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
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 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_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)
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])
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))
ixy = [ comp.runXY(list(i)) for i in qxy]
ixy = np.array(ixy)
ixy = ixy.reshape(400,400)
ixy = np.log(ixy)
plt.figure()
plt.imshow(ixy)
plt.show()