && {var}[{idx}] < {val_high}'
cut_str_shape = cut_str_base.format(var=cut_var_shape, idx=ph_idx_shape, val_low=cut_vals_shape[0], val_high=cut_vals_shape[1] )
cut_str_num = cut_str_base.format(var=cut_var_num, idx=ph_idx_num, val_low=cut_vals_num[0], val_high=cut_vals_num[1] )
cut_str_den_1 = cut_str_base.format(var=cut_var_shape, idx=ph_idx_den, val_low=cut_vals_shape[0], val_high=cut_vals_shape[1] )
cut_str_den_2 = cut_str_base.format(var=cut_var_num, idx=ph_idx_den, val_low=cut_vals_num[0], val_high=cut_vals_num[1] )
cut_str_den = cut_str_den_1 + ' && ' + cut_str_den_2
full_sel_shape = ' && '.join( [sel_base, ph_selection_shape, eta_str_shape, addtl_cuts_shape, cut_str_shape ] )
full_sel_num = ' && '.join( [sel_base, ph_selection_num, eta_str_num, addtl_cuts_num, cut_str_num] )
full_sel_den = ' && '.join( [sel_base, ph_selection_den, eta_str_den, addtl_cuts_den, cut_str_den] )
full_sel_sr = ' && '.join( [sel_base, ph_selection_sr, eta_str_sr, addtl_cuts_sr] )
label_shape = 'shape_%s' %suffix
label_num = 'num_%s' %suffix
label_den = 'den_%s' %suffix
label_sr = 'sr_%s' %suffix
if workspace is None :
ws = ROOT.RooWorkspace( 'ws')
else :
ws = workspace
hist_shape = clone_sample_and_draw( sampMan, sample, plot_var, full_sel_shape, binning )
result_shape= fit_distribution( 'dijet', 2, hist_shape, xvar, label_shape , sampMan, ws )
hist_num = clone_sample_and_draw( sampMan, sample, plot_var, full_sel_num , binning )
result_num = fit_distribution( 'dijet', 2, hist_num, xvar, label_num , sampMan, ws )
hist_den = clone_sample_and_draw( sampMan, sample, plot_var, full_sel_den , binning )
result_den = fit_distribution( 'dijet', 2, hist_den, xvar, label_den , sampMan, ws )
_integral_num = ws.pdf( 'dijet_%s' %label_num ).getNormIntegral(ROOT.RooArgSet( xvar ) )
func_integral_num = _integral_num.getValV()
_integral_den = ws.pdf( 'dijet_%s' %label_den).getNormIntegral(ROOT.RooArgSet( xvar ) )
func_integral_den = _integral_den.getValV()
_integral_shape = ws.pdf( 'dijet_%s' %label_shape).getNormIntegral(ROOT.RooArgSet( xvar ) )
func_integral_shape = _integral_shape.getValV()
hist_integral_num = result_num['integral'].n
hist_integral_den = result_den['integral'].n
hist_integral_shape = result_shape['integral'].n
norm_num = hist_integral_num / func_integral_num
norm_den = hist_integral_den / func_integral_den
norm_shape = hist_integral_shape / func_integral_shape
print 'func integral Num = ', func_integral_num
print 'hist integral Num = ', hist_integral_num
print 'normalization Num = ', norm_num
print 'func integral Den = ', func_integral_den
print 'hist integral Den = ', hist_integral_den
print 'normalization Den = ', norm_den
print 'func integral Shape = ', func_integral_shape
print 'hist integral Shape = ', hist_integral_shape
print 'normalization Shape = ', norm_shape
power_pred_name = 'power_pred_%s' %suffix
logcoef_pred_name = 'logcoef_pred_%s' %suffix
#power_ratio_name = 'power_ratio_%s' %suffix
#logcoef_ratio_name = 'logcoef_ratio_%s' %suffix
val_power_num = ws.var( 'power_dijet_%s' %label_num )
val_power_den = ws.var( 'power_dijet_%s' %label_den )
val_power_shape = ws.var( 'power_dijet_%s' %label_shape )
val_logcoef_num = ws.var( 'logcoef_dijet_%s' %label_num )
val_logcoef_den = ws.var( 'logcoef_dijet_%s' %label_den )
val_logcoef_shape = ws.var( 'logcoef_dijet_%s' %label_shape )
power_pred = ROOT.RooRealVar( power_pred_name , 'power' , (val_power_num.getValV() + val_power_shape.getValV() - val_power_den.getValV()) , -100, 100)
logcoef_pred = ROOT.RooRealVar( logcoef_pred_name , 'logcoef', (val_logcoef_num.getValV() + val_logcoef_shape.getValV() - val_logcoef_den.getValV()), -10, 10 )
#power_ratio = ROOT.RooRealVar( power_ratio_name , 'power' , val_power_num - val_power_den , -100, 100)
#logcoef_ratio = ROOT.RooRealVar( logcoef_ratio_name, 'logcoef', val_logcoef_num - val_logcoef_den, -10, 10 )
can_ratio = ROOT.TCanvas( str(uuid.uuid4()), '' )
func = 'TMath::Power(@0/13000, @1+@2*TMath::Log10(@0/13000))'
prediction = ROOT.RooGenericPdf('dijet_prediction_%s' %suffix , 'prediction', func, ROOT.RooArgList(xvar,power_pred, logcoef_pred))
norm_pred = ROOT.RooRealVar( 'dijet_prediction_%s_norm' %(suffix), 'prediction normalization', (hist_integral_shape * hist_integral_num) / hist_integral_den )
getattr( ws , 'import' ) ( norm_pred )
getattr( ws , 'import' ) ( prediction )
ratio_func = ROOT.TF1( 'ratio_func', '( [2]*TMath::Power(x/13000, [0] + [1]*TMath::Log10(x/13000) ) ) ', xmin, xmax )
ratio_func.SetParameter(0, result_num['power'].n - result_den['power'].n)
ratio_func.SetParameter(1, result_num['logcoef'].n - result_den['logcoef'].n )
ratio_func.SetParameter(2, norm_num / norm_den )
ratiohist = hist_num.Clone( 'closure_ratio_%s' %(suffix) )
ratiohist.Divide( hist_den )
ratiohist.SetMarkerStyle(20)
ratiohist.SetMarkerSize(1)
ratiohist.GetXaxis().SetTitle( 'Transverse Mass [GeV]' )
ratiohist.GetYaxis().SetTitle( 'ratio of passing to failing %s' %shape_var )
ratiohist.Draw()
ratiohist.SetStats(0)
ratio_func.Draw('same')
ratio_power_v = val_power_num.getValV() - val_power_den.getValV()
ratio_power_e = math.sqrt(val_power_num.getErrorHi()*val_power_num.getErrorHi() + val_power_den.getErrorHi()*val_power_den.getErrorHi() )
ratio_logcoef_v = val_logcoef_num.getValV() - val_logcoef_den.getValV()
ratio_logcoef_e = math.sqrt(val_logcoef_num.getErrorHi()*val_logcoef_num.getErrorHi() + val_logcoef_den.getErrorHi()*val_logcoef_den.getErrorHi() )
power_tex = ROOT.TLatex(0, 0, 'power = %.01f #pm %.02f' %( ratio_power_v, ratio_power_e ))
logcoef_tex = ROOT.TLatex(0, 0, 'logcoef = %.01f #pm %.02f' %( ratio_logcoef_v, ratio_logcoef_e ))
power_tex.SetNDC()
logcoef_tex.SetNDC()
power_tex .SetX( 0.6 )
power_tex .SetY( 0.84 )
logcoef_tex.SetX( 0.6 )
logcoef_tex.SetY( 0.78 )
power_tex.Draw()
logcoef_tex.Draw()
if closure :
sampMan.outputs['wjetsclosure_ratio_%s' %suffix] = can_ratio
else :
sampMan.outputs['wjets_ratio_%s' %suffix] = can_ratio
if closure :
hist_sr = clone_sample_and_draw( sampMan, sample, plot_var, full_sel_sr , binning )
result_sr = fit_distribution( 'dijet', 2, hist_sr, xvar, label_sr, sampMan, ws )
can_sr = ROOT.TCanvas( str(uuid.uuid4()), '' )
pred_val = (hist_integral_shape * hist_integral_num) / hist_integral_den
tot_sr = hist_sr.Integral( hist_sr.FindBin( xmin ), hist_sr.FindBin( xmax ) )
print 'SR integral = ', tot_sr
sr_func = ROOT.TF1( 'sr_func', '( [2]*TMath::Power(x/13000, [0] + [1]*TMath::Log10(x/13000) ) ) ', xmin, xmax )
sr_func.SetParameter(0, result_num['power'].n + result_shape['power'].n - result_den['power'].n )
sr_func.SetParameter(1, result_num['logcoef'].n + result_shape['logcoef'].n - result_den['logcoef'].n )
sr_func.SetParameter(2, 1 )
sr_int = sr_func.Integral( xmin, xmax )
print 'Normalization = ',(norm_shape*norm_num) / norm_den
print 'func Integral SR Before = ', sr_func.Integral( xmin, xmax )
sr_func.SetParameter(2, pred_val/sr_int )
hist_sr.Draw()
sr_func.Draw('same')
sampMan.outputs['wjetsclosure_pred_%s' %suffix] = can_sr
tot_ratio = result_num['integral']/result_den['integral']
def make_wjets_fit( sampMan, sample, sel_base, eta_cut, plot_var, shape_var, num_var, binning, xvar, suffix='', closure=False, workspace=None) :
#---------------------------------------
# Get the base selection for each region
#---------------------------------------
ph_selection_sr = '%s==1' %defs.get_phid_selection('medium')
ph_selection_den = '%s==1'%defs.get_phid_selection( num_var, shape_var )
ph_selection_num = '%s==1' %defs.get_phid_selection( num_var )
ph_selection_shape = '%s==1' %defs.get_phid_selection( shape_var )
#ph_selection_ABCD = '%s==1' %defs.get_phid_selection('all')
ph_idx_sr = defs.get_phid_idx( 'medium' )
ph_idx_den = defs.get_phid_idx( num_var, shape_var )
ph_idx_num = defs.get_phid_idx( num_var )
ph_idx_shape = defs.get_phid_idx( shape_var )
#ph_idx_ABCD = defs.get_phid_idx('all')
xmin = xvar.getMin()
xmax = xvar.getMax()
#---------------------------------------
# Add eta cuts, (IsEB, IsEE)
#---------------------------------------
eta_str_shape = 'ph_Is%s[%s]' %( eta_cut, ph_idx_shape )
eta_str_den = 'ph_Is%s[%s]' %( eta_cut, ph_idx_den)
eta_str_num = 'ph_Is%s[%s]' %( eta_cut, ph_idx_num )
eta_str_sr = 'ph_Is%s[%s]' %( eta_cut, ph_idx_sr )
#eta_str_ABCD = 'ph_Is%s[%s]' %( eta_cut, ph_idx_ABCD )
#---------------------------------------
# Add additional cuts, mainly restricting
# the fitted variable to the plotting limits
#---------------------------------------
addtl_cuts_sr = 'ph_pt[%s] > 50 && %s > %d && %s < %d ' %( ph_idx_sr, plot_var, xmin, plot_var, xmax )
addtl_cuts_den = 'ph_pt[%s] > 50 && %s > %d && %s < %d ' %( ph_idx_den, plot_var, xmin, plot_var, xmax )
addtl_cuts_num = 'ph_pt[%s] > 50 && %s > %d && %s < %d ' %( ph_idx_num, plot_var, xmin, plot_var, xmax )
addtl_cuts_shape = 'ph_pt[%s] > 50 && %s > %d && %s < %d ' %( ph_idx_shape, plot_var, xmin, plot_var, xmax )
#addtl_cuts_ABCD = 'ph_pt[%s] > 50 && %s > %d && %s < %d ' %( ph_idx_ABCD, plot_var, xmin, plot_var, xmax )
#---------------------------------------
# Get the cuts that define the photon
# sideband regions
#---------------------------------------
cut_str_base = ' {var}[{idx}] > {val_low} && {var}[{idx}] < {val_high}'
cut_var_shape = defs.get_phid_cut_var( shape_var )
cut_var_num = defs.get_phid_cut_var( num_var )
cut_vals_shape = get_cut_defaults( shape_var, eta_cut )
cut_vals_num = get_cut_defaults( num_var, eta_cut )
cut_str_shape = cut_str_base.format(var=cut_var_shape, idx=ph_idx_shape, val_low=cut_vals_shape[0], val_high=cut_vals_shape[1] )
cut_str_num = cut_str_base.format(var=cut_var_num, idx=ph_idx_num, val_low=cut_vals_num[0], val_high=cut_vals_num[1] )
cut_str_den_1 = cut_str_base.format(var=cut_var_shape, idx=ph_idx_den, val_low=cut_vals_shape[0], val_high=cut_vals_shape[1] )
cut_str_den_2 = cut_str_base.format(var=cut_var_num, idx=ph_idx_den, val_low=cut_vals_num[0], val_high=cut_vals_num[1] )
cut_str_den = cut_str_den_1 + ' && ' + cut_str_den_2
#---------------------------------------
# put the cuts together
#---------------------------------------
full_sel_shape = ' && '.join( [sel_base, ph_selection_shape, eta_str_shape, addtl_cuts_shape, cut_str_shape ] )
full_sel_num = ' && '.join( [sel_base, ph_selection_num, eta_str_num, addtl_cuts_num, cut_str_num] )
full_sel_den = ' && '.join( [sel_base, ph_selection_den, eta_str_den, addtl_cuts_den, cut_str_den] )
full_sel_sr = ' && '.join( [sel_base, ph_selection_sr, eta_str_sr, addtl_cuts_sr] )
#full_sel_ABCD = ' && '.join( [sel_base, ph_selection_ABCD, eta_str_ABCD, addtl_cuts_ABCD] )
label_shape = 'shape_%s_%s' %(sample,suffix)
label_num = 'num_%s_%s' %(sample,suffix)
label_den = 'den_%s_%s' %(sample,suffix)
label_sr = 'sr_%s_%s' %(sample,suffix)
if workspace is None :
ws = ROOT.RooWorkspace( 'ws')
else :
ws = workspace
jet_var = 'leadjet_pt' # 0 to 640 in bins of 5
jet_binning = (128,0.,640.)
subjet_var = 'subljet_pt' # 0 to 380 in bins of 3.8
subjet_binning = (100,0.,380.)
ht_var = 'trueht' # 0 to 7000 in bins of 50
ht_binning = (140,0.,7000.)
ABCD_var = 'ph_sigmaIEIE:ph_chIsoCorr'
ABCD_binning = (120,0.,12.,50,0.,0.05)
#chIsovar = 'ph_chIso'
#sigIEIEvar = 'ph_sigmaIEIE'
chIso_B_var = 'ph_chIsoCorr[ptSorted_ph_mediumNoChIso_idx[0]]'
sigIEIE_B_var = 'ph_sigmaIEIE[ptSorted_ph_mediumNoChIso_idx[0]]'
chIso_C_var = 'ph_chIsoCorr[ptSorted_ph_mediumNoSIEIE_idx[0]]'
sigIEIE_C_var = 'ph_sigmaIEIE[ptSorted_ph_mediumNoSIEIE_idx[0]]'
chIso_D_var = 'ph_chIsoCorr[ptSorted_ph_mediumNoSIEIENoChIso_idx[0]]'
sigIEIE_D_var = 'ph_sigmaIEIE[ptSorted_ph_mediumNoSIEIENoChIso_idx[0]]'
twoD_B_var = 'ph_sigmaIEIE[ptSorted_ph_mediumNoChIso_idx[0]]:ph_chIsoCorr[ptSorted_ph_mediumNoChIso_idx[0]]'
twoD_C_var = 'ph_sigmaIEIE[ptSorted_ph_mediumNoSIEIE_idx[0]]:ph_chIsoCorr[ptSorted_ph_mediumNoSIEIE_idx[0]]'
twoD_D_var = 'ph_sigmaIEIE[ptSorted_ph_mediumNoSIEIENoChIso_idx[0]]:ph_chIsoCorr[ptSorted_ph_mediumNoSIEIENoChIso_idx[0]]'
sigIEIE_binning = (50,0.,0.05)
chIso_binning = (120,0.,12.)
#---------------------------------------
# draw the histograms
#---------------------------------------
hist_shape = clone_sample_and_draw( sampMan, sample, plot_var, full_sel_shape, binning )
hist_num = clone_sample_and_draw( sampMan, sample, plot_var, full_sel_num , binning )
hist_den = clone_sample_and_draw( sampMan, sample, plot_var, full_sel_den , binning )
sampMan.outputs['%s_shapehist_%s' %(sample,suffix)] = hist_shape
sampMan.outputs['%s_numhist_%s' %(sample,suffix)] = hist_num
sampMan.outputs['%s_denhist_%s' %(sample,suffix)] = hist_den
#hist_ABCD = clone_sample_and_draw( sampMan, sample, ABCD_var, full_sel_ABCD, ABCD_binning )
hist_B = clone_sample_and_draw( sampMan, sample, twoD_B_var, full_sel_shape, ABCD_binning )
hist_C = clone_sample_and_draw( sampMan, sample, twoD_C_var, full_sel_num, ABCD_binning )
hist_D = clone_sample_and_draw( sampMan, sample, twoD_D_var, full_sel_den, ABCD_binning )
hist_1d_sigmaIEIE_B = clone_sample_and_draw( sampMan, sample, sigIEIE_B_var, full_sel_shape, sigIEIE_binning )
hist_1d_chIso_B = clone_sample_and_draw( sampMan, sample, chIso_B_var, full_sel_shape, chIso_binning )
hist_1d_sigmaIEIE_C = clone_sample_and_draw( sampMan, sample, sigIEIE_C_var, full_sel_num, sigIEIE_binning )
hist_1d_chIso_C = clone_sample_and_draw( sampMan, sample, chIso_C_var, full_sel_num, chIso_binning )
hist_1d_sigmaIEIE_D = clone_sample_and_draw( sampMan, sample, sigIEIE_D_var, full_sel_den, sigIEIE_binning )
hist_1d_chIso_D = clone_sample_and_draw( sampMan, sample, chIso_D_var, full_sel_den, chIso_binning )
sampMan.outputs['%s_sigmaIEIE_B_%s' %(sample,suffix)] = hist_1d_sigmaIEIE_B
sampMan.outputs['%s_sigmaIEIE_C_%s' %(sample,suffix)] = hist_1d_sigmaIEIE_C
sampMan.outputs['%s_sigmaIEIE_D_%s' %(sample,suffix)] = hist_1d_sigmaIEIE_D
sampMan.outputs['%s_chIso_B_%s' %(sample,suffix)] = hist_1d_chIso_B
sampMan.outputs['%s_chIso_C_%s' %(sample,suffix)] = hist_1d_chIso_C
sampMan.outputs['%s_chIso_D_%s' %(sample,suffix)] = hist_1d_chIso_D
#---------------------------------------
# make fit managers
#---------------------------------------
fitMan_shape = FitManager( 'dijet', 3, '%s_shape' %sample, hist_shape, plot_var, eta_cut, xvar, label_shape, useRooFit=False )
fitMan_num = FitManager( 'dijet', 3, '%s_num' %sample, hist_num, plot_var, eta_cut, xvar, label_num, useRooFit=False )
fitMan_den = FitManager( 'dijet', 3, '%s_den' %sample, hist_den, plot_var, eta_cut, xvar, label_den, useRooFit=False )
#---------------------------------------
# Do the fits
#---------------------------------------
result_shape= fitMan_shape.fit_histogram( ws )
result_num = fitMan_num.fit_histogram( ws )
result_den = fitMan_den.fit_histogram( ws )
print 'DEBUG: results of fit manager (shape, num, den): ',result_shape,result_num,result_den
#---------------------------------------
# save the results
#---------------------------------------
fitMan_den.save_fit( sampMan, ws, logy=True )
fitMan_shape.save_fit( sampMan, ws, logy=True )
fitMan_num.save_fit( sampMan, ws, logy=True )
#---------------------------------------
# calculate the function that describes
# the ratio of num/den
#---------------------------------------
_integral_num = ws.pdf( 'dijet_%s' %label_num ).getNormIntegral(ROOT.RooArgSet( xvar ) )
_integral_den = ws.pdf( 'dijet_%s' %label_den).getNormIntegral(ROOT.RooArgSet( xvar ) )
_integral_shape = ws.pdf( 'dijet_%s' %label_shape).getNormIntegral(ROOT.RooArgSet( xvar ) )
func_integral_num = _integral_num.getValV()
func_integral_den = _integral_den.getValV()
func_integral_shape = _integral_shape.getValV()
hist_integral_num = result_num['integral'].n
hist_integral_den = result_den['integral'].n
hist_integral_shape = result_shape['integral'].n
norm_num = hist_integral_num / func_integral_num
norm_den = hist_integral_den / func_integral_den
norm_shape = hist_integral_shape / func_integral_shape
print 'func integral Num = ', func_integral_num
print 'hist integral Num = ', hist_integral_num
print 'normalization Num = ', norm_num
print 'func integral Den = ', func_integral_den
print 'hist integral Den = ', hist_integral_den
print 'normalization Den = ', norm_den
print 'func integral Shape = ', func_integral_shape
print 'hist integral Shape = ', hist_integral_shape
print 'normalization Shape = ', norm_shape
power_pred_name = 'power_pred_%s' %suffix
logcoef_pred_name = 'logcoef_pred_%s' %suffix
log2coef_pred_name = 'log2coef_pred_%s' %suffix
power_ratio_name = 'power_ratio_%s' %suffix
logcoef_ratio_name = 'logcoef_ratio_%s' %suffix
#name_power_num ='power'
#name_power_den ='power'
#name_power_shape ='power'
#name_logcoef_num ='logcoef1'
#name_logcoef_den ='logcoef1'
#name_logcoef_shape ='logcoef1'
name_power_num ='dijet_order1_%s' %label_num
name_power_den ='dijet_order1_%s' %label_den
name_power_shape ='dijet_order1_%s' %label_shape
name_logcoef_num ='dijet_order2_%s' %label_num
name_logcoef_den ='dijet_order2_%s' %label_den
name_logcoef_shape ='dijet_order2_%s' %label_shape
name_log2coef_num ='dijet_order3_%s' %label_num
name_log2coef_den ='dijet_order3_%s' %label_den
name_log2coef_shape ='dijet_order3_%s' %label_shape
val_power_num = ws.var( name_power_num )
val_power_den = ws.var( name_power_den )
val_power_shape = ws.var( name_power_shape )
val_logcoef_num = ws.var( name_logcoef_num )
val_logcoef_den = ws.var( name_logcoef_den )
val_logcoef_shape = ws.var( name_logcoef_shape )
val_log2coef_num = ws.var( name_log2coef_num )
val_log2coef_den = ws.var( name_log2coef_den )
val_log2coef_shape = ws.var( name_log2coef_shape )
power_pred = ROOT.RooRealVar( power_pred_name , 'power' , (val_power_num.getValV() + val_power_shape.getValV() - val_power_den.getValV()) , -100, 100)
logcoef_pred = ROOT.RooRealVar( logcoef_pred_name , 'logcoef', (val_logcoef_num.getValV() + val_logcoef_shape.getValV() - val_logcoef_den.getValV()), -10, 10 )
log2coef_pred = ROOT.RooRealVar( log2coef_pred_name , 'log2coef', (val_log2coef_num.getValV() + val_log2coef_shape.getValV() - val_log2coef_den.getValV()), -10, 10 )
#power_ratio = ROOT.RooRealVar( power_ratio_name , 'power' , val_power_num - val_power_den , -100, 100)
#logcoef_ratio = ROOT.RooRealVar( logcoef_ratio_name, 'logcoef', val_logcoef_num - val_logcoef_den, -10, 10 )
#func = 'TMath::Power(@0/13000, @1+@2*TMath::Log10(@0/13000))'
#prediction = ROOT.RooGenericPdf('dijet_prediction_%s' %suffix , 'prediction', func, ROOT.RooArgList(xvar,power_pred, logcoef_pred))
func = 'TMath::Power(@0/13000, @1+@2*TMath::Log10(@0/13000)+@3*TMath::Log10(@0/13000)*TMath::Log10(@0/13000))'
prediction = ROOT.RooGenericPdf('dijet_prediction_%s' %suffix , 'prediction', func, ROOT.RooArgList(xvar,power_pred, logcoef_pred, log2coef_pred))
norm_pred = ROOT.RooRealVar( 'dijet_prediction_%s_norm' %(suffix), 'prediction normalization', (hist_integral_shape * hist_integral_num) / hist_integral_den )
getattr( ws , 'import' ) ( norm_pred )
getattr( ws , 'import' ) ( prediction )
#---------------------------------------
# Save the ABCD plots
#---------------------------------------
#can_ABCD = ROOT.TCanvas( str(uuid.uuid4()), '' )
#can_ABCD.SetLogz()
#hist_ABCD.Scale(1./hist_ABCD.Integral())
#hist_ABCD.Draw("colz")
#print "DEBUG: ABCD integral = ",hist_ABCD.Integral()
can_B = ROOT.TCanvas( str(uuid.uuid4()), '' )
can_B.SetLogz()
hist_B.Scale(1./hist_B.Integral())
hist_B.Draw("colz")
print "DEBUG: B integral = ",hist_B.Integral()
can_C = ROOT.TCanvas( str(uuid.uuid4()), '' )
hist_C.Scale(1./hist_C.Integral())
can_C.SetLogz()
hist_C.Draw("colz")
print "DEBUG: C integral = ",hist_C.Integral()
can_D = ROOT.TCanvas( str(uuid.uuid4()), '' )
hist_D.Scale(1./hist_D.Integral())
can_D.SetLogz()
hist_D.Draw("colz")
print "DEBUG: D integral = ",hist_D.Integral()
can_1d_sig_B = ROOT.TCanvas( str(uuid.uuid4()), '' )
can_1d_sig_B.SetLogy()
hist_1d_sigmaIEIE_B.Draw('l')
can_1d_iso_B = ROOT.TCanvas( str(uuid.uuid4()), '' )
can_1d_iso_B.SetLogy()
hist_1d_chIso_B.Draw('l')
can_1d_sig_C = ROOT.TCanvas( str(uuid.uuid4()), '' )
can_1d_sig_C.SetLogy()
hist_1d_sigmaIEIE_C.Draw('l')
can_1d_iso_C = ROOT.TCanvas( str(uuid.uuid4()), '' )
can_1d_iso_C.SetLogy()
hist_1d_chIso_C.Draw('l')
can_1d_sig_D = ROOT.TCanvas( str(uuid.uuid4()), '' )
can_1d_sig_D.SetLogy()
hist_1d_sigmaIEIE_D.Draw('l')
can_1d_iso_D = ROOT.TCanvas( str(uuid.uuid4()), '' )
can_1d_iso_D.SetLogy()
hist_1d_chIso_D.Draw('l')
#plot sigmaIEIE in regions B, C, D
can_1d_sig = ROOT.TCanvas( str(uuid.uuid4()), '' )
can_1d_sig.SetLogy()
hist_norm_1d_sigmaIEIE_B = ROOT.TH1F(str(uuid.uuid4()) , str(uuid.uuid4()), hist_1d_sigmaIEIE_B.GetNbinsX(), hist_1d_sigmaIEIE_B.GetXaxis().GetXmin(), hist_1d_sigmaIEIE_B.GetXaxis().GetXmax())
hist_norm_1d_sigmaIEIE_C = ROOT.TH1F(str(uuid.uuid4()) , str(uuid.uuid4()), hist_1d_sigmaIEIE_C.GetNbinsX(), hist_1d_sigmaIEIE_C.GetXaxis().GetXmin(), hist_1d_sigmaIEIE_C.GetXaxis().GetXmax())
hist_norm_1d_sigmaIEIE_D = ROOT.TH1F(str(uuid.uuid4()) , str(uuid.uuid4()), hist_1d_sigmaIEIE_D.GetNbinsX(), hist_1d_sigmaIEIE_D.GetXaxis().GetXmin(), hist_1d_sigmaIEIE_D.GetXaxis().GetXmax())
hist_1d_sigmaIEIE_B.Copy(hist_norm_1d_sigmaIEIE_B)
hist_1d_sigmaIEIE_C.Copy(hist_norm_1d_sigmaIEIE_C)
hist_1d_sigmaIEIE_D.Copy(hist_norm_1d_sigmaIEIE_D)
ROOT.SetOwnership( hist_norm_1d_sigmaIEIE_B, False )
ROOT.SetOwnership( hist_norm_1d_sigmaIEIE_C, False )
ROOT.SetOwnership( hist_norm_1d_sigmaIEIE_D, False )
hist_norm_1d_sigmaIEIE_B.Scale(1./hist_norm_1d_sigmaIEIE_B.Integral())
hist_norm_1d_sigmaIEIE_C.Scale(1./hist_norm_1d_sigmaIEIE_C.Integral())
hist_norm_1d_sigmaIEIE_D.Scale(1./hist_norm_1d_sigmaIEIE_D.Integral())
hist_norm_1d_sigmaIEIE_B.SetLineColor(2)
hist_norm_1d_sigmaIEIE_C.SetLineColor(1)
hist_norm_1d_sigmaIEIE_D.SetLineColor(4)
hist_norm_1d_sigmaIEIE_B.SetMarkerColor(2)
hist_norm_1d_sigmaIEIE_C.SetMarkerColor(1)
hist_norm_1d_sigmaIEIE_D.SetMarkerColor(4)
hist_norm_1d_sigmaIEIE_B.Draw('l')
hist_norm_1d_sigmaIEIE_C.Draw('same')
hist_norm_1d_sigmaIEIE_D.Draw('same')
#plot chIso in regions B, C, D
can_1d_iso = ROOT.TCanvas( str(uuid.uuid4()), '' )
can_1d_iso.SetLogy()
hist_norm_1d_chIso_B = ROOT.TH1F(str(uuid.uuid4()) , str(uuid.uuid4()), hist_1d_chIso_B.GetNbinsX(), hist_1d_chIso_B.GetXaxis().GetXmin(), hist_1d_chIso_B.GetXaxis().GetXmax())
hist_norm_1d_chIso_C = ROOT.TH1F(str(uuid.uuid4()) , str(uuid.uuid4()), hist_1d_chIso_C.GetNbinsX(), hist_1d_chIso_C.GetXaxis().GetXmin(), hist_1d_chIso_C.GetXaxis().GetXmax())
hist_norm_1d_chIso_D = ROOT.TH1F(str(uuid.uuid4()) , str(uuid.uuid4()), hist_1d_chIso_D.GetNbinsX(), hist_1d_chIso_D.GetXaxis().GetXmin(), hist_1d_chIso_D.GetXaxis().GetXmax())
hist_1d_chIso_B.Copy(hist_norm_1d_chIso_B)
hist_1d_chIso_C.Copy(hist_norm_1d_chIso_C)
hist_1d_chIso_D.Copy(hist_norm_1d_chIso_D)
ROOT.SetOwnership( hist_norm_1d_chIso_B, False )
ROOT.SetOwnership( hist_norm_1d_chIso_C, False )
ROOT.SetOwnership( hist_norm_1d_chIso_D, False )
hist_norm_1d_chIso_B.Scale(1./hist_norm_1d_chIso_B.Integral())
hist_norm_1d_chIso_C.Scale(1./hist_norm_1d_chIso_C.Integral())
hist_norm_1d_chIso_D.Scale(1./hist_norm_1d_chIso_D.Integral())
hist_norm_1d_chIso_B.SetLineColor(2)
hist_norm_1d_chIso_C.SetLineColor(1)
hist_norm_1d_chIso_D.SetLineColor(4)
hist_norm_1d_chIso_B.SetMarkerColor(2)
hist_norm_1d_chIso_C.SetMarkerColor(1)
hist_norm_1d_chIso_D.SetMarkerColor(4)
hist_norm_1d_chIso_C.Draw('l')
hist_norm_1d_chIso_B.Draw('same')
hist_norm_1d_chIso_D.Draw('same')
#---------------------------------------
# Save the ratio histogram
#---------------------------------------
# canvas for ratio histogram
can_ratio = ROOT.TCanvas( 'canv_ratio_%s' %(plot_var), 'canv_ratio_%s' %(plot_var) )
#ROOT.SetOwnership( can_ratio, False )
# ratio histogram = numerator histogram/denominator histogram
ratiohist = ROOT.TH1F( 'ratio_%s' %(plot_var), 'ratio_%s' %(plot_var), hist_num.GetNbinsX(), hist_num.GetXaxis().GetXmin(), hist_num.GetXaxis().GetXmax())
hist_num.Copy( ratiohist)
ROOT.SetOwnership( ratiohist, False )
print "DEBUG: first bin of num before div = ",ratiohist.GetBinContent(1)
print "DEBUG: first bin of den before div = ",hist_den.GetBinContent(1)
ratiohist.Divide( hist_den )
print "DEBUG: first bin of ratio after div = ",ratiohist.GetBinContent(1)
ratiohist.SetMarkerStyle(20)
ratiohist.SetMarkerSize(1)
ratiohist.GetXaxis().SetTitle( 'Transverse Mass [GeV]' )
ratiohist.GetYaxis().SetTitle( 'ratio of passing to failing %s' %shape_var )
ratiohist.Draw()
ratiohist.SetStats(0)
ratiohist.SetMinimum( 0)
ratiohist.SetMaximum( 5)
#ratio_func = ROOT.TF1( 'ratio_func_%s'%(str(uuid.uuid4())), '( [2]*TMath::Power(x/13000, [0] + [1]*TMath::Log10(x/13000) ) ) ', xmin, xmax )
ratio_func = ROOT.TF1( 'ratio_func', '[0]', xmin, xmax )
print 'DEBUG: ratio function = ',ratio_func
ROOT.SetOwnership( ratio_func, False )
print 'DEBUG: ratio function value at 200 GeV = ',ratio_func.Eval(200.)
print 'DEBUG: ratio of integrals = ',hist_num.Integral()/hist_den.Integral()
ratio_func.SetParameter(0, hist_num.Integral()/hist_den.Integral())
#ratio_func.SetParameter(0, result_num[name_power_num].n - result_den[name_power_den].n)
#ratio_func.SetParameter(1, result_num[name_logcoef_num].n - result_den[name_logcoef_den].n )
#ratio_func.SetParameter(2, norm_num / norm_den )
ratiohist.Fit("ratio_func","RL","",xmin,xmax)
print 'xmin and xmax are ',xmin,xmax
f = ratiohist.GetFunction("ratio_func")
fitvalue = ratio_func.GetParameter(0)
print 'DEBUG: fitted straight line at ',fitvalue
print 'DEBUG: chisquare of fit = ',f.GetChisquare()
print 'DEBUG: ndof of fit = ',f.GetNDF()
print 'DEBUG: testing f->GetParameter(0) = ',f.GetParameter(0)
print 'DEBUG: testing f->GetParError(0) = ',f.GetParError(0)
ratio_func.Draw('same')
print 'DEBUG: prediction in A from B*C/D histograms = ',hist_shape.Integral(hist_shape.FindBin( xmin ), hist_shape.FindBin( xmax ))*hist_num.Integral(hist_num.FindBin( xmin ), hist_num.FindBin( xmax ))/hist_den.Integral(hist_den.FindBin( xmin ), hist_den.FindBin( xmax ))
#ratio_power_v = val_power_num.getValV() - val_power_den.getValV()
#ratio_power_e = math.sqrt(val_power_num.getErrorHi()*val_power_num.getErrorHi() + val_power_den.getErrorHi()*val_power_den.getErrorHi() )
#ratio_logcoef_v = val_logcoef_num.getValV() - val_logcoef_den.getValV()
#ratio_logcoef_e = math.sqrt(val_logcoef_num.getErrorHi()*val_logcoef_num.getErrorHi() + val_logcoef_den.getErrorHi()*val_logcoef_den.getErrorHi() )
#power_tex = ROOT.TLatex(0, 0, 'power = %.01f #pm %.02f' %( ratio_power_v, ratio_power_e ))
#ROOT.SetOwnership( power_tex, False )
#logcoef_tex = ROOT.TLatex(0, 0, 'logcoef = %.01f #pm %.02f' %( ratio_logcoef_v, ratio_logcoef_e ))
#ROOT.SetOwnership( logcoef_tex, False )
#power_tex.SetNDC()
#logcoef_tex.SetNDC()
#power_tex .SetX( 0.6 )
#power_tex .SetY( 0.84 )
#logcoef_tex.SetX( 0.6 )
#logcoef_tex.SetY( 0.78 )
#power_tex.Draw('same')
#logcoef_tex.Draw('same')
#sampMan.outputs['wjets_ABCD_%s' %suffix] = can_ABCD
sampMan.outputs['%s_B_%s' %(sample,suffix)] = can_B
sampMan.outputs['%s_C_%s' %(sample,suffix)] = can_C
sampMan.outputs['%s_D_%s' %(sample,suffix)] = can_D
sampMan.outputs['%s_sigmaIEIE_B_%s' %(sample,suffix)] = can_1d_sig_B
sampMan.outputs['%s_chIso_B_%s' %(sample,suffix)] = can_1d_iso_B
sampMan.outputs['%s_sigmaIEIE_C_%s' %(sample,suffix)] = can_1d_sig_C
sampMan.outputs['%s_chIso_C_%s' %(sample,suffix)] = can_1d_iso_C
sampMan.outputs['%s_sigmaIEIE_D_%s' %(sample,suffix)] = can_1d_sig_D
sampMan.outputs['%s_chIso_D_%s' %(sample,suffix)] = can_1d_iso_D
sampMan.outputs['%s_sigmaIEIE_BCD_%s' %(sample,suffix)] = can_1d_sig
sampMan.outputs['%s_chIso_BCD_%s' %(sample,suffix)] = can_1d_iso
if closure :
# sampMan.outputs['wjetsclosure_ratio_%s' %suffix] = can_ratio
sampMan.outputs['%sclosure_ratio_hist_%s' %(sample,suffix)] = ratiohist
sampMan.outputs['%sclosure_ratio_func_%s' %(sample,suffix)] = ratio_func
#sampMan.outputs['wjetsclosure_ratio_power_%s' %suffix] = power_tex
#sampMan.outputs['wjetsclosure_ratio_logcoef_%s' %suffix] = logcoef_tex
else :
# sampMan.outputs['wjets_ratio_%s' %suffix] = can_ratio
sampMan.outputs['%s_ratio_hist_%s' %(sample,suffix)] = ratiohist
sampMan.outputs['%s_ratio_func_%s' %(sample,suffix)] = ratio_func
#sampMan.outputs['wjets_ratio_power_%s' %suffix] = power_tex
#sampMan.outputs['wjets_ratio_logcoef_%s' %suffix] = logcoef_tex
if closure :
# canvas for signal region predictions
can_sr = ROOT.TCanvas( 'canv_sig_%s_%s' %(sample,plot_var), 'canv_sig_%s_%s' %(sample,plot_var) )
ROOT.SetOwnership( can_sr, False )
hist_sr = clone_sample_and_draw( sampMan, sample, plot_var, full_sel_sr , binning )
ROOT.SetOwnership( hist_sr, False )
hist_sr.GetXaxis().SetTitle( 'Transverse Mass [GeV]' )
#hist_sr.GetYaxis().SetTitle( 'ratio of passing to failing %s' %shape_var ) ##really?
hist_sr.GetYaxis().SetTitle( 'Events in signal region' )
# fit a dijet2 function to region A
fitMan_sr = FitManager( 'dijet', 3, sample, hist_sr, plot_var, eta_cut, xvar, suffix, useRooFit=False )
result_sr = fitMan_sr.fit_histogram( ws )
# from histograms in MC regions B, C, and D
# predicted number of sr events
pred_val = (hist_integral_shape * hist_integral_num) / hist_integral_den
# from histogram in MC region A
# predicted number of sr events
tot_sr = hist_sr.Integral( hist_sr.FindBin( xmin ), hist_sr.FindBin( xmax ) )
print 'DEBUG: SR prediction from BCD MC = ',pred_val
print 'DEBUG: SR MC events from A = ',tot_sr
print 'SR integral = ', tot_sr
# from functions fitted to regions BCD
# predicted number of sr events
sr_func = ROOT.TF1( 'sr_func', '( [2]*TMath::Power(x/13000, [0] + [1]*TMath::Log10(x/13000) ) ) ', xmin, xmax )
#sr_func = ROOT.TF1( 'sr_func', '[3]*TMath::Power(x/13000, [0]+[1]*TMath::Log10(x/13000)+[2]*TMath::Log10(x/13000)*TMath::Log10(x/13000))', xmin, xmax )
ROOT.SetOwnership( sr_func, False )
sr_func.SetParameter(0, result_num[name_power_num].n + result_shape[name_power_shape].n - result_den[name_power_den].n )
sr_func.SetParameter(1, result_num[name_logcoef_num].n + result_shape[name_logcoef_shape].n - result_den[name_logcoef_den].n )
#sr_func.SetParameter(2, result_num[name_log2coef_num].n + result_shape[name_log2coef_shape].n - result_den[name_log2coef_den].n )
sr_func.SetParameter(2, 1)
sr_int = sr_func.Integral( xmin, xmax )
print 'normalizations (shape, num, den) = ',norm_shape,norm_num,norm_den
#print 'Normalization = ',(norm_shape*norm_num) / norm_den
print 'func Integral SR Before = ', sr_func.Integral( xmin, xmax )
sr_func.SetParameter(2, pred_val/sr_int )
print 'func Integral SR After = ', sr_func.Integral( xmin, xmax )
print 'DEBUG: closure SR fit function value at 200 GeV = ',sr_func.Eval(200.)
hist_sr.Draw()
sr_func.Draw('same')
sampMan.outputs['%sclosure_sr_pred_%s' %(sample,suffix)] = can_sr
sampMan.outputs['%sclosure_srhist_%s' %(sample,suffix)] = hist_sr
tot_ratio = result_num['integral']/result_den['integral']
print 'DEBUG: result_num[integral]/result_den[integral] = ',tot_ratio
hist_jetpt = clone_sample_and_draw( sampMan, sample, jet_var, sel_base, jet_binning )
hist_subjetpt = clone_sample_and_draw( sampMan, sample, subjet_var, sel_base, subjet_binning )
hist_trueht = clone_sample_and_draw( sampMan, sample, ht_var, sel_base, ht_binning )
sampMan.outputs['%sclosure_jetpt_%s' %(sample,suffix)] = hist_jetpt
sampMan.outputs['%sclosure_subjetpt_%s' %(sample,suffix)] = hist_subjetpt
sampMan.outputs['%sclosure_trueht_%s' %(sample,suffix)] = hist_trueht