def shape_variation(var, plot_range, varname, recreate, out_name):
tot_hists = OrderedDict()
tdrstyle()
for wn, w in [
("unw", Weight("1.0")),
("nominal", Weights.wjets_madgraph_shape_weight("nominal")),
("shape_up", Weights.wjets_madgraph_shape_weight("wjets_up")),
("shape_down", Weights.wjets_madgraph_shape_weight("wjets_down"))
]:
p = data_mc(
var, "2J0T_%s" % (wn),
cut, Weights.total("mu", "nominal")*w,
samples, ".", recreate, lumi_iso["mu"], plot_range=plot_range,
)
for hn, h in p.hists.items():
logger.debug("%s %.2f" % (hn, h.Integral()))
#sumw = [(sample.name, numpy.mean(root_numpy.root2array(sample.tfile.GetPath()[:-2], "trees/WJets_weights", branches=[str(w)])[str(w)])) for sample in samples]
s = sum(p.hists.values()).Clone()
tot_hists[wn] = s
hc = HistCollection(tot_hists, name=out_name)
for hn, h in hc.hists.items():
print hn, h.Integral()
canv = plot_hists_dict(hc.hists, do_chi2=False, do_ks=True, x_label=varname, legend_pos="top-left")
hc.hists.values()[0].SetTitle("shape variation")
canv.SaveAs(out_name + ".png")
hc.save(out_name)
return hc, canv
tree = args.tree
#The enabled systematic up/down variation sample prefixes to put on the ratio plot
if args.do_systs:
systs = {
"JES": ("EnUp", "EnDown"),
"JER": ["ResUp", "ResDown"],
"MET": ["UnclusteredEnUp", "UnclusteredEnDown"],
}
else:
systs = {}
for lepton_channel in args.channels:
logger.info("Plotting lepton channel %s" % lepton_channel)
weight = Weights.total(lepton_channel)*Weights.wjets_madgraph_shape_weight()*Weights.wjets_madgraph_flat_weight()
physics_processes = PhysicsProcess.get_proc_dict(lepton_channel=lepton_channel)#Contains the information about merging samples and proper pretty names for samples
merge_cmds = PhysicsProcess.get_merge_dict(physics_processes) #The actual merge dictionary
lumi = lumis[lepton_channel]
isoreg='iso'
use_antiiso = False
if args.use_antiiso:
isoreg='antiiso'
use_antiiso = True
#Get the file lists
flist = get_file_list(
merge_cmds,
flist = []
for i in ['sig','bg']:
for j in ['train','eval']:
flist += mvaFileList[i][j]
logger.debug('Used file list: %s',str(flist))
# Read in the samples
samples = {}
for f in flist:
samples[f] = Sample.fromFile(args.indir+'/%s/%s.root' % (proc,f), tree_name='Events_MVA')
# Set the weight expression
weightString = str(Weights.total(proc) *
Weights.wjets_madgraph_shape_weight() *
Weights.wjets_madgraph_flat_weight())
# Which file do we use to write our TMVA trainings
ext=''
if len(rVar):
ext='_sans'
for v in rVar:
ext+='_'+v
if len(cVar):
ext='_with'
for v in cVar:
ext+='_'+v
out = TFile('TMVA%s%s.root' % (proc,ext),'RECREATE')
