def smart_colormap(vmin, vmax, color_high='#b11902', hue_low=0.6):
    """
    Creates a "smart" colormap that is centered on zero, and accounts for
    asymmetrical vmin and vmax by matching saturation/value of high and low
    colors.

    It works by first creating a colormap from white to `color_high`.  Setting
    this color to the max(abs([vmin, vmax])), it then determines what the color
    of min(abs([vmin, vmax])) should be on that scale.  Then it shifts the
    color to the new hue `hue_low`, and finally creates a new colormap with the
    new hue-shifted as the low, `color_high` as the max, and centered on zero.

    :param color_high: a matplotlib color -- try "#b11902" for a nice red
    :param hue_low: float in [0, 1] -- try 0.6 for a nice blue
    :param vmin: lowest value in data you'll be plotting
    :param vmax: highest value in data you'll be plotting
    """
    # first go from white to color_high
    orig_cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
        'test', ['#FFFFFF', color_high], N=2048)

    # For example, say vmin=-3 and vmax=9.  If vmin were positive, what would
    # its color be?
    vmin = float(vmin)
    vmax = float(vmax)
    mx = max([vmin, vmax])
    mn = min([vmin, vmax])
    frac = abs(mn / mx)
    rgb = orig_cmap(frac)[:-1]

    # Convert to HSV and shift the hue
    hsv = list(colorsys.rgb_to_hsv(*rgb))
    hsv[0] = hue_low
    new_rgb = colorsys.hsv_to_rgb(*hsv)
    new_hex = matplotlib.colors.rgb2hex(new_rgb)

    zeropoint = abs(-vmin / (vmax - vmin))

    # Create a new colormap using the new hue-shifted color as the low end
    new_cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
        'test', [(0, new_rgb), (zeropoint, '#FFFFFF'), (1, color_high)],
        N=2048)

    return new_cmap