A* Algorithm implementation in python.

astar.py

# Enter your code here. Read input from STDIN. Print output to STDOUT
class Node:
    def __init__(self,value,point):
        self.value = value
        self.point = point
        self.parent = None
        self.H = 0
        self.G = 0
    def move_cost(self,other):
        return 0 if self.value == '.' else 1
        
def children(point,grid):
    x,y = point.point
    links = [grid[d[0]][d[1]] for d in [(x-1, y),(x,y - 1),(x,y + 1),(x+1,y)]]
    return [link for link in links if link.value != '%']
def manhattan(point,point2):
    return abs(point.point[0] - point2.point[0]) + abs(point.point[1]-point2.point[0])
def aStar(start, goal, grid):
    #The open and closed sets
    openset = set()
    closedset = set()
    #Current point is the starting point
    current = start
    #Add the starting point to the open set
    openset.add(current)
    #While the open set is not empty
    while openset:
        #Find the item in the open set with the lowest G + H score
        current = min(openset, key=lambda o:o.G + o.H)
        #If it is the item we want, retrace the path and return it
        if current == goal:
            path = []
            while current.parent:
                path.append(current)
                current = current.parent
            path.append(current)
            return path[::-1]
        #Remove the item from the open set
        openset.remove(current)
        #Add it to the closed set
        closedset.add(current)
        #Loop through the node's children/siblings
        for node in children(current,grid):
            #If it is already in the closed set, skip it
            if node in closedset:
                continue
            #Otherwise if it is already in the open set
            if node in openset:
                #Check if we beat the G score 
                new_g = current.G + current.move_cost(node)
                if node.G > new_g:
                    #If so, update the node to have a new parent
                    node.G = new_g
                    node.parent = current
            else:
                #If it isn't in the open set, calculate the G and H score for the node
                node.G = current.G + current.move_cost(node)
                node.H = manhattan(node, goal)
                #Set the parent to our current item
                node.parent = current
                #Add it to the set
                openset.add(node)
    #Throw an exception if there is no path
    raise ValueError('No Path Found')
def next_move(pacman,food,grid):
    #Convert all the points to instances of Node
    for x in xrange(len(grid)):
        for y in xrange(len(grid[x])):
            grid[x][y] = Node(grid[x][y],(x,y))
    #Get the path
    path = aStar(grid[pacman[0]][pacman[1]],grid[food[0]][food[1]],grid)
    #Output the path
    print len(path) - 1
    for node in path:
        x, y = node.point
        print x, y
pacman_x, pacman_y = [ int(i) for i in raw_input().strip().split() ]
food_x, food_y = [ int(i) for i in raw_input().strip().split() ]
x,y = [ int(i) for i in raw_input().strip().split() ]
 
grid = []
for i in xrange(0, x):
    grid.append(list(raw_input().strip()))
 
next_move((pacman_x, pacman_y),(food_x, food_y), grid)

The reason of why I want to see your code is because you might have modified something, even if just a line or a variable, and it might have some impact over the full code. After all, as the old ones said: "In my PC at home the code works, not sure why on yours it doesn't".

Why the aStar function is calling four times in next_move function?

If you are referring to my version, not the author's one, the answer I should give you is: my bad. I can't remember what I was trying to do at the time, maybe I just clicked the ctrl+v shortcut a few times by mistake. I just can't see a reason for executing 4 times the same thing.