def checklatex(texts, tag=""):
"""Check legend entries: colors, titles, ..."""
# https://root.cern.ch/doc/master/classTPaveText.html
LOG.header("checklegend" + tag.replace('_', ' '))
output = ensuredir('plots')
fname = "%s/testLatex%s" % (output, tag)
xdim = 500
ydim = 50 * (len(texts) + 2.5)
print ">>> Canvas: %sx%s (nlines=%d)" % (xdim, ydim, len(texts))
canvas = TCanvas('canvas', 'canvas', xdim, int(ydim))
#pave1 = TPaveText(0.0,0,0.5,1,'ARC') #,'BR')
pave2 = TPaveText(0.04, 0.04, 0.96, 0.96) #'ARC') #,'BR')
#pave1.SetBorderSize(0)
pave2.SetBorderSize(0)
#pave1.SetTextAlign(12)
pave2.SetTextAlign(12)
#pave1.SetTextFont(42)
pave2.SetTextFont(42)
#pave1.SetFillColor(0)
pave2.SetFillColor(0)
#pave1.SetCornerRadius(0.05)
#pave2.SetCornerRadius(0.05)
#pave1.SetMargin(0.12)
#pave1.SetTextSize(tsize)
#pave2.Copy(pave1)
for line in texts:
latex = makelatex(line)
print ">>> %r -> %r" % (line, latex)
#pave1.AddText(line)
pave2.AddText(latex)
#pave1.Draw()
pave2.Draw()
canvas.SaveAs(fname + ".png")
#canvas.SaveAs(fname+".pdf")
canvas.Close()
def plothist(xtitle,hists,ratio=False,logy=False,norm=False):
# SETTING
outdir = ensuredir("plots/")
fname = outdir+"testPlot"
if ratio:
fname += "_ratio"
if logy:
fname += "_logy"
if norm:
fname += "_norm" # normalize each histogram
rrange = 0.5
width = 0.2 # legend width
position = 'topright' # legend position
header = "Gaussians" # legend header
text = "#mu#tau_{h}" # corner text
grid = True #and False
staterr = True and False # add uncertainty band to first histogram
lstyle = 1 # solid lines
# PLOT
LOG.header(fname)
plot = Plot(xtitle,hists,norm=norm)
plot.draw(ratio=ratio,logy=logy,ratiorange=rrange,lstyle=lstyle,grid=grid,staterr=staterr)
plot.drawlegend(position,header=header,width=width)
plot.drawtext(text)
plot.saveas(fname+".png")
plot.saveas(fname+".pdf")
#plot.saveas(fname+".C")
#plot.saveas(fname+".png",fname+".C")
#plot.saveas(fname,ext=['png','pdf'])
plot.close()
print
def plothist(xtitle,hists,ratio=False,logy=False,norm=False,**kwargs):
# SETTING
outdir = ensuredir("plots/")
fname = outdir+"testLegend"
rrange = 0.5
header = "Gaussians" # legend header
text = "#mu#tau_{h}" # corner text
grid = True and False
staterr = True and False # add uncertainty band to first histogram
lstyle = 1 # solid lines
panel = 1 # 1 = main (top) panel, 2 = ratio (bottom) panel
hists = [h.Clone("%s_clone%d"%(h.GetName(),i)) for i, h in enumerate(hists)]
if kwargs.get('pos',False):
fname += "_"+kwargs['pos'].replace(';','')
header = kwargs['pos']
if ratio:
fname += "_ratio"
# PLOT
LOG.header(fname)
plot = Plot(xtitle,hists,norm=norm)
plot.draw(ratio=ratio,logy=logy,ratiorange=rrange,lstyle=lstyle,grid=grid,staterr=staterr)
plot.drawlegend(border=True,header=header,panel=panel,**kwargs)
plot.drawtext(text)
plot.saveas(fname+".png")
#plot.saveas(fname+".pdf")
plot.close()
print
def plotstack(xname,xtitle,datahist,exphists,ratio=False,logy=False):
"""Plot Stack objects for a given data hisogram and list of MC histograms."""
# SETTING
outdir = ensuredir("plots")
fname = "%s/testStack_%s"%(outdir,xname)
if ratio:
fname += "_ratio"
if logy:
fname += "_logy"
rrange = 0.5
text = "#mu#tau_{h} baseline"
grid = True and False
position = 'topright' if logy else 'right'
# PLOT
LOG.header(fname)
plot = Stack(xtitle,datahist,exphists)
plot.draw(ratio=ratio,logy=logy,ratiorange=rrange,grid=grid)
plot.drawlegend(position=position)
plot.drawtext(text)
plot.saveas(fname+".png")
plot.saveas(fname+".pdf")
#plot.saveas(fname+".C")
#plot.saveas(fname+".png",fname+".C")
#plot.saveas(fname,ext=['png','pdf'])
plot.close()
print
def makesamples(nevts=10000,**kwargs):
"""Create pseudo MC and data tree for quick and reproducible testing of Plotter tools."""
outdir = kwargs.get('outdir', 'plots' )
channel = kwargs.get('channel', 'mutau' )
samples = kwargs.get('sample', ['Data','ZTT','WJ','QCD','TT'])
scales = kwargs.get('scales', None )
samples = unwraplistargs(samples)
scaledict = { # relative contribtions to pseudo data
'ZTT': 1.00,
'WJ': 0.40,
'QCD': 0.25,
'TT': 0.15,
'DYJets': 1.0,
'DY1Jets': 0.5,
'DY2Jets': 0.3,
'DY3Jets': 0.2,
'DY4Jets': 0.1,
}
if scales:
scaledict.update(scales)
scaledict.pop('Data',None)
vardict = { # uncorrelated pseudo distributions for variables
'm_vis': {
'*': lambda gm: gRandom.Gaus( 72, 9) if gm==5 else gRandom.Gaus( 91, 2), # default
'ZTT': lambda gm: gRandom.Gaus( 72, 9),
'WJ': lambda gm: gRandom.Gaus( 65,28),
'QCD': lambda gm: gRandom.Gaus( 80,44),
'TT': lambda gm: gRandom.Gaus(110,70),
},
'genmatch_2': { # chance that genmatch_2==5 (real tau)
'*': 0.9, # default
'ZTT': 1.0,
'WJ': 0.5,
'QCD': 0.5,
'TT': 0.8,
},
'pt_1': {
'*': lambda: gRandom.Landau(30,2), # default
'QCD': lambda: gRandom.Landau(30,5),
'TT': lambda: gRandom.Landau(40,6),
},
'pt_2': {
'*': lambda: gRandom.Landau(24,2), # default
'WJ': lambda: gRandom.Landau(24,2),
'QCD': lambda: gRandom.Landau(24,5),
'TT': lambda: gRandom.Landau(30,6),
},
'eta_1': {
'*': lambda: gRandom.Uniform(-2.5,2.5), # default
},
'eta_2': {
'*': lambda: gRandom.Uniform(-2.5,2.5), # default
},
'dm_2': {
'*': lambda r: 0 if r<0.4 else 1 if r<0.6 else 10 if r<0.9 else 11, # default
},
'njets': {
'*': lambda: gRandom.Poisson(0.2,), # default
'ZTT': lambda: gRandom.Poisson(0.2,),
'WJ': lambda: gRandom.Poisson(1.2,),
'QCD': lambda: gRandom.Poisson(2.0,),
'TT': lambda: gRandom.Poisson(2.5,),
'DYJets': lambda: gRandom.Poisson(0.2,),
'DY1Jets': lambda: 1,
'DY2Jets': lambda: 2,
'DY3Jets': lambda: 3,
'DY4Jets': lambda: 4,
},
'NUP': {
'*': lambda: gRandom.Poisson(0.2,), # default
'ZTT': lambda: gRandom.Poisson(0.2,),
'WJ': lambda: gRandom.Poisson(1.2,),
'QCD': lambda: gRandom.Poisson(2.0,),
'TT': lambda: gRandom.Poisson(2.5,),
'DYJets': lambda: gRandom.Poisson(0.2,),
'DY1Jets': lambda: 1,
'DY2Jets': lambda: 2,
'DY3Jets': lambda: 3,
'DY4Jets': lambda: 4,
},
'weight': {
'*': lambda: gRandom.Gaus(1.0,0.10), # default
'ZTT': lambda: gRandom.Gaus(1.0,0.10),
'WJ': lambda: gRandom.Gaus(1.0,0.10),
'QCD': lambda: gRandom.Gaus(1.0,0.05),
'TT': lambda: gRandom.Gaus(1.0,0.08),
},
}
# PREPARE TREES
ensuredir(outdir)
filedict = { }
histdict = { }
m_vis = np.zeros(1,dtype='f')
pt_1 = np.zeros(1,dtype='f')
pt_2 = np.zeros(1,dtype='f')
dm_2 = np.zeros(1,dtype='f')
eta_1 = np.zeros(1,dtype='f')
eta_2 = np.zeros(1,dtype='f')
njets = np.zeros(1,dtype='i')
genmatch_2 = np.zeros(1,dtype='i') # genmatch_2: 5 = real tau; 0 = fake tau
NUP = np.zeros(1,dtype='i') # number of LHE-level partons for jet stitching
weight = np.zeros(1,dtype='f')
def makesample(sample): # help function to create file with tree
fname = "%s/%s_%s.root"%(outdir,sample,channel)
file = TFile(fname,'RECREATE')
hist = TH1D('cutflow','cutflow',20,0,20)
tree = TTree('tree','tree')
tree.Branch('m_vis', m_vis, 'm_vis/F')
tree.Branch('pt_1', pt_1, 'pt_1/F')
tree.Branch('pt_2', pt_2, 'pt_2/F')
tree.Branch('dm_2', dm_2, 'dm_2/I')
tree.Branch('eta_1', eta_1, 'eta_1/F')
tree.Branch('eta_2', eta_2, 'eta_2/F')
tree.Branch('njets', njets, 'njets/I')
tree.Branch('genmatch_2', genmatch_2, 'genmatch_2/I')
tree.Branch('NUP', NUP, 'NUP/I')
tree.Branch('weight', weight, 'weight/F')
tree.SetDirectory(file)
hist.SetDirectory(file)
hist.SetBinContent( 1,1)
hist.SetBinContent(17,1)
histdict[sample] = hist
filedict[sample] = (file,tree)
for sample in scaledict:
makesample(sample)
makesample('Data')
def getgenerator(var,sample): # get random generator from dictionary
if sample in vardict[var]: return vardict[var][sample]
else: return vardict[var]['*']
def fill(sample,tree,nevts): # help function to fill trees
for i in xrange(nevts):
genmatch_2[0] = 5 if gRandom.Uniform(1.)<getgenerator('genmatch_2',sample) else 0
m_vis[0] = getgenerator('m_vis',sample)(genmatch_2[0])
pt_1[0] = getgenerator('pt_1', sample)()
pt_2[0] = getgenerator('pt_2', sample)()
if m_vis[0]<0 or pt_1[0]<0 or pt_2[0]<0: continue
if pt_1[0]<pt_1[0]:
pt_1[0], pt_2[0] = pt_2[0], pt_1[0]
dm_2[0] = getgenerator('dm_2', sample)(gRandom.Uniform(1.))
eta_1[0] = getgenerator('eta_1', sample)()
eta_2[0] = getgenerator('eta_2', sample)()
njets[0] = getgenerator('njets', sample)()
weight[0] = getgenerator('weight',sample)() if sample!='Data' else 1.0
tree.Fill()
# PSEUDO MC
print ">>> Generating pseudo MC..."
#time0 = time.time()
for sample in samples:
if sample=='Data': continue
#print ">>> %r"%(sample)
file, tree = filedict[sample]
file.cd()
fill(sample,tree,nevts)
histdict[sample].Write()
tree.Write()
#print ">>> %.1f seconds"%(time.time()-time0)
# PSEUDO DATA
if 'Data' in samples:
print ">>> Generating pseudo data..."
file, tree = filedict['Data']
file.cd()
#time0 = time.time()
for sample in samples:
if sample=='Data': continue
prob = scaledict.get(sample,1.0)
ndevts = int(prob*nevts) # relative contribtion to pseudo data
fill(sample,tree,ndevts)
histdict[sample].Write()
tree.Write()
#print ">>> %.1f seconds"%(time.time()-time0)
return filedict