class AStar():
'''
Properties:
public:
- world: 2D array of Nodes
internal:
- size: (width, height) tuple of world
- open: Nodes queue to evaluate (heap-based priority queue)
'''
#----------------------------------------------------------------------
def __init__(self, world):
self.world = world
self.size = (len(world), len(world[0]))
# self.open = SortedList()
self.open = PriorityQueue()
self.openValue = 1
self.closedValue = 2
#----------------------------------------------------------------------
def initSearch(self, start, goal, obstacles):
''' first, check we can achieve the goal'''
if goal.type in obstacles:
return False
''' clear open list and setup new open/close value state to avoid the clearing of a closed list'''
self.open.clear()
self.openValue += 2
self.closedValue += 2
''' then init search variables'''
self.start = start
self.goal = goal
self.obstacles = obstacles
self.start.cost = 0
self.addToOpen(self.start)
self.goal.parent = None
return True
#----------------------------------------------------------------------
def search(self):
while not self.openIsEmpty():
current = self.popFromOpen()
if current == self.goal:
break
self.removeFromOpen(current)
self.addToClosed(current)
''' generator passes : look at the 8 neighbours around the current node from open'''
for (di, dj) in [(-1,-1), (-1,0), (-1,1), (0,-1), (0,1), (1,-1), (1,0), (1,1)]:
neighbour = self.getNode(current.i + di, current.j + dj)
if (not neighbour) or (neighbour.type in self.obstacles):
continue
'''the cost to get to this node is the current cost plus the movement
cost to reach this node. Note that the heuristic value is only used
in the open list'''
nextStepCost = current.cost + self.getNeighbourCost(current, neighbour)
'''if the new cost we've determined for this node is lower than
it has been previously makes sure the node has not been
determined that there might have been a better path to get to
this node, so it needs to be re-evaluated'''
if nextStepCost < neighbour.cost and (self.inOpenList(neighbour) or self.inClosedList(neighbour)):
self.invalidateState(neighbour)
'''if the node hasn't already been processed and discarded then
step (i.e. to the open list)'''
if (not self.inOpenList(neighbour)) and (not self.inClosedList(neighbour)):
neighbour.cost = nextStepCost
neighbour.heuristic = self.getHeuristicCost(neighbour, self.goal)
neighbour.parent = current
self.addToOpen(neighbour)
''' exit with None = path not yet found'''
yield None
'''since we've run out of search
there was no path. Just return'''
if self.goal.parent is None:
return
'''At this point we've definitely found a path so we can uses the parent
references of the nodes to find out way from the target location back
to the start recording the nodes on the way.'''
path = []
goal = self.goal
while goal is not self.start:
path.insert(0, (goal.i, goal.j))
goal = goal.parent
''' done, exit with path'''
yield path
#-----------------------------------------------------------------------------
def getNode(self, i, j):
if i >=0 and i < self.size[0] and j >= 0 and j < self.size[1]:
return self.world[i][j]
else:
return None
#----------------------------------------------------------------------
def getNeighbourCost(self, n1, n2):
return (abs(n2.i - n1.i) + abs(n2.j - n1.j))
#----------------------------------------------------------------------
def getHeuristicCost(self, n1, n2):
return (abs(n2.i - n1.i) + abs(n2.j - n1.j))
#----------------------------------------------------------------------
def invalidateState(self, node):
node.state = 0
#----------------------------------------------------------------------
def popFromOpen(self):
# return self.open.first()
return self.open.pop()
#----------------------------------------------------------------------
def addToOpen(self, node):
# self.open.add(node)
self.open.insert(node)
node.state = self.openValue
#----------------------------------------------------------------------
def inOpenList(self, node):
return node.state is self.openValue
#----------------------------------------------------------------------
def removeFromOpen(self, node):
# self.open.remove(node)
self.open.remove(node)
node.state = 0
#----------------------------------------------------------------------
def openIsEmpty(self):
# return not self.open.size()
return self.open.isEmpty()
#----------------------------------------------------------------------
def addToClosed(self, node):
node.state = self.closedValue
#----------------------------------------------------------------------
def inClosedList(self, node):
return node.state is self.closedValue
class AStar():
'''
Properties:
public:
- world: 2D array of Nodes
internal:
- size: (width, height) tuple of world
- open: Nodes queue to evaluate (heap-based priority queue)
'''
#----------------------------------------------------------------------
def __init__(self, world):
self.world = world
self.size = (len(world), len(world[0]))
# self.open = SortedList()
self.open = PriorityQueue()
self.openValue = 1
self.closedValue = 2
#----------------------------------------------------------------------
def initSearch(self, start, goal, obstacles):
''' first, check we can achieve the goal'''
if goal.type in obstacles:
return False
''' clear open list and setup new open/close value state to avoid the clearing of a closed list'''
self.open.clear()
self.openValue += 2
self.closedValue += 2
''' then init search variables'''
self.start = start
self.goal = goal
self.obstacles = obstacles
self.start.cost = 0
self.addToOpen(self.start)
self.goal.parent = None
return True
#----------------------------------------------------------------------
def search(self):
while not self.openIsEmpty():
current = self.popFromOpen()
if current == self.goal:
break
self.removeFromOpen(current)
self.addToClosed(current)
''' generator passes : look at the 8 neighbours around the current node from open'''
for (di, dj) in [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1),
(1, -1), (1, 0), (1, 1)]:
neighbour = self.getNode(current.i + di, current.j + dj)
if (not neighbour) or (neighbour.type in self.obstacles):
continue
'''the cost to get to this node is the current cost plus the movement
cost to reach this node. Note that the heuristic value is only used
in the open list'''
nextStepCost = current.cost + self.getNeighbourCost(
current, neighbour)
'''if the new cost we've determined for this node is lower than
it has been previously makes sure the node has not been
determined that there might have been a better path to get to
this node, so it needs to be re-evaluated'''
if nextStepCost < neighbour.cost and (
self.inOpenList(neighbour)
or self.inClosedList(neighbour)):
self.invalidateState(neighbour)
'''if the node hasn't already been processed and discarded then
step (i.e. to the open list)'''
if (not self.inOpenList(neighbour)) and (
not self.inClosedList(neighbour)):
neighbour.cost = nextStepCost
neighbour.heuristic = self.getHeuristicCost(
neighbour, self.goal)
neighbour.parent = current
self.addToOpen(neighbour)
''' exit with None = path not yet found'''
yield None
'''since we've run out of search
there was no path. Just return'''
if self.goal.parent is None:
return
'''At this point we've definitely found a path so we can uses the parent
references of the nodes to find out way from the target location back
to the start recording the nodes on the way.'''
path = []
goal = self.goal
while goal is not self.start:
path.insert(0, (goal.i, goal.j))
goal = goal.parent
''' done, exit with path'''
yield path
#-----------------------------------------------------------------------------
def getNode(self, i, j):
if i >= 0 and i < self.size[0] and j >= 0 and j < self.size[1]:
return self.world[i][j]
else:
return None
#----------------------------------------------------------------------
def getNeighbourCost(self, n1, n2):
return (abs(n2.i - n1.i) + abs(n2.j - n1.j))
#----------------------------------------------------------------------
def getHeuristicCost(self, n1, n2):
return (abs(n2.i - n1.i) + abs(n2.j - n1.j))
#----------------------------------------------------------------------
def invalidateState(self, node):
node.state = 0
#----------------------------------------------------------------------
def popFromOpen(self):
# return self.open.first()
return self.open.pop()
#----------------------------------------------------------------------
def addToOpen(self, node):
# self.open.add(node)
self.open.insert(node)
node.state = self.openValue
#----------------------------------------------------------------------
def inOpenList(self, node):
return node.state is self.openValue
#----------------------------------------------------------------------
def removeFromOpen(self, node):
# self.open.remove(node)
self.open.remove(node)
node.state = 0
#----------------------------------------------------------------------
def openIsEmpty(self):
# return not self.open.size()
return self.open.isEmpty()
#----------------------------------------------------------------------
def addToClosed(self, node):
node.state = self.closedValue
#----------------------------------------------------------------------
def inClosedList(self, node):
return node.state is self.closedValue
