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():
# 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():
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():
"""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():
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 fitRecipe(recipe):
"""We refine in stages to help the refinement converge."""
# Tune SAS.
recipe.setWeight(recipe.pdf, 0)
recipe.fix("all")
recipe.free("radius_a", "radius_b", iqscale = 1e8)
recipe.constrain('radius_b', 'radius_a')
scipyOptimize(recipe)
recipe.unconstrain('radius_b')
# Tune PDF
recipe.setWeight(recipe.pdf, 1)
recipe.setWeight(recipe.sas, 0)
recipe.fix("all")
recipe.free("a", "Biso_0", "scale", "delta2")
scipyOptimize(recipe)
# Tune all
recipe.setWeight(recipe.pdf, 1)
recipe.setWeight(recipe.sas, 1)
recipe.free("all")
scipyOptimize(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
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
def fitRecipe(recipe):
"""We refine in stages to help the refinement converge."""
# Tune SAS.
recipe.setWeight(recipe.pdf, 0)
recipe.fix("all")
recipe.free("radius_a", "radius_b", iqscale = 1e8)
scipyOptimize(recipe)
# Tune PDF
recipe.setWeight(recipe.pdf, 1)
recipe.setWeight(recipe.sas, 0)
recipe.fix("all")
recipe.free("a", "Biso_0", "scale", "delta2")
scipyOptimize(recipe)
# Tune all
recipe.setWeight(recipe.pdf, 1)
recipe.setWeight(recipe.sas, 1)
recipe.free("all")
scipyOptimize(recipe)
return
pylab.plot(nr,ndiff,'g-',label="G(r) neutron diff")
pylab.plot(nr,ndiffzero,'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
ciffile = "data/ni.cif"
xdata = "data/ni-q27r60nodg-xray.gr"
ndata = "data/ni-q27r100-neutron.gr"
# Make the recipe
recipe = makeRecipe(ciffile, xdata, ndata)
# Optimize
scipyOptimize(recipe)
# Generate and print the FitResults
res = FitResults(recipe)
res.printResults()
# Plot!
plotResults(recipe)
# End of file
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
