def draw(self):
## Make auxiliary lists of indices and hitogram names
indices = range(len(self.trees))
hnames = ['h_%s_%d' % (self.name, i) for i in indices]
## Make the histograms
for t, hname in zip(self.trees, hnames):
expr = '%s>>%s' % (self.expression, hname)
sel = '&'.join(['(%s)' % c for c in self.cuts if c.strip()])
if self.binning:
expr += '(%s)' % self.binning
t.Draw(expr, sel, 'goff')
else:
## No binning is specified.
## Take extra care to use the same binning for all.
## Use automatic binning fro the first tree and apply it
## to all others.
if id(t) == id(self.trees[0]):
## This is the first tree, use auto-binning.
t.Draw(expr, sel)
else:
## This is no the first tree, use same binning as
## for the first one
t.Draw(expr, sel, 'same')
## Retrieve the histograms in put them in a list
self.histos = [ROOT.gDirectory.Get(hn) for hn in hnames]
## Customize histos
for h, c, ms in zip(self.histos, self.colors, self.markerstyles):
h.Sumw2()
if self.normalize_to_unit_area:
nbins = h.GetXaxis().GetNbins()
area = h.GetBinWidth(1) * h.Integral(1, nbins)
h.Scale(1./area)
h.GetXaxis().SetTitle(self.xtitle)
h.GetYaxis().SetTitle(self.ytitle)
h.SetTitle(self.title)
h.SetStats(0)
h.SetLineColor(c)
h.SetMarkerColor(c)
h.SetMarkerStyle(ms)
## Find the histogram with greatest y range
maxima = [h.GetMaximum() for h in self.histos]
highest = max(maxima)
imax = maxima.index(highest)
## Draw the "highest" histo first to guarantee nice the y-range.
self.histos[imax].Draw(self.drawopts[imax])
## Draw all histograms
for h, opt in zip(self.histos, self.drawopts):
h.Draw(opt + 'same')
## Add the legend
legend = Legend(self.histos, self.ltitles, **self.legendkwargs)
legend.draw()
## Add latex labels
latex = Latex(self.labels, self.labels_layout)
latex.draw()
def draw(self):
## Make auxiliary lists of indices and hitogram names
indices = range(len(self.trees))
hnames = ['h_%s_%d' % (self.name, i) for i in indices]
## Make the histograms
for t, hname in zip(self.trees, hnames):
expr = '%s>>%s' % (self.expression, hname)
sel = '&'.join(['(%s)' % c for c in self.cuts if c.strip()])
if self.binning:
expr += '(%s)' % self.binning
t.Draw(expr, sel, 'goff')
else:
## No binning is specified.
## Take extra care to use the same binning for all.
## Use automatic binning fro the first tree and apply it
## to all others.
if id(t) == id(self.trees[0]):
## This is the first tree, use auto-binning.
t.Draw(expr, sel)
else:
## This is no the first tree, use same binning as
## for the first one
t.Draw(expr, sel, 'same')
## Retrieve the histograms in put them in a list
self.histos = [ROOT.gDirectory.Get(hn) for hn in hnames]
## Customize histos
for h, c, ms in zip(self.histos, self.colors, self.markerstyles):
h.Sumw2()
if self.normalize_to_unit_area:
nbins = h.GetXaxis().GetNbins()
area = h.GetBinWidth(1) * h.Integral(1, nbins)
h.Scale(1. / area)
h.GetXaxis().SetTitle(self.xtitle)
h.GetYaxis().SetTitle(self.ytitle)
h.SetTitle(self.title)
h.SetStats(0)
h.SetLineColor(c)
h.SetMarkerColor(c)
h.SetMarkerStyle(ms)
## Find the histogram with greatest y range
maxima = [h.GetMaximum() for h in self.histos]
highest = max(maxima)
imax = maxima.index(highest)
## Draw the "highest" histo first to guarantee nice the y-range.
self.histos[imax].Draw(self.drawopts[imax])
## Draw all histograms
for h, opt in zip(self.histos, self.drawopts):
h.Draw(opt + 'same')
## Add the legend
legend = Legend(self.histos, self.ltitles, **self.legendkwargs)
legend.draw()
## Add latex labels
latex = Latex(self.labels, self.labels_layout)
latex.draw()
def plot_mmgmass_with_fit_for_multiple_smearings(name, stargets, rtargets,
colors, plotrange=(60, 105)):
"""Plot the smeared mmg mass for a number of different smearings."""
canvases.next(name).SetGrid()
mmgMass.setRange('plot', *plotrange)
plot = mmgMass.frame(roo.Range('plot'))
plot.SetTitle("")
slabels = []
rlabels = []
## Loop over the various smearings.
for starget, rtarget, color in zip(stargets, rtargets, colors):
mydata = calibrator.get_smeared_data(starget, rtarget)
model = ParametrizedKeysPdf('model',
'model',
mmgMass, mmgMassSmearPeak,
mmgMassSmearWidth, mydata,
ROOT.RooKeysPdf.NoMirror, 1.5)
model.fitTo(mydata, roo.PrintLevel(-1), roo.Range(60, 120),
roo.SumW2Error(False))
mydata.plotOn(plot, roo.LineColor(color), roo.MarkerColor(color))
model.plotOn(plot, roo.LineColor(color), roo.Range('plot'),
roo.NormRange('plot'))
slabels.append([
's\' = % 3.f %%, ' % starget +
'#Delta m_{#mu#mu#gamma} = %.2f #pm %.2f %%' % (
100 * (mmgMassSmearPeak.getVal() / 91.2 - 1.),
100 * mmgMassSmearPeak.getError() / 91.2
),
])
rlabels.append([
'r\' = %.1f %%, ' % rtarget +
'#sigma_{eff}/#mu(m_{\mu\mu\gamma}) = % .2f #pm %.2f %%' % (
100 * mmgMassSmearWidth.getVal() / mmgMassSmearPeak.getVal(),
100 * mmgMassSmearWidth.getError() / mmgMassSmearPeak.getVal(),
),
])
## End of loop over the various smearings.
plot.Draw()
for i, (labels, color) in enumerate(zip(slabels, colors)):
latex = Latex(labels, position=(0.18, 0.85 - i*0.055))
latex.SetTextColor(color)
latex.draw()
for i, (labels, color) in enumerate(zip(rlabels, colors)):
latex = Latex(labels,
position=(0.18, 0.85 - (len(slabels)+1) * 0.055 - i*0.055))
latex.SetTextColor(color)
latex.draw()
def plot_mmgmass_for_multiple_smearings(name, stargets, rtargets,
colors, plotrange=(76, 106)):
canvases.next(name).SetGrid()
plot = mmgMass.frame(roo.Range(*plotrange))
plot.SetTitle("MC for multiple smearing scenarious")
multilabels = []
## Loop over the various smearings.
for starget, rtarget, color in zip(stargets, rtargets, colors):
mydata = calibrator.get_smeared_data(starget, rtarget)
mydata.plotOn(plot, roo.LineColor(color), roo.MarkerColor(color))
multilabels.append(['s_{target}: %.1f %%' % starget,
'r_{target}: %.1f %%'% rtarget,])
## End of loop over the various smearings.
plot.Draw()
for i, (labels, color) in enumerate(zip(multilabels, colors)):
latex = Latex(labels, position=(0.2, 0.85 - i*0.11))
latex.SetTextColor(color)
latex.draw()
def plot_phoeres_with_fit_for_multiple_smearings(name, stargets, rtargets,
colors, plotrange=(-30, 30)):
"""Plot the smeared photon energy response for a number of different
smearings."""
canvases.next(name).SetGrid()
phoERes.setRange('plot', *plotrange)
plot = phoERes.frame(roo.Range('plot'))
#plot.SetTitle("MC with paremetrized fit for multiple smearing scenarious")
plot.SetTitle("")
slabels = []
rlabels = []
## Loop over the various smearings.
for starget, rtarget, color in zip(stargets, rtargets, colors):
mydata = calibrator.get_smeared_data(starget, rtarget)
phoEResPdf.fitTo(mydata, roo.PrintLevel(-1), roo.SumW2Error(False))
mydata.plotOn(plot, roo.LineColor(color), roo.MarkerColor(color))
phoEResPdf.plotOn(plot, roo.LineColor(color), roo.Range('plot'), roo.NormRange('plot'))
slabels.append([
's\' = % 3.f %%, #Delta s_{fit} = % .2f #pm %.2f %%' % (
starget, phoScale.getVal() - starget, phoScale.getError()
),
])
rlabels.append([
'r\' = %3.1f %%, #Delta r_{fit} = % .2f #pm %.2f %%' % (
rtarget, phoRes.getVal() - rtarget, phoRes.getError()
),
])
## End of loop over the various smearings.
plot.Draw()
for i, (labels, color) in enumerate(zip(slabels, colors)):
latex = Latex(labels, position=(0.18, 0.85 - i*0.055))
latex.SetTextColor(color)
latex.draw()
for i, (labels, color) in enumerate(zip(rlabels, colors)):
latex = Latex(labels,
position=(0.18, 0.85 - (len(slabels) + 1) * 0.055 - i * 0.055))
latex.SetTextColor(color)
latex.draw()
# theory2.plotOn(plot, LineColor(kRed), LineStyle(kDashed))
plot.Draw()
plots.append(plot)
## Photon Resolution
canvases.next('phoERes').SetLogy()
phoERes.SetTitle('E^{#gamma}_{reco}/E^{#gamma}_{gen} - 1')
phoERes.setRange(-0.9,3.2)
plot = phoERes.frame()
plot.SetTitle('Photon Resolution Modeling Zoom Out')
reducedData['phoERes'].plotOn(plot)
phoEResShape.plotOn(plot)
plot.Draw()
plot.GetYaxis().SetRangeUser(1e-3, 1e4)
llabels.position = (0.6, 0.83)
llabels.draw()
plots.append(plot)
## Photon Resolution Zoom In
canvases.next('phoEResZoom')
plot = phoERes.frame(Range(-0.25 + phoEScaleMC, 0.33 + phoEScaleMC))
plot.SetTitle('Photon Resolution Modeling Zoom In')
reducedData['phoERes'].plotOn(plot)
phoEResShape.plotOn(plot)
plot.Draw()
llabels.draw()
llabels.position = (0.21, 0.83)
plots.append(plot)
## ## Photon Resolution Zoom Out
## canvases.next('phoEResZoomOut')
labels.append('#Deltas = %.2f #pm %.2f %%' %
(phoScale.getVal(), phoScale.getError()))
latexlabels = Latex(labels, position = (0.25, 0.8))
## Scan -log(L) vs photon scale
sframe = phoScale.frame(Bins(100),
Range(sFitted[-1] - 5*sFittedErr[-1],
sFitted[-1] + 5*sFittedErr[-1]))
nll = RooNLLVar('nll', 'nll', tmodel, data)
nll.plotOn(sframe, ShiftToZero())
canvases.next(name + '_nll')
phoScale.SetTitle('#Deltas')
# sframe.GetXaxis().SetTitle('#Deltas (%)')
sframe.GetYaxis().SetTitle('-#Delta log L(#Deltas)')
sframe.Draw()
latexlabels.draw()
canvases.canvases[-1].Update()
## Display data overlaid with fitted and extrapolated models
canvases.next(name + '_mmgMass')
frame = mmgMass.frame(Range(60,120))
frame.SetTitle('')
frame.GetXaxis().SetTitle(
'm_{#mu#mu#gamma} (GeV)'
)
data.plotOn(frame)
# m.plotOn(frame)
tmodel.plotOn(frame)
#tmodel.paramOn(frame)
frame.Draw()
latexlabels.draw()
