miss_projs = [128, 256]
# Select sinogram range to reconstruct.
start = 512
end = 2048
# Set number of data chunks for the reconstruction.
chunks = 64
num_sino = (end - start) // chunks
for m in range(chunks):
sino_start = start + num_sino * m
sino_end = start + num_sino * (m + 1)
# Read APS 32-ID raw data.
proj, flat, dark = tomopy.read_aps_32id(fname,
sino=(sino_start, sino_end))
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0])
# Remove the missing angles from data.
proj = np.concatenate(
(proj[0:miss_projs[0], :, :], proj[miss_projs[1] + 1:-1, :, :]),
axis=0)
theta = np.concatenate(
(theta[0:miss_projs[0]], theta[miss_projs[1] + 1:-1]))
# Flat-field correction of raw data.
proj = tomopy.normalize(proj, flat, dark)
# Reconstruct object using Gridrec algorithm.
miss_projs = [128, 256]
# Select sinogram range to reconstruct.
start = 512
end = 2048
# Set number of data chunks for the reconstruction.
chunks = 64
num_sino = (end - start) // chunks
for m in range(chunks):
sino_start = start + num_sino * m
sino_end = start + num_sino * (m + 1)
# Read APS 32-ID or 2-BM raw data.
proj, flat, dark = tomopy.read_aps_32id(fname, sino=(sino_start, sino_end))
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], 0, 180)
# Remove the missing angles from data.
proj = np.concatenate((proj[0:miss_projs[0], :, :], proj[miss_projs[1] + 1:-1, :, :]), axis=0)
theta = np.concatenate((theta[0:miss_projs[0]], theta[miss_projs[1] + 1:-1]))
# Flat-field correction of raw data.
proj = tomopy.normalize(proj, flat, dark)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(proj, theta, center=1024, algorithm='gridrec', emission=False)
# Write data as stack of TIFs.