def test_read_matlab_output(self):
self.hyp_file()
events = parse_hyp('hyptest.hyp')
event = events[0]
mdict, sdict = full_pdf_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., 2.], [2., 1.], [1., 2.], [2., 1.], [1., 2.],
[2., 1.]]),
'ln_pdf':
np.matrix([0, 0.7]),
'dV':
1,
'probability':
np.matrix([[1., 2.]]),
'total_number_samples':
400
})
MATLAB_output([mdict, sdict], 'hyptest.mat')
event, stations = read_matlab_output('hyptest.mat')
self.assertAlmostEqual(event, mdict['Events'], 10)
self.assertAlmostEqual(stations['azimuth'], mdict['Stations'][:, 1],
10)
self.assertAlmostEqual(stations['takeoff_angle'],
mdict['Stations'][:, 2], 10)
self.assertAlmostEqual(stations['polarity'], mdict['Stations'][:, 3],
10)
def test_parse_hyp(self):
self.hyp_file()
events = parse_hyp('hyptest.hyp')
self.assertEqual(len(events), 1)
self.assertTrue('PPolarity' in events[0].keys())
self.assertTrue('UID' in events[0].keys())
self.assertTrue('hyp_file' in events[0].keys())
self.assertEqual(events[0]['UID'], "20150126222324275")
self.assertEqual(len(events[0].keys()), 3)
def test_read_binary_output(self):
self.hyp_file()
events = parse_hyp('hyptest.hyp')
event = events[0]
x = {
'moment_tensor_space':
np.matrix([[1., 2.], [2., 1.], [1., 2.], [2., 1.], [1., 2.],
[2., 1.]]),
'dkl':
1.2,
'ln_pdf':
np.matrix([0, 0.7]),
'probability':
np.matrix([[1., 2.]]),
'total_number_samples':
400
}
a, b, c = hyp_output_dicts(event, False, x)
try:
os.remove('MTfitOUTPUTTEST.hyp')
except Exception:
pass
try:
os.remove('MTfitOUTPUTTEST.mt')
except Exception:
pass
self.assertFalse(os.path.exists('MTfitOUTPUTTEST.hyp'))
self.assertFalse(os.path.exists('MTfitOUTPUTTEST.mt'))
fid, out_str = hyp_output([a, b], fid='MTfitOUTPUTTEST.hyp')
y = read_binary_output('MTfitOUTPUTTEST.mt')
self.assertTrue(os.path.exists('MTfitOUTPUTTEST.hyp'))
self.assertTrue(os.path.exists('MTfitOUTPUTTEST.mt'))
self.assertEqual(sorted(y[0].keys()), sorted(x.keys()))
self.assertTrue(
(y[0]['moment_tensor_space'] == x['moment_tensor_space']).all())
self.assertEqual(y[0]['dkl'], x['dkl'])
try:
os.remove('MTfitOUTPUTTEST.hyp')
except Exception:
pass
try:
os.remove('MTfitOUTPUTTEST.mt')
except Exception:
pass
x = {
'moment_tensor_space':
np.matrix([[1., -0.51969334, 3.], [2., 0.22610635, 3.],
[1., 0.29358698, 3.], [2., 0.58532165, 3.],
[1., -0.27015115, 3.], [2., -0.42073549, 3.]]),
'probability':
np.matrix([[1., 2., 1.]]),
'dkl':
2.4,
'ln_pdf':
np.matrix([0, 0.7, 0]),
'total_number_samples':
400,
'g':
np.array([0.1, 0.2, 0.3]),
'd':
np.array([0.2, 0.2, 0.2]),
'k':
np.array([0.2, 0.2, 0.2]),
'h':
np.array([0.2, 0.2, 0.2]),
's':
np.array([0.2, 0.2, 0.2]),
'S1':
np.array([0.2, 0.2, 0.2]),
'D1':
np.array([0.2, 0.2, 0.2]),
'R1':
np.array([0.2, 0.2, 0.2]),
'u':
np.array([0.2, 0.2, 0.2]),
'v':
np.array([0.2, 0.2, 0.2]),
'S2':
np.array([0.2, 0.2, 0.2]),
'D2':
np.array([0.2, 0.2, 0.2]),
'R2':
np.array([0.2, 0.2, 0.2]),
'ln_bayesian_evidence':
1. + 10.
}
a, b, c = hyp_output_dicts(event, False, x)
try:
os.remove('MTfitOUTPUTTEST.hyp')
except Exception:
pass
try:
os.remove('MTfitOUTPUTTEST.mt')
except Exception:
pass
self.assertFalse(os.path.exists('MTfitOUTPUTTEST.hyp'))
self.assertFalse(os.path.exists('MTfitOUTPUTTEST.mt'))
fid, out_str = hyp_output([a, b], fid='MTfitOUTPUTTEST.hyp')
y = read_binary_output('MTfitOUTPUTTEST.mt')
self.assertTrue(os.path.exists('MTfitOUTPUTTEST.hyp'))
self.assertTrue(os.path.exists('MTfitOUTPUTTEST.mt'))
self.assertEqual(sorted(y[0].keys()), sorted(x.keys()))
self.assertTrue((y[0]['g'] == x['g']).all())
self.assertEqual(y[0]['ln_bayesian_evidence'],
x['ln_bayesian_evidence'])
self.assertEqual(y[0]['dkl'], x['dkl'])
try:
os.remove('MTfitOUTPUTTEST.hyp')
except Exception:
pass
try:
os.remove('MTfitOUTPUTTEST.mt')
except Exception:
pass
def test_hyp_output(self):
self.hyp_file()
events = parse_hyp('hyptest.hyp')
event = events[0]
a, b, c = hyp_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., 2.], [2., 1.], [1., 2.], [2., 1.], [1., 2.],
[2., 1.]]),
'ln_pdf':
np.matrix([0, 0.7, 0]),
'probability':
np.matrix([[1., 2.]]),
'total_number_samples':
400
})
try:
os.remove('MTfitOUTPUTTEST.hyp')
except Exception:
pass
try:
os.remove('MTfitOUTPUTTEST.mt')
except Exception:
pass
self.assertFalse(os.path.exists('MTfitOUTPUTTEST.hyp'))
self.assertFalse(os.path.exists('MTfitOUTPUTTEST.mt'))
fid, out_str = hyp_output([a, b], fid='MTfitOUTPUTTEST.hyp')
self.assertTrue(os.path.exists('MTfitOUTPUTTEST.hyp'))
self.assertTrue(os.path.exists('MTfitOUTPUTTEST.mt'))
try:
os.remove('MTfitOUTPUTTEST.hyp')
except Exception:
pass
try:
os.remove('MTfitOUTPUTTEST.mt')
except Exception:
pass
event['hyp_file'].pop(14)
a, b, c = hyp_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., -0.51969334, 3.], [2., 0.22610635, 3.],
[1., 0.29358698, 3.], [2., 0.58532165, 3.],
[1., -0.27015115, 3.], [2., -0.42073549, 3.]]),
'probability':
np.matrix([[1., 2., 1.]]),
'ln_pdf':
np.matrix([0, 0.7, 0]),
'total_number_samples':
400,
'g':
np.array([0.2, 0.2, 0.2]),
'd':
np.array([0.2, 0.2, 0.2]),
'k':
np.array([0.2, 0.2, 0.2]),
'h':
np.array([0.2, 0.2, 0.2]),
's':
np.array([0.2, 0.2, 0.2]),
'S1':
np.array([0.2, 0.2, 0.2]),
'D1':
np.array([0.2, 0.2, 0.2]),
'R1':
np.array([0.2, 0.2, 0.2]),
'u':
np.array([0.2, 0.2, 0.2]),
'v':
np.array([0.2, 0.2, 0.2]),
'S2':
np.array([0.2, 0.2, 0.2]),
'D2':
np.array([0.2, 0.2, 0.2]),
'R2':
np.array([0.2, 0.2, 0.2]),
'ln_bayesian_evidence':
1. + 10
})
try:
os.remove('MTfitOUTPUTTEST.hyp')
except Exception:
pass
try:
os.remove('MTfitOUTPUTTEST.mt')
except Exception:
pass
self.assertFalse(os.path.exists('MTfitOUTPUTTEST.hyp'))
self.assertFalse(os.path.exists('MTfitOUTPUTTEST.mt'))
fid, out_str = hyp_output([a, b], fid='MTfitOUTPUTTEST.hyp')
self.assertTrue(os.path.exists('MTfitOUTPUTTEST.hyp'))
self.assertTrue(os.path.exists('MTfitOUTPUTTEST.mt'))
try:
os.remove('MTfitOUTPUTTEST.hyp')
except Exception:
pass
try:
os.remove('MTfitOUTPUTTEST.mt')
except Exception:
pass
def test_hyp_output_dicts(self):
self.hyp_file()
events = parse_hyp('hyptest.hyp')
event = events[0]
a, b, c = hyp_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., 2.], [2., 1.], [1., 2.], [2., 1.], [1., 2.],
[2., 1.]]),
'ln_pdf':
np.matrix([0, 0.7]),
'probability':
np.matrix([[1., 2.]]),
'total_number_samples':
400
})
self.assertEqual(len(a.split('\n')), len(event['hyp_file']) + 1)
self.assertTrue('MOMENTTENSOR' in a)
self.assertEqual(float(a.split()[a.split().index('MTNN') + 1]), 2.0)
self.assertEqual(len(b), 169)
events = parse_hyp('hyptest.hyp')
event = events[0]
a, b, c = hyp_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., 2., 3.], [2., 1., 3.], [1., 2., 3.],
[2., 1., 3.], [1., 2., 3.], [2., 1., 3.]]),
'ln_pdf':
np.matrix([0, 0.7, 0]),
'probability':
np.matrix([[1., 2., 1.]]),
'total_number_samples':
400
})
self.assertEqual(len(a.split('\n')), len(event['hyp_file']) + 1)
self.assertTrue('MOMENTTENSOR' in a)
self.assertEqual(float(a.split()[a.split().index('MTNN') + 1]), 2.0)
self.assertEqual(len(b), 233)
events = parse_hyp('hyptest.hyp')
event = events[0]
event['hyp_file'].pop(14)
a, b, c = hyp_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., 2., 3.], [2., 1., 3.], [1., 2., 3.],
[2., 1., 3.], [1., 2., 3.], [2., 1., 3.]]),
'ln_pdf':
np.matrix([0, 0.7, 0]),
'probability':
np.matrix([[1., 2., 1.]]),
'total_number_samples':
400
})
self.assertEqual(len(a.split('\n')), len(event['hyp_file']) + 2)
self.assertTrue('MOMENTTENSOR' in a)
self.assertEqual(float(a.split()[a.split().index('MTNN') + 1]), 2.0)
self.assertEqual(len(b), 233)
event.pop('hyp_file')
a, b, c = hyp_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., 2., 3.], [2., 1., 3.], [1., 2., 3.],
[2., 1., 3.], [1., 2., 3.], [2., 1., 3.]]),
'ln_pdf':
np.matrix([0, 0.7, 0]),
'probability':
np.matrix([[1., 2., 1.]]),
'total_number_samples':
400
})
self.assertEqual(len(a.split('\n')), 36)
self.assertTrue('MOMENTTENSOR' in a)
self.assertEqual(float(a.split()[a.split().index('MTNN') + 1]), 2.0)
self.assertEqual(len(b), 233)
events = parse_hyp('hyptest.hyp')
event = events[0]
event['hyp_file'].pop(14)
a, b, c = hyp_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., -0.51969334, 3.], [2., 0.22610635, 3.],
[1., 0.29358698, 3.], [2., 0.58532165, 3.],
[1., -0.27015115, 3.], [2., -0.42073549, 3.]]),
'ln_pdf':
np.matrix([0, 0.7, 0]),
'probability':
np.matrix([[1., 2., 1.]]),
'total_number_samples':
400
})
self.assertEqual(len(a.split('\n')), len(event['hyp_file']) + 2)
self.assertFalse('MOMENTTENSOR' in a)
self.assertTrue('FOCALMECH' in a)
self.assertAlmostEqual(float(a.split()[a.split().index('Mech') + 1]),
0.5 * 180 / np.pi, 5)
self.assertEqual(len(b), 233)
a, b, c = hyp_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., -0.51969334, 3.], [2., 0.22610635, 3.],
[1., 0.29358698, 3.], [2., 0.58532165, 3.],
[1., -0.27015115, 3.], [2., -0.42073549, 3.]]),
'probability':
np.matrix([[1., 2., 1.]]),
'ln_pdf':
np.matrix([0, 0.7, 0]),
'total_number_samples':
400,
'g':
np.array([0.2, 0.2, 0.2]),
'd':
np.array([0.2, 0.2, 0.2]),
'k':
np.array([0.2, 0.2, 0.2]),
'h':
np.array([0.2, 0.2, 0.2]),
's':
np.array([0.2, 0.2, 0.2]),
'S1':
np.array([0.2, 0.2, 0.2]),
'D1':
np.array([0.2, 0.2, 0.2]),
'R1':
np.array([0.2, 0.2, 0.2]),
'u':
np.array([0.2, 0.2, 0.2]),
'v':
np.array([0.2, 0.2, 0.2]),
'S2':
np.array([0.2, 0.2, 0.2]),
'D2':
np.array([0.2, 0.2, 0.2]),
'R2':
np.array([0.2, 0.2, 0.2]),
'ln_bayesian_evidence': (1, 10)
})
self.assertEqual(len(a.split('\n')), len(event['hyp_file']) + 2)
self.assertFalse('MOMENTTENSOR' in a)
self.assertTrue('FOCALMECH' in a)
self.assertAlmostEqual(float(a.split()[a.split().index('Mech') + 1]),
0.5 * 180 / np.pi, 5)
self.assertEqual(len(b), 545)
def test_full_pdf_output_dicts(self):
self.hyp_file()
events = parse_hyp('hyptest.hyp')
event = events[0]
mdict, sdict = full_pdf_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., 2.], [2., 1.], [1., 2.], [2., 1.], [1., 2.],
[2., 1.]]),
'ln_pdf':
np.matrix([0, 0.7]),
'dV':
1,
'probability':
np.matrix([[1., 2.]]),
'total_number_samples':
400
})
self.assertTrue('Other' in mdict.keys())
self.assertTrue('Events' in mdict.keys())
self.assertTrue('Stations' in mdict.keys())
self.assertTrue('MTSpace' in mdict['Events'].keys())
self.assertTrue('Probability' in mdict['Events'].keys())
self.assertTrue('ln_pdf' in mdict['Events'].keys())
self.assertTrue('UID' in mdict['Events'].keys())
self.assertTrue('NSamples' in mdict['Events'].keys())
self.assertEqual(mdict['Stations'].shape, (20, 4))
event['PPolarityProbability'] = event['PPolarity'].copy()
event['PPolarityProbability']['Measured'] = np.matrix([[0.6, 0.4],
[0.7, 0.3],
[0.8, 0.2],
[0.67, 0.33],
[0.94, 0.06],
[0.32, 0.68],
[0.96, 0.04],
[0.76, 0.24],
[0.82, 0.18],
[0.12, 0.88],
[0.57, 0.43],
[0.68, 0.32],
[0.51, 0.49],
[0.68, 0.32],
[0.50, 0.50],
[0.02, 0.98],
[0.6, 0.4],
[0.7, 0.3],
[0.8, 0.2],
[0.67, 0.33]])
event.pop('PPolarity')
mdict, sdict = full_pdf_output_dicts(
event, ['PPolarityProbability'], {
'moment_tensor_space':
np.matrix([[1., 2.], [2., 1.], [1., 2.], [2., 1.], [1., 2.],
[2., 1.]]),
'ln_pdf':
np.matrix([0, 0.7]),
'dV':
1,
'probability':
np.matrix([[1., 2.]]),
'total_number_samples':
400
})
self.assertTrue('Other' in mdict.keys())
self.assertTrue('Events' in mdict.keys())
self.assertTrue('Stations' in mdict.keys())
self.assertTrue('MTSpace' in mdict['Events'].keys())
self.assertTrue('Probability' in mdict['Events'].keys())
self.assertTrue('ln_pdf' in mdict['Events'].keys())
self.assertTrue('UID' in mdict['Events'].keys())
self.assertTrue('NSamples' in mdict['Events'].keys())
self.assertEqual(mdict['Stations'].shape, (20, 4))
self.assertTrue(
all(mdict['Stations'][:, 3] == np.array([
0.6, 0.7, 0.8, 0.67, 0.94, -0.68, 0.96, 0.76, 0.82, -0.88,
0.57, 0.68, 0.51, 0.68, 0.5, -0.98, 0.6, 0.7, 0.8, 0.67
])))
mdict, sdict = full_pdf_output_dicts(
event, False, {
'moment_tensor_space':
np.matrix([[1., 2.], [2., 1.], [1., 2.], [2., 1.], [1., 2.],
[2., 1.]]),
'ln_pdf':
np.matrix([0, 0.7]),
'dV':
1,
'probability':
np.matrix([[1., 2.]]),
'total_number_samples':
400
})
self.assertTrue('Other' in mdict.keys())
self.assertTrue('Events' in mdict.keys())
self.assertTrue('Stations' in mdict.keys())
self.assertTrue('MTSpace' in mdict['Events'].keys())
self.assertTrue('Probability' in mdict['Events'].keys())
self.assertTrue('ln_pdf' in mdict['Events'].keys())
self.assertTrue('UID' in mdict['Events'].keys())
self.assertTrue('NSamples' in mdict['Events'].keys())
self.assertEqual(mdict['Stations'].shape, (20, 4))