From 6efac1a12c8ab0b7d96a8d342fbd74fce35e8c42 Mon Sep 17 00:00:00 2001
From: Vasil Zlatanov <vasil.zlatanov@gmail.com>
Date: Tue, 18 Aug 2015 02:38:33 +0200
Subject: Organise everything systematically.

---
 config/ratpoison/py/colorz.py | 71 -------------------------------------------
 1 file changed, 71 deletions(-)
 delete mode 100644 config/ratpoison/py/colorz.py

(limited to 'config/ratpoison/py/colorz.py')

diff --git a/config/ratpoison/py/colorz.py b/config/ratpoison/py/colorz.py
deleted file mode 100644
index 8c00f0c..0000000
--- a/config/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
-- 
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