def _makeRecipe(self, x, y, dy):
'''Make a FitRecipe for fitting a Gaussian curve to data.
'''
profile = Profile()
profile.setObservedProfile(x, y, dy)
contribution = FitContribution("g1")
contribution.setProfile(profile, xname="x")
contribution.registerStringFunction('1/sqrt(2 * pi * sig**2)',
name='gaussnorm')
contribution.setEquation(
"A * gaussnorm * exp(-0.5 * (x - x0)**2/sig**2)")
recipe = FitRecipe()
recipe.addContribution(contribution)
recipe.addVar(contribution.A)
recipe.addVar(contribution.x0)
recipe.addVar(contribution.sig)
recipe.clearFitHooks()
self.recipe = recipe
return
def _makeRecipe(self, x, y, dy):
'''Make a FitRecipe for fitting a Gaussian curve to data.
'''
profile = Profile()
profile.setObservedProfile(x, y, dy)
contribution = FitContribution("g1")
contribution.setProfile(profile, xname="x")
contribution.registerStringFunction(
'1/sqrt(2 * pi * sig**2)', name='gaussnorm')
contribution.setEquation(
"A * gaussnorm * exp(-0.5 * (x - x0)**2/sig**2)")
recipe = FitRecipe()
recipe.addContribution(contribution)
recipe.addVar(contribution.A)
recipe.addVar(contribution.x0)
recipe.addVar(contribution.sig)
recipe.clearFitHooks()
self.recipe = recipe
return
for par in spaceGroupParams.latpars:
MnOFit.addVar(par)
# Set initial value for the ADP parameters, because CIF had no ADP data.
for par in spaceGroupParams.adppars:
MnOFit.addVar(par, value=0.003, fixed=True)
# As usual, we add variables for the overall scale of the PDF and a delta2
# parameter for correlated motion of neighboring atoms.
MnOFit.addVar(MnOPDF.scale, 1)
MnOFit.addVar(MnOPDF.MnO.delta2, 1.5)
# We fix Qdamp based on prior information about our beamline.
MnOFit.addVar(MnOPDF.qdamp, 0.03, fixed=True)
# Turn off printout of iteration number.
MnOFit.clearFitHooks()
# We can now execute the fit using scipy's least square optimizer.
print("Refine PDF using scipy's least-squares optimizer:")
print(" variables:", MnOFit.names)
print(" initial values:", MnOFit.values)
leastsq(MnOFit.residual, MnOFit.values)
print(" final values:", MnOFit.values)
print()
# Obtain and display the fit results.
MnOResults = FitResults(MnOFit)
print("FIT RESULTS\n")
print(MnOResults)
# Get the experimental data from the recipe
for par in spaceGroupParams.latpars:
niFit.addVar(par)
# Set initial value for the ADP parameters, because CIF had no ADP data.
for par in spaceGroupParams.adppars:
niFit.addVar(par, value=0.005)
# As usual, we add variables for the overall scale of the PDF and a delta2
# parameter for correlated motion of neighboring atoms.
niFit.addVar(niPDF.scale, 1)
niFit.addVar(niPDF.nickel.delta2, 5)
# We fix Qdamp based on prior information about our beamline.
niFit.addVar(niPDF.qdamp, 0.03, fixed=True)
# Turn off printout of iteration number.
niFit.clearFitHooks()
# We can now execute the fit using scipy's least square optimizer.
print "Refine PDF using scipy's least-squares optimizer:"
print " variables:", niFit.names
print " initial values:", niFit.values
leastsq(niFit.residual, niFit.values)
print " final values:", niFit.values
print
# Obtain and display the fit results.
niResults = FitResults(niFit)
print "FIT RESULTS\n"
print niResults
# Plot the observed and refined PDF.
for par in sgpars.latpars:
mnofit.addVar(par)
# Set initial value for the ADP parameters, because CIF had no ADP data.
for par in sgpars.adppars:
mnofit.addVar(par, value=0.003)
# As usual, we add variables for the overall scale of the PDF and a delta2
# parameter for correlated motion of neighboring atoms.
mnofit.addVar(totpdf.nucscale, 1)
mnofit.addVar(nucpdf.delta2, 1.5)
# We fix Qdamp based on prior information about our beamline.
mnofit.addVar(nucpdf.qdamp, 0.03)
# Turn off printout of iteration number.
mnofit.clearFitHooks()
# Initial structural fit
print("Refine PDF using scipy's least-squares optimizer:")
print(" variables:", mnofit.names)
print(" initial values:", mnofit.values)
leastsq(mnofit.residual, mnofit.values)
print(" final values:", mnofit.values)
print()
# Obtain and display the fit results.
mnoresults = FitResults(mnofit)
print("FIT RESULTS\n")
print(mnoresults)
# Get the experimental data from the recipe
r = mnofit.totpdf.profile.x
for par in spaceGroupParams.latpars:
niFit.addVar(par)
# Set initial value for the ADP parameters, because CIF had no ADP data.
for par in spaceGroupParams.adppars:
niFit.addVar(par, value=0.005)
# As usual, we add variables for the overall scale of the PDF and a delta2
# parameter for correlated motion of neighboring atoms.
niFit.addVar(niPDF.scale, 1)
niFit.addVar(niPDF.nickel.delta2, 5)
# We fix Qdamp based on prior information about our beamline.
niFit.addVar(niPDF.qdamp, 0.03, fixed=True)
# Turn off printout of iteration number.
niFit.clearFitHooks()
# We can now execute the fit using scipy's least square optimizer.
print("Refine PDF using scipy's least-squares optimizer:")
print(" variables:", niFit.names)
print(" initial values:", niFit.values)
leastsq(niFit.residual, niFit.values)
print(" final values:", niFit.values)
print()
# Obtain and display the fit results.
niResults = FitResults(niFit)
print("FIT RESULTS\n")
print(niResults)
# Plot the observed and refined PDF.
def make(self, crystal, r, g, dg=None, meta=None):
"""
Construct new srfit recipe from CIF structure and PDF data
Parameters
----------
crystal : pyobjcryst.Crystal
The CIF structure to be fitted to the PDF data in-place.
r : array_like
The r-grid of the fitted PDF dataset in Angstroms.
g : array_like
The fitted PDF values per each `r` point.
dg : array_like, optional
The estimated standard deviations at each of `g` values.
When unspecified, *dg* is assumed 1 leading to underestimated
standard errors of the refined variables.
meta : dict, optional
A dictionary of extra metadata to be used when constructing
`PDFContribution` in the srfit recipe. The common recognized
keys are ``stype`` for radiation type ("X" or "N"), ``qmax``
for the Q-range used in the experiment, ``delta1``, ``delta2``,
``qbroad`` for peak sharpening and broadening factors and
``qdamp`` for the Q-resolution related signal dampening.
Returns
-------
recipe : FitRecipe
The new FitRecipe for in-place fitting of `crystal` to PDF data.
"""
if not isinstance(crystal, Crystal):
emsg = "crystal must be of the pyobjcryst.Crystal type."
raise TypeError(emsg)
cpdf = PDFContribution('cpdf')
cpdf.profile.setObservedProfile(r, g, dg)
m = {} if meta is None else dict(meta)
cpdf.profile.meta.update(m)
cpdf.addStructure('cif', crystal)
cpdf.setCalculationRange(self.rmin, self.rmax)
if self.nyquist:
if not 'qmax' in m:
emsg = "Nyquist spacing requires 'qmax' metadata."
raise ValueError(emsg)
assert m['qmax'] == cpdf.cif.getQmax()
cpdf.setCalculationRange(dx=numpy.pi / m['qmax'])
# create FitRecipe
recipe = FitRecipe()
recipe.clearFitHooks()
recipe.addContribution(cpdf)
recipe.addVar(cpdf.scale)
# get symmetry allowed structure parameters
sgpars = cpdf.cif.phase.sgpars
# constrain available lattice parameters
for p in sgpars.latpars:
recipe.addVar(p, tags=['phase', 'lattice'])
# constrain free atom positions
for p in sgpars.xyzpars:
recipe.addVar(p, tags=['phase', 'positions'])
# constrain adps
isosymbol = sgpars.isosymbol
fbbiso = self.fbbiso
# make a dummy diffpy.structure.Atom with isotropic Biso = fbbiso
afbiso = _dummyAtomWithBiso(crystal, self.fbbiso)
tags = ['phase', 'adps']
for p in sgpars.adppars:
if p.name.startswith(isosymbol):
recipe.addVar(p, value=p.value or fbbiso, tags=tags)
continue
# we have anisotropic site here, but constrain as isotropic
# if so requested
if self.isotropy:
# extract site index for this sg parameter. Use it to get
# the parameter for its Biso value.
idx = int(p.name.split('_')[-1])
psite = cpdf.cif.phase.scatterers[idx]
pbiso = psite.Biso
n = isosymbol + '_{}'.format(idx)
v = pbiso.value or fbbiso
# avoid applying duplicate constrain to pbiso
if recipe.get(n) is None:
recipe.addVar(pbiso, name=n, value=v, tags=tags)
continue
# here we constrain an anisotropic site.
# make sure its ADPs are nonzero.
spa = p.par.obj
if not all((spa.B11, spa.B22, spa.B33)):
spa.B11 = afbiso.B11
spa.B22 = afbiso.B22
spa.B33 = afbiso.B33
spa.B12 = afbiso.B12
spa.B13 = afbiso.B13
spa.B23 = afbiso.B23
recipe.addVar(p, tags=tags)
# constrain delta2, qdamp and qbroad
p = cpdf.cif.delta2
v = p.value or self.fbdelta2
recipe.addVar(p, value=v, tag='phase')
p = cpdf.qdamp
v = p.value or self.fbqdamp
recipe.addVar(p, value=v, tag='experiment')
p = cpdf.qbroad
v = p.value or self.fbqbroad
recipe.addVar(p, value=v, tag='experiment')
return recipe
mnofit.addVar(par, value=0.003, fixed=True) # As usual, we add variables for the overall scale of the PDF and a delta2 # parameter for correlated motion of neighboring atoms. mnofit.addVar(totpdf.nucscale, 1) mnofit.addVar(nucpdf.delta2, 1.5) # We fix Qdamp based on prior information about our beamline. mnofit.addVar(nucpdf.qdamp, 0.03, fixed=True) # add the mPDF variables mnofit.addVar(totpdf.parascale, 4) mnofit.addVar(totpdf.ordscale, 1.5) # Turn off printout of iteration number. mnofit.clearFitHooks() # Initial structural fit print "Refine PDF using scipy's least-squares optimizer:" print " variables:", mnofit.names print " initial values:", mnofit.values leastsq(mnofit.residual, mnofit.values) print " final values:", mnofit.values print # Obtain and display the fit results. mnoresults = FitResults(mnofit) print "FIT RESULTS\n" print mnoresults # Get the experimental data from the recipe
cdseFit.constrain(atom.Biso, SeBiso)
# Now we create a zoomscale factor which stretches the structure model, this is
# useful when you want to fit the bond length. Note that the relative position
# of atoms are not changed during the refinements
zoomscale = cdseFit.newVar('zoomscale', value=1.0)
# Here is a simple we to assign the zoomscale to the structure. Note that this
# only works for NON-PERIODIC structure
lattice = cdsePDF.CdSe.phase.getLattice()
cdseFit.constrain(lattice.a, zoomscale)
cdseFit.constrain(lattice.b, zoomscale)
cdseFit.constrain(lattice.c, zoomscale)
# Turn off printout of iteration number.
cdseFit.clearFitHooks()
# We can now execute the fit using scipy's least square optimizer.
print("Refine PDF using scipy's least-squares optimizer:")
print(" variables:", cdseFit.names)
print(" initial values:", cdseFit.values)
leastsq(cdseFit.residual, cdseFit.values)
print(" final values:", cdseFit.values)
print()
# Obtain and display the fit results.
cdseResults = FitResults(cdseFit)
print("FIT RESULTS\n")
print(cdseResults)
# Plot the observed and refined PDF.
print("linefit.evaluate() =", linefit.evaluate())
print("linefit.residual() =", linefit.residual())
plt.plot(xobs, yobs, 'x', linedata.x, linefit.evaluate(), '-')
plt.title('Line simulated at A=3, B=5')
# <demo> --- stop ---
# We want to find optimum model parameters that fit the simulated curve
# to the observations. This is done by associating FitContribution with
# a FitRecipe object. FitRecipe can manage multiple fit contributions and
# optimize all models to fit their respective profiles.
from diffpy.srfit.fitbase import FitRecipe
rec = FitRecipe()
# clearFitHooks suppresses printout of iteration number
rec.clearFitHooks()
rec.addContribution(linefit)
rec.show()
# <demo> --- stop ---
# FitContributions may have many parameters. We need to tell the recipe
# which of them should be tuned by the fit.
rec.addVar(rec.linefit.A)
rec.addVar(rec.linefit.B)
# The addVar function created two attributes A, B for the rec object
# which link ot the A and B parameters of the linefit contribution.
print("linefit.evaluate() =", linefit.evaluate())
print("linefit.residual() =", linefit.residual())
plt.plot(xobs, yobs, 'x', linedata.x, linefit.evaluate(), '-')
plt.title('Line simulated at A=3, B=5')
# <demo> --- stop ---
# We want to find optimum model parameters that fit the simulated curve
# to the observations. This is done by associating FitContribution with
# a FitRecipe object. FitRecipe can manage multiple fit contributions and
# optimize all models to fit their respective profiles.
from diffpy.srfit.fitbase import FitRecipe
rec = FitRecipe()
# clearFitHooks suppresses printout of iteration number
rec.clearFitHooks()
rec.addContribution(linefit)
rec.show()
# <demo> --- stop ---
# FitContributions may have many parameters. We need to tell the recipe
# which of them should be tuned by the fit.
rec.addVar(rec.linefit.A)
rec.addVar(rec.linefit.B)
# The addVar function created two attributes A, B for the rec object
# which link ot the A and B parameters of the linefit contribution.
plt.title(
'Two Gaussians simulated at A=25000, x0=40, sig=20 and A=500, x0=68, sig=3'
)
# <demo> --- stop ---
# We want to find optimum model parameters that fit the simulated curve
# to the observations. This is done by associating FitContribution with
# a FitRecipe object. FitRecipe can manage multiple fit contributions and
# optimize all models to fit their respective profiles.
from diffpy.srfit.fitbase import FitRecipe
recipe = FitRecipe()
# clearFitHooks suppresses printout of iteration number
recipe.clearFitHooks()
recipe.addContribution(large_gaussian)
recipe.addContribution(small_gaussian)
recipe.show()
# <demo> --- stop ---
# FitContributions may have many parameters. We need to tell the recipe
# which of them should be tuned by the fit.
recipe.addVar(large_gaussian.lgA)
recipe.addVar(large_gaussian.lgx0)
recipe.addVar(large_gaussian.lgsig)
import numpy as np
from pyobjcryst import loadCrystal
from diffpy.srfit.pdf import PDFContribution
from diffpy.srfit.fitbase import Profile, FitRecipe, FitResults
nphcrystal = loadCrystal('naphthalene.cif')
pdfcntb = PDFContribution('pdfcntb')
pdfcntb.loadData('naphthalene.gr')
pdfcntb.qdamp = 0.06
pdfcntb.setCalculationRange(1.1, 25)
pdfcntb.addStructure('nph', nphcrystal)
nphfit = FitRecipe()
nphfit.clearFitHooks()
nphfit.addContribution(pdfcntb)
nphfit.addVar(pdfcntb.scale, name='scale')
nphfit.addVar(pdfcntb.nph.delta2, value=1.0)
nphase = pdfcntb.nph.phase
# unit cell parameters
nphfit.addVar(nphase.a)
nphfit.addVar(nphase.b)
nphfit.addVar(nphase.c)
# cell-angle beta is in radians in ObjCryst Crystal
# we will refine angle in degrees.
nphfit.newVar('beta', value=np.degrees(nphase.beta.value))
nphfit.constrain(nphase.beta, 'radians(beta)')
# all carbon species have the same displacement parameter,
# it is sufficient to add constraint for the C1 atom
for par in spaceGroupParams.latpars:
MnOFit.addVar(par)
# Set initial value for the ADP parameters, because CIF had no ADP data.
for par in spaceGroupParams.adppars:
MnOFit.addVar(par, value=0.003,fixed=True)
# As usual, we add variables for the overall scale of the PDF and a delta2
# parameter for correlated motion of neighboring atoms.
MnOFit.addVar(MnOPDF.scale, 1)
MnOFit.addVar(MnOPDF.MnO.delta2, 1.5)
# We fix Qdamp based on prior information about our beamline.
MnOFit.addVar(MnOPDF.qdamp, 0.03, fixed=True)
# Turn off printout of iteration number.
MnOFit.clearFitHooks()
# We can now execute the fit using scipy's least square optimizer.
print("Refine PDF using scipy's least-squares optimizer:")
print(" variables:", MnOFit.names)
print(" initial values:", MnOFit.values)
leastsq(MnOFit.residual, MnOFit.values)
print(" final values:", MnOFit.values)
print()
# Obtain and display the fit results.
MnOResults = FitResults(MnOFit)
print("FIT RESULTS\n")
print(MnOResults)
# Get the experimental data from the recipe
