def METHOD_NAME(transition=0.5, width=0.1):
    """Colormap often used in H.E.S.S. collaboration publications.
    This colormap goes black -> blue -> red -> yellow -> white.
    A sharp blue -> red -> yellow transition is often used for significance images
    with a value of red at ``transition ~ 5`` or ``transition ~ 7``
    so that the following effect is achieved:
    - black, blue: non-significant features, not well visible
    - red: features at the detection threshold ``transition``
    - yellow, white: significant features, very well visible
    The transition parameter is defined between 0 and 1. To calculate the value
    from data units an `~astropy.visualization.mpl_normalize.ImageNormalize`
    instance should be used (see example below).
    Parameters
    ----------
    transition : float (default = 0.5)
        Value of the transition to red (between 0 and 1).
    width : float (default = 0.5)
        Width of the blue-red color transition (between 0 and 1).
    Returns
    -------
    colormap : `matplotlib.colors.LinearSegmentedColormap`
        Colormap
    Examples
    --------
    >>> from gammapy.visualization import colormap_hess
    >>> from astropy.visualization.mpl_normalize import ImageNormalize
    >>> from astropy.visualization import LinearStretch
    >>> normalize = ImageNormalize(vmin=-5, vmax=15, stretch=LinearStretch())
    >>> transition = normalize(5)
    >>> cmap = colormap_hess(transition=transition)
    """
    # Compute normalised values (range 0 to 1) that
    # correspond to red, blue, yellow.
    red = float(transition)
    if width > red:
        blue = 0.1 * red
    else:
        blue = red - width
    yellow = 2.0 / 3.0 * (1 - red) + red
    black, white = 0, 1
    # Create custom colormap
    # List entries: (value, (R, G, B))
    colors = [
        (black, "k"),
        (blue, (0, 0, 0.8)),
        (red, "r"),
        (yellow, (1.0, 1.0, 0)),
        (white, "w"),
    ]
    return LinearSegmentedColormap.from_list(name="hess", colors=colors)