max_s1 = max(s[1] for s in board_raw)
min_s1 = min(s[1] for s in board_raw)
norm_s1 = lambda x: (x - min_s1) / (max_s1 - min_s1)
+ max_s1 = max(s[1] for s in board_raw)
+ min_s1 = min(s[1] for s in board_raw)
+ norm_s1 = lambda x: (x - min_s1) / (max_s1 - min_s1)
color_data = [(norm_s0(s[0]), norm_s1(s[1])) for s in board_raw]
clusters = k_means.cluster(3, 2,zip(color_data, range(len(color_data))),
sum(p[1] for p in points) / norm,
sum(p[2] for p in points) / norm)
hue, luma, saturation = colorsys.rgb_to_hls(*color)
- return luma, saturation, color
+ color = colorsys.hls_to_rgb(hue, 0.5, 1.)
+ print color
+ return luma, saturation, color, hue