def smooth_along_centerline(data, fname_input_image, file_input_image, ext_input_image, mask_data, fname_centerline):

    # Find the voxels that belong to the spinal cord
    X, Y, Z = (mask_data > 0).nonzero()

    # Define useful variable
    N = len(X) # number of voxels in the spinal cord segmentation

    # Apply the script "sct_smooth_spinal_cord.py" to the original image
    print("\n \n \n Apply the script \"sct_smooth_spinal_cord.py\" to the original image\n \n ")
    os.system("python sct_smooth_spinal_cord.py -i " + str(fname_input_image) + " -c " + str(fname_centerline))

    # Read the nifti output file resulting from the previously run script
    print("\n \n Loading"+"./" + str(file_input_image) + "_smoothed" + str(ext_input_image)+"\n \n")
    smoothed_img = nibabel.load("./" + str(file_input_image) + "_smoothed" + str(ext_input_image))
    # 3d array for each x y z voxel values for the nifti image
    smoothed_sc_data = smoothed_img.get_data()

    # Replace the original spinal cord data by the smoothed one in the original data
    for i in range(0, N):
        data[X[i]][Y[i]][Z[i]] = smoothed_sc_data[X[i]][Y[i]][Z[i]]

    # Return the corrected data
    return data


#=======================================================================================================================
# usage
#=======================================================================================================================
Displays a list of API Queries.
dataset/ETHPy150Open neuropoly/spinalcordtoolbox/dev/sct_apply_local_filter.py/smooth_along_centerline--crossover with--dataset/ETHPy150Open googleanalytics/google-analytics-super-proxy/src/controllers/owner.py/AdminHandler.get