class TestPerlNecklace(unittest.TestCase):
""" Unit tests for PerlNecklace """
def setUp(self):
from sas.models.PearlNecklaceModel import PearlNecklaceModel
self.pnl = PearlNecklaceModel()
from sas.models.LinearPearlsModel import LinearPearlsModel
self.lpm = LinearPearlsModel()
from sas.models.SphereModel import SphereModel
self.sphere = SphereModel()
from sas.models.BarBellModel import BarBellModel
self.bar = BarBellModel()
def testwithsphere(self):
""" Compare 1D model with sphere """
self.pnl.setParam("radius", 60)
self.pnl.setParam("num_pearls", 1)
self.pnl.setParam("sld_pearl", 2e-06)
self.pnl.setParam("sld_solv", 1e-06)
self.assertAlmostEqual(self.pnl.run(0.001), self.sphere.run(0.001), 5)
self.assertAlmostEqual(self.pnl.run(0.005), self.sphere.run(0.005), 5)
self.assertAlmostEqual(self.pnl.run(0.01), self.sphere.run(0.01), 5)
self.assertAlmostEqual(self.pnl.run(0.05), self.sphere.run(0.05), 5)
self.assertAlmostEqual(self.pnl.run(0.1), self.sphere.run(0.1), 5)
self.assertAlmostEqual(self.pnl.run(0.5), self.sphere.run(0.5), 5)
def testwithbarbell(self):
"""
Compare 1D model with barbell
Note: pearlnecklace assumes infinite thin rod
"""
self.pnl.setParam("radius", 20)
self.pnl.setParam("num_pearls", 2)
self.pnl.setParam("sld_pearl", 1e-06)
self.pnl.setParam("sld_string", 1e-06)
self.pnl.setParam("sld_solv", 6.3e-06)
self.pnl.setParam("thick_string", 0.1)
self.pnl.setParam("edge_separation", 400)
self.bar.setParam("rad_bar", 0.1)
self.bar.setParam("rad_bell", 20)
self.lpm.setParam("radius", 20)
self.lpm.setParam("num_pearls", 2)
self.lpm.setParam("sld_pearl", 1e-06)
self.lpm.setParam("sld_solv", 6.3e-06)
self.lpm.setParam("edge_separation", 400)
self.assertAlmostEqual(self.pnl.run(0.001), self.bar.run(0.001), 1)
self.assertAlmostEqual(self.pnl.run(0.005), self.bar.run(0.005), 1)
self.assertAlmostEqual(self.pnl.run(0.01), self.bar.run(0.01), 1)
self.assertAlmostEqual(self.pnl.run(0.05), self.bar.run(0.05), 1)
self.assertAlmostEqual(self.pnl.run(0.1), self.bar.run(0.1), 1)
self.assertAlmostEqual(self.pnl.run(0.5), self.bar.run(0.5), 1)
self.assertAlmostEqual(self.pnl.run(0.001), self.lpm.run(0.001), 1)
self.assertAlmostEqual(self.pnl.run(0.005), self.lpm.run(0.005), 1)
self.assertAlmostEqual(self.pnl.run(0.01), self.lpm.run(0.01), 1)
self.assertAlmostEqual(self.pnl.run(0.05), self.lpm.run(0.05), 1)
self.assertAlmostEqual(self.pnl.run(0.1), self.lpm.run(0.1), 1)
self.assertAlmostEqual(self.pnl.run(0.5), self.lpm.run(0.5), 1)
class TestSphere(unittest.TestCase):
""" Unit tests for sphere model using evalDistribution function """
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
self.x = numpy.array([1.0, 2.0, 3.0, 4.0])
self.y = self.x + 1
def test1D(self):
""" Test 1D model for a sphere with vector as input"""
answer = numpy.array(
[5.63877831e-05, 2.57231782e-06, 2.73704050e-07, 2.54229069e-08])
testvector = self.comp.evalDistribution(self.x)
self.assertAlmostEqual(len(testvector), 4)
for i in xrange(len(answer)):
self.assertAlmostEqual(testvector[i], answer[i])
def test1D_1(self):
""" Test 2D model for a sphere with scalar as input"""
self.assertAlmostEqual(self.comp.run(1.0), 5.63877831e-05, 4)
def test1D_2(self):
""" Test 2D model for a sphere for 2 scalar """
self.assertAlmostEqual(self.comp.run([1.0, 1.3]), 56.3878e-06, 4)
def test1D_3(self):
""" Test 2D model for a Shpere for 2 vectors as input """
#x= numpy.reshape(self.x, [len(self.x),1])
#y= numpy.reshape(self.y, [1,len(self.y)])
vect = self.comp.evalDistribution([self.x, self.y])
self.assertAlmostEqual(vect[0], 9.2985e-07, 4)
self.assertAlmostEqual(vect[len(self.x) - 1], 1.3871e-08, 4)
class TestSphere(unittest.TestCase):
""" Unit tests for sphere model using evalDistribution function """
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
self.x = numpy.array([1.0,2.0,3.0, 4.0])
self.y = self.x +1
def test1D(self):
""" Test 1D model for a sphere with vector as input"""
answer = numpy.array([5.63877831e-05,2.57231782e-06,2.73704050e-07,2.54229069e-08])
testvector= self.comp.evalDistribution(self.x)
self.assertAlmostEqual(len(testvector),4)
for i in xrange(len(answer)):
self.assertAlmostEqual(testvector[i],answer[i])
def test1D_1(self):
""" Test 2D model for a sphere with scalar as input"""
self.assertAlmostEqual(self.comp.run(1.0),5.63877831e-05, 4)
def test1D_2(self):
""" Test 2D model for a sphere for 2 scalar """
self.assertAlmostEqual(self.comp.run([1.0, 1.3]), 56.3878e-06, 4)
def test1D_3(self):
""" Test 2D model for a Shpere for 2 vectors as input """
#x= numpy.reshape(self.x, [len(self.x),1])
#y= numpy.reshape(self.y, [1,len(self.y)])
vect = self.comp.evalDistribution([self.x,self.y])
self.assertAlmostEqual(vect[0],9.2985e-07, 4)
self.assertAlmostEqual(vect[len(self.x)-1],1.3871e-08, 4)
class TestPerlNecklace(unittest.TestCase):
""" Unit tests for PerlNecklace """
def setUp(self):
from sas.models.PearlNecklaceModel import PearlNecklaceModel
self.pnl = PearlNecklaceModel()
from sas.models.LinearPearlsModel import LinearPearlsModel
self.lpm = LinearPearlsModel()
from sas.models.SphereModel import SphereModel
self.sphere = SphereModel()
from sas.models.BarBellModel import BarBellModel
self.bar = BarBellModel()
def testwithsphere(self):
""" Compare 1D model with sphere """
self.pnl.setParam("radius", 60)
self.pnl.setParam("num_pearls", 1)
self.pnl.setParam("sld_pearl", 2e-06)
self.pnl.setParam("sld_solv", 1e-06)
self.assertAlmostEqual(self.pnl.run(0.001), self.sphere.run(0.001), 5)
self.assertAlmostEqual(self.pnl.run(0.005), self.sphere.run(0.005), 5)
self.assertAlmostEqual(self.pnl.run(0.01), self.sphere.run(0.01), 5)
self.assertAlmostEqual(self.pnl.run(0.05), self.sphere.run(0.05), 5)
self.assertAlmostEqual(self.pnl.run(0.1), self.sphere.run(0.1), 5)
self.assertAlmostEqual(self.pnl.run(0.5), self.sphere.run(0.5), 5)
def testwithbarbell(self):
"""
Compare 1D model with barbell
Note: pearlnecklace assumes infinite thin rod
"""
self.pnl.setParam("radius", 20)
self.pnl.setParam("num_pearls", 2)
self.pnl.setParam("sld_pearl", 1e-06)
self.pnl.setParam("sld_string", 1e-06)
self.pnl.setParam("sld_solv", 6.3e-06)
self.pnl.setParam("thick_string", 0.1)
self.pnl.setParam("edge_separation", 400)
self.bar.setParam("rad_bar", 0.1)
self.bar.setParam("rad_bell", 20)
self.lpm.setParam("radius", 20)
self.lpm.setParam("num_pearls", 2)
self.lpm.setParam("sld_pearl", 1e-06)
self.lpm.setParam("sld_solv", 6.3e-06)
self.lpm.setParam("edge_separation", 400)
self.assertAlmostEqual(self.pnl.run(0.001), self.bar.run(0.001), 1)
self.assertAlmostEqual(self.pnl.run(0.005), self.bar.run(0.005), 1)
self.assertAlmostEqual(self.pnl.run(0.01), self.bar.run(0.01), 1)
self.assertAlmostEqual(self.pnl.run(0.05), self.bar.run(0.05), 1)
self.assertAlmostEqual(self.pnl.run(0.1), self.bar.run(0.1), 1)
self.assertAlmostEqual(self.pnl.run(0.5), self.bar.run(0.5), 1)
self.assertAlmostEqual(self.pnl.run(0.001), self.lpm.run(0.001), 1)
self.assertAlmostEqual(self.pnl.run(0.005), self.lpm.run(0.005), 1)
self.assertAlmostEqual(self.pnl.run(0.01), self.lpm.run(0.01), 1)
self.assertAlmostEqual(self.pnl.run(0.05), self.lpm.run(0.05), 1)
self.assertAlmostEqual(self.pnl.run(0.1), self.lpm.run(0.1), 1)
self.assertAlmostEqual(self.pnl.run(0.5), self.lpm.run(0.5), 1)
def setUp(self):
from sas.models.PearlNecklaceModel import PearlNecklaceModel
self.pnl = PearlNecklaceModel()
from sas.models.LinearPearlsModel import LinearPearlsModel
self.lpm = LinearPearlsModel()
from sas.models.SphereModel import SphereModel
self.sphere = SphereModel()
from sas.models.BarBellModel import BarBellModel
self.bar = BarBellModel()
def setUp(self):
from sas.models.SphereModel import SphereModel
self.model= SphereModel()
self.model.setParam('scale', 1.0)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSph', 2.0)
self.model.setParam('sldSolv', 1.0)
self.model.setParam('background', 0.0)
def setUp(self):
from sas.models.SphereModel import SphereModel
from sas.models.HardsphereStructure import HardsphereStructure
from sas.models.DiamCylFunc import DiamCylFunc
from sas.models.MultiplicationModel import MultiplicationModel
self.model = SphereModel()
self.model2 = HardsphereStructure()
self.model3 = MultiplicationModel(self.model, self.model2)
self.modelD = DiamCylFunc()
class TestSphere(unittest.TestCase):
""" Unit tests for calculate_ER (sphere model) """
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
def test(self):
""" Test 1D model for a sphere """
self.comp.setParam("radius", 20)
self.assertAlmostEqual(self.comp.calculate_ER(), 20)
def test_4():
radius = 15
density = .1
vol = 4 / 3 * math.pi * radius * radius * radius
npts = vol * density
canvas = VolumeCanvas.VolumeCanvas()
canvas.setParam('lores_density', density)
#handle = canvas.add('sphere')
#canvas.setParam('%s.radius' % handle, radius)
#canvas.setParam('%s.contrast' % handle, 1.0)
pdb = canvas.add('test.pdb')
sphere = SphereModel()
sphere.setParam('radius', radius)
sphere.setParam('scale', 1.0)
sphere.setParam('contrast', 1.0)
# Simple sphere sum(Pr) = (rho*V)^2
# each p(r) point has a volume of 1/density
for i in range(35):
q = 0.001 + 0.01 * i
#sim_1 = 1.0e8*canvas.getIq(q)*4/3*math.pi/(density*density*density)
sim_1 = canvas.getIq(q)
ana_1 = sphere.run(q)
#ana_1 = form_factor(q, radius)
print("q=%g sim=%g ana=%g ratio=%g" %
(q, sim_1, ana_1, sim_1 / ana_1))
def setUp(self):
data = Loader().load("latex_smeared.xml")
self.data_res = data[0]
self.data_slit = data[1]
self.sphere = SphereModel()
self.sphere.setParam('background', 0)
self.sphere.setParam('radius', 5000.0)
self.sphere.setParam('scale', 0.4)
self.sphere.setParam('sldSolv',0)
self.sphere.setParam('sldSph',1e-6)
class TestSphere(unittest.TestCase):
""" Unit tests for calculate_ER (sphere model) """
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
def test(self):
""" Test 1D model for a sphere """
self.comp.setParam("radius", 20)
self.assertAlmostEqual(self.comp.calculate_ER(), 20)
def __init__(self, radius=15, density = 0.01):
from sas.models.SphereModel import SphereModel
self.name = 'sphere'
self.radius = radius
self.density = density
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', radius)
self.create()
def setUp(self):
# NIST sample data
self.data = Loader().load("CMSphere5.txt")
# NIST smeared sphere w/ param values below
self.answer = Loader().load("CMSphere5smearsphere.txt")
# call spheremodel
self.model = SphereModel()
# setparams consistent with Igor default
self.model.setParam('scale', 1.0)
self.model.setParam('background', 0.01)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSolv', 6.3e-06)
self.model.setParam('sldSph', 1.0e-06)
class TestSphere(unittest.TestCase):
""" Unit tests for sphere model """
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
def test1D(self):
""" Test 1D model for a sphere """
self.assertAlmostEqual(self.comp.run(1.0), 5.6387e-5, 4)
def test1D_2(self):
""" Test 2D model for a sphere """
self.assertAlmostEqual(self.comp.run([1.0, 1.3]), 5.6387e-5, 4)
def setUp(self):
loader = Loader()
## IGOR/NIST computation
self.output_gauss = loader.load('Gausssphere.txt')
self.output_shulz = loader.load('Schulzsphere.txt')
from sas.models.SphereModel import SphereModel
self.model = SphereModel()
self.model.setParam('scale', 0.01)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSph', 1.e-6)
self.model.setParam('sldSolv', 3.e-6)
self.model.setParam('background', 0.001)
class SphereValidator(Validator):
def __init__(self, radius=15, density = 0.01):
from sas.models.SphereModel import SphereModel
self.name = 'sphere'
self.radius = radius
self.density = density
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', radius)
self.create()
def create(self):
canvas = VolumeCanvas.VolumeCanvas()
canvas.setParam('lores_density', self.density)
handle = canvas.add('sphere')
canvas.setParam('%s.radius' % handle, self.radius)
canvas.setParam('scale' , 1.0)
canvas.setParam('%s.contrast' % handle, 1.0)
canvas.setParam('background' , 0.0)
self.canvas = canvas
class TestSphere(unittest.TestCase):
""" Unit tests for sphere model """
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
def test1D(self):
""" Test 1D model for a sphere """
self.assertAlmostEqual(self.comp.run(1.0), 5.6387e-5, 4)
def test1D_2(self):
""" Test 2D model for a sphere """
self.assertAlmostEqual(self.comp.run([1.0, 1.3]), 5.6387e-5, 4)
def test_state_IO(self):
"""
Check that a state oject is independent from the model object it
was generated with
"""
self.sphere.setParam('radius', 44.0)
_, _, state, _, _ = self.sphere.__reduce_ex__(0)
sphere_copy = SphereModel()
sphere_copy.__setstate__(state)
sphere_clone = sphere_copy.clone()
self.assertEqual(sphere_copy.getParam('radius'), 44)
self.sphere.setParam('radius', 33.0)
self.assertEqual(sphere_clone.getParam('radius'), 44)
def test_1():
radius = 15
density = .1
vol = 4 / 3 * math.pi * radius * radius * radius
npts = vol * density
canvas = VolumeCanvas.VolumeCanvas()
canvas.setParam('lores_density', density)
handle = canvas.add('sphere')
canvas.setParam('%s.radius' % handle, radius)
canvas.setParam('%s.contrast' % handle, 1.0)
if False:
# Time test
t_0 = time.time()
value_1 = 1.0e8 * canvas.getIq(0.1)
print("density = 0.1: output=%g time=%g" %
(value_1, time.time() - t_0))
t_0 = time.time()
canvas.setParam('lores_density', 1)
value_1 = 1.0e8 * canvas.getIq(0.1)
print("density = 1000: output=%g time=%g" %
(value_1, time.time() - t_0))
t_0 = time.time()
canvas.setParam('lores_density', 0.01)
value_1 = 1.0e8 * canvas.getIq(0.1)
print("density = 0.00001: output=%g time=%g" %
(value_1, time.time() - t_0))
print()
sphere = SphereModel()
sphere.setParam('radius', radius)
sphere.setParam('scale', 1.0)
sphere.setParam('contrast', 1.0)
# Simple sphere sum(Pr) = (rho*V)^2
# each p(r) point has a volume of 1/density
for i in range(35):
q = 0.001 + 0.01 * i
#sim_1 = 1.0e8*canvas.getIq(q)*4/3*math.pi/(density*density*density)
sim_1 = canvas.getIq(q)
ana_1 = sphere.run(q)
#ana_1 = form_factor(q, radius)
print("q=%g sim=%g ana=%g ratio=%g" %
(q, sim_1, ana_1, sim_1 / ana_1))
def testWrongOrder(self):
from sas.models.SphereModel import SphereModel
self.set_coreshell_on_canvas(1, 0)
# Core shell model
sphere = SphereModel()
# Core radius
sphere.setParam('radius', self.outer_radius)
# Shell thickness
sphere.setParam('contrast', self.shell_sld)
sphere.setParam('background', 0.0)
sphere.setParam('scale', 1.0)
ana = sphere.run(0.05)
val, err = self.canvas.getIqError(0.05)
#print 'wrong', ana, val, err
self.assert_(math.fabs(ana-val)/ana < 1.1)
def setUp(self):
from sas.models.PearlNecklaceModel import PearlNecklaceModel
self.pnl = PearlNecklaceModel()
from sas.models.LinearPearlsModel import LinearPearlsModel
self.lpm = LinearPearlsModel()
from sas.models.SphereModel import SphereModel
self.sphere = SphereModel()
from sas.models.BarBellModel import BarBellModel
self.bar = BarBellModel()
def test_1():
radius = 15
density = .1
vol = 4/3*math.pi*radius*radius*radius
npts = vol*density
canvas = VolumeCanvas.VolumeCanvas()
canvas.setParam('lores_density', density)
handle = canvas.add('sphere')
canvas.setParam('%s.radius' % handle, radius)
canvas.setParam('%s.contrast' % handle, 1.0)
if False:
# Time test
t_0 = time.time()
value_1 = 1.0e8*canvas.getIq(0.1)
print "density = 0.1: output=%g time=%g" % (value_1, time.time()-t_0)
t_0 = time.time()
canvas.setParam('lores_density', 1)
value_1 = 1.0e8*canvas.getIq(0.1)
print "density = 1000: output=%g time=%g" % (value_1, time.time()-t_0)
t_0 = time.time()
canvas.setParam('lores_density', 0.01)
value_1 = 1.0e8*canvas.getIq(0.1)
print "density = 0.00001: output=%g time=%g" % (value_1, time.time()-t_0)
print
sphere = SphereModel()
sphere.setParam('radius', radius)
sphere.setParam('scale', 1.0)
sphere.setParam('contrast', 1.0)
# Simple sphere sum(Pr) = (rho*V)^2
# each p(r) point has a volume of 1/density
for i in range(35):
q = 0.001 + 0.01*i
#sim_1 = 1.0e8*canvas.getIq(q)*4/3*math.pi/(density*density*density)
sim_1 = canvas.getIq(q)
ana_1 = sphere.run(q)
#ana_1 = form_factor(q, radius)
print "q=%g sim=%g ana=%g ratio=%g" % (q, sim_1, ana_1, sim_1/ana_1)
def setUp(self):
from sas.models.SphereModel import SphereModel
from sas.models.HardsphereStructure import HardsphereStructure
from sas.models.DiamCylFunc import DiamCylFunc
from sas.models.MultiplicationModel import MultiplicationModel
self.model = SphereModel()
self.model2 = HardsphereStructure()
self.model3 = MultiplicationModel(self.model, self.model2)
self.modelD = DiamCylFunc()
def setUp(self):
data = Loader().load("latex_smeared.xml")
self.data_res = data[0]
self.data_slit = data[1]
self.sphere = SphereModel()
self.sphere.setParam('background', 0)
self.sphere.setParam('radius', 5000.0)
self.sphere.setParam('scale', 0.4)
self.sphere.setParam('sldSolv',0)
self.sphere.setParam('sldSph',1e-6)
class TestSphere(unittest.TestCase):
def setUp(self):
self.sphere = SphereModel()
def test_state_IO(self):
"""
Check that a state oject is independent from the model object it
was generated with
"""
self.sphere.setParam('radius', 44.0)
_, _, state, _, _ = self.sphere.__reduce_ex__(0)
sphere_copy = SphereModel()
sphere_copy.__setstate__(state)
sphere_clone = sphere_copy.clone()
self.assertEqual(sphere_copy.getParam('radius'), 44)
self.sphere.setParam('radius', 33.0)
self.assertEqual(sphere_clone.getParam('radius'), 44)
def setUp(self):
# NIST sample data
self.data = Loader().load("CMSphere5.txt")
# NIST smeared sphere w/ param values below
self.answer = Loader().load("CMSphere5smearsphere.txt")
# call spheremodel
self.model = SphereModel()
# setparams consistent with Igor default
self.model.setParam('scale', 1.0)
self.model.setParam('background', 0.01)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSolv', 6.3e-06)
self.model.setParam('sldSph', 1.0e-06)
def setUp(self):
loader = Loader()
## IGOR/NIST computation
self.output_gauss=loader.load('Gausssphere.txt')
self.output_shulz=loader.load('Schulzsphere.txt')
from sas.models.SphereModel import SphereModel
self.model= SphereModel()
self.model.setParam('scale', 0.01)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSph', 1.e-6)
self.model.setParam('sldSolv', 3.e-6)
self.model.setParam('background', 0.001)
def setUp(self):
"""
Set up canvas
"""
from sas.models.SphereModel import SphereModel
self.model = VolumeCanvas.VolumeCanvas()
handle = self.model.add('sphere')
radius = 10
density = .1
ana = SphereModel()
ana.setParam('scale', 1.0)
ana.setParam('contrast', 1.0)
ana.setParam('background', 0.0)
ana.setParam('radius', radius)
self.ana = ana
self.model.setParam('lores_density', density)
self.model.setParam('%s.radius' % handle, radius)
self.model.setParam('scale' , 1.0)
self.model.setParam('%s.contrast' % handle, 1.0)
self.model.setParam('background' , 0.0)
def test_5():
from sas.models.SphereModel import SphereModel
model = VolumeCanvas.VolumeCanvas()
handle = model.add('sphere')
radius = 10
density = .1
ana = SphereModel()
ana.setParam('scale', 1.0)
ana.setParam('contrast', 1.0)
ana.setParam('background', 0.0)
ana.setParam('radius', radius)
model.setParam('lores_density', density)
model.setParam('%s.radius' % handle, radius)
model.setParam('scale' , 1.0)
model.setParam('%s.contrast' % handle, 1.0)
model.setParam('background' , 0.0)
ana = ana.runXY([0.1, 0.1])
sim = model.getIq2D(0.1, 0.1)
print ana, sim, sim/ana, ana/sim
def test_4():
radius = 15
density = .1
vol = 4/3*math.pi*radius*radius*radius
npts = vol*density
canvas = VolumeCanvas.VolumeCanvas()
canvas.setParam('lores_density', density)
#handle = canvas.add('sphere')
#canvas.setParam('%s.radius' % handle, radius)
#canvas.setParam('%s.contrast' % handle, 1.0)
pdb = canvas.add('test.pdb')
sphere = SphereModel()
sphere.setParam('radius', radius)
sphere.setParam('scale', 1.0)
sphere.setParam('contrast', 1.0)
# Simple sphere sum(Pr) = (rho*V)^2
# each p(r) point has a volume of 1/density
for i in range(35):
q = 0.001 + 0.01*i
#sim_1 = 1.0e8*canvas.getIq(q)*4/3*math.pi/(density*density*density)
sim_1 = canvas.getIq(q)
ana_1 = sphere.run(q)
#ana_1 = form_factor(q, radius)
print "q=%g sim=%g ana=%g ratio=%g" % (q, sim_1, ana_1, sim_1/ana_1)
import sys
sys.path.append('../build/temp.macosx-10.11-x86_64-2.7/src/sas')
"""
Test plotting sphere
"""
import numpy as np
import matplotlib.pyplot as plt
from sas.models.SphereModel import SphereModel
comp = SphereModel()
comp.setParam("radius", 30.0)
comp.setParam("background", 0.01)
# Generate Q and calculate I
q = np.linspace(0.001, 1, num=200)
i = map(comp.run,q)
# Plot I(q)
plt.figure()
plt.plot(q,np.log(i))
qx = np.linspace(-1, 1, num=400)
qy = qx
xv, yv = np.meshgrid(qx, qy)
xv= xv.flatten()
yv= yv.flatten()
qxy = np.column_stack((xv,yv))
def setUp(self):
self.sphere = SphereModel()
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
self.x = numpy.array([1.0,2.0,3.0, 4.0])
self.y = self.x +1
def testGetIq(self):
""" Test the output of I(q) to the analytical solution
If the normalization is wrong, we will have to fix it.
getIq() should call getPr() behind the scenes so that
the user doesnt have to do it if he doesn't need to.
"""
from sas.models.SphereModel import SphereModel
sphere = SphereModel()
sphere.setParam('radius', 10.0)
sphere.setParam('contrast', 1.0)
sphere.setParam('background', 0.0)
sphere.setParam('scale', 1.0)
handle = self.canvas.add('sphere')
self.canvas.setParam('%s.radius' % handle, 10.0)
self.canvas.setParam('%s.contrast' % handle, 1.0)
sim_1 = self.canvas.getIq(0.001)
ana_1 = sphere.run(0.001)
sim_2 = self.canvas.getIq(0.01)
ana_2 = sphere.run(0.01)
# test the shape of the curve (calculate relative error
# on the output and it should be compatible with zero
# THIS WILL DEPEND ON THE NUMBER OF SPACE POINTS:
# that why we need some error analysis.
self.assert_( (sim_2*ana_1/sim_1 - ana_2)/ana_2 < 0.1)
# test the absolute amplitude
self.assert_( math.fabs(sim_2-ana_2)/ana_2 < 0.1)
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
self.x = numpy.array([1.0, 2.0, 3.0, 4.0])
self.y = self.x + 1
class smear_test_1Dpinhole(unittest.TestCase):
def setUp(self):
# NIST sample data
self.data = Loader().load("CMSphere5.txt")
# NIST smeared sphere w/ param values below
self.answer = Loader().load("CMSphere5smearsphere.txt")
# call spheremodel
self.model = SphereModel()
# setparams consistent with Igor default
self.model.setParam('scale', 1.0)
self.model.setParam('background', 0.01)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSolv', 6.3e-06)
self.model.setParam('sldSph', 1.0e-06)
def test_q(self):
"""
Compare Pinhole resolution smearing with NIST
"""
# x values
input = numpy.zeros(len(self.data.x))
# set time
st1 = time()
# cal I w/o smear
input = self.model.evalDistribution(self.data.x)
# Cal_smear (first call)
for i in range(1000):
s = QSmearer(self.data, self.model)
# stop and record time taken
first_call_time = time()-st1
# set new time
st = time()
# cal I w/o smear (this is not neccessary to call but just to be fare
input = self.model.evalDistribution(self.data.x)
# smear cal (after first call done above)
for i in range(1000):
output = s(input)
# record time taken
last_call_time = time()-st
# compare the ratio of ((NIST_answer-SsanView_answer)/NIST_answer)
# If the ratio less than 1%, pass the test
for i in range(len(self.data.x)):
ratio = math.fabs((self.answer.y[i]-output[i])/self.answer.y[i])
if ratio > 0.006:
ratio = 0.006
self.assertEqual(math.fabs((self.answer.y[i]-output[i])/ \
self.answer.y[i]), ratio)
# print
print "\n NIST_time = 10sec:"
print "Cal_time(1000 times of first_calls; ) = ", first_call_time
print "Cal_time(1000 times of calls) = ", last_call_time
def setUp(self):
self.sphere = SphereModel()
class smear_testdata(unittest.TestCase):
"""
Test fitting with the smearing operations
The output of the fits should be compated to fits
done with IGOR for the same models and data sets.
"""
def setUp(self):
data = Loader().load("latex_smeared.xml")
self.data_res = data[0]
self.data_slit = data[1]
self.sphere = SphereModel()
self.sphere.setParam('background', 0)
self.sphere.setParam('radius', 5000.0)
self.sphere.setParam('scale', 0.4)
self.sphere.setParam('sldSolv',0)
self.sphere.setParam('sldSph',1e-6)
#self.sphere.setParam('radius.npts', 30)
#self.sphere.setParam('radius.width',50)
def test_reso(self):
# Let the data module find out what smearing the
# data needs
smear = smear_selection(self.data_res)
#self.assertEqual(smear.__class__.__name__, 'QSmearer')
#self.assertEqual(smear.__class__.__name__, 'PySmearer')
# Fit
fitter = Fit()
# Data: right now this is the only way to set the smearer object
# We should improve that and have a way to get access to the
# data for a given fit.
fitter.set_data(self.data_res,1)
fitter.fit_arrange_dict[1].data_list[0].smearer = smear
# Model: maybe there's a better way to do this.
# Ideally we should have to create a new model from our sas model.
fitter.set_model(Model(self.sphere),1, ['radius','scale', 'background'])
# Why do we have to do this...?
fitter.select_problem_for_fit(id=1,value=1)
# Perform the fit (might take a while)
result1, = fitter.fit()
#print "v",result1.pvec
#print "dv",result1.stderr
#print "chisq(v)",result1.fitness
self.assertTrue( math.fabs(result1.pvec[0]-5000) < 20 )
self.assertTrue( math.fabs(result1.pvec[1]-0.48) < 0.02 )
self.assertTrue( math.fabs(result1.pvec[2]-0.060) < 0.002 )
def test_slit(self):
smear = smear_selection(self.data_slit)
#self.assertEqual(smear.__class__.__name__, 'SlitSmearer')
#self.assertEqual(smear.__class__.__name__, 'PySmearer')
fitter = Fit()
# Data: right now this is the only way to set the smearer object
# We should improve that and have a way to get access to the
# data for a given fit.
fitter.set_data(self.data_slit,1)
fitter.fit_arrange_dict[1].data_list[0].smearer = smear
fitter.fit_arrange_dict[1].data_list[0].qmax = 0.003
# Model
fitter.set_model(Model(self.sphere),1, ['radius','scale'])
fitter.select_problem_for_fit(id=1,value=1)
result1, = fitter.fit()
#print "v",result1.pvec
#print "dv",result1.stderr
#print "chisq(v)",result1.fitness
numpy.testing.assert_allclose(result1.pvec, [2323.466,0.22137], rtol=0.001)
def setUp(self):
self.model= SphereModel()
class TestEvalMethods(unittest.TestCase):
""" Testing evalDistribution for C models """
def setUp(self):
self.model = SphereModel()
def test_scalar_methods(self):
"""
Simple test comparing the run(), runXY() and
evalDistribution methods
"""
q1 = self.model.run(0.001)
q2 = self.model.runXY(0.001)
q4 = self.model.run(0.002)
qlist3 = numpy.asarray([0.001, 0.002])
q3 = self.model.evalDistribution(qlist3)
self.assertEqual(q1, q2)
self.assertEqual(q1, q3[0])
self.assertEqual(q4, q3[1])
def test_XY_methods(self):
"""
Compare to the runXY() method for 2D models.
+--------+--------+--------+
qy=0.009 | | | |
+--------+--------+--------+
qy-0.006 | | | |
+--------+--------+--------+
qy=0.003 | | | |
+--------+--------+--------+
qx=0.001 0.002 0.003
"""
# These are the expected values for all bins
expected = numpy.zeros([3, 3])
for i in range(3):
for j in range(3):
q_length = math.sqrt((0.001 * (i + 1.0)) * (0.001 *
(i + 1.0)) +
(0.003 * (j + 1.0)) * (0.003 * (j + 1.0)))
expected[i][j] = self.model.run(q_length)
qx_values = [0.001, 0.002, 0.003]
qy_values = [0.003, 0.006, 0.009]
qx = numpy.asarray(qx_values)
qy = numpy.asarray(qy_values)
new_x = numpy.tile(qx, (len(qy), 1))
new_y = numpy.tile(qy, (len(qx), 1))
new_y = new_y.swapaxes(0, 1)
#iq is 1d array now (since 03-12-2010)
qx_prime = new_x.flatten()
qy_prime = new_y.flatten()
iq = self.model.evalDistribution([qx_prime, qy_prime])
for i in range(3):
for j in range(3):
# convert index into 1d array
k = i + len(qx) * j
self.assertAlmostEquals(iq[k], expected[i][j])
class TestEvalMethods(unittest.TestCase):
""" Testing evalDistribution for C models """
def setUp(self):
self.model= SphereModel()
def test_scalar_methods(self):
"""
Simple test comparing the run(), runXY() and
evalDistribution methods
"""
q1 = self.model.run(0.001)
q2 = self.model.runXY(0.001)
q4 = self.model.run(0.002)
qlist3 = numpy.asarray([0.001, 0.002])
q3 = self.model.evalDistribution(qlist3)
self.assertEqual(q1, q2)
self.assertEqual(q1, q3[0])
self.assertEqual(q4, q3[1])
def test_XY_methods(self):
"""
Compare to the runXY() method for 2D models.
+--------+--------+--------+
qy=0.009 | | | |
+--------+--------+--------+
qy-0.006 | | | |
+--------+--------+--------+
qy=0.003 | | | |
+--------+--------+--------+
qx=0.001 0.002 0.003
"""
# These are the expected values for all bins
expected = numpy.zeros([3,3])
for i in range(3):
for j in range(3):
q_length = math.sqrt( (0.001*(i+1.0))*(0.001*(i+1.0)) + (0.003*(j+1.0))*(0.003*(j+1.0)) )
expected[i][j] = self.model.run(q_length)
qx_values = [0.001, 0.002, 0.003]
qy_values = [0.003, 0.006, 0.009]
qx = numpy.asarray(qx_values)
qy = numpy.asarray(qy_values)
new_x = numpy.tile(qx, (len(qy),1))
new_y = numpy.tile(qy, (len(qx),1))
new_y = new_y.swapaxes(0,1)
#iq is 1d array now (since 03-12-2010)
qx_prime = new_x.flatten()
qy_prime = new_y.flatten()
iq = self.model.evalDistribution([qx_prime, qy_prime])
for i in range(3):
for j in range(3):
# convert index into 1d array
k = i+len(qx)*j
self.assertAlmostEquals(iq[k], expected[i][j])
def testWrongOrder(self):
from sas.models.SphereModel import SphereModel
self.set_coreshell_on_canvas(1, 0)
# Core shell model
sphere = SphereModel()
# Core radius
sphere.setParam('radius', self.outer_radius)
# Shell thickness
sphere.setParam('contrast', self.shell_sld)
sphere.setParam('background', 0.0)
sphere.setParam('scale', 1.0)
ana = sphere.run(0.05)
val, err = self.canvas.getIqError(0.05)
#print 'wrong', ana, val, err
self.assert_(math.fabs(ana - val) / ana < 1.1)
def testGetIq(self):
""" Test the output of I(q) to the analytical solution
If the normalization is wrong, we will have to fix it.
getIq() should call getPr() behind the scenes so that
the user doesnt have to do it if he doesn't need to.
"""
from sas.models.SphereModel import SphereModel
sphere = SphereModel()
sphere.setParam('radius', 10.0)
sphere.setParam('contrast', 1.0)
sphere.setParam('background', 0.0)
sphere.setParam('scale', 1.0)
handle = self.canvas.add('sphere')
self.canvas.setParam('%s.radius' % handle, 10.0)
self.canvas.setParam('%s.contrast' % handle, 1.0)
sim_1 = self.canvas.getIq(0.001)
ana_1 = sphere.run(0.001)
sim_2 = self.canvas.getIq(0.01)
ana_2 = sphere.run(0.01)
# test the shape of the curve (calculate relative error
# on the output and it should be compatible with zero
# THIS WILL DEPEND ON THE NUMBER OF SPACE POINTS:
# that why we need some error analysis.
self.assert_((sim_2 * ana_1 / sim_1 - ana_2) / ana_2 < 0.1)
# test the absolute amplitude
self.assert_(math.fabs(sim_2 - ana_2) / ana_2 < 0.1)
def setUp(self):
self.model = SphereModel()
class TestSphere(unittest.TestCase):
"""
Testing C++ Cylinder model
"""
def setUp(self):
from sas.models.SphereModel import SphereModel
self.model= SphereModel()
self.model.setParam('scale', 1.0)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSph', 2.0)
self.model.setParam('sldSolv', 1.0)
self.model.setParam('background', 0.0)
def test_simple(self):
"""
Test simple 1D and 2D values
Numbers taken from model that passed validation, before
the update to C++ underlying class.
"""
self.assertTrue(math.fabs(self.model.run(0.001)-90412744456148.094)<=50.0)
self.assertAlmostEqual(self.model.runXY([0.001,0.001]),
90347660670656.391, 1)
def test_dispersion(self):
"""
Test with dispersion
"""
from sas.models.DisperseModel import DisperseModel
disp = DisperseModel(self.model, ['radius'], [10])
disp.setParam('n_pts', 10)
disp.setParam('radius.npts', 10)
disp.setParam('radius.nsigmas', 2.5)
self.assertTrue(math.fabs(disp.run(0.001)-96795008379475.219<50.0))
def test_new_disp(self):
from sas.models.dispersion_models import GaussianDispersion
disp_rm = GaussianDispersion()
self.model.set_dispersion('radius', disp_rm)
self.model.dispersion['radius']['width'] = 0.1666666667
self.model.dispersion['radius']['npts'] = 10
self.model.dispersion['radius']['nsigmas'] = 2
def setUp(self):
self.sldloader = sas_gen.SLDReader()
self.pdbloader = sas_gen.PDBReader()
self.omfloader = sas_gen.OMFReader()
self.comp = SphereModel()
def test_5():
from sas.models.SphereModel import SphereModel
model = VolumeCanvas.VolumeCanvas()
handle = model.add('sphere')
radius = 10
density = .1
ana = SphereModel()
ana.setParam('scale', 1.0)
ana.setParam('contrast', 1.0)
ana.setParam('background', 0.0)
ana.setParam('radius', radius)
model.setParam('lores_density', density)
model.setParam('%s.radius' % handle, radius)
model.setParam('scale', 1.0)
model.setParam('%s.contrast' % handle, 1.0)
model.setParam('background', 0.0)
ana = ana.runXY([0.1, 0.1])
sim = model.getIq2D(0.1, 0.1)
print(ana, sim, sim / ana, ana / sim)
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
class TestSphereGauss(unittest.TestCase):
"""
Testing C++ Polydispersion w/ sphere comparing to IGOR/NIST computation
"""
def setUp(self):
loader = Loader()
## IGOR/NIST computation
self.output_gauss=loader.load('Gausssphere.txt')
self.output_shulz=loader.load('Schulzsphere.txt')
from sas.models.SphereModel import SphereModel
self.model= SphereModel()
self.model.setParam('scale', 0.01)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSph', 1.e-6)
self.model.setParam('sldSolv', 3.e-6)
self.model.setParam('background', 0.001)
def test_gauss(self):
from sas.models.dispersion_models import GaussianDispersion
disp_g = GaussianDispersion()
self.model.set_dispersion('radius', disp_g)
self.model.dispersion['radius']['width'] = 0.2
self.model.dispersion['radius']['npts'] = 100
self.model.dispersion['radius']['nsigmas'] = 10
for ind in range(len(self.output_gauss.x)):
self.assertAlmostEqual(self.model.run(self.output_gauss.x[ind]),
self.output_gauss.y[ind], 2)
def test_shulz(self):
from sas.models.dispersion_models import SchulzDispersion
disp_s = SchulzDispersion()
self.model.set_dispersion('radius', disp_s)
self.model.dispersion['radius']['width'] = 0.2
self.model.dispersion['radius']['npts'] = 100
self.model.dispersion['radius']['nsigmas'] = 10
for ind in range(len(self.output_shulz.x)):
self.assertAlmostEqual(self.model.run(self.output_gauss.x[ind]),
self.output_shulz.y[ind], 3)
class smear_test_1Dpinhole(unittest.TestCase):
def setUp(self):
# NIST sample data
self.data = Loader().load("CMSphere5.txt")
# NIST smeared sphere w/ param values below
self.answer = Loader().load("CMSphere5smearsphere.txt")
# call spheremodel
self.model = SphereModel()
# setparams consistent with Igor default
self.model.setParam('scale', 1.0)
self.model.setParam('background', 0.01)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSolv', 6.3e-06)
self.model.setParam('sldSph', 1.0e-06)
def test_q(self):
"""
Compare Pinhole resolution smearing with NIST
"""
# x values
input = numpy.zeros(len(self.data.x))
# set time
st1 = time()
# cal I w/o smear
input = self.model.evalDistribution(self.data.x)
# Cal_smear (first call)
for i in range(1000):
s = QSmearer(self.data, self.model)
# stop and record time taken
first_call_time = time()-st1
# set new time
st = time()
# cal I w/o smear (this is not neccessary to call but just to be fare
input = self.model.evalDistribution(self.data.x)
# smear cal (after first call done above)
for i in range(1000):
output = s(input)
# record time taken
last_call_time = time()-st
# compare the ratio of ((NIST_answer-SsanView_answer)/NIST_answer)
# If the ratio less than 1%, pass the test
for i in range(len(self.data.x)):
ratio = math.fabs((self.answer.y[i]-output[i])/self.answer.y[i])
if ratio > 0.006:
ratio = 0.006
self.assertEqual(math.fabs((self.answer.y[i]-output[i])/ \
self.answer.y[i]), ratio)
# print
print "\n NIST_time = 10sec:"
print "Cal_time(1000 times of first_calls; ) = ", first_call_time
print "Cal_time(1000 times of calls) = ", last_call_time
def setUp(self):
from sas.models.SphereModel import SphereModel
self.comp = SphereModel()
class smear_testdata(unittest.TestCase):
"""
Test fitting with the smearing operations
The output of the fits should be compated to fits
done with IGOR for the same models and data sets.
"""
def setUp(self):
data = Loader().load("latex_smeared.xml")
self.data_res = data[0]
self.data_slit = data[1]
self.sphere = SphereModel()
self.sphere.setParam('background', 0)
self.sphere.setParam('radius', 5000.0)
self.sphere.setParam('scale', 0.4)
self.sphere.setParam('sldSolv',0)
self.sphere.setParam('sldSph',1e-6)
#self.sphere.setParam('radius.npts', 30)
#self.sphere.setParam('radius.width',50)
def test_reso(self):
# Let the data module find out what smearing the
# data needs
smear = smear_selection(self.data_res)
#self.assertEqual(smear.__class__.__name__, 'QSmearer')
#self.assertEqual(smear.__class__.__name__, 'PySmearer')
# Fit
fitter = Fit()
# Data: right now this is the only way to set the smearer object
# We should improve that and have a way to get access to the
# data for a given fit.
fitter.set_data(self.data_res,1)
fitter.fit_arrange_dict[1].data_list[0].smearer = smear
# Model: maybe there's a better way to do this.
# Ideally we should have to create a new model from our sas model.
fitter.set_model(Model(self.sphere),1, ['radius','scale', 'background'])
# Why do we have to do this...?
fitter.select_problem_for_fit(id=1,value=1)
# Perform the fit (might take a while)
result1, = fitter.fit()
#print "v",result1.pvec
#print "dv",result1.stderr
#print "chisq(v)",result1.fitness
self.assertTrue( math.fabs(result1.pvec[0]-5000) < 20 )
self.assertTrue( math.fabs(result1.pvec[1]-0.48) < 0.02 )
self.assertTrue( math.fabs(result1.pvec[2]-0.060) < 0.002 )
def test_slit(self):
smear = smear_selection(self.data_slit)
#self.assertEqual(smear.__class__.__name__, 'SlitSmearer')
#self.assertEqual(smear.__class__.__name__, 'PySmearer')
fitter = Fit()
# Data: right now this is the only way to set the smearer object
# We should improve that and have a way to get access to the
# data for a given fit.
fitter.set_data(self.data_slit,1)
fitter.fit_arrange_dict[1].data_list[0].smearer = smear
fitter.fit_arrange_dict[1].data_list[0].qmax = 0.003
# Model
fitter.set_model(Model(self.sphere),1, ['radius','scale'])
fitter.select_problem_for_fit(id=1,value=1)
result1, = fitter.fit()
#print "v",result1.pvec
#print "dv",result1.stderr
#print "chisq(v)",result1.fitness
numpy.testing.assert_allclose(result1.pvec, [2323.466,0.22137], rtol=0.001)
class TestSphereGauss(unittest.TestCase):
"""
Testing C++ Polydispersion w/ sphere comparing to IGOR/NIST computation
"""
def setUp(self):
loader = Loader()
## IGOR/NIST computation
self.output_gauss = loader.load('Gausssphere.txt')
self.output_shulz = loader.load('Schulzsphere.txt')
from sas.models.SphereModel import SphereModel
self.model = SphereModel()
self.model.setParam('scale', 0.01)
self.model.setParam('radius', 60.0)
self.model.setParam('sldSph', 1.e-6)
self.model.setParam('sldSolv', 3.e-6)
self.model.setParam('background', 0.001)
def test_gauss(self):
from sas.models.dispersion_models import GaussianDispersion
disp_g = GaussianDispersion()
self.model.set_dispersion('radius', disp_g)
self.model.dispersion['radius']['width'] = 0.2
self.model.dispersion['radius']['npts'] = 100
self.model.dispersion['radius']['nsigmas'] = 10
for ind in range(len(self.output_gauss.x)):
self.assertAlmostEqual(self.model.run(self.output_gauss.x[ind]),
self.output_gauss.y[ind], 2)
def test_shulz(self):
from sas.models.dispersion_models import SchulzDispersion
disp_s = SchulzDispersion()
self.model.set_dispersion('radius', disp_s)
self.model.dispersion['radius']['width'] = 0.2
self.model.dispersion['radius']['npts'] = 100
self.model.dispersion['radius']['nsigmas'] = 10
for ind in range(len(self.output_shulz.x)):
self.assertAlmostEqual(self.model.run(self.output_gauss.x[ind]),
self.output_shulz.y[ind], 3)
class TestsphereHardS(unittest.TestCase):
"""
Unit tests for SphereModel(Q) * HardsphereStructure(Q)
"""
def setUp(self):
from sas.models.SphereModel import SphereModel
from sas.models.HardsphereStructure import HardsphereStructure
from sas.models.DiamCylFunc import DiamCylFunc
from sas.models.MultiplicationModel import MultiplicationModel
self.model = SphereModel()
self.model2 = HardsphereStructure()
self.model3 = MultiplicationModel(self.model, self.model2)
self.modelD = DiamCylFunc()
#Radius of model1.calculate_ER should be equal to the output/2 of DiamFunctions
def test_multplication_radius(self):
"""
test multiplication model (check the effective radius & the output
of the multiplication)
"""
self.model.setParam("radius", 60)
modelDrun = 60
self.model2.setParam("volfraction", 0.2)
self.model2.setParam("effect_radius", modelDrun )
#Compare new method with old method
self.assertEqual(self.model3.run(0.1), self.model.run(0.1)*self.model2.run(0.1))
#Compare radius from two different calculations. Note: modelD.run(0.0) is DIAMETER
self.assertEqual(self.model.calculate_ER(), modelDrun)
def testMultiplicationParam(self):
""" Test Multiplication (check the parameters)"""
## test details dictionary
## test parameters list
list3= self.model3.getParamList()
for item in self.model.getParamList():
if not 'scale' in item:
self.assert_(item in list3)
for item in self.model2.getParamList():
#model3 parameters should not include effect_radius*
if not 'effect_radius' in item:
self.assert_(item in list3)
## test set value for parameters and get paramaters
self.model3.setParam("scale_factor", 15)
self.assertEqual(self.model3.getParam("scale_factor"), 15)
self.model3.setParam("radius", 20)
self.assertEqual(self.model3.getParam("radius"), 20)
self.model3.setParam("radius.width", 15)
self.assertEqual(self.model3.getParam("radius.width"), 15)
self.model3.setParam("scale_factor", 15)
self.assertEqual(self.model3.getParam("scale_factor"), 15)
self.assertEqual(self.model3.getParam("volfraction"), self.model.getParam("scale"))
## Dispersity
list3= self.model3.getDispParamList()
self.assertEqual(list3, ['radius.npts', 'radius.nsigmas', 'radius.width'])
from sas.models.dispersion_models import ArrayDispersion
disp_th = ArrayDispersion()
values_th = numpy.zeros(100)
weights = numpy.zeros(100)
for i in range(100):
values_th[i]=(math.pi/99.0*i)
weights[i]=(1.0)
disp_th.set_weights(values_th, weights)
self.model3.set_dispersion('radius', disp_th)
val_1d = self.model3.run(math.sqrt(0.0002))
val_2d = self.model3.runXY([0.01,0.01])
self.assertTrue(math.fabs(val_1d-val_2d)/val_1d < 0.02)
model4= self.model3.clone()
self.assertEqual(model4.getParam("radius"), 20)