def test_fit_multi_ion_and_spectra(self):
from CrystalField.fitting import makeWorkspace
from CrystalField import CrystalField, CrystalFieldFit
from mantid.simpleapi import CalculateChiSquared
params = {'B20': 0.37737, 'B22': 3.9770, 'B40': -0.031787, 'B42': -0.11611, 'B44': -0.12544,
'Temperature': [44.0, 50.0], 'FWHM': [1.1, 0.9]}
cf1 = CrystalField('Ce', 'C2v', **params)
cf2 = CrystalField('Pr', 'C2v', **params)
cf = cf1 + cf2
r = [0.0, 1.45, 2.4, 3.0, 3.85]
ws1 = makeWorkspace(*cf.getSpectrum(0, r))
ws2 = makeWorkspace(*cf.getSpectrum(1, r))
params = {'ion0.B20': 0.37737, 'ion0.B22': 3.9770, 'ion0.B40': -0.031787, 'ion0.B42': -0.11611,
'ion0.B44': -0.12544, 'ion1.B20': 0.37737, 'ion1.B22': 3.9770, 'ion1.B40': -0.031787,
'ion1.B42': -0.11611, 'ion1.B44': -0.12544}
cf = CrystalFieldMultiSite(Ions=['Ce', 'Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44.0, 50.0],
FWHM=[1.0, 1.0], ToleranceIntensity=6.0, ToleranceEnergy=1.0, FixAllPeaks=True,
parameters=params)
cf.fix('ion0.BmolX', 'ion0.BmolY', 'ion0.BmolZ', 'ion0.BextX', 'ion0.BextY', 'ion0.BextZ', 'ion0.B40',
'ion0.B42', 'ion0.B44', 'ion0.B60', 'ion0.B62', 'ion0.B64', 'ion0.B66', 'ion0.IntensityScaling',
'ion1.BmolX', 'ion1.BmolY', 'ion1.BmolZ', 'ion1.BextX', 'ion1.BextY', 'ion1.BextZ', 'ion1.B40',
'ion1.B42', 'ion1.B44', 'ion1.B60', 'ion1.B62', 'ion1.B64', 'ion1.B66', 'ion1.IntensityScaling',
'sp0.IntensityScaling', 'sp1.IntensityScaling')
chi2 = CalculateChiSquared(str(cf.function), InputWorkspace=ws1, InputWorkspace_1=ws2)[1]
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws1, ws2], MaxIterations=10)
fit.fit()
self.assertTrue(cf.chi2 > 0.0)
self.assertTrue(cf.chi2 < chi2)
def test_fit_multi_ion_and_spectra(self):
from CrystalField.fitting import makeWorkspace
from CrystalField import CrystalField, CrystalFieldFit
from mantid.simpleapi import CalculateChiSquared
params = {'B20': 0.37737, 'B22': 3.9770, 'B40': -0.031787, 'B42': -0.11611, 'B44': -0.12544,
'Temperature': [44.0, 50.0], 'FWHM': [1.1, 0.9]}
cf1 = CrystalField('Ce', 'C2v', **params)
cf2 = CrystalField('Pr', 'C2v', **params)
cf = cf1 + cf2
r = [0.0, 1.45, 2.4, 3.0, 3.85]
ws1 = makeWorkspace(*cf.getSpectrum(0, r))
ws2 = makeWorkspace(*cf.getSpectrum(1, r))
params = {'ion0.B20': 0.37737, 'ion0.B22': 3.9770, 'ion0.B40': -0.031787, 'ion0.B42': -0.11611,
'ion0.B44': -0.12544, 'ion1.B20': 0.37737, 'ion1.B22': 3.9770, 'ion1.B40': -0.031787,
'ion1.B42': -0.11611, 'ion1.B44': -0.12544}
cf = CrystalFieldMultiSite(Ions=['Ce', 'Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44.0, 50.0],
FWHM=[1.0, 1.0], ToleranceIntensity=6.0, ToleranceEnergy=1.0, FixAllPeaks=True,
parameters=params)
cf.fix('ion0.BmolX', 'ion0.BmolY', 'ion0.BmolZ', 'ion0.BextX', 'ion0.BextY', 'ion0.BextZ', 'ion0.B40',
'ion0.B42', 'ion0.B44', 'ion0.B60', 'ion0.B62', 'ion0.B64', 'ion0.B66', 'ion0.IntensityScaling',
'ion1.BmolX', 'ion1.BmolY', 'ion1.BmolZ', 'ion1.BextX', 'ion1.BextY', 'ion1.BextZ', 'ion1.B40',
'ion1.B42', 'ion1.B44', 'ion1.B60', 'ion1.B62', 'ion1.B64', 'ion1.B66', 'ion1.IntensityScaling',
'sp0.IntensityScaling', 'sp1.IntensityScaling')
chi2 = CalculateChiSquared(str(cf.function), InputWorkspace=ws1, InputWorkspace_1=ws2)[1]
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws1, ws2], MaxIterations=10)
fit.fit()
self.assertTrue(cf.chi2 > 0.0)
self.assertTrue(cf.chi2 < chi2)
def test_fit_multi_ion_single_spectrum(self):
from CrystalField.fitting import makeWorkspace
from CrystalField import CrystalField, CrystalFieldFit
from mantid.simpleapi import CalculateChiSquared
params = {
'B20': 0.37737,
'B22': 3.9770,
'B40': -0.031787,
'B42': -0.11611,
'B44': -0.12544,
'Temperature': 44.0,
'FWHM': 1.1
}
cf1 = CrystalField('Ce', 'C2v', **params)
cf2 = CrystalField('Pr', 'C2v', **params)
cf = cf1 + cf2
r = [0.0, 1.45, 2.4, 3.0, 3.85]
x, y = cf.getSpectrum(r)
ws = makeWorkspace(x, y)
params = {
'ion0.B20': 0.37737,
'ion0.B22': 3.9770,
'ion0.B40': -0.031787,
'ion0.B42': -0.11611,
'ion0.B44': -0.12544,
'ion1.B20': 0.37737,
'ion1.B22': 3.9770,
'ion1.B40': -0.031787,
'ion1.B42': -0.11611,
'ion1.B44': -0.12544
}
cf = CrystalFieldMultiSite(Ions=['Ce', 'Pr'],
Symmetries=['C2v', 'C2v'],
Temperatures=[44.0],
FWHM=[1.1],
ToleranceIntensity=6.0,
ToleranceEnergy=1.0,
FixAllPeaks=True,
parameters=params)
cf.fix('ion0.BmolX', 'ion0.BmolY', 'ion0.BmolZ', 'ion0.BextX',
'ion0.BextY', 'ion0.BextZ', 'ion0.B40', 'ion0.B42', 'ion0.B44',
'ion0.B60', 'ion0.B62', 'ion0.B64', 'ion0.B66',
'ion0.IntensityScaling', 'ion1.BmolX', 'ion1.BmolY',
'ion1.BmolZ', 'ion1.BextX', 'ion1.BextY', 'ion1.BextZ',
'ion1.B40', 'ion1.B42', 'ion1.B44', 'ion1.B60', 'ion1.B62',
'ion1.B64', 'ion1.B66', 'ion1.IntensityScaling')
chi2 = CalculateChiSquared(cf.makeSpectrumFunction(),
InputWorkspace=ws)[1]
fit = CrystalFieldFit(Model=cf, InputWorkspace=ws, MaxIterations=10)
fit.fit()
self.assertGreater(cf.chi2, 0.0)
self.assertLess(cf.chi2, chi2)
Temperature=5,
FWHM=1,
B20=0.19,
B22=0.11,
B40=-0.0004,
B42=-0.002,
B44=-0.012,
B60=5.e-05,
B62=0.00054,
B64=-0.0006,
B66=0.0008)
cf.PeakShape = 'Lorentzian'
cf.IntensityScaling = 2 # Scale factor if data is not in absolute units (mbarn/sr/f.u./meV), will be fitted.
# Runs the fit
fit = CrystalFieldFit(Model=cf, InputWorkspace=data_ws1, MaxIterations=200)
fit.fit()
# Plots the data and print fitted parameters
l = plotSpectrum('fit_Workspace', [0, 1, 2], error_bars=False)
l.activeLayer().setAxisScale(Layer.Left, 0, 100)
l.activeLayer().setAxisScale(Layer.Bottom, -10, 30)
for parnames in [
'B20', 'B22', 'B40', 'B42', 'B44', 'B60', 'B62', 'B64', 'B66'
]:
print parnames + ' = ' + str(fit.model[parnames]) + ' meV'
table = mtd['fit_Parameters']
print 'Cost function value = ' + str(table.row(table.rowCount() - 1)['Value'])
def runTest(self):
from CrystalField import CrystalField, CrystalFieldFit, CrystalFieldMultiSite, Background, Function, ResolutionModel
cf = CrystalField('Ce', 'C2v')
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770)
cf['B40'] = -0.031
b = cf['B40']
# Calculate and return the Hamiltonian matrix as a 2D numpy array.
h = cf.getHamiltonian()
print(h)
# Calculate and return the eigenvalues of the Hamiltonian as a 1D numpy array.
e = cf.getEigenvalues()
print(e)
# Calculate and return the eigenvectors of the Hamiltonian as a 2D numpy array.
w = cf.getEigenvectors()
print(w)
# Using the keyword argument
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44)
# Using the property
cf.Temperature = 44
print(cf.getPeakList())
#[[ 0.00000000e+00 2.44006198e+01 4.24977124e+01 1.80970926e+01 -2.44006198e+01]
# [ 2.16711565e+02 8.83098530e+01 5.04430056e+00 1.71153708e-01 1.41609425e-01]]
cf.ToleranceIntensity = 1
print(cf.getPeakList())
#[[ 0. 24.40061976 42.49771237]
# [ 216.71156467 88.30985303 5.04430056]]
cf.PeakShape = 'Gaussian'
cf.FWHM = 0.9
sp = cf.getSpectrum()
print(cf.function)
CrystalField_Ce = CreateWorkspace(*sp)
print(CrystalField_Ce)
# If the peak shape is Gaussian
cf.peaks.param[1]['Sigma'] = 2.0
cf.peaks.param[2]['Sigma'] = 0.01
# If the peak shape is Lorentzian
cf.PeakShape = 'Lorentzian'
cf.peaks.param[1]['FWHM'] = 2.0
cf.peaks.param[2]['FWHM'] = 0.01
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
Temperature=44.0, FWHM=1.1)
cf.background = Background(peak=Function('Gaussian', Height=10, Sigma=1),
background=Function('LinearBackground', A0=1.0, A1=0.01))
h = cf.background.peak.param['Height']
a1 = cf.background.background.param['A1']
print(h)
print(a1)
cf.ties(B20=1.0, B40='B20/2')
cf.constraints('1 < B22 <= 2', 'B22 < 4')
print(cf.function[1].getTies())
print(cf.function[1].getConstraints())
cf.background.peak.ties(Height=10.1)
cf.background.peak.constraints('Sigma > 0')
cf.background.background.ties(A0=0.1)
cf.background.background.constraints('A1 > 0')
print(cf.function[0][0].getConstraints())
print(cf.function[0][1].getConstraints())
print(cf.function.getTies())
print(cf.function.getConstraints())
cf.peaks.ties({'f2.FWHM': '2*f1.FWHM', 'f3.FWHM': '2*f2.FWHM'})
cf.peaks.constraints('f0.FWHM < 2.2', 'f1.FWHM >= 0.1')
cf.PeakShape = 'Gaussian'
cf.peaks.tieAll('Sigma=0.1', 3)
cf.peaks.constrainAll('0 < Sigma < 0.1', 4)
cf.peaks.tieAll('Sigma=f0.Sigma', 1, 3)
cf.peaks.ties({'f1.Sigma': 'f0.Sigma', 'f2.Sigma': 'f0.Sigma', 'f3.Sigma': 'f0.Sigma'})
rm = ResolutionModel(([1, 2, 3, 100], [0.1, 0.3, 0.35, 2.1]))
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0, ResolutionModel=rm)
rm = ResolutionModel(self.my_func, xstart=0.0, xend=24.0, accuracy=0.01)
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0, ResolutionModel=rm)
marires = Instrument('MARI')
marires.setChopper('S')
marires.setFrequency(250)
marires.setEi(30)
rm = ResolutionModel(marires.getResolution, xstart=0.0, xend=29.0, accuracy=0.01)
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0, ResolutionModel=rm)
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0, ResolutionModel=rm, FWHMVariation=0.1)
# ---------------------------
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
Temperature=[44.0, 50.], FWHM=[1.1, 0.9])
cf.PeakShape = 'Lorentzian'
cf.peaks[0].param[0]['FWHM'] = 1.11
cf.peaks[1].param[1]['FWHM'] = 1.12
cf.background = Background(peak=Function('Gaussian', Height=10, Sigma=0.3),
background=Function('FlatBackground', A0=1.0))
cf.background[1].peak.param['Sigma'] = 0.8
cf.background[1].background.param['A0'] = 1.1
# The B parameters are common for all spectra - syntax doesn't change
cf.ties(B20=1.0, B40='B20/2')
cf.constraints('1 < B22 <= 2', 'B22 < 4')
# Backgrounds and peaks are different for different spectra - must be indexed
cf.background[0].peak.ties(Height=10.1)
cf.background[0].peak.constraints('Sigma > 0.1')
cf.background[1].peak.ties(Height=20.2)
cf.background[1].peak.constraints('Sigma > 0.2')
cf.peaks[1].tieAll('FWHM=2*f1.FWHM', 2, 5)
cf.peaks[0].constrainAll('FWHM < 2.2', 1, 4)
rm = ResolutionModel([self.my_func, marires.getResolution], 0, 100, accuracy = 0.01)
cf.ResolutionModel = rm
# Calculate second spectrum, use the generated x-values
sp = cf.getSpectrum(1)
# Calculate second spectrum, use the first spectrum of a workspace
sp = cf.getSpectrum(1, 'CrystalField_Ce')
# Calculate first spectrum, use the i-th spectrum of a workspace
i=0
sp = cf.getSpectrum(0, 'CrystalField_Ce', i)
print(cf.function)
cf.Temperature = [5, 50, 150]
print()
print(cf.function)
ws = 'CrystalField_Ce'
ws1 = 'CrystalField_Ce'
ws2 = 'CrystalField_Ce'
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544, Temperature=5)
# In case of a single spectrum (ws is a workspace)
fit = CrystalFieldFit(Model=cf, InputWorkspace=ws)
# Or for multiple spectra
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws1, ws2])
cf.Temperature = [5, 50]
fit.fit()
params = {'B20': 0.377, 'B22': 3.9, 'B40': -0.03, 'B42': -0.116, 'B44': -0.125,
'Temperature': [44.0, 50.], 'FWHM': [1.1, 0.9]}
cf1 = CrystalField('Ce', 'C2v', **params)
cf2 = CrystalField('Pr', 'C2v', **params)
cfms = cf1 + cf2
cf = 2*cf1 + cf2
cfms = CrystalFieldMultiSite(Ions=['Ce', 'Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44.0], FWHMs=[1.1])
cfms['ion0.B40'] = -0.031
cfms['ion1.B20'] = 0.37737
b = cfms['ion0.B22']
print(b)
print(cfms.function)
cfms = CrystalFieldMultiSite(Ions=['Ce', 'Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44.0], FWHMs=[1.1],
parameters={'ion0.B20': 0.37737, 'ion0.B22': 3.9770, 'ion1.B40':-0.031787,
'ion1.B42':-0.11611, 'ion1.B44':-0.12544})
cfms = CrystalFieldMultiSite(Ions='Ce', Symmetries='C2v', Temperatures=[20], FWHMs=[1.0],
Background='name=Gaussian,Height=0,PeakCentre=1,Sigma=0;name=LinearBackground,A0=0,A1=0')
cfms = CrystalFieldMultiSite(Ions=['Ce'], Symmetries=['C2v'], Temperatures=[50], FWHMs=[0.9],
Background=LinearBackground(A0=1.0), BackgroundPeak=Gaussian(Height=10, Sigma=0.3))
cfms = CrystalFieldMultiSite(Ions='Ce', Symmetries='C2v', Temperatures=[20], FWHMs=[1.0],
Background= Gaussian(PeakCentre=1) + LinearBackground())
cfms = CrystalFieldMultiSite(Ions=['Ce','Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44, 50], FWHMs=[1.1, 0.9],
Background=FlatBackground(), BackgroundPeak=Gaussian(Height=10, Sigma=0.3),
parameters={'ion0.B20': 0.37737, 'ion0.B22': 3.9770, 'ion1.B40':-0.031787,
'ion1.B42':-0.11611, 'ion1.B44':-0.12544})
cfms.ties({'sp0.bg.f0.Height': 10.1})
cfms.constraints('sp0.bg.f0.Sigma > 0.1')
cfms.constraints('ion0.sp0.pk1.FWHM < 2.2')
cfms.ties({'ion0.sp1.pk2.FWHM': '2*ion0.sp1.pk1.FWHM', 'ion1.sp1.pk3.FWHM': '2*ion1.sp1.pk2.FWHM'})
# --------------------------
params = {'ion0.B20': 0.37737, 'ion0.B22': 3.9770, 'ion1.B40':-0.031787, 'ion1.B42':-0.11611, 'ion1.B44':-0.12544}
cf = CrystalFieldMultiSite(Ions=['Ce', 'Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44.0, 50.0],
FWHMs=[1.0, 1.0], ToleranceIntensity=6.0, ToleranceEnergy=1.0, FixAllPeaks=True,
parameters=params)
cf.fix('ion0.BmolX', 'ion0.BmolY', 'ion0.BmolZ', 'ion0.BextX', 'ion0.BextY', 'ion0.BextZ', 'ion0.B40',
'ion0.B42', 'ion0.B44', 'ion0.B60', 'ion0.B62', 'ion0.B64', 'ion0.B66', 'ion0.IntensityScaling',
'ion1.BmolX', 'ion1.BmolY', 'ion1.BmolZ', 'ion1.BextX', 'ion1.BextY', 'ion1.BextZ', 'ion1.B40',
'ion1.B42', 'ion1.B44', 'ion1.B60', 'ion1.B62', 'ion1.B64', 'ion1.B66', 'ion1.IntensityScaling',
'sp0.IntensityScaling', 'sp1.IntensityScaling')
chi2 = CalculateChiSquared(str(cf.function), InputWorkspace=ws1, InputWorkspace_1=ws2)[1]
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws1, ws2], MaxIterations=10)
fit.fit()
print(chi2)
cfms = CrystalFieldMultiSite(Ions='Ce', Symmetries='C2', Temperatures=[25], FWHMs=[1.0], PeakShape='Gaussian',
BmolX=1.0, B40=-0.02)
print(str(cfms.function).split(',')[0])
# --------------------------
# Create some crystal field data
origin = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
Temperature=44.0, FWHM=1.1)
x, y = origin.getSpectrum()
ws = makeWorkspace(x, y)
# Define a CrystalField object with parameters slightly shifted.
cf = CrystalField('Ce', 'C2v', B20=0, B22=0, B40=0, B42=0, B44=0,
Temperature=44.0, FWHM=1.0, ResolutionModel=([0, 100], [1, 1]), FWHMVariation=0)
# Set any ties on the field parameters.
cf.ties(B20=0.37737)
# Create a fit object
fit = CrystalFieldFit(cf, InputWorkspace=ws)
# Find initial values for the field parameters.
# You need to define the energy splitting and names of parameters to estimate.
# Optionally additional constraints can be set on tied parameters (eg, peak centres).
fit.estimate_parameters(EnergySplitting=50,
Parameters=['B22', 'B40', 'B42', 'B44'],
Constraints='20<f1.PeakCentre<45,20<f2.PeakCentre<45',
NSamples=1000)
print('Returned', fit.get_number_estimates(), 'sets of parameters.')
# The first set (the smallest chi squared) is selected by default.
# Select a different parameter set if required
fit.select_estimated_parameters(3)
print(cf['B22'], cf['B40'], cf['B42'], cf['B44'])
# Run fit
fit.fit()
# --------------------------
from CrystalField import PointCharge
axial_pc_model = PointCharge([[-2, 0, 0, -4], [-2, 0, 0, 4]], 'Nd')
axial_blm = axial_pc_model.calculate()
print(axial_blm)
from CrystalField import PointCharge
from mantid.geometry import CrystalStructure
perovskite_structure = CrystalStructure('4 4 4 90 90 90', 'P m -3 m', 'Ce 0 0 0 1 0; Al 0.5 0.5 0.5 1 0; O 0.5 0.5 0 1 0')
cubic_pc_model = PointCharge(perovskite_structure, 'Ce', Charges={'Ce':3, 'Al':3, 'O':-2}, MaxDistance=7.5)
cubic_pc_model = PointCharge(perovskite_structure, 'Ce', Charges={'Ce':3, 'Al':3, 'O':-2}, Neighbour=2)
print(cubic_pc_model)
cif_pc_model = PointCharge('Sm2O3.cif')
print(cif_pc_model.getIons())
cif_pc_model.Charges = {'O1':-2, 'O2':-2, 'Sm1':3, 'Sm2':3, 'Sm3':3}
cif_pc_model.IonLabel = 'Sm2'
cif_pc_model.Neighbour = 1
cif_blm = cif_pc_model.calculate()
print(cif_blm)
bad_pc_model = PointCharge('Sm2O3.cif', MaxDistance=7.5, Neighbour=2)
print(bad_pc_model.Neighbour)
print(bad_pc_model.MaxDistance)
cif_pc_model.Charges = {'O':-2, 'Sm':3}
cif_blm = cif_pc_model.calculate()
print(cif_blm)
cf = CrystalField('Sm', 'C2', Temperature=5, FWHM=10, **cif_pc_model.calculate())
fit = CrystalFieldFit(cf, InputWorkspace=ws)
fit = CrystalFieldFit(cf, InputWorkspace=ws)
fit.fit()
# --------------------------
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0)
Cv = cf.getHeatCapacity() # Calculates Cv(T) for 1<T<300K in 1K steps (default)
T = np.arange(1,900,5)
Cv = cf.getHeatCapacity(T) # Calculates Cv(T) for specified values of T (1 to 900K in 5K steps here)
# Temperatures from a single spectrum workspace
ws = CreateWorkspace(T, T, T)
Cv = cf.getHeatCapacity(ws) # Use the x-values of a workspace as the temperatures
ws_cp = CreateWorkspace(*Cv)
# Temperatures from a multi-spectrum workspace
ws = CreateWorkspace(T, T, T, NSpec=2)
Cv = cf.getHeatCapacity(ws, 1) # Uses the second spectrum's x-values for T (e.g. 450<T<900)
chi_v = cf.getSusceptibility(T, Hdir=[1, 1, 1])
chi_v_powder = cf.getSusceptibility(T, Hdir='powder')
chi_v_cgs = cf.getSusceptibility(T, Hdir=[1, 1, 0], Unit='SI')
chi_v_bohr = cf.getSusceptibility(T, Unit='bohr')
print(type([chi_v, chi_v_powder, chi_v_cgs, chi_v_bohr]))
moment_t = cf.getMagneticMoment(Temperature=T, Hdir=[1, 1, 1], Hmag=0.1) # Calcs M(T) with at 0.1T field||[111]
H = np.linspace(0, 30, 121)
moment_h = cf.getMagneticMoment(Hmag=H, Hdir='powder', Temperature=10) # Calcs M(H) at 10K for powder sample
moment_SI = cf.getMagneticMoment(H, [1, 1, 1], Unit='SI') # M(H) in Am^2/mol at 1K for H||[111]
moment_cgs = cf.getMagneticMoment(100, Temperature=T, Unit='cgs') # M(T) in emu/mol in a field of 100G || [001]
print(type([moment_t, moment_h, moment_SI, moment_cgs]))
# --------------------------
from CrystalField import CrystalField, CrystalFieldFit, PhysicalProperties
# Fits a heat capacity dataset - you must have subtracted the phonon contribution by some method already
# and the data must be in J/mol/K.
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
PhysicalProperty=PhysicalProperties('Cv'))
fitcv = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fitcv.fit()
params = {'B20':0.37737, 'B22':3.9770, 'B40':-0.031787, 'B42':-0.11611, 'B44':-0.12544}
cf = CrystalField('Ce', 'C2v', **params)
cf.PhysicalProperty = PhysicalProperties('Cv')
fitcv = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fitcv.fit()
# Fits a susceptibility dataset. Data is the volume susceptibility in SI units
cf = CrystalField('Ce', 'C2v', **params)
cf.PhysicalProperty = PhysicalProperties('susc', Hdir='powder', Unit='SI')
fit_chi = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fit_chi.fit()
# Fits a magnetisation dataset. Data is in emu/mol, and was measured at 5K with the field || [111].
cf = CrystalField('Ce', 'C2v', **params)
cf.PhysicalProperty = PhysicalProperties('M(H)', Temperature=5, Hdir=[1, 1, 1], Unit='cgs')
fit_mag = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fit_mag.fit()
# Fits a magnetisation vs temperature dataset. Data is in Am^2/mol, measured with a 0.1T field || [110]
cf = CrystalField('Ce', 'C2v', **params)
cf.PhysicalProperty = PhysicalProperties('M(T)', Hmag=0.1, Hdir=[1, 1, 0], Unit='SI')
fit_moment = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fit_moment.fit()
# --------------------------
# Pregenerate the required workspaces
for tt in [10, 44, 50]:
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544, Temperature=tt, FWHM=0.5)
x, y = cf.getSpectrum()
self.my_create_ws('ws_ins_'+str(tt)+'K', x, y)
ws_ins_10K = mtd['ws_ins_10K']
ws_ins_44K = mtd['ws_ins_44K']
ws_ins_50K = mtd['ws_ins_50K']
ws_cp = self.my_create_ws('ws_cp', *cf.getHeatCapacity())
ws_chi = self.my_create_ws('ws_chi', *cf.getSusceptibility(np.linspace(1,300,100), Hdir='powder', Unit='cgs'))
ws_mag = self.my_create_ws('ws_mag', *cf.getMagneticMoment(Hmag=np.linspace(0, 30, 100), Hdir=[1,1,1], Unit='bohr', Temperature=5))
# --------------------------
# Fits an INS spectrum (at 10K) and the heat capacity simultaneously
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544)
cf.Temperature = 10
cf.FWHM = 1.5
cf.PhysicalProperty = PhysicalProperties('Cv')
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws_ins_10K, ws_cp])
fit.fit()
# Fits two INS spectra (at 44K and 50K) and the heat capacity, susceptibility and magnetisation simultaneously.
PPCv = PhysicalProperties('Cv')
PPchi = PhysicalProperties('susc', 'powder', Unit='cgs')
PPMag = PhysicalProperties('M(H)', [1, 1, 1], 5, 'bohr')
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
Temperature=[44.0, 50.], FWHM=[1.1, 0.9], PhysicalProperty=[PPCv, PPchi, PPMag] )
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws_ins_44K, ws_ins_50K, ws_cp, ws_chi, ws_mag])
fit.fit()
def runTest(self):
from CrystalField import CrystalField, CrystalFieldFit, CrystalFieldMultiSite, Background, Function, ResolutionModel
cf = CrystalField('Ce', 'C2v')
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770)
cf['B40'] = -0.031
b = cf['B40']
# Calculate and return the Hamiltonian matrix as a 2D numpy array.
h = cf.getHamiltonian()
print(h)
# Calculate and return the eigenvalues of the Hamiltonian as a 1D numpy array.
e = cf.getEigenvalues()
print(e)
# Calculate and return the eigenvectors of the Hamiltonian as a 2D numpy array.
w = cf.getEigenvectors()
print(w)
# Using the keyword argument
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44)
# Using the property
cf.Temperature = 44
print(cf.getPeakList())
#[[ 0.00000000e+00 2.44006198e+01 4.24977124e+01 1.80970926e+01 -2.44006198e+01]
# [ 2.16711565e+02 8.83098530e+01 5.04430056e+00 1.71153708e-01 1.41609425e-01]]
cf.ToleranceIntensity = 1
print(cf.getPeakList())
#[[ 0. 24.40061976 42.49771237]
# [ 216.71156467 88.30985303 5.04430056]]
cf.PeakShape = 'Gaussian'
cf.FWHM = 0.9
sp = cf.getSpectrum()
print(cf.function)
CrystalField_Ce = CreateWorkspace(*sp)
print(CrystalField_Ce)
# If the peak shape is Gaussian
cf.peaks.param[1]['Sigma'] = 2.0
cf.peaks.param[2]['Sigma'] = 0.01
# If the peak shape is Lorentzian
cf.PeakShape = 'Lorentzian'
cf.peaks.param[1]['FWHM'] = 2.0
cf.peaks.param[2]['FWHM'] = 0.01
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
Temperature=44.0, FWHM=1.1)
cf.background = Background(peak=Function('Gaussian', Height=10, Sigma=1),
background=Function('LinearBackground', A0=1.0, A1=0.01))
h = cf.background.peak.param['Height']
a1 = cf.background.background.param['A1']
print(h)
print(a1)
cf.ties(B20=1.0, B40='B20/2')
cf.constraints('1 < B22 <= 2', 'B22 < 4')
print(cf.function[1].getTies())
print(cf.function[1].getConstraints())
cf.background.peak.ties(Height=10.1)
cf.background.peak.constraints('Sigma > 0')
cf.background.background.ties(A0=0.1)
cf.background.background.constraints('A1 > 0')
print(cf.function[0][0].getConstraints())
print(cf.function[0][1].getConstraints())
print(cf.function.getTies())
print(cf.function.getConstraints())
cf.peaks.ties({'f2.FWHM': '2*f1.FWHM', 'f3.FWHM': '2*f2.FWHM'})
cf.peaks.constraints('f0.FWHM < 2.2', 'f1.FWHM >= 0.1')
cf.PeakShape = 'Gaussian'
cf.peaks.tieAll('Sigma=0.1', 3)
cf.peaks.constrainAll('0 < Sigma < 0.1', 4)
cf.peaks.tieAll('Sigma=f0.Sigma', 1, 3)
cf.peaks.ties({'f1.Sigma': 'f0.Sigma', 'f2.Sigma': 'f0.Sigma', 'f3.Sigma': 'f0.Sigma'})
rm = ResolutionModel(([1, 2, 3, 100], [0.1, 0.3, 0.35, 2.1]))
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0, ResolutionModel=rm)
rm = ResolutionModel(self.my_func, xstart=0.0, xend=24.0, accuracy=0.01)
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0, ResolutionModel=rm)
marires = PyChop2('MARI')
marires.setChopper('S')
marires.setFrequency(250)
marires.setEi(30)
rm = ResolutionModel(marires.getResolution, xstart=0.0, xend=29.0, accuracy=0.01)
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0, ResolutionModel=rm)
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0, ResolutionModel=rm, FWHMVariation=0.1)
# ---------------------------
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
Temperature=[44.0, 50], FWHM=[1.1, 0.9])
cf.PeakShape = 'Lorentzian'
cf.peaks[0].param[0]['FWHM'] = 1.11
cf.peaks[1].param[1]['FWHM'] = 1.12
cf.background = Background(peak=Function('Gaussian', Height=10, Sigma=0.3),
background=Function('FlatBackground', A0=1.0))
cf.background[1].peak.param['Sigma'] = 0.8
cf.background[1].background.param['A0'] = 1.1
# The B parameters are common for all spectra - syntax doesn't change
cf.ties(B20=1.0, B40='B20/2')
cf.constraints('1 < B22 <= 2', 'B22 < 4')
# Backgrounds and peaks are different for different spectra - must be indexed
cf.background[0].peak.ties(Height=10.1)
cf.background[0].peak.constraints('Sigma > 0.1')
cf.background[1].peak.ties(Height=20.2)
cf.background[1].peak.constraints('Sigma > 0.2')
cf.peaks[1].tieAll('FWHM=2*f1.FWHM', 2, 5)
cf.peaks[0].constrainAll('FWHM < 2.2', 1, 4)
rm = ResolutionModel([self.my_func, marires.getResolution], 0, 100, accuracy = 0.01)
cf.ResolutionModel = rm
# Calculate second spectrum, use the generated x-values
sp = cf.getSpectrum(1)
# Calculate second spectrum, use the first spectrum of a workspace
sp = cf.getSpectrum(1, 'CrystalField_Ce')
# Calculate first spectrum, use the i-th spectrum of a workspace
i=0
sp = cf.getSpectrum(0, 'CrystalField_Ce', i)
print(cf.function)
cf.Temperature = [5, 50, 150]
print()
print(cf.function)
ws = 'CrystalField_Ce'
ws1 = 'CrystalField_Ce'
ws2 = 'CrystalField_Ce'
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544, Temperature=5)
# In case of a single spectrum (ws is a workspace)
fit = CrystalFieldFit(Model=cf, InputWorkspace=ws)
# Or for multiple spectra
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws1, ws2])
cf.Temperature = [5, 50]
fit.fit()
params = {'B20': 0.377, 'B22': 3.9, 'B40': -0.03, 'B42': -0.116, 'B44': -0.125,
'Temperature': [44.0, 50], 'FWHM': [1.1, 0.9]}
cf1 = CrystalField('Ce', 'C2v', **params)
cf2 = CrystalField('Pr', 'C2v', **params)
cfms = cf1 + cf2
cf = 2*cf1 + cf2
cfms = CrystalFieldMultiSite(Ions=['Ce', 'Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44.0], FWHMs=[1.1])
cfms['ion0.B40'] = -0.031
cfms['ion1.B20'] = 0.37737
b = cfms['ion0.B22']
print(b)
print(cfms.function)
cfms = CrystalFieldMultiSite(Ions=['Ce', 'Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44.0], FWHMs=[1.1],
parameters={'ion0.B20': 0.37737, 'ion0.B22': 3.9770, 'ion1.B40':-0.031787,
'ion1.B42':-0.11611, 'ion1.B44':-0.12544})
cfms = CrystalFieldMultiSite(Ions='Ce', Symmetries='C2v', Temperatures=[20], FWHMs=[1.0],
Background='name=Gaussian,Height=0,PeakCentre=1,Sigma=0;name=LinearBackground,A0=0,A1=0')
cfms = CrystalFieldMultiSite(Ions=['Ce'], Symmetries=['C2v'], Temperatures=[50], FWHMs=[0.9],
Background=LinearBackground(A0=1.0), BackgroundPeak=Gaussian(Height=10, Sigma=0.3))
cfms = CrystalFieldMultiSite(Ions='Ce', Symmetries='C2v', Temperatures=[20], FWHMs=[1.0],
Background= Gaussian(PeakCentre=1) + LinearBackground())
cfms = CrystalFieldMultiSite(Ions=['Ce','Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44, 50], FWHMs=[1.1, 0.9],
Background=FlatBackground(), BackgroundPeak=Gaussian(Height=10, Sigma=0.3),
parameters={'ion0.B20': 0.37737, 'ion0.B22': 3.9770, 'ion1.B40':-0.031787,
'ion1.B42':-0.11611, 'ion1.B44':-0.12544})
cfms.ties({'sp0.bg.f0.Height': 10.1})
cfms.constraints('sp0.bg.f0.Sigma > 0.1')
cfms.constraints('ion0.sp0.pk1.FWHM < 2.2')
cfms.ties({'ion0.sp1.pk2.FWHM': '2*ion0.sp1.pk1.FWHM', 'ion1.sp1.pk3.FWHM': '2*ion1.sp1.pk2.FWHM'})
# --------------------------
params = {'ion0.B20': 0.37737, 'ion0.B22': 3.9770, 'ion1.B40':-0.031787, 'ion1.B42':-0.11611, 'ion1.B44':-0.12544}
cf = CrystalFieldMultiSite(Ions=['Ce', 'Pr'], Symmetries=['C2v', 'C2v'], Temperatures=[44.0, 50.0],
FWHMs=[1.0, 1.0], ToleranceIntensity=6.0, ToleranceEnergy=1.0, FixAllPeaks=True,
parameters=params)
cf.fix('ion0.BmolX', 'ion0.BmolY', 'ion0.BmolZ', 'ion0.BextX', 'ion0.BextY', 'ion0.BextZ', 'ion0.B40',
'ion0.B42', 'ion0.B44', 'ion0.B60', 'ion0.B62', 'ion0.B64', 'ion0.B66', 'ion0.IntensityScaling',
'ion1.BmolX', 'ion1.BmolY', 'ion1.BmolZ', 'ion1.BextX', 'ion1.BextY', 'ion1.BextZ', 'ion1.B40',
'ion1.B42', 'ion1.B44', 'ion1.B60', 'ion1.B62', 'ion1.B64', 'ion1.B66', 'ion1.IntensityScaling',
'sp0.IntensityScaling', 'sp1.IntensityScaling')
chi2 = CalculateChiSquared(str(cf.function), InputWorkspace=ws1, InputWorkspace_1=ws2)[1]
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws1, ws2], MaxIterations=10)
fit.fit()
print(chi2)
cfms = CrystalFieldMultiSite(Ions='Ce', Symmetries='C2', Temperatures=[25], FWHMs=[1.0], PeakShape='Gaussian',
BmolX=1.0, B40=-0.02)
print(str(cfms.function).split(',')[0])
# --------------------------
# Create some crystal field data
origin = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
Temperature=44.0, FWHM=1.1)
x, y = origin.getSpectrum()
ws = makeWorkspace(x, y)
# Define a CrystalField object with parameters slightly shifted.
cf = CrystalField('Ce', 'C2v', B20=0, B22=0, B40=0, B42=0, B44=0,
Temperature=44.0, FWHM=1.0, ResolutionModel=([0, 100], [1, 1]), FWHMVariation=0)
# Set any ties on the field parameters.
cf.ties(B20=0.37737)
# Create a fit object
fit = CrystalFieldFit(cf, InputWorkspace=ws)
# Find initial values for the field parameters.
# You need to define the energy splitting and names of parameters to estimate.
# Optionally additional constraints can be set on tied parameters (eg, peak centres).
fit.estimate_parameters(EnergySplitting=50,
Parameters=['B22', 'B40', 'B42', 'B44'],
Constraints='20<f1.PeakCentre<45,20<f2.PeakCentre<45',
NSamples=1000)
print('Returned', fit.get_number_estimates(), 'sets of parameters.')
# The first set (the smallest chi squared) is selected by default.
# Select a different parameter set if required
fit.select_estimated_parameters(3)
print(cf['B22'], cf['B40'], cf['B42'], cf['B44'])
# Run fit
fit.fit()
# --------------------------
from CrystalField import PointCharge
axial_pc_model = PointCharge([[-2, 0, 0, -4], [-2, 0, 0, 4]], 'Nd')
axial_blm = axial_pc_model.calculate()
print(axial_blm)
from CrystalField import PointCharge
from mantid.geometry import CrystalStructure
perovskite_structure = CrystalStructure('4 4 4 90 90 90', 'P m -3 m', 'Ce 0 0 0 1 0; Al 0.5 0.5 0.5 1 0; O 0.5 0.5 0 1 0')
cubic_pc_model = PointCharge(perovskite_structure, 'Ce', Charges={'Ce':3, 'Al':3, 'O':-2}, MaxDistance=7.5)
cubic_pc_model = PointCharge(perovskite_structure, 'Ce', Charges={'Ce':3, 'Al':3, 'O':-2}, Neighbour=2)
print(cubic_pc_model)
cif_pc_model = PointCharge('Sm2O3.cif')
print(cif_pc_model.getIons())
cif_pc_model.Charges = {'O1':-2, 'O2':-2, 'Sm1':3, 'Sm2':3, 'Sm3':3}
cif_pc_model.IonLabel = 'Sm2'
cif_pc_model.Neighbour = 1
cif_blm = cif_pc_model.calculate()
print(cif_blm)
bad_pc_model = PointCharge('Sm2O3.cif', MaxDistance=7.5, Neighbour=2)
print(bad_pc_model.Neighbour)
print(bad_pc_model.MaxDistance)
cif_pc_model.Charges = {'O':-2, 'Sm':3}
cif_blm = cif_pc_model.calculate()
print(cif_blm)
cf = CrystalField('Sm', 'C2', Temperature=5, FWHM=10, **cif_pc_model.calculate())
fit = CrystalFieldFit(cf, InputWorkspace=ws)
fit = CrystalFieldFit(cf, InputWorkspace=ws)
fit.fit()
# --------------------------
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, Temperature=44.0)
Cv = cf.getHeatCapacity() # Calculates Cv(T) for 1<T<300K in 1K steps (default)
T = np.arange(1,900,5)
Cv = cf.getHeatCapacity(T) # Calculates Cv(T) for specified values of T (1 to 900K in 5K steps here)
# Temperatures from a single spectrum workspace
ws = CreateWorkspace(T, T, T)
Cv = cf.getHeatCapacity(ws) # Use the x-values of a workspace as the temperatures
ws_cp = CreateWorkspace(*Cv)
# Temperatures from a multi-spectrum workspace
ws = CreateWorkspace(T, T, T, NSpec=2)
Cv = cf.getHeatCapacity(ws, 1) # Uses the second spectrum's x-values for T (e.g. 450<T<900)
chi_v = cf.getSusceptibility(T, Hdir=[1, 1, 1])
chi_v_powder = cf.getSusceptibility(T, Hdir='powder')
chi_v_cgs = cf.getSusceptibility(T, Hdir=[1, 1, 0], Unit='SI')
chi_v_bohr = cf.getSusceptibility(T, Unit='bohr')
print(type([chi_v, chi_v_powder, chi_v_cgs, chi_v_bohr]))
moment_t = cf.getMagneticMoment(Temperature=T, Hdir=[1, 1, 1], Hmag=0.1) # Calcs M(T) with at 0.1T field||[111]
H = np.linspace(0, 30, 121)
moment_h = cf.getMagneticMoment(Hmag=H, Hdir='powder', Temperature=10) # Calcs M(H) at 10K for powder sample
moment_SI = cf.getMagneticMoment(H, [1, 1, 1], Unit='SI') # M(H) in Am^2/mol at 1K for H||[111]
moment_cgs = cf.getMagneticMoment(100, Temperature=T, Unit='cgs') # M(T) in emu/mol in a field of 100G || [001]
print(type([moment_t, moment_h, moment_SI, moment_cgs]))
# --------------------------
from CrystalField import CrystalField, CrystalFieldFit, PhysicalProperties
# Fits a heat capacity dataset - you must have subtracted the phonon contribution by some method already
# and the data must be in J/mol/K.
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
PhysicalProperty=PhysicalProperties('Cv'))
fitcv = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fitcv.fit()
params = {'B20':0.37737, 'B22':3.9770, 'B40':-0.031787, 'B42':-0.11611, 'B44':-0.12544}
cf = CrystalField('Ce', 'C2v', **params)
cf.PhysicalProperty = PhysicalProperties('Cv')
fitcv = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fitcv.fit()
# Fits a susceptibility dataset. Data is the volume susceptibility in SI units
cf = CrystalField('Ce', 'C2v', **params)
cf.PhysicalProperty = PhysicalProperties('susc', Hdir='powder', Unit='SI')
fit_chi = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fit_chi.fit()
# Fits a magnetisation dataset. Data is in emu/mol, and was measured at 5K with the field || [111].
cf = CrystalField('Ce', 'C2v', **params)
cf.PhysicalProperty = PhysicalProperties('M(H)', Temperature=5, Hdir=[1, 1, 1], Unit='cgs')
fit_mag = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fit_mag.fit()
# Fits a magnetisation vs temperature dataset. Data is in Am^2/mol, measured with a 0.1T field || [110]
cf = CrystalField('Ce', 'C2v', **params)
cf.PhysicalProperty = PhysicalProperties('M(T)', Hmag=0.1, Hdir=[1, 1, 0], Unit='SI')
fit_moment = CrystalFieldFit(Model=cf, InputWorkspace=ws)
fit_moment.fit()
# --------------------------
# Pregenerate the required workspaces
for tt in [10, 44, 50]:
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544, Temperature=tt, FWHM=0.5)
x, y = cf.getSpectrum()
self.my_create_ws('ws_ins_'+str(tt)+'K', x, y)
ws_ins_10K = mtd['ws_ins_10K']
ws_ins_44K = mtd['ws_ins_44K']
ws_ins_50K = mtd['ws_ins_50K']
ws_cp = self.my_create_ws('ws_cp', *cf.getHeatCapacity())
ws_chi = self.my_create_ws('ws_chi', *cf.getSusceptibility(np.linspace(1,300,100), Hdir='powder', Unit='cgs'))
ws_mag = self.my_create_ws('ws_mag', *cf.getMagneticMoment(Hmag=np.linspace(0, 30, 100), Hdir=[1,1,1], Unit='bohr', Temperature=5))
# --------------------------
# Fits an INS spectrum (at 10K) and the heat capacity simultaneously
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544)
cf.Temperature = 10
cf.FWHM = 1.5
cf.PhysicalProperty = PhysicalProperties('Cv')
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws_ins_10K, ws_cp])
fit.fit()
# Fits two INS spectra (at 44K and 50K) and the heat capacity, susceptibility and magnetisation simultaneously.
PPCv = PhysicalProperties('Cv')
PPchi = PhysicalProperties('susc', 'powder', Unit='cgs')
PPMag = PhysicalProperties('M(H)', [1, 1, 1], 5, 'bohr')
cf = CrystalField('Ce', 'C2v', B20=0.37737, B22=3.9770, B40=-0.031787, B42=-0.11611, B44=-0.12544,
Temperature=[44.0, 50], FWHM=[1.1, 0.9], PhysicalProperty=[PPCv, PPchi, PPMag] )
fit = CrystalFieldFit(Model=cf, InputWorkspace=[ws_ins_44K, ws_ins_50K, ws_cp, ws_chi, ws_mag])
fit.fit()
lit_par = {
'B20': -.27,
'B22': -.34,
'B40': 1e-6,
'B42': 0.04158,
'B44': 0.01369,
'B60': 0.112e-3,
'B62': 0.4185e-3,
'B64': -0.555e-3,
'B66': 0.588e-3
} # K
for parameter in lit_par.keys():
lit_par[parameter] *= Kelvin_to_meV
# Set up the crystal field model.
cf = CrystalField('Ho', 'D2', Temperature=[55, 159], FWHM=0.3, **lit_par)
cf.PeakShape = 'Lorentzian'
cf.IntensityScaling = [
0.2, 0.2
] # Scale factor if data is not in absolute units (mbarn/sr/f.u./meV), will be fitted.
cf.background = Background(
peak=Function('Gaussian', Height=700, Sigma=0.4 / 2.3))
cf.ResolutionModel = [resmod, resmod]
# Runs the fit
fit = CrystalFieldFit(Model=cf,
InputWorkspace=[data_ws3, data_ws4],
MaxIterations=2000,
Output='Fit_159K')
fit.fit()
B42=-0.002,
B44=-0.012,
B60=5.e-05,
B62=0.00054,
B64=-0.0006,
B66=0.0008)
# Simultaneously fit data measured in a, b and c directions
cf.PhysicalProperty = [
PhysicalProperties('susc', Hdir=[1, 0, 0], Inverse=True, Unit='cgs'),
PhysicalProperties('susc', Hdir=[0, 1, 0], Inverse=True, Unit='cgs'),
PhysicalProperties('susc', Hdir=[0, 0, 1], Inverse=True, Unit='cgs')
]
fit = CrystalFieldFit(Model=cf,
InputWorkspace=[sus_a, sus_b, sus_c],
MaxIterations=100,
Output='fit_susc')
fit.fit()
# Print fitted parameters and plot results
blm = {}
for parname in ['B20', 'B22', 'B40', 'B42', 'B44', 'B60', 'B62', 'B64', 'B66']:
blm[parname] = cf[parname]
print parname + "=" + str(cf[parname])
calc_a = mtd['fit_susc_Workspaces'][0]
calc_b = mtd['fit_susc_Workspaces'][1]
calc_c = mtd['fit_susc_Workspaces'][2]
plt.plot(calc_a.readX(1), calc_a.readY(1), '-k', label='$\chi^a$ Fit')
plt.plot(mtd['sus_a'].readX(0),
mtd['sus_a'].readY(0),
'ok',