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 _get_mctruth_scale_and_resolution(self):
## Enlarge the range of the observable to get vanishing tails for
## the photon energy scale resolution
# savrange = (self.phoERes.getMin(), self.phoERes.getMax())
# self.phoERes.setRange(savrange[0] - 10, savrange[1] + 10)
## Build the model for the photon energy resolution.
self.phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf',
self.phoERes, self.s, self.r,
self.data,
ROOT.RooKeysPdf.NoMirror,
self.rho)
# self.phoERes.setRange(*savrange)
## Set sensible initial values
self.s.setVal(self.phoEResPdf.shapemode)
self.r.setVal(self.phoEResPdf.shapewidth)
## Extract the MC truth scale and resolution from MC
self.fitresult_mctruth = self.phoEResPdf.fitTo(
self.data, roo.PrintLevel(self.printlevel), roo.SumW2Error(False),
roo.Range(-50, 50), roo.Save(), roo.Strategy(2))
self.w.Import(self.fitresult_mctruth)
## Store the MC truth scale and resolution
for source, target in zip([self.s, self.r], [self.s0, self.r0]):
target.setVal(source.getVal())
target.setError(source.getError())
target.setAsymError(source.getErrorLo(), source.getErrorHi())
# self.s0.setVal(self.s.getVal())
# self.r0.setVal(self.r.getVal())
self.w.saveSnapshot('sr_mctruth', self.sr)
self.w.saveSnapshot('sr0_mctruth', self.sr0)
self.s0.setConstant(True)
self.r0.setConstant(True)
def _fit_smeared_data(self, name):
'Fit the current smeared data to get the smeared s and r.'
self.fitresult_sdata = self.phoEResPdf.fitTo(
self.sdata, roo.PrintLevel(self.printlevel), roo.SumW2Error(False),
roo.Range(-50, 50), roo.Save(), roo.Strategy(2))
self.w.saveSnapshot(name + '_sr', self.sr)
self.w.Import(self.fitresult_sdata, name + '_fitresult')
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()
phoScale = w.factory('phoScale[0,-50,50]')
phoRes = w.factory('phoRes[5,0.01,50]')
for x in [phoScale, phoRes]:
x.setUnit('%')
range_save = (phoERes.getMin(), phoERes.getMax())
## Enlarge the range of the observable to get vanishing tails.
phoERes.setRange(-90, 150)
phoEResPdf = ParametrizedKeysPdf('phoEResPdf', 'phoEResPdf', phoERes, phoScale,
phoRes, data, ROOT.RooKeysPdf.NoMirror, 1.5)
phoERes.setRange(*range_save)
##------------------------------------------------------------------------------
## Extract the MC truth scale and resolution from MC
phoEResPdf.fitTo(data, roo.PrintLevel(-1), roo.SumW2Error(False))
phoScaleRef = phoScale.getVal()
phoResRef = phoRes.getVal()
##------------------------------------------------------------------------------
## Define the smearing formulas for photon energy and mmg invariant mass
phoEResSmear = w.factory('phoEResSmear[-100,200]')
phoEResSmearFunc = w.factory('''expr::phoEResSmearFunc(
"{m} + {s} * ({x} - {m0}) / {s0}",
{{{x}}}
)'''.format(x='phoERes',
m=targets,
s=targetr,
m0=phoScaleRef,
s0=phoResRef))
