# Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source
# & Institut Laue - Langevin
# SPDX - License - Identifier: GPL - 3.0 +
from __future__ import absolute_import, division, print_function
import numpy as np
from scipy import signal, ndimage, interpolate, optimize
from mantid.api import AlgorithmFactory, CommonBinsValidator, MatrixWorkspaceProperty, PythonAlgorithm, PropertyMode
from mantid.kernel import Direction, StringListValidator, StringMandatoryValidator
from mantid.simpleapi import CreateWorkspace, Rebin, SplineSmoothing, AnalysisDataService
class FitIncidentSpectrum(PythonAlgorithm):
_input_ws = None
_output_ws = None
def category(self):
return 'Diffraction\\Fitting'
def name(self):
return 'FitIncidentSpectrum'
def summary(self):
return 'Calculate a fit for an incident spectrum using different methods. ' \
'Outputs a workspace containing the functionalized fit and its first ' \
'derivative.'
def seeAlso(self):
return [""]
def version(self):
return 1
def PyInit(self):
self.declareProperty(
MatrixWorkspaceProperty('InputWorkspace', '',
direction=Direction.Input,
),
doc='Incident spectrum to be fit.')
self.declareProperty(
MatrixWorkspaceProperty('OutputWorkspace', '',
direction=Direction.Output),
doc='Output workspace containing the fit and it\'s first derivative.')
self.declareProperty(
name='BinningForCalc',
defaultValue='',
doc='Bin range for calculation given as a comma separated string in the format '
'\"[Start],[Increment],[End]\". If empty use default binning. The calculated '
'spectrum will use this binning')
self.declareProperty(
name='BinningForFit',
defaultValue='',
doc='Bin range for fitting given as a comma separated string in the format '
'\"[Start],[Increment],[End]\". If empty use BinningForCalc. The '
'incident spectrum will be rebined to this range before being fit.')
self.declareProperty(
name='FitSpectrumWith',
defaultValue='GaussConvCubicSpline',
validator=StringListValidator(['GaussConvCubicSpline', 'CubicSpline', 'CubicSplineViaMantid',
'HowellsFunction']),
doc='The method for fitting the incident spectrum.')
def validateInputs(self):
issues = dict()
input_ws = self.getProperty('InputWorkspace').value
range_min = input_ws.readX(0)[0]
bin_width = input_ws.readX(0)[1] - input_ws.readX(0)[0]
range_max = input_ws.readX(0)[-1]
binning_for_calc = self.getProperty('BinningForCalc').value
if not binning_for_calc == "":
parsed_calc = self.parse_binning(binning_for_calc)
if parsed_calc[0] < range_min - bin_width:
issues['BinningForCalc'] = "Lower bin range outside of InputWorkspace range, minimum value: %f" % (
range_min - bin_width)
if parsed_calc[-1] > range_max + bin_width:
issues['BinningForCalc'] = "Upper bin range outside of InputWorkspace range, maximum values: %f" % (
range_max + bin_width)
if parsed_calc.size == 0:
issues['BinningForCalc'] = "Invalid parameters for bin range"
binning_for_fit = self.getProperty('BinningForFit').value
if not binning_for_fit == "":
parsed_fit = self.parse_binning(binning_for_fit)
if parsed_fit[0] < range_min - bin_width:
issues['BinningForFit'] = "Lower bin range outside of InputWorkspace range, minimum value: %f" % (
range_min - bin_width)
if parsed_fit[-1] > range_max + bin_width:
issues['BinningForFit'] = "Upper bin range outside of InputWorkspace range, maximum values: %f" % (
range_max + bin_width)
if parsed_calc.size == 0:
issues['BinningForFit'] = "Invalid parameters for bin range"
return issues
def _setup(self):
self._input_ws = self.getProperty('InputWorkspace').value
self._output_ws = self.getProperty('OutputWorkspace').valueAsStr
self._binning_for_fit = self.getProperty('BinningForFit').value
self._fit_spectrum_with = self.getProperty('FitSpectrumWith').value
self._binning_for_calc = self.getProperty('BinningForCalc').value
if self._binning_for_calc is "":
x = self._input_ws.readX(0)
binning = [str(i) for i in [min(x), x[1] - x[0], max(x) + x[1] - x[0]]]
self._binning_for_calc = ",".join(binning)
if self._binning_for_fit == "":
self._binning_for_fit = self._binning_for_calc
def PyExec(self):
self._setup()
x = self.parse_binning(self._binning_for_calc)
rebinned = Rebin(
self._input_ws,
Params=self._binning_for_fit,
PreserveEvents=True,
StoreInADS=False)
incident_index = 0
x_fit = np.array(rebinned.readX(incident_index))
y_fit = np.array(rebinned.readY(incident_index))
if len(x_fit) != len(y_fit):
x_fit = 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, 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,
ParamsInput=self._binning_for_fit,
ParamsOutput=self._binning_for_calc,
Error=0.0001,
MaxNumberOfBreaks=0)
elif self._fit_spectrum_with == 'HowellsFunction':
# Fit using Howells function
fit, fit_prime = self.fit_howells_function(x_fit, y_fit, x)
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, sigma=0.5)
# Create output workspace
output_workspace = CreateWorkspace(
DataX=x,
DataY=np.append(fit, fit_prime),
UnitX='Wavelength',
NSpec=2,
Distribution=False,
StoreInADS=False)
self.setProperty("OutputWorkspace", output_workspace)
def parse_binning(self, binning):
# convert binning [min, width, max] into a np array
try:
params = [float(x) for x in binning.split(',')]
except AttributeError:
params = [float(x) for x in binning]
xlo, binsize, xhi = params
return np.arange(xlo, xhi, binsize)
def fit_cubic_spline_with_gauss_conv(self, x_fit, y_fit, x, sigma=3):
# Fit with Cubic Spline using a Gaussian Convolution to get weights
def moving_average(y, sig=sigma):
b = signal.gaussian(39, sig)
average = ndimage.filters.convolve1d(y, b / b.sum())
var = ndimage.filters.convolve1d(np.power(y - average, 2), b / b.sum())
return average, var
avg, var = moving_average(y_fit)
spline_fit = interpolate.UnivariateSpline(x_fit, y_fit, w=1. / np.sqrt(var))
spline_fit_prime = spline_fit.derivative()
fit = spline_fit(x)
fit_prime = spline_fit_prime(x)
return fit, fit_prime
def fit_cubic_spline(self, x_fit, y_fit, x, s=1e15):
# Fit with Cubic Spline
tck = interpolate.splrep(x_fit, y_fit, s=s)
fit = interpolate.splev(x, tck, der=0)
fit_prime = interpolate.splev(x, tck, der=1)
return fit, fit_prime
def fit_cubic_spline_via_mantid_spline_smoothing(self, InputWorkspace, ParamsInput, ParamsOutput, **kwargs):
# Fit with Cubic Spline using the mantid SplineSmoothing algorithm
Rebin(
InputWorkspace=InputWorkspace,
OutputWorkspace='fit',
Params=ParamsInput,
PreserveEvents=True)
SplineSmoothing(
InputWorkspace='fit',
OutputWorkspace='fit',
OutputWorkspaceDeriv='fit_prime',
DerivOrder=1,
**kwargs)
Rebin(
InputWorkspace='fit',
OutputWorkspace='fit',
Params=ParamsOutput,
PreserveEvents=True)
Rebin(
InputWorkspace='fit_prime_1',
OutputWorkspace='fit_prime_1',
Params=ParamsOutput,
PreserveEvents=True)
return AnalysisDataService.retrieve('fit').readY(0), AnalysisDataService.retrieve('fit_prime_1').readY(0)
def fit_howells_function(self, x_fit, y_fit, x):
# Fit with analytical function from HowellsEtAl
def calc_howells_function(lambdas, phi_max, phi_epi, lam_t, lam_1, lam_2, a):
term1 = phi_max * ((lam_t**4.) / lambdas**5.) * \
np.exp(-(lam_t / lambdas)**2.)
term2 = (phi_epi / (lambdas**(1. + 2. * a))) * \
(1. / (1 + np.exp((lambdas - lam_1) / lam_2)))
return term1 + term2
def calc_howells_function_1st_derivative(lambdas, phi_max, phi_epi, lam_t, lam_1, lam_2, a):
term1 = (((2 * lam_t**2) / lambdas**2) - 5.) * (1. / lambdas) * \
phi_max * ((lam_t**4.) / lambdas**5.) * np.exp(-(lam_t / lambdas)**2.)
term2 = - (phi_epi / lambdas**(1 + 2 * a)) * (1 / (1+np.exp((lambdas - lam_1) / lam_2))) \
* ((1 + 2 * a) / lambdas
+ (np.exp((lambdas - lam_1) / lam_2) / lam_2) * (1 / (1+np.exp((lambdas - lam_1) / lam_2))))
return term1 + term2
params = [1., 1., 1., 0., 1., 1.]
params, convergence = optimize.curve_fit(
calc_howells_function, x_fit, y_fit, params)
fit = calc_howells_function(x, *params)
fit_prime = calc_howells_function_1st_derivative(x, *params)
return fit, fit_prime
AlgorithmFactory.subscribe(FitIncidentSpectrum)