def _reset_helper(self, path=None, npts=NPTS):
"""
Set value to fitter and prepare inputs for map function
"""
for i in range(npts):
data = Loader().load(path)
fitter = Fit()
#create model
model = CylinderModel()
model.setParam('scale', 1.0)
model.setParam('radius', 20.0)
model.setParam('length', 400.0)
model.setParam('sldCyl', 4e-006)
model.setParam('sldSolv', 1e-006)
model.setParam('background', 0.0)
for param in model.dispersion.keys():
model.set_dispersion(param, self.polydisp['gaussian']())
model.setParam('cyl_phi.width', 10)
model.setParam('cyl_phi.npts', 3)
model.setParam('cyl_theta.nsigmas', 10)
# for 2 data cyl_theta = 60.0 [deg] cyl_phi= 60.0 [deg]
fitter.set_model(model, i, self.param_to_fit,
self.list_of_constraints)
#smear data
current_smearer = smear_selection(data, model)
import cPickle
p = cPickle.dumps(current_smearer)
sm = cPickle.loads(p)
fitter.set_data(data=data, id=i,
smearer=current_smearer, qmin=self.qmin, qmax=self.qmax)
fitter.select_problem_for_fit(id=i, value=1)
self.list_of_fitter.append(copy.deepcopy(fitter))
self.list_of_function.append('fit')
self.list_of_mapper.append(classMapper)
class TestCylinder(unittest.TestCase):
"""
Testing C++ Cylinder model
"""
def setUp(self):
from sas.models.CylinderModel import CylinderModel
self.model= CylinderModel()
self.model.setParam('scale', 1.0)
self.model.setParam('radius', 20.0)
self.model.setParam('length', 400.0)
self.model.setParam('sldCyl', 4.e-6)
self.model.setParam('sldSolv', 1.e-6)
self.model.setParam('background', 0.0)
self.model.setParam('cyl_theta', 0.0)
self.model.setParam('cyl_phi', 90.0)
def test_simple(self):
self.assertAlmostEqual(self.model.run(0.001), 450.355, 3)
self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 452.299, 3)
def test_constant(self):
from sas.models.dispersion_models import DispersionModel
disp = DispersionModel()
self.model.setParam('scale', 10.0)
self.model.set_dispersion('radius', disp)
self.model.dispersion['radius']['width'] = 0.25
self.model.dispersion['radius']['npts'] = 100
self.model.dispersion['radius']['nsigmas'] = 2.5
self.assertAlmostEqual(self.model.run(0.001), 1.021051*4527.47250339, 3)
self.assertAlmostEqual(self.model.runXY([0.001, 0.001]),
1.021048*4546.997777604715, 2)
def test_gaussian(self):
from sas.models.dispersion_models import GaussianDispersion
disp = GaussianDispersion()
self.model.set_dispersion('radius', disp)
self.model.dispersion['radius']['width'] = 0.25
self.model.dispersion['radius']['npts'] = 100
self.model.dispersion['radius']['nsigmas'] = 2
self.model.setParam('scale', 10.0)
self.assertAlmostEqual(self.model.run(0.001),
1.1804794*4723.32213339, 3)
self.assertAlmostEqual(self.model.runXY([0.001,0.001]),
1.180454*4743.56, 2)
def test_clone(self):
from sas.models.dispersion_models import GaussianDispersion
disp = GaussianDispersion()
self.model.set_dispersion('radius', disp)
self.model.dispersion['radius']['width'] = 0.25
self.model.dispersion['radius']['npts'] = 100
self.model.dispersion['radius']['nsigmas'] = 2
self.model.setParam('scale', 10.0)
new_model = self.model.clone()
self.assertAlmostEqual(new_model.run(0.001),
1.1804794*4723.32213339, 3)
self.assertAlmostEqual(new_model.runXY([0.001,0.001]),
1.180454*4743.56, 2)
def test_gaussian_zero(self):
from sas.models.dispersion_models import GaussianDispersion
disp = GaussianDispersion()
self.model.set_dispersion('radius', disp)
self.model.dispersion['radius']['width'] = 0.0
self.model.dispersion['radius']['npts'] = 100
self.model.dispersion['radius']['nsigmas'] = 2.5
self.model.setParam('scale', 1.0)
self.assertAlmostEqual(self.model.run(0.001), 450.355, 3)
self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 452.299, 3)
def test_array(self):
"""
Perform complete rotational average and
compare to 1D
"""
from sas.models.dispersion_models import ArrayDispersion
disp_ph = ArrayDispersion()
disp_th = ArrayDispersion()
values_ph = numpy.zeros(100)
values_th = numpy.zeros(100)
weights = numpy.zeros(100)
for i in range(100):
values_ph[i]=(360/99.0*i)
values_th[i]=(180/99.0*i)
weights[i]=(1.0)
disp_ph.set_weights(values_ph, weights)
disp_th.set_weights(values_th, weights)
self.model.set_dispersion('cyl_theta', disp_th)
self.model.set_dispersion('cyl_phi', disp_ph)
val_1d = self.model.run(math.sqrt(0.0002))
val_2d = self.model.runXY([0.01,0.01])
self.assertTrue(math.fabs(val_1d-val_2d)/val_1d < 0.02)
