Python fitbase.FitResults類代碼示例

def main():
    """Refine the C60 molecule.

    From previous examples we know that the radius of the model structure is
    slightly too small. The annealing algorithm has to determine the proper
    radius, and must also account for thermal vibrations in the PDF.

    """

    molecule = makeC60()
    recipe = makeRecipe(molecule, "data/C60.gr")
    recipe.fithooks[0].verbose = 0

    # Optimize
    # Group related atomic positions for the optimizer
    parlist = [getXYZNames(i) for i in xrange(60)]
    groupAnneal(recipe, parlist)

    # Print results
    recipe.fix("all")
    res = FitResults(recipe, showfixed = False)
    res.printResults()

    # Save the structure
    molecule.write("C60_refined.stru", "pdffit")

    # Plot results
    plotResults(recipe)

    return

def main():

    # Make the data and the recipe
    strufile = "data/C60.stru"
    q = numpy.arange(1, 20, 0.05)
    makeData(strufile, q, "C60.iq", 1.0, 100.68, 0.005, 0.13, 2)

    # Make the recipe
    recipe = makeRecipe(strufile, "C60.iq")

    # Optimize
    scipyOptimize(recipe)

    # Generate and print the FitResults
    res = FitResults(recipe)
    # We want to see how much speed-up we get from bringing the scale and
    # background outside of the intensity generator.  Get the number of calls
    # to the residual function from the FitRecipe, and the number of calls to
    # 'iofq' from the IntensityGenerator.
    rescount = recipe.fithooks[0].count
    calcount = recipe.bucky.I.count
    footer = "iofq called %i%% of the time"%int(100.0*calcount/rescount)
    res.printResults(footer = footer)

    # Plot!
    plotResults(recipe)

    return

def main():
    """Set up and refine the recipe."""

    # Make the data and the recipe
    cdsciffile = "data/CdS.cif"
    znsciffile = "data/ZnS.cif"
    data = "data/CdS_ZnS_nano.gr"

    # Make the recipe
    stru1 = loadCrystal(cdsciffile)
    stru2 = loadCrystal(znsciffile)
    recipe = makeRecipe(stru1, stru2, data)
    from diffpy.srfit.fitbase.fithook import PlotFitHook
    recipe.pushFitHook(PlotFitHook())
    recipe.fithooks[0].verbose = 3

    # Optimize - we do this in steps to help convergence
    recipe.fix("all")

    # Start with the lattice parameters. In makeRecipe, these were tagged with
    # "lat". Here is how we use that.
    recipe.free("lat")
    leastsq(recipe.residual, recipe.values, maxfev = 50)

    # Now the scale and phase fraction.
    recipe.free("scale", "scale_CdS")
    leastsq(recipe.residual, recipe.values, maxfev = 50)

    # The ADPs.
    recipe.free("adp")
    leastsq(recipe.residual, recipe.values, maxfev = 100)

    # The delta2 parameters.
    recipe.free("delta2_cds", "delta2_zns")
    leastsq(recipe.residual, recipe.values, maxfev = 50)

    # The shape parameters.
    recipe.free("radius", "thickness")
    leastsq(recipe.residual, recipe.values, maxfev = 50)

    # The positional parameters.
    recipe.free("xyz")
    leastsq(recipe.residual, recipe.values)

    # Generate and print the FitResults
    res = FitResults(recipe)
    res.printResults()

    # Plot!
    plotResults(recipe)
    return

def gaussianfit(x, y, dy=None, A=1.0, sig=1.0, x0=0.0):
#def gaussianfit(arg):
    '''main function to fit and plot gaussian curve
        x, y, dy:   gaussian date to be fitted 
        A, sig, x0: initial value
    '''
    #print type(arg)
    dy = np.ones_like(x) if dy==None else dy
    
    recipe = makeRecipe(x, y, dy, A, sig, x0)
    scipyOptimize(recipe)
    res = FitResults(recipe)
    res.printResults()
    plotResults(recipe)
    return 

def main():

    # Create the recipe
    recipe = makeRecipeII()

    # Refine using the optimizer of your choice
    scipyOptimize(recipe)

    # Get the results in a FitResults object.
    res = FitResults(recipe)

    # Print the results
    res.printResults()

    # Plot the results
    plotResults(recipe)

    return

def main():

    p = Profile()
    p.loadtxt("data/gaussian.dat")

    # FitContribution operations
    # "|="  -   Union of necessary components.
    # "<<"  -   Inject a parameter value
    c = FitContribution("g1")
    c |= p
    c |= "A * exp(-0.5*(x-x0)**2/sigma**2)"
    c.A << 0.5
    c.x0 << 5
    c.sigma << 1

    # FitRecipe operations
    # "|="  -   Union of necessary components.
    # "+="  -   Add Parameter or create a new one. Each tuple is a set of
    #           arguments for either setVar or addVar.
    # "*="  -   Constrain a parameter. Think of "*" as a push-pin holding one
    #           parameter's value to that of another.
    # "%="  -   Restrain a parameter or equation. Think of "%" as a rope
    #           loosely tying parameters to a value.
    r = FitRecipe()
    r |= c
    r += (c.A, 0.5), (c.x0, 5), "sig"
    r *= c.sigma, "sig"
    r %= c.A, 0.5, 0.5

    from gaussianrecipe import scipyOptimize

    scipyOptimize(r)

    res = FitResults(r)

    # Print the results.
    res.printResults()

    # Plot the results.
    from gaussianrecipe import plotResults

    plotResults(r)

    return

def main():
    """The workflow of creating, running and inspecting a fit."""

    # Create the recipe
    recipe = makeRecipe()

    # Refine using the optimizer of your choice
    scipyOptimize(recipe)

    # Get the results.
    res = FitResults(recipe)

    # Print the results
    res.printResults()

    # Plot the results
    plotResults(recipe)

    return

def main():

    molecule = makeC60()
    # Make the data and the recipe
    recipe = makeRecipe(molecule, "data/C60.gr")
    # Tell the fithook that we want very verbose output.
    recipe.fithooks[0].verbose = 3

    # Optimize
    from scipy.optimize import leastsq
    leastsq(recipe.residual, recipe.getValues())

    # Print results
    res = FitResults(recipe)
    res.printResults()

    # Plot results
    plotResults(recipe)

    return

def main():

    # Make two different data sets, each from the same structure, but with
    # different scale, noise, broadening and background.
    strufile = "data/C60.stru"
    q = numpy.arange(1, 20, 0.05)
    makeData(strufile, q, "C60_1.iq", 8.1, 101.68, 0.008, 0.12, 2, 0.01)
    makeData(strufile, q, "C60_2.iq", 3.2, 101.68, 0.02, 0.003, 0, 1)

    # Make the recipe
    recipe = makeRecipe(strufile, "C60_1.iq", "C60_2.iq")

    # Optimize
    # Since the backgrounds have a large effect on the profile, we will refine
    # them first, but do so separately.
    # To refine the background from the first contribution, we will fix
    # all other parameters and give the second contribution no weight in the
    # fit.
    recipe.fix("all")
    recipe.free("bcoeffs1")
    recipe.setWeight(recipe.bucky2, 0)
    scipyOptimize(recipe)
    # Now do the same for the second background
    recipe.fix("all")
    recipe.free("bcoeffs1")
    recipe.setWeight(recipe.bucky2, 1)
    recipe.setWeight(recipe.bucky1, 0)
    scipyOptimize(recipe)
    # Now refine everything with the structure parameters included
    recipe.free("all")
    recipe.setWeight(recipe.bucky1, 1)
    scipyOptimize(recipe)

    # Generate and print the FitResults
    res = FitResults(recipe)
    res.printResults()

    # Plot!
    plotResults(recipe)

    return

    pylab.plot(r,diffzero,'k-')
    pylab.xlabel("$r (\AA)$")
    pylab.ylabel("$G (\AA^{-2})$")
    pylab.legend(loc=1)

    pylab.show()
    return


if __name__ == "__main__":

    # Make the data and the recipe
    niciffile = "data/ni.cif"
    siciffile = "data/si.cif"
    data = "data/si90ni10-q27r60-xray.gr"

    # Make the recipe
    recipe = makeRecipe(niciffile, siciffile, data)

    # Optimize
    scipyOptimize(recipe)

    # Generate and print the FitResults
    res = FitResults(recipe)
    res.printResults()

    # Plot!
    plotResults(recipe)

# End of file

    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

if __name__ == "__main__":

    # Make the data and the recipe
    ciffile = "data/ni.cif"
    data = "data/ni-q27r100-neutron.gr"

    # Make the recipe
    recipe = makeRecipe(ciffile, data)

    # Optimize
    scipyOptimize(recipe)
    # Save the file
    recipe.nickel.savetxt("nickel_example.fit")

    # Generate, print and save the FitResults
    res = FitResults(recipe)
    res.printResults()
    res.saveResults("nickel_example.res")

    # Plot!
    plotResults(recipe)

# End of file