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import math
from agarnet.agarnet.vec import Vec
"""
pathfinding works by performing an A* search on a graph, built as follows:
there is a equally spaced rectangular grid, where each node is connected
to its 8 neighbours, with the appropriate euclidean distance.
additionally, for each food or ejected mass blob, a node is created. they're
additionally, for each food or ejected mass blob, a node is created. they're
connected by straight lines with each other, if no enemy cell is in between.
those "wormhole connections" have a cost of less than the euclidean distance.
"""
"""class Graph:
def __init__(self, center, width, height, spacing):
self.center = center
self.spacing = spacing
self.width = width
self.height = height
def nearest_node(self, pt):
rel = pt - self.center
rel.x = round(rel.x / spacing)
rel.y = round(rel.x / spacing)
nearest_blob = min(blobs, key = lambda blob : (blob.pos - pt).len())
dist_to_blob = (nearest_blob.pos - pt).len()
dist_to_grid = (spacing*rel + self.center - pt).len()
if dist_to_grid < dist_to_blob:
return self.get_gridnode(rel.x, rel.y)
else:
return self.get_blobnode(nearest_blob)
"""
class Graph:
def __init__(self, grid, blobs):
self.grid = grid
self.blobs = blobs
# A* code taken and adapted from https://gist.github.com/jamiees2/5531924
class Node:
def __init__(self,value,point,point_in_grid, is_in_wormhole_plane, graph, cell):
self.value = value
self.point = point
self.point_in_grid = point_in_grid
self.parent = None
self.H = 0
self.G = 0
self.graph = graph
self.is_in_wormhole_plane = is_in_wormhole_plane
self.find_near_wormholes(50)
def find_near_wormholes(self, radius):
self.near_wormholes = list(filter(lambda blob : (self.point - blob.point).len() < radius, self.graph.blobs))
def move_cost(self,other):
dist = distance(self, other)
if not (self.is_in_wormhole_plane or other.is_in_wormhole_plane):
# assert other in siblings(self,grid). otherwise this makes no sense
#return 5*(distance(self, other) + (self.value + other.value)/2)
return 5*dist
else:
return max(dist, 5*dist - 500)
def siblings(self):
x,y = self.point_in_grid
links = [self.graph.grid[d[0]][d[1]] for d in [(x-1, y),(x-1,y-1),(x,y - 1),(x+1,y-1),(x+1,y),(x+1,y+1),(x,y + 1),(x-1,y+1)]]
return [link for link in links if link.value != None] + self.near_wormholes
def distance(point,point2):
return math.sqrt((point.point[0] - point2.point[0])**2 + (point.point[1]-point2.point[1])**2)
def aStar(start, goal, grid):
print("aStar("+str(start.point)+"="+str(start.point_in_grid)+", "+str(goal.point)+"="+str(goal.point_in_grid)+")")
openset = set()
closedset = set()
current = start
openset.add(current)
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]
openset.remove(current)
closedset.add(current)
for node in current.siblings():
if node in closedset:
continue
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 = distance(node, goal)
node.parent = current
openset.add(node)
raise ValueError('No Path Found')
grid_radius=int(1100/30)*30
grid_density=30
class PathfindingTesterStrategy:
def __init__(self, c, gui):
self.c = c
self.path = None
self.gui = gui
def build_grid(self):
graph = Graph(None, [])
graph.blobs = [ Node(0, c.pos, Vec( int((c.pos.x - self.c.player.center.x + grid_radius) // grid_density), int((c.pos.y - self.c.player.center.y + grid_radius) // grid_density) ), True, graph, c) for c in self.c.world.cells.values() if c.is_food ]
graph.grid = [[0 for x in range(int(2*grid_radius//grid_density+1))] for x in range(int(2*grid_radius//grid_density+1))]
interesting_cells = list(filter(lambda c : not (c.is_food or c in self.c.player.own_cells), self.c.player.world.cells.values()))
for cell in interesting_cells:
for x in range(-grid_radius,grid_radius+1,grid_density):
gridx = (x+grid_radius) // grid_density
for y in range(-grid_radius,grid_radius+1,grid_density):
gridy = (y+grid_radius) // grid_density
relpos = (cell.pos.x - (x+self.c.player.center.x), cell.pos.y - (y+self.c.player.center.y))
dist_sq = relpos[0]**2 + relpos[1]**2
if dist_sq < cell.size**2 *3:
graph.grid[gridx][gridy] += 100000000
for x in range(-grid_radius,grid_radius+1,grid_density):
gridx = (x+grid_radius) // grid_density
for y in range(-grid_radius,grid_radius+1,grid_density):
gridy = (y+grid_radius) // grid_density
if (gridx in [0,len(graph.grid)-1] or gridy in [0, len(graph.grid[gridx])-1]):
val = None
else:
val = graph.grid[gridx][gridy]
graph.grid[gridx][gridy] = Node(val, self.c.player.center+Vec(x,y), Vec(gridx, gridy), False, graph, None)
for blob in graph.blobs:
blob.find_near_wormholes(200)
return graph.grid
def plan_path(self):
goalx = int((self.gui.marker[0][0] - self.c.player.center[0] + grid_radius)/grid_density)
goaly = int((self.gui.marker[0][1] - self.c.player.center[1] + grid_radius)/grid_density)
grid = self.build_grid()
path = aStar(grid[int(grid_radius/grid_density)][int(grid_radius/grid_density)], grid[goalx][goaly], grid)
return path
def path_is_valid(self, path):
interesting_cells = list(filter(lambda c : not (c.is_food or c in self.c.player.own_cells), self.c.player.world.cells.values()))
for node in path:
for cell in interesting_cells:
relpos = (cell.pos.x - node.point[0], cell.pos.y - node.point[1])
dist_sq = relpos[0]**2 + relpos[1]**2
if dist_sq < cell.size**2 *2:
return False
return True
def process_frame(self):
if self.gui.marker_updated[0]:
self.gui.marker_updated[0]=False
self.path = self.plan_path()
for node in self.path:
print (node.point_in_grid)
print("="*10)
for (node1,node2) in zip(self.path,self.path[1:]):
self.gui.draw_line(node1.point, node2.point, (0,0,0))
if self.path:
relx, rely = self.path[0].point[0]-self.c.player.center.x, self.path[0].point[1]-self.c.player.center.y
if relx*relx + rely*rely < (2*grid_density)**2:
self.path=self.path[1:]
if self.path and not self.path_is_valid(self.path):
print("recalculating!")
self.path = self.plan_path()
if self.path:
return self.path[0].point
return self.gui.marker[0]
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