Пример #1
0
    titer = 4
    NITER = 512
    maxint = 3
    voxelsize = 1e-6
    energy = 5

    # Load a 3D object
    beta = dxchange.read_tiff(
        'data/test-beta-128.tiff').astype('float32')[::2, ::2, ::2]
    delta = dxchange.read_tiff(
        'data/test-delta-128.tiff').astype('float32')[::2, ::2, ::2]

    # Create object.
    obj = objects.Object(beta, delta, voxelsize)
    # Create probe
    prb = objects.Probe(objects.gaussian(15, rin=0.8, rout=1.0), maxint=maxint)
    # Detector parameters
    det = objects.Detector(63, 63)
    # Define rotation angles
    theta = np.linspace(0, 2*np.pi, 400).astype('float32')
    # Scanner positions
    scanax, scanay = objects.scanner3(theta, beta.shape, 10, 10, margin=[
        prb.size, prb.size], offset=[0, 0], spiral=1)
    # tomography data shape
    tomoshape = [len(theta), obj.shape[0], obj.shape[2]]

    # Class solver
    slv = solver_gpu.Solver(prb, scanax, scanay,
                            theta, det, voxelsize, energy, tomoshape)

    def signal_handler(sig, frame):
Пример #2
0
    # Load a 3D object.
    beta = dxchange.read_tiff(
        'data/test-beta-128.tiff').astype('float32')
    delta = dxchange.read_tiff(
        'data/test-delta-128.tiff').astype('float32')

    obj = objects.Object(beta, delta, voxelsize)
    det = objects.Detector(128, 128)
    theta = np.linspace(0, 2*np.pi, 720).astype('float32')
    tomoshape = [len(theta), obj.shape[0], obj.shape[2]]

    if(sys.argv[1] == "True"):
        print('compute regular')
        maxint = 10
        prb = objects.Probe(gaussian(16, rin=0.8, rout=1.0), maxint=maxint)
        scan, scanax, scanay = scanner3(theta, beta.shape, 6, 6, margin=[
                                        prb.size, prb.size], offset=[0, 0], spiral=1)
        slv = solver_gpu.Solver(prb, scanax, scanay,
                                 theta, det, voxelsize, energy, tomoshape)
        # data
        psis = slv.fwd_tomo(obj.complexform)
        data = np.abs(slv.fwd_ptycho(slv.exptomo(psis)))**2

        # rec
        tau = 1e-16
        alpha = 1e-16
        h = np.ones(tomoshape, dtype='complex64', order='C')
        psi = np.ones(tomoshape, dtype='complex64', order='C')
        lamd = np.zeros(psi.shape, dtype='complex64', order='C')
        phi = np.zeros([3, *obj.shape], dtype='complex64', order='C')