CrystalFieldSpectrum¶

import numpy as np

# Build a reference data set
fun = 'name=CrystalFieldSpectrum,Ion=Ce,Temperature=44,ToleranceIntensity=0.001,B20=0.37737,B22=3.9770,B40=-0.031787,B42=-0.11611,B44=-0.12544,'
fun += 'f0.FWHM=1.6,f1.FWHM=2.0,f2.FWHM=2.3'

# This creates a (empty) workspace to use with EvaluateFunction
x = np.linspace(0, 55, 200)
y = x * 0
e = y + 1
ws = CreateWorkspace(x, y, e)

# The calculated data will be in 'data', WorkspaceIndex=1
EvaluateFunction(fun, ws, OutputWorkspace='data')

 # Change parameters slightly and fit to the reference data
fun = 'name=CrystalFieldSpectrum,Ion=Ce,Symmetry=C2v,Temperature=44,ToleranceIntensity=0.002,B20=0.37,B22=3.9,B40=-0.03,B42=-0.1,B44=-0.12,'
fun += 'f0.FWHM=2.2,f1.FWHM=1.8,f2.FWHM=2.1,'
fun += 'ties=(B60=0,B62=0,B64=0,B66=0,BmolX=0,BmolY=0,BmolZ=0,BextX=0,BextY=0,BextZ=0)'

# (set MaxIterations=0 to see the starting point)
Fit(fun, 'data', WorkspaceIndex=1, Output='fit',MaxIterations=100, CostFunction='Unweighted least squares')
# Using Unweighted least squares fit because the data has no errors.