def test_xcorr_all_again(self):
seed(42)
data1 = random(1200) * np.sin(np.arange(1200)) * np.arange(1200) - 0.5
data2 = random(1000) * np.sin(np.arange(1000) * 0.4) * 500 - 0.5
data1 -= np.mean(data1[100:1100])
data2 -= np.mean(data2)
data2_pad = np.hstack((np.zeros(100), data2, np.zeros(100)))
cor1a = correlate(data1, data2, 'valid')
cor1a2 = xcorr_obspy(data1, data2_pad, 100)
cor1b = xcorrt(data1, data2, 100)
cor1c = xcorrf(data1, data2, 100)
cor1a *= max(cor1b) / max(cor1a)
cor1a2 *= max(cor1b) / max(cor1a2)
cor3 = xcorrf(data2, data1, 100)[::-1] #@UnusedVariable
cor4 = xcorrt(data1, data2, 100, demean=False, normalize=False)
cor5 = xcorrf(data1, data2, 100, demean=False, normalize=False)
val = max (cor4)
cor4 /= val
cor5 /= val
cor6 = xcorrt(data1, data2, 100, shift_zero=100)
cor7 = xcorrf(data1, data2, 100, shift_zero=100)
cor8 = xcorrt(data1, data2, 500, window=200)
cor9 = xcorrf(data1, data2, 500, window=200)
# from pylab import plot, show, subplot, legend
# subplot(411)
# plot(data1)
# plot(data2)
# subplot(412)
# plot(cor1a, label='convolve')
# plot(cor1b, label='xcorrt')
# plot(cor1c, label='xcorrf')
# plot(cor1a2, label='xcorr_obspy')
# plot(cor6, label='xcorrt shift')
# plot(cor7, label='xcorrf shift')
# legend()
# subplot(413)
# plot(cor4, label='xcorrt all not demeaned not normalized')
# plot(cor5, label='xcorrf')
# legend()
# subplot(414)
# plot(cor8, label='xcorrt window')
# plot(cor9, label='xcorrf window')
# legend()
# show()
np.testing.assert_array_almost_equal(cor1a, cor1b)
np.testing.assert_array_almost_equal(cor1a, cor1a2)
np.testing.assert_array_almost_equal(cor1b, cor1c)
np.testing.assert_array_almost_equal(cor1c[20:30], cor7[120:130])
np.testing.assert_array_almost_equal(cor4, cor5)
np.testing.assert_array_almost_equal(cor6, cor7)
np.testing.assert_array_almost_equal(cor8, cor9)
def test_xcorr_all_again(self):
seed(42)
data1 = random(1200) * np.sin(np.arange(1200)) * np.arange(1200) - 0.5
data2 = random(1000) * np.sin(np.arange(1000) * 0.4) * 500 - 0.5
data1 -= np.mean(data1[100:1100])
data2 -= np.mean(data2)
data2_pad = np.hstack((np.zeros(100), data2, np.zeros(100)))
cor1a = correlate(data1, data2, 'valid')
cor1a2 = xcorr_obspy(data1, data2_pad, 100)
cor1b = xcorrt(data1, data2, 100)
cor1c = xcorrf(data1, data2, 100)
cor1a *= max(cor1b) / max(cor1a)
cor1a2 *= max(cor1b) / max(cor1a2)
cor3 = xcorrf(data2, data1, 100)[::-1] #@UnusedVariable
cor4 = xcorrt(data1, data2, 100, demean=False, normalize=False)
cor5 = xcorrf(data1, data2, 100, demean=False, normalize=False)
val = max(cor4)
cor4 /= val
cor5 /= val
cor6 = xcorrt(data1, data2, 100, shift_zero=100)
cor7 = xcorrf(data1, data2, 100, shift_zero=100)
cor8 = xcorrt(data1, data2, 500, window=200)
cor9 = xcorrf(data1, data2, 500, window=200)
# from pylab import plot, show, subplot, legend
# subplot(411)
# plot(data1)
# plot(data2)
# subplot(412)
# plot(cor1a, label='convolve')
# plot(cor1b, label='xcorrt')
# plot(cor1c, label='xcorrf')
# plot(cor1a2, label='xcorr_obspy')
# plot(cor6, label='xcorrt shift')
# plot(cor7, label='xcorrf shift')
# legend()
# subplot(413)
# plot(cor4, label='xcorrt all not demeaned not normalized')
# plot(cor5, label='xcorrf')
# legend()
# subplot(414)
# plot(cor8, label='xcorrt window')
# plot(cor9, label='xcorrf window')
# legend()
# show()
np.testing.assert_array_almost_equal(cor1a, cor1b)
np.testing.assert_array_almost_equal(cor1a, cor1a2)
np.testing.assert_array_almost_equal(cor1b, cor1c)
np.testing.assert_array_almost_equal(cor1c[20:30], cor7[120:130])
np.testing.assert_array_almost_equal(cor4, cor5)
np.testing.assert_array_almost_equal(cor6, cor7)
np.testing.assert_array_almost_equal(cor8, cor9)
def test_acorr(self):
cor1 = xcorrf(self.data1, self.data1, self.N // 4, oneside=True)
cor2 = acorrf(self.data1, self.N // 4)
# from pylab import plot, show
# plot(cor1)
# plot(cor2)
# show()
np.testing.assert_array_almost_equal(cor1, cor2, 3)
self.assertTrue((cor2[0] - 1) ** 2 < 1e-5)
def test_acorr(self):
cor1 = xcorrf(self.data1, self.data1, self.N // 4, oneside=True)
cor2 = acorrf(self.data1, self.N // 4)
# from pylab import plot, show
# plot(cor1)
# plot(cor2)
# show()
np.testing.assert_array_almost_equal(cor1, cor2, 3)
self.assertTrue((cor2[0] - 1)**2 < 1e-5)
def test_xcorr_realdata(self):
stream = self.stream.copy()
data = stream.select(component='Z')[0].data
shift = 100
cor1 = xcorrf(data, data[shift:-shift], shift)
cor2 = xcorrt(data, data[shift:-shift], shift)
# from pylab import plot, show
# plot(cor1)
# plot(cor2)
# show()
np.testing.assert_array_almost_equal(cor1, cor2)
def test_xcorr_realdata(self):
stream = self.stream.copy()
data = stream.select(component='Z')[0].data
shift = 100
cor1 = xcorrf(data, data[shift:-shift], shift)
cor2 = xcorrt(data, data[shift:-shift], shift)
# from pylab import plot, show
# plot(cor1)
# plot(cor2)
# show()
np.testing.assert_array_almost_equal(cor1, cor2)
def test_xcorr(self):
cor1 = correlate(self.data1, self.data2, 'same')
cor2 = xcorrf(self.data1.copy(), self.data2.copy(), self.N // 2, demean=False, normalize=False)
# from pylab import plot, show, subplot
# subplot(211)
# plot(data1)
# plot(data2)
# subplot(212)
# plot(cor1)
# plot(cor2)
# show()
np.testing.assert_array_almost_equal(cor1, cor2)
def test_xcorr_xcorrf(self):
N = 1001
data1 = np.sin(np.arange(N // 2 + 1) / 100.)
data2 = np.e**(-(np.arange(N) - 500)**2 / 100.) - np.e**(
-(np.arange(N) - 50)**2 /
100.) + 5 * np.e**(-(np.arange(N) - 950)**2 / 100.)
cor1 = xcorrt(data1, data2, 750)
cor2 = xcorrf(data1, data2, 750)
cor3 = xcorrf(data2, data1, 750)[::-1]
cor4 = xcorrt(data1, data2, 750, demean=False, normalize=False)
cor5 = xcorrf(data1, data2, 750, demean=False, normalize=False)
cor6 = xcorrt(data1, data2, 750, shift_zero=100)
cor7 = xcorrf(data1, data2, 750, shift_zero=100)
cor8 = xcorrt(data1, data2, 750, window=200)
cor9 = xcorrf(data1, data2, 750, window=200)
# from pylab import plot, show, subplot, legend
# subplot(411)
# plot(data1)
# plot(data2)
# subplot(412)
# plot(cor1, label='xcorrt')
# plot(cor2, label='xcorrf ndat1 > ndat2')
# plot(cor3, label='xcorrf ndat2 > ndat1')
# legend()
# subplot(413)
# plot(cor4, label='xcorrt all not demeaned not normalized')
# plot(cor5, label='xcorrf')
# legend()
# subplot(414)
# plot(cor6, label='xcorrt shift')
# plot(cor7, label='xcorrf shift')
# plot(cor8, label='xcorrt window')
# plot(cor9, label='xcorrf window')
# legend()
# show()
np.testing.assert_array_almost_equal(cor1, cor2)
np.testing.assert_array_almost_equal(cor1, cor3)
np.testing.assert_array_almost_equal(cor4, cor5, 5)
np.testing.assert_array_almost_equal(cor6, cor7)
np.testing.assert_array_almost_equal(cor8, cor9)
def test_xcorr_xcorrf(self):
N = 1001
data1 = np.sin(np.arange(N // 2 + 1) / 100.)
data2 = np.e ** (-(np.arange(N) - 500) ** 2 / 100.) - np.e ** (-(np.arange(N) - 50) ** 2 / 100.) + 5 * np.e ** (-(np.arange(N) - 950) ** 2 / 100.)
cor1 = xcorrt(data1, data2, 750)
cor2 = xcorrf(data1, data2, 750)
cor3 = xcorrf(data2, data1, 750)[::-1]
cor4 = xcorrt(data1, data2, 750, demean=False, normalize=False)
cor5 = xcorrf(data1, data2, 750, demean=False, normalize=False)
cor6 = xcorrt(data1, data2, 750, shift_zero=100)
cor7 = xcorrf(data1, data2, 750, shift_zero=100)
cor8 = xcorrt(data1, data2, 750, window=200)
cor9 = xcorrf(data1, data2, 750, window=200)
# from pylab import plot, show, subplot, legend
# subplot(411)
# plot(data1)
# plot(data2)
# subplot(412)
# plot(cor1, label='xcorrt')
# plot(cor2, label='xcorrf ndat1 > ndat2')
# plot(cor3, label='xcorrf ndat2 > ndat1')
# legend()
# subplot(413)
# plot(cor4, label='xcorrt all not demeaned not normalized')
# plot(cor5, label='xcorrf')
# legend()
# subplot(414)
# plot(cor6, label='xcorrt shift')
# plot(cor7, label='xcorrf shift')
# plot(cor8, label='xcorrt window')
# plot(cor9, label='xcorrf window')
# legend()
# show()
np.testing.assert_array_almost_equal(cor1, cor2)
np.testing.assert_array_almost_equal(cor1, cor3)
np.testing.assert_array_almost_equal(cor4, cor5, 5)
np.testing.assert_array_almost_equal(cor6, cor7)
np.testing.assert_array_almost_equal(cor8, cor9)
def test_xcorr(self):
cor1 = correlate(self.data1, self.data2, 'same')
cor2 = xcorrf(self.data1.copy(),
self.data2.copy(),
self.N // 2,
demean=False,
normalize=False)
# from pylab import plot, show, subplot
# subplot(211)
# plot(data1)
# plot(data2)
# subplot(212)
# plot(cor1)
# plot(cor2)
# show()
np.testing.assert_array_almost_equal(cor1, cor2)
def _noisexcorr_traces(tr1, tr2, shift_sec, correlation):
sr = tr1.stats.sampling_rate
if 'is_fft' not in tr1.stats or not tr1.stats.is_fft:
tr1.fft()
if 'is_fft' not in tr2.stats or not tr2.stats.is_fft:
tr2.fft()
cor = xcorrf(tr1.data, tr2.data, int(shift_sec * sr), freq_domain=True,
N1=tr1.stats.npts_data, N2=tr2.stats.npts_data,
stdev1=tr1.stats.stdev, stdev2=tr2.stats.stdev)
# mirror cor function so that it displays waves from station1 to
# station2 for positive lag times
tr_cor = Trace(data=cor[::-1], header=tr1.stats.copy())
tr_cor.stats.npts = len(cor)
tr_cor.stats.sampling_rate = (tr_cor.stats.npts - 1.) / 2 / shift_sec
tr_cor.stats.station = '-'.join(correlation)
tr_cor.stats.is_fft = False
tr_cor.stats.filter += 'IFFTXcorr%s' % shift_sec
return tr_cor