def convert(fname_in, fname_out, squeeze_data=True, type=None, verbose=1):
    from msct_image import Image
    from sct_utils import printv
    printv('sct_convert -i '+fname_in+' -o '+fname_out, verbose, 'code')
    # Open file
    im = Image(fname_in)
    # Save file
    im.setFileName(fname_out)
    if type is not None:
        im.changeType(type=type)
    im.save(squeeze_data=squeeze_data)
    return im


# MAIN
# ==========================================================================================
The inverse transform of the above normalization
dataset/ETHPy150Open neuropoly/spinalcordtoolbox/scripts/sct_convert.py/convert--crossover with--dataset/ETHPy150Open nipy/nitime/nitime/utils.py/normal_coherence_to_unit