def main(fname_anat, fname_centerline, degree_poly, centerline_fitting, interp, remove_temp_files, verbose):
    
    # extract path of the script
    path_script = os.path.dirname(__file__)+'/'
    
    # Parameters for debug mode
    if param.debug == 1:
        print '\n*** WARNING: DEBUG MODE ON ***\n'
        status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
        fname_anat = path_sct_data+'/t2/t2.nii.gz'
        fname_centerline = path_sct_data+'/t2/t2_seg.nii.gz'
    
    # extract path/file/extension
    path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
    
    # Display arguments
    print '\nCheck input arguments...'
    print '  Input volume ...................... '+fname_anat
    print '  Centerline ........................ '+fname_centerline
    print ''
    
    # Get input image orientation
    im_anat = Image(fname_anat)
    input_image_orientation = get_orientation_3d(im_anat)

    # Reorient input data into RL PA IS orientation
    im_centerline = Image(fname_centerline)
    im_anat_orient = set_orientation(im_anat, 'RPI')
    im_anat_orient.setFileName('tmp.anat_orient.nii')
    im_centerline_orient = set_orientation(im_centerline, 'RPI')
    im_centerline_orient.setFileName('tmp.centerline_orient.nii')

    # Open centerline
    #==========================================================================================
    print '\nGet dimensions of input centerline...'
    nx, ny, nz, nt, px, py, pz, pt = im_centerline_orient.dim
    print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
    print '.. voxel size:  '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
    
    print '\nOpen centerline volume...'
    data = im_centerline_orient.data

    X, Y, Z = (data>0).nonzero()
    min_z_index, max_z_index = min(Z), max(Z)
    
    
    # loop across z and associate x,y coordinate with the point having maximum intensity
    x_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
    y_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
    z_centerline = [iz for iz in range(min_z_index, max_z_index+1, 1)]

    # Two possible scenario:
    # 1. the centerline is probabilistic: each slices contains voxels with the probability of containing the centerline [0:...:1]
    # We only take the maximum value of the image to aproximate the centerline.
    # 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
    # We take all the points and approximate the centerline on all these points.

    X, Y, Z = ((data<1)*(data>0)).nonzero() # X is empty if binary image
    if (len(X) > 0): # Scenario 1
        for iz in range(min_z_index, max_z_index+1, 1):
            x_centerline[iz-min_z_index], y_centerline[iz-min_z_index] = numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape)
    else: # Scenario 2
        for iz in range(min_z_index, max_z_index+1, 1):
            x_seg, y_seg = (data[:,:,iz]>0).nonzero()
            if len(x_seg) > 0:
                x_centerline[iz-min_z_index] = numpy.mean(x_seg)
                y_centerline[iz-min_z_index] = numpy.mean(y_seg)

    # TODO: find a way to do the previous loop with this, which is more neat:
    # [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
    
    # clear variable
    del data
    
    # Fit the centerline points with the kind of curve given as argument of the script and return the new smoothed coordinates
    if centerline_fitting == 'nurbs':
        try:
            x_centerline_fit, y_centerline_fit = b_spline_centerline(x_centerline,y_centerline,z_centerline)
        except __HOLE__:
            print "splines fitting doesn't work, trying with polynomial fitting...\n"
            x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)
    elif centerline_fitting == 'polynome':
        x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)

    #==========================================================================================
    # Split input volume
    print '\nSplit input volume...'
    im_anat_orient_split_list = split_data(im_anat_orient, 2)
    file_anat_split = []
    for im in im_anat_orient_split_list:
        file_anat_split.append(im.absolutepath)
        im.save()

    # initialize variables
    file_mat_inv_cumul = ['tmp.mat_inv_cumul_Z'+str(z).zfill(4) for z in range(0,nz,1)]
    z_init = min_z_index
    displacement_max_z_index = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[max_z_index-min_z_index]

    # write centerline as text file
    print '\nGenerate fitted transformation matrices...'
    file_mat_inv_cumul_fit = ['tmp.mat_inv_cumul_fit_Z'+str(z).zfill(4) for z in range(0,nz,1)]
    for iz in range(min_z_index, max_z_index+1, 1):
        # compute inverse cumulative fitted transformation matrix
        fid = open(file_mat_inv_cumul_fit[iz], 'w')
        if (x_centerline[iz-min_z_index] == 0 and y_centerline[iz-min_z_index] == 0):
            displacement = 0
        else:
            displacement = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[iz-min_z_index]
        fid.write('%i %i %i %f\n' %(1, 0, 0, displacement) )
        fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
        fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
        fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
        fid.close()

    # we complete the displacement matrix in z direction
    for iz in range(0, min_z_index, 1):
        fid = open(file_mat_inv_cumul_fit[iz], 'w')
        fid.write('%i %i %i %f\n' %(1, 0, 0, 0) )
        fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
        fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
        fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
        fid.close()
    for iz in range(max_z_index+1, nz, 1):
        fid = open(file_mat_inv_cumul_fit[iz], 'w')
        fid.write('%i %i %i %f\n' %(1, 0, 0, displacement_max_z_index) )
        fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
        fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
        fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
        fid.close()

    # apply transformations to data
    print '\nApply fitted transformation matrices...'
    file_anat_split_fit = ['tmp.anat_orient_fit_Z'+str(z).zfill(4) for z in range(0,nz,1)]
    for iz in range(0, nz, 1):
        # forward cumulative transformation to data
        sct.run(fsloutput+'flirt -in '+file_anat_split[iz]+' -ref '+file_anat_split[iz]+' -applyxfm -init '+file_mat_inv_cumul_fit[iz]+' -out '+file_anat_split_fit[iz]+' -interp '+interp)

    # Merge into 4D volume
    print '\nMerge into 4D volume...'
    from glob import glob
    im_to_concat_list = [Image(fname) for fname in glob('tmp.anat_orient_fit_Z*.nii')]
    im_concat_out = concat_data(im_to_concat_list, 2)
    im_concat_out.setFileName('tmp.anat_orient_fit.nii')
    im_concat_out.save()
    # sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*')

    # Reorient data as it was before
    print '\nReorient data back into native orientation...'
    fname_anat_fit_orient = set_orientation(im_concat_out.absolutepath, input_image_orientation, filename=True)
    move(fname_anat_fit_orient, 'tmp.anat_orient_fit_reorient.nii')

    # Generate output file (in current folder)
    print '\nGenerate output file (in current folder)...'
    sct.generate_output_file('tmp.anat_orient_fit_reorient.nii', file_anat+'_flatten'+ext_anat)

    # Delete temporary files
    if remove_temp_files == 1:
        print '\nDelete temporary files...'
        sct.run('rm -rf tmp.*')

    # to view results
    print '\nDone! To view results, type:'
    print 'fslview '+file_anat+ext_anat+' '+file_anat+'_flatten'+ext_anat+' &\n'
dataset/ETHPy150Open neuropoly/spinalcordtoolbox/scripts/sct_flatten_sagittal.py/main