def buildSpline(self,h,region='PP',shift=''):
'''
Get the signal spline for a given Higgs mass.
Required arguments:
h = higgs mass
'''
histMap = self.histMap[region][shift]
tag= '{}{}'.format(region,'_'+shift if shift else '')
# initial fit
results = {}
errors = {}
results[h] = {}
errors[h] = {}
for a in self.AMASSES:
ws = ROOT.RooWorkspace('sig')
ws.factory('x[{0}, {1}]'.format(*self.XRANGE))
ws.var('x').setUnit('GeV')
ws.var('x').setPlotLabel(self.XLABEL)
ws.var('x').SetTitle(self.XLABEL)
model = Models.Voigtian('sig',
mean = [a,0,30],
width = [0.01*a,0,5],
sigma = [0.01*a,0,5],
)
model.build(ws, 'sig')
hist = histMap[self.SIGNAME.format(h=h,a=a)]
results[h][a], errors[h][a] = model.fit(ws, hist, 'h{}_a{}_{}'.format(h,a,tag), saveDir=self.plotDir, save=True, doErrors=True)
models = {
'mean' : Models.Chebychev('mean', order = 1, p0 = [0,-1,1], p1 = [0.1,-1,1], p2 = [0.03,-1,1]),
'width': Models.Chebychev('width', order = 1, p0 = [0,-1,1], p1 = [0.1,-1,1], p2 = [0.03,-1,1]),
'sigma': Models.Chebychev('sigma', order = 1, p0 = [0,-1,1], p1 = [0.1,-1,1], p2 = [0.03,-1,1]),
}
for param in ['mean', 'width', 'sigma']:
ws = ROOT.RooWorkspace(param)
ws.factory('x[{},{}]'.format(*self.XRANGE))
ws.var('x').setUnit('GeV')
ws.var('x').setPlotLabel(self.XLABEL)
ws.var('x').SetTitle(self.XLABEL)
model = models[param]
model.build(ws, param)
name = '{}_{}{}'.format(param,h,tag)
hist = ROOT.TH1D(name, name, len(self.AMASSES), 4, 22)
vals = [results[h][a]['{}_h{}_a{}_{}'.format(param,h,a,tag)] for a in self.AMASSES]
errs = [errors[h][a]['{}_h{}_a{}_{}'.format(param,h,a,tag)] for a in self.AMASSES]
for i,a in enumerate(self.AMASSES):
b = hist.FindBin(a)
hist.SetBinContent(b,vals[i])
hist.SetBinError(b,errs[i])
model.fit(ws, hist, name, saveDir=self.plotDir, save=True)
# create model
for a in self.AMASSES:
print h, a, results[h][a]
model = Models.VoigtianSpline(self.SPLINENAME.format(h=h),
**{
'masses' : self.AMASSES,
'means' : [results[h][a]['mean_h{0}_a{1}_{2}'.format(h,a,tag)] for a in self.AMASSES],
'widths' : [results[h][a]['width_h{0}_a{1}_{2}'.format(h,a,tag)] for a in self.AMASSES],
'sigmas' : [results[h][a]['sigma_h{0}_a{1}_{2}'.format(h,a,tag)] for a in self.AMASSES],
}
)
if self.binned:
integrals = [histMap[self.SIGNAME.format(h=h,a=a)].Integral() for a in self.AMASSES]
else:
integrals = [histMap[self.SIGNAME.format(h=h,a=a)].sumEntries('x>{} && x<{}'.format(*self.XRANGE)) for a in self.AMASSES]
model.setIntegral(self.AMASSES,integrals)
model.build(self.workspace,'{}_{}'.format(self.SPLINENAME.format(h=h),tag))
model.buildIntegral(self.workspace,'integral_{}_{}'.format(self.SPLINENAME.format(h=h),tag))
def buildSpline(self,h,region='PP',shift='',**kwargs):
'''
Get the signal spline for a given Higgs mass.
Required arguments:
h = higgs mass
'''
fit = kwargs.get('fit',False) # will fit the spline parameters rather than a simple spline
amasses = self.AMASSES
if h>125: amasses = [a for a in amasses if a not in ['3p6',4,6]]
avals = [float(str(x).replace('p','.')) for x in amasses]
histMap = self.histMap[region][shift]
tag= '{}{}'.format(region,'_'+shift if shift else '')
# initial fit
results = {}
errors = {}
results[h] = {}
errors[h] = {}
for a in amasses:
aval = float(str(a).replace('p','.'))
ws = ROOT.RooWorkspace('sig')
ws.factory('x[{0}, {1}]'.format(*self.XRANGE))
ws.var('x').setUnit('GeV')
ws.var('x').setPlotLabel(self.XLABEL)
ws.var('x').SetTitle(self.XLABEL)
model = Models.Voigtian('sig',
mean = [aval,0,30],
width = [0.01*aval,0,5],
sigma = [0.01*aval,0,5],
)
model.build(ws, 'sig')
hist = histMap[self.SIGNAME.format(h=h,a=a)]
saveDir = '{}/{}'.format(self.plotDir,shift if shift else 'central')
results[h][a], errors[h][a] = model.fit(ws, hist, 'h{}_a{}_{}'.format(h,a,tag), saveDir=saveDir, save=True, doErrors=True)
# Fit using ROOT rather than RooFit for the splines
fitFuncs = {
'mean' : 'pol1',
'width': 'pol2',
'sigma': 'pol2',
}
xs = []
x = self.XRANGE[0]
while x<=self.XRANGE[1]:
xs += [x]
x += float(self.XRANGE[1]-self.XRANGE[0])/100
fittedParams = {}
for param in ['mean','width','sigma']:
name = '{}_{}{}'.format(param,h,tag)
xerrs = [0]*len(amasses)
vals = [results[h][a]['{}_h{}_a{}_{}'.format(param,h,a,tag)] for a in amasses]
errs = [errors[h][a]['{}_h{}_a{}_{}'.format(param,h,a,tag)] for a in amasses]
graph = ROOT.TGraphErrors(len(avals),array('d',avals),array('d',vals),array('d',xerrs),array('d',errs))
savedir = '{}/{}'.format(self.plotDir,shift if shift else 'central')
python_mkdir(savedir)
savename = '{}/{}_Fit'.format(savedir,name)
canvas = ROOT.TCanvas(savename,savename,800,800)
graph.Draw()
graph.SetTitle('')
graph.GetHistogram().GetXaxis().SetTitle(self.SPLINELABEL)
graph.GetHistogram().GetYaxis().SetTitle(param)
if fit:
fitResult = graph.Fit(fitFuncs[param])
func = graph.GetFunction(fitFuncs[param])
fittedParams[param] = [func.Eval(x) for x in xs]
canvas.Print('{}.png'.format(savename))
# create model
for a in amasses:
print h, a, results[h][a]
if fit:
model = Models.VoigtianSpline(self.SPLINENAME.format(h=h),
**{
'masses' : xs,
'means' : fittedParams['mean'],
'widths' : fittedParams['width'],
'sigmas' : fittedParams['sigma'],
}
)
else:
model = Models.VoigtianSpline(self.SPLINENAME.format(h=h),
**{
'masses' : avals,
'means' : [results[h][a]['mean_h{0}_a{1}_{2}'.format(h,a,tag)] for a in amasses],
'widths' : [results[h][a]['width_h{0}_a{1}_{2}'.format(h,a,tag)] for a in amasses],
'sigmas' : [results[h][a]['sigma_h{0}_a{1}_{2}'.format(h,a,tag)] for a in amasses],
}
)
if self.binned:
integrals = [histMap[self.SIGNAME.format(h=h,a=a)].Integral() for a in amasses]
else:
integrals = [histMap[self.SIGNAME.format(h=h,a=a)].sumEntries('x>{} && x<{}'.format(*self.XRANGE)) for a in amasses]
print 'Integrals', tag, h, integrals
param = 'integral'
funcname = 'pol2'
name = '{}_{}{}'.format(param,h,tag)
vals = integrals
graph = ROOT.TGraph(len(avals),array('d',avals),array('d',vals))
savedir = '{}/{}'.format(self.plotDir,shift if shift else 'central')
python_mkdir(savedir)
savename = '{}/{}_Fit'.format(savedir,name)
canvas = ROOT.TCanvas(savename,savename,800,800)
graph.Draw()
graph.SetTitle('')
graph.GetHistogram().GetXaxis().SetTitle(self.SPLINELABEL)
graph.GetHistogram().GetYaxis().SetTitle('integral')
if fit:
fitResult = graph.Fit(funcname)
func = graph.GetFunction(funcname)
newintegrals = [func.Eval(x) for x in xs]
# dont fit integrals
#model.setIntegral(xs,newintegrals)
canvas.Print('{}.png'.format(savename))
model.setIntegral(avals,integrals)
model.build(self.workspace,'{}_{}'.format(self.SPLINENAME.format(h=h),tag))
model.buildIntegral(self.workspace,'integral_{}_{}'.format(self.SPLINENAME.format(h=h),tag))
savedir = '{}/{}'.format(self.fitsDir,shift if shift else 'central')
python_mkdir(savedir)
savename = '{}/h{}_{}.json'.format(savedir,h,tag)
jsonData = {'vals': results, 'errs': errors, 'integrals': {a:integral for a,integral in zip(avals,integrals)}}
self.dump(savename,jsonData)
# return the model
# this can be used to determine if you want to keep this shift
return model