def test_output(self):
self.test_append()
self.assertTrue((self.Sample.output()[0]['moment_tensor_space'] == np.matrix([[2, 2, 3, 2], [1, 1, 5, 1], [2, 2, 1, 2], [1, 1, 1, 1], [2, 2, 2, 2], [1, 1, 1, 1]])).all())
output_ln_pdf = np.sum(np.exp(np.matrix([[1, 1, 0, 1], [2, 2, 1, 2]])), 0)
output_ln_pdf /= np.sum(output_ln_pdf)
self.assertAlmostEqual(self.Sample.output()[0]['probability'][0], output_ln_pdf[0, 0])
self.assertAlmostEqual(self.Sample.output()[0]['probability'][1], output_ln_pdf[0, 1])
self.assertAlmostEqual(self.Sample.output()[0]['probability'][2], output_ln_pdf[0, 2])
self.assertAlmostEqual(self.Sample.output()[0]['probability'][3], output_ln_pdf[0, 3])
self.assertEqual(self.Sample.output()[0]['dV'], 1)
output_ln_pdf = np.sum(np.exp(np.matrix([[1, 1, 0, 1], [2, 2, 1, 2]])), 0)
self.assertAlmostEqual(self.Sample.output(False)[0]['probability'][0, 0], output_ln_pdf[0, 0])
self.assertAlmostEqual(self.Sample.output(False)[0]['probability'][0, 1], output_ln_pdf[0, 1])
self.assertAlmostEqual(self.Sample.output(False)[0]['probability'][0, 2], output_ln_pdf[0, 2])
self.assertAlmostEqual(self.Sample.output(False)[0]['probability'][0, 3], output_ln_pdf[0, 3])
self.assertEqual(self.Sample.output(False)[0]['dV'], 1)
self.tearDown()
self.Sample = Sample(initial_sample_size=1, number_events=2)
moment_tensors = [np.matrix([[2], [1], [2], [1], [2], [1]]), np.matrix([[3], [1], [2], [1], [2], [1]])]
ln_pdf = LnPDF(np.matrix([[1]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue((self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1], [2], [1], [3], [1], [2], [1], [2], [1]])).all())
self.assertTrue((self.Sample.output()[0]['moment_tensor_space_1'] == np.matrix([[2], [1], [2], [1], [2], [1]])).all())
self.assertTrue((self.Sample.output()[0]['moment_tensor_space_2'] == np.matrix([[3], [1], [2], [1], [2], [1]])).all())
self.assertTrue((self.Sample.output()[0]['probability'] == np.matrix([[1]])).all(), str(self.Sample.output()[0]['probability']))
self.assertEqual(self.Sample.output()[0]['dV'], 1)
self.tearDown()
self.Sample = Sample(initial_sample_size=1, number_events=2)
moment_tensors = [np.matrix([[2], [1], [2], [1], [2], [1]]), np.matrix([[2], [1], [2], [1], [2], [1]])]
ln_pdf = LnPDF(np.matrix([[1]]))
scale_factor = np.array([{'mu': np.array([[[2, 3], [3, 2]]]), 'sigma': np.array([[1, 1], [1, 1]])}])
self.assertFalse(self.Sample.__dict__.__contains__('scale_factor'))
self.Sample.append(moment_tensors, ln_pdf, 1, scale_factor)
self.assertTrue((self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1], [2], [1], [2], [1], [2], [1], [2], [1]])).all())
self.assertTrue(self.Sample.__dict__.__contains__('scale_factor'))
self.assertTrue((self.Sample.scale_factor[0]['mu'] == np.array([[2, 3], [3, 2]])).all())
self.assertTrue((self.Sample.output()[0]['scale_factors'][0]['mu'] == np.array([[[2, 3], [3, 2]]])).all(), str(self.Sample.output()[0]['scale_factors'][0]['mu']))
self.tearDown()
self.setUp()
self.test_append()
out, out_str = self.Sample.output(convert=True)
self.assertTrue('dV' in out.keys())
self.assertTrue('moment_tensor_space' in out.keys())
self.assertTrue('probability' in out.keys())
self.assertTrue('ln_pdf' in out.keys())
self.assertTrue('g' in out.keys())
def test_append(self):
moment_tensors = np.matrix([[2], [1], [2], [1], [2], [1]])
ln_pdf = LnPDF(np.matrix([[1], [2]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue((self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1], [2], [1]])).all())
self.assertEqual(self.Sample.n, 1)
self.assertEqual(self.Sample.ln_pdf[0, 0], 1.)
moment_tensors = np.matrix([[2, 3], [1, 5], [2, 1], [1, 1], [2, 2], [1, 1]])
ln_pdf = LnPDF(np.matrix([[1, 0], [2, 1]]))
self.Sample.append(moment_tensors, ln_pdf, 2)
self.assertTrue((self.Sample.moment_tensors[:, 0:3] == np.matrix([[2, 2, 3], [1, 1, 5], [2, 2, 1], [1, 1, 1], [2, 2, 2], [1, 1, 1]])).all())
moment_tensors = np.matrix([[2], [1], [2], [1], [2], [1]])
ln_pdf = LnPDF(np.matrix([[0], [0]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue((self.Sample.moment_tensors[:, 0:3] == np.matrix([[2, 2, 3], [1, 1, 5], [2, 2, 1], [1, 1, 1], [2, 2, 2], [1, 1, 1]])).all())
self.Sample.append(np.matrix([[], [], [], [], [], []]), np.matrix([[]]), 1)
self.assertTrue((self.Sample.moment_tensors[:, 0:3] == np.matrix([[2, 2, 3], [1, 1, 5], [2, 2, 1], [1, 1, 1], [2, 2, 2], [1, 1, 1]])).all())
self.assertEqual(self.Sample.n, 5)
# test multiple events
self.tearDown()
self.Sample = Sample(initial_sample_size=1, number_events=2)
moment_tensors = [np.matrix([[2], [1], [2], [1], [2], [1]]), np.matrix([[2], [1], [2], [1], [2], [1]])]
ln_pdf = LnPDF(np.matrix([[1]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue((self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1], [2], [1], [2], [1], [2], [1], [2], [1]])).all())
# test scale_factor - multiple events
self.tearDown()
self.Sample = Sample(initial_sample_size=1, number_events=2)
moment_tensors = [np.matrix([[2], [1], [2], [1], [2], [1]]), np.matrix([[2], [1], [2], [1], [2], [1]])]
ln_pdf = LnPDF(np.matrix([[1]]))
scale_factor = np.array([{'mu': np.array([[[2, 3], [3, 2]]]), 'sigma': np.array([[1, 1], [1, 1]])}])
self.assertFalse(self.Sample.__dict__.__contains__('scale_factor'))
self.Sample.append(moment_tensors, ln_pdf, 1, scale_factor)
self.assertTrue((self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1], [2], [1], [2], [1], [2], [1], [2], [1]])).all())
self.assertTrue(self.Sample.__dict__.__contains__('scale_factor'))
self.assertTrue((self.Sample.scale_factor[0]['mu'] == np.array([[2, 3], [3, 2]])).all())
self.tearDown()
self.Sample = Sample(initial_sample_size=3)
# test initial samples
moment_tensors = np.matrix([[2], [1], [2], [1], [2], [1]])
ln_pdf = LnPDF(np.matrix([[1], [2]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue((self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1], [2], [1]])).all())
self.assertEqual(self.Sample.n, 1)
self.assertEqual(self.Sample.ln_pdf[0, 0], 1.)
moment_tensors = np.matrix([[2, 3], [1, 5], [2, 1], [1, 1], [2, 2], [1, 1]])
ln_pdf = LnPDF(np.matrix([[1, 0], [2, 1]]))
self.Sample.append(moment_tensors, ln_pdf, 2)
self.assertTrue((self.Sample.moment_tensors[:, 0:3] == np.matrix([[2, 2, 3], [1, 1, 5], [2, 2, 1], [1, 1, 1], [2, 2, 2], [1, 1, 1]])).all())
moment_tensors = np.matrix([[2], [1], [2], [1], [2], [1]])
ln_pdf = LnPDF(np.matrix([[1], [2]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue((self.Sample.moment_tensors[:, 0:4] == np.matrix([[2, 2, 3, 2], [1, 1, 5, 1], [2, 2, 1, 2], [1, 1, 1, 1], [2, 2, 2, 2], [1, 1, 1, 1]])).all())
def setUp(self):
self.Sample = Sample()
class SampleTestCase(unittest.TestCase):
def setUp(self):
self.Sample = Sample()
def tearDown(self):
del self.Sample
def test_append(self):
moment_tensors = np.matrix([[2], [1], [2], [1], [2], [1]])
ln_pdf = LnPDF(np.matrix([[1], [2]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue(
(self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1],
[2], [1]])).all())
self.assertEqual(self.Sample.n, 1)
self.assertEqual(self.Sample.ln_pdf[0, 0], 1.)
moment_tensors = np.matrix([[2, 3], [1, 5], [2, 1], [1, 1], [2, 2],
[1, 1]])
ln_pdf = LnPDF(np.matrix([[1, 0], [2, 1]]))
self.Sample.append(moment_tensors, ln_pdf, 2)
self.assertTrue(
(self.Sample.moment_tensors[:, 0:3] == np.matrix([[2, 2, 3],
[1, 1, 5],
[2, 2, 1],
[1, 1, 1],
[2, 2, 2],
[1, 1,
1]])).all())
moment_tensors = np.matrix([[2], [1], [2], [1], [2], [1]])
ln_pdf = LnPDF(np.matrix([[0], [0]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue(
(self.Sample.moment_tensors[:, 0:3] == np.matrix([[2, 2, 3],
[1, 1, 5],
[2, 2, 1],
[1, 1, 1],
[2, 2, 2],
[1, 1,
1]])).all())
self.Sample.append(np.matrix([[], [], [], [], [], []]),
np.matrix([[]]), 1)
self.assertTrue(
(self.Sample.moment_tensors[:, 0:3] == np.matrix([[2, 2, 3],
[1, 1, 5],
[2, 2, 1],
[1, 1, 1],
[2, 2, 2],
[1, 1,
1]])).all())
self.assertEqual(self.Sample.n, 5)
# test multiple events
self.tearDown()
self.Sample = Sample(initial_sample_size=1, number_events=2)
moment_tensors = [
np.matrix([[2], [1], [2], [1], [2], [1]]),
np.matrix([[2], [1], [2], [1], [2], [1]])
]
ln_pdf = LnPDF(np.matrix([[1]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue(
(self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1],
[2], [1], [2], [1],
[2], [1], [2],
[1]])).all())
# test scale_factor - multiple events
self.tearDown()
self.Sample = Sample(initial_sample_size=1, number_events=2)
moment_tensors = [
np.matrix([[2], [1], [2], [1], [2], [1]]),
np.matrix([[2], [1], [2], [1], [2], [1]])
]
ln_pdf = LnPDF(np.matrix([[1]]))
scale_factor = np.array([{
'mu': np.array([[[2, 3], [3, 2]]]),
'sigma': np.array([[1, 1], [1, 1]])
}])
self.assertFalse(self.Sample.__dict__.__contains__('scale_factor'))
self.Sample.append(moment_tensors, ln_pdf, 1, scale_factor)
self.assertTrue(
(self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1],
[2], [1], [2], [1],
[2], [1], [2],
[1]])).all())
self.assertTrue(self.Sample.__dict__.__contains__('scale_factor'))
self.assertTrue(
(self.Sample.scale_factor[0]['mu'] == np.array([[2, 3],
[3, 2]])).all())
self.tearDown()
self.Sample = Sample(initial_sample_size=3)
# test initial samples
moment_tensors = np.matrix([[2], [1], [2], [1], [2], [1]])
ln_pdf = LnPDF(np.matrix([[1], [2]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue(
(self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1],
[2], [1]])).all())
self.assertEqual(self.Sample.n, 1)
self.assertEqual(self.Sample.ln_pdf[0, 0], 1.)
moment_tensors = np.matrix([[2, 3], [1, 5], [2, 1], [1, 1], [2, 2],
[1, 1]])
ln_pdf = LnPDF(np.matrix([[1, 0], [2, 1]]))
self.Sample.append(moment_tensors, ln_pdf, 2)
self.assertTrue(
(self.Sample.moment_tensors[:, 0:3] == np.matrix([[2, 2, 3],
[1, 1, 5],
[2, 2, 1],
[1, 1, 1],
[2, 2, 2],
[1, 1,
1]])).all())
moment_tensors = np.matrix([[2], [1], [2], [1], [2], [1]])
ln_pdf = LnPDF(np.matrix([[1], [2]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue(
(self.Sample.moment_tensors[:, 0:4] == np.matrix([[2, 2, 3, 2],
[1, 1, 5, 1],
[2, 2, 1, 2],
[1, 1, 1, 1],
[2, 2, 2, 2],
[1, 1, 1,
1]])).all())
def test_output(self):
self.test_append()
self.assertTrue(
(self.Sample.output()[0]['moment_tensor_space'] == np.matrix(
[[2, 2, 3, 2], [1, 1, 5, 1], [2, 2, 1, 2], [1, 1, 1, 1],
[2, 2, 2, 2], [1, 1, 1, 1]])).all())
output_ln_pdf = np.sum(
np.exp(np.matrix([[1., 1., 0., 1.], [2., 2., 1., 2.]])), 0)
output_ln_pdf /= np.sum(output_ln_pdf)
self.assertAlmostEqual(self.Sample.output()[0]['probability'][0],
output_ln_pdf[0, 0])
self.assertAlmostEqual(self.Sample.output()[0]['probability'][1],
output_ln_pdf[0, 1])
self.assertAlmostEqual(self.Sample.output()[0]['probability'][2],
output_ln_pdf[0, 2])
self.assertAlmostEqual(self.Sample.output()[0]['probability'][3],
output_ln_pdf[0, 3])
self.assertEqual(self.Sample.output()[0]['dV'], 1)
output_ln_pdf = np.sum(
np.exp(np.matrix([[1., 1., 0., 1.], [2., 2., 1., 2.]])), 0)
self.assertAlmostEqual(
self.Sample.output(False)[0]['probability'][0], output_ln_pdf[0,
0])
self.assertAlmostEqual(
self.Sample.output(False)[0]['probability'][1], output_ln_pdf[0,
1])
self.assertAlmostEqual(
self.Sample.output(False)[0]['probability'][2], output_ln_pdf[0,
2])
self.assertAlmostEqual(
self.Sample.output(False)[0]['probability'][3], output_ln_pdf[0,
3])
self.assertEqual(self.Sample.output(False)[0]['dV'], 1)
self.tearDown()
self.Sample = Sample(initial_sample_size=1, number_events=2)
moment_tensors = [
np.matrix([[2], [1], [2], [1], [2], [1]]),
np.matrix([[3], [1], [2], [1], [2], [1]])
]
ln_pdf = LnPDF(np.matrix([[1]]))
self.Sample.append(moment_tensors, ln_pdf, 1)
self.assertTrue(
(self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1],
[2], [1], [3], [1],
[2], [1], [2],
[1]])).all())
self.assertTrue(
(self.Sample.output()[0]['moment_tensor_space_1'] == np.matrix(
[[2], [1], [2], [1], [2], [1]])).all())
self.assertTrue(
(self.Sample.output()[0]['moment_tensor_space_2'] == np.matrix(
[[3], [1], [2], [1], [2], [1]])).all())
self.assertTrue(
(self.Sample.output()[0]['probability'] == (np.array([1]))).all(),
str(self.Sample.output()[0]['probability']))
self.assertEqual(self.Sample.output()[0]['dV'], 1)
self.tearDown()
self.Sample = Sample(initial_sample_size=1, number_events=2)
moment_tensors = [
np.matrix([[2], [1], [2], [1], [2], [1]]),
np.matrix([[2], [1], [2], [1], [2], [1]])
]
ln_pdf = LnPDF(np.matrix([[1.]]))
scale_factor = np.array([{
'mu': np.array([[[2, 3], [3, 2]]]),
'sigma': np.array([[1, 1], [1, 1]])
}])
self.assertFalse(self.Sample.__dict__.__contains__('scale_factor'))
self.Sample.append(moment_tensors, ln_pdf, 1, scale_factor)
self.assertTrue(
(self.Sample.moment_tensors[:, 0] == np.matrix([[2], [1], [2], [1],
[2], [1], [2], [1],
[2], [1], [2],
[1]])).all())
self.assertTrue(self.Sample.__dict__.__contains__('scale_factor'))
self.assertTrue(
(self.Sample.scale_factor[0]['mu'] == np.array([[2, 3],
[3, 2]])).all())
self.assertTrue(
(self.Sample.output()[0]['scale_factors'][0]['mu'] == np.array(
[[[2, 3], [3, 2]]])).all(),
str(self.Sample.output()[0]['scale_factors'][0]['mu']))
self.tearDown()
self.setUp()
self.test_append()
out, out_str = self.Sample.output(convert=True)
self.assertTrue('dV' in out.keys())
self.assertTrue('moment_tensor_space' in out.keys())
self.assertTrue('probability' in out.keys())
self.assertTrue('ln_pdf' in out.keys())
self.assertTrue('g' in out.keys())