class TestEllipsoid(unittest.TestCase):
"""
Testing C++ Cylinder model
"""
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 test_simple(self):
"""
Test simple 1D and 2D values
Numbers taken from model that passed validation, before
the update to C++ underlying class.
"""
self.assertAlmostEqual(self.model.run(0.001),
11808.842896863147, 3)
self.assertAlmostEqual(self.model.runXY([0.001,0.001]),
11681.990374929677, 3)
def test_dispersion(self):
"""
Test with dispersion
"""
from sas.models.DisperseModel import DisperseModel
disp = DisperseModel(self.model, ['radius_a', 'radius_b'], [5, 50])
disp.setParam('n_pts', 10)
self.assertAlmostEqual(disp.run(0.001), 11948.72581312305, 3)
self.assertAlmostEqual(disp.runXY([0.001,0.001]), 11811.972359807551, 3)
def test_new_disp(self):
from sas.models.dispersion_models import GaussianDispersion
disp_rm = GaussianDispersion()
self.model.set_dispersion('radius_a', disp_rm)
self.model.dispersion['radius_a']['width'] = 0.25
self.model.dispersion['radius_a']['npts'] = 10
self.model.dispersion['radius_a']['nsigmas'] = 2
disp_rr = GaussianDispersion()
self.model.set_dispersion('radius_b', disp_rr)
self.model.dispersion['radius_b']['width'] = 0.125
self.model.dispersion['radius_b']['npts'] = 10
self.model.dispersion['radius_b']['nsigmas'] = 2
self.assertAlmostEqual(self.model.run(0.001),
1.10650710*11948.72581312305, 3)
self.assertAlmostEqual(self.model.runXY([0.001,0.001]),
1.105898*11811.972359807551, 2)
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('axis_theta', disp_th)
self.model.set_dispersion('axis_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)
