are downsampled by a factor of two. The rotational axis is the `y`-axis.
A total of 180 projections are used for the reconstruction. A detailed
description of this phantom is given in :cite:`Mueller2015`.
.. _`meep`: http://ab-initio.mit.edu/wiki/index.php/Meep
"""
import matplotlib.pylab as plt
import numpy as np
import odtbrain as odt
from example_helper import load_data
sino, angles, phantom, cfg = \
load_data("fdtd_3d_sino_A180_R6.500.tar.lzma")
A = angles.shape[0]
print("Example: Backpropagation from 3D FDTD simulations")
print("Refractive index of medium:", cfg["nm"])
print("Measurement position from object center:", cfg["lD"])
print("Wavelength sampling:", cfg["res"])
print("Number of projections:", A)
print("Performing backpropagation.")
# Apply the Rytov approximation
sinoRytov = odt.sinogram_as_rytov(sino)
# perform backpropagation to obtain object function f
f = odt.backpropagate_3d(uSin=sinoRytov,
projections. The second column shows the reconstruction without angular
weights and the third column shows the reconstruction with angular
weights. The keyword argument `weight_angles` was introduced in version
0.1.1.
"""
import matplotlib.pylab as plt
import numpy as np
import unwrap
import odtbrain as odt
from example_helper import load_data
sino, angles, cfg = load_data("mie_2d_noncentered_cylinder_A250_R2.zip",
f_angles="mie_angles.txt",
f_sino_real="sino_real.txt",
f_sino_imag="sino_imag.txt",
f_info="mie_info.txt")
A, size = sino.shape
# background sinogram computed with Mie theory
# miefield.GetSinogramCylinderRotation(radius, nmed, nmed, lD, lC, size, A,res)
u0 = load_data("mie_2d_noncentered_cylinder_A250_R2.zip",
f_sino_imag="u0_imag.txt",
f_sino_real="u0_real.txt")
# create 2d array
u0 = np.tile(u0, size).reshape(A, size).transpose()
# background field necessary to compute initial born field
# u0_single = mie.GetFieldCylinder(radius, nmed, nmed, lD, size, res)
u0_single = load_data("mie_2d_noncentered_cylinder_A250_R2.zip",
reconstruction of the refractive index with the Rytov approximation
is in good agreement with the phantom that was used in the
simulation.
.. _`meep`: http://ab-initio.mit.edu/wiki/index.php/Meep
"""
import matplotlib.pylab as plt
import numpy as np
import odtbrain as odt
from example_helper import load_data
sino, angles, phantom, cfg = load_data(
"fdtd_2d_sino_A100_R13.zip",
f_angles="fdtd_angles.txt",
f_sino_imag="fdtd_imag.txt",
f_sino_real="fdtd_real.txt",
f_info="fdtd_info.txt",
f_phantom="fdtd_phantom.txt",
)
print("Example: Backpropagation from 2D FDTD simulations")
print("Refractive index of medium:", cfg["nm"])
print("Measurement position from object center:", cfg["lD"])
print("Wavelength sampling:", cfg["res"])
print("Performing backpropagation.")
# Apply the Rytov approximation
sino_rytov = odt.sinogram_as_rytov(sino)
# perform backpropagation to obtain object function f
f = odt.backpropagate_2d(uSin=sino_rytov,
reconstruction that does not take into account the tilted axis of
rotation; the result is a blurry reconstruction. The third column
shows the improved reconstruction; the known tilted axis of rotation
is used in the reconstruction process.
.. _`meep`: http://ab-initio.mit.edu/wiki/index.php/Meep
"""
import matplotlib.pylab as plt
import numpy as np
import odtbrain as odt
from example_helper import load_data
sino, angles, phantom, cfg = \
load_data("fdtd_3d_sino_A220_R6.500_tiltyz0.2.tar.lzma")
A = angles.shape[0]
print("Example: Backpropagation from 3D FDTD simulations")
print("Refractive index of medium:", cfg["nm"])
print("Measurement position from object center:", cfg["lD"])
print("Wavelength sampling:", cfg["res"])
print("Axis tilt in y-z direction:", cfg["tilt_yz"])
print("Number of projections:", A)
print("Performing normal backpropagation.")
# Apply the Rytov approximation
sinoRytov = odt.sinogram_as_rytov(sino)
# Perform naive backpropagation
perfect sphere using 200 projections. Missing angle artifacts are
visible along the :math:`y`-axis due to the :math:`2\pi`-only
coverage in 3D Fourier space.
.. _`GMM-field`: https://code.google.com/p/scatterlib/wiki/Nearfield
"""
import matplotlib.pylab as plt
import nrefocus
import numpy as np
import odtbrain as odt
from example_helper import load_data
Ex, cfg = load_data("mie_3d_sphere_field.zip",
f_sino_imag="mie_sphere_imag.txt",
f_sino_real="mie_sphere_real.txt",
f_info="mie_info.txt")
# Manually set number of angles:
A = 200
print("Example: Backpropagation from 3D Mie scattering")
print("Refractive index of medium:", cfg["nm"])
print("Measurement position from object center:", cfg["lD"])
print("Wavelength sampling:", cfg["res"])
print("Number of angles for reconstruction:", A)
print("Performing backpropagation.")
# Reconstruction angles
angles = np.linspace(0, 2 * np.pi, A, endpoint=False)