def test_complex_abs(shape):
shape = shape + [2]
x = create_input(shape)
out_torch = fastmri.complex_abs(x).numpy()
input_numpy = transforms.tensor_to_complex_np(x)
out_numpy = np.abs(input_numpy)
assert np.allclose(out_torch, out_numpy)
def test_ifft2(shape):
shape = shape + [2]
x = create_input(shape)
out_torch = fastmri.ifft2c(x).numpy()
out_torch = out_torch[..., 0] + 1j * out_torch[..., 1]
input_numpy = transforms.tensor_to_complex_np(x)
input_numpy = np.fft.ifftshift(input_numpy, (-2, -1))
out_numpy = np.fft.ifft2(input_numpy, norm="ortho")
out_numpy = np.fft.fftshift(out_numpy, (-2, -1))
assert np.allclose(out_torch, out_numpy)
volume_kspace = orig['kspace'][()]
kspace_list = []
reconstruction_list = []
total_slices = volume_kspace.shape[0]
for i in range(total_slices // 2 - num_middle_slices // 2,
total_slices // 2 + num_middle_slices // 2 + 1):
slice_kspace = volume_kspace[i]
slice_kspace2 = to_tensor(
slice_kspace) # Convert from numpy array to pytorch tensor
kspace_crop = complex_center_crop(slice_kspace2, (size, size))
ift = fastmri.ifft2c(
kspace_crop
) # Apply Inverse Fourier Transform to get the complex image
reconstruction = fastmri.complex_abs(ift)
kspace_list.append(tensor_to_complex_np(kspace_crop))
reconstruction_list.append(reconstruction)
stacked_kspace = np.stack(kspace_list)
stacked_reconstruction_esc = np.stack(reconstruction_list)
dest['kspace'] = stacked_kspace
dest['reconstruction_esc'] = stacked_reconstruction_esc
dest['ismrmrd_header'] = orig['ismrmrd_header'][()]
dest.attrs['norm'] = np.linalg.norm(stacked_reconstruction_esc)
dest.attrs['max'] = np.max(stacked_reconstruction_esc)
dest.close()
orig.close()