diff --git a/src/closest_colors.py b/src/closest_colors.py
new file mode 100644
index 0000000..4b990b9
--- /dev/null
+++ b/src/closest_colors.py
@@ -0,0 +1,84 @@
+import requests
+import json
+from vector_utils import Vector as vec
+from mpl_toolkits import mplot3d
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+def get_content(url):
+    response = requests.get(url)
+    content = response.content.decode()
+    return content
+
+
+def get_json(url):
+    content = get_content(url)
+    data = json.loads(content)
+    return data
+
+
+def hex_to_rgb(s):
+    s = s.lstrip('#')
+    return [int(s[:2], 16), int(s[2:4], 16), int(s[4:6], 16)]
+
+
+def parse_colors(data_set):
+    return dict((item['color'], hex_to_rgb(item['hex'])) for item in data_set['colors'])
+
+
+def calculate_size(dist):
+    pass
+
+
+def closest(colors, to, n):
+    closest_colors = ((c, vec.distance(to, c)) for c in colors.values())
+    return sorted(closest_colors, key=lambda c: c[1])[:n]
+
+
+def plot_data(ax, values, colors, sizes):
+    xdata, ydata, zdata = zip(*values)
+    ax.scatter3D(xdata, ydata, zdata, c=colors /
+                 255.0, s=sizes, depthshade=False)
+
+
+def plot_closest(ax, colors, to):
+    distances = closest(colors, to, n=len(colors)//2)
+    max_distance = max(distances, key=lambda x: x[1])[1]
+    sizes = np.array(
+        list(map(lambda x: (math.log2(max_distance) - math.log2(x[1] + 1))*100, distances)))
+    rgb_values = list(map(lambda x: x[0], distances))
+    plot_data(ax, rgb_values, np.array(rgb_values), sizes)
+
+
+def init_projection(size=(13, 10)):
+    plt.figure(figsize=size, dpi=90)
+    ax = plt.axes(projection='3d')
+    ax.set_axis_off()
+    return ax
+
+
+def print_colors(colors):
+    print("\n-----COLOR LIST-----")
+    for color in colors.keys():
+        print(color)
+
+
+if __name__ == '__main__':
+    data_set_url = "https://raw.githubusercontent.com/dariusk/corpora/master/data/colors/xkcd.json"
+    data_set = get_json(data_set_url)
+    colors = parse_colors(data_set)
+
+    print_colors(colors)
+
+    color = input(
+        "\nEnter a color from the color list or just enter an hexadecimal value: ")
+    
+    if color in colors:
+        color = colors[color]
+    else:
+        color = hex_to_rgb(color)
+    ax = init_projection()
+    plot_closest(ax, colors, color)
+    plt.show()
+
diff --git a/src/colors_plot.py b/src/colors_plot.py
deleted file mode 100644
index 3ad7d11..0000000
--- a/src/colors_plot.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import requests
-import json
-from vector_utils import Vector as vec
-
-from mpl_toolkits import mplot3d
-import numpy as np 
-import matplotlib.pyplot as plt
-
-def get_content(url):
-    response = requests.get(url)
-    content = response.content.decode()
-    return content
-
-
-def get_json(url):
-    content = get_content(url)
-    data = json.loads(content)
-    return data
-
-
-def hex_to_rgb(s):
-    s = s.lstrip('#')
-    return [int(s[:2], 16), int(s[2:4], 16), int(s[4:6], 16)]
-
-
-def parse_colors(data_set):
-    return dict((item['color'], hex_to_rgb(item['hex'])) for item in data_set['colors'])
-
-
-def closest(space, coord, n=10):
-    return sorted(space.keys(), key=lambda x: vec.distance(space[x], coord))[:n]
-
-
-if __name__ == '__main__':
-    data_set_url = "https://raw.githubusercontent.com/dariusk/corpora/master/data/colors/xkcd.json"
-    data_set = get_json(data_set_url)
-    colors = parse_colors(data_set)
-
-    fig = plt.figure()
-    ax = plt.axes(projection='3d')
-
-    plt.show(fig)
-
diff --git a/src/vector_utils.py b/src/vector_utils.py
index 0779f54..413f6f6 100644
--- a/src/vector_utils.py
+++ b/src/vector_utils.py
@@ -1,5 +1,6 @@
 import math
 from functools import reduce
+import operator
 
 class Vector:
     @staticmethod
@@ -8,15 +9,15 @@ class Vector:
 
     @staticmethod
     def add(*vecs):
-        return [i + j for i, j in zip(*vecs)]
+        return [sum(t) for t in zip(*vecs)]
 
     @staticmethod
     def subtract(*vecs):
-        return [i - j for i, j in zip(*vecs)]
+        return [reduce(operator.__sub__, t) for t in zip(*vecs)]
 
     @staticmethod
-    def mean(*vecs):
-        return [float(coord) / len(vecs) for coord in reduce(Vector.add, vecs)]
+    def mean(*vectors):
+        return [float(coord) / len(vectors) for coord in reduce(Vector.add, vectors)]
 
 
 
diff --git a/test/test_vector.py b/test/test_vector.py
index 80cf348..7d0cb3f 100644
--- a/test/test_vector.py
+++ b/test/test_vector.py
@@ -2,7 +2,7 @@ import unittest
 from src.vector_utils import Vector
 
 
-class TestStringMethods(unittest.TestCase):
+class TestVector(unittest.TestCase):
 
     def test_add(self):
         v1 = [2, 3]