def main():
oversamp_factor = 1.25 #For NUFFT
parser = argparse.ArgumentParser()
inputs = bl832inputs_parser(parser)
num_slice = inputs['z_numElts']
num_angles= inputs['num_views']/inputs['view_subsmpl_fact']
pad_size = np.int16(inputs['x_width']*oversamp_factor)
num_iter = inputs['num_iter']
nufft_scaling = (np.pi/pad_size)**2
tomo, flats, darks, floc = tomopy.read_als_832h5(inputs['input_hdf5'],ind_tomo=range(1,inputs['num_views']+1,inputs['view_subsmpl_fact']),sino=(inputs['z_start'], inputs['z_start']+inputs['z_numElts'], 1))
print('Data read complete')
print('Generating angles')
theta = tomopy.angles(num_angles)
#Need to modify to return the raw counts for noise estimation
print('Normalization')
tomo,weight = normalize_bo(tomo, flats, darks,inputs['num_dark'])
print('Ring removal')
tomo = tomopy.remove_stripe_fw(tomo)
################## GPU MBIR ######################
input_params={}
input_params['gpu_device']=inputs['gpu_device']
input_params['oversamp_factor']=oversamp_factor
input_params['num_iter']=num_iter
input_params['p']=inputs['p']
input_params['smoothness']=inputs['smoothness']
t=time.time()
rec_mbir_final = gpuMBIR(tomo,theta,inputs['rot_center'],input_params)
elapsed_time = (time.time()-t)
print('Time for reconstucting using GPU-MBIR of %d slices with %d iter : %f' % (num_slice,num_iter,elapsed_time))
pg.image(rec_mbir_final);pg.QtGui.QApplication.exec_()
# np.save('/home/svvenkatakrishnan/results/mbir_notch1080_70slice',rec_mbir_final)
# fig = plt.figure()
# sirt_camTomo = rec_sirt_final[124]
# plt.imshow(sirt_camTomo,cmap=plt.cm.Greys_r,vmin=0,vmax=0.00075)
# plt.colorbar()
# fig.suptitle('Tomopy GPU-SIRT Reconstruction')
# plt.draw()
# plt.show()
print 'main: Done!'
#script to test tomopy
from __future__ import print_function, division
import matplotlib.pyplot as plt
from skimage.io import imread
from skimage import data_dir
from skimage.transform import radon, rescale, iradon
import tomopy
from normalize import normalize_bo
dataset = '/home/svvenkatakrishnan/data/20131106_074854_S45L3_notch_OP_10x.h5'
#20130807_234356_OIM121R_SAXS_5x.h5'
algo = 'fbp'
num_sets = 2
tomo, flats, darks, floc = tomopy.read_als_832h5(dataset, sino=(1000, 1010, 1))
print('Data read complete')
print('Displaying sinogram')
imgplot = plt.imshow(tomo[:, 0, :])
plt.show()
print('Generating angles')
theta = tomopy.angles(tomo.shape[0])
print('Normalization')
tomo, weight = normalize_bo(tomo, flats, darks, num_sets)
#print('Ring removal')
#tomo = tomopy.remove_stripe_fw(tomo)
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 3 16:20:30 2015
@author: lbluque
"""
import tomopy
first, flats, darks, floc = tomopy.read_als_832h5('../../TestDatasets/20151021_105637_ALS10_RT_210lbs_10x.h5', ind_tomo=(0,))
first = tomopy.normalize_nf(first, flats, darks, floc)
base = '20151021_105637_ALS10_RT_210lbs_10x_tomopy_phase_'
dist = (0.1, 0.5, 1.0, 2.5, 5.0, 10.0, 13.2284, 100.0, 500.0)
alpha = (1.0e-3, 1.0e-2, 1.0e-1)
for d in dist:
for a in alpha:
print a
phase = tomopy.retrieve_phase(first,
pixel_size=0.000129,
dist=d,
alpha=a,
energy=40)
name = base + 'dist{0:.1f}_alpha{1:.1e}'.format(d*10.0, a)
print(name)
tomopy.write_tiff(phase, fname='filters/'+name)
def fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger("fast_tomopy.fast_tomo_recon")
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", type=str, help="path to input raw " "dataset", required=True)
parser.add_argument(
"-o",
"--output-file",
type=str,
help="full path to h5 output " "file",
default=os.path.join(os.getcwd(), "fast-tomopy.h5"),
)
parser.add_argument("-sn", "--sino-num", type=int, help="Number of slices " "to reconstruct", default=5)
parser.add_argument(
"-a",
"--algorithm",
type=str,
help="Reconstruction" " algorithm",
default="gridrec",
choices=[
"art",
"bart",
"fbp",
"gridrec",
"mlem",
"ospml_hybrid",
"ospml_quad",
"pml_hybrid",
"pml_quad",
"sirt",
],
)
parser.add_argument("-c", "--center", type=float, help="Center of rotation", default=None)
parser.add_argument(
"-fn",
"--filter-name",
type=str,
help="Name of filter" " used for reconstruction",
choices=["none", "shepp", "cosine", "hann", "hamming", "ramlak", "parzen", "butterworth"],
default="butterworth",
)
parser.add_argument(
"-rr",
"--ring-remove",
type=str,
help="Ring removal " "method",
choices=["Octopus", "Tomopy-FW", "Tomopy-T"],
default="Tomopy-T",
)
parser.add_argument("-lf", "--log-file", type=str, help="log file name", default="fast-tomopy.log")
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, "Directory of output file does not exist", args.output_file)
# Read file metadata
logger.info("Reading input file metadata")
fdata, gdata = read_als_832h5_metadata(args.input)
proj_total = int(gdata["nangles"])
last = proj_total - 1
sino_total = int(gdata["nslices"])
ray_total = int(gdata["nrays"])
px_size = float(gdata["pxsize"]) / 10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step * (args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info("Reading full first and last projection for COR")
first_last, flats, darks, floc = tomopy.read_als_832h5(args.input, ind_tomo=(0, last))
first_last = tomopy.normalize(first_last, flats, darks)
args.center = tomopy.find_center_pc(first_last[0, :, :], first_last[1, :, :], tol=0.1)
logger.info("Detected center: %f", args.center)
# Read and normalize raw sinograms
logger.info("Reading raw data")
tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino)
logger.info("Normalizing raw data")
tomo = tomopy.normalize_nf(tomo, flats, darks, floc)
# Remove stripes from sinograms (remove rings)
logger.info("Preprocessing normalized data")
if args.ring_remove == "Tomopy-FW":
logger.info("Removing stripes from sinograms with %s", args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == "Tomopy-T":
logger.info("Removing stripes from sinograms with %s", args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total * np.sqrt(2)) - ray_total) // 2
tomo = tomopy.pad(tomo, 2, npad=npad, mode="edge")
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(proj_total, 270, 90)
logger.info("Reconstructing normalized data")
# Reconstruct sinograms
# rec = tomopy.minus_log(tomo, out=tomo)
rec = tomopy.recon(
tomo, theta, center=args.center, emission=False, algorithm=args.algorithm, filter_name=filter_name
)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec / px_size
# Remove rings from reconstruction
if args.ring_remove == "Octopus":
logger.info("Removing rings from reconstructions with %s", args.ring_remove)
thresh = float(gdata["ring_threshold"])
thresh_max = float(gdata["upp_ring_value"])
thresh_min = float(gdata["low_ring_value"])
theta_min = int(gdata["max_arc_length"])
rwidth = int(gdata["max_ring_size"])
rec = tomopy.remove_rings(
rec,
center_x=args.center,
thresh=thresh,
thresh_max=thresh_max,
thresh_min=thresh_min,
theta_min=theta_min,
rwidth=rwidth,
)
# Write reconstruction data to new hdf5 file
fdata["stage"] = "fast-tomopy"
fdata["stage_flow"] = "/raw/" + fdata["stage"]
fdata["stage_version"] = "fast-tomopy-0.1"
# Generate a new uuid based on host ID and current time
fdata["uuid"] = str(uuid.uuid1())
gdata["Reconstruction_Type"] = "tomopy-gridrec"
gdata["ring_removal_method"] = args.ring_remove
gdata["rfilter"] = args.filter_name
logger.info("Writing reconstructed data to h5 file")
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return
# import sirtfbp
datasets ={ '/home/svvenkatakrishnan/data/20130807_234356_OIM121R_SAXS_5x.h5': 1272}
algorithms = ('fbp',)
sinos = (10,10,1)
cores = (1,) #range(mp.cpu_count(), 0, -4)
f = open('benchmark_results.txt', 'a')
for dataset in datasets:
f.write('*****************************************************************************************************\n')
f.write(dataset + '\n\n')
for algorithm in algorithms:
for sino in sinos:
for core in cores:
start_time = time.time()
tomo, flats, darks, floc = tomopy.read_als_832h5(dataset,
sino=(0, sino, 1))
end_time = time.time() - start_time
f.write('Function: {0}, Number of sinos: {1}, Runtime (s): {2}\n'.format('read', sino, end_time))
theta = tomopy.angles(tomo.shape[0])
tomo = tomopy.normalize(tomo, flats, darks, ncore=core)
end_time = time.time() - start_time - end_time
f.write('Function: {0}, Number of sinos: {1}, Number of cores: {2}, Runtime (s): {3}\n'.format('normalize', sino, core, end_time))
tomo = tomopy.remove_stripe_fw(tomo, ncore=core)
end_time = time.time() - start_time - end_time
f.write('Function: {0}, Number of sinos: {1}, Number of cores: {2}, Runtime (s): {3}\n'.format('stripe_fw', sino, core, end_time))
rec = tomopy.recon(tomo, theta, center=datasets[dataset],
algorithm=algorithm, emission=False,
ncore=core)
end_time = time.time() - start_time - end_time
rec = tomopy.circ_mask(rec, 0)
f.write('Function: {0}, Number of sinos: {1}, Number of cores: {2}, Algorithm: {3}, Runtime (s): {4}\n'.format('recon', sino, core, algorithm, end_time))
def fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger('fast_tomopy.fast_tomo_recon')
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, help='path to input raw '
'dataset', required=True)
parser.add_argument('-o', '--output-file', type=str, help='full path to h5 output '
'file', default=os.path.join(os.getcwd(), "fast-tomopy.h5"))
parser.add_argument('-sn', '--sino-num', type=int, help='Number of slices '
'to reconstruct', default=5)
parser.add_argument('-a', '--algorithm', type=str, help='Reconstruction'
' algorithm', default='gridrec',
choices=['art', 'bart', 'fbp', 'gridrec', 'mlem',
'ospml_hybrid', 'ospml_quad', 'pml_hybrid',
'pml_quad', 'sirt'])
parser.add_argument('-c', '--center', type=float, help='Center of rotation',
default=None)
parser.add_argument('-fn', '--filter-name', type=str, help='Name of filter'
' used for reconstruction',
choices=['none', 'shepp', 'cosine', 'hann', 'hamming',
'ramlak', 'parzen', 'butterworth'],
default='butterworth')
parser.add_argument('-rr', '--ring-remove', type=str, help='Ring removal '
'method', choices=['Octopus', 'Tomopy-FW', 'Tomopy-T'],
default='Tomopy-T')
parser.add_argument('-lf', '--log-file', type=str, help='log file name',
default='fast-tomopy.log')
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, 'Directory of output file does not exist', args.output_file)
# Read file metadata
logger.info('Reading input file metadata')
fdata, gdata = read_als_832h5_metadata(args.input)
proj_total = int(gdata['nangles'])
last = proj_total - 1
sino_total = int(gdata['nslices'])
ray_total = int(gdata['nrays'])
px_size = float(gdata['pxsize'])/10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step*(args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info('Reading full first and last projection for COR')
first_last, flats, darks, floc = tomopy.read_als_832h5(args.input,
ind_tomo=(0, last))
first_last = tomopy.normalize(first_last, flats, darks)
args.center = tomopy.find_center_pc(first_last[0, :, :],
first_last[1, :, :], tol=0.1)
logger.info('Detected center: %f', args.center)
# Read and normalize raw sinograms
logger.info('Reading raw data')
tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino)
logger.info('Normalizing raw data')
tomo = tomopy.normalize_nf(tomo, flats, darks, floc)
# Remove stripes from sinograms (remove rings)
logger.info('Preprocessing normalized data')
if args.ring_remove == 'Tomopy-FW':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == 'Tomopy-T':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total*np.sqrt(2)) - ray_total)//2
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(proj_total, 270, 90)
logger.info('Reconstructing normalized data')
# Reconstruct sinograms
# rec = tomopy.minus_log(tomo, out=tomo)
rec = tomopy.recon(tomo, theta, center=args.center, emission=False,
algorithm=args.algorithm, filter_name=filter_name)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec/px_size
# Remove rings from reconstruction
if args.ring_remove == 'Octopus':
logger.info('Removing rings from reconstructions with %s',
args.ring_remove)
thresh = float(gdata['ring_threshold'])
thresh_max = float(gdata['upp_ring_value'])
thresh_min = float(gdata['low_ring_value'])
theta_min = int(gdata['max_arc_length'])
rwidth = int(gdata['max_ring_size'])
rec = tomopy.remove_rings(rec, center_x=args.center, thresh=thresh,
thresh_max=thresh_max, thresh_min=thresh_min,
theta_min=theta_min, rwidth=rwidth)
# Write reconstruction data to new hdf5 file
fdata['stage'] = 'fast-tomopy'
fdata['stage_flow'] = '/raw/' + fdata['stage']
fdata['stage_version'] = 'fast-tomopy-0.1'
# Generate a new uuid based on host ID and current time
fdata['uuid'] = str(uuid.uuid1())
gdata['Reconstruction_Type'] = 'tomopy-gridrec'
gdata['ring_removal_method'] = args.ring_remove
gdata['rfilter'] = args.filter_name
logger.info('Writing reconstructed data to h5 file')
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return
axis : int
Axis to crop along
Returns
-------
ndarray:
Cropped array
"""
slc = []
pts = [p11, p12, p21, p22, p31, p32]
for n in range(len(arr.shape)):
slc.append(slice(pts.pop(0), pts.pop(0)))
return arr[slc]
if __name__ == '__main__':
import tomopy
from matplotlib.pyplot import imshow, show, figure
d = tomopy.read_als_832h5('/home/lbluque/TestDatasetsLocal/dleucopodia.h5',
ind_tomo=(500, 501, 502, 503))
# d = np.array(d[0], dtype=np.float32)
d = d[0]
c = convert_data(d, imin=0, imax=0)
print d.dtype
print c.dtype
figure(0)
imshow(d[0])
figure(1)
imshow(c[0])
show()
def main():
parser = argparse.ArgumentParser()
inputs = bl832inputs_parser(parser)
num_slice = inputs['z_numElts']
num_angles = inputs['num_views'] / inputs['view_subsmpl_fact']
oversamp_factor = 1.25
pad_size = np.int16(inputs['x_width'] * oversamp_factor)
fbp_filter_param = inputs['fbp_filter_param']
nufft_scaling = (np.pi / pad_size)**2
algorithm = 'gridrec'
tomo, flats, darks, floc = tomopy.read_als_832h5(
inputs['input_hdf5'],
ind_tomo=range(1, inputs['num_views'] + 1,
inputs['view_subsmpl_fact']),
sino=(inputs['z_start'], inputs['z_start'] + inputs['z_numElts'], 1))
print('Data read complete')
print('Displaying sinogram')
#imgplot = plt.imshow(tomo[:,0,:])
print('Generating angles')
theta = tomopy.angles(num_angles)
#Need to modify to return the raw counts for noise estimation
print('Normalization')
tomo, weight = normalize_bo(tomo, flats, darks, inputs['num_dark'])
print('Ring removal')
tomo = tomopy.remove_stripe_fw(tomo)
# fig = plt.figure()
# plt.imshow(tomo[:,1,:],cmap=plt.cm.Greys_r)
# fig.suptitle('Sinogram')
################## GPU gridrec() ######################
input_params = {}
input_params['gpu_device'] = inputs['gpu_device']
input_params['fbp_filter_param'] = inputs['fbp_filter_param']
input_params['oversamp_factor'] = oversamp_factor
t = time.time()
rec_nufft_final = gpuGridrec(tomo, theta, inputs['rot_center'],
input_params)
elapsed_time = time.time() - t
print('Time for NUFFT Gridrec of %d slices : %f' %
(num_slice, elapsed_time))
pg.image(rec_nufft_final)
pg.QtGui.QApplication.exec_()
# np.save('/home/svvenkatakrishnan/results/gridrec_notch1080_70slice',rec_nufft_final)
##################TomoPy Recon#####################
# print('Recon - tomopy GridRec')
# t=time.time()
# rec_tomopy = tomopy.recon(tomo, theta, center=inputs['rot_center'],algorithm=algorithm)#emission=False)
# elapsed_time = (time.time()-t)
# print('Time for reconstucting using Tomopy GridRec of %d slices : %f' % (num_slice,elapsed_time))
# print('Recon - tomopy Astra')
# t=time.time()
# options = {'proj_type':'cuda', 'method':'FBP_CUDA'}
# rec_astra = tomopy.recon(tomo, theta, center=inputs['rot_center'], algorithm=tomopy.astra, options=options)
# elapsed_time = (time.time()-t)
# print('Time for reconstucting using Tomopy Astra of %d slices : %f' % (num_slice,elapsed_time))
# fig = plt.figure()
# plt.imshow(np.abs(np.flipud(rec_tomopy[0])),cmap=plt.cm.Greys_r)
# plt.colorbar()
# fig.suptitle('Tomopy Gridrec Reconstruction')
# fig = plt.figure()
# plt.imshow(rec_nufft[0].real,cmap=plt.cm.Greys_r)
# plt.colorbar()
# fig.suptitle('GPU NUFFT Reconstruction')
print 'main: Done!'
''' bl832 test hdf5 file: center of rotation '20150820_130628_Dleucopodia_10458_pieceA_10x_z30mm.h5': 1155.75 '20150820_124324_Dleucopodia_10458_pieceA_10x_z80mm_moreangles.h5': 1223, '20140829_132953_euc60mesh_1M-KI_10percLoad_time0_5x_33p5keV_10cmSampDet.h5': 1334.85 These files can be found in /global/cscratch1/sd/lbluque/tomobench_datasets ''' fname = "path/to/bl832.h5" # Load real data from ALS 832 ------------ # load DataExchange data t = time.time() prj, flt, drk, flc = tomopy.read_als_832h5(fname) # sino=(1000, 1032)) #sino kwarg only if you only what to read in a subset of sinograms # ignore the flc variable... print prj.shape, flt.shape, drk.shape print time.time() - t # define projection angles ang = tomopy.angles(prj.shape[0]) # data normalization t = time.time() prj = tomopy.normalize(prj, flt, drk) print time.time() - t # Processing funcs --------------
def fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger('fast_tomopy.fast_tomo_recon')
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, help='path to input raw '
'dataset', required=True)
parser.add_argument('-o', '--output-file', type=str, help='full path to h5 output '
'file', default=os.path.join(os.getcwd(), "fast-tomopy.h5"))
parser.add_argument('-sn', '--sino-num', type=int, help='Number of slices '
'to reconstruct', default=5)
parser.add_argument('-a', '--algorithm', type=str, help='Reconstruction'
' algorithm', default='gridrec',
choices=['art', 'bart', 'fbp', 'gridrec', 'mlem',
'ospml_hybrid', 'ospml_quad', 'pml_hybrid',
'pml_quad', 'sirt'])
parser.add_argument('-c', '--center', type=float, help='Center of rotation',
default=None)
parser.add_argument('-fn', '--filter-name', type=str, help='Name of filter'
' used for reconstruction',
choices=['none', 'shepp', 'cosine', 'hann', 'hamming',
'ramlak', 'parzen', 'butterworth'],
default='butterworth')
parser.add_argument('-rr', '--ring-remove', type=str, help='Ring removal '
'method', choices=['Octopus', 'Tomopy-FW', 'Tomopy-T'],
default='Tomopy-FW')
parser.add_argument('-lf', '--log-file', type=str, help='log file name',
default='fast-tomopy.log')
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, 'Directory of output file does not exist', args.output_file)
# Read file metadata
logger.info('Reading input file metadata')
fdata, gdata = tomopy.read_als_832h5_metadata(args.input)
proj_total = int(gdata['nangles'])
last = proj_total - 1
sino_total = int(gdata['nslices'])
ray_total = int(gdata['nrays'])
px_size = float(gdata['pxsize'])/10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step*(args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info('Reading full first and last projection for COR')
first_last, flats, darks, floc = tomopy.read_als_832h5(args.input,
ind_tomo=(0, last))
first_last = tomopy.normalize_nf(first_last, flats, darks, floc)
args.center = tomopy.find_center_pc(first_last[0, :, :],
first_last[1, :, :], tol=0.1)
logger.info('Detected center: %f', args.center)
# Read and normalize raw sinograms
logger.info('Reading raw data')
tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino)
logger.info('Normalizing raw data')
tomo = tomopy.normalize_nf(tomo, flats, darks, floc)
# Remove stripes from sinograms (remove rings)
logger.info('Preprocessing normalized data')
if args.ring_remove == 'Tomopy-FW':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == 'Tomopy-T':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total*np.sqrt(2)) - ray_total)//2
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(tomo.shape[0], 270, 90)
logger.info('Reconstructing normalized data')
# Reconstruct sinograms
rec = tomopy.recon(tomo, theta, center=args.center, emission=False,
algorithm=args.algorithm, filter_name=filter_name)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec/px_size
# Remove rings from reconstruction
if args.ring_remove == 'Octopus':
logger.info('Removing rings from reconstructions with %s',
args.ring_remove)
thresh = float(gdata['ring_threshold'])
thresh_max = float(gdata['upp_ring_value'])
thresh_min = float(gdata['low_ring_value'])
theta_min = int(gdata['max_arc_length'])
rwidth = int(gdata['max_ring_size'])
rec = tomopy.remove_rings(rec, center_x=args.center, thresh=thresh,
thresh_max=thresh_max, thresh_min=thresh_min,
theta_min=theta_min, rwidth=rwidth)
# Write reconstruction data to new hdf5 file
fdata['stage'] = 'fast-tomopy'
fdata['stage_flow'] = '/raw/' + fdata['stage']
fdata['stage_version'] = 'fast-tomopy-0.1'
# WHAT ABOUT uuid ????? Who asigns this???
del fdata['uuid'] # I'll get rid of it altogether then...
gdata['Reconstruction_Type'] = 'tomopy-gridrec'
gdata['ring_removal_method'] = args.ring_remove
gdata['rfilter'] = args.filter_name
logger.info('Writing reconstructed data to h5 file')
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return