def main():
'''This is the entry point to execution.'''
global data, model, x, fitresult
data = getdata()
x = data['raw'].get()[variable]
if fit_range:
x.setRange('fit', *fit_range)
if plot_range:
x.setRange('plot', *plot_range)
initialize_fit_parameters(data['raw'], x)
old_precision = set_default_integrator_precision(1e-8, 1e-8)
## Reduce data for model building
data_kde_training = data['raw']
if data['raw'].numEntries() > kde_training_max_events:
resampler = Resampler(data['raw'])
data['raw_reduced'] = resampler.bootstrap(
name = data['raw'].GetName() + '_boot',
title = data['raw'].GetTitle() + ' Bootstrap Replica',
size = kde_training_max_events,
seed = 12345,
)
data_kde_training = data['raw_reduced']
model = ParameterizedKeysPdf('model', 'model', x, mode, effsigma,
data_kde_training, rho=0.3, forcerange=True)
fitresult = {}
for name, dataset in data.items():
dataset.SetName(name + 'Data')
## Reduce data for debugging
# dataset = dataset.reduce(roo.EventRange(0, 10000))
mode.setVal(model.shapemodevar.getVal())
effsigma.setVal(model.shapewidthvar.getVal())
fitresult[name] = model.fitTo(dataset, roo.SumW2Error(True),
roo.NumCPU(8), roo.Strategy(2),
roo.Save(), roo.Range("fit"))
make_plot(x, dataset, model)
print '\n== Fitted parameters =='
mode.Print()
effsigma.Print()
save_result(fitresult[name])
set_default_integrator_precision(*old_precision)
x_corr = get_correction(x, fitresult['raw'].floatParsFinal(),
fitresult['target'].floatParsFinal())
make_comparison_plot(data['raw'], data['target'], x_corr)
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
