def save_fit(self,
sampMan=None,
workspace=None,
logy=False,
stats_pos='right'):
if sampMan is not None:
for name, param in self.fit_params.iteritems():
param.SetName(name)
can = ROOT.TCanvas(str(uuid.uuid4()), '')
frame = self.xvar.frame()
self.datahist.plotOn(frame)
self.func_pdf.plotOn(frame)
if stats_pos == 'left':
self.func_pdf.paramOn(frame, rf.ShowConstants(True),
rf.Layout(0.1, 0.5, 0.9),
rf.Format("NEU", rf.AutoPrecision(3)))
if stats_pos == 'right':
self.func_pdf.paramOn(frame, rf.ShowConstants(True),
rf.Layout(0.5, 0.9, 0.9),
rf.Format("NEU", rf.AutoPrecision(2)))
frame.Draw()
if logy:
ymax = frame.GetMaximum()
frame.SetMinimum(0.001)
frame.SetMaximum(ymax * 10)
can.SetLogy()
can.SetLogx()
sampMan.outputs[self.label] = can
def plot_fit_params(self, wsp):
"""
Plot all free fit parameters onto canvas
Args:
wsp (ROOT.RooWorkspace): workspace where the model is stored
"""
fit_var = get_var(wsp, self.fit_var)
cans = OrderedDict()
for bin_name in self.bins:
frame = fit_var.frame(rf.Title('Fit Results'))
plotname = '{}_{}'.format(self.full_model, bin_name)
full_pdf = wsp.pdf(plotname)
full_pdf.paramOn(frame, rf.Layout(0.1, 0.9, 0.9),
rf.Format('NEU', rf.AutoPrecision(2)))
can = r.TCanvas(create_random_str(32), 'rcan', 600, 600)
can.cd()
frame.findObject('{}_paramBox'.format(full_pdf.GetName())).Draw()
cans[bin_name] = can
#can.SaveAs(pdfname)
# returns fit params for each fit
return cans
def plot_fit_params(self, wsp, pdfname, snapname=''):
"""
Plot all free fit parameters onto canvas and save as a pdf.
Args:
wsp (ROOT.RooWorkspace): workspace where the model is stored
pdfname (str): Name of the created pdf under which the plot will be
stored
snapname (str, optional): Name of snapshot that will be loaded
before plotting
"""
if snapname:
wsp.loadSnapshot(snapname)
frame = get_var(wsp, self.mname).frame(rf.Title('Fit Results'))
full_pdf = wsp.pdf(self.full_model)
full_pdf.paramOn(frame, rf.Layout(0.1, 0.9, 0.9),
rf.Format('NEU', rf.AutoPrecision(2)))
can = r.TCanvas(create_random_str(32), 'rcan', 600, 600)
can.cd()
frame.findObject('{}_paramBox'.format(full_pdf.GetName())).Draw()
can.SaveAs(pdfname)
mean = RooRealVar("mean", "MeanofGaussian", 0, -10, 10)
sigma = RooRealVar("sigma", "WidthofGaussian", 3, -10, 10)
gauss = RooGaussian("gauss", "gauss(x,mean,sigma)", x, mean, sigma)
g1sig = RooRealVar("g1sig", "fraction of gauss1", 10, 0, 100000.0)
# Build signal PDF: f1
signalPDF = RooAddPdf("signalPDF", "g1sig * g1", RooArgList(gauss),
RooArgList(g1sig))
#mean.setConstant(kTRUE); #fix mean
sigma.setRange(0.1, 3)
# change range for sigma
gauss.fitTo(data, RooFit.PrintLevel(-1))
# ffit gauss on data
signalPDF.fitTo(data, RooFit.PrintLevel(-1))
#PrintLevel(-1) almost no output
# Minos(kTRUE)?
mean.Print()
sigma.Print()
#Show parameters on plot
signalPDF.paramOn(xframe, RooFit.Layout(0.55, 0.9, 0.9),
RooFit.Format("NEU", RooFit.AutoPrecision(1)))
signalPDF.plotOn(xframe)
xframe.Draw()
chi2txt = TLatex()
chi2txt.SetNDC()
chi2txt.DrawLatex(0.13, 0.83, "\chi^2/n.d.f = %0.3f" %
xframe.chiSquare()) #Put chi² on plot
