def test_main(rec=False, verb=False, throw=True):
pet.MessageRedirector()
for scheme in ("file", "memory"):
pet.AcquisitionData.set_storage_scheme(scheme)
original_verb = pet.get_verbosity()
pet.set_verbosity(False)
# create an acq_model that is explicitly a RayTracingMatrix
am = pet.AcquisitionModelUsingRayTracingMatrix()
# load sample data
data_path = pet.examples_data_path('PET')
raw_data_file = pet.existing_filepath(data_path,
'Utahscat600k_ca_seg4.hs')
ad = pet.AcquisitionData(raw_data_file)
# create sample image
image = pet.ImageData()
image.initialise(dim=(31, 111, 111), vsize=(2.25, 2.25, 2.25))
# set up Acquisition Model
am.set_up(ad, image)
# test for adjointnesss
if not is_operator_adjoint(am, verbose=verb):
raise AssertionError(
'AcquisitionModelUsingRayTracingMatrix is not adjoint')
# Reset original verbose-ness
pet.set_verbosity(original_verb)
return 0, 1
def test_main(rec=False, verb=False, throw=True):
msg_red = pet.MessageRedirector()
data_path = pet.examples_data_path('PET')
raw_data_file = pet.existing_filepath(data_path, 'mMR/list.l.hdr')
lm2sino = pet.ListmodeToSinograms()
lm2sino.set_input(raw_data_file)
num_prompts_threshold = 73036.
known_time = 22.
time_at_which_num_prompts_exceeds_threshold = \
lm2sino.get_time_at_which_num_prompts_exceeds_threshold(num_prompts_threshold)
if abs(time_at_which_num_prompts_exceeds_threshold - known_time) > 1.e-4:
raise AssertionError(
"ListmodeToSinograms::get_time_at_which_num_prompts_exceeds_threshold failed"
)
return 0, 1
def test_main(rec=False, verb=False, throw=True):
# Set STIR verbosity to off
original_verb = pet.get_verbosity()
pet.set_verbosity(0)
time.sleep(0.5)
sys.stderr.write("Testing NiftyPET projector...")
time.sleep(0.5)
data_path = pet.examples_data_path('PET')
raw_data_file = pet.existing_filepath(data_path,
'mMR/mMR_template_span11.hs')
template_acq_data = pet.AcquisitionData(raw_data_file)
# Get image
image = get_image()
# Get AM
try:
acq_model = pet.AcquisitionModelUsingNiftyPET()
except:
return 1, 1
acq_model.set_cuda_verbosity(verb)
acq_model.set_up(template_acq_data, image)
# Test operator adjointness
if verb:
print('testing adjointness')
if not is_operator_adjoint(acq_model, num_tests=1, verbose=True):
raise AssertionError('NiftyPet AcquisitionModel is not adjoint')
# Generate test data
simulated_acq_data = acq_model.forward(image)
simulated_acq_data_w_noise = add_noise(simulated_acq_data, 10)
obj_fun = pet.make_Poisson_loglikelihood(template_acq_data)
obj_fun.set_acquisition_model(acq_model)
recon = pet.OSMAPOSLReconstructor()
recon.set_objective_function(obj_fun)
recon.set_num_subsets(1)
recon.set_num_subiterations(1)
recon.set_input(simulated_acq_data_w_noise)
if verb:
print('setting up, please wait...')
initial_estimate = image.get_uniform_copy()
recon.set_up(initial_estimate)
if verb:
print('reconstructing...')
recon.set_current_estimate(initial_estimate)
recon.process()
reconstructed_im = recon.get_output()
if not reconstructed_im:
raise AssertionError()
# Reset original verbose-ness
pet.set_verbosity(original_verb)
return 0, 1
# import engine module
import sirf.STIR as PET
from sirf.Utilities import show_2D_array
import PET_plot_functions
#import os
# process command-line options
raw_data_file = args['--file']
randoms_data_file = args['--randoms']
acf_file = args['--attenuation_correction_factors']
data_path = args['--path']
if data_path is None:
data_path = PET.examples_data_path('PET') + '/mMR'
norm_file = PET.existing_filepath(data_path, args['--norm'])
mu_map_file = PET.existing_filepath(data_path, args['--attenuation_image'])
output_prefix = args['--output']
def main():
# direct all engine's messages to files
msg_red = PET.MessageRedirector('info.txt', 'warn.txt', 'errr.txt')
PET.AcquisitionData.set_storage_scheme('memory')
# Create the Scatter Estimator
# We can use a STIR parameter file like this
# par_file_path = os.path.join(os.path.dirname(__file__), '..', '..', 'parameter_files')
