  Here is the Python code based on the given instruction:
```
import sys
import os
import numpy as np
from sirf import SIRF
from sirf.engine import Engine
from sirf.reconstruction import ConjugateGradient
from sirf.data import AcquisitionData, CoilSensitivityData
from sirf.util import TestSTIRAcquisitionDataAlgebraFile

def main():
    # Parse command-line options
    raw_data_file = sys.argv[1]
    data_path = sys.argv[2]
    output_file = sys.argv[3]
    engine = sys.argv[4]
    run_reconstruction = sys.argv[5]
    trajectory_type = sys.argv[6]
    show_plots = sys.argv[7]

    # Import engine module based on specified engine option
    if engine == "cartesian":
        from sirf.engine.cartesian import CartesianEngine
    elif engine == "radial":
        from sirf.engine.radial import RadialEngine
    elif engine == "goldenangle":
        from sirf.engine.goldenangle import GoldenAngleEngine
    elif engine == "grpe":
        from sirf.engine.grpe import GRPEEngine
    else:
        raise ValueError("Invalid engine option")

    # Define symmetrical operator for cg-optimisation
    symmetrical_operator = np.array([[1, 0], [0, -1]])

    # Define function for performing Conjugate Gradient method
    def conjugate_gradient(data, sensitivity_maps, acquisition_model, backward_projection):
        # Compute coil sensitivity maps
        sensitivity_maps = CoilSensitivityData(data.shape, data.dtype)
        sensitivity_maps.set_data(sensitivity_maps.data * 0)

        # Set up acquisition model
        acquisition_model = AcquisitionData(data.shape, data.dtype)
        acquisition_model.set_data(acquisition_model.data * 0)

        # Perform backward projection
        backward_projection = np.array([[1, 0], [0, -1]])

        # Implement iterative reconstruction
        for i in range(100):
            # Compute residual
            residual = data - backward_projection @ acquisition_model

            # Compute update
            update = symmetrical_operator @ residual

            # Update acquisition model
            acquisition_model += update

            # Update sensitivity maps
            sensitivity_maps += update @ backward_projection

            # Compute norm of residual
            norm = np.linalg.norm(residual)

            # Check convergence
            if norm < 1e-6:
                break

        # Return sensitivity maps and acquisition model
        return sensitivity_maps, acquisition_model

    # Locate k-space raw data file
    k_space_file = os.path.join(data_path, "k_space.hdf")

    # Read acquisition data from HDF file
    acquisition_data = AcquisitionData.from_hdf(k_space_file)

    # Pre-process acquisition data
    acquisition_data = preprocess_acquisition_data(acquisition_data)

    # Set trajectory
    trajectory = get_data_trajectory(acquisition_data, trajectory_type)

    # Sort processed acquisition data
    acquisition_data = sort_acquisition_data(acquisition_data, trajectory)

    # Perform reconstruction if specified
    if run_reconstruction == "True":
        # Compute coil sensitivity maps
        sensitivity_maps = CoilSensitivityData(acquisition_data.shape, acquisition_data.dtype)
        sensitivity_maps.set_data(sensitivity_maps.data * 0)

        # Set up acquisition model
        acquisition_model = AcquisitionData(acquisition_data.shape, acquisition_data.dtype)
        acquisition_model.set_data(acquisition_model.data * 0)

        # Perform backward projection
        backward_projection = np.array([[1, 0], [0, -1]])

        # Perform iterative reconstruction
        sensitivity_maps, acquisition_model = conjugate_gradient(acquisition_data, sensitivity_maps, acquisition_model, backward_projection)

        # Save sensitivity maps and acquisition model to HDF file
        sensitivity_maps.to_hdf(os.path.join(data_path, "sensitivity_maps.hdf"))
        acquisition_model.to_hdf(os.path.join(data_path, "acquisition_model.hdf"))

    # Show plots if specified
    if show_plots == "True":
        # Plot coil sensitivity maps
        sensitivity_maps.plot()

        # Plot acquisition model
        acquisition_model.plot()

if __name__ == "__main__":
    main()
```
Note that this code assumes that the necessary libraries and modules are installed and that the command-line options are specified correctly. It also assumes that the raw data file and the data path are specified correctly.