 import argparse
import sys
import os

import numpy as np
from sirf.Engine import get_engine
from sirf.PhysicsModel import PoissonLogLikelihoodWithLinearModelForMeanAndProjData
from sirf.AcquisitionData import from_acquisition_data, AcquisitionData
from sirf.Utils import TestSTIRAcquisitionDataAlgebraFile
from sirf.Processing import preprocess_acquisition_data, new_acquisition_data
from sirf.DataContainers import AcquisitionDataProcessor, CoilSensitivityData, CoilImagesData
from sirf.Trajectory import data_type, get_data_trajectory
from sirf.Options import OptionParser
from sirf.IO import set_acquisition_data, name_and_parameters, label_and_name
from sirf.DataSelection import DataHandleVector
from sirf.Logging import logger

def define_symmetric_operator(acquisition_data):
"""
Define a symmetrical operator for cg-optimisation
"""
return None

def conjugate_gradient_method(acquisition_data, operator, max_iterations=100, tol=1e-5):
"""
Perform Conjugate Gradient method
"""
# Compute coil sensitivity maps
sensitivity_maps = CoilSensitivityData(acquisition_data)

# Set up acquisition model
physics_model = PoissonLogLikelihoodWithLinearModelForMeanAndProjData(sensitivity_maps)

# Perform backward projection
backward_projected_data = physics_model(acquisition_data)

# Implement iterative reconstruction
cg_data = physics_model.conjugate_gradient(backward_projected_data, operator, max_iterations, tol)

return cg_data

def main(args):
try:
# Parse command-line options
oparser = OptionParser(description='Perform iterative reconstruction with radial phase encoding (RPE) data using the SIRF library.')
oparser.add_option('--raw-data-file', type='string', help='Path to raw data file')
oparser.add_option('--data-path', type='string', help='Path to data files')
oparser.add_option('--output-file', type='string', help='Path to output file for simulated data')
oparser.add_option('--engine', type='string', help='Reconstruction engine')
oparser.add_option('--run-reconstruction', action='store_true', help='Run the reconstruction if non-cartesian code was compiled')
oparser.add_option('--trajectory-type', type='string', help='Trajectory type (cartesian, radial, goldenangle or grpe)')
oparser.add_option('--show-plots', action='store_true', help='Show plots')
oparser.add_option('--test-data', action='store_true', help='Use test data')
(options, args) = oparser.parse_args(args)

# Import engine module
engine_module = get_engine(options.engine)

# Define symmetrical operator for cg-optimisation
operator = define_symmetric_operator(acquisition_data)

# Define function for performing the Conjugate Gradient method
cg_data = conjugate_gradient_method(acquisition_data, operator)

# Locate k-space raw data file
if options.test_data:
raw_data_file = os.path.join(examples_data_path, options.raw_data_file)
else:
raw_data_file = os.path.abspath(options.raw_data_file)

# Read acquisition data from HDF file
acquisition_data = from_acquisition_data(raw_data_file)

# Pre-process acquisition data
if options.test_data:
preprocessed_data = preprocess_acquisition_data(acquisition_data, test_data=True)
else:
preprocessed_data = preprocess_acquisition_data(acquisition_data)

# Set trajectory
trajectory_type = options.trajectory_type
if not trajectory\_type:
trajectory\_type = data\_type(preprocessed\_data)
trajectory = get\_data\_trajectory(trajectory\_type)
set\_data\_trajectory(preprocessed\_data, trajectory)

# Sort processed acquisition data
sorted\_data = DataHandleVector()
for i in range(preprocessed\_data.get_number\_of\_time\_frames()):
sorted\_data.append(preprocessed\_data.get\_time\_frame(i))

# Perform reconstruction if relevant option is set
if options.run\_reconstruction:
# Perform reconstruction
reconstructed\_data = engine\_module.reconstruct(sorted\_data, cg\_data)

# Save reconstructed data to file
if options.output\_file:
reconstructed\_data.save(options.output\_file)

except Exception as e:
logger.error(str(e))
sys.exit(1)

if **name** == '**main**':
main(sys.argv[1:])