def test_fwt_ifwt_mackey_haar():
"""Test the Haar case for a long signal."""
wavelet = pywt.Wavelet("haar")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
generator = MackeyGenerator(batch_size=2,
tmax=512,
delta_t=1,
device=device)
wavelet = pywt.Wavelet("haar")
pt_data = torch.squeeze(generator())
coeffs_max = pywt.wavedec(pt_data.cpu().numpy(), wavelet, level=9)
matrix_wavedec = MatrixWavedec(wavelet, 9)
coeffs_mat_max = matrix_wavedec(pt_data)
test_lst = []
for test_no in range(9):
test_lst.append(
np.sum(
np.abs(coeffs_max[test_no] -
coeffs_mat_max[test_no].cpu().numpy())) < 0.001)
print(test_lst)
# test the inverse fwt.
matrix_waverec = MatrixWaverec(wavelet)
reconstructed_data = matrix_waverec(coeffs_mat_max)
err1 = torch.mean(torch.abs(pt_data - reconstructed_data))
print("abs ifwt reconstruction error", err1)
assert np.allclose(pt_data.cpu().numpy(), reconstructed_data.cpu().numpy())
def test_conv_fwt():
"""Test multiple convolution fwt, for various levels and padding options."""
generator = MackeyGenerator(batch_size=2,
tmax=128,
delta_t=1,
device="cpu")
mackey_data_1 = torch.squeeze(generator())
for level in [1, 2, 3, None]:
for wavelet_string in ["db1", "db2", "db3", "db4", "db5"]:
for mode in ["reflect", "zero"]:
wavelet = pywt.Wavelet(wavelet_string)
ptcoeff = wavedec(mackey_data_1,
wavelet,
level=level,
mode=mode)
pycoeff = pywt.wavedec(mackey_data_1[0, :].numpy(),
wavelet,
level=level,
mode=mode)
cptcoeff = torch.cat(ptcoeff, -1)[0, :]
cpycoeff = np.concatenate(pycoeff, -1)
err = np.mean(np.abs(cpycoeff - cptcoeff.numpy()))
print(
"db5 coefficient error scale 3:",
err,
["ok" if err < 1e-4 else "failed!"],
"mode",
mode,
)
assert np.allclose(cptcoeff.numpy(), cpycoeff, atol=1e-6)
res = waverec(
wavedec(mackey_data_1, wavelet, level=3, mode=mode),
wavelet,
)
err = torch.mean(torch.abs(mackey_data_1 - res)).numpy()
print(
"db5 reconstruction error scale 3:",
err,
["ok" if err < 1e-4 else "failed!"],
"mode",
mode,
)
assert np.allclose(mackey_data_1.numpy(), res.numpy())
res = waverec(
wavedec(mackey_data_1, wavelet, level=4, mode=mode),
wavelet,
)
err = torch.mean(torch.abs(mackey_data_1 - res)).numpy()
print(
"db5 reconstruction error scale 4:",
err,
["ok" if err < 1e-4 else "failed!"],
"mode",
mode,
)
assert np.allclose(mackey_data_1.numpy(), res.numpy())
def test_conv_fwt_db5_lvl3():
"""Test a third level db5 conv-fwt."""
generator = MackeyGenerator(batch_size=2,
tmax=128,
delta_t=1,
device="cpu")
mackey_data_1 = torch.squeeze(generator())
wavelet = pywt.Wavelet("db5")
for mode in ["reflect", "zero"]:
ptcoeff = wavedec(mackey_data_1, wavelet, level=3, mode=mode)
pycoeff = pywt.wavedec(mackey_data_1[0, :].numpy(),
wavelet,
level=3,
mode=mode)
cptcoeff = torch.cat(ptcoeff, -1)[0, :]
cpycoeff = np.concatenate(pycoeff, -1)
err = np.mean(np.abs(cpycoeff - cptcoeff.numpy()))
print(
"db5 coefficient error scale 3:",
err,
["ok" if err < 1e-4 else "failed!"],
"mode",
mode,
)
assert np.allclose(cpycoeff, cptcoeff.numpy(), atol=1e-6)
res = waverec(wavedec(mackey_data_1, wavelet, level=3, mode=mode),
wavelet)
err = torch.mean(torch.abs(mackey_data_1 - res)).numpy()
print(
"db5 reconstruction error scale 3:",
err,
["ok" if err < 1e-4 else "failed!"],
"mode",
mode,
)
assert np.allclose(mackey_data_1.numpy(), res.numpy())
res = waverec(wavedec(mackey_data_1, wavelet, level=4, mode=mode),
wavelet)
err = torch.mean(torch.abs(mackey_data_1 - res)).numpy()
print(
"db5 reconstruction error scale 4:",
err,
["ok" if err < 1e-4 else "failed!"],
"mode",
mode,
)
assert np.allclose(mackey_data_1.numpy(), res.numpy())
def test_fwt_ifwt_mackey_db2():
"""Test the db2 case for a long signal."""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
wavelet = pywt.Wavelet("db2")
generator = MackeyGenerator(batch_size=2,
tmax=512,
delta_t=1,
device=device)
pt_data = torch.squeeze(generator()).cpu()
matrix_wavedec = MatrixWavedec(wavelet, 4)
coeffs_mat_max = matrix_wavedec(pt_data)
matrix_waverec = MatrixWaverec(wavelet)
reconstructed_data = matrix_waverec(coeffs_mat_max)
err = torch.mean(torch.abs(pt_data - reconstructed_data))
print("reconstruction error:", err)
assert err < 1e-6
def test_orth_wavelet():
"""Test an orthogonal wavelet fwt."""
generator = MackeyGenerator(batch_size=2, tmax=64, delta_t=1, device="cpu")
mackey_data_1 = torch.squeeze(generator())
# orthogonal wavelet object test
wavelet = pywt.Wavelet("db5")
orthwave = SoftOrthogonalWavelet(
torch.tensor(wavelet.rec_lo),
torch.tensor(wavelet.rec_hi),
torch.tensor(wavelet.dec_lo),
torch.tensor(wavelet.dec_hi),
)
res = waverec(wavedec(mackey_data_1, orthwave), orthwave)
err = torch.mean(torch.abs(mackey_data_1 - res.detach())).numpy()
print("orth reconstruction error scale 4:", err,
["ok" if err < 1e-4 else "failed!"])
assert np.allclose(res.detach().numpy(), mackey_data_1.numpy())
def test_conv_fwt_haar_lvl4():
"""Test a fourth level Haar wavelet conv-fwt."""
generator = MackeyGenerator(batch_size=2, tmax=64, delta_t=1, device="cpu")
mackey_data_1 = torch.squeeze(generator())
wavelet = pywt.Wavelet("haar")
ptcoeff = wavedec(mackey_data_1, wavelet, level=4)
pycoeff = pywt.wavedec(mackey_data_1[0, :].numpy(), wavelet, level=4)
ptwt_coeff = torch.cat(ptcoeff, -1)[0, :].numpy()
pywt_coeff = np.concatenate(pycoeff)
err = np.mean(np.abs(pywt_coeff - ptwt_coeff))
print("haar coefficient error scale 4:", err,
["ok" if err < 1e-4 else "failed!"])
assert np.allclose(pywt_coeff, ptwt_coeff, atol=1e-06)
reconstruction = waverec(wavedec(mackey_data_1, wavelet), wavelet)
err = torch.mean(torch.abs(mackey_data_1 - reconstruction)).numpy()
print("haar reconstruction error scale 4:", err,
["ok" if err < 1e-4 else "failed!"])
assert np.allclose(reconstruction.numpy(), mackey_data_1.numpy())