def setUp(self):
""" Set up ellipsoid """
from sas.models.EllipsoidModel import EllipsoidModel
radius_a = 10
radius_b = 15
density = 5
self.ana = EllipsoidModel()
self.ana.setParam('scale', 1.0)
self.ana.setParam('contrast', 1.0)
self.ana.setParam('background', 0.0)
self.ana.setParam('radius_a', radius_a)
self.ana.setParam('radius_b', radius_b)
canvas = VolumeCanvas.VolumeCanvas()
canvas.setParam('lores_density', density)
self.handle = canvas.add('ellipsoid')
canvas.setParam('%s.radius_x' % self.handle, radius_a)
canvas.setParam('%s.radius_y' % self.handle, radius_b)
canvas.setParam('%s.radius_z' % self.handle, radius_b)
canvas.setParam('scale' , 1.0)
canvas.setParam('%s.contrast' % self.handle, 1.0)
canvas.setParam('background' , 0.0)
self.canvas = canvas
self.ana.setParam('axis_theta', 1.57)
self.ana.setParam('axis_phi', 0)
self.canvas.setParam('%s.orientation' % self.handle, [0,0,0])
def setUp(self):
""" Set up ellipsoid """
from sas.models.EllipsoidModel import EllipsoidModel
radius_a = 60
radius_b = 10
density = 30
self.ana = EllipsoidModel()
self.ana.setParam('scale', 1.0)
self.ana.setParam('contrast', 1.0)
self.ana.setParam('background', 0.0)
self.ana.setParam('radius_a', radius_a)
self.ana.setParam('radius_b', radius_b)
# Default orientation is there=1.57, phi=0
# Radius_a is along the x direction
canvas = VolumeCanvas.VolumeCanvas()
canvas.setParam('lores_density', density)
self.handle = canvas.add('ellipsoid')
canvas.setParam('%s.radius_x' % self.handle, radius_a)
canvas.setParam('%s.radius_y' % self.handle, radius_b)
canvas.setParam('%s.radius_z' % self.handle, radius_b)
canvas.setParam('scale' , 1.0)
canvas.setParam('%s.contrast' % self.handle, 1.0)
canvas.setParam('background' , 0.0)
self.canvas = canvas
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 sas model to configure and serve as the calculation engine of the
# generator. This allows us to use the full sas model creation
# capabilities, and tie this into SrFit when we want to fit a model to
# data. The documentation for the various sas models can be found at
# http://www.sasview.org.
from sas.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
def setUp(self):
from sas.models.EllipsoidModel import EllipsoidModel
self.model= EllipsoidModel()
self.model.setParam('scale', 1.0)
self.model.setParam('radius_a', 20.0)
self.model.setParam('radius_b', 400.0)
self.model.setParam('sldEll', 4.e-6)
self.model.setParam('sldSolv', 1.e-6)
self.model.setParam('background', 0.0)
self.model.setParam('axis_theta', 0.0)
self.model.setParam('axis_phi', 0.0)
def __init__(self, radius_a=60, radius_b=10, density = 0.01):
from sas.models.EllipsoidModel import EllipsoidModel
#from sas.models.SphereModel import SphereModel
self.name = 'ellipsoid'
self.radius_a = radius_a
self.radius_b = radius_b
self.density = density
self.ana = EllipsoidModel()
#self.ana = SphereModel()
self.ana.setParam('scale', 1.0)
self.ana.setParam('contrast', 1.0)
self.ana.setParam('background', 0.0)
self.ana.setParam('radius_a', radius_a)
self.ana.setParam('radius_b', radius_b)
#self.ana.setParam('radius', radius_a)
# Default orientation is there=1.57, phi=0
# Radius_a is along the x direction
self.create()
def setUp(self):
from sas.models.EllipsoidModel import EllipsoidModel
from sas.models.DiamEllipFunc import DiamEllipFunc
self.comp = EllipsoidModel()
self.diam = DiamEllipFunc()
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 = loadCrystal(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)
if all(sasprofile.dy == 0):
sasprofile.dy[:] = 1
sascontribution = FitContribution("sas")
sascontribution.setProfile(sasprofile)
from sas.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
