Spring cleanup!

2 files changed, 0 insertions, 179 deletions

diff --git a/ratpoison/py/color_detect.py b/ratpoison/py/color_detect.py
deleted file mode 100644
index 30661f8..0000000
--- a/ratpoison/py/color_detect.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import sys
-import colorsys
-from colorz import colorz
-from math import sqrt
-
-try:
-    import Image
-except ImportError:
-    from PIL import Image
-
-if len(sys.argv) < 2:
-  print "Usage: {0} FILENAME [num_colors]".format(sys.argv[0])
-  sys.exit()
-
-
-print sys.argv[1]
-
-WALLPAPER = sys.argv[1]
-filename = WALLPAPER.split('/').pop()
-COLORS = ".{0}.colors".format(filename)
-XRESOURCES = ".{0}.Xres".format(filename)
-SAMPLE = ".{0}.sample.png".format(filename)
-
-cols = ''
-xres = ''
-
-def torgb(hexv):
-    hexv = hexv[1:]
-    r, g, b = (
-        int(hexv[0:2], 16) / 256.0,
-        int(hexv[2:4], 16) / 256.0,
-        int(hexv[4:6], 16) / 256.0,
-    )
-    return r, g, b
-
-def normalize(hexv, minv=128, maxv=256):
-    r, g, b = torgb(hexv)
-    h, s, v = colorsys.rgb_to_hsv(r, g, b)
-    minv = minv / 256.0
-    maxv = maxv / 256.0
-    if v < minv:
-        v = minv
-    if v > maxv:
-        v = maxv
-    r, g, b = colorsys.hsv_to_rgb(h, s, v)
-    return '#{:02x}{:02x}{:02x}'.format(int(r * 256), int(g * 256), int(b * 256))
-
-def darkness(hexv):
-  r, g, b = torgb(hexv)
-  darkness = sqrt((255 - r) ** 2 + (255 - g) ** 2 + (255 - b) ** 2)
-  return darkness
-
-def to_hsv(c):
-    r, g, b = torgb(c)
-    h, s, v = colorsys.rgb_to_hsv(r, g, b)
-    return h, s, v
-
-def hex_color_to_rgb(color):
-    color = color[1:] if color[0]=="#" else color
-    return (
-        int(color[:2], 16),
-        int(color[2:4], 16),
-        int(color[4:], 16)
-        )
-
-def create_sample(f, colors):
-    im = Image.new("RGB", (1000, 100), "white")
-    pix = im.load()
-
-    width_sample = im.size[0]/len(colors)
-
-    for i, c in enumerate(colors):
-        for j in range(width_sample*i, width_sample*i+width_sample):
-            for k in range(0, 100):
-                pix[j, k] = hex_color_to_rgb(c)
-
-    im.save(f)
-
-if __name__ == '__main__':
-    if len(sys.argv) == 2:
-        n = 16
-    else:
-        n = int(sys.argv[2])
-
-
-    i = 0
-    # sort by value, saturation, then hue
-    colors = colorz(WALLPAPER, n=n)
-    colors.sort(key=lambda  x:darkness(x), reverse=True)
-    for c in colors:
-        if i == 0:
-            c = normalize(c, minv=0, maxv=32)
-        elif i == 8:
-            c = normalize(c, minv=128, maxv=192)
-        elif i < 8:
-            c = normalize(c, minv=160, maxv=224)
-        else:
-            c = normalize(c, minv=200, maxv=256)
-        c = normalize(c, minv=32, maxv=224)
-        xres += """*color{}: {}\n""".format(i, c)
-        cols += """export COLOR{}="{}"\n""".format(i, c)
-        i += 1
-
-    create_sample(SAMPLE, colors)
-    with open(XRESOURCES, 'w') as f:
-        f.write(xres)
-    with open(COLORS, 'w') as f:
-        f.write(cols)
diff --git a/ratpoison/py/colorz.py b/ratpoison/py/colorz.py
deleted file mode 100644
index 8c00f0c..0000000
--- a/ratpoison/py/colorz.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from collections import namedtuple
-from math import sqrt
-import random
-try:
-    import Image
-except ImportError:
-    from PIL import Image
-
-Point = namedtuple('Point', ('coords', 'n', 'ct'))
-Cluster = namedtuple('Cluster', ('points', 'center', 'n'))
-
-def get_points(img):
-    points = []
-    w, h = img.size
-    for count, color in img.getcolors(w * h):
-        points.append(Point(color, 3, count))
-    return points
-
-rtoh = lambda rgb: '#%s' % ''.join(('%02x' % p for p in rgb))
-
-def colorz(filename, n=3):
-    img = Image.open(filename)
-    img.thumbnail((200, 200))
-    w, h = img.size
-
-    points = get_points(img)
-    clusters = kmeans(points, n, 1)
-    rgbs = [map(int, c.center.coords) for c in clusters]
-    return map(rtoh, rgbs)
-
-def euclidean(p1, p2):
-    return sqrt(sum([
-        (p1.coords[i] - p2.coords[i]) ** 2 for i in range(p1.n)
-    ]))
-
-def calculate_center(points, n):
-    vals = [0.0 for i in range(n)]
-    plen = 0
-    for p in points:
-        plen += p.ct
-        for i in range(n):
-            vals[i] += (p.coords[i] * p.ct)
-    return Point([(v / plen) for v in vals], n, 1)
-
-def kmeans(points, k, min_diff):
-    clusters = [Cluster([p], p, p.n) for p in random.sample(points, k)]
-
-    while 1:
-        plists = [[] for i in range(k)]
-
-        for p in points:
-            smallest_distance = float('Inf')
-            for i in range(k):
-                distance = euclidean(p, clusters[i].center)
-                if distance < smallest_distance:
-                    smallest_distance = distance
-                    idx = i
-            plists[idx].append(p)
-
-        diff = 0
-        for i in range(k):
-            old = clusters[i]
-            center = calculate_center(plists[i], old.n)
-            new = Cluster(plists[i], center, old.n)
-            clusters[i] = new
-            diff = max(diff, euclidean(old.center, new.center))
-
-        if diff < min_diff:
-            break
-
-    return clusters