def plot_data_mc(hists_mc, hist_data, name):
canv = ROOT.TCanvas()
p1 = ROOT.TPad("p1", "p1", 0, 0.3, 1, 1)
p1.Draw()
p1.SetTicks(1, 1);
p1.SetGrid();
p1.SetFillStyle(0);
p1.cd()
stacks_d = OrderedDict()
print "MC",hists_mc
print "VAL",hists_mc.values()
stacks_d["mc"] = hists_mc.values()
stacks_d["data"] = [hist_data]
stacks = plot_hists_stacked(
p1,
stacks_d,
x_label=var,
y_label="",
do_log_y=True
)
leg = legend([hist_data] + list(reversed(hists_mc.values())), styles=["p", "f"])
print canv, hist_data
print get_stack_total_hist(stacks["mc"])
ratio_pad, hratio = plot_data_mc_ratio(canv, get_stack_total_hist(stacks["mc"]), hist_data)
plot_info = PlotMetaInfo(
name,
"CUT",
"WEIGHT",
[infile],
subdir=fit.name,
comments=str(result[SIGNAL])
)
of.savePlot(canv, plot_info)
canv.Close()
hists_tot_mc_syst[name] = tuple(tots)
#Draw the ratio plot with the systematics
do_norm = plot_def.get("normalize", False)
if not do_norm and len(hists_tot_mc_syst.items())>0:
syst_hists = total_syst(htot_mc, hists_tot_mc_syst)
for h in list(syst_hists):
try:
logger.info("Chi2 = %.2f" % (htot_mc.Chi2Test(h, "WW CHI2/NDF")))
except rootpy.ROOTError as e:
logger.error("Couldn't calculate the chi2: %s" % str(e))
else:
syst_hists = None
ratio_pad, hratio = plot_data_mc_ratio(canv, htot_data, htot_mc, syst_hists=syst_hists)
#This is adopted in the AN
if lepton_channel=="ele":
_lepton_channel = "el"
else:
_lepton_channel = "mu"
if use_antiiso:
_lepton_channel+='_aiso'
subpath = ""
if "dir" in plot_def.keys():
subpath = plot_def["dir"]
#Set the plot metadata
def data_mc_plot(pd):
hists = load_theta_format(pd.infile, styles)
for (variable, sample, systtype, systdir), hist in hists.items_flat():
#Scale all MC samples except QCD to the luminosity
if sample_types.is_mc(sample) and not sample=="qcd":
hist.Scale(pd.lumi)
if hasattr(pd, "rebin"):
hist.Rebin(pd.rebin)
if sample=="qcd" and hasattr(pd, "qcd_yield"):
hist.Scale(pd.qcd_yield / hist.Integral())
rescale_to_fit(sample, hist, pd.process_scale_factor)
hist.SetTitle(sample)
hist.SetName(sample)
#Assuming we only have 1 variable
hists = hists[pd.var]
hists_nominal = hists.pop("nominal")[None]
hists_nom_data = hists_nominal.pop('data')
hists_nom_mc = hists_nominal.values()
hists_syst = hists
hists_nom_data.SetTitle('data')
#A list of all the systematic up, down variation templates as 2-tuples
all_systs = [
]
all_systs = hists_syst.keys()
systs_to_consider = []
#See which systematics where asked to switch on
for syst in all_systs:
for sm in pd.systematics:
if re.match(sm, syst):
systs_to_consider.append(syst)
#The total nominal MC histogram
nom = sum(hists_nom_mc)
if pd.normalize:
ratio = hists_nom_data.Integral() / nom.Integral()
hists_nom_data.Scale(1.0/ratio)
#Get all the variated up/down total templates
#A list with all the up/down total templates
all_systs = []
sumsqs = []
logger.info("Considering systematics %s" % str(systs_to_consider))
for syst in systs_to_consider:
#A list with the up/down variated template for a particular systematic
totupdown = []
sumsq = []
for systdir in ["up", "down"]:
#Get all the templates corresponding to a systematic scenario and a variation
_hists = hists_syst[syst][systdir]
for k, h in _hists.items():
"""
Consider only the shape variation of the systematic,
hence the variated template is normalized to the corresponding
unvariated template.
"""
if pd.systematics_shapeonly:
if h.Integral()>0:
h.Scale(hists_nominal[k].Integral() / h.Integral())
#For the missing variated templates, use the nominal ones, but warn the user
present = set(_hists.keys())
all_mc = set(hists_nominal.keys())
missing = list(all_mc.difference(present))
for m in missing:
logger.warning("Missing systematic template for %s:%s" % (syst, systdir))
#Calculate the total variated template
tot = sum(_hists.values()) + sum([hists_nominal[m] for m in missing])
totupdown.append(tot)
sumsq.append(
math.sqrt(numpy.sum(numpy.power(numpy.array(list(nom.y())) - numpy.array(list(tot.y())), 2)))
)
logger.debug("Systematic %s: sumsq=%.2Eu, %.2Ed" % (syst, sumsq[0], sumsq[1]))
sumsqs.append((syst, max(sumsq)))
all_systs.append(
(syst, tuple(totupdown))
)
sumsqs = sorted(sumsqs, key=lambda x: x[1], reverse=True)
for syst, sumsq in sumsqs[0:7]:
logger.info("Systematic %s, %.4f" % (syst, sumsq))
#Calculate the total up/down variated templates by summing in quadrature
syst_stat_up, syst_stat_down = total_syst(
nom, all_systs,
)
for k, v in hists_nominal.items():
if hasattr(PhysicsProcess, k):
pp = getattr(PhysicsProcess, k)
v.SetTitle(pp.pretty_name)
else:
logger.warning("Not setting pretty name for %s" % k)
#If QCD is high-stats, put it in the bottom
plotorder = copy.copy(PhysicsProcess.desired_plot_order_mc)
if hists_nominal["qcd"].GetEntries()>100:
plotorder.pop(plotorder.index("qcd"))
plotorder.insert(0, "qcd")
stacks_d = OrderedDict()
stacks_d['mc'] = reorder(hists_nominal, plotorder)
stacks_d['data'] = [hists_nom_data]
#Systematic style
for s in [syst_stat_up, syst_stat_down]:
s.SetFillStyle(0)
s.SetLineWidth(3)
s.SetMarkerSize(0)
s.SetLineColor(ROOT.kBlue+2)
s.SetLineStyle('dashed')
s.SetTitle("stat. + syst.")
#c = ROOT.TCanvas("c", "c", 1000, 1000)
c = ROOT.TCanvas("c", "c")
p1 = ROOT.TPad("p1", "p1", 0, 0.3, 1, 1)
p1.Draw()
p1.SetTicks(1, 1);
p1.SetGrid();
p1.SetFillStyle(0);
p1.cd()
stacks = plot_hists_stacked(
p1, stacks_d,
x_label=pd.get_x_label(), max_bin_mult=pd.get_max_bin_mult(),
min_bin=pd.get_min_bin()
)
p1.SetLogy(pd.log)
syst_stat_up.Draw("SAME hist")
syst_stat_down.Draw("SAME hist")
ratio_pad, hratio = plot_data_mc_ratio(
c, hists_nom_data,
nom, syst_hists=(syst_stat_down, syst_stat_up), min_max=pd.get_ratio_minmax()
)
p1.cd()
leg = legend(
stacks_d['data'] +
list(reversed(stacks_d['mc'])) +
[syst_stat_up],
nudge_x=pd.legend_nudge_x,
nudge_y=pd.legend_nudge_y,
**pd.__dict__
)
lb = lumi_textbox(pd.lumi,
line2=pd.get_lumibox_comments(channel=pd.channel_pretty),
pos=pd.get_lumi_pos()
)
c.children = [p1, ratio_pad, stacks, leg, lb]
tot = 0
for k, v in hists_nominal.items():
print k, v.Integral(), v.GetEntries()
tot += v.Integral()
tot_data = hists_nom_data.Integral()
print "MC: %.2f Data: %.2f" % (tot, tot_data)
#import pdb; pdb.set_trace()
return c, (hists_nominal, hists_nom_data)
