def test_xcorr_obspy(self):
N = 1001
data1 = np.sin(np.arange(N) / 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.)
data1[:] = data1[:] - np.mean(data1)
data2[:] = data2[:] - np.mean(data2)
cor1 = correlate(data1, data2, 'same')
cor2 = xcorr_obspy(data1, data2, N // 2)
#cor3 = xcorr_obspy(data1, data1, N)
cor1 *= max(cor2) / max(cor1)
def test_xcorr_obspy(self):
N = 1001
data1 = np.sin(np.arange(N) / 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.)
data1[:] = data1[:] - np.mean(data1)
data2[:] = data2[:] - np.mean(data2)
cor1 = correlate(data1, data2, 'same')
cor2 = xcorr_obspy(data1, data2, N // 2)
#cor3 = xcorr_obspy(data1, data1, N)
cor1 *= max(cor2) / max(cor1)
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)
