def mc_amount(cut, weight, lumi=12210, ref=None):
histsD = dict()
for samp in samples:
histsD[samp.name] = samp.drawHistogram("mu_pt",
str(cut),
dtype="float",
weight=weight,
plot_range=[100, 0, 100000000])
for name, hist in histsD.items():
hist.normalize_lumi(lumi)
for name, hist in histsD.items():
histsD[name] = hist.hist
merge_cmd = dict()
merge_cmd["t-channel incl"] = ["T_t", "Tbar_t"]
merge_cmd["t-channel excl"] = ["T_t_ToLeptons", "Tbar_t_ToLeptons"]
merge_cmd["t#bar{t} excl"] = ["TTJets_FullLept", "TTJets_SemiLept"]
merge_cmd["QCD (MC)"] = ["QCDMu"]
merge_cmd["t#bar{t} incl"] = ["TTJets_MassiveBinDECAY"]
merge_cmd["WJets incl"] = ["WJets_inclusive"]
merge_cmd["WJets excl"] = [
"W1Jets_exclusive", "W2Jets_exclusive", "W3Jets_exclusive",
"W4Jets_exclusive"
]
merged_hists = merge_hists(histsD, merge_cmd)
normd = dict()
for hn, h in merged_hists.items():
normd[hn] = get_hist_int_err(h)
return merged_hists, normd
def compare_plot(plot_def):
"""
Draws a comparison plot based on a plot definition dictionary, which has the following arguments:
*mandatory*
var: a string with the TTree branch name / TFormula expression to plot
plot_range: the plot range definition for Sample.drawHistogram
items: a list of tuples, containing the plots to be drawn in the following format
[
(name_of_plot, pathname_of_file, filename, cut_object, weight_object)
]
*optional*
lumi: the luminosity to normalize to in /pb
merge_cmds: a merge command for the merge_hists method, which merges the histograms
according to the names in the items list
hist_callback: a method that is called on each of the histograms after merging,
having the form:
def f(hist_name, hist):
dostuff
return hist
x_label: a string the the label for the x axis
*returns*
a tuple (canvas, final_histogram_dict)
"""
proc_cuts = plot_def['items']
var = plot_def.get('var')
plot_range = plot_def.get('range')
lumi = plot_def.get('lumi', 20000)
merge_cmds = plot_def.get('merge_cmds', None)
hist_callback = plot_def.get('hist_callback', None)
x_label = plot_def.get('xlab', 'xlab')
hists = {}
for name, path, fname, cut, weight in proc_cuts:
fname = join(path, fname)
sample = Sample.fromFile(fname)
hists[name] = sample.drawHistogram(var, str(cut), weight=str(weight), plot_range=plot_range)
hists[name].SetName(name)
hists[name] = copy.deepcopy(hists[name])
hists[name].Scale(sample.lumiScaleFactor(lumi))
if merge_cmds:
hists_merged = merge_hists(hists, merge_cmds)
hists = hists_merged
if hist_callback:
for hn, h in hists.items():
hists[hn] = hist_callback(hn, h)
canv = plot_hists_dict(hists, x_label=x_label)
return canv, hists
def mc_amount(cut, weight, lumi=12210, ref=None):
histsD = dict()
for samp in samples:
histsD[samp.name] = samp.drawHistogram("mu_pt", str(cut), dtype="float", weight=weight, plot_range=[100, 0, 100000000])
histsD[samp.name].Scale(samp.lumiScaleFactor(lumi))
merge_cmd = dict()
merge_cmd["t-channel incl"] = ["T_t", "Tbar_t"]
merge_cmd["t-channel excl"] = ["T_t_ToLeptons", "Tbar_t_ToLeptons"]
merge_cmd["t#bar{t} excl"] = ["TTJets_FullLept", "TTJets_SemiLept"]
merge_cmd["QCD (MC)"] = ["QCDMu"]
merge_cmd["t#bar{t} incl"] = ["TTJets_MassiveBinDECAY"]
merge_cmd["WJets incl"] = ["WJets_inclusive"]
merge_cmd["WJets excl"] = ["W1Jets_exclusive", "W2Jets_exclusive", "W3Jets_exclusive", "W4Jets_exclusive"]
merged_hists = merge_hists(histsD, merge_cmd)
normd = dict()
for hn, h in merged_hists.items():
normd[hn] = get_hist_int_err(h)
return merged_hists, normd
}
recreate = True
if recreate:
for sn in sampnames:
sample = Sample.fromFile(path + "/" + sn + ".root")
for wn, w in weights:
hi = sample.drawHistogram(var, str(cut), weight=w, plot_range=plot_range)
hi.Scale(sample.lumiScaleFactor(lumi))
hists[wn][sample.name] = hi
merged = dict()
for wn, w in weights:
merged[wn] = merge_hists(hists[wn], merge_cmds)
hc = HistCollection(merged[wn], name="bweight_%s" % wn)
hc.save(".")
colls = dict()
for wn, w in weights:
hc = HistCollection.load("./bweight_%s.root" % wn)
colls[wn] = hc
for proc in merge_cmds.keys():
hists = OrderedDict([(wn, colls[wn].hists[proc]) for wn, w in weights])
for hn, h in hists.items():
h.SetTitle(pretty_names_weights[hn])
hists_normed = copy.deepcopy(hists)
def combine_templates(templates, patterns, conf):
"""
Args:
"""
hists = {}
hsources = []
for k in ["data", "mc_nom", "mc_varsamp", "mc_varproc"]:
items = templates[patterns[k]]
if len(items)==0:
raise MatchingException("Nothing matched to %s:%s" % (k, patterns[k]))
hsources += items
if len(hsources)==0:
raise ValueError("No histograms matched")
hqcd = NestedDict()
hqcd["nominal"][None] = []
for syst in ["yield", "iso"]:
for sdir in ["up", "down"]:
hqcd[syst][sdir] = []
templates_qcd = templates[patterns["data_antiiso"]]
if len(templates_qcd)==0:
raise MatchingException("Nothing matched to %s:%s" % ("data_antiiso", patterns["data_antiiso"]))
for keys, hist in templates[patterns["data_antiiso"]]:
if keys[1].startswith("antiiso"):
#We have isolation variations
isodir = keys[1].split("_")[1]
if isodir=="nominal":
hqcd["nominal"][None].append(hist)
hup = hist.Clone()
hdown = hist.Clone()
hup.Scale(qcd_yield_variations[0])
hdown.Scale(qcd_yield_variations[1])
hqcd["yield"]["up"].append(hup)
hqcd["yield"]["down"].append(hdown)
elif isodir in ["up", "down"]:
hqcd["iso"][isodir].append(hist)
else:
raise ValueError("Undefined isolation variation direction: %s" % isodir)
#We only have the nominal QCD shape
elif keys[1]=="weight__unweighted":
hqcd["nominal"][None].append(hist)
hup = hist.Clone()
hdown = hist.Clone()
hup.Scale(qcd_yield_variations[0])
hdown.Scale(qcd_yield_variations[1])
hqcd["yield"]["up"].append(hup)
hqcd["yield"]["down"].append(hdown)
#Placeholders for the isolation variation
for isodir in ["up", "down"]:
h = hist.Clone()
hqcd["iso"][isodir].append(h)
else:
raise Exception("Couldn't parse the QCD pattern: %s" % str(keys))
def map_leaves(di, f, equate=True):
for k, v in di.items():
if isinstance(v, dict):
map_leaves(v, f)
else:
if equate:
di[k] = f(v)
else:
f(v)
return di
#Sum the anti-iso data subsamples
map_leaves(hqcd, lambda li: reduce(lambda x,y: x+y, li))
#Normalize the isolation variations to the nominal
map_leaves(hqcd["iso"],
lambda hi:
hi.Scale(hqcd["nominal"][None].Integral() / hi.Integral()) if hi.Integral()>0 else 0,
equate=False
)
#Add the variated data-driven QCD templates
hsources += [
(("data", "qcd", "weight__unweighted"), hqcd["nominal"][None]),
(("data", "qcd", "weight__qcd_yield_up"), hqcd["yield"]["up"]),
(("data", "qcd", "weight__qcd_yield_down"), hqcd["yield"]["down"]),
(("data", "qcd", "weight__qcd_iso_up"), hqcd["iso"]["up"]),
(("data", "qcd", "weight__qcd_iso_down"), hqcd["iso"]["down"]),
]
#f = open('temp.pickle','wb')
#pickle.dump(hsources, f)
#f.close()
#load the histos from the temporary pickle
#f = open('temp.pickle','rb')
#hsources = pickle.load(f)
syst_scenarios = NestedDict()
for (sample_var, sample, weight_var), hist in hsources:
make_hist(hist)
# if "__ele" in weight_var:
# continue
if ".root" in sample:
sample = sample[:sample.index(".root")]
if "__" in weight_var:
spl = weight_var.split("__")
wn = spl[1]
else:
wn = weight_var
sample_var = sample_var.lower()
wtype = None
wdir = None
stype = None
sdir = None
syst = None
#Nominal weight, look for variated samples
if wn=="nominal":
syst = sample_var
elif wn=="unweighted":
syst="unweighted"
else:
#Variated weight, use only nominal sample or data in case of data-driven shapes
if not (sample_var=="nominal" or sample_var=="data"):
continue
syst = wn
if wn==conf.get_nominal_weight() and sample_var=="nominal":
logger.info("Using %s:%s as nominal sample for %s" % (wn, sample_var, sample))
syst_scenarios[sample]["nominal"][None] = hist
#A systematic scenario which has a separate systematic sample
elif sample_var == "syst":
try:
r = get_syst_from_sample_name(sample)
except Exception as e:
logger.warning("Unhandled systematic: %s" % str(e))
r = None
if not r:
continue
sample, systname, d = r
#sample = map_syst_sample_to_nominal(sample)
syst_scenarios[sample][systname][d] = hist
else:
logger.debug("Systematically variated weight: %s:%s %s" % (wn, sample_var, sample))
systname, d = get_updown(syst)
syst_scenarios[sample][systname][d] = hist
logger.info("histogram W3Jets_exclusive nominal: " + "%f %d" % (
syst_scenarios["W3Jets_exclusive"]["nominal"][None].Integral(),
syst_scenarios["W3Jets_exclusive"]["nominal"][None].GetEntries())
)
######################################
### Save systematics, fill missing ###
######################################
#########
# tchan #
#########
#T_t_ToLeptons mass_up is missing, take the mass down and flip the difference with the nominal
mnomt = syst_scenarios["T_t_ToLeptons"]["nominal"][None].Clone()
mdownt = syst_scenarios["T_t_ToLeptons"]["mass"]["down"].Clone()
mupt = (mnomt+mnomt-mdownt)
syst_scenarios["T_t_ToLeptons"]["mass"]["up"] = mupt
#########
# TTBar #
#########
#TTbar variations are provided for the inclusive only, fill them for the exclusive
nom_ttbar = syst_scenarios["TTJets_FullLept"]["nominal"][None] + syst_scenarios["TTJets_SemiLept"]["nominal"][None]
for syst in ["mass", "ttbar_scale", "ttbar_matching"]:
for sample in ["TTJets_FullLept", "TTJets_SemiLept"]:
for sd in ["up", "down"]:
syst_scenarios[sample][syst][sd] = syst_scenarios[sample]["nominal"][None] * syst_scenarios["TTJets"][syst][sd] / nom_ttbar
syst_scenarios.pop("TTJets")
syst_scenarios = syst_scenarios.as_dict()
#Create the output file
p = os.path.dirname(conf.get_outfile_unmerged())
if not os.path.exists(p):
os.makedirs(p)
of = ROOT.TFile(conf.get_outfile_unmerged() , "RECREATE")
of.cd()
#Get the list of all possible systematic scenarios that we have available
allsysts = get_all_systs(syst_scenarios)
for sampn, h1 in syst_scenarios.items():
#Consider all the possible systematic scenarios
for systname in allsysts:
#If we have it available, fine, use it
if systname in h1.keys():
h2 = h1[systname]
#If not, in case of MC and a non-trivial variation
elif not sampn.startswith("Single") and systname not in ["unweighted", "nominal"]:
#Try to get the unvariated template as a placeholder
h = h1.get("nominal", None)
if not h:
h = h1.get("unweighted", None)
if not h:
raise Exception("Could not get the nominal template for %s:%s" % (sampn, systname))
#Our convention is that even the unvariated template is a dict with a single
#key for the direction of variation, which is 'None'
h = h[None]
#Add placeholder templates
for systdir in ["up", "down"]:
h = h.Clone(hname_encode(conf.varname, sampn, systname, systdir))
set_missing_hist(h)
#Save to file
h.SetDirectory(of)
h.Write()
continue
else:
continue
for systdir, h in h2.items():
if systdir==None and systname=="nominal" or not sample_types.is_mc(sampn):
h = h.Clone(hname_encode(conf.varname, sampn))
elif systdir==None and systname=="unweighted":
h = h.Clone(hname_encode(conf.varname, sampn, "unweighted"))
else:
h = h.Clone(hname_encode(conf.varname, sampn, systname, systdir))
h.SetDirectory(of)
h.Write()
nkeys = len(of.GetListOfKeys())
logger.info("Saved %d histograms to file %s" % (nkeys, of.GetPath()))
# of.Close()
########################
### Load systematics ###
########################
# of_unmerged = File(conf.get_outfile_unmerged())
hists = dict()
# ROOT.gROOT.cd()
for k in of.GetListOfKeys():
hists[k.GetName()] = of.Get(k.GetName())
h = hists[k.GetName()]
#hists[k.GetName()].Rebin(2)
logger.info("Loaded %d histograms from file %s" % (len(hists), of.GetPath()))
#of_unmerged.Close()
########################
### Merge ###
########################
from plots.common.utils import merge_hists, PhysicsProcess
merge_cmds = PhysicsProcess.get_merge_dict(
PhysicsProcess.get_proc_dict(conf.channel)
)
hsysts = NestedDict()
for k, v in hists.items():
spl = split_name(k)
hsysts[spl["type"]][spl["dir"]][spl["sample"]] = v
hsysts = hsysts.as_dict()
p = os.path.dirname(conf.get_outfile_merged())
if not os.path.exists(p):
os.makedirs(p)
of = ROOT.TFile(conf.get_outfile_merged(), "RECREATE")
of.cd()
for syst, h1 in hsysts.items():
if syst in skipped_systs:
continue
for sdir, h2 in h1.items():
hmc = merge_hists(h2, merge_cmds)
for hn, h in hmc.items():
if syst=="nominal" or syst=="unweighted":
h.SetName("__".join([spl["var"], hn]))
else:
h.SetName("__".join([spl["var"], hn, syst, sdir]))
h.SetDirectory(of)
h.Write()
nkeys = len(of.GetListOfKeys())
logger.info("Saved %d histograms to file %s" % (nkeys, of.GetPath()))
of.Close()
hists = load_theta_format(conf.get_outfile_merged())
processes = []
systs = []
for (variable, sample, syst, systdir), v in hists.items_flat():
processes.append(sample)
systs.append(syst)
processes = set(processes)
systs = set(systs)
logger.info("Processes: %s" % processes)
if not processes == set(['diboson', 'schan', 'tWchan', 'TTJets', 'tchan', 'WJets', 'qcd', 'DYJets', 'data']):
raise Exception("Combined file did not contain the necessary processes: %s" % str(processes))
logger.info("Systematic scenarios: %s" % systs)
for k, v in histsA["cos_theta"][N_jets][N_tags]["unweighted"].items():
if re.match("W.*Jets.*", k):
for flavour, hist in v.items():
wjets_hists[flavour][k] = hist
wjets_hists = wjets_hists.as_dict()
merged_wjets = NestedDict()
wjets_merges = OrderedDict()
wjets_merges["sherpa"] = ["WJets_sherpa"]
wjets_merges["madgraph"] = ["W[1-4]Jets_exclusive"]
hists_ratio = NestedDict()
logger.info("Merging histograms by flavour")
for flavour, hists in wjets_hists.items():
logger.debug("Merging flavour %s: %s" % (flavour, str(hists)))
merged_wjets[flavour] = merge_hists(hists, wjets_merges, order=wjets_merges.keys())
madgraph_rew_hists = dict(
filter(lambda x:
make_pattern(
N_jets=N_jets, N_tags=N_tags, weight="weighted_wjets_mg_flavour_nominal",
var="cos_theta", flavour=flavour, extra="/sample__W[1-4]Jets_exclusive"
).match(x[0]), histsB.items()
)
).values()
madgraph_rew = sum(madgraph_rew_hists)
cloned = dc(merged_wjets[flavour])
logger.debug("Merge output %s" % str(cloned))
norm(cloned["sherpa"])
norm(cloned["madgraph"])
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
if sample.isMC:
hist = sample.drawHistogram(var, cut_str, weight=weight_str, plot_range=plot_range)
hist.Scale(sample.lumiScaleFactor(lumi)*mc_sf)
hists_mc[sample.name] = hist.hist
Styling.mc_style(hists_mc[sample.name], sample.name)
elif name == "SingleMu":
hist_data = sample.drawHistogram(var, cut_str, weight="1.0", plot_range=plot_range)
Styling.data_style(hist_data)
elif name == "SingleMu_aiso":
hist_qcd = sample.drawHistogram(var, cut_str, weight="1.0", plot_range=plot_range)
#hist_qcd.
pass
#Combine the subsamples to physical processes
merged_hists = merge_hists(hists_mc, merge_cmds).values()
#Some printout
for h in merged_hists + [hist_data]:
print h.GetName(), h.GetTitle(), h.Integral()
canv = ROOT.TCanvas()
stacks_d = OrderedDict()
stacks_d["mc"] = merged_hists
stacks_d["data"] = [hist_data]
stacks = plot_hists_stacked(canv, stacks_d)
canv.Draw()
#Create the dir if it doesn't exits
try:
def plot_sherpa_vs_madgraph(var, cut_name, cut, samples, out_dir, recreate=False, **kwargs):
hname = var["varname"]
out_dir = out_dir + "/" + cut_name
if recreate and os.path.exists(out_dir):
logger.info("Output directory %s exists, removing" % out_dir)
shutil.rmtree(out_dir)
mkdir_p(out_dir)
logger.info("Using output directory %s" % out_dir)
logger.info("Using output directory %s" % out_dir)
coll = data_mc(var["var"], cut_name, cut, Weights.total()*Weights.mu, samples, out_dir, recreate, LUMI_TOTAL, reweight_madgraph=True, flavour_split=True, plot_range=var["range"], **kwargs)
logging.debug(str(coll.hists))
for hn, hist in coll.hists.items():
sample_name = coll.metadata[hn].sample_name
process_name = coll.metadata[hn].process_name
match = re.match(".*/cut__flavour__(W_[Hl][Hl])/.*", hn)
if match:
flavour_scenario = match.group(1)
else:
flavour_scenario = None
try:
if sample_types.is_mc(sample_name):
Styling.mc_style(hist, process_name)
else:
Styling.data_style(hist)
except KeyError as e:
logger.warning("Couldn't style histogram %s" % hn)
if flavour_scenario:
logger.debug("Matched flavour split histogram %s, %s" % (hn, flavour_scenario))
#Styling.mc_style(hist, process_name)
if re.match("W_H[lH]", flavour_scenario):
logger.debug("Changing colour of %s" % (hn))
hist.SetFillColor(hist.GetFillColor()+1)
hist.SetLineColor(hist.GetLineColor()+1)
logger.debug("pre merge: %s" % str([ (hn, coll.hists[hn].GetLineColor()) for hn in coll.hists.keys() if "sherpa" in hn]))
merges = dict()
merge_cmds = get_merge_cmds()
merge_cmds.pop("WJets")
merges["madgraph/unweighted"] = merge_cmds.copy()
merges["madgraph/weighted"] = merge_cmds.copy()
merges["sherpa/unweighted"] = merge_cmds.copy()
merges["sherpa/weighted"] = merge_cmds.copy()
merges["sherpa/unweighted"]["WJets_hf"] = ["weight__nominal/cut__flavour__W_heavy/WJets_sherpa_nominal"]
merges["sherpa/unweighted"]["WJets_lf"] = ["weight__nominal/cut__flavour__W_light/WJets_sherpa_nominal"]
merges["sherpa/weighted"]["WJets_hf"] = ["weight__sherpa_flavour/cut__flavour__W_heavy/WJets_sherpa_nominal"]
merges["sherpa/weighted"]["WJets_lf"] = ["weight__sherpa_flavour/cut__flavour__W_light/WJets_sherpa_nominal"]
merges["madgraph/unweighted"]["WJets_hf"] = ["weight__nominal/cut__flavour__W_heavy/W[1-4]Jets_exclusive"]
merges["madgraph/unweighted"]["WJets_lf"] = ["weight__nominal/cut__flavour__W_light/W[1-4]Jets_exclusive"]
merges["madgraph/weighted"]["WJets_hf"] = ["weight__reweight_madgraph/cut__flavour__W_heavy/W[1-4]Jets_exclusive"]
merges["madgraph/weighted"]["WJets_lf"] = ["weight__reweight_madgraph/cut__flavour__W_light/W[1-4]Jets_exclusive"]
hmerged = dict()
for k in merges.keys():
hmerged[k] = merge_hists(copy.deepcopy(coll.hists), merges[k])
logger.debug("post merge: %s" % str([ (hn, hmerged["sherpa/weighted"][hn].GetLineColor()) for hn in hmerged["sherpa/weighted"].keys()]))
#w_mg_sh = 1.0416259307303726 #sherpa to madgraph ratio
w_mg_sh = 1.0821535639376414
hmerged["sherpa/weighted"]["WJets_hf"].Scale(w_mg_sh)
hmerged["sherpa/weighted"]["WJets_lf"].Scale(w_mg_sh)
logger.info("Drawing madgraph unweighted plot")
canv = ROOT.TCanvas("c2", "c2")
suffix = "__%s__%s" % (var["var"], cut_name)
suffix = escape(suffix)
plot(canv, "madgraph_unw"+suffix, hmerged["madgraph/unweighted"], out_dir, **kwargs)
kwargs = dict({"x_label": var["varname"]}, **kwargs)
for k, v in hmerged.items():
logger.debug("Group %s" % k)
for hn, h in v.items():
logger.debug("Sample %s = %.2f" % (hn, h.Integral()))
logger.info("%s data=%.2f" % (k, v["data"].Integral()))
logger.info("%s MC=%.2f" % (k, sum([h.Integral() for k, h in v.items() if k!="data"])))
hists_flavours_merged = dict()
hists_flavours_merged["madgraph/weighted"] = merge_hists(hmerged["madgraph/weighted"], {"WJets": ["WJets_hf", "WJets_lf"]})
hists_flavours_merged["madgraph/unweighted"] = merge_hists(hmerged["madgraph/unweighted"], {"WJets": ["WJets_hf", "WJets_lf"]})
hists_flavours_merged["sherpa/unweighted"] = merge_hists(hmerged["sherpa/unweighted"], {"WJets": ["WJets_hf", "WJets_lf"]})
hists_flavours_merged["sherpa/weighted"] = merge_hists(hmerged["sherpa/weighted"], {"WJets": ["WJets_hf", "WJets_lf"]})
logger.info("Drawing sherpa weighted plot")
canv = ROOT.TCanvas("c1", "c1")
plot(canv, "sherpa_rew"+suffix, hmerged["sherpa/weighted"], out_dir, **kwargs)
logger.info("Drawing sherpa unweighted plot")
canv = ROOT.TCanvas("c1", "c1")
plot(canv, "sherpa_unw"+suffix, hmerged["sherpa/unweighted"], out_dir, **kwargs)
logger.info("Drawing madgraph plot")
canv = ROOT.TCanvas("c2", "c2")
plot(canv, "madgraph_rew"+suffix, hmerged["madgraph/weighted"], out_dir, **kwargs)
total_madgraph = copy.deepcopy(hmerged["madgraph/unweighted"])
merged_colls = dict()
for k, v in hmerged.items():
merged_colls[k] = HistCollection(copy.deepcopy(v), name=k)
logger.info("Drawing sherpa vs. madgraph shape comparison plots")
hists = [
("sherpa unw hf", hmerged["sherpa/unweighted"]["WJets_hf"]),
("sherpa rew hf", hmerged["sherpa/weighted"]["WJets_hf"]),
("madgraph unw hf", hmerged["madgraph/unweighted"]["WJets_hf"]),
("madgraph rew hf", hmerged["madgraph/weighted"]["WJets_hf"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
canv.SaveAs(out_dir + "/weighted_flavour_hf_%s.png" % hname)
canv.Close()
hists = [
("data", hmerged["madgraph/unweighted"]["data"]),
("sherpa", hists_flavours_merged["sherpa/unweighted"]["WJets"]),
("madgraph", hists_flavours_merged["madgraph/unweighted"]["WJets"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
canv.SaveAs(out_dir + "/unweighted_sherpa_mg_%s.png" % hname)
canv.Close()
hists = [
("data", hmerged["madgraph/unweighted"]["data"]),
("sherpa", hists_flavours_merged["sherpa/weighted"]["WJets"]),
("madgraph", hists_flavours_merged["madgraph/weighted"]["WJets"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
canv.SaveAs(out_dir + "/weighted_sherpa_mg_%s.png" % hname)
canv.Close()
hists = [
("sherpa unw lf", hmerged["sherpa/unweighted"]["WJets_lf"]),
("sherpa rew lf", hmerged["sherpa/weighted"]["WJets_lf"]),
("madgraph unw lf", hmerged["madgraph/unweighted"]["WJets_lf"]),
("madgraph rew lf", hmerged["madgraph/weighted"]["WJets_lf"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
canv.SaveAs(out_dir + "/weighted_flavour_lf_%s.png" % hname)
canv.Close()
hists = [
("data", hmerged["madgraph/unweighted"]["data"]),
("madgraph unw", hists_flavours_merged["madgraph/unweighted"]["WJets"]),
("madgraph rew", hists_flavours_merged["madgraph/weighted"]["WJets"]),
("sherpa unw", hists_flavours_merged["sherpa/unweighted"]["WJets"]),
("sherpa rew", hists_flavours_merged["sherpa/weighted"]["WJets"]),
]
hists = copy.deepcopy(hists)
for hn, h in hists:
h.SetTitle(hn + " %.2f" % h.Integral())
h.Scale(1.0/h.Integral())
hists = [h[1] for h in hists]
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
leg = legend(hists, styles=["f", "f"], **kwargs)
hists[0].SetTitle("")
canv.Update()
canv.SaveAs(out_dir + "/shapes_%s.png" % hname)
canv.Close()
# hists = [
# ("sherpa hf", hmerged["sherpa"]["WJets_hf"]),
# ("madgraph unw hf", hmerged["madgraph/unweighted"]["WJets_hf"]),
# ("madgraph rew hf", hmerged["madgraph/weighted"]["WJets_hf"]),
# ]
# hists = copy.deepcopy(hists)
# for hn, h in hists:
# h.SetTitle(hn + " %.2f" % h.Integral())
# h.Scale(1.0/h.Integral())
# hists = [h[1] for h in hists]
# ColorStyleGen.style_hists(hists)
# canv = plot_hists(hists, x_label=var["varname"], do_chi2=True)
# leg = legend(hists, styles=["f", "f"], **kwargs)
# hists[0].SetTitle("madgraph sherpa rew hf")
# canv.SaveAs(out_dir + "/shapes_hf_%s.png" % hname)
# canv.Close()
return coll, merged_colls
def plot_ratios(cut_name, cut, samples, out_dir, recreate, flavour_scenario=flavour_scenarios[0]):
out_dir += "/" + cut_name
mkdir_p(out_dir)
colls = dict()
samples_WJets = filter(lambda x: sample_types.is_wjets(x.name), samples)
for sc in flavour_scenario:
logger.info("Drawing ratio with cut %s" % sc)
cut_ = cut*getattr(Cuts, sc)
colls[sc] = data_mc(costheta["var"], cut_name + "__" + sc, cut_, Weights.total()*Weights.mu, samples_WJets, out_dir, recreate, LUMI_TOTAL, plot_range=costheta["range"])
logger.debug(colls[flavour_scenario[0]].hists["weight__nominal/cut__all/WJets_sherpa_nominal"].Integral())
logger.debug(colls[flavour_scenario[1]].hists["weight__nominal/cut__all/WJets_sherpa_nominal"].Integral())
coll = dict()
for k, c in colls.items():
for hn, h in c.hists.items():
coll[hn + "/" + k] = h
for k, h in coll.items():
logger.debug("%s = %s" % (k, str([y for y in h.y()])))
logger.debug(coll)
#coll = HistCollection(coll, name=cut_name)
merges = {}
for sc in flavour_scenario:
merges["madgraph/%s" % sc] = ["weight__nominal/cut__all/W[1-4]Jets_exclusive/%s" % sc]
merges["sherpa/unweighted/%s" % sc] = ["weight__nominal/cut__all/WJets_sherpa_nominal/%s" % sc]
merges["sherpa/weighted/%s" % sc] = ["weight__sherpa_flavour/cut__all/WJets_sherpa_nominal/%s" % sc]
merged = merge_hists(coll, merges)
for k, h in merged.items():
logger.debug("%s = %s" % (k, str([y for y in h.y()])))
hists_flavour = dict()
hists_flavour["madgraph"] = ROOT.TH1F("madgraph", "madgraph", len(flavour_scenario), 0, len(flavour_scenario)-1)
hists_flavour["sherpa/unweighted"] = ROOT.TH1F("sherpa_unw", "sherpa unweighted", len(flavour_scenario), 0, len(flavour_scenario)-1)
hists_flavour["sherpa/weighted"] = ROOT.TH1F("sherpa_rew", "sherpa weighted", len(flavour_scenario), 0, len(flavour_scenario)-1)
for i, sc in zip(range(1,len(flavour_scenario)+1), flavour_scenario):
sh1_int, sh1_err = calc_int_err(merged["sherpa/unweighted/%s" % sc])
sh2_int, sh2_err = calc_int_err(merged["sherpa/weighted/%s" % sc])
mg_int, mg_err = calc_int_err(merged["madgraph/%s" % sc])
logger.debug("%.2f %.2f" % (sh1_int, sh1_err))
logger.debug("%.2f %.2f" % (sh2_int, sh2_err))
logger.debug("%.2f %.2f" % (mg_int, mg_err))
hists_flavour["madgraph"].SetBinContent(i, mg_int)
hists_flavour["madgraph"].SetBinError(i, mg_err)
hists_flavour["sherpa/unweighted"].SetBinContent(i, sh1_int)
hists_flavour["sherpa/unweighted"].SetBinError(i, sh1_err)
hists_flavour["sherpa/weighted"].SetBinContent(i, sh2_int)
hists_flavour["sherpa/weighted"].SetBinError(i, sh2_err)
hists_flavour["madgraph"].GetXaxis().SetBinLabel(i, sc)
hists_flavour["sherpa/unweighted"].GetXaxis().SetBinLabel(i, sc)
hists_flavour["sherpa/weighted"].GetXaxis().SetBinLabel(i, sc)
hists_flavour["sherpa/weighted"].Sumw2()
hists_flavour["sherpa/unweighted"].Sumw2()
hists_flavour["madgraph"].Sumw2()
hists_flavour["ratio/unweighted"] = hists_flavour["madgraph"].Clone("ratio_unw")
hists_flavour["ratio/unweighted"].Divide(hists_flavour["sherpa/unweighted"])
hists_flavour["ratio/weighted"] = hists_flavour["madgraph"].Clone("ratio_rew")
hists_flavour["ratio/weighted"].Divide(hists_flavour["sherpa/weighted"])
for i, sc in zip(range(1,len(flavour_scenario)+1), flavour_scenario):
logger.info("weights[%s] = %.6f; //error=%.6f [%d]" % (sc, hists_flavour["ratio/unweighted"].GetBinContent(i), hists_flavour["ratio/unweighted"].GetBinError(i), i))
flavour_ratio_coll = HistCollection(hists_flavour, name="hists__flavour_ratios")
flavour_ratio_coll.save(out_dir)
for sc in flavour_scenario:
hists = [merged["madgraph/%s" % sc], merged["sherpa/unweighted/%s" % sc], merged["sherpa/weighted/%s" % sc]]
for hist in hists:
norm(hist)
#hist.SetName(sc)
#hist.SetTitle(sc)
ColorStyleGen.style_hists(hists)
canv = plot_hists(hists, x_label=costheta["varname"])
leg = legend(hists, styles=["f", "f"], nudge_x=-0.2)
chi2 = hists[0].Chi2Test(hists[1], "WW CHI2/NDF")
hists[0].SetTitle("madgraph to sherpa comparison #chi^{2}/ndf=%.2f" % chi2)
canv.Update()
canv.SaveAs(out_dir + "/flavours__%s.png" % (sc))
md_merged = dict()
for sc in flavour_scenario:
logger.info("Calculating ratio for %s" % sc)
hi = merged["sherpa/unweighted/%s" % sc].Clone("ratio__%s" % sc)
hi.Divide(merged["madgraph/%s" % sc])
merged[hi.GetName()] = hi
hc_merged = HistCollection(merged, md_merged, "hists__costheta_flavours_merged")
hc_merged.save(out_dir)
logger.info("Saved merged histogram collection")
hists = {}
for fn in fnames:
files[re.match(".*__(.*).root", fn).group(1)] = File(fn)
logging.info("files = %s" % str(files))
for sn, fi in files.items():
hi = fi.Get("flavour_counts")
hi.SetName(sn)
pn = sn
Styling.mc_style(hi, pn)
hists[sn] = hi
merges = OrderedDict()
merges["W (madgraph)"] = ["W1Jets_exclusive", "W2Jets_exclusive", "W3Jets_exclusive", "W4Jets_exclusive"]
merges["W (sherpa)"] = ["WJets_sherpa"]
hists_merged = merge_hists(hists, merges)
hists = hists_merged.values()
ColorStyleGen.style_hists(hists)
for h in hists:
logging.info(list(h.y()))
h.SetFillColor(ROOT.kWhite)
h.Scale(1.0 / h.Integral())
c = plot_hists(hists, x_label="flavour(j,j)", y_label="", draw_cmd="HIST E1")
leg = legend(hists, styles=len(hists) * ["f"], nudge_x=-0.1)
hists[0].SetTitle("Jet flavour fraction n %s" % cut_name)
# hists[0].SetFillColor(ROOT.kGreen+2)
hists[0].SetMinimum(10 ** -3)
hists[0].SetMaximum(10)
# hists[0].SetLineColor(ROOT.kGreen+2)
c.SetLogy()
