utest_evaldist.py @ 04349fe

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 04349fe was 7e99f5c, checked in by Jae Cho <jhjcho@…>, 15 years ago
updated data input
Property mode set to `100644`
File size: 6.3 KB

Rev	Line
[ecc58e72]	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)
[7e99f5c]	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()
[ecc58e72]	66
	67	iq = self.model.evalDistribution([qx_prime, qy_prime])
	68
	69	for i in range(3):
	70	for j in range(3):
[7e99f5c]	71	k = i+len(qx)*j
	72	self.assertAlmostEquals(iq[k], expected[i][j])
[ecc58e72]	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)
[7e99f5c]	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()
[ecc58e72]	108
	109	iq = self.model.evalDistribution([qx_prime, qy_prime])
	110
	111	for i in range(3):
	112	for j in range(2):
[7e99f5c]	113	k = i+len(qx)*j
	114	self.assertAlmostEquals(iq[k], expected[i][j])
[ecc58e72]	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)
[7e99f5c]	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()
[ecc58e72]	173
	174	iq = self.model.evalDistribution([qx_prime, qy_prime])
	175
	176	for i in range(3):
	177	for j in range(3):
[7e99f5c]	178	# convert index into 1d array
	179	k = i+len(qx)*j
	180	self.assertAlmostEquals(iq[k], expected[i][j])
[ecc58e72]	181
	182
	183	if __name__ == '__main__':
	184	unittest.main()

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source: sasview/sansmodels/src/sans/models/test/utest_evaldist.py @ 04349fe

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