class TestAMP(unittest.TestCase):
'''Make sure we line up with Stanford results.'''
def setUp(self):
data = load_test_data('mr_utils/test_data/tests/cs/thresholding/amp',
['cdf97', 'mask', 'x0', 'y'])
self.cdf97, self.mask, self.x0, self.y = data[:] #pylint: disable=W0632
self.uft = UFT(self.mask)
self.level = 5
def test_uft(self):
'''Test undersampled fourier encoding.'''
y0 = self.uft.forward_ortho(self.x0)
self.assertTrue(np.allclose(self.y, y0))
@unittest.skip('Currently do not know how to match cdf97 using pywavelets')
def test_wavelet_decomposition(self):
'''Make sure we decompose using the same wavelet transformation.'''
wavelet_transform, locations = cdf97_2d_forward(self.x0, self.level)
# # Check 'em out
# view(np.stack((np.log(np.abs(self.cdf97)),
# np.log(np.abs(wavelet_transform)))))
# view(np.stack((self.cdf97 - wavelet_transform)), log=True)
#
# # Make sure we can go back
inverse = cdf97_2d_inverse(wavelet_transform, locations)
self.assertTrue(np.allclose(self.x0, inverse))
# view(self.x0 - inverse)
# view(cdf97_2d_inverse(wavelet_transform, locations))
# Currently failing...
self.assertTrue(np.allclose(wavelet_transform, self.cdf97))
from mr_utils.sim.traj import cartesian_pe
if __name__ == '__main__':
# Same binary smiley face example
do_reordering = True
N = 1000
x = binary_smiley(N)
k = np.sum(np.abs(np.diff(x)) > 0)
np.random.seed(5)
samp = cartesian_pe(x.shape, undersample=.2, reflines=5)
uft = UFT(samp)
# Make the complex measurement in kspace
# Note this is different than uft.forward, as fftshift must be performed
y = uft.forward_ortho(x)
# Solve inverse problem using gradient descent with TV sparsity constraint
x_hat = GD_TV(y,
forward_fun=uft.forward_ortho,
inverse_fun=uft.inverse_ortho,
alpha=.5,
lam=.022,
do_reordering=do_reordering,
x=x,
ignore_residual=True,
disp=True,
maxiter=50)
# Look at the before/after shots
view(np.stack((uft.inverse_ortho(y), x_hat)))
run = ['monosort', 'lagrangian'] # none is always run to get prior
# Need a reasonable numerical phantom
x = np.rot90(modified_shepp_logan((N, N, N))[:, :, int(N / 2)])
# view(x)
# Sparsifying transform
level = 3
wvlt, locations = cdf97_2d_forward(x, level)
sparsify = lambda x: cdf97_2d_forward(x, level)[0]
unsparsify = lambda x: cdf97_2d_inverse(x, locations)
# Do radial golden-angle sampling
mask = radial(x.shape, num_spokes, skinny=True, extend=False)
uft = UFT(mask)
kspace_u = uft.forward_ortho(x)
view(kspace_u, fft=True)
# # We need to find the best alpha for the no ordering recon
# pGD = partial(
# proximal_GD, y=kspace_u, forward_fun=uft.forward_ortho,
# inverse_fun=uft.inverse_ortho, sparsify=sparsify,
# unsparsify=unsparsify, mode='soft', thresh_sep=True,
# selective=None, x=x, ignore_residual=False, disp=False,
# maxiter=500)
# obj = lambda alpha0: compare_mse(
# np.abs(x), np.abs(pGD(alpha=alpha0)))
# alpha0 = 0.05
# res = minimize(obj, alpha0)
# print(res)
# The best is alpha0 = 0.01743203 for 500 iterations, N=64
