# generate fake data
data = recoWithFakes
# Create unfolding class
m = Unfolder(bkg, mig, eff, truth)
m.setUniformPrior()
#m.setGaussianPrior()
#m.setCurvaturePrior()
#m.setFirstDerivativePrior()
m.run(data)
m.setAlpha(1.0)
m.sample(50000)
# plot marginal distributions
m.plotMarginal("plotMarginal.%s" % extension)
# plot correlations
#m.plotPairs("pairPlot.%s" % extension) # takes forever
m.plotCov("covPlot.%s" % extension)
m.plotCorr("corrPlot.%s" % extension)
m.plotCorrWithNP("corrPlotWithNP.%s" % extension)
m.plotSkewness("skewPlot.%s" % extension)
m.plotKurtosis("kurtosisPlot.%s" % extension)
m.plotNP("plotNP.%s" % extension)
# plot unfolded spectrum
m.plotUnfolded("plotUnfolded.%s" % extension)
m.plotOnlyUnfolded(1.0, False, "", "plotOnlyUnfolded.%s" % extension)
bestAlphaNormBias[i], bestAlphaNormBiasStd[i])
# do the rest with the best alpha from stat. test only
m.setAlpha(alpha[""])
m.run(pseudo_data)
m.setData(pseudo_data)
m.sample(100000)
# plot marginal distributions
m.plotMarginal("plotMarginal_pseudo.%s" % extension)
for i in uncList:
m.plotNPMarginal(i, "plotNPMarginal_pseudo_%s.%s" % (i, extension))
# plot unfolded spectrum
m.plotUnfolded("plotUnfolded_pseudo.%s" % extension)
m.plotOnlyUnfolded(luminosity * 1e-3, True, "fb/GeV",
"plotOnlyUnfolded_pseudo.%s" % extension)
# plot correlations graphically
# it takes forever and it is just pretty
# do it only if you really want to see it
# I just get annoyed waiting for it ...
#m.plotPairs("pairPlot.%s" % extension) # takes forever!
suf = "_pseudo"
m.plotCov("covPlot%s.%s" % (suf, extension))
m.plotCorr("corrPlot%s.%s" % (suf, extension))
m.plotCorrWithNP("corrPlotWithNP%s.%s" % (suf, extension))
m.plotSkewness("skewPlot%s.%s" % (suf, extension))
m.plotKurtosis("kurtosisPlot%s.%s" % (suf, extension))