import ast
import sys

file = 'aggregation.config'
algorithm = 'Astar'

if len(sys.argv) > 1:
file = sys.argv[1]
if len(sys.argv) > 2:
algorithm = sys.argv[2]

'''
Graph Superclass
'''
class Graph:

# BFS Search Algorithm
def bfs(self, source):
   S = [source]
   Q = [source]
   result = []
   space = 1
   visited = 0
   while len(Q) != 0:
         visited += 1
         space = max(space, len(Q))
         # BFS -- traverse first visited node
         current = Q.pop(0)
         result.append(current)
         for edge in self.getEdges(current):
            other = edge[0]
            if other not in S:
               S.append(other)
               Q.append(other)
   time = len(S)
   cost = self.cost(result)
   return result, time, space, visited, cost

# Unicost Search Algorithm
def unicost(self, source):
   S = [source]
   Q = [(source, 0)]
   result = []
   edges = self.getEdges(source)
   space = 1
   visited = 0
   while len(Q) != 0:
         visited += 1
         space = max(space, len(Q))
         Q = sorted(Q, key=lambda x: x[1])
         # Unicost -- traverse first node
         current = Q.pop(0)[0]
         result.append(current)
         for edge in self.getEdges(current):
            other = edge[0]
            if other not in S:
               S.append(other)
               # Assign every node the same cost
               Q.append((other, 1))
   time = len(S)
   cost = self.cost(result)
   return result, time, space, visited, cost

# Greedy Search Algorithm
def greedy(self, source):
   S = [source]
   Q = [(source, 0)]
   result = []
   edges = self.getEdges(source)
   space = 1
   visited = 0
   while len(Q) != 0:
         visited += 1
         space = max(space, len(Q))
         Q = sorted(Q, key=lambda x: x[1])
         # Greedy -- traverse best node
         current = Q.pop(0)[0]
         result.append(current)
         for edge in self.getEdges(current):
            other = edge[0]
            weight = edge[1]
            if other not in S:
               S.append(other)
               Q.append((other, weight))
   time = len(S)
   cost = self.cost(result)
   return result, time, space, visited, cost

# IDDFS Search Algorithm
def iddfs(self, source):
   S = [source

Carryout the following activities on lists. Copy ListA = [1, 3,...