def dofit(self):
mi = ModalInterval(self.data)
mi.setSigmaLevel(1)
self.mode.setVal(mi.halfSampleMode())
self.effsigma.setVal(0.5 * mi.length())
self.bootdata.add(self.bootset)
resampler = Resampler(self.data)
for iboot in range(self.nboot):
mi = ModalInterval(resampler.bootstrap())
mi.setSigmaLevel(1)
self.mode.setVal(mi.halfSampleMode())
self.effsigma.setVal(0.5 * mi.length())
self.bootdata.add(self.bootset)
self.mode.setVal(self.bootdata.mean(self.mode))
self.effsigma.setVal(self.bootdata.mean(self.effsigma))
self.mode.setError(self.bootdata.rmsVar(self.mode).getVal())
self.effsigma.setError(self.bootdata.rmsVar(self.effsigma).getVal())
def dofit(self):
mi = ModalInterval(self.data)
mi.setSigmaLevel(1)
self.mode.setVal(mi.halfSampleMode())
self.effsigma.setVal(0.5 * mi.length())
self.bootdata.add(self.bootset)
resampler = Resampler(self.data)
for iboot in range(self.nboot):
mi = ModalInterval(resampler.bootstrap())
mi.setSigmaLevel(1)
self.mode.setVal(mi.halfSampleMode())
self.effsigma.setVal(0.5 * mi.length())
self.bootdata.add(self.bootset)
self.mode.setVal(self.bootdata.mean(self.mode))
self.effsigma.setVal(self.bootdata.mean(self.effsigma))
self.mode.setError(self.bootdata.rmsVar(self.mode).getVal())
self.effsigma.setError(self.bootdata.rmsVar(self.effsigma).getVal())
def bootstrap(self, repeat=10):
nbinsx = len(self.fractions)
xlow = 0.5 * self.fractions[0]
xup = self.fractions[-1] + xlow
errors_rms = ROOT.TProfile(self.name + '_errors_rms', self.title,
nbinsx, xlow, xup, 's')
errors_mi1 = ROOT.TGraphAsymmErrors(nbinsx)
errors_mi2 = ROOT.TGraphAsymmErrors(nbinsx)
for graph in [errors_mi1, errors_mi2]:
graph.SetTitle(self.title)
resampler = Resampler(self.data)
bootdata = {}
for iteration in range(repeat):
replica = resampler.bootstrap()
boot = self.get_width_ratio(replica, self.fractions)
for i in range(boot.GetN()):
x = boot.GetX()[i]
y = boot.GetY()[i]
#y = boot.GetY()[i] - self.width_ratio.GetY()[i]
errors_rms.Fill(x, y)
bootdata.setdefault(x, []).append(y)
for i, (x, ydist) in enumerate(sorted(bootdata.items())):
ysize = len(ydist)
yarray = array.array('d', ydist)
y = ROOT.TMath.Median(ysize, yarray)
errors_mi1.SetPoint(i, x, y)
errors_mi2.SetPoint(i, x, y)
exh = exl = 0.
mi = ModalInterval(ysize, yarray)
mi.setSigmaLevel(1)
eyl = y - mi.lowerBound()
eyh = mi.upperBound() - y
errors_mi1.SetPointError(i, exl, exh, eyl, eyh)
mi.setSigmaLevel(2)
eyl = y - mi.lowerBound()
eyh = mi.upperBound() - y
errors_mi2.SetPointError(i, exl, exh, eyl, eyh)
return errors_rms, errors_mi1, errors_mi2
def bootstrap(self, repeat=10):
nbinsx = len(self.fractions)
xlow = 0.5 * self.fractions[0]
xup = self.fractions[-1] + xlow
errors_rms = ROOT.TProfile(self.name + '_errors_rms', self.title,
nbinsx, xlow, xup, 's')
errors_mi1 = ROOT.TGraphAsymmErrors(nbinsx)
errors_mi2 = ROOT.TGraphAsymmErrors(nbinsx)
for graph in [errors_mi1, errors_mi2]:
graph.SetTitle(self.title)
resampler = Resampler(self.data)
bootdata = {}
for iteration in range(repeat):
replica = resampler.bootstrap()
boot = self.get_width_ratio(replica, self.fractions)
for i in range(boot.GetN()):
x = boot.GetX()[i]
y = boot.GetY()[i]
#y = boot.GetY()[i] - self.width_ratio.GetY()[i]
errors_rms.Fill(x, y)
bootdata.setdefault(x, []).append(y)
for i, (x, ydist) in enumerate(sorted(bootdata.items())):
ysize = len(ydist)
yarray = array.array('d', ydist)
y = ROOT.TMath.Median(ysize, yarray)
errors_mi1.SetPoint(i, x, y)
errors_mi2.SetPoint(i, x, y)
exh = exl = 0.
mi = ModalInterval(ysize, yarray)
mi.setSigmaLevel(1)
eyl = y - mi.lowerBound()
eyh = mi.upperBound() - y
errors_mi1.SetPointError(i, exl, exh, eyl, eyh)
mi.setSigmaLevel(2)
eyl = y - mi.lowerBound()
eyh = mi.upperBound() - y
errors_mi2.SetPointError(i, exl, exh, eyl, eyh)
return errors_rms, errors_mi1, errors_mi2
