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)