def analysis_tree_all_reweighed(graph, cuts, snodes, **kwargs):
cutnodes = []
for cut_name, cut in cuts:
lepton = cut_name.split("_")[0]
cutnode = tree.CutNode(cut, graph, cut_name, snodes, [],
filter_funcs=[
lambda x,lepton=lepton: tree.is_samp(x, lepton)
])
cutnodes.append(
cutnode
)
#an extra QCD cleaning cut on top of the previous cut, which is only done in antiiso
for syst in ["nominal", "up", "down"]:
cn = tree.CutNode(
Cuts.antiiso(lepton, syst) * Cuts.deltaR_QCD(),
graph, "antiiso_%s" % syst, [cutnode], [],
filter_funcs=[
lambda p: tree.is_samp(p, "antiiso")
]
)
cutnodes.append(
cn
)
from histo_descs import create_plots
create_plots(graph, cutnodes, **kwargs)
def data_mc_plot(samples, plot_def, name, lepton_channel, lumi, weight, physics_processes, use_antiiso=False):
logger.info('Plot in progress %s' % name)
merge_cmds = PhysicsProcess.get_merge_dict(physics_processes) #The actual merge dictionary
var = plot_def['var']
#Id var is a list/tuple, assume
if not isinstance(var, basestring):
try:
if lepton_channel == 'ele':
var = var[0]
elif lepton_channel == 'mu':
var = var[1]
except Exception as e:
logger.error("Plot variable 'var' specification incorrect for multi-variable plot: %s" % str(var))
raise e
cut = None
if lepton_channel == 'ele':
cut = plot_def['elecut']
elif lepton_channel == 'mu':
cut = plot_def['mucut']
cut_str = str(cut)
plot_range = plot_def['range']
do_norm = False
if 'normalize' in plot_def.keys() and plot_def['normalize']:
do_norm = True
hists_mc = dict()
hists_data = dict()
for name, sample in samples.items():
logger.debug("Starting to plot %s" % name)
if sample.isMC:
hist = sample.drawHistogram(var, cut_str, weight=str(weight), plot_range=plot_range)
hist.Scale(sample.lumiScaleFactor(lumi))
hists_mc[sample.name] = hist
if do_norm:
Styling.mc_style_nostack(hists_mc[sample.name], sample.name)
else:
Styling.mc_style(hists_mc[sample.name], sample.name)
if "fitpars" in plot_def.keys():
rescale_to_fit(sample.name, hist, plot_def["fitpars"][lepton_channel])
elif "antiiso" in name and plot_def['estQcd'] and not use_antiiso:
# Make loose template
#Y U NO LOOP :) -JP
region = '2j1t'
if '2j0t' in plot_def['estQcd']: region='2j0t'
if '3j0t' in plot_def['estQcd']: region='3j0t'
if '3j1t' in plot_def['estQcd']: region='3j1t'
if '3j2t' in plot_def['estQcd']: region='3j2t'
qcd_extra_cut = Cuts.deltaR(0.3)*Cuts.antiiso(lepton_channel)
#Take the loose template with a good shape from the N-jet, M-tag, post lepton selection region with high statistics
qcd_loose_cut = cutlist[region]*cutlist['presel_'+lepton_channel]*qcd_extra_cut
#Take the template which can be correctly normalized from the actual region with inverted isolation cuts
qcd_cut = cut*qcd_extra_cut
hist_qcd_loose = sample.drawHistogram(var, str(qcd_loose_cut), weight="1.0", plot_range=plot_range)
hist_qcd = sample.drawHistogram(var, str(qcd_cut), weight="1.0", plot_range=plot_range)
logger.debug("Using the QCD scale factor %s: %.2f" % (plot_def['estQcd'], qcdScale[lepton_channel][plot_def['estQcd']]))
hist_qcd.Scale(qcdScale[lepton_channel][plot_def['estQcd']])
hist_qcd_loose.Scale(hist_qcd.Integral()/hist_qcd_loose.Integral())
if var=='cos_theta':
hist_qcd=hist_qcd_loose
sampn = "QCD"+sample.name
#Rescale the QCD histogram to the eta_lj fit
if "fitpars" in plot_def.keys():
rescale_to_fit(sampn, hist_qcd, plot_def["fitpars"][lepton_channel])
hists_mc[sampn] = hist_qcd
hists_mc[sampn].SetTitle('QCD')
if do_norm:
Styling.mc_style_nostack(hists_mc[sampn], 'QCD')
else:
Styling.mc_style(hists_mc[sampn], 'QCD')
#Real ordinary data in the isolated region
elif not "antiiso" in name or use_antiiso:
hist_data = sample.drawHistogram(var, cut_str, weight="1.0", plot_range=plot_range)
hist_data.SetTitle('Data')
Styling.data_style(hist_data)
hists_data[name] = hist_data
if len(hists_data.values())==0:
raise Exception("Couldn't draw the data histogram")
#Combine the subsamples to physical processes
hist_data = sum(hists_data.values())
merge_cmds['QCD']=["QCD"+merge_cmds['data'][0]]
order=['QCD']+PhysicsProcess.desired_plot_order
if plot_def['log']:
order = PhysicsProcess.desired_plot_order_log+['QCD']
merged_hists = merge_hists(hists_mc, merge_cmds, order=order)
if hist_data.Integral()<=0:
logger.error(hists_data)
logger.error("hist_data.entries = %d" % hist_data.GetEntries())
logger.error("hist_data.integral = %d" % hist_data.Integral())
raise Exception("Histogram for data was empty. Something went wrong, please check.")
if do_norm:
for k,v in merged_hists.items():
v.Scale(1./v.Integral())
hist_data.Scale(1./hist_data.Integral())
htot = sum(merged_hists.values())
chi2 = hist_data.Chi2Test(htot, "UW CHI2/NDF")
if chi2>20:#FIXME: uglyness
logger.error("The chi2 between data and MC is large (%s, chi2=%.2f). You may have errors with your samples!" %
(name, chi2)
)
logger.info("MC : %s" % " ".join(map(lambda x: "%.1f" % x, list(htot.y()))))
logger.info("DATA: %s" % " ".join(map(lambda x: "%.1f" % x, list(hist_data.y()))))
logger.info("diff: %s" % str(
" ".join(map(lambda x: "%.1f" % x, numpy.abs(numpy.array(list(htot.y())) - numpy.array(list(hist_data.y())))))
))
merged_hists_l = merged_hists.values()
PhysicsProcess.name_histograms(physics_processes, merged_hists)
leg_style = ['p','f']
if do_norm:
leg_style=['p','l']
leg = legend([hist_data] + list(reversed(merged_hists_l)), legend_pos=plot_def['labloc'], styles=leg_style)
canv = ROOT.TCanvas()
#Make the stacks
stacks_d = OrderedDict()
stacks_d["mc"] = merged_hists_l
stacks_d["data"] = [hist_data]
#label
xlab = plot_def['xlab']
if not isinstance(xlab, basestring):
if lepton_channel == 'ele':
xlab = xlab[0]
else:
xlab = xlab[1]
ylab = 'N / '+str((1.*(plot_range[2]-plot_range[1])/plot_range[0]))
if plot_def['gev']:
ylab+=' GeV'
fact = 1.5
if plot_def['log']:
fact = 10
plow=0.3
if do_norm:
plow=0
#Make a separate pad for the stack plot
p1 = ROOT.TPad("p1", "p1", 0, plow, 1, 1)
p1.Draw()
p1.SetTicks(1, 1);
p1.SetGrid();
p1.SetFillStyle(0);
p1.cd()
stacks = plot_hists_stacked(p1, stacks_d, x_label=xlab, y_label=ylab, max_bin_mult = fact, do_log_y = plot_def['log'], stack = (not do_norm))
#Put the the lumi box where the legend is not
boxloc = 'top-right'
if plot_def['labloc'] == 'top-right':
boxloc = 'top-left'
chan = 'Electron'
if lepton_channel == "mu":
chan = 'Muon'
additional_comments = ""
if 'cutname' in plot_def.keys():
additional_comments += ", " + plot_def['cutname'][lepton_channel]
lbox = lumi_textbox(lumi,
boxloc,
'preliminary',
chan + ' channel' + additional_comments
)
#Draw everything
lbox.Draw()
leg.Draw()
canv.Draw()
#Keep the handles just in case
canv.PAD1 = p1
canv.STACKS = stacks
canv.LEGEND = legend
canv.LUMIBOX = lbox
return canv, merged_hists, htot, hist_data
isos[lep] = dict()
isos[lep]['iso'] = CutNode(
Cut("1.0"), #At present no special cuts have to be applied on the ISO region
graph, lep + "__iso", par, [],
filter_funcs=[lambda x: "/iso/" in x[0].name]
)
isol.append(isos[lep]['iso'])
#Antiiso with variations in the isolation cut
for aiso_syst in ["nominal", "up", "down"]:
#dR with variations
for dr_syst in ["nominal", "up", "down"]:
cn = 'antiiso_' + aiso_syst + '_dR_' + dr_syst
isos[lep][cn] = CutNode(
#Apply any additional anti-iso cuts (like dR) along with antiiso variations.
Cuts.antiiso(lep, aiso_syst) * Cuts.deltaR_QCD(dr_syst),
graph, lep + "__" + cn, par, [],
filter_funcs=[
lambda x: is_samp(x, "antiiso") and is_samp(x, "data")
]
)
isol.append(isos[lep][cn])
# [iso, antiiso] --> jet --> [jets2-3]
jet = Node(graph, "jet", isol, [])
jets = dict()
for i in [2,3]:
jets[i] = CutNode(Cuts.n_jets(i), graph, "%dj"%i, [jet], [])
tag = Node(graph, "tag", jet.children(), [])
tags = dict()