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 decorate_canvas(self, canvas):
'''
Decorate the new canvas with the results of the fit and other labels.
'''
gpad_save = ROOT.gPad
canvas.cd()
phoScale = self.workspace.var('phoScale')
phoRes = self.workspace.var('phoRes')
## Draw fit results:
Latex([
'Fit Parameters:',
'#mu(E_{#gamma}) = %.2f #pm %.2f %%' % (
phoScale.getVal(), phoScale.getError()
),
'#sigma(E_{#gamma}) = %.2f #pm %.2f %%' % (
phoRes.getVal(), phoRes.getError()
),
],
position=(0.65, 0.8), textsize=22, rowheight=0.07
).draw()
## CMS Preliminary:
Latex(['CMS Preliminary 2011, #sqrt{s} = 7 TeV'],
position=(0.17, 0.93), textsize=22).draw()
labels = []
## Data or MC
if 'data' in self.name:
labels.append('Data, L = 4.89 fb^{-1}')
else:
labels.append('Simulation')
## EB or EE
if 'EB' in self.name:
labels.append('ECAL Barrel')
else:
labels.append('ECAL Endcaps')
if 'highR9' in self.name:
labels.append('R_{9} > 0.94')
Latex(labels, position=(0.22, 0.8), textsize=22,
rowheight=0.07
).draw()
canvas.Modified()
canvas.Update()
gpad_save.cd()
def plot_training_phoeres_with_shape_and_fit():
"""Plot the nominal MC photon energy smearing overlayed with the pdf shape
and fit."""
canvases.next('TrainingPhoEResWithShapeAndFit')
plot = phoERes.frame(roo.Range(-7.5, 5))
plot.SetTitle("Photon energy smearing overlayed with PDF shape (blue) "
"and it's parametrized fit (dashed red)")
data.plotOn(plot)
## Define model for the photon energy smearing function Ereco/Etrue - 1.
phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf', phoERes,
phoScale, phoRes, data,
ROOT.RooKeysPdf.NoMirror, 1.5)
## PDF shape
phoEResPdf.shape.plotOn(plot)
## Parametrized fit of the PDF shape
phoEResPdf.fitTo(data, roo.Range(-50, 50), roo.PrintLevel(-1))
phoEResPdf.plotOn(plot, roo.LineColor(ROOT.kRed),
roo.LineStyle(ROOT.kDashed))
plot.Draw()
Latex([
's_{shape}: %.3f %%' % phoEResPdf.shapemode,
's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' %
(phoScale.getVal() - phoEResPdf.shapemode, phoScale.getError()),
'r_{shape}: %.3f %%' % phoEResPdf.shapewidth,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit} - r_{shape}: %.4f #pm %.4f %%' %
(phoRes.getVal() - phoEResPdf.shapewidth, phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(phoRes.getVal() / phoEResPdf.shapewidth,
phoRes.getError() / phoEResPdf.shapewidth),
],
position=(0.2, 0.8)).draw()
def draw_latex_for_fit_to_real_data():
'''
Draw latex results to the plot of the fit to real data.
'''
global fsr_purity
Latex([
'E^{#gamma} Scale (%)',
' MC Truth: %.2f #pm %.2f' % (fit_calibrator.s.getVal(),
fit_calibrator.s.getError()),
' Data Fit: %.2f #pm %.2f ^{+%.2f}_{%.2f}' % (
phoScale.getVal(), phoScale.getError(), phoScale.getErrorHi(),
phoScale.getErrorLo()
),
'',
'E^{#gamma} Resolution (%)',
' MC Truth: %.2f #pm %.2f' % (fit_calibrator.r.getVal(),
fit_calibrator.r.getError()),
' Data Fit: %.2f #pm %.2f ^{+%.2f}_{%.2f}' % (
phoRes.getVal(), phoRes.getError(), phoRes.getErrorHi(),
phoRes.getErrorLo()
),
'',
'Signal Purity (%)',
' MC Truth: %.2f' % fsr_purity,
' Data Fit: %.2f #pm %.2f' % (
100 * w.var('signal_f').getVal(),
100 * w.var('signal_f').getError()
)
],
position=(0.2, 0.8)
).draw()
print 'E^{#gamma} Scale (%)',' MC Truth: %.2f #pm %.2f' % (fit_calibrator.s.getVal(),fit_calibrator.s.getError()),' Data Fit: %.2f #pm %.2f ^{+%.2f}_{%.2f}' % (phoScale.getVal(), phoScale.getError(), phoScale.getErrorHi(),phoScale.getErrorLo())
def plot_nominal_mmgmass_with_shape_and_fit():
"""Plot the nominal MC mmg mass data overlayed with the pdf shape and
fit."""
canvases.next('NominalMmgMassWithShapeAndFit')
plot = mmgMass.frame(roo.Range(75, 105))
plot.SetTitle("m(#mu#mu#gamma) overlayed with PDF shape (blue) "
"and it's parametrized fit (dashed red)")
data.plotOn(plot)
## Define the mmg mass model.
mmgMassPdf = ParametrizedKeysPdf('mmgMassPdf', 'mmgMassPdf', mmgMass,
massPeak, massWidth, data,
ROOT.RooKeysPdf.NoMirror, 1.5)
## PDF shape
mmgMassPdf.shape.plotOn(plot)
## Parametrized fit of the PDF shape
mmgMassPdf.fitTo(data, roo.Range(60, 120), roo.PrintLevel(-1))
mmgMassPdf.plotOn(plot, roo.LineColor(ROOT.kRed),
roo.LineStyle(ROOT.kDashed))
plot.Draw()
sshape = 100 * (mmgMassPdf.shapemode / mZ.getVal() - 1)
rshape = 100 * mmgMassPdf.shapewidth / mmgMassPdf.shapemode
Latex([
's_{shape}: %.3f %%' % sshape,
's_{fit}: %.3f #pm %.3f %%' %
(massScale.getVal(), massScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' %
(massScale.getVal() - sshape, massScale.getError()),
'r_{shape}: %.3f %%' % rshape,
'r_{fit}: %.3f #pm %.3f %%' % (massRes.getVal(), massRes.getError()),
'r_{fit} - r_{shape}: %.4f #pm %.4f %%' %
(massRes.getVal() - rshape, massRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(massRes.getVal() / rshape, massRes.getError() / rshape),
],
position=(0.2, 0.8)).draw()
def decorate_plot(self):
## Draw the functional form
Latex(['#frac{#sigma_{eff}}{E} = #frac{S}{#sqrt{E_{T}}} #oplus '
'#frac{N}{E_{T}} #oplus C'],
position = (0.18, 0.8), textsize = 22).draw()
## Draw the fit result
chi2 = self.fresult.minNll()
npars = self.fresult.floatParsFinal().getSize()
ndof = self.dxy.numEntries() - npars
prob = ROOT.TMath.Prob(chi2, ndof)
Latex([self.root_latex_for_realvar(self.S, 1),
self.root_latex_for_realvar(self.N, 1),
self.root_latex_for_realvar(self.C, 1),
'#chi^{2} / N_{dof}: %.2g / %d' % (chi2, ndof),
'#chi^{2} Prob.: %.3g %%' % (100 * prob)],
position = (0.53, 0.8), textsize = 22).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_nominal_and_smeared_mmgmass():
canvases.next('SmearedMMGMass').SetGrid()
plot = mmgMass.frame(roo.Range(76, 106))
plot.SetTitle("Nominal (black) and smeared (red) mmg mass")
data.plotOn(plot)
datasmeared.plotOn(plot, roo.MarkerColor(ROOT.kRed),
roo.LineColor(ROOT.kRed))
plot.Draw()
Latex([
's_{0}: %.2g %%, s: %.2g %%' % (phoScaleRef, targets),
'r_{0}: %.2g %%, r: %.2g %%' % (phoResRef, targetr)
],
position=(0.2, 0.8)).draw()
def plot_smeared_phoeres_with_fit():
phoEResPdf.fitTo(datasmeared, roo.PrintLevel(-1), roo.SumW2Error(False))
canvases.next('SmearedSampleWithFit')
plot = phoERes.frame(roo.Range(-30, 30))
plot.SetTitle("Smeared MC with paremetrized fit")
datasmeared.plotOn(plot)
phoEResPdf.plotOn(plot)
phoEResPdf.paramOn(plot)
plot.Draw()
Latex([
'target s: %.3g %%' % targets,
'target r: %.3g %%' % targetr,
],
position=(0.2, 0.75)).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_training_phoeres_with_shape_and_fit():
canvases.next('TrainingSampleWithShapeAndFit')
plot = phoERes.frame(roo.Range(-7.5, 7.5))
plot.SetTitle(
"MC overlayed with PDF shape (blue) and it's parametrized fit"
"(dashed red)")
data.plotOn(plot)
phoEResPdf.shape.plotOn(plot)
phoEResPdf.plotOn(plot, roo.LineColor(ROOT.kRed),
roo.LineStyle(ROOT.kDashed))
plot.Draw()
Latex([
's_{shape}: %.3f %%' % phoEResPdf.shapemode,
's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f' %
(phoScale.getVal() - phoEResPdf.shapemode, phoScale.getError()),
'r_{shape}: %.3f %%' % phoEResPdf.shapewidth,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(phoRes.getVal() / phoEResPdf.shapewidth,
phoRes.getError() / phoEResPdf.shapewidth),
],
position=(0.2, 0.75)).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()
## Plot fT1(t1) with training data.
canvases.next('t1pdf').SetGrid()
t.setRange(5, 10)
t.SetTitle('log(m_{#mu#mu#gamma,E_{gen}^{#gamma}}^{2} - m_{#mu#mu}^{2})')
plot = t.frame(roo.Range(6, 9))
t1data.plotOn(plot)
t1pdf.shape.plotOn(plot)
t1pdf.plotOn(plot, roo.LineColor(ROOT.kRed), roo.LineStyle(ROOT.kDashed))
plot.Draw()
Latex([
's_{shape}: %.3f' % t1pdf.shapemode,
's_{fit}: %.3f #pm %.3f' % (t1mode.getVal(), t1mode.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f' %
(t1mode.getVal() - t1pdf.shapemode, t1mode.getError()),
'r_{shape}: %.3f' % t1pdf.shapewidth,
'r_{fit}: %.3f #pm %.3f' % (t1width.getVal(), t1width.getError()),
'r_{fit} - r_{shape}: %.4f #pm %.4f' %
(t1width.getVal() - t1pdf.shapewidth, t1width.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(t1width.getVal() / t1pdf.shapewidth,
t1width.getError() / t1pdf.shapewidth),
],
position=(0.2, 0.8)).draw()
## Plot fT2(t2|s,r) fitted to training data.
canvases.next('t2pdf').SetGrid()
t.setRange(-1, 1)
t.setVal(0)
t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})')
phoScale.setVal(t2pdf.s0val)
phoRes.setVal(t2pdf.r0val)
t2pdf.fitTo(t2data, roo.Range(ROOT.TMath.Log(0.5), ROOT.TMath.Log(1.5)))
data,
rho=1.5)
## Fit nominal data with ft2(t2|r,s)
canvases.next('t2pdf').SetGrid()
t2pdf.fitTo(data, roo.Range(ROOT.TMath.Log(0.5), ROOT.TMath.Log(2.0)))
plot = t.frame(roo.Range(-0.15, 0.15))
data.plotOn(plot)
t2pdf.plotOn(plot)
plot.Draw()
Latex([
's_{shape}: %.3f %%' % t2pdf.s0val,
's_{sfit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' %
(phoScale.getVal() - t2pdf.s0val, phoScale.getError()),
'r_{shape}: %.3f %%' % t2pdf.r0val,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' %
(phoRes.getVal() / t2pdf.r0val, phoRes.getError() / t2pdf.r0val),
],
position=(0.2, 0.75)).draw()
## Fit scale s=-5 smeared data with ft2(t2|r,s)
canvases.next('t2pdf_sm5').SetGrid()
sdata = calibrator.get_smeared_data(-5, 'nominal')
sdata.addColumn(tfunc)
sdata.SetName('sdata_sm5')
plot = t.frame(roo.Range(-0.15, 0.15))
sdata.plotOn(plot)
t2pdf.fitTo(sdata, roo.Range(-0.7, 0.4))
t2pdf.plotOn(plot)
labels.append('Endcaps')
labels = ['E_{T}^{#gamma} #in [%g,%g] GeV' % phoPtRange]
for c in cuts:
if 'phoR9' in c:
labels.append(c.replace('phoR9', 'R_{9}'))
break
labels.append('Moduel 1')
labels.append('True Resolution (#sigma_{eff}): %.3g %%' % phoEResMC)
labels.append('True Scale: %.3g %%' % (100*phoEScaleMC))
labels.append('<EdM/dE>: %.3g GeV' % phoF.getVal())
llabels = Latex(labels, (0.21, 0.83))
## Make plots
plots = []
phoRes = w.var('phoRes')
phoRes.SetTitle('#sigma_{eff}(E_{#gamma})')
phoScale.SetTitle('E_{#gamma} Scale')
## for x in [phoRes, phoScale]:
## x.SetTitle(x.GetName())
## Theory for unsmeared photon
canvases.next('theory')#.SetLogy()
mmgMassShiftedTitle = mmgMassShifted.GetTitle().replace('mmgMass', 'm(#mu#mu#gamma)')
mmgMassShifted.setUnit('GeV')
mmgMassShifted.SetTitle(mmgMassShiftedTitle.replace('m(#mu#mu#gamma)', 'm(#mu#mu + gen #gamma)'))
def decorate_canvas(self, canvas):
'''
Decorate the new canvas with the results of the fit and other labels.
'''
gpad_save = ROOT.gPad
canvas.cd()
phoScale = self.workspace.var('phoScale')
phoRes = self.workspace.var('phoRes')
## Draw fit results:
Latex([
'Fit Parameters:',
'#mu(E_{#gamma}) = %.2f #pm %.2f %%' % (
phoScale.getVal(), phoScale.getError()
),
'#sigma(E_{#gamma}) = %.2f #pm %.2f %%' % (
phoRes.getVal(), phoRes.getError()
),
],
position=(0.65, 0.8), textsize=22, rowheight=0.07,
textfont=42
).draw()
## CMS Preliminary:
Latex(['CMS 2011, #sqrt{s} = 7 TeV'],
position=(0.17, 0.93), textsize=22,
textfont=42).draw()
labels = []
tokens = self.name.split('_')
## Data or MC
if 'data' in tokens:
labels.append('Data, L = 4.89 fb^{-1}')
else:
labels.append('Simulation')
## EB or EE
if 'EB' in tokens:
labels.append('ECAL Barrel')
else:
labels.append('ECAL Endcaps')
if 'highR9' in tokens:
labels.append('R_{9} > 0.94')
## Find the name token with the pt information
pt_token = ''
for token in tokens:
print token
if ('pt' in token) and ('to' in token):
pt_token = token
print 'Pt range:', pt_token
if pt_token:
## We found it. Parse it to get the pt range
lo, hi = pt_token.replace('pt', '').split('to')
labels.append('Photon E_{T} #in [%s, %s) GeV' % (lo, hi))
Latex(labels, position=(0.22, 0.8), textsize=22,
rowheight=0.07, textfont = 42
).draw()
canvas.Modified()
canvas.Update()
gpad_save.cd()
def decorate_canvas(self, canvas):
'''
Decorate the new canvas with the results of the fit and other labels.
'''
gpad_save = ROOT.gPad
canvas.cd()
phoScale = self.workspace.var('phoScale')
phoRes = self.workspace.var('phoRes')
## Draw fit results:
Latex(
[
#'Fit Parameters:',
's = %.2f #pm %.2f %%' %
(phoScale.getVal(), phoScale.getError()),
'r = %.2f #pm %.2f %%' % (phoRes.getVal(), phoRes.getError()),
],
position=(0.59, 0.8),
textsize=22,
rowheight=0.07).draw()
## CMS Preliminary:
## Data or MC
if 'data' in self.name:
Latex([
'CMS Preliminary, #sqrt{s} = 8 TeV, #int L dt = 19.6 fb^{-1}'
],
position=(0.15, 0.93),
textsize=22).draw()
else:
Latex(['CMS Preliminary, #sqrt{s} = 8 TeV, Simulation'],
position=(0.15, 0.93),
textsize=22).draw()
labels = []
## Data or MC
#if 'data' in self.name:
#labels.extend([
#'Data',
#])
#else:
#labels.append('Simulation')
## EB or EE
#if 'EB' in self.name:
#labels.append('ECAL Barrel')
#else:
#labels.append('ECAL Endcaps')
#if 'highR9' in self.name:
#labels.append('R_{9} > 0.94')
#labels.append('E_{T}^{#gamma} > 20 GeV')
## Regression or not
if 'HggRegression' in self.subdir.split('_'):
labels.append('Regression')
else:
labels.append('No Regression')
Latex(labels, position=(0.2, 0.8), textsize=22, rowheight=0.07).draw()
canvas.Modified()
canvas.Update()
gpad_save.cd()
t.setVal(0)
t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})')
phoScale.setVal(t2pdf.s0val)
phoRes.setVal(t2pdf.r0val)
t2pdf.fitTo(t2data, roo.Range(ROOT.TMath.Log(0.5), ROOT.TMath.Log(1.5)))
plot = t.frame(roo.Range(-0.3, 0.3))
t2data.plotOn(plot)
t2pdf.plotOn(plot)
plot.Draw()
Latex([
's_{shape}: %.3f %%' % t2pdf.s0val,
's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(), phoScale.getError()),
's_{fit} - s_{shape}: %.4f #pm %.4f %%' % (
phoScale.getVal() - t2pdf.s0val,
phoScale.getError()
),
'r_{shape}: %.3f %%' % t2pdf.r0val,
'r_{fit}: %.3f #pm %.3f %%' % (phoRes.getVal(), phoRes.getError()),
'r_{fit}/r_{shape}: %.4f #pm %.4f' % (
phoRes.getVal() / t2pdf.r0val,
phoRes.getError() / t2pdf.r0val),
], position=(0.2, 0.75)).draw()
canvases.update()
t.setRange(*t1range)
xtpdf.fitTo(xtdata, roo.NumCPU(8), roo.Verbose(True), roo.Timer(True),
roo.SumW2Error(True), roo.Minos(ROOT.RooArgSet(phoRes)))
## Plot fXT(t|s,r) fitted to data
## canvases.next('tpdf').SetGrid()
## Import the data in the workspace
data.SetName('data_%s' % name)
w.Import(data)
## Also build the PDF's
## m = w.factory('KeysPdf::model%d(mmgMass, data%d, NoMirror, 2)' % (i, i))
## dataCollection.append(data)
## models.append(m)
## fit the transformed model to the test data
tmodel.fitTo(data)
sFitted.append(phoScale.getVal())
sFittedErr.append(phoScale.getError())
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()
tdata = calibrator.get_smeared_data(stest, rtest)
tdata.SetName('tdata')
pdf.fitTo(tdata, roo.Range(60, 120), roo.Minos())
calibrator.phoEResPdf.fitTo(tdata, roo.Range(-50, 50))
canvases.next('test_fit')
plot = mmgMass.frame(roo.Range(70, 110))
tdata.plotOn(plot)
pdf.plotOn(plot)
plot.Draw()
Latex(
[
's_{true}: %.3f #pm %.3f %%' %
(calibrator.s.getVal(), calibrator.s.getError()),
## 's_{fit}: %.3f #pm %.3f %%' % (phoScale.getVal(),
## phoScale.getError()),
'r_{true}: %.3f #pm %.3f %%' %
(calibrator.r.getVal(), calibrator.r.getError()),
'r_{fit}: %.3f ^{+%.3f}_{%.3f} %%' %
(phoRes.getVal(), phoRes.getErrorHi(), phoRes.getErrorLo()),
],
position=(0.2, 0.8)).draw()
canvases.next('test_nll').SetGrid()
nll = pdf.createNLL(tdata, roo.Range(60, 120))
rframe = phoRes.frame(
roo.Range(phoRes.getVal() + 3 * phoRes.getErrorLo(),
phoRes.getVal() + 3 * phoRes.getErrorHi()))
nll.plotOn(rframe, roo.ShiftToZero())
rframe.Draw()
canvases.update()
def process_monte_carlo():
'''
Get, fit and plot monte carlo.
'''
global fitdata1
fitdata1 = fit_calibrator.get_smeared_data(
sfit, rfit, 'fitdata1', 'fitdata1', True
)
fitdata1.reduce(ROOT.RooArgSet(mmgMass, mmMass))
fitdata1.append(data['zj1'])
fitdata1.SetName('fitdata1')
data['fit1'] = fitdata1
if reduce_data == True:
fitdata1 = fitdata1.reduce(roo.Range(reduced_entries,
fitdata1.numEntries()))
check_timer('3. get fit data (%d entries)' % fitdata1.numEntries())
nll = pm.createNLL(fitdata1, roo.Range('fit'), roo.NumCPU(8))
minuit = ROOT.RooMinuit(nll)
minuit.setProfile()
minuit.setVerbose()
phoScale.setError(1)
phoRes.setError(1)
## Initial HESSE
status = minuit.hesse()
fitres = minuit.save(name + '_fitres1_inithesse')
w.Import(fitres, fitres.GetName())
check_timer('4. initial hesse (status: %d)' % status)
## Minimization
minuit.setStrategy(2)
status = minuit.migrad()
fitres = minuit.save(name + '_fitres2_migrad')
w.Import(fitres, fitres.GetName())
check_timer('5. migrad (status: %d)' % status)
## Parabolic errors
status = minuit.hesse()
fitres = minuit.save(name + '_fitres3_hesse')
w.Import(fitres, fitres.GetName())
check_timer('6. hesse (status: %d)' % status)
## Minos errors
status = minuit.minos()
fitres = minuit.save(name + '_fitres4_minos')
w.Import(fitres, fitres.GetName())
check_timer('7. minos (status: %d)' % status)
#fres = pm.fitTo(fitdata1, roo.SumW2Error(True),
#roo.Range('fit'),
## roo.Strategy(2),
#roo.InitialHesse(True),
#roo.Minos(),
#roo.Verbose(True),
#roo.NumCPU(8), roo.Save(), roo.Timer())
signal_model._phorhist.GetXaxis().SetRangeUser(75, 105)
signal_model._phorhist.GetYaxis().SetRangeUser(0, 15)
signal_model._phorhist.GetXaxis().SetTitle('%s (%s)' % (mmgMass.GetTitle(),
mmgMass.getUnit()))
signal_model._phorhist.GetYaxis().SetTitle('E^{#gamma} Resolution (%)')
signal_model._phorhist.GetZaxis().SetTitle('Probability Density (1/GeV/%)')
signal_model._phorhist.SetTitle(latex_title)
signal_model._phorhist.GetXaxis().SetTitleOffset(1.5)
signal_model._phorhist.GetYaxis().SetTitleOffset(1.5)
signal_model._phorhist.GetZaxis().SetTitleOffset(1.5)
signal_model._phorhist.SetStats(False)
canvases.next(name + '_phorhist')
signal_model._phorhist.Draw('surf1')
global graph
graph = signal_model.make_mctrue_graph()
graph.GetXaxis().SetTitle('E^{#gamma} resolution (%)')
graph.GetYaxis().SetTitle('m_{#mu^{+}#mu^{-}#gamma} effective #sigma (GeV)')
graph.SetTitle(latex_title)
canvases.next(name + '_mwidth_vs_phor').SetGrid()
graph.Draw('ap')
mmgMass.setBins(80)
plot = mmgMass.frame(roo.Range('plot'))
plot.SetTitle('Fall11 MC, ' + latex_title)
fitdata1.plotOn(plot)
pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot'))
pm.plotOn(plot, roo.Range('plot'), roo.NormRange('plot'),
roo.Components('*zj*'), roo.LineStyle(ROOT.kDashed))
canvases.next(name + '_fit').SetGrid()
plot.Draw()
## Estimate the MC truth phos and phor:
old_precision = set_default_integrator_precision(2e-9, 2e-9)
calibrator0.s.setRange(-15, 15)
calibrator0.r.setRange(0,25)
calibrator0.phoEResPdf.fitTo(fitdata1, roo.Range(-50, 50), roo.Strategy(2))
set_default_integrator_precision(*old_precision)
set_mc_truth(calibrator0.s, calibrator0.r)
## Store the result in the workspace:
w.saveSnapshot('mc_fit', ROOT.RooArgSet(phoScale, phoRes,
phoScaleTrue, phoResTrue))
## Draw the results on the canvas:
Latex([
'E^{#gamma} Scale (%)',
' MC Truth: %.2f #pm %.2f' % (calibrator0.s.getVal(),
calibrator0.s.getError()),
' MC Fit: %.2f #pm %.2f ^{+%.2f}_{%.2f}' % (
phoScale.getVal(), phoScale.getError(), phoScale.getErrorHi(),
phoScale.getErrorLo()
),
'',
'E^{#gamma} Resolution (%)',
' MC Truth: %.2f #pm %.2f' % (calibrator0.r.getVal(),
calibrator0.r.getError()),
' MC Fit: %.2f #pm %.2f ^{+%.2f}_{%.2f}' % (
phoRes.getVal(), phoRes.getError(), phoRes.getErrorHi(),
phoRes.getErrorLo()
),
'',
'Signal Purity (%)',
' MC Truth: %.2f' % fsr_purity,
' MC Fit: %.2f #pm %.2f' % (
100 * w.var('signal_f').getVal(),
100 * w.var('signal_f').getError()
)
],
position=(0.2, 0.8)
).draw()
check_timer('8. fast plots')
