utest_evaldist.py @ 162c778

ESS_GUIESS_GUI_DocsESS_GUI_batch_fittingESS_GUI_bumps_abstractionESS_GUI_iss1116ESS_GUI_iss879ESS_GUI_iss959ESS_GUI_openclESS_GUI_orderingESS_GUI_sync_sascalccostrafo411magnetic_scattrelease-4.1.1release-4.1.2release-4.2.2release_4.0.1ticket-1009ticket-1094-headlessticket-1242-2d-resolutionticket-1243ticket-1249ticket885unittest-saveload

Last change on this file since 162c778 was 18e250c, checked in by Gervaise Alina <gervyh@…>, 13 years ago
move test to sansmodels top level
Property mode set to `100644`
File size: 6.3 KB

Line
1	"""
2	Test for the BaseComponent.evalDistribution
3	See method documentation for more details
4	"""
5	import unittest
6	import numpy, math
7	from sans.models.SphereModel import SphereModel
8	from sans.models.Cos import Cos
9
10
11	class TestEvalPythonMethods(unittest.TestCase):
12	""" Testing evalDistribution for pure python models """
13
14	def setUp(self):
15	self.model= Cos()
16
17	def test_scalar_methods(self):
18	"""
19	Simple test comparing the run(), runXY() and
20	evalDistribution methods
21	"""
22	q1 = self.model.run(0.001)
23	q2 = self.model.runXY(0.001)
24	qlist3 = numpy.asarray([0.001, 0.002])
25	q3 = self.model.evalDistribution(qlist3)
26	q4 = self.model.run(0.002)
27
28	self.assertEqual(q1, q2)
29	self.assertEqual(q1, q3[0])
30	self.assertEqual(q4, q3[1])
31
32	def test_XY_methods(self):
33	"""
34	Compare to the runXY() method for 2D models.
35
36	+--------+--------+--------+
37	qy=0.009 \| \| \| \|
38	+--------+--------+--------+
39	qy-0.006 \| \| \| \|
40	+--------+--------+--------+
41	qy=0.003 \| \| \| \|
42	+--------+--------+--------+
43	qx=0.001 0.002 0.003
44
45	"""
46	# These are the expected values for all bins
47	expected = numpy.zeros([3,3])
48	for i in range(3):
49	for j in range(3):
50	q_length = math.sqrt( (0.001(i+1.0))(0.001(i+1.0)) + (0.003(j+1.0))(0.003(j+1.0)) )
51	expected[i][j] = self.model.run(q_length)
52
53	qx_values = [0.001, 0.002, 0.003]
54	qy_values = [0.003, 0.006, 0.009]
55
56	qx = numpy.asarray(qx_values)
57	qy = numpy.asarray(qy_values)
58
59	new_x = numpy.tile(qx, (len(qy),1))
60	new_y = numpy.tile(qy, (len(qx),1))
61	new_y = new_y.swapaxes(0,1)
62
63	#iq is 1d array now (since 03-12-2010)
64	qx_prime = new_x.flatten()
65	qy_prime = new_y.flatten()
66
67	iq = self.model.evalDistribution([qx_prime, qy_prime])
68
69	for i in range(3):
70	for j in range(3):
71	k = i+len(qx)*j
72	self.assertAlmostEquals(iq[k], expected[i][j])
73
74	def test_rectangle_methods(self):
75	"""
76	Compare to the runXY() method for 2D models
77	with a non-square matrix.
78	TODO: Doesn't work for C models apparently
79
80	+--------+--------+--------+
81	qy-0.006 \| \| \| \|
82	+--------+--------+--------+
83	qy=0.003 \| \| \| \|
84	+--------+--------+--------+
85	qx=0.001 0.002 0.003
86
87	"""
88	# These are the expected values for all bins
89	expected = numpy.zeros([3,3])
90	for i in range(3):
91	for j in range(2):
92	q_length = math.sqrt( (0.001(i+1.0))(0.001(i+1.0)) + (0.003(j+1.0))(0.003(j+1.0)) )
93	expected[i][j] = self.model.run(q_length)
94
95	qx_values = [0.001, 0.002, 0.003]
96	qy_values = [0.003, 0.006]
97
98	qx = numpy.asarray(qx_values)
99	qy = numpy.asarray(qy_values)
100
101	new_x = numpy.tile(qx, (len(qy),1))
102	new_y = numpy.tile(qy, (len(qx),1))
103	new_y = new_y.swapaxes(0,1)
104
105	#iq is 1d array now (since 03-12-2010)
106	qx_prime = new_x.flatten()
107	qy_prime = new_y.flatten()
108
109	iq = self.model.evalDistribution([qx_prime, qy_prime])
110
111	for i in range(3):
112	for j in range(2):
113	k = i+len(qx)*j
114	self.assertAlmostEquals(iq[k], expected[i][j])
115
116
117
118	class TestEvalMethods(unittest.TestCase):
119	""" Testing evalDistribution for C models """
120
121	def setUp(self):
122	self.model= SphereModel()
123
124	def test_scalar_methods(self):
125	"""
126	Simple test comparing the run(), runXY() and
127	evalDistribution methods
128	"""
129	q1 = self.model.run(0.001)
130	q2 = self.model.runXY(0.001)
131	q4 = self.model.run(0.002)
132	qlist3 = numpy.asarray([0.001, 0.002])
133	q3 = self.model.evalDistribution(qlist3)
134
135	self.assertEqual(q1, q2)
136	self.assertEqual(q1, q3[0])
137	self.assertEqual(q4, q3[1])
138
139	def test_XY_methods(self):
140	"""
141	Compare to the runXY() method for 2D models.
142
143	+--------+--------+--------+
144	qy=0.009 \| \| \| \|
145	+--------+--------+--------+
146	qy-0.006 \| \| \| \|
147	+--------+--------+--------+
148	qy=0.003 \| \| \| \|
149	+--------+--------+--------+
150	qx=0.001 0.002 0.003
151
152	"""
153	# These are the expected values for all bins
154	expected = numpy.zeros([3,3])
155	for i in range(3):
156	for j in range(3):
157	q_length = math.sqrt( (0.001(i+1.0))(0.001(i+1.0)) + (0.003(j+1.0))(0.003(j+1.0)) )
158	expected[i][j] = self.model.run(q_length)
159
160	qx_values = [0.001, 0.002, 0.003]
161	qy_values = [0.003, 0.006, 0.009]
162
163	qx = numpy.asarray(qx_values)
164	qy = numpy.asarray(qy_values)
165
166	new_x = numpy.tile(qx, (len(qy),1))
167	new_y = numpy.tile(qy, (len(qx),1))
168	new_y = new_y.swapaxes(0,1)
169
170	#iq is 1d array now (since 03-12-2010)
171	qx_prime = new_x.flatten()
172	qy_prime = new_y.flatten()
173
174	iq = self.model.evalDistribution([qx_prime, qy_prime])
175
176	for i in range(3):
177	for j in range(3):
178	# convert index into 1d array
179	k = i+len(qx)*j
180	self.assertAlmostEquals(iq[k], expected[i][j])
181
182
183	if __name__ == '__main__':
184	unittest.main()

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