def METHOD_NAME():
    # 1. Prepare data:
    # locate the input data file
    input_file = existing_filepath(data_path, data_file)
    #
    # acquisition data will be read from an HDF file input_file
    AcquisitionData.set_storage_scheme('memory')
    acq_data = AcquisitionData(input_file)
    #
    # pre-process acquisition data
    processed_data = preprocess_acquisition_data(acq_data)
    #
    # sort k-space data into a 2D Cartesian matrix for each coil
    processed_data.sort()
    # 2. Calculate coil sensitivity maps directly from the raw k-space data:
    # create coil sensitivity object
    CSMs = CoilSensitivityData()
    #
    # set number of smoothing iterations to suppress noise
    CSMs.smoothness = nit
    # calculate coil sensitivity maps directly from the raw k-space data by the
    # Square-Root-of-the-Sum-of-Squares over all coils (SRSS) method
    print('using default Square-Root-of-the-Sum-of-Squares (SRSS) method...')
    print('A) calculating from raw data...')
    CSMs.calculate(processed_data)
    #
    # check fill & as_array compatibility
    zero = CSMs - CSMs.fill(CSMs.as_array())
    print('CSMs - CSMs.fill(CSMs.as_array()) = %f' % zero.norm())
    if show_plot:
        # display coil sensitivity maps
        csms_array = CSMs.as_array()
        nz = csms_array.shape[1]
        title = 'SRSS from raw data (magnitude)'
        show_3D_array(abs(csms_array[:, nz//2, :, :]), suptitle=title, \
                xlabel='samples', ylabel='readouts', label='coil', show=False)
    # 3. Now compute coil sensitivity maps from coil images in order to compare
    # SSRS and Inati methods:
    # create coil images object
    CIs = CoilImagesData()
    # calculate coil sensitivity maps by dividing each coil image data by the
    # Square-Root-of-the-Sum-of-Squares over all coils (SRSS);
    # (niter = nit) sets the number of smoothing iterations applied
    # to the image data prior to the calculation of the coil sensitivity maps
    CIs.calculate(processed_data)
    CSs = CoilSensitivityData()
    print('B) calculating from coil images...')
    CSs.calculate(CIs, method='SRSS(niter=%d)' % nit)
    diff = CSs - CSMs
    print('difference between A and B: %f' % diff.norm())
    try:
        from ismrmrdtools import coils
    except:
        print('Inati method requires ismrmrd-python-tools')
        if show_plot:
            import matplotlib.pyplot as plt
            plt.show()
        return
    # calculate coil sensitivity maps using an approach suggested by 
    #   Inati SJ, Hansen MS, Kellman P.
    #   A solution to the phase problem in adaptive coil combination.
    #   In: ISMRM proceeding; April; Salt Lake City, Utah, USA; 2013. 2672.  
    # for more details please see 
    # gadgetron/toolboxes/mri_core/mri_core_coil_map_estimation.h
    print('using Inati method...')
    print('A) calculating from raw data...')
    CSMs.calculate(processed_data, method='Inati()')
    print('B) calculating from coil images...')
    CSs.calculate(CIs, method='Inati()')
    if show_plot:
        csms_array = CSMs.as_array()
        nz = csms_array.shape[1]
        #
        # display coil sensitivity maps
        title = 'Inati (magnitude)'
        show_3D_array(abs(csms_array[:, nz//2, :, :]), suptitle=title, \
                  xlabel='samples', ylabel='readouts', label='coil')
    diff = CSs - CSMs
    print('difference between A and B: %f' % diff.norm())