def test_lpca_rmse():
S0_w_noise = 100 + 2 * np.random.standard_normal((22, 23, 30, 20))
rmse_w_noise = np.sqrt(np.mean((S0_w_noise - 100) ** 2))
S0_denoised, _, _ = randommatrix_lpca(S0_w_noise)
rmse_denoised = np.sqrt(np.mean((S0_denoised - 100) ** 2))
# Denoising should always improve the RMSE:
assert_(rmse_denoised < rmse_w_noise)
def test_lpca_sharpness():
S0 = np.ones((30, 30, 30, 20), dtype=np.float64) * 100
S0[10:20, 10:20, 10:20, :] = 50
S0[20:30, 20:30, 20:30, :] = 0
S0 = S0 + 20 * np.random.standard_normal((30, 30, 30, 20))
S0ns, _, _ = randommatrix_lpca(S0)
# check the edge gradient
edgs = np.abs(np.mean(S0ns[8, 10:20, 10:20, :] -
S0ns[12, 10:20, 10:20, :]) - 50)
assert_(edgs < 2)
def test_lpca_dtype():
# If out_dtype is not specified, we retain the original precision:
S0 = 200 * np.ones((20, 20, 20, 3), dtype=np.float64)
S0ns, _, _ = randommatrix_lpca(S0)
assert_equal(S0.dtype, S0ns.dtype)
S0 = 200 * np.ones((20, 20, 20, 20), dtype=np.uint16)
S0ns, _, _ = randommatrix_lpca(S0)
assert_equal(S0.dtype, S0ns.dtype)
# If we set out_dtype, we get what we asked for:
S0 = 200 * np.ones((20, 20, 20, 20), dtype=np.uint16)
S0ns, _, _ = randommatrix_lpca(S0, out_dtype=np.float32)
assert_equal(np.float32, S0ns.dtype)
# If we set a few entries to zero, this induces negative entries in the
# Resulting denoised array:
S0[5:8, 5:8, 5:8] = 0
# But if we should always get all non-negative results:
S0ns, _, _ = randommatrix_lpca(S0)
assert_(np.all(S0ns >= 0))
# And no wrap-around to crazy high values:
assert_(np.all(S0ns <= 200))
def test_lpca_boundary_behaviour():
# check is first slice is getting denoised or not ?
S0 = 100 * np.ones((20, 20, 20, 20), dtype='f8')
S0[:, :, 0, :] = S0[:, :, 0, :] + 2 * \
np.random.standard_normal((20, 20, 20))
S0_first = S0[:, :, 0, :]
S0ns, _, _ = randommatrix_lpca(S0)
S0ns_first = S0ns[:, :, 0, :]
rmses = np.sum(np.abs(S0ns_first - S0_first)) / \
(100.0 * 20.0 * 20.0 * 20.0)
# shows that S0n_first is not very close to S0_first
assert_(rmses > 0.0001)
assert_equal(np.round(S0ns_first.mean()), 100)
rmses = np.sum(np.abs(S0ns_first - S0_first)) / \
(100.0 * 20.0 * 20.0 * 20.0)
# shows that S0n_first is not very close to S0_first
assert_(rmses > 0.0001)
assert_equal(np.round(S0ns_first.mean()), 100)
def test_lpca_random_noise():
S0 = 100 + 2 * np.random.standard_normal((22, 23, 30, 20))
S0ns, _, _ = randommatrix_lpca(S0)
assert_(S0ns.min() > S0.min())
assert_(S0ns.max() < S0.max())
assert_equal(np.round(S0ns.mean()), 100)
def test_lpca_static():
S0 = 100 * np.ones((20, 20, 20, 20), dtype='f8')
S0ns, _, _ = randommatrix_lpca(S0)
assert_array_almost_equal(S0, S0ns)