def METHOD_NAME():
    # Acquisitions will be read from this HDF file
    input_file = existing_filepath(data_path, data_file)
    # Initially we create a container that points to the h5 file. Data is
    # not read from file until the gadgetron is called using
    # the 'process' method.
    # Create an acquisition container of type pGadgetron.AcquisitionData
    print('---\n reading in file %s...' % input_file)
    acq_data = AcquisitionData(input_file)
    # Pre-process this input data using three preparation gadgets
    # from gadgetron.
    # List gadgets to use (not all may be required for this test data).
    prep_gadgets = ['NoiseAdjustGadget', 'AsymmetricEchoAdjustROGadget', \
                                'RemoveROOversamplingGadget' ]
    # Call gadgetron by using the 'process' method. This runs the gadgets
    # specified in prep_gadgets, returning an instance
    # of an mGadgetron.AcquisitionsContainer
    preprocessed_data = acq_data.process(prep_gadgets)
    if show_plot:
        preprocessed_data.show(title = 'Acquisition data (magnitude)')
    # Perform reconstruction of the preprocessed data.
    # 1) Create a recon object for the desired reconstruction.
    # In this demo, the recon object is created using the class
    # Reconstructor(). A simpler class is available in the SIRF code
    # for a GRAPPA reconstruction:
    #   recon = CartesianGRAPPAReconstructor()
    recon_gadgets = ['AcquisitionAccumulateTriggerGadget',
        'BucketToBufferGadget', 
        'GenericReconCartesianReferencePrepGadget', 
        'GRAPPA:GenericReconCartesianGrappaGadget', 
        'GenericReconFieldOfViewAdjustmentGadget', 
        'GenericReconImageArrayScalingGadget', 
        'ImageArraySplitGadget'
        ]
    recon = Reconstructor(recon_gadgets)
    # 2) The GRAPPA gadget can compute G-factors in addition to
    # reconstructed images. We can set a gadget property as below if the gadget
    # has been identified with a label. In the above list of recon_gadgets,
    # the 4th is labelled 'GRAPPA' and we can use this label as below:
    recon.set_gadget_property('GRAPPA', 'send_out_gfactor', True)
    # If the chain had been set using
    # recon = CartesianGRAPPAReconstructor(), an alternative method
    # would be available:
    #  recon.compute_gfactors(True)
    # 3) set the reconstruction input to be the data we just preprocessed.
    recon.set_input(preprocessed_data)
    # 4) Run the reconstruction using 'process' to call gadgetron.
    print('---\n reconstructing...\n')
    recon.process()
    # Output
    # Reconstructed data sits in memory. We need to first get data
    # for both the reconstructed images and g-factors, before extracting the
    # data as Python arrays.
    # Get image and gfactor data as objects of type pGadgetron.ImageData
    # (Note this syntax may change in the future with the addition of a
    #  method '.get_gfactor'.)
    image_data = recon.get_output('image')
    gfact_data = recon.get_output('gfactor')
    if show_plot:
        image_data.show(title = 'Reconstructed image data (magnitude)')
        gfact_data.show(title = 'G-factor data (magnitude)')