def make_cmap(color, rotation=.5, white=False, transparent_zero=False):
h, s, v = rgb_to_hsv(color)
h = h + rotation
if h > 1:
h -= 1
r, g, b = color
ri, gi, bi = hsv_to_rgb((h, s, v))
colors = {'direct': (ri, gi, bi), 'inverted': (r, g, b)}
cdict = {}
for direction, (r, g, b) in colors.items():
if white:
cdict[direction] = {color: [(0.0, 0.0416, 0.0416),
(0.18, c, c),
(0.5, 1, 1),
(0.62, 0.0, 0.0),
(1.0, 0.0416, 0.0416)] for color, c in
[('blue', b), ('red', r), ('green', g)]}
else:
cdict[direction] = {color: [(0.0, 1, 1),
(0.32, c, c),
(0.5, 0.0416, 0.0416),
(0.5, 0.0, 0.0),
(0.87, 0.0, 0.0),
(1.0, 1, 1)] for color, c in
[('blue', b), ('red', r), ('green', g)]}
if transparent_zero:
cdict[direction]['alpha']: [(0, 1, 1), (0.5, 0, 0), (1, 1, 1)]
cmap = LinearSegmentedColormap('cmap', cdict['direct'])
cmapi = LinearSegmentedColormap('cmap', cdict['inverted'])
cmap._init()
cmapi._init()
cmap._lut = np.maximum(cmap._lut, cmapi._lut[::-1])
# Big hack from nilearn (WTF !?)
cmap._lut[-1, -1] = 0
return cmap
