def _process(self,xDataSet, yDataSet):
dyDataSet = Maths.derivative(xDataSet._jdataset(), yDataSet._jdataset(), self.smoothwidth)
minVal, maxVal = dyDataSet.min(), dyDataSet.max()
if maxVal - minVal == 0:
raise ValueError("There is no edge")
labels = [label if label != 'slope' else 'top' for label in self.labelList]
return GaussianPeak(self.name, labels, self.formatString, self.plotPanel)._process(xDataSet, dyDataSet)
def _process(self, xDataSet, yDataSet):
dyDataSet = dnp.array(Maths.derivative(xDataSet._jdataset(), yDataSet._jdataset(), self.smoothwidth))
uposC, ufwhmC, uareaC, dposC, dfwhmC, dareaC = self.coarseProcess(xDataSet, dyDataSet)
gaussian = dnp.fit.function.gaussian
if abs(dareaC) < 0.2 * uareaC:
r = dnp.fit.fit([gaussian], xDataSet, dyDataSet,
[uposC, ufwhmC, uareaC],
bounds=[
(uposC - 2 * ufwhmC, uposC + 2 * ufwhmC),
(0, 2 * ufwhmC),
(0, 2 * uareaC)],
ptol=1e-10, optimizer=self.optimizer)
upos, ufwhm, _uarea = r.parameters
results = {'upos': upos, 'ufwhm': ufwhm, 'area': _uarea, 'uarea': _uarea, 'fwhm': ufwhm}
elif uareaC < 0.2 * abs(dareaC):
r = dnp.fit.fit([gaussian], xDataSet, dyDataSet,
[dposC, dfwhmC, dareaC],
bounds=[
(dposC - 2 * dfwhmC, dposC + 2 * dfwhmC),
(0, 2 * dfwhmC),
(2 * dareaC, 0)],
ptol=1e-10, optimizer=self.optimizer)
dpos, dfwhm, _darea = r.parameters
results = {'dpos': dpos, 'dfwhm': dfwhm, 'area': abs(_darea), 'darea': _darea, 'fwhm': dfwhm}
else:
r = dnp.fit.fit([gaussian, gaussian], xDataSet, dyDataSet,
[uposC, ufwhmC, uareaC,dposC, dfwhmC, dareaC],
bounds=[
(uposC - 2 * ufwhmC, uposC + 2 * ufwhmC),
(0, 2 * ufwhmC),
(0, 2 * uareaC),
(dposC - 2 * dfwhmC, dposC + 2 * dfwhmC),
(0, 2 * dfwhmC),
(2 * dareaC, 0)],
ptol=1e-10, optimizer=self.optimizer)
upos, ufwhm, _uarea, dpos, dfwhm, _darea = r.parameters
results = {'upos': upos,
'dpos': dpos,
'ufwhm': ufwhm,
'dfwhm': dfwhm,
'uarea': _uarea,
'darea': _darea,
'centre': (upos + dpos) / 2.0,
'width': abs(upos - dpos),
'area': (_uarea + abs(_darea)) / 2.0,
'fwhm': (ufwhm + dfwhm) / 2.0}
self.plotResult(r)
results['residual'] = r.residual
return [results.get(label, float('NaN')) for label in self.labelList]
def _process(self, xDataSet, yDataSet):
dyDataSet = dnp.array(
Maths.derivative(xDataSet._jdataset(), yDataSet._jdataset(),
self.smoothwidth))
uposC, ufwhmC, uareaC, dposC, dfwhmC, dareaC = self.coarseProcess(
xDataSet, dyDataSet)
gaussian = dnp.fit.function.gaussian
if abs(dareaC) < 0.2 * uareaC:
r = dnp.fit.fit([gaussian],
xDataSet,
dyDataSet, [uposC, ufwhmC, uareaC],
bounds=[(uposC - 2 * ufwhmC, uposC + 2 * ufwhmC),
(0, 2 * ufwhmC), (0, 2 * uareaC)],
ptol=1e-10,
optimizer=self.optimizer)
upos, ufwhm, _uarea = r.parameters
results = {
'upos': upos,
'ufwhm': ufwhm,
'area': _uarea,
'uarea': _uarea,
'fwhm': ufwhm
}
elif uareaC < 0.2 * abs(dareaC):
r = dnp.fit.fit([gaussian],
xDataSet,
dyDataSet, [dposC, dfwhmC, dareaC],
bounds=[(dposC - 2 * dfwhmC, dposC + 2 * dfwhmC),
(0, 2 * dfwhmC), (2 * dareaC, 0)],
ptol=1e-10,
optimizer=self.optimizer)
dpos, dfwhm, _darea = r.parameters
results = {
'dpos': dpos,
'dfwhm': dfwhm,
'area': abs(_darea),
'darea': _darea,
'fwhm': dfwhm
}
else:
r = dnp.fit.fit([gaussian, gaussian],
xDataSet,
dyDataSet,
[uposC, ufwhmC, uareaC, dposC, dfwhmC, dareaC],
bounds=[(uposC - 2 * ufwhmC, uposC + 2 * ufwhmC),
(0, 2 * ufwhmC), (0, 2 * uareaC),
(dposC - 2 * dfwhmC, dposC + 2 * dfwhmC),
(0, 2 * dfwhmC), (2 * dareaC, 0)],
ptol=1e-10,
optimizer=self.optimizer)
upos, ufwhm, _uarea, dpos, dfwhm, _darea = r.parameters
results = {
'upos': upos,
'dpos': dpos,
'ufwhm': ufwhm,
'dfwhm': dfwhm,
'uarea': _uarea,
'darea': _darea,
'centre': (upos + dpos) / 2.0,
'width': abs(upos - dpos),
'area': (_uarea + abs(_darea)) / 2.0,
'fwhm': (ufwhm + dfwhm) / 2.0
}
self.plotResult(r)
results['residual'] = r.residual
return [results.get(label, float('NaN')) for label in self.labelList]
