fix styling according to spec

Anthex
1 parent 1d9d7a9c
Showing 6 changed files with 22 additions and 27 deletions Show diff stats
README.md
main.py
structure.py
test_structure.py
test_visuals.py
visuals.py
-
-  
-
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-# [UTBM - LO53] - Python fingerprinting
-**Documentation** : https://dantz.fr/LO53/structure.html  
+# UTBM - LO53 : Python fingerprinting
+**Documentation** : [dantz.fr/LO53/structure.html](https://dantz.fr/LO53/structure.html) or _Documentation.md_
 **Author** : Achille Dantz <achille@dantz.fr>
  
 ## Description
@@ -16,6 +13,5 @@ This program aims to compute the approximate location of a mobile terminal for a
  
 ## Annex : N-Lateration 3D visualisation
 visuals.py provides a cool function to graphically represent the N-Lateration distances in a 3D space by creating an animated gif (RMI style)
-### Example : 
+### Example
 [Click here to view output gif example](https://dantz.fr/LO53/out.gif?) (too big to be displayed)
-
-from structure import RSSVector, Location, Cell, newCell, KNeighbors, resolve_barycenter, MarkovModel, NLateration
+from structure import RSSVector, Location, newCell, KNeighbors, resolve_barycenter, MarkovModel, NLateration
 from random import random
 from math import floor
  
@@ -35,7 +35,7 @@ def main():
         print(a.toString())
  
         #### Markov ####
-        MM = MarkovModel(Tf)    
+        MM = MarkovModel(Tf)
  
         # small set fixed definition
         MM.path([8,7,8,7,8,7,8,5,8,2,9,8,1,9,8,9,5,4,3,2,3,2,4,5,4,5,6,6,7,6,9,5,9,3,2,4,3,5,3,4,3,3,5,6,7,6,7,6,5,4,3,4,3,4])
@@ -50,7 +50,7 @@ def main():
         MM.printPercentages()
  
         print("\r\ncurrent cell is \033[1;32;40m#" + str(MM.previousCell) + "\033[1;37;40m , most likely next cell is \033[1;32;40m#" + str(MM.getMostLikely()) + "\033[1;37;40m which is located at \033[1;32;40m" + str(Location.fromID(MM.getMostLikely()).toString()) + "\033[1;37;40m")
-        
+
         while(1):
                 print("Input next location ID (between 1 and 9)\r\n>>", end='')
                 in_char = int(input())
 from operator import itemgetter as ig
-from math import floor, sqrt, ceil, log, exp
+from math import floor, sqrt, ceil, exp
 from numpy import arange
 class RSSVector():
     distances = []
@@ -30,7 +30,7 @@ class Location():
  
     def __mul__(self, multiplier):
         returnValue = Location(self.x, self.y, self.z)
-        returnValue.x *= multiplier 
+        returnValue.x *= multiplier
         returnValue.y *= multiplier
         returnValue.z *= multiplier
         return returnValue
@@ -117,14 +117,14 @@ class MarkovModel():
             self.MarkovValues.append([])
             for _ in range (0, 10):
                 self.MarkovValues[i].append(MarkovValue())
-        self.MarkovValues[10][0].nb = 1 #initial position sigma increment  
+        self.MarkovValues[10][0].nb = 1 #initial position sigma increment
  
     def moveToCellID(self, nextCell):
         '''
         Registers a movement from the current cell to a specified location by its ID
         :param nextCell: The ID of the new location
         '''
-        self.MarkovValues[nextCell][self.previousCell].nb += 1     
+        self.MarkovValues[nextCell][self.previousCell].nb += 1
         self.MarkovValues[10][nextCell].nb += 1
         self.refreshPercentage(self.previousCell)
         self.previousCell = nextCell
@@ -201,7 +201,7 @@ class MarkovModel():
         Returns the ID of the most likely next location with a given previous cell ID
         Typically called by getMostLikely() function
         Convert to coordinates using the Location.fromID() function
-        :param currentCell: ID of the last cell 
+        :param currentCell: ID of the last cell
         :return: ID of the most likely next location
         '''
         max_value=0
@@ -210,7 +210,7 @@ class MarkovModel():
             if self.MarkovValues[k][currentCell].nb > max_value:
                 max_value = self.MarkovValues[k][currentCell].nb
                 max_id = k
-        return max_id 
+        return max_id
  
     def path(self, locationIDs):
         '''
@@ -233,8 +233,8 @@ def newCell(n1, n2, n3, n4, l1, l2):
     :return: Cell with given characteristics
     '''
     return Cell(RSSVector(n1,n2,n3,n4), Location(l1,l2))
-    
-def KNeighbors(fingerprints, sample):  
+
+def KNeighbors(fingerprints, sample):
     '''
     Returns the 4 closest cells to the given sample and fills sample distance data
     :param fingerprints: 2D array of all the cells
@@ -247,7 +247,7 @@ def KNeighbors(fingerprints, sample):
             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.n4 - sample.n4)
             distances.append((dist, currentItem))
     distances = sorted(distances, key=ig(0))
     sample.distances = [x[0] for x in distances][:4]
@@ -266,7 +266,7 @@ def resolve_barycenter(nC, d):
           1 / (1+d[0]/d[1]+d[0]/d[2]+d[0]/d[3])*nC[0].location \
         + 1 / (1+d[1]/d[0]+d[1]/d[2]+d[1]/d[3])*nC[1].location \
         + 1 / (1+d[2]/d[1]+d[2]/d[0]+d[2]/d[3])*nC[2].location \
-        + 1 / (1+d[3]/d[1]+d[3]/d[2]+d[3]/d[0])*nC[3].location 
+        + 1 / (1+d[3]/d[1]+d[3]/d[2]+d[3]/d[0])*nC[3].location
  
  
 def NLateration(data, step=.1, xSize=0.0, ySize=0.0, zSize=0.0):
@@ -68,9 +68,9 @@ def test_getPositionInArray():
  
 def test_fromID():
       test_loc = Location.fromID(3)
-      assert test_loc == Location(2,10)      
+      assert test_loc == Location(2,10)
       test_loc = Location.fromID(9)
-      assert test_loc == Location(10,10)      
+      assert test_loc == Location(10,10)
  
 def test_getModeLikely():
       test_MM = MarkovModel(Tf)
@@ -84,7 +84,7 @@ def test_getModeLikely():
 def test_printValues():
       test_MM = MarkovModel(Tf)
       test_MM.path([1,2,3,2,3,4,3,4])
-      
+
       with OutputBuffer() as output:
             test_MM.printValues()
       assert len(output.out) > 2500
@@ -4,7 +4,7 @@ from PIL import Image
  
 def test_createFrame():
     result = createFrame(100,100,1)
-    assert type(result) is Image.Image
+    assert isinstance(result, Image.Image)
  
 def test_exportGif():
     exportGif()
-from structure import RSSVector, Location, Cell, newCell, KNeighbors, resolve_barycenter, MarkovModel, NLateration
-from random import random
-from math import floor, sqrt, ceil
+from structure import NLateration, Location
+from math import floor, sqrt
 from PIL import Image, ImageDraw
  
 dataset = [(Location(.5,.5,.5), 3.0), (Location(4.0,.0,.0), 2.0), (Location(4.0,5.0,5.0), 4.2), (Location(3.0,3.0,3.0), 2.5)]