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 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 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_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 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()
## pdflist=','.join(spdflist),
## mreflist=','.join(sreflist)))
## w.Import(pdf)
w.Print()
pdf.fitTo(data, roo.Range(60, 120), roo.Minos())
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()
phoERes.setRange(-90, 150)
phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf',
phoERes, phoScale, phoRes, data,
ROOT.RooKeysPdf.NoMirror, 1.5)
phoERes.setRange(*range_save)
##------------------------------------------------------------------------------
## Plot the phoEResPdf for various values of the scale
canvases.next('ShapeScaleScan').SetGrid()
phoRes.setVal(1)
plot = phoERes.frame(roo.Range(-10, 10))
latexlabels = []
for i, color in enumerate('Red Yellow Green Blue Black'.split()):
scale = -4 + 2*i
phoScale.setVal(scale)
phoEResPdf.plotOn(plot, roo.LineColor(getattr(ROOT, 'k' + color)))
label = Latex(['s: %d %%' % scale,],
position=(0.8, 0.75 - (i+1) * 0.055))
label.SetTextColor(getattr(ROOT, 'k' + color))
latexlabels.append(label)
plot.SetTitle('Scale Parametrization Closure')
plot.GetYaxis().SetTitle('a. u.')
plot.Draw()
Latex(cutlabels, position=(0.2, 0.75)).draw()
Latex(['r: 1 %',], position=(0.8, 0.75)).draw()
for l in latexlabels:
l.draw()
##------------------------------------------------------------------------------
## Plot the phoEResPdf for various values of the effective sigma
## Build the model for fT(t|s,r) = fT1(t1) * fT2(t2|s,r)
t.setRange(5, 10)
t.setBins(1000, "cache")
tpdf = ROOT.RooFFTConvPdf('tpdf', 'tpdf', t, t1pdf, t2pdf)
tpdf.setBufferFraction(0.1)
##------------------------------------------------------------------------------
## 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' % (
res1 = (phoScale.getVal(), phoScale.getError())
mmgMassModel2.fitTo(fitdata, roo.Range(60, 120), roo.SumW2Error(True),
roo.Strategy(2), roo.InitialHesse(True), roo.Minos(True),
roo.Timer(True))
res2 = (phoScale.getVal(), phoScale.getError())
##------------------------------------------------------------------------------
canvases.next('data')
plot = mmgMass.frame(roo.Range(70, 110))
data.plotOn(plot)
mmgMassPdf.shape.plotOn(plot)
## mmgMass.setRange(50, 130)
## mmgMassPdf.fitTo(data, roo.Range(60,120), roo.SumW2Error(True),
## roo.InitialHesse(True), roo.Minos(True), roo.Strategy(2),
## roo.Timer(True))
mmgMassPdf.plotOn(plot, roo.LineColor(ROOT.kRed), roo.LineStyle(ROOT.kDashed))
print 'Plotting', plot.GetName(), 'on', canvases.canvases[-1].GetName()
plot.Draw()
Latex(
['m(E^{#gamma}_{reco}/E^{#gamma}_{gen}-1): %.3f #pm %.3f %%' % (
calibrator.s.getVal(), calibrator.s.getError()
),
'#sigma_{eff}(E^{#gamma}_{reco}/E^{#gamma}_{gen}-1): %.3f #pm %.3f %%' % (
calibrator.r.getVal(), calibrator.r.getError()
),
'm(m_{#mu#mu#gamma})',
' shape: %.3f GeV' % mmgMassPdf.shapemode,
' fit: %.3f #pm %.3f GeV' % (
mmgMassPeak.getVal(), mmgMassPeak.getError()
),
'#sigma_{eff}(m_{#mu#mu#gamma})',
rho=1.5)
## Build the model for fT(t|s,r) = fT1(t1) * fT2(t2|s,r)
t.setRange(5, 10)
t.setBins(1000, "cache")
tpdf = ROOT.RooFFTConvPdf('tpdf', 'tpdf', t, t1pdf, t2pdf)
tpdf.setBufferFraction(0.1)
## 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()
## pdflist=','.join(spdflist),
## mreflist=','.join(sreflist)))
## w.Import(pdf)
w.Print()
pdf.fitTo(data, roo.Range(60, 120), roo.Minos())
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()
sw.Start()
init()
get_data()
phoEResPdf = ParametrizedKeysPdf(
'phoEResPdf', 'phoEResPdf', phoERes, phoScale, phoRes, data,
ROOT.RooKeysPdf.NoMirror, 1.5
)
phoEResPdf.fitTo(data, roo.Range(-50, 50))
canvases.next('phoEResPdf').SetGrid()
plot = phoERes.frame(roo.Range(-10, 10))
data.plotOn(plot)
phoEResPdf.plotOn(plot)
phoEResPdf.paramOn(plot)
plot.Draw()
t = w.factory('t[0,-1,1]')
t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})')
tfunc = w.factory('expr::tfunc("log(0.01 * phoERes + 1)", {phoERes})')
tfunc.SetName('t')
data.addColumn(tfunc)
## Build the model for log(Ereco/Egen) ft2(t2|r,s)
t2pdf = LogPhoeresKeysPdf('t2pdf', 't2pdf', phoERes, t, phoScale, phoRes, data,
rho=1.5)
## Fit nominal data with ft2(t2|r,s)
canvases.next('t2pdf').SetGrid()
##------------------------------------------------------------------------------
sw = ROOT.TStopwatch()
sw.Start()
init()
get_data()
phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf', phoERes, phoScale,
phoRes, data, ROOT.RooKeysPdf.NoMirror, 1.5)
phoEResPdf.fitTo(data, roo.Range(-50, 50))
canvases.next('phoEResPdf').SetGrid()
plot = phoERes.frame(roo.Range(-10, 10))
data.plotOn(plot)
phoEResPdf.plotOn(plot)
phoEResPdf.paramOn(plot)
plot.Draw()
t = w.factory('t[0,-1,1]')
t.SetTitle('log(E_{reco}^{#gamma}/E_{gen}^{#gamma})')
tfunc = w.factory('expr::tfunc("log(0.01 * phoERes + 1)", {phoERes})')
tfunc.SetName('t')
data.addColumn(tfunc)
## Build the model for log(Ereco/Egen) ft2(t2|r,s)
t2pdf = LogPhoeresKeysPdf('t2pdf',
't2pdf',
phoERes,
t,
phoScale,