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Diffstat (limited to 'pathfinding.py')
| -rw-r--r-- | pathfinding.py | 313 |
1 files changed, 313 insertions, 0 deletions
diff --git a/pathfinding.py b/pathfinding.py new file mode 100644 index 0000000..951a055 --- /dev/null +++ b/pathfinding.py @@ -0,0 +1,313 @@ +import heapq +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 + + +class Grid: + def __init__(self, origin, radius, density, default=None): + self.radius = radius + self.density = density + self.origin = origin + if not hasattr(default, '__call__'): + self.data = [[default for x in range(int(2*radius//density+1))] for x in range(int(2*radius//density+1))] + else: + self.data = [[default() for x in range(int(2*radius//density+1))] for x in range(int(2*radius//density+1))] + + def getpos(self, x, y = None): + if y == None: + x,y=x[0],x[1] + return ( int(x-self.origin.x+self.radius)//self.density, int(y-self.origin.y+self.radius)//self.density ) + + def distance(self, x, y = None): + if y == None: + x,y=x[0],x[1] + + xx,yy = self.getpos(x,y) + + return (Vec(x,y) - Vec(xx*self.density+self.origin.x-self.radius, yy*self.density+self.origin.y-self.radius)).len() + + + def at(self, x, y = None): + xx,yy = self.getpos(x,y) + return self.data[xx][yy] + + def points_near(self, radius, x, y = None): + r = int(radius / self.density) + xx,yy = self.getpos(x,y) + + result = [] + for xxx in range(xx-r, xx+r+1): + for yyy in range(yy-r, yy+r+1): + if self.contains_raw(xxx,yyy): + result.append(self.data[xxx][yyy]) + return result + + def set(self, val, x, y = None): + xx,yy = self.getpos(x,y) + self.data[xx][yy] = val + + def is_border(self, x, y): + xx,yy = self.getpos(x,y) + return (xx in [0,len(self.data)-1] or yy in [0, len(self.data[xx])-1]) + + def contains(self, x, y): + xx,yy = self.getpos(x,y) + return contains_raw(xx,yy) + + def contains_raw(self, xx, yy): + return (0 <= xx and xx < len(self.data)) and (0 <= yy and yy < len(self.data[yy])) + + +# A* code taken and adapted from https://gist.github.com/jamiees2/5531924 + +class Node: + def __init__(self,value,point, is_in_wormhole_plane, graph, cell, near_wormholes = []): + self.value = value + self.point = point + self.parent = None + self.H = 0 + self.G = 0 + self.F = 0 + self.graph = graph + self.is_in_wormhole_plane = is_in_wormhole_plane + self.near_wormholes = near_wormholes + self.is_open = False + self.is_closed = False + + def __lt__(self, other): + return False + + 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): + # MUST NOT be called when other not in self.siblings()! + + + 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) + xd, yd = abs(self.point.x-other.point.x), abs(self.point.y-other.point.y) + dist=0 + if xd == 0 or yd == 0: + dist = xd+yd + else: + dist = 1.41*xd + + return 5*dist + (self.value + other.value)/2 + else: + dist = distance(self, other) + return max(dist, 5*dist - 500) + + def siblings(self): + x,y = self.graph.grid.getpos(self.point) + links = [self.graph.grid.data[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): + openheap = [] + + current = start + current.is_open = True + openheap.append((0,current)) + + while openheap: + #Find the item in the open set with the lowest F = G + H score + current = heapq.heappop(openheap)[1] + + #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] + + current.is_open = False + current.is_closed = True + + for node in current.siblings(): + if node.is_closed: + continue + + if node.is_open: + #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.F = node.G + node.H + node.parent = current + heapq.heappush(openheap, (node.F, node)) + 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.F = node.G + node.H + + node.parent = current + + node.is_open=True + heapq.heappush(openheap, (node.F, node)) + + raise ValueError('No Path Found') + +grid_density=30 +grid_radius=int(1100/grid_density)*grid_density + +class PathfindingTesterStrategy: + def __init__(self, c, gui): + self.c = c + self.path = None + self.gui = gui + + def build_graph(self): + graph = Graph(None, []) + + graph.blobs = [ Node(0, c.pos, True, graph, c) for c in self.c.world.cells.values() if c.is_food ] + + + + graph.grid = Grid(self.c.player.center, grid_radius, grid_density, 0) + + + tempgrid = Grid(self.c.player.center, grid_radius, grid_density, lambda : []) + for blob in graph.blobs: + for l in tempgrid.points_near(100, blob.point): + l.append(blob) + #dist = tempgrid.distance(cell.pos) + + + + + + interesting_cells = list(filter(lambda c : not (c.is_food or c in self.c.player.own_cells), self.c.player.world.cells.values())) + + xmin,xmax = int(self.c.player.center.x-grid_radius), int(self.c.player.center.x+grid_radius+1) + ymin,ymax = int(self.c.player.center.y-grid_radius), int(self.c.player.center.y+grid_radius+1) + + for cell in interesting_cells: + x1,x2 = max(xmin, cell.pos.x - 3*cell.size - grid_density), min(xmax, cell.pos.x + 3*cell.size + grid_density) + y1,y2 = max(ymin, cell.pos.y - 3*cell.size - grid_density), min(ymax, cell.pos.y + 3*cell.size + grid_density) + xx1,yy1 = graph.grid.getpos(x1,y1) + xx2,yy2 = graph.grid.getpos(x2,y2) + for (x,xx) in zip( range(x1,x2, grid_density), range(xx1,xx2) ): + for (y,yy) in zip( range(y1,y2, grid_density), range(yy1,yy2) ): + relpos = (cell.pos.x - x, cell.pos.y - y) + dist = math.sqrt(relpos[0]**2 + relpos[1]**2) + if dist < cell.size + 100: + graph.grid.data[xx][yy] = 100000000 + + xx1,yy1 = graph.grid.getpos(xmin,ymin) + xx2,yy2 = graph.grid.getpos(xmax+1,ymax+1) + for xx in range(xx1,xx2): + graph.grid.data[xx][yy1+1] = None + graph.grid.data[xx][yy2-1] = None + for yy in range(yy1,yy2): + graph.grid.data[xx1+1][yy] = None + graph.grid.data[xx2-1][yy] = None + + for x,xx in zip( range(xmin, xmax+1, grid_density), range(xx1,xx2) ): + for y,yy in zip( range(ymin, ymax+1, grid_density), range(yy1,yy2) ): + val = graph.grid.data[xx][yy] + graph.grid.data[xx][yy] = Node(val, Vec(x,y), False, graph, None, tempgrid.data[xx][yy]) + + for blob in graph.blobs: + blob.find_near_wormholes(100) + + return graph + + def plan_path(self): + graph = self.build_graph() + + path = aStar(graph.grid.at(self.c.player.center), graph.grid.at(self.gui.marker[0])) + 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): + for x in range(0,grid_radius, grid_density): + color=(192,192,192) + self.gui.draw_line((-8000,self.c.player.center.y + x), (8000, self.c.player.center.y + x), color) + self.gui.draw_line((-8000,self.c.player.center.y - x), (8000, self.c.player.center.y - x), color) + self.gui.draw_line((self.c.player.center.x - x,-8000), (self.c.player.center.x - x, 8000), color) + self.gui.draw_line((self.c.player.center.x + x,-8000), (self.c.player.center.x + x, 8000), color) + + + 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) + 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] |