def test_xcorr(self):
"""
"""
# example 1 - all samples are equal
np.random.seed(815) # make test reproducible
tr1 = np.random.randn(10000).astype(np.float32)
tr2 = tr1.copy()
shift, corr = xcorr(tr1, tr2, 100)
self.assertEqual(shift, 0)
self.assertAlmostEqual(corr, 1, 2)
# example 2 - all samples are different
tr1 = np.ones(10000, dtype=np.float32)
tr2 = np.zeros(10000, dtype=np.float32)
shift, corr = xcorr(tr1, tr2, 100)
self.assertEqual(shift, 0)
self.assertAlmostEqual(corr, 0, 2)
# example 3 - shift of 10 samples
tr1 = np.random.randn(10000).astype(np.float32)
tr2 = np.concatenate((np.zeros(10), tr1[0:-10]))
shift, corr = xcorr(tr1, tr2, 100)
self.assertEqual(shift, -10)
self.assertAlmostEqual(corr, 1, 2)
shift, corr = xcorr(tr2, tr1, 100)
self.assertEqual(shift, 10)
self.assertAlmostEqual(corr, 1, 2)
# example 4 - shift of 10 samples + small sine disturbance
tr1 = (np.random.randn(10000) * 100).astype(np.float32)
var = np.sin(np.arange(10000, dtype=np.float32) * 0.1)
tr2 = np.concatenate((np.zeros(10), tr1[0:-10])) * 0.9
tr2 += var
shift, corr = xcorr(tr1, tr2, 100)
self.assertEqual(shift, -10)
self.assertAlmostEqual(corr, 1, 2)
shift, corr = xcorr(tr2, tr1, 100)
self.assertEqual(shift, 10)
self.assertAlmostEqual(corr, 1, 2)
def test_xcorr(self):
"""
"""
# example 1 - all samples are equal
np.random.seed(815) # make test reproducable
tr1 = np.random.randn(10000).astype('float32')
tr2 = tr1.copy()
shift, corr = xcorr(tr1, tr2, 100)
self.assertEquals(shift, 0)
self.assertAlmostEqual(corr, 1, 2)
# example 2 - all samples are different
tr1 = np.ones(10000, dtype='float32')
tr2 = np.zeros(10000, dtype='float32')
shift, corr = xcorr(tr1, tr2, 100)
self.assertEquals(shift, 0)
self.assertAlmostEqual(corr, 0, 2)
# example 3 - shift of 10 samples
tr1 = np.random.randn(10000).astype('float32')
tr2 = np.concatenate((np.zeros(10), tr1[0:-10]))
shift, corr = xcorr(tr1, tr2, 100)
self.assertEquals(shift, -10)
self.assertAlmostEqual(corr, 1, 2)
shift, corr = xcorr(tr2, tr1, 100)
self.assertEquals(shift, 10)
self.assertAlmostEqual(corr, 1, 2)
# example 4 - shift of 10 samples + small sine disturbance
tr1 = (np.random.randn(10000) * 100).astype('float32')
var = np.sin(np.arange(10000, dtype='float32') * 0.1)
tr2 = np.concatenate((np.zeros(10), tr1[0:-10])) * 0.9
tr2 += var
shift, corr = xcorr(tr1, tr2, 100)
self.assertEquals(shift, -10)
self.assertAlmostEqual(corr, 1, 2)
shift, corr = xcorr(tr2, tr1, 100)
self.assertEquals(shift, 10)
self.assertAlmostEqual(corr, 1, 2)
continue
sta_name_split = sta_name.split('.')
real_tr = read(os.path.join(real_add, ev_name, 'BH', 'dis.' + sta_name_split[1] + '.' +
sta_name_split[2] + '.' + sta_name_split[-1]))[0]
mfi_tr = convSTF(grf_tr, STF_tr, grp)
# preprocessing all three waveforms
mfi_tr = preprocess(tr=mfi_tr, lfreq=lfreq, hfreq=hfreq)
real_tr = preprocess(tr=real_tr, lfreq=lfreq, hfreq=hfreq)
print 'WARNING: you are using resample!!!'
real_tr.resample(mfi_tr.stats.sampling_rate)
real_tr = real_tr.slice(ev_time + phase_time - tb, ev_time + phase_time + ta)
mfi_tr = mfi_tr.slice(ev_time + phase_time - tb, ev_time + phase_time + ta)
np_xcorr, fac_xcorr = xcorr(mfi_tr.data, real_tr.data, int(15.*real_tr.stats.sampling_rate))
print fac_xcorr
sys.stdout.flush()
if fac_xcorr < min_xcorr:
print '.',
continue
if np_xcorr > int(1.*real_tr.stats.sampling_rate):
print '.',
continue
print '\n******************'
print sta_name
print 'Sampling rate difference: ',
print real_tr.stats.sampling_rate - mfi_tr.stats.sampling_rate
print 'xcorr: ',
print np_xcorr, fac_xcorr
