Python FitContribution.addProfileGenerator方法代码示例

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(datname):
    """Create a fitting recipe for ellipsoidal SAS data."""

    ## The Profile
    # This will be used to store the observed and calculated I(Q) data.
    profile = Profile()

    # Load data and add it to the Profile. We use a SASParser to load the data
    # properly and pass the metadata along.
    parser = SASParser()
    parser.parseFile(datname)
    profile.loadParsedData(parser)

    ## The ProfileGenerator
    # The SASGenerator is for configuring and calculating a SAS profile. We use
    # a sans model to configure and serve as the calculation engine of the
    # generator. This allows us to use the full sans model creation
    # capabilities, and tie this into SrFit when we want to fit a model to
    # data. The documentation for the various sans models can be found at
    # http://danse.chem.utk.edu/sansview.html.
    from sans.models.EllipsoidModel import EllipsoidModel
    model = EllipsoidModel()
    generator = SASGenerator("generator", model)

    ## The FitContribution
    # Here we associate the Profile and ProfileGenerator, as has been done
    # before. 
    contribution = FitContribution("ellipsoid")
    contribution.addProfileGenerator(generator)
    contribution.setProfile(profile, xname = "q")

    # We want to fit the log of the signal to the log of the data so that the
    # higher-Q information remains significant. There are no I(Q) uncertainty
    # values with the data, so we do not need to worry about the effect this
    # will have on the estimated parameter uncertainties.
    contribution.setResidualEquation("log(eq) - log(y)")

    ## Make the FitRecipe and add the FitContribution.
    recipe = FitRecipe()
    recipe.addContribution(contribution)

    ## Configure the fit variables
    # The SASGenerator uses the parameters from the params and dispersion
    # attribues of the model. These vary from model to model, but are adopted
    # as SrFit Parameters within the generator. Whereas the dispersion
    # parameters are accessible as, e.g. "radius.width", within the
    # SASGenerator these are named like "radius_width".
    #
    # We want to fit the scale factor, radii and background factors.
    recipe.addVar(generator.scale, 1)
    recipe.addVar(generator.radius_a, 50)
    recipe.addVar(generator.radius_b, 500)
    recipe.addVar(generator.background, 0)
    
    # Give the recipe away so it can be used!
    return recipe

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(molecule, datname):
    """Create a recipe that uses the DebyePDFGenerator."""

    ## The Profile
    profile = Profile()

    # Load data and add it to the profile
    profile.loadtxt(datname)
    profile.setCalculationRange(xmin=1.2, xmax=8)

    ## The ProfileGenerator
    # Create a DebyePDFGenerator named "G".
    generator = DebyePDFGenerator("G")
    generator.setStructure(molecule)
    # These are metadata needed by the generator
    generator.setQmin(0.68)
    generator.setQmax(22)

    ## The FitContribution
    contribution = FitContribution("bucky")
    contribution.addProfileGenerator(generator)
    contribution.setProfile(profile, xname = "r")

    # Make a FitRecipe.
    recipe = FitRecipe()
    recipe.addContribution(contribution)

    # Specify which parameters we want to refine.
    c60 = generator.phase

    # We're not going to refine the ADPs. However, every atom must have a
    # small, but finite ADP for the PDF calculator to work properly.
    atoms = c60.getScatterers()
    for atom in atoms:
        atom.Uiso.value = 0.001

    # Scale factor. We cannot optimize this efficiently, so we take the value
    # from a previous refinement. In general, care must be taken to properly
    # determine the scale of the profile, or to make sure that the residual is
    # not affected by the scale.
    generator.scale.value = 1.24457360e+4

    # Allow every atom to move.  We define the bounds to be a window of radius
    # 0.1 centered on the current value of the position.
    win = 0.1
    for idx, atom in enumerate(atoms):
        xname, yname, zname = getXYZNames(idx)
        recipe.addVar(atom.x, name = xname).boundWindow(win)
        recipe.addVar(atom.y, name = yname).boundWindow(win)
        recipe.addVar(atom.z, name = zname).boundWindow(win)

    return recipe

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe():
    """Create a recipe that uses the GaussianGenerator.

    This will create a FitContribution that uses the GaussianGenerator,
    associate this with a Profile, and use this to define a FitRecipe.

    """

    ## The Profile
    # Create a Profile to hold the experimental and calculated signal.
    profile = Profile()

    # Load data and add it to the profile. This uses the loadtxt function from
    # numpy.
    profile.loadtxt("data/gaussian.dat")

    ## The ProfileGenerator
    # Create a GaussianGenerator named "g". This will be the name we use to
    # refer to the generator from within the FitContribution equation.  
    generator = GaussianGenerator("g")
    
    ## The FitContribution
    # Create a FitContribution that will associate the Profile with the
    # GaussianGenerator.  The GaussianGenerator will be accessible as an
    # attribute of the FitContribution by its name ("g"). Note that this will
    # set the fitting equation to "g", which calls the GaussianGenerator.
    contribution = FitContribution("g1")
    contribution.addProfileGenerator(generator)
    contribution.setProfile(profile)

    ## The FitRecipe
    # Now we create the FitRecipe and add the FitContribution.
    recipe = FitRecipe()
    recipe.addContribution(contribution)

    # Specify which Parameters we want to vary in the fit.  This will add
    # Variables to the FitRecipe that directly modify the Parameters of the
    # FitContribution.
    # 
    # We create a variable for each Parameter of the GaussianGenerator. Note
    # that the Parameters belong to the GaussianGenerator, not the
    # FitContribution as in gaussianrecipe.py. We initialize parameters as in
    # gaussianrecipe.py so we can expect the same output.
    recipe.addVar(generator.A, 1)
    recipe.addVar(generator.x0, 5)
    recipe.addVar(generator.sigma, name = "sig")
    recipe.sig.value = 1

    # Give the recipe away so it can be used!
    return recipe

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(ciffile, grdata):
    """Make a recipe to model a crystal-like nanoparticle PDF."""

    # Set up a PDF fit as has been done in other examples.
    pdfprofile = Profile()

    pdfparser = PDFParser()
    pdfparser.parseFile(grdata)
    pdfprofile.loadParsedData(pdfparser)
    pdfprofile.setCalculationRange(xmin = 0.1, xmax = 20)

    pdfcontribution = FitContribution("pdf")
    pdfcontribution.setProfile(pdfprofile, xname = "r")

    pdfgenerator = PDFGenerator("G")
    pdfgenerator.setQmax(30.0)
    stru = CreateCrystalFromCIF(file(ciffile))
    pdfgenerator.setStructure(stru)
    pdfcontribution.addProfileGenerator(pdfgenerator)

    # Register the nanoparticle shape factor.
    from diffpy.srfit.pdf.characteristicfunctions import sphericalCF
    pdfcontribution.registerFunction(sphericalCF, name = "f")

    # Now we set up the fitting equation.
    pdfcontribution.setEquation("f * G")

    # Now make the recipe. Make sure we fit the characteristic function shape
    # parameters, in this case 'psize', which is the diameter of the particle.
    recipe = FitRecipe()
    recipe.addContribution(pdfcontribution)

    phase = pdfgenerator.phase
    for par in phase.sgpars:
        recipe.addVar(par)

    recipe.addVar(pdfcontribution.psize, 20)
    recipe.addVar(pdfgenerator.scale, 1)
    recipe.addVar(pdfgenerator.delta2, 0)
    recipe.B11_0 = 0.1

    return recipe

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(strufile, datname):
    """Create a recipe that uses the IntensityGenerator.

    This will create a FitContribution that uses the IntensityGenerator,
    associate this with a Profile, and use this to define a FitRecipe.

    """

    ## The Profile
    # Create a Profile. This will hold the experimental and calculated signal.
    profile = Profile()

    # Load data and add it to the profile
    x, y, u = profile.loadtxt(datname)

    ## The ProfileGenerator
    # Create an IntensityGenerator named "I". This will be the name we use to
    # refer to the generator from within the FitContribution equation.  We also
    # need to load the model structure we're using.
    generator = IntensityGenerator("I")
    generator.setStructure(strufile)
    
    ## The FitContribution
    # Create a FitContribution, that will associate the Profile with the
    # ProfileGenerator.  The ProfileGenerator will be accessible as an
    # attribute of the FitContribution by its name ("I").  We also want to tell
    # the FitContribution to name the x-variable of the profile "q", so we can
    # use it in equations with this name.
    contribution = FitContribution("bucky")
    contribution.addProfileGenerator(generator)
    contribution.setProfile(profile, xname = "q")

    # Now we're ready to define the fitting equation for the FitContribution.
    # We need to modify the intensity calculation, and we'll do that from
    # within the fitting equation for the sake of instruction. We want to
    # modify the calculation in three ways.  We want to scale it, add a
    # polynomial background, and broaden the peaks. 
    #
    # There is added benefit for defining these operations outside of the
    # IntensityGenerator. By combining the different parts of the calculation
    # within the fitting equation, the time-consuming iofq calculation is only
    # performed when a structural Parameter is changed. If only non-structural
    # parameters are changed, such as the background and broadening Parameters,
    # then then previously computed iofq value will be used to compute the
    # contribution equation.  The benefit in this is very apparent when
    # refining the recipe with the LM optimizer, which only changes two
    # variables at a time most of the time. Note in the refinement output how
    # many times the residual is calculated, versus how many times iofq is
    # called when using the scipyOptimize function.

    # We will define the background as a string.

    bkgdstr = "b0 + b1*q + b2*q**2 + b3*q**3 + b4*q**4 + b5*q**5 + b6*q**6 +\
               b7*q**7 + b8*q**8 + b9*q**9"

    # This creates a callable equation named "bkgd" within the FitContribution,
    # and turns the polynomial coefficients into Parameters.
    eq = contribution.registerStringFunction(bkgdstr, "bkgd")

    # We will create the broadening function that we need by creating a python
    # function and registering it with the FitContribution.
    pi = numpy.pi
    exp = numpy.exp
    def gaussian(q, q0, width):
        return 1/(2*pi*width**2)**0.5 * exp(-0.5 * ((q-q0)/width)**2)

    # This registers the python function and extracts the name and creates
    # Parameters from the arguments.
    contribution.registerFunction(gaussian)

    # Center the Gaussian so it is not truncated.
    contribution.q0.value = x[len(x)/2]

    # Now we can incorporate the scale and bkgd into our calculation. We also
    # convolve the signal with the Gaussian to broaden it. Recall that we don't
    # need to supply arguments to the registered functions unless we want to
    # make changes to their input values.
    contribution.setEquation("scale * convolve(I, gaussian) + bkgd")

    # Make the FitRecipe and add the FitContribution.
    recipe = FitRecipe()
    recipe.addContribution(contribution)

    # Specify which parameters we want to refine.
    recipe.addVar(contribution.b0, 0)
    recipe.addVar(contribution.b1, 0)
    recipe.addVar(contribution.b2, 0)
    recipe.addVar(contribution.b3, 0)
    recipe.addVar(contribution.b4, 0)
    recipe.addVar(contribution.b5, 0)
    recipe.addVar(contribution.b6, 0)
    recipe.addVar(contribution.b7, 0)
    recipe.addVar(contribution.b8, 0)
    recipe.addVar(contribution.b9, 0)

    # We also want to adjust the scale and the convolution width
    recipe.addVar(contribution.scale, 1)
    recipe.addVar(contribution.width, 0.1)

    # We can also refine structural parameters. Here we extract the
#.........这里部分代码省略.........

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(niciffile, siciffile, datname):
    """Create a fitting recipe for crystalline PDF data."""

    ## The Profile
    profile = Profile()

    # Load data and add it to the profile
    parser = PDFParser()
    parser.parseFile(datname)
    profile.loadParsedData(parser)
    profile.setCalculationRange(xmax = 20)

    ## The ProfileGenerator
    # In order to fit two phases simultaneously, we must use two PDFGenerators.
    # PDFGenerator is designed to take care of as little information as it
    # must. (Don't do too much, and do it well.) A PDFGenerator can generate
    # the signal from only a single phase at a time. So, we will create one
    # PDFGenerator for each phase and compose them within the same
    # FitContribution. Note that both generators will be associated with the
    # same Profile within the FitContribution, so they will both be
    # automatically configured according to the metadata.
    #
    # The generator for the nickel phase. We call it "G_ni" and will use this
    # name later when we set the fitting equation in the FitContribution.
    generator_ni = PDFGenerator("G_ni")
    stru = CreateCrystalFromCIF(file(niciffile))
    generator_ni.setStructure(stru)
    # The generator for the silicon phase. We call it "G_si".
    generator_si = PDFGenerator("G_si")
    stru = CreateCrystalFromCIF(file(siciffile))
    generator_si.setStructure(stru)

    ## The FitContribution
    # Add both generators to the FitContribution. Add the Profile. This will
    # send the metadata to the generators.
    contribution = FitContribution("nisi")
    contribution.addProfileGenerator(generator_ni)
    contribution.addProfileGenerator(generator_si)
    contribution.setProfile(profile, xname = "r")

    # Write the fitting equation. We want to sum the PDFs from each phase and
    # multiply it by a scaling factor. We also want a certain phase scaling
    # relationship between the PDFs which we will enforce with constraints in
    # the FitRecipe.
    contribution.setEquation("scale * (G_ni +  G_si)")

    # Make the FitRecipe and add the FitContribution.
    recipe = FitRecipe()
    recipe.addContribution(contribution)

    ## Configure the fit variables
    # Start by configuring the scale factor and resolution factors.
    # We want the sum of the phase scale factors to be 1.
    recipe.newVar("scale_ni", 0.1)
    recipe.constrain(generator_ni.scale, "scale_ni")
    recipe.constrain(generator_si.scale, "1 - scale_ni")
    # We also want the resolution factor to be the same on each.
    recipe.newVar("qdamp", 0.03)
    recipe.constrain(generator_ni.qdamp, "qdamp")
    recipe.constrain(generator_si.qdamp, "qdamp")

    # Vary the gloabal scale as well.
    recipe.addVar(contribution.scale, 1)

    # Now we can configure the structural parameters. Since we're using
    # ObjCrystCrystalParSets, the space group constraints are automatically
    # applied to each phase. We must selectively vary the free parameters.
    #
    # First the nickel parameters
    phase_ni = generator_ni.phase
    for par in phase_ni.sgpars:
        recipe.addVar(par, name = par.name + "_ni")
    recipe.addVar(generator_ni.delta2, name = "delta2_ni")
    # Next the silicon parameters
    phase_si = generator_si.phase
    for par in phase_si.sgpars:
        recipe.addVar(par, name = par.name + "_si")
    recipe.addVar(generator_si.delta2, name = "delta2_si")

    # We have prior information from the earlier examples so we'll use it here
    # in the form of restraints.
    #
    # The nickel lattice parameter was measured to be 3.527. The uncertainty
    # values are invalid for that measurement, since the data from which it is
    # derived has no uncertainty. Thus, we will tell the recipe to scale the
    # residual, which means that it will be weighted as much as the average
    # data point during the fit.
    recipe.restrain("a_ni", lb = 3.527, ub = 3.527, scaled = True)
    # Now we do the same with the delta2 and Biso parameters (remember that
    # Biso = 8*pi**2*Uiso)
    recipe.restrain("delta2_ni", lb = 2.22, ub = 2.22, scaled = True)
    recipe.restrain("Biso_0_ni", lb = 0.454, ub = 0.454, scaled = True)
    #
    # We can do the same with the silicon values. We haven't done a thorough
    # job of measuring the uncertainties in the results, so we'll scale these
    # as well.
    recipe.restrain("a_si", lb = 5.430, ub = 5.430, scaled = True)
    recipe.restrain("delta2_si", lb = 3.54, ub = 3.54, scaled = True)
    recipe.restrain("Biso_0_si", lb = 0.645, ub = 0.645, scaled = True)

#.........这里部分代码省略.........

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
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. As before we use a PDFParser. The
    # metadata is still passed to the PDFGenerator later on. The interaction
    # between the PDFGenerator and the metadata does not depend on type of
    # structure being refined.
    parser = PDFParser()
    parser.parseFile(datname)
    profile.loadParsedData(parser)
    profile.setCalculationRange(xmax = 20)

    ## The ProfileGenerator
    # This time we use the CreateCrystalFromCIF method of pyobjcryst.crystal to
    # create a Crystal object. That object is passed to the PDFGenerator as in
    # the previous example.
    generator = PDFGenerator("G")
    stru = CreateCrystalFromCIF(file(ciffile))
    generator.setStructure(stru)
    generator.setQmax(40.0)
    
    ## The FitContribution
    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

    # As before, we get a handle to the structure parameter set. In this case,
    # it is a ObjCrystCrystalParSet instance that was created when we called
    # 'setStructure' above. The ObjCrystCrystalParSet has different Parameters
    # and options than the DiffpyStructureParSet used in the last example. See
    # its documentation in diffpy.srfit.structure.objcrystparset.
    phase = generator.phase

    # Here is where we created space group constraints in the previous example.
    # The difference in this example is that the ObjCrystCrystalParSet is aware
    # of space groups, and the DiffpyStructureParSet is not. Constraints are
    # created internally when "sgpars" attribute is called for. These
    # constriants get enforced within the ObjCrystCrystalParSet. Free
    # Parameters are stored within the 'sgpars' member of the
    # ObjCrystCrystalParSet, which is the same as the object returned from
    # 'constrainAsSpaceGroup'.
    #
    # As before, we have one free lattice parameter ('a'). We can simplify
    # things by iterating through all the sgpars.
    for par in phase.sgpars: 
        recipe.addVar(par)
    # set the initial thermal factor to a non-zero value
    assert hasattr(recipe, 'B11_0')
    recipe.B11_0 = 0.1

    # 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

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(stru1, stru2, datname):
    """Create a fitting recipe for crystalline PDF data."""

    ## The Profile
    profile = Profile()

    # Load data and add it to the profile
    parser = PDFParser()
    parser.parseFile(datname)
    profile.loadParsedData(parser)
    profile.setCalculationRange(xmin=1.5, xmax = 45, dx = 0.1)

    ## The ProfileGenerator
    # In order to fit the core and shell phases simultaneously, we must use two
    # PDFGenerators.
    #
    # The generator for the CdS core. We call it "G_CdS" and will use this name
    # later when we set the fitting equation in the FitContribution.
    generator_cds = PDFGenerator("G_CdS")
    generator_cds.setStructure(stru1)
    generator_cds.setQmax(26)
    generator_cds.qdamp.value = 0.0396
    # The generator for the ZnS shell. We call it "G_ZnS".
    generator_zns = PDFGenerator("G_ZnS")
    generator_zns.setStructure(stru2)
    generator_zns.setQmax(26)
    generator_zns.qdamp.value = 0.0396

    ## The FitContribution
    # Add both generators and the profile to the FitContribution.
    contribution = FitContribution("cdszns")
    contribution.addProfileGenerator(generator_cds)
    contribution.addProfileGenerator(generator_zns)
    contribution.setProfile(profile, xname = "r")

    # Set up the characteristic functions. We use a spherical CF for the core
    # and a spherical shell CF for the shell. Since this is set up as two
    # phases, we implicitly assume that the core-shell correlations contribute
    # very little to the PDF.
    from diffpy.srfit.pdf.characteristicfunctions import sphericalCF, shellCF
    contribution.registerFunction(sphericalCF, name = "f_CdS")
    contribution.registerFunction(shellCF, name = "f_ZnS")

    # Write the fitting equation. We want to sum the PDFs from each phase and
    # multiply it by a scaling factor.
    contribution.setEquation("scale * (f_CdS * G_CdS +  f_ZnS * G_ZnS)")

    # Make the FitRecipe and add the FitContribution.
    recipe = FitRecipe()
    recipe.addContribution(contribution)

    # Vary the inner radius and thickness of the shell. Constrain the core
    # diameter to twice the shell radius.
    recipe.addVar(contribution.radius, 15)
    recipe.addVar(contribution.thickness, 11)
    recipe.constrain(contribution.psize, "2 * radius")

    ## Configure the fit variables
    # Start by configuring the scale factor and resolution factors.
    # We want the sum of the phase scale factors to be 1.
    recipe.newVar("scale_CdS", 0.7)
    recipe.constrain(generator_cds.scale, "scale_CdS")
    recipe.constrain(generator_zns.scale, "1 - scale_CdS")
    # We also want the resolution factor to be the same on each.

    # Vary the gloabal scale as well.
    recipe.addVar(contribution.scale, 0.3)

    # Now we can configure the structural parameters. We tag the different
    # structural variables so we can easily turn them on and off in the
    # subsequent refinement.
    phase_cds = generator_cds.phase
    for par in phase_cds.sgpars.latpars:
        recipe.addVar(par, name = par.name + "_cds", tag = "lat")
    for par in phase_cds.sgpars.adppars:
        recipe.addVar(par, 1, name = par.name + "_cds", tag = "adp")
    recipe.addVar(phase_cds.sgpars.xyzpars.z_1, name = "z_1_cds", tag = "xyz")
    # Since we know these have stacking disorder, constrain the B33 adps for
    # each atom type.
    recipe.constrain("B33_1_cds", "B33_0_cds")
    recipe.addVar(generator_cds.delta2, name = "delta2_cds", value = 5)

    phase_zns = generator_zns.phase
    for par in phase_zns.sgpars.latpars:
        recipe.addVar(par, name = par.name + "_zns", tag = "lat")
    for par in phase_zns.sgpars.adppars:
        recipe.addVar(par, 1, name = par.name + "_zns", tag = "adp")
    recipe.addVar(phase_zns.sgpars.xyzpars.z_1, name = "z_1_zns", tag = "xyz")
    recipe.constrain("B33_1_zns", "B33_0_zns")
    recipe.addVar(generator_zns.delta2, name = "delta2_zns", value = 2.5)

    # Give the recipe away so it can be used!
    return recipe

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeContribution(name, generator, profile):
    """Make a FitContribution and add a generator and profile."""
    contribution = FitContribution(name)
    contribution.addProfileGenerator(generator)
    contribution.setProfile(profile, xname = "r")
    return contribution

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(strufile, datname1, datname2):
    """Create a recipe that uses the IntensityGenerator.

    We will create two FitContributions that use the IntensityGenerator from
    npintensitygenerator.py and associate each of these with a Profile, and use
    this to define a FitRecipe.

    Both simulated data sets come from the same structure. We're going to make
    two FitContributions that are identical, except for the profile that is
    held in each. We're going to assure that the structures are identical by
    using the same DiffpyStructureParSet (which is generated by the
    IntensityGenerator when we load the structure) in both generators.

    """

    ## The Profiles
    # Create two Profiles for the two FitContributions.
    profile1 = Profile()
    profile2 = Profile()

    # Load data into the Profiles
    profile1.loadtxt(datname1)
    x, y, u = profile2.loadtxt(datname2)

    ## The ProfileGenerators
    # Create two IntensityGenerators named "I". There will not be a name
    # conflict, since the name is only meaningful within the FitContribution
    # that holds the ProfileGenerator.  Load the structure into one and make
    # sure that the second ProfileGenerator is using the same
    # DiffyStructureParSet.  This will assure that both ProfileGenerators are
    # using the exact same Parameters, and underlying Structure object in the
    # calculation of the profile.
    generator1 = IntensityGenerator("I")
    generator1.setStructure(strufile)
    generator2 = IntensityGenerator("I")
    generator2.addParameterSet(generator1.phase)

    ## The FitContributions
    # Create the FitContributions.
    contribution1 = FitContribution("bucky1")
    contribution1.addProfileGenerator(generator1)
    contribution1.setProfile(profile1, xname = "q")
    contribution2 = FitContribution("bucky2")
    contribution2.addProfileGenerator(generator2)
    contribution2.setProfile(profile2, xname = "q")

    # Now we're ready to define the fitting equation for each FitContribution.
    # The functions registered below will be independent, even though they take
    # the same form and use the same Parameter names.  By default, Parameters
    # in different contributions are different Parameters even if they have the
    # same names.  FitContributions are isolated namespaces than only share
    # information if you tell them to by using addParameter or addParameterSet.
    bkgdstr = "b0 + b1*q + b2*q**2 + b3*q**3 + b4*q**4 + b5*q**5 + b6*q**6 +\
               b7*q**7 +b8*q**8 + b9*q**9"

    contribution1.registerStringFunction(bkgdstr, "bkgd")
    contribution2.registerStringFunction(bkgdstr, "bkgd")

    # We will create the broadening function by registering a python function.
    pi = numpy.pi
    exp = numpy.exp
    def gaussian(q, q0, width):
        return 1/(2*pi*width**2)**0.5 * exp(-0.5 * ((q-q0)/width)**2)

    contribution1.registerFunction(gaussian)
    contribution2.registerFunction(gaussian)
    # Center the gaussian
    contribution1.q0.value = x[len(x) // 2]
    contribution2.q0.value = x[len(x) // 2]

    # Now we can incorporate the scale and bkgd into our calculation. We also
    # convolve the signal with the gaussian to broaden it.
    contribution1.setEquation("scale * convolve(I, gaussian) + bkgd")
    contribution2.setEquation("scale * convolve(I, gaussian) + bkgd")

    # Make a FitRecipe and associate the FitContributions.
    recipe = FitRecipe()
    recipe.addContribution(contribution1)
    recipe.addContribution(contribution2)

    # Specify which Parameters we want to refine. We want to refine the
    # background that we just defined in the FitContributions. We have to do
    # this separately for each FitContribution. We tag the variables so it is
    # easy to retrieve the background variables.
    recipe.addVar(contribution1.b0, 0, name = "b1_0", tag = "bcoeffs1")
    recipe.addVar(contribution1.b1, 0, name = "b1_1", tag = "bcoeffs1")
    recipe.addVar(contribution1.b2, 0, name = "b1_2", tag = "bcoeffs1")
    recipe.addVar(contribution1.b3, 0, name = "b1_3", tag = "bcoeffs1")
    recipe.addVar(contribution1.b4, 0, name = "b1_4", tag = "bcoeffs1")
    recipe.addVar(contribution1.b5, 0, name = "b1_5", tag = "bcoeffs1")
    recipe.addVar(contribution1.b6, 0, name = "b1_6", tag = "bcoeffs1")
    recipe.addVar(contribution1.b7, 0, name = "b1_7", tag = "bcoeffs1")
    recipe.addVar(contribution1.b8, 0, name = "b1_8", tag = "bcoeffs1")
    recipe.addVar(contribution1.b9, 0, name = "b1_9", tag = "bcoeffs1")
    recipe.addVar(contribution2.b0, 0, name = "b2_0", tag = "bcoeffs2")
    recipe.addVar(contribution2.b1, 0, name = "b2_1", tag = "bcoeffs2")
    recipe.addVar(contribution2.b2, 0, name = "b2_2", tag = "bcoeffs2")
    recipe.addVar(contribution2.b3, 0, name = "b2_3", tag = "bcoeffs2")
    recipe.addVar(contribution2.b4, 0, name = "b2_4", tag = "bcoeffs2")
    recipe.addVar(contribution2.b5, 0, name = "b2_5", tag = "bcoeffs2")
#.........这里部分代码省略.........

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(molecule, datname):
    """Create a recipe that uses the DebyePDFGenerator."""

    ## The Profile
    profile = Profile()

    # Load data and add it to the profile
    profile.loadtxt(datname)
    profile.setCalculationRange(xmin=1.2, xmax=8)

    ## The ProfileGenerator
    # Create a DebyePDFGenerator named "G".
    generator = DebyePDFGenerator("G")
    generator.setStructure(molecule)
    # These are metadata needed by the generator
    generator.setQmin(0.68)
    generator.setQmax(22)

    ## The FitContribution
    contribution = FitContribution("bucky")
    contribution.addProfileGenerator(generator)
    contribution.setProfile(profile, xname = "r")

    # Make a FitRecipe.
    recipe = FitRecipe()
    recipe.addContribution(contribution)

    # Specify which parameters we want to refine. We'll be using the
    # MoleculeParSet within the generator, so let's get a handle to it. See the
    # diffpy.srfit.structure.objcryststructure module for more information
    # about the MoleculeParSet hierarchy.
    c60 = generator.phase

    # First, the isotropic thermal displacement factor.
    Biso = recipe.newVar("Biso")
    for atom in c60.getScatterers():

        # We have defined a 'center' atom that is a dummy, which means that it
        # has no scattering power. It is only used as a reference point for
        # our bond length. We don't want to constrain it.
        if not atom.isDummy():
            recipe.constrain(atom.Biso, Biso)

    # We need to let the molecule expand. If we were modeling it as a crystal,
    # we could let the unit cell expand. For instruction purposes, we use a
    # Molecule to model C60, and molecules have different modeling options than
    # crystals. To make the molecule expand from a central point, we will
    # constrain the distance from each atom to a dummy center atom that was
    # created with the molecule, and allow that distance to vary. (We could
    # also let the nearest-neighbor bond lengths vary, but that would be much
    # more difficult to set up.)
    center = c60.center
    # Create a new Parameter that represents the radius of the molecule. Note
    # that we don't give it an initial value. Since the variable is being
    # directly constrained to further below, its initial value will be inferred
    # from the constraint.
    radius = recipe.newVar("radius")
    for i, atom in enumerate(c60.getScatterers()):

        if atom.isDummy():
            continue

        # This creates a Parameter that moves the second atom according to the
        # bond length. Note that each Parameter needs a unique name.
        par = c60.addBondLengthParameter("rad%i"%i, center, atom)
        recipe.constrain(par, radius)

    # Add the correlation term, scale. The scale is too short to effectively
    # determine qdamp.
    recipe.addVar(generator.delta2, 2)
    recipe.addVar(generator.scale, 1.3e4)

    # Give the recipe away so it can be used!
    return recipe

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
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

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(ciffile, grdata, iqdata):
    """Make complex-modeling recipe where I(q) and G(r) are fit
    simultaneously.

    The fit I(q) is fed into the calculation of G(r), which provides feedback
    for the fit parameters of both.
    
    """

    # Create a PDF contribution as before
    pdfprofile = Profile()
    pdfparser = PDFParser()
    pdfparser.parseFile(grdata)
    pdfprofile.loadParsedData(pdfparser)
    pdfprofile.setCalculationRange(xmin = 0.1, xmax = 20)

    pdfcontribution = FitContribution("pdf")
    pdfcontribution.setProfile(pdfprofile, xname = "r")

    pdfgenerator = PDFGenerator("G")
    pdfgenerator.setQmax(30.0)
    stru = CreateCrystalFromCIF(file(ciffile))
    pdfgenerator.setStructure(stru)
    pdfcontribution.addProfileGenerator(pdfgenerator)
    pdfcontribution.setResidualEquation("resv")

    # Create a SAS contribution as well. We assume the nanoparticle is roughly
    # elliptical.
    sasprofile = Profile()
    sasparser = SASParser()
    sasparser.parseFile(iqdata)
    sasprofile.loadParsedData(sasparser)

    sascontribution = FitContribution("sas")
    sascontribution.setProfile(sasprofile)

    from sans.models.EllipsoidModel import EllipsoidModel
    model = EllipsoidModel()
    sasgenerator = SASGenerator("generator", model)
    sascontribution.addProfileGenerator(sasgenerator)
    sascontribution.setResidualEquation("resv")

    # Now we set up a characteristic function calculator that depends on the
    # sas model.
    cfcalculator = SASCF("f", model)

    # Register the calculator with the pdf contribution and define the fitting
    # equation.
    pdfcontribution.registerCalculator(cfcalculator)
    # The PDF for a nanoscale crystalline is approximated by
    # Gnano = f * Gcryst
    pdfcontribution.setEquation("f * G")

    # Moving on
    recipe = FitRecipe()
    recipe.addContribution(pdfcontribution)
    recipe.addContribution(sascontribution)

    # PDF
    phase = pdfgenerator.phase
    for par in phase.sgpars:
        recipe.addVar(par)

    recipe.addVar(pdfgenerator.scale, 1)
    recipe.addVar(pdfgenerator.delta2, 0)

    # SAS
    recipe.addVar(sasgenerator.scale, 1, name = "iqscale")
    recipe.addVar(sasgenerator.radius_a, 10)
    recipe.addVar(sasgenerator.radius_b, 10)

    # Even though the cfcalculator and sasgenerator depend on the same sas
    # model, we must still constrain the cfcalculator Parameters so that it is
    # informed of changes in the refined parameters.
    recipe.constrain(cfcalculator.radius_a, "radius_a")
    recipe.constrain(cfcalculator.radius_b, "radius_b")

    return recipe

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
# Set up the mPDF calculator.

mc=mPDFcalculator(magstruc=mstr,rmin=rmin,rmax=rmax,
                  rstep=rstep, gaussPeakWidth=0.2)

def magpdf(parascale, ordscale):
    mc.paraScale = parascale
    mc.ordScale = ordscale
    mc.magstruc.makeAtoms()
    mc.magstruc.makeSpins()
    rv = mc.calc(both=True)[2]
    return rv

totpdf = FitContribution('totpdf')
totpdf.addProfileGenerator(nucpdf)
totpdf.setProfile(profile)

# Add mPDF to the FitContribution
totpdf.registerFunction(magpdf)
totpdf.setEquation("nucscale * nucpdf + magpdf(parascale, ordscale)")

# Make magnetic PDF depend on any changes to the atomic structure.
# 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()

# 需要导入模块: from diffpy.srfit.fitbase import FitContribution [as 别名]
# 或者: from diffpy.srfit.fitbase.FitContribution import addProfileGenerator [as 别名]
def makeRecipe(ciffile, xdatname, ndatname):
    """Create a fitting recipe for crystalline PDF data."""

    ## The Profiles
    # We need a profile for each data set. This means that we will need two
    # FitContributions as well.
    xprofile = Profile()
    nprofile = Profile()

    # Load data and add it to the proper Profile.
    parser = PDFParser()
    parser.parseFile(xdatname)
    xprofile.loadParsedData(parser)
    xprofile.setCalculationRange(xmax = 20)

    parser = PDFParser()
    parser.parseFile(ndatname)
    nprofile.loadParsedData(parser)
    nprofile.setCalculationRange(xmax = 20)

    ## The ProfileGenerators
    # We need one of these for the x-ray data.
    xgenerator = PDFGenerator("G")
    stru = CreateCrystalFromCIF(file(ciffile))
    xgenerator.setStructure(stru)

    # And we need one for the neutron data. We want to refine the same
    # structure object in each PDFGenerator. This would suggest that we add the
    # same Crystal to each. However, if we do that then we will have two
    # Parameters for each Crystal data member (two Parameters for the "a"
    # lattice parameter, etc.), held in different ObjCrystCrystalParSets, each
    # managed by its own PDFGenerator. Thus, changes made to the Crystal
    # through one PDFGenerator will not be known to the other PDFGenerator
    # since their ObjCrystCrystalParSets don't know about each other. The
    # solution is to share ObjCrystCrystalParSets rather than Crystals. This
    # way there is only one Parameter for each Crystal data member. (An
    # alternative to this is to constrain each structure Parameter to be varied
    # to the same variable. The present approach is easier and less error
    # prone.)
    #
    # Tell the neutron PDFGenerator to use the phase from the x-ray
    # PDFGenerator.
    ngenerator = PDFGenerator("G")
    ngenerator.setPhase(xgenerator.phase)

    ## The FitContributions
    # We associate the x-ray PDFGenerator and Profile in one FitContribution...
    xcontribution = FitContribution("xnickel")
    xcontribution.addProfileGenerator(xgenerator)
    xcontribution.setProfile(xprofile, xname = "r")
    # and the neutron objects in another.
    ncontribution = FitContribution("nnickel")
    ncontribution.addProfileGenerator(ngenerator)
    ncontribution.setProfile(nprofile, xname = "r")

    # This example is different than the previous ones in that we are composing
    # a residual function from other residuals (one for the x-ray contribution
    # and one for the neutron contribution). The relative magnitude of these
    # residuals effectively determines the influence of each contribution over
    # the fit. This is a problem in this case because the x-ray data has
    # uncertainty values associated with it (on the order of 1e-4), and the
    # chi^2 residual is proportional to 1 / uncertainty**2. The neutron has no
    # uncertainty, so it's chi^2 is proportional to 1. Thus, my optimizing
    # chi^2 we would give the neutron data practically no weight in the fit. To
    # get around this, we will optimize a different metric.
    #
    # The contribution's residual can be either chi^2, Rw^2, or custom crafted.
    # In this case, we should minimize Rw^2 of each contribution so that each
    # one can contribute roughly equally to the fit.
    xcontribution.setResidualEquation("resv")
    ncontribution.setResidualEquation("resv")

    # Make the FitRecipe and add the FitContributions.
    recipe = FitRecipe()
    recipe.addContribution(xcontribution)
    recipe.addContribution(ncontribution)

    # Now we vary and constrain Parameters as before.
    recipe.addVar(xgenerator.scale, 1, "xscale")
    recipe.addVar(ngenerator.scale, 1, "nscale")
    recipe.addVar(xgenerator.qdamp, 0.01, "xqdamp")
    recipe.addVar(ngenerator.qdamp, 0.01, "nqdamp")
    # delta2 is a non-structual material propery. Thus, we constrain together
    # delta2 Parameter from each PDFGenerator.
    delta2 = recipe.newVar("delta2", 2)
    recipe.constrain(xgenerator.delta2, delta2)
    recipe.constrain(ngenerator.delta2, delta2)

    # We only need to constrain phase properties once since there is a single
    # ObjCrystCrystalParSet for the Crystal.
    phase = xgenerator.phase
    for par in phase.sgpars:
        recipe.addVar(par)
    recipe.B11_0 = 0.1

    # Give the recipe away so it can be used!
    return recipe