From 0326eafd5205b9e008f62ea237ff3cd1941e7658 Mon Sep 17 00:00:00 2001
From: xiaoxiae <tomas@slama.dev>
Date: Thu, 24 Apr 2025 00:42:21 +0200
Subject: [PATCH] =?UTF-8?q?Python=20=C5=99e=C5=A1en=C3=AD?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
asteracer-python-search/README.md | 3 +
asteracer-python-search/__main__.py | 569 +
asteracer-python-search/asteracer.py | 429 +
asteracer-python-search/marathon/14290.txt | 14291 +++++++++++++++++++
asteracer-python-search/sprint/1151.txt | 1152 ++
asteracer-python-search/test/307.txt | 308 +
6 files changed, 16752 insertions(+)
create mode 100644 asteracer-python-search/README.md
create mode 100644 asteracer-python-search/__main__.py
create mode 100644 asteracer-python-search/asteracer.py
create mode 100644 asteracer-python-search/marathon/14290.txt
create mode 100644 asteracer-python-search/sprint/1151.txt
create mode 100644 asteracer-python-search/test/307.txt
diff --git a/asteracer-python-search/README.md b/asteracer-python-search/README.md
new file mode 100644
index 0000000..dc5e695
--- /dev/null
+++ b/asteracer-python-search/README.md
@@ -0,0 +1,3 @@
+# Asteracer v Pythonu
+
+Mělo by stačit spustit `__main__.py`.
diff --git a/asteracer-python-search/__main__.py b/asteracer-python-search/__main__.py
new file mode 100644
index 0000000..4b92259
--- /dev/null
+++ b/asteracer-python-search/__main__.py
@@ -0,0 +1,569 @@
+"""Tom's solution to Asteracer. Moves on the vertices of the asteroid graph by simulating all angles."""
+import os
+from typing import Optional
+
+import drawsvg as draw
+import networkx as nx
+import numpy as np
+
+from asteracer import *
+
+directional_instructions_cache = {}
+
+Vertex = tuple[int, int]
+
+
+def load_asteroid_graph(path: str):
+ with open(path) as file:
+ lines = [line.strip() for line in file.readlines()]
+
+ # Filter out comments and empty lines
+ contents = [line for line in lines if line and not line.startswith('#')]
+
+ iter_lines = iter(contents)
+ first_line = list(map(int, next(iter_lines).split()))
+ n_racer, n_asteroid, n_goal, m = first_line
+
+ vertices = []
+ edges = []
+ vertex_objects = []
+
+ # Load vertices
+ for i in range(n_racer + n_asteroid + n_goal):
+ line = list(map(int, next(iter_lines).split()))
+ vertices.append((line[0], line[1]))
+
+ if i < n_racer:
+ vertex_objects.append(('S', i))
+ elif i < (n_racer + n_asteroid):
+ vertex_objects.append(('A', line[2]))
+ else:
+ vertex_objects.append(('G', line[2]))
+
+ # Load edges
+ for _ in range(m):
+ line = list(map(int, next(iter_lines).split()))
+ edges.append((line[0], line[1]))
+
+ return vertices, edges, vertex_objects
+
+
+def _yield_points_at_distance(x: float, y: float, r: float, n: int):
+ """Generate n points uniformly at distance r from the coordinates (x, y)."""
+ for i in range(n):
+ t = (i / n) * np.pi * 2
+ yield x + np.cos(t) * r, y + np.sin(t) * r
+
+
+def _get_directional_instructions(
+ n: int = 1000,
+ sort_by_angle_to: tuple[float, float] = None,
+ limit_angle=None
+) -> list[Instruction]:
+ """Generate normalized instructions in at most n different angles with maximum velocity."""
+ instructions = []
+
+ if n in directional_instructions_cache:
+ instructions = directional_instructions_cache[n]
+
+ else:
+ for point in _yield_points_at_distance(0, 0, 127, n):
+ i1 = Instruction(*point)
+
+ if i1 in instructions:
+ continue
+
+ for i2 in instructions:
+ if np.sign(i1.vx) == np.sign(i2.vx) and abs(i1.vx) <= abs(i2.vx) \
+ and np.sign(i1.vy) == np.sign(i2.vy) and abs(i1.vy) <= abs(i2.vy):
+ break
+ else:
+ instructions.append(i1)
+
+ directional_instructions_cache[n] = instructions
+
+ def instr_vector_angle(i_from: Instruction, v_to: tuple[float, float]) -> float:
+ """The angle between an instruction and a vector."""
+ i_vec = np.array([i_from.vx, i_from.vy])
+ o_vec = np.array(v_to)
+ return (1 - np.dot(i_vec, o_vec) / (np.linalg.norm(i_vec) * np.linalg.norm(o_vec))) / 2
+
+ # possibly sort by an angle to a given instruction
+ if sort_by_angle_to:
+ instructions = sorted(instructions, key=lambda x: instr_vector_angle(x, sort_by_angle_to))
+
+ if limit_angle:
+ return [i for i in instructions if abs(instr_vector_angle(i, sort_by_angle_to)) <= limit_angle]
+ else:
+ return instructions
+ else:
+ return instructions
+
+
+def instructions_to_position(
+ simulation: Simulation,
+ position: Vertex,
+ try_ticks=7,
+ max_distance=1.25,
+) -> Optional[list[Instruction]]:
+ """Return a sequence of instructions that brings us the closest to the provided coordinates.
+ Returns None if there is no such set that brings us at most max_distance * racer_radius away."""
+ x, y = position
+
+ best_distance = float('inf')
+ best_instructions = None
+
+ def is_safe(simulation: Simulation) -> bool:
+ """Returns True if the racer has a sequence of instructions to not crash at this stage of the simulation."""
+ simulation.push()
+ for instruction in _get_directional_instructions(10):
+ simulation.apply()
+
+ for _ in range(try_ticks):
+ result = simulation.tick(instruction)
+
+ if result & TickFlag.COLLIDED:
+ break
+ else:
+ simulation.pop()
+ return True
+
+ simulation.pop()
+ return False
+
+ simulation.push()
+ for inst in _get_directional_instructions(
+ sort_by_angle_to=(x - int(simulation.racer.x), y - int(simulation.racer.y)),
+ limit_angle=0.5
+ ):
+ simulation.apply()
+ started_improving = False
+ prev_dist = 0
+
+ i = 0
+ while True:
+ result = simulation.tick(inst)
+
+ if result & TickFlag.COLLIDED:
+ break
+
+ distance = euclidean_distance(simulation.racer.x, simulation.racer.y, x, y)
+
+ # if we're closer than before, we started improving
+ if distance < prev_dist:
+ started_improving = True
+
+ # if we've started improving but are now getting worse, terminate this instruction
+ if started_improving and distance > prev_dist:
+ break
+
+ prev_dist = distance
+
+ # if we improved and are safe, save results
+ if distance < best_distance and is_safe(simulation):
+ best_distance = distance
+ best_instructions = [inst] * i
+
+ i += 1
+
+ simulation.pop()
+
+ if best_distance > max_distance * simulation.racer.radius:
+ return None
+
+ return best_instructions
+
+
+def get_preview(simulation: Simulation, size=1000):
+ """Generate preview of the simulation as an SVG."""
+ d = draw.Drawing(size, size, origin='center')
+
+ # background
+ d.append(
+ draw.Rectangle(
+ -size / 2, -size / 2,
+ size, size,
+ fill="White"
+ )
+ )
+
+ def draw_circles(circles: list[Asteroid | Goal | Racer], color: str):
+ """Draw a circle with a specific color on the SVG."""
+ s = simulation.bounding_box.width() / size
+
+ for i, circle in enumerate(circles):
+ d.append(draw.Circle(circle.x / s, circle.y / s, circle.radius / s, fill=color, stroke=color))
+
+ draw_circles(simulation.asteroids, "Black")
+ draw_circles([g for i, g in enumerate(simulation.goals) if simulation.reached_goals[i]], "LightGreen")
+ draw_circles([g for i, g in enumerate(simulation.goals) if not simulation.reached_goals[i]], "Red")
+ draw_circles([simulation.racer], "Gray")
+
+ return d
+
+
+def get_solution_preview(
+ simulation: Simulation,
+ simulation_path: Optional[list[Vertex]],
+ color: str = "Green"
+) -> draw.Drawing:
+ """Return an SVG object with the solution graph, possibly with the simulation too."""
+ d = get_preview(simulation)
+ s = simulation.bounding_box.width() / d.width
+
+ def draw_path(path, color, stroke_width, stroke_opacity) -> draw.Path:
+ p = draw.Path(stroke_width=stroke_width, stroke=color, opacity=stroke_opacity, fill_opacity=0)
+
+ p.M(path[0][0] / s, path[0][1] / s)
+ for i in range(1, len(path)):
+ p.L(path[i][0] / s, path[i][1] / s)
+ d.append(p)
+
+ if simulation_path:
+ d.append(draw_path(simulation_path, color, simulation.racer.radius / (s * 2), 1))
+
+ return d
+
+
+def get_checkpoints_path(
+ simulation, vertices, edges, goal_vertices,
+ longest_edges,
+):
+ """Get a path that the rocket should follow to obtain all goals."""
+
+ def astar_heuristic(p1, p2):
+ return np.linalg.norm(np.array(p1) - np.array(p2))
+
+ def perturb_shortest_path(G, best_path, penalty_factor=2.0):
+ """ Perturbs the shortest path by increasing the weight of a random edge. """
+ if len(best_path) < 2:
+ return best_path # No modification possible
+
+ # Randomly choose an edge along the best path
+ idx = random.randint(2, len(best_path) - 3)
+ u, v = best_path[idx], best_path[idx + 1]
+
+ if G.has_edge(u, v):
+ original_weight = G[u][v].get("weight", 1.0)
+ G[u][v]["weight"] = original_weight * penalty_factor
+ if G.has_edge(v, u): # If the graph is undirected
+ G[v][u]["weight"] = original_weight * penalty_factor
+
+ perturbed_path = nx.astar_path(G, best_path[0], best_path[-1], heuristic=astar_heuristic)
+
+ return perturbed_path
+
+ # convert vertices, edges to nx.Graph
+ G = nx.Graph()
+ for v in vertices:
+ G.add_node(v)
+ for u, v in edges:
+ G.add_edge(u, v, weight=np.linalg.norm(np.array(u) - np.array(v)))
+
+ # for more than 1 goal, we have to solve TSP (at least approximately)
+ if len(simulation.goals) > 1:
+ G_tsp = nx.DiGraph()
+
+ tsp_vertices = [(simulation.racer.x, simulation.racer.y)]
+
+ for g in simulation.goals:
+ tsp_vertices.append((g.x, g.y))
+ G_tsp.add_node(tsp_vertices[-1])
+
+ count = 0
+ for i, u in enumerate(tsp_vertices):
+ for v in tsp_vertices[i + 1:]:
+ print(f"TSP: building graph {count + 1}/{len(tsp_vertices) * (len(tsp_vertices) - 1) // 2}")
+ w = nx.astar_path_length(G, u, v, heuristic=astar_heuristic)
+ G_tsp.add_edge(u, v, weight=w)
+ G_tsp.add_edge(v, u, weight=w)
+
+ count += 1
+
+ print(f"TSP: graph built")
+
+ # nx doesn't play nice with infinite values, but this might as well be
+ INF = 100000000000
+
+ # we solve TSP by adding a hack vertex to force us to try all possible ending vertices
+ # this means that we have to solve #number_of_vertices instances of TSP
+ G_tsp.add_node("hack")
+ G_tsp.add_edge(vertices[0], "hack", weight=0)
+ G_tsp.add_edge("hack", vertices[0], weight=INF)
+
+ best_cost = float('inf')
+ best_path = None
+
+ for i, g in enumerate(simulation.goals):
+ print(f"TSP: solving {i + 1}/{len(simulation.goals)}")
+
+ for g_hack in simulation.goals:
+ v = (g_hack.x, g_hack.y)
+ G_tsp.add_edge("hack", v, weight=0 if g is g_hack else INF)
+ G_tsp.add_edge(v, "hack", weight=INF)
+
+ path = nx.approximation.simulated_annealing_tsp(G_tsp, init_cycle="greedy")
+ path.pop()
+
+ cost = 0
+ for j in range(len(path)):
+ cost += G_tsp[path[j - 1]][path[j]]["weight"]
+
+ for g_hack in simulation.goals:
+ v = (g_hack.x, g_hack.y)
+ G_tsp.remove_edge("hack", v)
+ G_tsp.remove_edge(v, "hack")
+
+ path = list(reversed(path))
+ j = path.index("hack")
+ path.remove("hack")
+ path = path[j:] + path[:j]
+
+ assert len(path) == len(simulation.goals) + 1, "Not all goals visited!"
+
+ if cost < best_cost:
+ best_path = path
+ best_cost = cost
+ print(f"TSP: new optimum of length {best_cost}")
+
+ path = [(int(simulation.racer.x), int(simulation.racer.y))]
+ for i in range(len(best_path) - 1):
+ path += nx.astar_path(G, best_path[i], best_path[i + 1], heuristic=astar_heuristic)[1:]
+
+ # last vertex is a center of some goal
+ path = path[:-1]
+
+ # shorten the path by cutting out vertices
+ while True:
+ # try to remove vertices for goals
+ for i in range(len(path) - 2):
+ if not G.has_edge(path[i], path[i + 2]):
+ continue
+
+ # if it's not a part of a goal, we can remove for free
+ if path[i + 1] not in goal_vertices:
+ path.pop(i + 1)
+ break
+
+ # if it is and is not the only one, we can remove it too
+ goal_vertices_count = 0
+ for v in path:
+ if v in goal_vertices and goal_vertices[v] == goal_vertices[path[i + 1]]:
+ goal_vertices_count += 1
+
+ if goal_vertices_count != 1:
+ path.pop(i + 1)
+ break
+ else:
+ break
+
+ # rotate goal vertices (we can take an arbitrary one to get the goal)
+ while True:
+ improved = False
+
+ for i in range(len(path) - 2):
+ u = path[i + 1]
+
+ if u not in goal_vertices:
+ continue
+
+ for v in vertices:
+ if v not in goal_vertices:
+ continue
+
+ if goal_vertices[u] != goal_vertices[v]:
+ continue
+
+ if not G.has_edge(path[i], v) or not G.has_edge(v, path[i + 2]):
+ continue
+
+ u_dist = G[path[i]][u]["weight"] + G[u][path[i + 2]]["weight"]
+ v_dist = G[path[i]][v]["weight"] + G[v][path[i + 2]]["weight"]
+
+ if u_dist > v_dist:
+ path = path[:i + 1] + [v] + path[i + 2:]
+ improved = True
+ break
+
+ if not improved:
+ break
+
+ # TODO: remove goal vertices entirely if the edge exists and intersects it
+ # TODO: for each goal, attempt to remove vertex and connect astar to next and to previous
+
+ print("Solved with TSP.")
+ else:
+ path = nx.astar_path(
+ G,
+ (simulation.racer.x, simulation.racer.y),
+ (simulation.goals[0].x, simulation.goals[0].y),
+ heuristic=astar_heuristic,
+ )
+
+ path = perturb_shortest_path(G, path, penalty_factor=1000)
+
+ print("Solved with A*.")
+
+ # split long edges
+ while True:
+ split = False
+
+ i = 0
+ while i < len(path) - 1:
+ if euclidean_distance(*path[i], *path[i + 1]) > longest_edges:
+ x = path[i][0] + path[i + 1][0]
+ y = path[i][1] + path[i + 1][1]
+ path.insert(i + 1, (x / 2, y / 2))
+ split = True
+ i += 1
+
+ if not split:
+ break
+
+ return path
+
+
+def finalize_instructions(simulation, instructions):
+ """Finalize the instructions (cut useless ones + extend if the simulation isn't finished)."""
+ simulation.restart()
+ for i, instruction in enumerate(instructions):
+ simulation.tick(instruction)
+
+ # there might be some redundant instructions at the very end
+ if simulation.finished():
+ break
+
+ instructions = instructions[:i + 1]
+
+ # if we ended just before the goal, repeat last instruction
+ simulation.simulate(instructions)
+ i = 0
+ while not simulation.finished():
+ simulation.tick(instruction)
+ instructions.append(instruction)
+ i += 1
+
+ if i > 1000:
+ break
+
+ return instructions
+
+
+def get_solution_path(simulation, instructions):
+ """Generate the path the racer took using the instructions."""
+ simulation.restart()
+ solution_path = [(simulation.racer.x, simulation.racer.y)]
+ for i, instruction in enumerate(instructions):
+ simulation.tick(instruction)
+ solution_path.append((simulation.racer.x, simulation.racer.y))
+
+ return solution_path
+
+
+def solve(simulation, path):
+ """Solve the simulation by following the path."""
+ steps_instructions = []
+ randomize_step_counter = 0
+ i = 1
+ while i < len(path):
+ v = path[i]
+
+ for goal in simulation.goals:
+ if euclidean_distance(goal.x, goal.y, v[0], v[1]) < goal.radius + simulation.racer.radius:
+ is_goal_position = True
+ break
+ else:
+ is_goal_position = False
+
+ print(f"Simulating {i}/{len(path) - 1}{'' if not is_goal_position else ' to goal'}", end=": ", flush=True)
+
+ if randomize_step_counter:
+ print("randomized, ", end="")
+
+ # only move towards the goal
+ if is_goal_position:
+ p2g_vector = np.array([goal.x, goal.y]) - np.array(v)
+ v = (p2g_vector / np.linalg.norm(p2g_vector)) * np.random.normal(0, simulation.racer.radius, 1)
+ else:
+ v = np.array(v) + np.random.normal(0, simulation.racer.radius, 2)
+
+ new_instructions = instructions_to_position(simulation, v, max_distance=2 if not is_goal_position else 0.75)
+
+ if new_instructions is None:
+ if len(steps_instructions) != 0:
+ randomize_step_counter += 1
+
+ i -= randomize_step_counter
+ for _ in range(randomize_step_counter):
+ steps_instructions.pop()
+
+ print(f"(- backtrack {randomize_step_counter}x)")
+
+ if len(steps_instructions) != 0:
+ simulation.simulate(list(np.hstack(steps_instructions)))
+ else:
+ simulation.restart()
+
+ continue
+ else:
+ randomize_step_counter = max(randomize_step_counter - 1, 0)
+
+ steps_instructions.append(new_instructions)
+ simulation.simulate(list(np.hstack(steps_instructions)))
+
+ print(f"(+ {len(new_instructions)})")
+
+ i += 1
+
+ return finalize_instructions(simulation, list(np.hstack(steps_instructions)))
+
+
+if __name__ == "__main__":
+ iterations = 5000
+
+ for task in ["sprint"]:
+ print(f"Solving {task} ({iterations} attempts):")
+
+ os.makedirs(task, exist_ok=True)
+
+ simulation = Simulation.load(f"../mapy/{task}.txt")
+ print("Simulation loaded.")
+
+ vertices, edges, object_types = load_asteroid_graph(f"../grafy/{task}.txt")
+ edges = [(vertices[u], vertices[v]) for u, v in edges]
+
+ print("Graph loaded.")
+
+ average_asteroid_radius = sum([asteroid.radius for asteroid in simulation.asteroids]) / len(simulation.asteroids)
+ goal_vertices = {
+ vertices[i]: (simulation.goals[j].x, simulation.goals[j].y)
+ for (i, (c, j)) in enumerate(object_types)
+ if c == "G"
+ }
+
+ best_instructions = None
+ best_instructions_length = float('inf')
+
+ i = 0
+ while i < iterations:
+ simulation.restart()
+ path = get_checkpoints_path(
+ simulation,
+ vertices, edges, goal_vertices,
+ longest_edges=average_asteroid_radius * np.random.uniform(0.5, 5),
+ )
+
+ instructions = solve(simulation, path)
+ i += 1
+
+ if len(instructions) < best_instructions_length:
+ best_instructions = instructions
+ best_instructions_length = len(instructions)
+ print(f"Solved with {len(instructions)} instructions.")
+
+ solution_path = get_solution_path(simulation, best_instructions)
+ d = get_solution_preview(simulation, solution_path, "Green")
+ d.save_svg(f"{task}/solution.svg")
+ save_instructions(f"{task}/solution.txt", best_instructions)
+
+ print(f"Best solution: {len(best_instructions)} instructions.")
diff --git a/asteracer-python-search/asteracer.py b/asteracer-python-search/asteracer.py
new file mode 100644
index 0000000..8968a89
--- /dev/null
+++ b/asteracer-python-search/asteracer.py
@@ -0,0 +1,429 @@
+"""The Asteracer game implementation. Includes the base movement code + couple of QOL additions (eg. save states)."""
+from __future__ import annotations
+
+import dataclasses
+import random
+from collections import defaultdict
+from dataclasses import dataclass
+from math import isqrt
+
+
+class TickFlag:
+ """Flags returned by simulation.tick() for various events that can occur during a tick."""
+ COLLIDED = 1
+ GOAL_REACHED = 2
+
+
+@dataclass
+class Racer:
+ x: int = 0
+ y: int = 0
+ vx: int = 0
+ vy: int = 0
+ radius: int = 1
+
+
+@dataclass(frozen=True)
+class Asteroid:
+ x: int = 0
+ y: int = 0
+ radius: int = 1
+
+Goal = Asteroid
+
+
+class Instruction:
+ MAX_ACCELERATION = 127
+
+ def __init__(self, vx: int | float = 0, vy: int | float = 0):
+ """Whatever values we get, normalize them."""
+ vx = int(vx)
+ vy = int(vy)
+
+ if distance_squared(vx, vy) > Instruction.MAX_ACCELERATION ** 2:
+ vx = float(vx)
+ vy = float(vy)
+
+ # use float to properly normalize here
+ distance = (vx ** 2 + vy ** 2) ** (1/2)
+
+ vx = int(vx / distance * Instruction.MAX_ACCELERATION)
+ vy = int(vy / distance * Instruction.MAX_ACCELERATION)
+
+ # if we're still over, decrement both values
+ if distance_squared(vx, vy) > Instruction.MAX_ACCELERATION ** 2:
+ vx -= signum(vx)
+ vy -= signum(vy)
+
+ assert distance_squared(vx, vy) <= Instruction.MAX_ACCELERATION ** 2
+
+ self.vx = vx
+ self.vy = vy
+
+ def __hash__(self):
+ return hash((self.vx, self.vy))
+
+ def __eq__(self, other):
+ return self.vx == other.vx and self.vy == other.vy
+
+ def __str__(self):
+ return f"Instruction({self.vx}, {self.vy})"
+
+ @classmethod
+ def random(cls):
+ return cls(
+ random.randint(
+ -cls.MAX_ACCELERATION,
+ cls.MAX_ACCELERATION
+ ),
+ random.randint(
+ -cls.MAX_ACCELERATION,
+ cls.MAX_ACCELERATION
+ ),
+ )
+
+
+@dataclass
+class BoundingBox:
+ min_x: int
+ min_y: int
+ max_x: int
+ max_y: int
+
+ def width(self) -> int:
+ return int(self.max_x - self.min_x)
+
+ def height(self) -> int:
+ return int(self.max_y - self.min_y)
+
+
+def distance_squared(x1, y1, x2=0, y2=0) -> int:
+ """Squared Euclidean distance between two points."""
+ return (int(x1) - int(x2)) ** 2 + (int(y1) - int(y2)) ** 2
+
+
+def euclidean_distance(x1, y1, x2=0, y2=0):
+ """Integer Euclidean distance between two points. Uses integer square root."""
+ return int(isqrt(distance_squared(x1, y1, x2, y2)))
+
+
+def signum(x):
+ return -1 if x < 0 else 0 if x == 0 else 1
+
+
+def division(a, b):
+ """Correctly implemented division, removing the fractional component."""
+ return (abs(int(a)) // int(b)) * signum(a)
+
+
+
+class Simulation:
+ DRAG_FRACTION = (9, 10) # slowdown of the racer's velocity after each tick
+ COLLISION_FRACTION = (1, 2) # slowdown of the racer's velocity after a tick where a collision occurred
+ MAX_COLLISION_RESOLUTIONS = 5 # at most how many collision iterations to perform
+
+ CELL_SIZE = 10_000
+
+ def __init__(
+ self,
+ racer: Racer = Racer(),
+ asteroids: list[Asteroid] = None,
+ goals: list[Goal] = None,
+ bounding_box: BoundingBox = None,
+ ):
+ # the initial racer state (used when resetting the simulation)
+ self.initial_racer = dataclasses.replace(racer)
+
+ self.racer = racer
+ self.asteroids = asteroids or []
+ self.goals = goals or []
+ self.bounding_box = bounding_box
+
+ # to speed up the computation, we divide the bounding box (if we have one) into a grid
+ # we do this so we don't need to check all asteroids at each tick, only those that could collide with the racer
+ self._grid: dict[tuple[int, int], list[Asteroid]] = defaultdict(list)
+
+ for asteroid in asteroids:
+ min_x, min_y = self._coordinate_to_grid(
+ asteroid.x - asteroid.radius - racer.radius,
+ asteroid.y - asteroid.radius - racer.radius,
+ )
+
+ max_x, max_y = self._coordinate_to_grid(
+ asteroid.x + asteroid.radius + racer.radius,
+ asteroid.y + asteroid.radius + racer.radius,
+ )
+
+ for grid_x in range(min_x, max_x + 1):
+ for grid_y in range(min_y, max_y + 1):
+ self._grid[(grid_x, grid_y)].append(asteroid)
+
+ self.reached_goals: list[bool] = [False] * len(self.goals)
+
+ # a list of simulation states that can be popped (restored to)
+ self._pushed_states = []
+
+ def _coordinate_to_grid(self, x: float, y: float) -> tuple[int, int]:
+ """Translate an (x,y) coordinate into a coordinate of the grid."""
+ return (x // self.CELL_SIZE, y // self.CELL_SIZE)
+
+ def _move_racer(self, instruction: Instruction):
+ """Move the racer in the given direction."""
+ vx, vy = instruction.vx, instruction.vy
+
+ # drag
+ self.racer.vx = division(self.racer.vx * self.DRAG_FRACTION[0], self.DRAG_FRACTION[1])
+ self.racer.vy = division(self.racer.vy * self.DRAG_FRACTION[0], self.DRAG_FRACTION[1])
+
+ # velocity
+ self.racer.vx += int(vx)
+ self.racer.vy += int(vy)
+
+ # movement
+ self.racer.x += self.racer.vx
+ self.racer.y += self.racer.vy
+
+ def _push_out(self, obj: Asteroid | BoundingBox) -> bool:
+ """Attempt to push the racer out of the object (if he's colliding), adjusting
+ his velocity accordingly (based on the angle of collision). Returns True if the
+ racer was pushed out, otherwise returns False."""
+ if isinstance(obj, Asteroid):
+ # not colliding, nothing to be done
+ if euclidean_distance(self.racer.x, self.racer.y, obj.x, obj.y) > (self.racer.radius + obj.radius):
+ return False
+
+ # the vector to push the racer out by
+ nx = self.racer.x - obj.x
+ ny = self.racer.y - obj.y
+
+ # how much to push by
+ distance = euclidean_distance(self.racer.x, self.racer.y, obj.x, obj.y)
+ push_by = distance - (self.racer.radius + obj.radius)
+
+ # the actual push
+ self.racer.x -= division(nx * push_by, distance)
+ self.racer.y -= division(ny * push_by, distance)
+
+ return True
+
+ elif isinstance(obj, BoundingBox):
+ # not pretty but easy to read :)
+ collided = False
+
+ if self.racer.x - self.racer.radius < obj.min_x:
+ self.racer.x = obj.min_x + self.racer.radius
+ collided = True
+ if self.racer.x + self.racer.radius > obj.max_x:
+ self.racer.x = obj.max_x - self.racer.radius
+ collided = True
+ if self.racer.y - self.racer.radius < obj.min_y:
+ self.racer.y = obj.min_y + self.racer.radius
+ collided = True
+ if self.racer.y + self.racer.radius > obj.max_y:
+ self.racer.y = obj.max_y - self.racer.radius
+ collided = True
+
+ return collided
+
+ else:
+ raise Exception("Attempted to collide with something other than asteroid / bounding box!")
+
+ def _check_goal(self) -> bool:
+ """Sets the _reached_goals variable to True according to if the racer is intersecting them, returning True if
+ a new one was reached."""
+ new_goal_reached = False
+
+ for i, goal in enumerate(self.goals):
+ if euclidean_distance(self.racer.x, self.racer.y, goal.x, goal.y) <= (self.racer.radius + goal.radius):
+ if not self.reached_goals[i]:
+ new_goal_reached = True
+
+ self.reached_goals[i] = True
+
+ return new_goal_reached
+
+ def _resolve_collisions(self) -> bool:
+ """Resolve all collisions of the racer and asteroids, returning True if a collison occurred."""
+ collided = False
+ for _ in range(self.MAX_COLLISION_RESOLUTIONS):
+ collided_this_iteration = False
+
+ for asteroid in self._grid[self._coordinate_to_grid(self.racer.x, self.racer.y)]:
+ if self._push_out(asteroid):
+ collided_this_iteration = collided = True
+ break
+
+ if self.bounding_box is not None and self._push_out(self.bounding_box):
+ collided_this_iteration = collided = True
+
+ if not collided_this_iteration:
+ break
+
+ if collided:
+ self.racer.vx = division(self.racer.vx * self.COLLISION_FRACTION[0], self.COLLISION_FRACTION[1])
+ self.racer.vy = division(self.racer.vy * self.COLLISION_FRACTION[0], self.COLLISION_FRACTION[1])
+
+ return collided
+
+ def finished(self) -> bool:
+ """Returns True if the racer reached all goals."""
+ return all(self.reached_goals)
+
+ def restart(self):
+ """Restart the simulation to its initial state."""
+ self.racer.x = self.initial_racer.x
+ self.racer.y = self.initial_racer.y
+ self.racer.vx = 0
+ self.racer.vy = 0
+
+ for i in range(len(self.reached_goals)):
+ self.reached_goals[i] = False
+
+ def tick(self, instruction: Instruction):
+ """Simulate a single tick of the simulation."""
+ self._move_racer(instruction)
+ collided = self._resolve_collisions()
+ goal = self._check_goal()
+
+ return (TickFlag.COLLIDED if collided else 0) | (TickFlag.GOAL_REACHED if goal else 0)
+
+ def simulate(self, instructions: list[Instruction]):
+ """Simulate a number of instructions for the simulation (from the start)."""
+ self.restart()
+
+ results = []
+
+ for instruction in instructions:
+ results.append(self.tick(instruction))
+
+ return results
+
+ def save(self, path: str):
+ """Save the simulation to a file:
+ | 0 0 5
+ | -100 -100 100 100 // bounding box (min_x/min_y/max_x/max_y)
+ | 5 // number of asteroids
+ | 10 -10 10 // asteroid 1 x/y/radius
+ | 20 20 50 // asteroid 2 x/y/radius
+ | -10 10 30 // asteroid 3 x/y/radius
+ | 10 10 70 // asteroid 4 x/y/radius
+ | -10 -10 10 // asteroid 5 x/y/radius
+ | 1 // number of goals
+ | 100 100 10 // goal 1 x/y/radius
+ """
+ with open(path, "w") as f:
+ f.write(f"{self.racer.x} {self.racer.y} {self.racer.radius}\n")
+
+ bbox = self.bounding_box
+ f.write(f"{bbox.min_x} {bbox.min_y} {bbox.max_x} {bbox.max_y}\n")
+
+ f.write(f"{len(self.asteroids)}\n")
+ for asteroid in self.asteroids:
+ f.write(f"{asteroid.x} {asteroid.y} {asteroid.radius}\n")
+
+ f.write(f"{len(self.goals)}\n")
+ for goal in self.goals:
+ f.write(f"{goal.x} {goal.y} {goal.radius}\n")
+
+ @classmethod
+ def load(cls, path: str) -> Simulation:
+ """Load the simulation from a file (see self.save for the format description)."""
+ with open(path) as f:
+ lines = f.read().splitlines()
+
+ racer_parts = lines[0].split()
+ racer = Racer(x=int(racer_parts[0]), y=int(racer_parts[1]), radius=int(racer_parts[2]))
+
+ bb_parts = lines[1].split()
+ bb = BoundingBox(int(bb_parts[0]), int(bb_parts[1]), int(bb_parts[2]), int(bb_parts[2]))
+
+ asteroid_count = int(lines[2])
+
+ asteroids = []
+ for i in range(3, 3 + asteroid_count):
+ asteroid_parts = lines[i].split()
+ asteroids.append(
+ Asteroid(
+ x=int(asteroid_parts[0]),
+ y=int(asteroid_parts[1]),
+ radius=int(asteroid_parts[2]),
+ )
+ )
+
+ goal_count = int(lines[3 + asteroid_count])
+
+ goals = []
+ for i in range(4 + asteroid_count, 4 + asteroid_count + goal_count):
+ goal_parts = lines[i].split()
+ goals.append(
+ Asteroid(
+ x=int(goal_parts[0]),
+ y=int(goal_parts[1]),
+ radius=int(goal_parts[2]),
+ )
+ )
+
+ return Simulation(racer=racer, bounding_box=bb, asteroids=asteroids, goals=goals)
+
+ def push(self):
+ """Push (save) the current state of the simulation. Can be popped (restored) later."""
+ self._pushed_states.append(
+ (
+ dataclasses.replace(self.racer),
+ list(self.reached_goals),
+ )
+ )
+
+ def pop(self):
+ """Pop (restore) the previously pushed state."""
+ assert len(self._pushed_states) != 0, "No states to pop!"
+ self.racer, self.reached_goals = self._pushed_states.pop()
+
+ def apply(self):
+ """Apply the previously pushed state without popping it."""
+ self.racer = dataclasses.replace(self._pushed_states[-1][0])
+ self.reached_goals = list(self._pushed_states[-1][1])
+
+
+def save_instructions(path: str, instructions: list[Instruction]):
+ """Save a list of instructions to a file:
+ | 4 // number if instructions
+ | -16 -127 // instructions...
+ | -16 -127
+ | -26 -125
+ | -30 -124
+ """
+ with open(path, "w") as f:
+ f.write(f"{len(instructions)}\n")
+
+ for instruction in instructions:
+ f.write(f"{instruction.vx} {instruction.vy}\n")
+
+
+def load_instructions(path: str) -> list[Instruction]:
+ """Load a list of instructions from a file (see save_instructions for the format description)."""
+ instructions = []
+
+ with open(path) as f:
+ for line in f.read().splitlines()[1:]:
+ instruction_parts = list(map(int, line.split()))
+ instructions.append(Instruction(*instruction_parts))
+
+ return instructions
+
+
+if __name__ == "__main__":
+ map_path = "../../maps/test.txt"
+
+ simulation = Simulation.load(map_path)
+
+ tick_result = 0
+
+ print("Running simulation until collision...")
+
+ while tick_result & TickFlag.COLLIDED == 0:
+ tick_result = simulation.tick(Instruction(0, Instruction.MAX_ACCELERATION))
+
+ print(simulation.racer)
+
+ print("Bam!")
diff --git a/asteracer-python-search/marathon/14290.txt b/asteracer-python-search/marathon/14290.txt
new file mode 100644
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diff --git a/asteracer-python-search/sprint/1151.txt b/asteracer-python-search/sprint/1151.txt
new file mode 100644
index 0000000..2e52ac7
--- /dev/null
+++ b/asteracer-python-search/sprint/1151.txt
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diff --git a/asteracer-python-search/test/307.txt b/asteracer-python-search/test/307.txt
new file mode 100644
index 0000000..0ac5a16
--- /dev/null
+++ b/asteracer-python-search/test/307.txt
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