def _set_fitInput(self, fitPairs):
ln_fitPairs = Pairs(conv_func=log)
for p in fitPairs:
ln_fitPairs.add(p[0], p[1])
_Efficiency._set_fitInput(self, ln_fitPairs)
def _get_fitInput(self):
fitPairs = Pairs(conv_func=exp)
for p in self._fitInput:
fitPairs.add(p[0], p[1])
return fitPairs
def _get_background_pairs(self):
if self.bgMarkers.IsPending():
hdtv.ui.warning("Not all background regions are closed.")
backgrounds = Pairs()
for m in self.bgMarkers:
try:
backgrounds.add(m.p1.pos_uncal, m.p2.pos_uncal)
except AttributeError:
pos = m.p1.pos_cal if m.p1.pos_cal else m.p2.pos_cal
hdtv.ui.warning("Background region at {:.2f} without second marker was ignored".format(pos))
return backgrounds
def RestorePeaks(self,
cal=None,
region=Pairs(),
peaks=list(),
chisquare=0.0,
coeffs=list()):
"""
Create the Peak Fitter object and
restore all peaks
"""
# create the fitter
peaklist = [p.pos.nominal_value for p in peaks]
self.peakFitter = self.peakModel.GetFitter(region, peaklist, cal)
# restore first the fitter and afterwards the peaks
if self.bgFitter:
# external background
self.peakFitter.Restore(self.bgFitter, chisquare)
else:
# internal background
values = ROOT.TArrayD(self.bgdeg + 1)
errors = ROOT.TArrayD(self.bgdeg + 1)
for i in range(0, self.bgdeg + 1):
values[i] = coeffs[i].nominal_value
errors[i] = coeffs[i].std_dev
self.peakFitter.Restore(values, errors, chisquare)
if not len(peaks) == self.peakFitter.GetNumPeaks():
raise RuntimeError("Number of peaks does not match")
for i, peak in enumerate(peaks):
self.peakModel.RestoreParams(peak, self.peakFitter.GetPeak(i))
def __init__(self, num_pars=0, pars=None, norm=True):
pars = pars or []
self._numPars = num_pars
self.parameter = pars
self.fCov = [[None for j in range(self._numPars)]
for i in range(self._numPars)
] # Simple matrix replacement
self._dEff_dP = list()
self.TGraph = TGraphErrors()
# Normalization factors
self._doNorm = norm
self.norm = 1.0
self.TF1.FixParameter(0, self.norm) # Normalization
self.TF1.SetRange(0, 10000) # Default range for efficiency function
self._fitInput = Pairs(lambda x: ufloat(x, 0))
# if self.parameter: # Parameters were given
# map(lambda i: self.TF1.SetParameter(i + 1, self.parameter[i]), range(1, len(pars))) # Set initial parameters
# else:
# self.parameter = [None for i in range(1, self._numPars + 1)]
#
self.TF1.SetParName(0, "N") # Normalization
for i in range(0, num_pars):
self._dEff_dP.append(None)
if num_pars <= len(string.ascii_lowercase):
self.TF1.SetParName(i + 1, string.ascii_lowercase[i])
def Restore(self, spec):
# do not call Erase() while setting spec!
self._spec = weakref(spec)
self.cal = spec.cal
self.color = spec.color
self.FixMarkerInUncal()
if len(self.bgMarkers) > 0 and not self.bgMarkers.IsPending():
backgrounds = Pairs()
for m in self.bgMarkers:
backgrounds.add(m.p1.pos_uncal, m.p2.pos_uncal)
self.fitter.RestoreBackground(backgrounds=backgrounds,
params=self.bgParams,
chisquare=self.bgChi)
region = sorted([
self.regionMarkers[0].p1.pos_uncal,
self.regionMarkers[0].p2.pos_uncal
])
print(self.bgParams)
if self.peaks:
self.fitter.RestorePeaks(
cal=self.cal,
region=region,
peaks=self.peaks,
chisquare=self.chi,
coeffs=self.bgParams,
)
# get background function
func = self.fitter.peakFitter.GetBgFunc()
self.dispBgFunc = ROOT.HDTV.Display.DisplayFunc(func, self.color)
self.dispBgFunc.SetCal(self.cal)
# get peak function
func = self.fitter.peakFitter.GetSumFunc()
self.dispPeakFunc = ROOT.HDTV.Display.DisplayFunc(func, self.color)
self.dispPeakFunc.SetCal(self.cal)
# restore display functions of single peaks
for i in range(0, self.fitter.peakFitter.GetNumPeaks()):
cpeak = self.fitter.peakFitter.GetPeak(i)
func = cpeak.GetPeakFunc()
self.peaks[i].displayObj = ROOT.HDTV.Display.DisplayFunc(
func, self.color)
self.peaks[i].displayObj.SetCal(self.cal)
# create non-existant integral
if not self.integral:
self.integral = hdtv.integral.Integrate(
self.spec, self.fitter.bgFitter, region)
def FitBackground(self, spec, backgrounds=Pairs()):
"""
Create Background Fitter object and do the background fit
"""
# create fitter
bgfitter = ROOT.HDTV.Fit.PolyBg(self.bgdeg)
for bg in backgrounds:
bgfitter.AddRegion(bg[0], bg[1])
self.bgFitter = bgfitter
# do the background fit
self.bgFitter.Fit(spec.hist.hist)
def FitPeaks(self, spec, region=Pairs(), peaklist=list()):
"""
Create the Peak Fitter object and do the peak fit
"""
# create the fitter
self.peakFitter = self.peakModel.GetFitter(region, peaklist, spec.cal)
# Do the fitpeak
if self.bgFitter:
# external background
self.peakFitter.Fit(spec.hist.hist, self.bgFitter)
else:
# internal background
self.peakFitter.Fit(spec.hist.hist, self.bgdeg)
def RestoreBackground(self,
backgrounds=Pairs(),
params=list(),
chisquare=0.0):
"""
Create Background Fitter object and
restore the background polynom from coeffs
"""
self.bgFitter = self.backgroundModel.GetFitter(nparams=len(params),
nbg=len(backgrounds))
# restore the fitter
valueArray = ROOT.TArrayD(len(params))
errorArray = ROOT.TArrayD(len(params))
for i, param in enumerate(params):
valueArray[i] = param.nominal_value
errorArray[i] = param.std_dev
self.bgFitter.Restore(valueArray, errorArray, chisquare)
def FitBackground(self, spec, backgrounds=Pairs()):
"""
Create Background Fitter object and do the background fit
"""
# create fitter
self.bgFitter = self.backgroundModel.GetFitter(
nparams=self.backgroundModel.fParStatus['nparams'],
nbg=len(backgrounds))
if self.bgFitter is None:
msg = (
"Background model %s needs at least %i background regions to execute a fit. Found %i."
% (self.backgroundModel.name,
self.backgroundModel.requiredBgRegions, len(backgrounds)))
raise ValueError(msg)
else:
for bg in backgrounds:
self.bgFitter.AddRegion(bg[0], bg[1])
# do the background fit
self.bgFitter.Fit(spec.hist.hist)
def RestoreBackground(self,
backgrounds=Pairs(),
coeffs=list(),
chisquare=0.0):
"""
Create Background Fitter object and
restore the background polynom from coeffs
"""
# create fitter
bgfitter = ROOT.HDTV.Fit.PolyBg(self.bgdeg)
for bg in backgrounds:
bgfitter.AddRegion(bg[0], bg[1])
self.bgFitter = bgfitter
# restore the fitter
valueArray = ROOT.TArrayD(len(coeffs))
errorArray = ROOT.TArrayD(len(coeffs))
for i, coeff in enumerate(coeffs):
valueArray[i] = coeff.nominal_value
errorArray[i] = coeff.std_dev
self.bgFitter.Restore(valueArray, errorArray, chisquare)
def FitBackground(self, spec, backgrounds=Pairs()):
"""
Create Background Fitter object and do the background fit
"""
# create fitter
self.bgFitter = self.backgroundModel.GetFitter(
integrate=self.peakModel.GetOption("integrate"),
likelihood=self.peakModel.GetOption("likelihood"),
nparams=self.backgroundModel.fParStatus["nparams"],
nbg=len(backgrounds),
)
if self.bgFitter is None:
msg = "Background model %s needs at least %i background regions to execute a fit. Found %i.".format(
self.backgroundModel.name,
self.backgroundModel.requiredBgRegions,
len(backgrounds),
)
raise ValueError(msg)
else:
for bg in backgrounds:
self.bgFitter.AddRegion(bg[0], bg[1])
# do the background fit
self.bgFitter.Fit(spec.hist.hist)