Modernize Python 2 code to get ready for Python 3

Author: cclauss

File_Transfer_Protocol/ftp_send_receive.py

@@ -1,34 +1,34 @@
-		"""
-			File transfer protocol used to send and receive files using FTP server.
-			Use credentials to provide access to the FTP client
+"""
+	File transfer protocol used to send and receive files using FTP server.
+	Use credentials to provide access to the FTP client
 
-			Note: Do not use root username & password for security reasons
-				  Create a seperate user and provide access to a home directory of the user
-				  Use login id and password of the user created 
-				  cwd here stands for current working directory
-		"""
+	Note: Do not use root username & password for security reasons
+	  Create a seperate user and provide access to a home directory of the user
+	  Use login id and password of the user created 
+	  cwd here stands for current working directory
+"""
 
 from ftplib import FTP
-ftp = FTP('xxx.xxx.x.x')    """ Enter the ip address or the domain name here """   
+ftp = FTP('xxx.xxx.x.x')  # Enter the ip address or the domain name here
 ftp.login(user='username', passwd='password')
 ftp.cwd('/Enter the directory here/')
 
-		"""
-			The file which will be received via the FTP server
-			Enter the location of the file where the file is received
-		"""
+"""
+	The file which will be received via the FTP server
+	Enter the location of the file where the file is received
+"""
 
 def ReceiveFile():
 	FileName = 'example.txt'   """ Enter the location of the file """
 	LocalFile = open(FileName, 'wb')
-	ftp.retrbinary('RETR ' + filename, LocalFile.write, 1024)
+	ftp.retrbinary('RETR ' + FileName, LocalFile.write, 1024)
 	ftp.quit()
 	LocalFile.close()
 
-		"""
-			The file which will be sent via the FTP server
-			The file send will be send to the current working directory
-		"""
+"""
+	The file which will be sent via the FTP server
+	The file send will be send to the current working directory
+"""
 
 def SendFile():
 	FileName = 'example.txt'   """ Enter the name of the file """

Graphs/a_star.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 
 grid = [[0, 1, 0, 0, 0, 0],
         [0, 1, 0, 0, 0, 0],#0 are free path whereas 1's are obstacles
@@ -80,7 +81,7 @@ def search(grid,init,goal,cost,heuristic):
     y = goal[1]
     invpath.append([x, y])#we get the reverse path from here
     while x != init[0] or y != init[1]:
-    	x2 = x - delta[action[x][y]][0]
+        x2 = x - delta[action[x][y]][0]
         y2 = y - delta[action[x][y]][1]
         x = x2
         y = y2
@@ -89,13 +90,13 @@ def search(grid,init,goal,cost,heuristic):
     path = []
     for i in range(len(invpath)):
     	path.append(invpath[len(invpath) - 1 - i])
-    print "ACTION MAP"
+    print("ACTION MAP")
     for i in range(len(action)):
-        print action[i]
+        print(action[i])
 
     return path
 
 a = search(grid,init,goal,cost,heuristic)
 for i in range(len(a)):
-	print a[i] 
+	print(a[i]) 
 

Graphs/basic-graphs.py

@@ -1,3 +1,15 @@
+from __future__ import print_function
+
+try:
+    raw_input          # Python 2
+except NameError:
+    raw_input = input  # Python 3
+
+try:
+    xrange             # Python 2
+except NameError:
+    xrange = range     # Python 3
+
 # Accept No. of Nodes and edges
 n, m = map(int, raw_input().split(" "))
 
@@ -48,15 +60,15 @@
 
 def dfs(G, s):
     vis, S = set([s]), [s]
-    print s
+    print(s)
     while S:
         flag = 0
         for i in G[S[-1]]:
             if i not in vis:
                 S.append(i)
                 vis.add(i)
                 flag = 1
-                print i
+                print(i)
                 break
         if not flag:
             S.pop()
@@ -76,14 +88,14 @@ def dfs(G, s):
 
 def bfs(G, s):
     vis, Q = set([s]), deque([s])
-    print s
+    print(s)
     while Q:
         u = Q.popleft()
         for v in G[u]:
             if v not in vis:
                 vis.add(v)
                 Q.append(v)
-                print v
+                print(v)
 
 
 """
@@ -116,7 +128,7 @@ def dijk(G, s):
                     path[v[0]] = u
     for i in dist:
         if i != s:
-            print dist[i]
+            print(dist[i])
 
 
 """
@@ -140,7 +152,7 @@ def topo(G, ind=None, Q=[1]):
     if len(Q) == 0:
         return
     v = Q.popleft()
-    print v
+    print(v)
     for w in G[v]:
         ind[w] -= 1
         if ind[w] == 0:
@@ -175,7 +187,8 @@ def adjm():
 """
 
 
-def floy((A, n)):
+def floy(xxx_todo_changeme):
+    (A, n) = xxx_todo_changeme
     dist = list(A)
     path = [[0] * n for i in xrange(n)]
     for k in xrange(n):
@@ -184,7 +197,7 @@ def floy((A, n)):
                 if dist[i][j] > dist[i][k] + dist[k][j]:
                     dist[i][j] = dist[i][k] + dist[k][j]
                     path[i][k] = k
-    print dist
+    print(dist)
 
 
 """
@@ -246,14 +259,15 @@ def edglist():
 """
 
 
-def krusk((E, n)):
+def krusk(xxx_todo_changeme1):
     # Sort edges on the basis of distance
+    (E, n) = xxx_todo_changeme1
     E.sort(reverse=True, key=lambda x: x[2])
     s = [set([i]) for i in range(1, n + 1)]
     while True:
         if len(s) == 1:
             break
-        print s
+        print(s)
         x = E.pop()
         for i in xrange(len(s)):
             if x[0] in s[i]:

Graphs/minimum_spanning_tree_kruskal.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 num_nodes, num_edges = list(map(int,input().split()))
 
 edges = []

Graphs/scc_kosaraju.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 # n - no of nodes, m - no of edges
 n, m = list(map(int,input().split()))
 

Multi_Hueristic_Astar.py

@@ -1,8 +1,14 @@
+from __future__ import print_function
 import heapq
 import numpy as np
 import math
 import copy
 
+try:
+    xrange          # Python 2
+except NameError:
+    xrange = range  # Python 3
+
 
 class PriorityQueue:
 	def __init__(self):
@@ -95,22 +101,22 @@ def do_something(back_pointer, goal, start):
 	for i in xrange(n):
 		for j in range(n):
 			if (i, j) == (0, n-1):
-				print grid[i][j],
-				print "<-- End position",
+				print(grid[i][j], end=' ')
+				print("<-- End position", end=' ')
 			else:
-				print grid[i][j],
-		print
+				print(grid[i][j], end=' ')
+		print()
 	print("^")
 	print("Start position")
-	print
+	print()
 	print("# is an obstacle")
 	print("- is the path taken by algorithm")
 	print("PATH TAKEN BY THE ALGORITHM IS:-")
 	x = back_pointer[goal]
 	while x != start:
-		print x, 
+		print(x, end=' ') 
 		x = back_pointer[x]
-	print x
+	print(x)
 	quit()
 
 def valid(p):
@@ -239,24 +245,24 @@ def multi_a_star(start, goal, n_hueristic):
 					expand_state(get_s, 0, visited, g_function, close_list_anchor, close_list_inad, open_list, back_pointer)
 					close_list_anchor.append(get_s)
 	print("No path found to goal")
-	print
+	print()
 	for i in range(n-1,-1, -1):
 		for j in range(n):
 			if (j, i) in blocks:
-				print '#',
+				print('#', end=' ')
 			elif (j, i) in back_pointer:
 				if (j, i) == (n-1, n-1):
-					print '*',
+					print('*', end=' ')
 				else:
-					print '-',
+					print('-', end=' ')
 			else:
-				print '*',
+				print('*', end=' ')
 			if (j, i) == (n-1, n-1):
-				print '<-- End position',
-		print
+				print('<-- End position', end=' ')
+		print()
 	print("^")
 	print("Start position")
-	print
+	print()
 	print("# is an obstacle")
 	print("- is the path taken by algorithm")
-multi_a_star(start, goal, n_hueristic)
+multi_a_star(start, goal, n_hueristic)

Neural_Network/convolution_neural_network.py

@@ -15,6 +15,7 @@
     Date: 2017.9.20
     - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
           '''
+from __future__ import print_function
 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -192,8 +193,8 @@ def _calculate_gradient_from_pool(self,out_map,pd_pool,num_map,size_map,size_poo
     def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_e = bool):
         #model traning
         print('----------------------Start Training-------------------------')
-        print(' - - Shape: Train_Data  ',np.shape(datas_train))
-        print(' - - Shape: Teach_Data  ',np.shape(datas_teach))
+        print((' - - Shape: Train_Data  ',np.shape(datas_train)))
+        print((' - - Shape: Teach_Data  ',np.shape(datas_teach)))
         rp = 0
         all_mse = []
         mse  = 10000
@@ -262,7 +263,7 @@ def draw_error():
             plt.grid(True, alpha=0.5)
             plt.show()
         print('------------------Training Complished---------------------')
-        print(' - - Training epoch: ', rp, '     - - Mse: %.6f' % mse)
+        print((' - - Training epoch: ', rp, '     - - Mse: %.6f' % mse))
         if draw_e:
             draw_error()
         return mse
@@ -271,7 +272,7 @@ def predict(self,datas_test):
         #model predict
         produce_out = []
         print('-------------------Start Testing-------------------------')
-        print(' - - Shape: Test_Data  ',np.shape(datas_test))
+        print((' - - Shape: Test_Data  ',np.shape(datas_test)))
         for p in range(len(datas_test)):
             data_test = np.asmatrix(datas_test[p])
             data_focus1, data_conved1 = self.convolute(data_test, self.conv1, self.w_conv1,

Neural_Network/perceptron.py

@@ -9,6 +9,7 @@
 	p2 = 1
 
 '''
+from __future__ import print_function
 
 import random
 
@@ -52,7 +53,7 @@ def trannig(self):
             epoch_count = epoch_count + 1
             # if you want controle the epoch or just by erro
             if erro == False:
-                print('\nEpoch:\n',epoch_count)
+                print(('\nEpoch:\n',epoch_count))
                 print('------------------------\n')
             #if epoch_count > self.epoch_number or not erro:
                 break
@@ -66,10 +67,10 @@ def sort(self, sample):
         y = self.sign(u)
 
         if  y == -1:
-            print('Sample: ', sample)
+            print(('Sample: ', sample))
             print('classification: P1')
         else:
-            print('Sample: ', sample)
+            print(('Sample: ', sample))
             print('classification: P2')
 
     def sign(self, u):

Project Euler/Problem 01/sol1.py

@@ -4,9 +4,14 @@
 we get 3,5,6 and 9. The sum of these multiples is 23.
 Find the sum of all the multiples of 3 or 5 below N.
 '''
+from __future__ import print_function
+try:
+    raw_input          # Python 2
+except NameError:
+    raw_input = input  # Python 3
 n = int(raw_input().strip())
-sum=0;
+sum=0
 for a in range(3,n):
     if(a%3==0 or a%5==0):
         sum+=a
-print sum;
+print(sum)

Project Euler/Problem 01/sol2.py

@@ -4,6 +4,11 @@
 we get 3,5,6 and 9. The sum of these multiples is 23.
 Find the sum of all the multiples of 3 or 5 below N.
 '''
+from __future__ import print_function
+try:
+    raw_input          # Python 2
+except NameError:
+    raw_input = input  # Python 3
 n = int(raw_input().strip())
 sum = 0
 terms = (n-1)/3
@@ -12,4 +17,4 @@
 sum+= ((terms)*(10+(terms-1)*5))/2
 terms = (n-1)/15
 sum-= ((terms)*(30+(terms-1)*15))/2
-print sum
+print(sum)