def makeRecipe(ciffile, datname):
"""Create a fitting recipe for crystalline PDF data."""
# Work directly with a custom PDFContribution to load the data
contribution = PDFContribution("nickel")
contribution.loadData(datname)
contribution.setCalculationRange(xmin=1, xmax=20, dx=0.1)
# and the phase
stru = Structure()
stru.read(ciffile)
contribution.addStructure("nickel", stru)
## Make the FitRecipe and add the FitContribution.
recipe = FitRecipe()
recipe.addContribution(contribution)
## Configure the fit variables
phase = contribution.nickel.phase
from diffpy.srfit.structure import constrainAsSpaceGroup
sgpars = constrainAsSpaceGroup(phase, "Fm-3m")
for par in sgpars.latpars:
recipe.addVar(par)
for par in sgpars.adppars:
recipe.addVar(par, 0.005)
recipe.addVar(contribution.scale, 1)
recipe.addVar(contribution.qdamp, 0.03, fixed=True)
recipe.addVar(contribution.nickel.delta2, 5)
# Give the recipe away so it can be used!
return recipe
def makeRecipe(ciffile, datname):
"""Create a fitting recipe for crystalline PDF data."""
# Work directly with a custom PDFContribution to load the data
contribution = PDFContribution("nickel")
contribution.loadData(datname)
contribution.setCalculationRange(xmin = 1, xmax = 20, dx = 0.1)
# and the phase
stru = Structure()
stru.read(ciffile)
contribution.addStructure("nickel", stru)
## Make the FitRecipe and add the FitContribution.
recipe = FitRecipe()
recipe.addContribution(contribution)
## Configure the fit variables
phase = contribution.nickel.phase
from diffpy.srfit.structure import constrainAsSpaceGroup
sgpars = constrainAsSpaceGroup(phase, "Fm-3m")
for par in sgpars.latpars:
recipe.addVar(par)
for par in sgpars.adppars:
recipe.addVar(par, 0.005)
recipe.addVar(contribution.scale, 1)
recipe.addVar(contribution.qdamp, 0.03, fixed = True)
recipe.addVar(contribution.nickel.delta2, 5)
# Give the recipe away so it can be used!
return recipe
# Add the structure from our cif file to the contribution
MnOPDF.addStructure("MnO", mnostructure)
# The FitRecipe does the work of calculating the PDF with the fit variable
# that we give it.
MnOFit = FitRecipe()
# give the PDFContribution to the FitRecipe
MnOFit.addContribution(MnOPDF)
# Configure the fit variables and give them to the recipe. We can use the
# srfit function constrainAsSpaceGroup to constrain the lattice and ADP
# parameters according to the H-3m space group.
from diffpy.srfit.structure import constrainAsSpaceGroup
spaceGroupParams = constrainAsSpaceGroup(MnOPDF.MnO.phase, spaceGroup)
print("Space group parameters are:", end=' ')
print(', '.join([p.name for p in spaceGroupParams]))
print()
# We can now cycle through the parameters and activate them in the recipe as
# variables
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)
# Add the structure from our cif file to the contribution
niStructure = loadStructure(structureFile)
niPDF.addStructure("nickel", niStructure)
# The FitRecipe does the work of calculating the PDF with the fit variable
# that we give it.
niFit = FitRecipe()
# give the PDFContribution to the FitRecipe
niFit.addContribution(niPDF)
# Configure the fit variables and give them to the recipe. We can use the
# srfit function constrainAsSpaceGroup to constrain the lattice and ADP
# parameters according to the Fm-3m space group.
from diffpy.srfit.structure import constrainAsSpaceGroup
spaceGroupParams = constrainAsSpaceGroup(niPDF.nickel.phase, spaceGroup)
print "Space group parameters are:",
print ', '.join([p.name for p in spaceGroupParams])
print
# We can now cycle through the parameters and activate them in the recipe as
# variables
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)
def main():
"""
This will run by default when the file is executed using
"python file.py" in the command line
Parameters
----------
None
Returns
----------
None
"""
# Make some folders to store our output files.
resdir = Path("res")
fitdir = Path("fit")
figdir = Path("fig")
folders = [resdir, fitdir, figdir]
# Loop over all folders
for folder in folders:
# If the folder does not exist...
if not folder.exists():
# ...then we create it.
folder.mkdir()
# Let the user know what fit we are running by printing to terminal.
basename = FIT_ID
print(f"\n{basename}\n")
# Establish the full location of the data.
data = DPATH / GR_NAME
# Establish the location of the cif file with the structure of interest
# and load it into a diffpy structure object.
strudir = DPATH
cif_file = strudir / CIF_NAME
# Initialize the Fit Recipe by giving it this diffpy structure
# as well as the path to the data file.
p_cif = getParser('cif')
structure = p_cif.parseFile(str(cif_file))
space_group = p_cif.spacegroup.short_name
# Initialize the Fit Recipe by giving it this diffpy structure
# as well as the path to the data file.
recipe = makerecipe(cif_file, data)
# Let's set the calculation range!
recipe.crystal.profile.setCalculationRange(xmin=PDF_RMIN,
xmax=PDF_RMAX,
dx=PDF_RSTEP)
# Add, initialize, and tag variables in the Fit Recipe object.
# In this case we also add psize, which is the NP size.
recipe.addVar(recipe.crystal.s1, SCALE_I, tag="scale")
# Set an equation, based on your PDF generators. Here we add an extra layer
# of complexity, incorporating "f" int our equation. This new term
# incorporates damping to our PDF to model the effect of finite crystallite size.
# In this case we use a function which models a spherical NP.
from diffpy.srfit.pdf.characteristicfunctions import sphericalCF
recipe.crystal.registerFunction(sphericalCF, name="f")
recipe.crystal.setEquation("s1*G1*f")
recipe.addVar(recipe.crystal.psize, PSIZE_I, tag="psize")
# Initialize the instrument parameters, Q_damp and Q_broad, and
# assign Q_max and Q_min.
# Note, here we do not add the qdamp and qbroad parameters to the fit!!!
# They are fixed here, because we refined them in the Ni standard fit!
recipe.crystal.G1.qdamp.value = QDAMP_I
recipe.crystal.G1.qbroad.value = QBROAD_I
recipe.crystal.G1.setQmax(QMAX)
recipe.crystal.G1.setQmin(QMIN)
# Use the srfit function constrainAsSpaceGroup to constrain
# the lattice and ADP parameters according to the Fm-3m space group.
from diffpy.srfit.structure import constrainAsSpaceGroup
spacegroupparams = constrainAsSpaceGroup(recipe.crystal.G1.phase,
space_group)
# Add and initialize delta, the lattice parameter, and a thermal parameter,
# but not instrumental parameters to Fit Recipe.
# The instrumental parameters will remain fixed at values obtained from
# the Ni calibrant in our previous example. As we have not added them through
# recipe.addVar, they cannot be refined.
for par in spacegroupparams.latpars:
recipe.addVar(par, value=CUBICLAT_I, name="fcc_Lat", tag="lat")
for par in spacegroupparams.adppars:
recipe.addVar(par, value=UISO_I, name="fcc_ADP", tag="adp")
recipe.addVar(recipe.crystal.G1.delta2,
name="Pt_Delta2",
value=DELTA2_I,
tag="d2")
# Tell the Fit Recipe we want to write the maximum amount of
# information to the terminal during fitting.
recipe.fithooks[0].verbose = 0
refine_params = ["scale", "lat", "psize", "adp", "d2", "all"]
recipe.fix("all")
for params in refine_params:
recipe.free(params)
print(f"\n****\nFitting {recipe.getNames()} against "
f"{GR_NAME} with {CIF_NAME}\n")
least_squares(recipe.residual, recipe.values, x_scale="jac")
# We use the savetxt method of the profile to write a text file
# containing the measured and fitted PDF to disk.
# The file is named based on the basename we created earlier, and
# written to the fitdir directory.
profile = recipe.crystal.profile
profile.savetxt(fitdir / (basename + ".fit"))
# We use the FitResults function to parse out the results from
# the optimized Fit Recipe.
res = FitResults(recipe)
# We print these results to the terminal.
res.printResults()
# We grab the fit Rw
rw = res.rw
# We use the saveResults method of FitResults to write a text file
# containing the fitted parameters and fit quality indices to disk.
# The file is named based on the basename we created earlier, and
# written to the resdir directory.
header = "crystal_HF.\n"
res.saveResults(resdir / (basename + ".res"), header=header)
# We use the plotresults function we created earlier to make a plot of
# the measured, calculated, and difference curves. We show this
# as an interactive window and then write a pdf file to disk.
# The file is named based on the basename we created earlier, and
# written to the figdir directory.
plotresults(recipe, figdir / basename)
# Let make a dictionary to hold our results. This way make reloading the
# fit parameters easier later
refined_dict = dict()
refined_dict['rw'] = rw.item()
# We loop over the variable names, the variable values, and the variable uncertainties (esd)
for name, val, unc in zip(res.varnames, res.varvals, res.varunc):
# We store the refined value for this variable using the "value" key.
# We use the ".item()" method because "res.varvals" exist as
# numpy.float64 objects, and we want them as regular python floats.
if name not in refined_dict:
refined_dict[name] = dict()
refined_dict[name]["value"] = val.item()
refined_dict[name]["uncert"] = unc.item()
with open(basename + ".yml", 'w') as outfile:
yaml.safe_dump(refined_dict, outfile)
totpdf.addProfileGenerator(nucpdf)
totpdf.setProfile(profile)
totpdf.setEquation("nucscale*nucpdf")
# The FitRecipe does the work of calculating the PDF with the fit variables
# that we give it.
mnofit = FitRecipe()
# give the FitContribution to the FitRecipe
mnofit.addContribution(totpdf)
# Configure the fit variables and give them to the recipe. We can use the
# srfit function constrainAsSpaceGroup to constrain the lattice and ADP
# parameters according to the CIF-loaded space group.
from diffpy.srfit.structure import constrainAsSpaceGroup
sgpars = constrainAsSpaceGroup(nucpdf.phase, pcif.spacegroup.short_name)
print("Space group parameters are:", end=' ')
print(', '.join([p.name for p in sgpars]))
print()
# We can now cycle through the parameters and activate them in the recipe as
# variables
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)
niStructure = loadStructure(structureFile)
niPDF.addStructure("nickel", niStructure)
# The FitRecipe does the work of calculating the PDF with the fit variable
# that we give it.
niFit = FitRecipe()
# give the PDFContribution to the FitRecipe
niFit.addContribution(niPDF)
# Configure the fit variables and give them to the recipe. We can use the
# srfit function constrainAsSpaceGroup to constrain the lattice and ADP
# parameters according to the Fm-3m space group.
from diffpy.srfit.structure import constrainAsSpaceGroup
spaceGroupParams = constrainAsSpaceGroup(niPDF.nickel.phase, spaceGroup)
print("Space group parameters are:",
', '.join(p.name for p in spaceGroupParams))
print()
# We can now cycle through the parameters and activate them in the recipe as
# variables
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)
def make_recipe(self, structure, sg):
""" Construct PDF with diffpy. """
# construct a PDFContribution object
pdf = PDFContribution("Contribution")
# read experimental data
try:
pdf.loadData(self.input_file)
except:
print_failure('Failed to parse ' + self.input_file +
'. Exiting...')
exit()
print('Constructing PDF object for', structure.title)
pdf.setCalculationRange(self.xmin, self.xmax, self.dx)
pdf.addStructure("Contribution", structure)
# create FitRecipe to calculate PDF with chosen fit variable
fit = FitRecipe()
fit.addContribution(pdf)
# configure variables and add to recipe
if sg != 'xxx' and sg is not None:
print(sg)
spacegroup_params = constrainAsSpaceGroup(pdf.Contribution.phase,
sg)
else:
cart_lat = abc2cart([[
structure.lattice.a, structure.lattice.b, structure.lattice.c
],
[
structure.lattice.alpha,
structure.lattice.beta,
structure.lattice.gamma
]])
positions_frac = structure.xyz
atomic_numbers = []
for atom in structure.element:
atomic_numbers.append(get_atomic_number(atom))
cell = (cart_lat, positions_frac, atomic_numbers)
sg = int(
spg.get_spacegroup(cell, symprec=1e-2).split(' ')[1].replace(
'(', '').replace(')', ''))
spacegroup_params = constrainAsSpaceGroup(pdf.Contribution.phase,
sg)
# print('Space group parameters:')
# print(', '.join([param.name for param in spacegroup_params]))
# iterate through spacegroup params and activate them
for param in spacegroup_params.latpars:
fit.addVar(param)
for param in spacegroup_params.xyzpars:
fit.addVar(param, fixed=True)
# these next parameters are taken from Martin's PDFht.py,
# though I have a feeling that was not their origin...
# set initial ADP parameters
for param in spacegroup_params.adppars:
fit.addVar(param, value=0.03, fixed=True)
# overall scale of PDF and delta2 parameter for correlated motion - from PDFht.py
fit.addVar(pdf.scale, 1, fixed=True)
fit.restrain(pdf.scale, lb=0, ub=0.1, scaled=True)
fit.addVar(pdf.Contribution.delta2, 5, fixed=True)
fit.restrain(pdf.Contribution.delta2, lb=1, ub=10, scaled=True)
# fix Qdamp based on information about "our beamline": yep, no idea
fit.addVar(pdf.qdamp, 0.03, fixed=True)
fit.restrain(pdf.qdamp, lb=0, ub=0.1, scaled=True)
return fit
def makeRecipe(ciffile, datname):
"""Create a fitting recipe for crystalline PDF data."""
## The Profile
# This will be used to store the observed and calculated PDF profile.
profile = Profile()
# Load data and add it to the Profile. Unlike in other examples, we use a
# class (PDFParser) to help us load the data. This class will read the data
# and relevant metadata from a two- to four-column data file generated
# with PDFGetX2 or PDFGetN. The metadata will be passed to the PDFGenerator
# when they are associated in the FitContribution, which saves some
# configuration steps.
parser = PDFParser()
parser.parseFile(datname)
profile.loadParsedData(parser)
profile.setCalculationRange(xmax = 20)
## The ProfileGenerator
# The PDFGenerator is for configuring and calculating a PDF profile. Here,
# we want to refine a Structure object from diffpy.structure. We tell the
# PDFGenerator that with the 'setStructure' method. All other configuration
# options will be inferred from the metadata that is read by the PDFParser.
# In particular, this will set the scattering type (x-ray or neutron), the
# Qmax value, as well as initial values for the non-structural Parameters.
generator = PDFGenerator("G")
stru = Structure()
stru.read(ciffile)
generator.setStructure(stru)
## The FitContribution
# Here we associate the Profile and ProfileGenerator, as has been done
# before.
contribution = FitContribution("nickel")
contribution.addProfileGenerator(generator)
contribution.setProfile(profile, xname = "r")
## Make the FitRecipe and add the FitContribution.
recipe = FitRecipe()
recipe.addContribution(contribution)
## Configure the fit variables
# The PDFGenerator class holds the ParameterSet associated with the
# Structure passed above in a data member named "phase". (We could have
# given the ParameterSet a name other than "phase" when we added it to the
# PDFGenerator.) The ParameterSet in this case is a StructureParameterSet,
# the documentation for which is found in the
# diffpy.srfit.structure.diffpystructure module.
phase = generator.phase
# We start by constraining the phase to the known space group. We could do
# this by hand, but there is a method in diffpy.srfit.structure named
# 'constrainAsSpaceGroup' for this purpose. The constraints will by default
# be applied to the sites, the lattice and to the ADPs. See the method
# documentation for more details. The 'constrainAsSpaceGroup' method may
# create new Parameters, which it returns in a SpaceGroupParameters object.
from diffpy.srfit.structure import constrainAsSpaceGroup
sgpars = constrainAsSpaceGroup(phase, "Fm-3m")
# The SpaceGroupParameters object returned by 'constrainAsSpaceGroup' holds
# the free Parameters allowed by the space group constraints. Once a
# structure is constrained, we need (should) only use the Parameters
# provided in the SpaceGroupParameters, as the relevant structure
# Parameters are constrained to these.
#
# We know that the space group does not allow for any free sites because
# each atom is on a special position. There is one free (cubic) lattice
# parameter and one free (isotropic) ADP. We can access these Parameters in
# the xyzpars, latpars, and adppars members of the SpaceGroupParameters
# object.
for par in sgpars.latpars:
recipe.addVar(par)
for par in sgpars.adppars:
recipe.addVar(par, 0.005)
# We now select non-structural parameters to refine.
# This controls the scaling of the PDF.
recipe.addVar(generator.scale, 1)
# This is a peak-damping resolution term.
recipe.addVar(generator.qdamp, 0.01)
# This is a vibrational correlation term that sharpens peaks at low-r.
recipe.addVar(generator.delta2, 5)
# Give the recipe away so it can be used!
return recipe
# Cover your eyes, but a structure change will now trigger the same
# reevaluations as if ordscale were modified.
nucpdf.phase.addObserver(totpdf.ordscale.notify)
# The FitRecipe does the work of calculating the PDF with the fit variable
# that we give it.
mnofit = FitRecipe()
# give the PDFContribution to the FitRecipe
mnofit.addContribution(totpdf)
# Configure the fit variables and give them to the recipe. We can use the
# srfit function constrainAsSpaceGroup to constrain the lattice and ADP
# parameters according to the CIF-loaded space group.
from diffpy.srfit.structure import constrainAsSpaceGroup
sgpars = constrainAsSpaceGroup(nucpdf.phase, pcif.spacegroup.short_name)
print "Space group parameters are:",
print ', '.join([p.name for p in sgpars])
print
# We can now cycle through the parameters and activate them in the recipe as
# variables
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, 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)
def main():
"""
This will run by default when the file is executed using
"python file.py" in the command line
Parameters
----------
None
Returns
----------
None
"""
# Make some folders to store our output files.
resdir = Path("res")
fitdir = Path("fit")
figdir = Path("fig")
folders = [resdir, fitdir, figdir]
# Loop over all folders
for folder in folders:
# If the folder does not exist...
if not folder.exists():
# ...then we create it.
folder.mkdir()
# Let the user know what fit we are running by printing to terminal.
basename = FIT_ID
print(f"\n{basename}\n")
# Establish the full location of the two datasets.
xray_data = DPATH / XRAY_GR_NAME
nuetron_data = DPATH / NEUTRON_GR_NAME
# Establish the location of the cif file with the structure of interest
# and load it into a diffpy structure object.
strudir = DPATH
cif_file = strudir / CIF_NAME
p_cif = getParser('cif')
structure = p_cif.parseFile(str(cif_file))
space_group = p_cif.spacegroup.short_name
# Initialize the Fit Recipe by giving it this diffpy structure
# as well as the path to the data file.
# Here we use a new function, which takes both datasets.
recipe = makerecipe_coref(cif_file, xray_data, nuetron_data)
# We first want to add two scale parameters to our fit recipe,
# one for each dataset.
recipe.addVar(recipe.xray.s1, XRAY_SCALE_I, tag="scale")
recipe.addVar(recipe.neutron.s2, NEUTRON_SCALE_I, tag="scale")
# Let's set the calculation range!
# Here we use a loop to make it easier to edit both ranges.
for cont in recipe._contributions.values():
cont.profile.setCalculationRange(xmin=PDF_RMIN, xmax=PDF_RMAX, dx=PDF_RSTEP)
# assign Q_max and Q_min, all part of the PDF Generator object.
# It's possible that the PDFParse function we used above
# already parsed out ths information, but in case it didn't, we set it
# explicitly again here.
# We do it for both neutron and PDF configurations
recipe.xray.xray_G.setQmax(XRAY_QMAX)
recipe.xray.xray_G.setQmin(XRAY_QMIN)
recipe.neutron.neutron_G.setQmax(NEUTRON_QMAX)
recipe.neutron.neutron_G.setQmin(NEUTRON_QMAX)
# Initialize and add the instrument parameters, Q_damp and Q_broad, and
# delta and instrumental parameters to Fit Recipe.
# We give them unique names, and tag them with our choice of relevant strings.
# Again, two datasets means we need to do this for each.
recipe.addVar(recipe.xray.xray_G.delta2,
name="Ni_Delta2",
value=DELTA2_I,
tag="d2")
recipe.constrain(recipe.neutron.neutron_G.delta2,
"Ni_Delta2")
recipe.addVar(recipe.xray.xray_G.qdamp,
name="xray_Calib_Qdamp",
value=XRAY_QDAMP_I,
tag="inst")
recipe.addVar(recipe.xray.xray_G.qbroad,
name="xray_Calib_Qbroad",
value=XRAY_QBROAD_I,
tag="inst")
recipe.addVar(recipe.neutron.neutron_G.qdamp,
name="neutron_Calib_Qdamp",
value=NEUTRON_QDAMP_I,
tag="inst")
recipe.addVar(recipe.neutron.neutron_G.qbroad,
name="neutron_Calib_Qbroad",
value=NEUTRON_QBROAD_I,
tag="inst")
# Configure some additional fit variables pertaining to symmetry.
# We can use the srfit function constrainAsSpaceGroup to constrain
# the lattice and ADP parameters according to the Fm-3m space group.
# First we establish the relevant parameters, then we cycle through
# the parameters and activate and tag them.
# We must explicitly set the ADP parameters, because in this case, the
# CIF had no ADP data.
from diffpy.srfit.structure import constrainAsSpaceGroup
# Create the symmetry distinct parameter sets, and constrain them
# in the generator.
neutron_spacegroupparams = constrainAsSpaceGroup(recipe.neutron.neutron_G.phase,
space_group)
xray_spacegroupparams = constrainAsSpaceGroup(recipe.xray.xray_G.phase,
space_group)
# Loop over all the symmetry distinct lattice parameters and add
# them to the recipe.
# We give them unique names, and tag them with our choice of a relevant string.
for xray_par, neutron_par in zip(xray_spacegroupparams.latpars, neutron_spacegroupparams.latpars):
recipe.addVar(xray_par,
value=CUBICLAT_I,
name="fcc_Lat",
tag="lat")
recipe.constrain(neutron_par,
"fcc_Lat")
# Loop over all the symmetry distinct ADPs and add
# them to the recipe.
# We give them unique names, and tag them with our choice of a relevant string.
for xray_par, neutron_par in zip(xray_spacegroupparams.adppars, neutron_spacegroupparams.adppars):
recipe.addVar(xray_par,
value=UISO_I,
name="fcc_ADP",
tag="adp")
recipe.constrain(neutron_par,
"fcc_ADP")
# Tell the Fit Recipe we want to write the maximum amount of
# information to the terminal during fitting.
recipe.fithooks[0].verbose = 3
# During the optimization, we fix and free parameters sequentially
# as you would in PDFgui. This leads to more stability in the refinement.
# We first fix all variables. "all" is a tag which incorporates
# every parameter.
recipe.fix("all")
# Here will will set the weight of each contribution. In this case, we give each equal weight
conts = list(recipe._contributions.values())
for cont in conts:
recipe.setWeight(cont, 1.0/len(conts))
# We then run a fit using the SciPy function "least_squares" which
# takes as its arguments the function to be optimized, here recipe.residual,
# as well as initial values for the fitted parameters, provided by
# recipe.values. The x_scale="jac" argument is an optional argument
# that provides for a bit more stability in the refinement.
# "least_squares" is a bit more robust than "leastsq,"
# which is another optimization function provided by SciPy.
# "least_squares" supports bounds on refined parameters,
# while "leastsq" does not.
refine_params = ["scale", "lat", "adp", "d2", "all"]
for params in refine_params:
recipe.free(params)
print(f"\n****\nFitting {recipe.getNames()} against "
f"{XRAY_GR_NAME} and {NEUTRON_GR_NAME} with {CIF_NAME}\n")
least_squares(recipe.residual, recipe.values, x_scale="jac")
# We use the savetxt method of the profile to write a text file
# containing the measured and fitted PDF to disk.
# The file is named based on the basename we created earlier, and
# written to the fitdir directory.
profile = recipe.crystal.profile
profile.savetxt(fitdir / (basename + ".fit"))
# We use the FitResults function to parse out the results from
# the optimized Fit Recipe.
res = FitResults(recipe)
# We print these results to the terminal.
res.printResults()
# We grab the fit Rw
rw = res.rw
# We use the saveResults method of FitResults to write a text file
# containing the fitted parameters and fit quality indices to disk.
# The file is named based on the basename we created earlier, and
# written to the resdir directory.
header = "crystal_HF.\n"
res.saveResults(resdir / (basename + ".res"), header=header)
# We use the plotresults function we created earlier to make a plot of
# the measured, calculated, and difference curves. We show this
# as an interactive window and then write a pdf file to disk.
# The file is named based on the basename we created earlier, and
# written to the figdir directory.
plotresults(recipe, figdir / basename)
# Let make a dictionary to hold our results. This way make reloading the
# fit parameters easier later
refined_dict = dict()
refined_dict['rw'] = rw.item()
recipe.free("all")
# We loop over the variable names, the variable values, and the variable uncertainties (esd)
for name, val, unc in zip(res.varnames, res.varvals, res.varunc):
# We store the refined value for this variable using the "value" key.
# We use the ".item()" method because "res.varvals" exist as
# numpy.float64 objects, and we want them as regular python floats.
if name not in refined_dict:
refined_dict[name] = dict()
refined_dict[name]["value"] = val.item()
refined_dict[name]["uncert"] = unc.item()
# Finally, let's write our dictionary to a yaml file!
with open(basename + ".yml", 'w') as outfile:
yaml.safe_dump(refined_dict, outfile)
# Add the structure from our cif file to the contribution
MnOPDF.addStructure("MnO", mnostructure)
# The FitRecipe does the work of calculating the PDF with the fit variable
# that we give it.
MnOFit = FitRecipe()
# give the PDFContribution to the FitRecipe
MnOFit.addContribution(MnOPDF)
# Configure the fit variables and give them to the recipe. We can use the
# srfit function constrainAsSpaceGroup to constrain the lattice and ADP
# parameters according to the H-3m space group.
from diffpy.srfit.structure import constrainAsSpaceGroup
spaceGroupParams = constrainAsSpaceGroup(MnOPDF.MnO.phase, spaceGroup)
print("Space group parameters are:", end=' ')
print(', '.join([p.name for p in spaceGroupParams]))
print()
# We can now cycle through the parameters and activate them in the recipe as
# variables
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)
def main():
"""
This will run by default when the file is executed using
"python file.py" in the command line
Parameters
----------
None
Returns
----------
None
"""
# Make some folders to store our output files.
resdir = "res"
fitdir = "fit"
figdir = "fig"
folders = [resdir, fitdir, figdir]
# Let's define our working directory.
base_dir = Path()
yaml_file = base_dir / (FIT_ID_BASE + "refined_params.yml")
# This is a bit different than what we've done before.
# We are going to look at a set of temperatures, so we want
# to find all the relevant data files in the "DPATH" folder
# we identified earlier which match a certain pattern.
# To do this we will use list comprehension
# For every file we find, we will link it up with the data path
data_files = list(DPATH.glob(f"*{GR_NAME_BASE}*.gr"))
# We now want to grab the temperature at which each file was measured.
# We again use list comprehension, and we re-use the variable "temp"
# This specific procedure depends on how the file is named.
# In our case, we carefully named each file as:
# "composition_TTTK.gr" where TTT is the temperature in Kelvin.
# First we strip off the directory information for every file in "data_files"
# This gives us a list of just full file names, without directories.
temps = [f.stem for f in data_files]
# Next we split every base filename into a list, delimited by "_"
# We keep the second entry from this list, because we know it has the
# temperature in the form TTTK.
temps = [t.split('_')[1] for t in temps]
# We want the temperature as an integer, so we need to drop the "K"
# from each string and cast the values as integers using "int()"
# Strings can be slides like arrays, so "[:-1]" means "take all the
# values except the last one."
temps = [int(t[:-1]) for t in temps]
# This will sort the data files and temperatures in descending order
# based on the temperature values.
temps, data_files = zip(*sorted(zip(temps, data_files), reverse=True))
# We want to test two structure models, so we should find both the cif files.
# Similar to how we found the data files, we use list comprehension
# For every file we find, we will link it up with the data path
cif_files = list(DPATH.glob(f"*{CIF_NAME_BASE}*.cif"))
# We initialize and empty dictionary, where we will save all
# the details of the refined parameters.
if yaml_file.exists():
print(f"\n{yaml_file.name} exists, loading!\n")
with open(yaml_file, 'r') as infile:
refined_dict = yaml.safe_load(infile)
else:
print(f"\n{yaml_file.name} does not exist, creating!\n")
refined_dict = dict()
# We want to do a separate temperature series on each of the structures,
# so we will use a loop on all the cif files we found.
for cif in cif_files:
# Let's get the space group, so we can refer to it later.
p_cif = getParser('cif')
p_cif.parseFile(str(cif))
space_group = p_cif.spacegroup.short_name
# Backslashes are bad form, so let's remove them...
structure_string = space_group.replace("/", "_on_")
# Lets check if we already ran this fit...so we dont duplicate work
if structure_string not in refined_dict:
print(f"\n{structure_string} IS NOT in dictionary!\n")
# Nest a dictionary inside "refined_dict" with a key defined by "structure_string"
refined_dict[structure_string] = dict()
# This is just for ease of coding/readability
sg_dict = refined_dict[structure_string]
done = False
elif structure_string in refined_dict:
print(f"\n{structure_string} IS IN dictionary!\n")
sg_dict = refined_dict[structure_string]
done = True
# Where will we work? here!
work_dir = base_dir / structure_string
# Make our folders!
for folder in folders:
new_folder = work_dir / folder
if not new_folder.exists():
new_folder.mkdir(parents=True)
# Make a recipe based on this cif file, and the first data file in
# "data_files"
# We can pass in any data file at this point, this is only to initialize
# the recipe, and we will replace this data before refining.
recipe = makerecipe(cif, data_files[0])
# Let's set the calculation range!
recipe.crystal.profile.setCalculationRange(xmin=PDF_RMIN,
xmax=PDF_RMAX,
dx=PDF_RSTEP)
# Initialize the instrument parameters, Q_damp and Q_broad, and
# assign Q_max and Q_min.
recipe.crystal.G1.qdamp.value = QDAMP_I
recipe.crystal.G1.qbroad.value = QBROAD_I
recipe.crystal.G1.setQmax(QMAX)
recipe.crystal.G1.setQmin(QMIN)
# Add, initialize, and tag the scale variable.
recipe.addVar(recipe.crystal.s1, SCALE_I, tag="scale")
# Use the srfit function constrainAsSpaceGroup to constrain
# the lattice and ADP parameters according to the space group setting,
# in this case, contained in the function argument "sg"
#
from diffpy.srfit.structure import constrainAsSpaceGroup
spacegroupparams = constrainAsSpaceGroup(recipe.crystal.G1.phase,
space_group)
for par in spacegroupparams.latpars:
recipe.addVar(par, fixed=False, tag="lat")
for par in spacegroupparams.adppars:
recipe.addVar(par, fixed=False, tag="adp")
# Note: here we also can refine atomic coordinates.
# In our previous examples, all the atoms were on symmetry
# operators, so their positions could not be refined.
for par in spacegroupparams.xyzpars:
recipe.addVar(par, fixed=False, tag="xyz")
# Add delta, but not instrumental parameters to Fit Recipe.
recipe.addVar(recipe.crystal.G1.delta2,
name="Delta2",
value=DELTA1_I,
tag="d2")
# Tell the Fit Recipe we want to write the maximum amount of
# information to the terminal during fitting.
recipe.fithooks[0].verbose = 0
# As we are doing a temperature series through a phase transition, we want to fit many
# different data sets, each at a different temperature.
# for this we will loop over both "temps" and "data_files"
for file, temp in zip(data_files, temps):
print(f"\nProcessing {file.name}!\n")
if temp not in sg_dict:
print(
f"\nT = {temp} K NOT IN {structure_string} dictionary, creating!\n"
)
# Nest a dictionary inside the nested dictionary "refined_dict[stru_type]"
# with a key defined by "temp"
sg_dict[temp] = dict()
temp_dict = sg_dict[temp]
done = False
elif temp in sg_dict:
print(f"\nT = {temp} K IS IN {structure_string} dictionary!\n")
temp_dict = sg_dict[temp]
done = True
# We create a unique string to identify our fit,
# using the structure type and the temperature.
# This will be used when we write files later.
basename = f"{FIT_ID_BASE}{structure_string}_{str(temp)}_K"
# Print the basename to the terminal.
print(f"\nWorking on {basename}!\n")
# We now want to load in the proper dataset for the given temperature.
# We do this by creating a new profile object and loading it into the fit recipe.
profile = Profile()
parser = PDFParser()
parser.parseFile(file)
profile.loadParsedData(parser)
recipe.crystal.setProfile(profile)
if not done:
print(
f"{basename} is NOT DONE with structure {structure_string} at T = {temp} K\n"
)
# We are now ready to start the refinement.
# During the optimization, fix and free parameters as you would
# PDFgui. This leads to more stability in the refinement
recipe.fix("all")
refine_params = ["scale", "lat", "adp", "d2", "all"]
for params in refine_params:
recipe.free(params)
print(f"\n****\nFitting {recipe.getNames()} against "
f"{file.name} with {cif.name}\n")
least_squares(recipe.residual,
recipe.values,
x_scale="jac")
elif done:
print(
f"{basename} IS done with structure {structure_string} at T = {temp} K\n"
)
recipe.free("all")
print("\nLoading parameters...\n")
for var in recipe.getNames():
if var not in temp_dict:
print(
f"{var} is not in the dictionary!! Let's try to fix it..."
)
recipe.fix("all")
recipe.free(var)
print(f"\nFitting {recipe.getNames()}\n")
least_squares(recipe.residual,
recipe.values,
x_scale="jac")
recipe.free("all")
elif var in temp_dict:
var_dict = temp_dict[var]
val = var_dict["value"]
recipe.get(var).setValue(val)
if not SKIP_DONE:
print("\nPolishing...\n")
recipe.free("all")
print(f"\nFitting {recipe.getNames()}\n")
least_squares(recipe.residual,
recipe.values,
x_scale="jac")
print("\nPolishing done\n")
if (done and not SKIP_DONE) or not done or REPLOT_DONE:
print(f"\nStarting to write results for {basename}"
f" with structure {structure_string} at T = {temp} K\n")
# Print the fit results to the terminal.
res = FitResults(recipe)
print("\n******************\n")
res.printResults()
print("\n******************\n")
rw = res.rw
# Write the fitted data to a file.
profile = recipe.crystal.profile
profile.savetxt(work_dir / fitdir / (basename + ".fit"))
# Now, as we will use the optimized fit recipe in the next loop,
# we want to keep all the refined parameters for this part of the loop
# we do this by recording everything in the nested dictionaries we made earlier.
# We loop over the variable names, the variable values, and the variable uncertainties (esd)
for name, val, unc in zip(res.varnames, res.varvals,
res.varunc):
# We create a new nested dictionary based on each variable name
if name not in temp_dict:
temp_dict[name] = dict()
var_dict = temp_dict[name]
# We store the refined value for this variable using the "value" key.
# We use the ".item()" method because "res.varvals" exist as
# numpy.float64 objects, and we want them as regular python floats.
var_dict["value"] = val.item()
var_dict["uncert"] = unc.item()
# We also store the fit rw, for posterity.
temp_dict['rw'] = rw.item()
# Write the fit results to a file.
header = "crystal_HF.\n"
res.saveResults(work_dir / resdir / (basename + ".res"),
header=header)
# Write a plot of the fit to a (pdf) file.
plotresults(recipe, work_dir / figdir / basename)
# plt.ion()
# We now write this dictionary to a file for later use.
with open(yaml_file, 'w') as outfile:
yaml.safe_dump(refined_dict, outfile)
# We now write this dictionary to a file for later use.
with open(yaml_file, 'w') as outfile:
yaml.safe_dump(refined_dict, outfile)
