def fft_cross_corr(a):
M = a.shape[0]
L = a.shape[1]
cross_correlations = np.zeros((M**2, 2 * L - 1), dtype=np.complex128)
for i in range(0, M):
for j in range(0, M):
cross_correlations[i * M + j, :] = sc.cross_correlate(a[i], a[j])
return cross_correlations
def fft_cross_corr(a):
M = a.shape[0]
L = a.shape[1]
cross_correlations = np.zeros((M ** 2, 2 * L - 1), dtype=np.complex128)
for i in range(0, M):
for j in range(0, M):
cross_correlations[i * M + j, :] = sc.cross_correlate(a[i], a[j])
return cross_correlations
def test_cross_correlate(self):
inp1 = np.linspace(1, 4, 4)
inp2 = np.linspace(4, 7, 4)
out = sc.cross_correlate(inp1, inp2, maxlag=2, unbiased=False)
ref = np.array([20, 38, 60, 47, 32])
np.testing.assert_array_almost_equal(out, ref)
def test_cross_correlate(self):
inp1 = np.linspace(1, 4, 4)
inp2 = np.linspace(4, 7, 4)
out = sc.cross_correlate(inp1, inp2, maxlag=2, unbiased=False)
ref = np.array([20, 38, 60, 47, 32])
np.testing.assert_array_almost_equal(out, ref)