Day 17: Clumsy Crucible

Megathread guidelines

  • Keep top level comments as only solutions, if you want to say something other than a solution put it in a new post. (replies to comments can be whatever)
  • You can send code in code blocks by using three backticks, the code, and then three backticks or use something such as https://topaz.github.io/paste/ if you prefer sending it through a URL

FAQ

  • hades
    link
    fedilink
    arrow-up
    3
    ·
    edit-2
    3 months ago

    Python

    749 line-seconds

    import collections
    import dataclasses
    import heapq
    
    import numpy as np
    
    from .solver import Solver
    
    
    @dataclasses.dataclass(order=True)
    class QueueEntry:
      price: int
      x: int
      y: int
      momentum_x: int
      momentum_y: int
      deleted: bool
    
    
    class Day17(Solver):
      lines: list[str]
      sx: int
      sy: int
      lower_bounds: np.ndarray
    
      def __init__(self):
        super().__init__(17)
    
      def presolve(self, input: str):
        self.lines = input.splitlines()
        self.sx = len(self.lines[0])
        self.sy = len(self.lines)
        start = (self.sx - 1, self.sy - 1)
        self.lower_bounds = np.zeros((self.sx, self.sy)) + np.inf
        self.lower_bounds[start] = 0
        queue: list[QueueEntry] = [QueueEntry(0, self.sx - 1, self.sy - 1, 0, 0, False)]
        queue_entries: dict[tuple[int, int], QueueEntry] = {start: queue[0]}
        while queue:
          cur_price, x, y, _, _, deleted = dataclasses.astuple(heapq.heappop(queue))
          if deleted:
            continue
          del queue_entries[(x, y)]
          self.lower_bounds[x, y] = cur_price
          price = cur_price + int(self.lines[y][x])
          for dx, dy in ((-1, 0), (1, 0), (0, -1), (0, 1)):
            nx, ny = x + dx, y + dy
            if not (0 <= nx < self.sx) or not (0 <= ny < self.sy):
              continue
            if price < self.lower_bounds[nx, ny]:
              self.lower_bounds[nx, ny] = price
              if (nx, ny) in queue_entries:
                queue_entries[(nx, ny)].deleted = True
              queue_entries[(nx, ny)] = QueueEntry(price, nx, ny, 0, 0, False)
              heapq.heappush(queue, queue_entries[(nx, ny)])
    
      def _solve(self, maximum_run: int, minimum_run_to_turn: int):
        came_from: dict[tuple[int, int, int, int], tuple[int, int, int, int]] = {}
        start = (0, 0, 0, 0)
        queue: list[QueueEntry] = [QueueEntry(self.lower_bounds[0, 0], *start, False)]
        queue_entries: dict[tuple[int, int, int, int], QueueEntry] = {start: queue[0]}
        route: list[tuple[int, int]] = []
        prices: dict[tuple[int, int, int, int], float] = collections.defaultdict(lambda: np.inf)
        prices[start] = 0
        while queue:
          _, current_x, current_y, momentum_x, momentum_y, deleted = dataclasses.astuple(heapq.heappop(queue))
          cur_price = prices[(current_x, current_y, momentum_x, momentum_y)]
          if deleted:
            continue
          if ((current_x, current_y) == (self.sx - 1, self.sy - 1) and
              (momentum_x >= minimum_run_to_turn or momentum_y >= minimum_run_to_turn)):
            previous = came_from.get((current_x, current_y, momentum_x, momentum_y))
            route.append((current_x, current_y))
            while previous:
              x, y, *_ = previous
              if x != 0 or y != 0:
                route.append((x, y))
              previous = came_from.get(previous)
            break
          for dx, dy in ((-1, 0), (1, 0), (0, -1), (0, 1)):
            dot_product = dx * momentum_x + dy * momentum_y
            if dot_product < 0 or dot_product >= maximum_run:
              continue
            if ((momentum_x or momentum_y) and dot_product == 0 and
                abs(momentum_x) < minimum_run_to_turn and abs(momentum_y) < minimum_run_to_turn):
              continue
            new_x, new_y = current_x + dx, current_y + dy
            if not (0 <= new_x < self.sx) or not (0 <= new_y < self.sy):
              continue
            new_momentum_x, new_momentum_y = (dx, dy) if dot_product == 0 else (momentum_x + dx, momentum_y + dy)
            new_position = (new_x, new_y, new_momentum_x, new_momentum_y)
            potential_new_price = cur_price + int(self.lines[new_y][new_x])
            if potential_new_price < prices[new_position]:
              queue_entry = queue_entries.get(new_position)
              if queue_entry:
                queue_entry.deleted = True
              queue_entries[new_position] = QueueEntry(potential_new_price + self.lower_bounds[new_x, new_y],
                                                       *new_position, False)
              came_from[new_position] = (current_x, current_y, momentum_x, momentum_y)
              prices[new_position] = potential_new_price
              heapq.heappush(queue, queue_entries[new_position])
        return sum(int(self.lines[y][x]) for x, y in route)
    
      def solve_first_star(self) -> int:
        return self._solve(3, 0)
    
      def solve_second_star(self) -> int:
        return self._solve(10, 4)