class TestCos(unittest.TestCase):
"""Unit tests for Cos(x) function"""
def setUp(self):
from sas.models.Cos import Cos
self.cos = Cos()
def test1D(self):
self.assertEqual(self.cos.run(1.13), math.cos(1.13))
def test2D(self):
self.assertEqual(self.cos.run([1.13,0.56]), math.cos(1.13*math.cos(0.56))*math.cos(1.13*math.sin(0.56)))
self.assertEqual(self.cos.runXY([1.13,0.56]), math.cos(1.13)*math.cos(0.56))
class TestCos(unittest.TestCase):
"""Unit tests for Cos(x) function"""
def setUp(self):
from sas.models.Cos import Cos
self.cos = Cos()
def test1D(self):
self.assertEqual(self.cos.run(1.13), math.cos(1.13))
def test2D(self):
self.assertEqual(
self.cos.run([1.13, 0.56]),
math.cos(1.13 * math.cos(0.56)) * math.cos(1.13 * math.sin(0.56)))
self.assertEqual(self.cos.runXY([1.13, 0.56]),
math.cos(1.13) * math.cos(0.56))
def setUp(self):
from sas.models.Cos import Cos
self.cos = Cos()
def setUp(self):
self.model = Cos()
class TestEvalPythonMethods(unittest.TestCase):
""" Testing evalDistribution for pure python models """
def setUp(self):
self.model = Cos()
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)
qlist3 = numpy.asarray([0.001, 0.002])
q3 = self.model.evalDistribution(qlist3)
q4 = self.model.run(0.002)
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):
k = i + len(qx) * j
self.assertAlmostEquals(iq[k], expected[i][j])
def test_rectangle_methods(self):
"""
Compare to the runXY() method for 2D models
with a non-square matrix.
TODO: Doesn't work for C models apparently
+--------+--------+--------+
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(2):
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]
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(2):
k = i + len(qx) * j
self.assertAlmostEquals(iq[k], expected[i][j])
def setUp(self):
self.model= Cos()
class TestEvalPythonMethods(unittest.TestCase):
""" Testing evalDistribution for pure python models """
def setUp(self):
self.model= Cos()
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)
qlist3 = numpy.asarray([0.001, 0.002])
q3 = self.model.evalDistribution(qlist3)
q4 = self.model.run(0.002)
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):
k = i+len(qx)*j
self.assertAlmostEquals(iq[k], expected[i][j])
def test_rectangle_methods(self):
"""
Compare to the runXY() method for 2D models
with a non-square matrix.
TODO: Doesn't work for C models apparently
+--------+--------+--------+
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(2):
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]
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(2):
k = i+len(qx)*j
self.assertAlmostEquals(iq[k], expected[i][j])
def setUp(self):
from sas.models.Cos import Cos
self.cos = Cos()
