if options.function == 'erfexp':
fitter.erfexp('model', 'x')
parameterization['type'] = 'erfexp'
if options.function == 'erfexpCB':
fitter.erfexpCB('model', 'x')
parameterization['type'] = 'erfexpCB'
if options.function == 'erfexpTimesCB':
fitter.erfexpTimesCB('model', 'x')
parameterization['type'] = 'erfexpTimesCB'
if options.function.find('bernstein') != -1:
order = int(options.function.split('_')[1])
fitter.bernstein('model', 'x', order)
parameterization['type'] = 'bernstein'
parameterization['order'] = order
fitter.fit('model', 'data', [ROOT.RooFit.SumW2Error(1)])
fitter.fit('model', 'data', [ROOT.RooFit.SumW2Error(1)])
fitter.projection("model", "data", "x", "debug" + options.output + ".png")
if parameterization['type'] == 'expo':
val, err = fitter.fetch('c_0')
parameterization['c_0'] = val
parameterization['c_0Err'] = err
if parameterization['type'] == 'erfexp':
for i in range(0, 3):
def interpolate(histos, axis, options):
#create 2D histogram
histo2D = ROOT.TH2D("histo2D", "histo2D", options.binsx, options.minx,
options.maxx, options.binsy, options.miny,
options.maxy)
if options.fitFunc == "bernstein3":
order = 3
if options.fitFunc == "bernstein4":
order = 4
if options.fitFunc == "bernstein5":
order = 5
if options.fitFunc == "bifur":
order = 3
graphs = []
for i in range(0, order):
graphs.append(ROOT.TGraphErrors())
#run the fits
for N, h in enumerate(histos):
center = axis.GetBinCenter(N + 1)
fitter = Fitter(['mjj'])
fitter.w.var("mjj").setVal(40)
fitter.w.var("mjj").setMax(options.maxy)
fitter.w.var("mjj").setMin(options.miny)
if options.fitFunc == "bernstein3":
fitter.bernstein('model', 'mjj', 3)
if options.fitFunc == "bernstein4":
fitter.bernstein('model', 'mjj', 4)
if options.fitFunc == "bernstein5":
fitter.bernstein('model', 'mjj', 5)
if options.fitFunc == "bifur":
fitter.bifur('model', 'mjj')
fitter.importBinnedData(h, ['mjj'], 'data')
fitter.fit('model', 'data')
fitter.fit('model', 'data')
fitter.projection("model", "data", "mjj",
"debugProjectionFit_" + str(N) + ".png")
for j, g in enumerate(graphs):
c, cerr = fitter.fetch("c_" + str(j))
g.SetPoint(N, center, c)
g.SetPointError(N, 0.0, cerr)
##OK now interpolate and make histograms!
#first make the function
fitter = Fitter(['mjj'])
if options.fitFunc == "bernstein3":
fitter.bernstein('model', 'mjj', 3)
order = 3
if options.fitFunc == "bernstein4":
fitter.bernstein('model', 'mjj', 4)
order = 4
if options.fitFunc == "bernstein5":
fitter.bernstein('model', 'mjj', 5)
order = 5
if options.fitFunc == "bifur":
fitter.bifur('model', 'mjj')
order = 3
fitter.w.var("mjj").setVal(80)
fitter.w.var("mjj").setMax(options.maxy)
fitter.w.var("mjj").setMin(options.miny)
for i in range(1, histo2D.GetNbinsX() + 1):
x = histo2D.GetXaxis().GetBinCenter(i)
print 'Bin:', i, 'Evaluating function at x=', x
for j, g in enumerate(graphs):
print "c_" + str(j), "=", g.Eval(x)
fitter.w.var("c_" + str(j)).setVal(g.Eval(x, 0, "S"))
histogram = fitter.w.pdf("model").createHistogram("mjj", options.binsy)
for k in range(1, histo2D.GetNbinsY() + 1):
bin = histo2D.GetBin(i, k)
histo2D.SetBinContent(bin, histogram.GetBinContent(k))
return graphs, histo2D