import math from interval_utils import * import gui import random class Strategy: def __init__(self): self.target = (0,0) self.has_target = False self.target_cell = None self.color = (0,0,0) def process_frame(self,c): runaway = False my_smallest = min(map(lambda cell : cell.mass, c.player.own_cells)) my_largest = max(map(lambda cell : cell.mass, c.player.own_cells)) # enemy/virus avoidance forbidden_intervals = [] for cell in c.world.cells.values(): relpos = ((cell.pos[0]-c.player.center[0]),(cell.pos[1]-c.player.center[1])) dist = math.sqrt(relpos[0]**2+relpos[1]**2) if (not cell.is_virus and dist < ((500+2*cell.size) if cell.mass > 1.25*my_smallest*2 else (300+cell.size)) and cell.mass > 1.25 * my_smallest) or (cell.is_virus and dist < my_largest and cell.mass < my_largest): angle = math.atan2(relpos[1],relpos[0]) corridor_halfwidth = math.asin(cell.size / dist) forbidden_intervals += canonicalize_angle_interval((angle-corridor_halfwidth, angle+corridor_halfwidth)) runaway = True # wall avoidance if c.player.center[0] < c.world.top_left[1]+(c.player.total_size*2): forbidden_intervals += [(0.5*pi, 1.5*pi)] if c.player.center[0] > c.world.bottom_right[1]-(c.player.total_size*2): forbidden_intervals += [(0,0.5*pi), (1.5*pi, 2*pi)] if c.player.center[1] < c.world.top_left[0]+(c.player.total_size*2): forbidden_intervals += [(pi, 2*pi)] if c.player.center[1] > c.world.bottom_right[0]-(c.player.total_size*2): forbidden_intervals += [(0, pi)] # if there's actually an enemy to avoid: if (runaway): # find the largest non-forbidden interval, and run into this direction. forbidden_intervals = merge_intervals(forbidden_intervals) allowed_intervals = invert_angle_intervals(forbidden_intervals) (a,b) = find_largest_angle_interval(allowed_intervals) runaway_angle = (a+b)/2 runaway_x, runaway_y = (c.player.center[0]+int(100*math.cos(runaway_angle))), (c.player.center[1]+int(100*math.sin(runaway_angle))) self.target = (runaway_x, runaway_y) self.has_target = False self.target_cell = None self.color = (255,0,0) print ("Running away: " + str((runaway_x-c.player.center[0], runaway_y-c.player.center[1]))) # a bit of debugging information for i in forbidden_intervals: gui.draw_arc(c.player.center, c.player.total_size+10, i, (255,0,255)) # if however there's no enemy to avoid, chase food or jizz randomly around else: def edible(cell): return (cell.is_food) or (cell.mass <= sorted(c.player.own_cells, key = lambda x: x.mass)[0].mass * 0.75) and not (cell.is_virus) def rival(cell, food): if cell.is_virus or cell.is_food: return False if cell.cid in c.player.own_ids: return False if cell.mass < 1.25*my_smallest: return food.is_food or cell.size > 1.25*food.size else: return False def splitkiller(cell): return not cell.is_virus and not cell.is_food and cell.mass > 1.25*2*my_smallest def nonsplitkiller(cell): return not cell.is_virus and not cell.is_food and 1.20*my_smallest < cell.mass and cell.mass < 1.25*2*my_smallest if self.target_cell != None: self.target = tuple(self.target_cell.pos) if self.target_cell not in c.world.cells.values() or not edible(self.target_cell): self.target_cell = None self.has_target = False print("target_cell does not exist any more") elif self.target == tuple(c.player.center): self.has_target = False print("Reached random destination") if not self.has_target: food = list(filter(edible, c.world.cells.values())) def quality(cell): dd_sq = max((cell.pos[0]-c.player.center[0])**2 + (cell.pos[1]-c.player.center[1])**2,0.001) sigma = 500 dist_score = -math.exp(-dd_sq/(2*sigma**2)) rivals = filter(lambda r : rival(r,cell), c.world.cells.values()) splitkillers = filter(splitkiller, c.world.cells.values()) nonsplitkillers = filter(nonsplitkiller, c.world.cells.values()) rival_score = 0 for r in rivals: dd_sq = max(0.001, (r.pos[0]-cell.pos[0])**2 + (r.pos[1]-cell.pos[1])**2) sigma = r.size + 100 rival_score += math.exp(-dd_sq/(2*sigma**2)) splitkill_score = 0 for s in splitkillers: dd_sq = max(0.001, (s.pos[0]-cell.pos[0])**2 + (s.pos[1]-cell.pos[1])**2) sigma = (500+2*s.size) splitkill_score += math.exp(-dd_sq/(2*sigma**2)) nonsplitkill_score = 0 for s in nonsplitkillers: dd_sq = max(0.001, (s.pos[0]-cell.pos[0])**2 + (s.pos[1]-cell.pos[1])**2) sigma = (300+s.size) nonsplitkill_score += math.exp(-dd_sq/(2*sigma**2)) density_score = 0 sigma = 300 for f in filter(lambda c : c.is_food and c!=cell, c.world.cells.values()): dd_sq = (f.pos[0]-cell.pos[0])**2 + (f.pos[1]-cell.pos[1])**2 density_score -= math.exp(-dd_sq/(2*sigma**2)) wall_score = 0 wall_dist = min( cell.pos[0]-c.world.top_left[1], c.world.bottom_right[1]-cell.pos[0], cell.pos[1]-c.world.top_left[0], c.world.bottom_right[0]-cell.pos[1] ) sigma = 100 wall_score = math.exp(-wall_dist**2/(2*sigma**2)) return dist_score + 0.2*rival_score + nonsplitkill_score + 5*splitkill_score + 0.1*density_score + 5*wall_score ##print (density_score) #return density_score food = sorted(food, key = quality) if len(food) > 0: self.target = (food[0].pos[0], food[0].pos[1]) self.target_cell = food[0] self.has_target = True self.color = (0,0,255) print("Found food at: " + str(food[0].pos)) else: rx = c.player.center[0] + random.randrange(-400, 401) ry = c.player.center[1] + random.randrange(-400, 401) self.target = (rx, ry) self.has_target = True self.color = (0,255,0) print("Nothing to do, heading to random targetination: " + str((rx, ry))) # more debugging gui.draw_line(c.player.center, self.target, self.color) return self.target