diff --git a/main.py b/main.py
index cd0b0c6..b61bdc0 100644
--- a/main.py
+++ b/main.py
@@ -1,15 +1,11 @@
 
-from structure import *
+from structure import RSSVector, Location, Cell, newCell, KNeighbors, resolve_barycenter
 import sys 
 
 Tf = [] #cells table
 
-testSample = RSSVector(-26, -42, -13, -46)
-testSample2 = RSSVector(-26, -42, -13, -46)
-testSample3 = RSSVector(-26, -42, -13, -46)
-
-testSamples = []
-testSamples.extend([testSample, testSample2, testSample3])
+testSamples = [RSSVector(-26, -42, -13, -46), RSSVector(-26, -42, -13, -46), RSSVector(-26, -42, -13, -46)]
+testSample = testSamples[0]
 
 #cells Table initialization
 for i in range (0, 3):
@@ -18,15 +14,15 @@ for i in range (0, 3):
         Tf[i].append([])
 
 #known fingerprints 
-Tf[0][0] = Cell(RSSVector(-38,-27,-54,-13), Location(2,2))
-Tf[0][1] = Cell(RSSVector(-74,-62,-48,-33), Location(2,6))
-Tf[0][2] = Cell(RSSVector(-13,-28,-12,-40), Location(2,10))
-Tf[1][0] = Cell(RSSVector(-34,-27,-38,-41), Location(6,2))
-Tf[1][1] = Cell(RSSVector(-64,-48,-72,-35), Location(6,6))
-Tf[1][2] = Cell(RSSVector(-45,-37,-20,-15), Location(6,10))
-Tf[2][0] = Cell(RSSVector(-17,-50,-44,-33), Location(10,2))
-Tf[2][1] = Cell(RSSVector(-27,-28,-32,-45), Location(10,6))
-Tf[2][2] = Cell(RSSVector(-30,-20,-60,-40), Location(10,10))
+Tf[0][0] = newCell(-38,-27,-54,-13,2,2)
+Tf[0][1] = newCell(-74,-62,-48,-33,2,6)
+Tf[0][2] = newCell(-13,-28,-12,-40,2,10)
+Tf[1][0] = newCell(-34,-27,-38,-41,6,2)
+Tf[1][1] = newCell(-64,-48,-72,-35,6,6)
+Tf[1][2] = newCell(-45,-37,-20,-15,6,10)
+Tf[2][0] = newCell(-17,-50,-44,-33,10,2)
+Tf[2][1] = newCell(-27,-28,-32,-45,10,6)
+Tf[2][2] = newCell(-30,-20,-60,-40,10,10)
 
 def main(args):
         #### K neighbours ####
@@ -44,5 +40,5 @@ def main(args):
 
         #### Markov ####
 
-        return 0;
+        return 0
 main(sys.argv)
\ No newline at end of file
diff --git a/structure.py b/structure.py
index f3d9756..3753507 100644
--- a/structure.py
+++ b/structure.py
@@ -44,6 +44,9 @@ class Cell():
         self.Likeliness = .0 # Probability of Markov model
         self.pastCount = 1
 
+def newCell(n1, n2, n3, n4, l1, l2):
+    return Cell(RSSVector(n1,n2,n3,n4), Location(l1,l2))
+    
 def KNeighbors(fingerprints, sample):  
     '''
     Returns the 4 closest cells to the given sample and fills sample distance data
@@ -52,15 +55,15 @@ def KNeighbors(fingerprints, sample):
     :return Cell[4]: the 4 nearest cells
     '''
     distances = []
+    neighbours = []
     for row in fingerprints:
         for currentItem in row:
             dist = abs(currentItem.v.n1 - sample.n1) \
-                    + abs(currentItem.v.n2 - sample.n2) \
-                    + abs(currentItem.v.n3 - sample.n3) \
-                    + abs(currentItem.v.n4 - sample.n4) 
+                 + abs(currentItem.v.n2 - sample.n2) \
+                 + abs(currentItem.v.n3 - sample.n3) \
+                 + abs(currentItem.v.n4 - sample.n4) 
             distances.append((dist, currentItem))
-            distances = sorted(distances, key=ig(0))
-    neighbours = []
+    distances = sorted(distances, key=ig(0))
     for k in range (0,4):
         neighbours.append(distances[k][1])
         sample.distances.append(distances[k][0])
--
libgit2 0.21.4