'param_init_data_' + fitter.name,
'Initial values for for fits',
fitter.parameters
)
param_fitted_data = ROOT.RooDataSet(
'param_fitted_data_' + fitter.name,
'Fit results for different initial values',
fitter.parameters
)
## Loop over extra fits to study the systematics
for j in range(10):
## Loop over parameters
for pname in '#Deltas #sigmaL #sigmaR'.split():
par = ws1.var(pname)
## Randomize it's value uniformly in range
pmin = par.getMin()
pmax = par.getMax()
rand = ROOT.gRandom.Gaus(0.5*(pmin + pmax), (pmax-pmin)/10.)
par.setVal(rand)
param_init_data.add(fitter.parameters)
## Make the fit
fitter.name = (name + '_fit%d' % j)
fitter.doAutoFitRange = False
fitter.fitToData(ws1)
fitter.doAutoFitRange = True
fitter.fitToData(ws1)
fitter.labels = labels[:]
## Get mass window, only perform fit once for a given selection and
cutsav = ' & '.join(fitter.cuts)
if not fitter.massWindow:
fitter.massWindow = mwindows.get(cutsav)
fitter.getMassCut(ws1)
## Store the resulting mass window in mwindows
mwindows[cutsav] = fitter.massWindow
## Get the data
fitter.getData(ws1)
try:
fitScale = fitter.fitScale
except AttributeError:
fitScale = fSigma
name = fitter.name
Deltas = ws1.var('#Deltas')
DeltasOld = Deltas.getVal()
sigmaL = ws1.var('#sigmaL')
sigmaR = ws1.var('#sigmaR')
sigma = ws1.var('#sigma')
k = ws1.function('k')
m0 = ws1.function('m0')
for iteration in range(maxIterations):
print "++ begin iteration", iteration
if iteration == 0:
if not hasattr(fitter, 'fitRange'):
## Set the fit range automatically to include all data
xlo = fitter.data.tree().GetMinimum(fitter.x.GetName())
xhi = fitter.data.tree().GetMaximum(fitter.x.GetName())
## Loop over plots
for fitter in _fits[:4]:
## Log the current fit configuration
print "++ Processing", fitter.title
print "++ Configuration:"
print fitter.pydump()
## Get the data
fitter.getData(ws1)
try:
fitScale = fitter.fitScale
except AttributeError:
fitScale = fSigma
name = fitter.name
Deltas = ws1.var('#Deltas')
DeltasOld = Deltas.getVal()
sigmaL = ws1.var('#sigmaL')
sigmaR = ws1.var('#sigmaR')
sigma = ws1.var('#sigma')
k = ws1.function('k')
m0 = ws1.function('m0')
for iteration in range(maxIterations):
print "++ begin iteration", iteration
if iteration == 0:
pass
## Set the fit range automatically to include all data
# xlo = fitter.data.tree().GetMinimum(fitter.x.GetName())
# xhi = fitter.data.tree().GetMaximum(fitter.x.GetName())
#
## Get mass window, only perform fit once for a given selection and
cutsav = " & ".join(fitter.cuts)
if not fitter.massWindow:
fitter.massWindow = mwindows.get(cutsav)
fitter.getMassCut(ws1)
## Store the resulting mass window in mwindows
mwindows[cutsav] = fitter.massWindow
## Get the data
fitter.getData(ws1)
try:
fitScale = fitter.fitScale
except AttributeError:
fitScale = fSigma
name = fitter.name
Deltas = ws1.var("#Deltas")
DeltasOld = Deltas.getVal()
sigmaL = ws1.var("#sigmaL")
sigmaR = ws1.var("#sigmaR")
sigma = ws1.var("#sigma")
k = ws1.function("k")
m0 = ws1.function("m0")
for iteration in range(maxIterations):
print "++ begin iteration", iteration
if iteration == 0:
# pass
## Set the fit range automatically to include all data
xlo = fitter.data.tree().GetMinimum(fitter.x.GetName())
xhi = fitter.data.tree().GetMaximum(fitter.x.GetName())
## Loop over plots
for fitter in _fits[:9]:
## Log the current fit configuration
print "++ Processing", fitter.title
print "++ Configuration:"
print fitter.pydump()
## Get the data
fitter.getData(ws1)
try:
fitScale = fitter.fitScale
except AttributeError:
fitScale = fSigma
name = fitter.name
Deltas = ws1.var('#Deltas')
DeltasOld = Deltas.getVal()
sigmaL = ws1.var('#sigmaL')
sigmaR = ws1.var('#sigmaR')
sigma = ws1.var('#sigma')
k = ws1.function('k')
m0 = ws1.function('m0')
for iteration in range(maxIterations):
print "++ begin iteration", iteration
if iteration == 0:
pass
## Set the fit range automatically to include all data
# xlo = fitter.data.tree().GetMinimum(fitter.x.GetName())
# xhi = fitter.data.tree().GetMaximum(fitter.x.GetName())
#
is_first_srecofit = False
print srecofit.title
elif "struefit" in vars() and struefit.title in plot.title:
if is_first_struefit:
is_first_struefit = False
print struefit.title
elif "sgenfit" in vars() and sgenfit.title in plot.title:
if is_first_sgenfit:
is_first_sgenfit = False
print sgenfit.title
elif "shybfit" in vars() and shybfit.title in plot.title:
if is_first_shybfit:
is_first_shybfit = False
print shybfit.title
print "%6.2f +/- %4.2f" % (ws1.var("#Deltas").getVal(), ws1.var("#Deltas").getError()),
if "srecofit" in vars() and srecofit.title in plot.title:
print plot.title[len(srecofit.title) + 2 :],
elif "struefit" in vars() and struefit.title in plot.title:
print plot.title[len(struefit.title) + 2 :],
elif "sgenfit" in vars() and sgenfit.title in plot.title:
print plot.title[len(sgenfit.title) + 2 :],
elif "shybfit" in vars() and shybfit.title in plot.title:
print plot.title[len(shybfit.title) + 2 :],
else:
print plot.title,
print i, "%.3g" % plot.chi2s[i]
## <-- loop over plots