def PyExec(self):
self._setup()
if self._binning_for_calc.size == 0:
x = np.array(self._input_ws.readX(self._incident_index))
self._binning_for_calc = [
i for i in [min(x), x[1] -
x[0], max(x) + x[1] - x[0]]
]
else:
x = np.arange(self._binning_for_calc[0], self._binning_for_calc[2],
self._binning_for_calc[1])
if self._binning_for_fit.size == 0:
x_fit = np.array(self._input_ws.readX(self._incident_index))
y_fit = np.array(self._input_ws.readY(self._incident_index))
else:
rebinned = Rebin(InputWorkspace=self._input_ws,
Params=self._binning_for_fit,
PreserveEvents=True,
StoreInADS=False)
x_fit = np.array(rebinned.readX(self._incident_index))
y_fit = np.array(rebinned.readY(self._incident_index))
rebin_norm = x.size / x_fit.size
x_bin_centers = 0.5 * (x[:-1] + x[1:])
if len(x_fit) != len(y_fit):
x_fit = 0.5 * (x_fit[:-1] + x_fit[1:])
if self._fit_spectrum_with == 'CubicSpline':
# Fit using cubic spline
fit, fit_prime = self.fit_cubic_spline(x_fit,
y_fit,
x_bin_centers,
s=1e7)
elif self._fit_spectrum_with == 'CubicSplineViaMantid':
# Fit using cubic spline via Mantid
fit, fit_prime = self.fit_cubic_spline_via_mantid_spline_smoothing(
self._input_ws,
params_input=self._binning_for_fit,
params_output=self._binning_for_calc,
Error=0.0001,
MaxNumberOfBreaks=0)
elif self._fit_spectrum_with == 'GaussConvCubicSpline':
# Fit using Gauss conv cubic spline
fit, fit_prime = self.fit_cubic_spline_with_gauss_conv(
x_fit, y_fit, x_bin_centers, sigma=0.5)
# Create output workspace
unit = self._input_ws.getAxis(0).getUnit().unitID()
output_workspace = CreateWorkspace(DataX=x,
DataY=np.append(fit, fit_prime) /
rebin_norm,
UnitX=unit,
NSpec=2,
Distribution=False,
ParentWorkspace=self._input_ws,
StoreInADS=False)
self.setProperty("OutputWorkspace", output_workspace)
def fit_cubic_spline_via_mantid_spline_smoothing(self, InputWorkspace,
params_input,
params_output, **kwargs):
# Fit with Cubic Spline using the mantid SplineSmoothing algorithm
rebinned = Rebin(InputWorkspace=InputWorkspace,
Params=params_input,
PreserveEvents=True,
StoreInADS=False)
fit_tuple = SplineSmoothing(InputWorkspace=rebinned,
OutputWorkspaceDeriv='fit_prime',
DerivOrder=1,
StoreInADS=False,
**kwargs)
fit = Rebin(InputWorkspace=fit_tuple.OutputWorkspace,
Params=params_output,
PreserveEvents=True,
StoreInADS=False)
fit_prime = Rebin(InputWorkspace=fit_tuple.OutputWorkspaceDeriv[0],
Params=params_output,
PreserveEvents=True,
StoreInADS=False)
fit_array = copy(fit.readY(0))
fit_prime_array = copy(fit_prime.readY(0))
return fit_array, fit_prime_array
