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+#!/usr/bin/env python
+########################################################################
+#
+# diffpy.srfit      by DANSE Diffraction group
+#                   Simon J. L. Billinge
+#                   (c) 2009 The Trustees of Columbia University
+#                   in the City of New York.  All rights reserved.
+#
+# File coded by:    Chris Farrow
+#
+# See AUTHORS.txt for a list of people who contributed.
+# See LICENSE_DANSE.txt for license information.
+#
+########################################################################
+
+"""Example of a PDF refinement using diffpy.structure and PDFGenerator.
+
+This is example of fitting the fcc nickel structure to measured PDF data. The
+purpose of this example is to demonstrate and describe the classes in
+configuraiton options involved with setting up a fit in this way. The main
+benefit of using SrFit for PDF refinement is the flexibility of modifying the
+PDF profile function for specific needs, adding restraints to a fit and the
+ability to simultaneously refine a structure to PDF data and data from other
+sources. This example demonstrates only the basic configuration.
+
+"""
+
+import numpy
+
+from diffpy.structure import Structure
+from diffpy.srfit.pdf import PDFGenerator, PDFParser
+from diffpy.srfit.fitbase import Profile
+from diffpy.srfit.fitbase import FitContribution, FitRecipe
+from diffpy.srfit.fitbase import FitResults
+
+from gaussianrecipe import scipyOptimize
+
+####### Example Code
+
+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
+
+def plotResults(recipe):
+    """Plot the results contained within a refined FitRecipe."""
+
+    # All this should be pretty familiar by now.
+    r = recipe.nickel.profile.x
+    g = recipe.nickel.profile.y
+    gcalc = recipe.nickel.profile.ycalc
+    diffzero = -0.8 * max(g) * numpy.ones_like(g)
+    diff = g - gcalc + diffzero
+
+    import pylab
+    pylab.plot(r,g,'bo',label="G(r) Data")
+    pylab.plot(r, gcalc,'r-',label="G(r) Fit")
+    pylab.plot(r,diff,'g-',label="G(r) diff")
+    pylab.plot(r,diffzero,'k-')
+    pylab.xlabel(r"$r (\AA)$")
+    pylab.ylabel(r"$G (\AA^{-2})$")
+    pylab.legend(loc=1)
+
+    pylab.show()
+    return
+
+
+def fit_wrapper(recipe):
+    """helper function"""
+    from scipy.optimize.minpack import leastsq
+    leastsq(recipe.residual, recipe.getValues())
+    return recipe
+
+if __name__ == "__main__":
+
+    # Make the data and the recipe
+    ciffile = "data/ni.cif"
+    data = "data/ni-q27r100-neutron.gr"
+
+    # sanity check
+    print("==== Rw before refinements ====")
+    recipe = makeRecipe(ciffile, data)
+    recipe.clearFitHooks()
+    res = FitResults(recipe)
+    print(res.rw)
+
+    # Make the recipe
+    recipe_list = []
+    for i in range(5):
+        recipe = makeRecipe(ciffile, data)
+        recipe.clearFitHooks()
+        recipe_list.append(recipe)
+
+    # Optimize
+    print("==== Rw: Sequencial refinements ====")
+    for recipe in recipe_list:
+        recipe = fit_wrapper(recipe)
+        res = FitResults(recipe)
+        print(res.rw)
+
+    # Make the recipe
+    recipe_list = []
+    for i in range(5):
+        recipe = makeRecipe(ciffile, data)
+        recipe.clearFitHooks()
+        recipe_list.append(recipe)
+
+    # Optimize
+    print("==== Rw: Parallel refinements ====")
+    import multiprocessing as mp
+    n_process = 4
+    with mp.Pool(n_process) as p:
+        rv = p.map(fit_wrapper, recipe_list)
+    for recipe in rv:
+        res = FitResults(recipe)
+        print(res.rw)