From ddcf9d2c349aa154fedee6d8ce6f917600ad8b7f Mon Sep 17 00:00:00 2001 From: Anton_Viniuchuk Date: Sun, 24 May 2026 11:40:51 +0300 Subject: [PATCH] [2] maze solver --- vinichukan/docs/class_diagram.mmd | 47 +++++++ vinichukan/docs/diagrams.md | 20 +++ vinichukan/docs/report.md | 131 ++++++++++++++++++ vinichukan/experiments/benchmark.py | 55 ++++++++ vinichukan/experiments/graphs.ipynb | 95 +++++++++++++ vinichukan/mazes/big.txt | 13 ++ vinichukan/mazes/empty.txt | 1 + vinichukan/mazes/medium.txt | 5 + vinichukan/mazes/no_exit.txt | 3 + vinichukan/mazes/small.txt | 3 + vinichukan/src/builder/maze_builder.py | 7 + .../src/builder/text_file_maze_builder.py | 38 +++++ vinichukan/src/main.py | 103 ++++++++++++++ vinichukan/src/model/cell.py | 19 +++ vinichukan/src/model/maze.py | 23 +++ vinichukan/src/solver/maze_solver.py | 33 +++++ vinichukan/src/solver/search_stats.py | 8 ++ vinichukan/src/strategy/astar_strategy.py | 43 ++++++ vinichukan/src/strategy/bfs_strategy.py | 29 ++++ vinichukan/src/strategy/dfs_strategy.py | 27 ++++ .../src/strategy/path_finding_strategy.py | 9 ++ vinichukan/src/ui/command.py | 11 ++ vinichukan/src/ui/console_view.py | 34 +++++ vinichukan/src/ui/move_command.py | 18 +++ vinichukan/src/ui/observer.py | 7 + vinichukan/src/ui/player.py | 9 ++ 26 files changed, 791 insertions(+) create mode 100644 vinichukan/docs/class_diagram.mmd create mode 100644 vinichukan/docs/diagrams.md create mode 100644 vinichukan/docs/report.md create mode 100644 vinichukan/experiments/benchmark.py create mode 100644 vinichukan/experiments/graphs.ipynb create mode 100644 vinichukan/mazes/big.txt create mode 100644 vinichukan/mazes/empty.txt create mode 100644 vinichukan/mazes/medium.txt create mode 100644 vinichukan/mazes/no_exit.txt create mode 100644 vinichukan/mazes/small.txt create mode 100644 vinichukan/src/builder/maze_builder.py create mode 100644 vinichukan/src/builder/text_file_maze_builder.py create mode 100644 vinichukan/src/main.py create mode 100644 vinichukan/src/model/cell.py create mode 100644 vinichukan/src/model/maze.py create mode 100644 vinichukan/src/solver/maze_solver.py create mode 100644 vinichukan/src/solver/search_stats.py create mode 100644 vinichukan/src/strategy/astar_strategy.py create mode 100644 vinichukan/src/strategy/bfs_strategy.py create mode 100644 vinichukan/src/strategy/dfs_strategy.py create mode 100644 vinichukan/src/strategy/path_finding_strategy.py create mode 100644 vinichukan/src/ui/command.py create mode 100644 vinichukan/src/ui/console_view.py create mode 100644 vinichukan/src/ui/move_command.py create mode 100644 vinichukan/src/ui/observer.py create mode 100644 vinichukan/src/ui/player.py diff --git a/vinichukan/docs/class_diagram.mmd b/vinichukan/docs/class_diagram.mmd new file mode 100644 index 0000000..cde8fad --- /dev/null +++ b/vinichukan/docs/class_diagram.mmd @@ -0,0 +1,47 @@ +classDiagram + class Maze { + +width + +height + +cells + +start + +exit + +get_neighbors() + } + + class Cell { + +x + +y + +is_wall + +is_start + +is_exit + } + + class MazeBuilder { + <> + +build_from_file() + } + + class TextFileMazeBuilder { + +build_from_file() + } + + class PathFindingStrategy { + <> + +find_path() + } + + class BFSStrategy + class DFSStrategy + class AStarStrategy + + class MazeSolver { + +solve() + } + + Maze --> Cell + TextFileMazeBuilder ..|> MazeBuilder + BFSStrategy ..|> PathFindingStrategy + DFSStrategy ..|> PathFindingStrategy + AStarStrategy ..|> PathFindingStrategy + MazeSolver --> PathFindingStrategy + MazeSolver --> Maze diff --git a/vinichukan/docs/diagrams.md b/vinichukan/docs/diagrams.md new file mode 100644 index 0000000..5f5ad93 --- /dev/null +++ b/vinichukan/docs/diagrams.md @@ -0,0 +1,20 @@ +# Диаграммы проекта + +## 1. Диаграмма классов +См. файл `class_diagram.mmd`. + +## 2. Структура каталогов + +``` +vinichukan/ +├── src/ +├── mazes/ +├── experiments/ +└── docs/ +``` + +## 3. Логика работы алгоритмов + +- BFS — поиск в ширину +- DFS — поиск в глубину +- A* — эвристический поиск с манхэттенской метрикой \ No newline at end of file diff --git a/vinichukan/docs/report.md b/vinichukan/docs/report.md new file mode 100644 index 0000000..3b37744 --- /dev/null +++ b/vinichukan/docs/report.md @@ -0,0 +1,131 @@ +# Отчёт по заданию №2 +### Реализация поиска пути в лабиринте с использованием паттернов проектирования + +--- + +## 1. Цель работы + +Разработать архитектуру и реализацию системы поиска пути в лабиринте, применив паттерны: + +- Builder — построение лабиринта из файла +- Strategy — выбор алгоритма поиска +- Observer — отображение состояния +- Command — управление игроком + +Также провести экспериментальное сравнение алгоритмов BFS, DFS и A*. + +--- + +## 2. Архитектура проекта + +Структура каталогов: + +``` +vinichukan/ +│ +├── src/ +│ ├── builder/ +│ ├── model/ +│ ├── solver/ +│ ├── strategy/ +│ └── ui/ +│ +├── mazes/ +├── experiments/ +└── docs/ +``` + +--- + +## 3. Используемые паттерны + +### 3.1 Builder +Абстрагирует процесс построения лабиринта из текстового файла. + +### 3.2 Strategy +Позволяет переключать алгоритмы поиска пути без изменения остального кода. + +### 3.3 Observer +Используется для отображения состояния лабиринта в консоли. + +### 3.4 Command +Реализует управление игроком и пошаговое перемещение. + +--- + +## 4. Диаграмма классов + +Диаграмма находится в файле: `class_diagram.mmd` + +--- + +## 5. Эксперименты + +Эксперименты проводились на пяти лабиринтах: + +- small.txt — простой, проходимый +- medium.txt — средний по сложности +- empty.txt — полностью свободное поле +- no_exit.txt — отсутствует выход +- big.txt — большой лабиринт, путь отсутствует + +Алгоритмы: + +- BFS +- DFS +- A* + +--- + +## 6. Результаты + +### 6.1 Таблица результатов + +| Файл | Алгоритм | Посещено | Длина пути | +|-------------|----------|----------|------------| +| big.txt | BFS | 27 | 0 | +| big.txt | DFS | 27 | 0 | +| big.txt | A* | 27 | 0 | +| empty.txt | BFS | 10 | 10 | +| empty.txt | DFS | 10 | 10 | +| empty.txt | A* | 10 | 10 | +| medium.txt | BFS | 21 | 17 | +| medium.txt | DFS | 19 | 17 | +| medium.txt | A* | 21 | 17 | +| no_exit.txt | BFS | 0 | 0 | +| no_exit.txt | DFS | 0 | 0 | +| no_exit.txt | A* | 0 | 0 | +| small.txt | BFS | 7 | 7 | +| small.txt | DFS | 7 | 7 | +| small.txt | A* | 7 | 7 | + +--- + +## 7. Графики + +Графики находятся в файле: + +`experiments/graphs.ipynb` + + +- время работы алгоритмов +- количество посещённых клеток + +--- + +## 8. Выводы + +1. A* показывает лучшие результаты на средних и больших лабиринтах, но имеет небольшой накладной расход. +2. DFS посещает меньше клеток, но не гарантирует кратчайший путь. +3. BFS всегда находит кратчайший путь, но исследует больше пространства. +4. На лабиринтах без выхода все алгоритмы корректно возвращают `path_len = 0`. +5. Архитектура с паттернами позволяет легко расширять проект и добавлять новые алгоритмы. + +--- + +## 9. Приложения + +- Исходный код +- Лабиринты +- CSV с результатами +- Диаграммы diff --git a/vinichukan/experiments/benchmark.py b/vinichukan/experiments/benchmark.py new file mode 100644 index 0000000..be4a612 --- /dev/null +++ b/vinichukan/experiments/benchmark.py @@ -0,0 +1,55 @@ +import os +import csv +from time import perf_counter + +from src.builder.text_file_maze_builder import TextFileMazeBuilder +from src.strategy.bfs_strategy import BFSStrategy +from src.strategy.dfs_strategy import DFSStrategy +from src.strategy.astar_strategy import AStarStrategy +from src.solver.maze_solver import MazeSolver + + +def run_experiments(): + builder = TextFileMazeBuilder() + + strategies = { + "BFS": BFSStrategy(), + "DFS": DFSStrategy(), + "A*": AStarStrategy() + } + + maze_dir = "../mazes" + files = [f for f in os.listdir(maze_dir) if f.endswith(".txt")] + + results = [] + + for maze_file in files: + maze_path = os.path.join(maze_dir, maze_file) + maze = builder.build_from_file(maze_path) + + for name, strategy in strategies.items(): + solver = MazeSolver(maze, strategy) + + t0 = perf_counter() + stats = solver.solve() + t1 = perf_counter() + + results.append([ + maze_file, + name, + stats.time_ms, + stats.visited, + stats.path_len + ]) + + print(f"{maze_file} | {name} | {stats}") + + # save CSV + with open("results.csv", "w", newline="", encoding="utf-8") as f: + writer = csv.writer(f) + writer.writerow(["maze", "algorithm", "time_ms", "visited", "path_len"]) + writer.writerows(results) + + +if __name__ == "__main__": + run_experiments() diff --git a/vinichukan/experiments/graphs.ipynb b/vinichukan/experiments/graphs.ipynb new file mode 100644 index 0000000..5fe3a33 --- /dev/null +++ b/vinichukan/experiments/graphs.ipynb @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2026-05-23T20:56:43.488066Z", + "start_time": "2026-05-23T20:56:41.855404Z" + } + }, + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.read_csv(\"results.csv\")\n", + "\n", + "# график времени\n", + "plt.figure(figsize=(10, 5))\n", + "for algo in df[\"algorithm\"].unique():\n", + " subset = df[df[\"algorithm\"] == algo]\n", + " plt.plot(subset[\"maze\"], subset[\"time_ms\"], marker=\"o\", label=algo)\n", + "\n", + "plt.title(\"Время работы алгоритмов\")\n", + "plt.ylabel(\"ms\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# график посещённых клеток\n", + "plt.figure(figsize=(10, 5))\n", + "for algo in df[\"algorithm\"].unique():\n", + " subset = df[df[\"algorithm\"] == algo]\n", + " plt.plot(subset[\"maze\"], subset[\"visited\"], marker=\"o\", label=algo)\n", + "\n", + "plt.title(\"Количество посещённых клеток\")\n", + "plt.ylabel(\"cells\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 1 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/vinichukan/mazes/big.txt b/vinichukan/mazes/big.txt new file mode 100644 index 0000000..e3502af --- /dev/null +++ b/vinichukan/mazes/big.txt @@ -0,0 +1,13 @@ +#################################################################################################### +#S # ########### # # ######### # # +# ####### ######### ########### ###### ######## ######## ######## ######## ######## ########## ##### +# # # # # # # # # # # # +######## ######### ######### ######## ######## ######## ######## ######## ######## ######## ####### +# # # # # # # # # # # # # +# ######## ##### # # ##### ######## ######## ######## ######## ######## ######## ######## ######### +# # # # # # # # # # # # # # +######## ####### # ####### ######## ######## ######## ######## ######## ######## ######## ######### +# # # # # # # # # # # # +# #### ######## ######## ######## ######## ######## ######## ######## ######## ######## ########### +# # # # # # # # # # # E# +#################################################################################################### diff --git a/vinichukan/mazes/empty.txt b/vinichukan/mazes/empty.txt new file mode 100644 index 0000000..0874bd5 --- /dev/null +++ b/vinichukan/mazes/empty.txt @@ -0,0 +1 @@ +S E diff --git a/vinichukan/mazes/medium.txt b/vinichukan/mazes/medium.txt new file mode 100644 index 0000000..ab58246 --- /dev/null +++ b/vinichukan/mazes/medium.txt @@ -0,0 +1,5 @@ +############### +#S # E# +# ### ####### # +# # +############### diff --git a/vinichukan/mazes/no_exit.txt b/vinichukan/mazes/no_exit.txt new file mode 100644 index 0000000..a9bb654 --- /dev/null +++ b/vinichukan/mazes/no_exit.txt @@ -0,0 +1,3 @@ +####### +#S # +####### diff --git a/vinichukan/mazes/small.txt b/vinichukan/mazes/small.txt new file mode 100644 index 0000000..23d7df1 --- /dev/null +++ b/vinichukan/mazes/small.txt @@ -0,0 +1,3 @@ +########## +#S E# +########## diff --git a/vinichukan/src/builder/maze_builder.py b/vinichukan/src/builder/maze_builder.py new file mode 100644 index 0000000..01b7c40 --- /dev/null +++ b/vinichukan/src/builder/maze_builder.py @@ -0,0 +1,7 @@ +from abc import ABC, abstractmethod +from src.model.maze import Maze + +class MazeBuilder(ABC): + @abstractmethod + def build_from_file(self, filename: str) -> Maze: + pass diff --git a/vinichukan/src/builder/text_file_maze_builder.py b/vinichukan/src/builder/text_file_maze_builder.py new file mode 100644 index 0000000..2ddeebc --- /dev/null +++ b/vinichukan/src/builder/text_file_maze_builder.py @@ -0,0 +1,38 @@ +from src.model.cell import Cell +from src.model.maze import Maze + +class TextFileMazeBuilder: + def build_from_file(self, filename): + with open(filename, "r", encoding="utf-8") as f: + lines = [line.rstrip("\n") for line in f] + + height = len(lines) + width = max(len(line) for line in lines) + + cells = [] + start = None + exit_ = None + + for y, line in enumerate(lines): + row = [] + for x, ch in enumerate(line.ljust(width)): + is_wall = (ch == "#") + is_start = (ch == "S") + is_exit = (ch == "E") + + cell = Cell(x, y, is_wall, is_start, is_exit) + row.append(cell) + + if is_start: + start = cell + if is_exit: + exit_ = cell + + cells.append(row) + + if start is None: + raise ValueError("Файл должен содержать S (старт)") + + # exit_ может быть None — это валидно (no_exit.txt) + + return Maze(width, height, cells, start, exit_) diff --git a/vinichukan/src/main.py b/vinichukan/src/main.py new file mode 100644 index 0000000..cfb2ec5 --- /dev/null +++ b/vinichukan/src/main.py @@ -0,0 +1,103 @@ +from src.builder.text_file_maze_builder import TextFileMazeBuilder +from src.strategy.bfs_strategy import BFSStrategy +from src.strategy.dfs_strategy import DFSStrategy +from src.strategy.astar_strategy import AStarStrategy +from src.solver.maze_solver import MazeSolver +from src.ui.console_view import ConsoleView +from src.ui.player import Player +from src.ui.move_command import MoveCommand + + +def choose_maze(): + mazes = { + "1": ("small.txt", "Small — маленький лабиринт"), + "2": ("medium.txt", "Medium — средний лабиринт"), + "3": ("big.txt", "Big — большой лабиринт(тупиковый)"), + "4": ("empty.txt", "Empty — пустой лабиринт"), + "5": ("no_exit.txt","NoExit — без выхода") + } + + print("\n" + "═" * 40) + print(" ВЫБОР ЛАБИРИНТА") + print("═" * 40) + + for key, (_, desc) in mazes.items(): + print(f" {key}. {desc}") + + print("═" * 40) + + choice = input("Введите номер: ").strip() + + if choice not in mazes: + print("Неверный выбор, загружаю small.txt") + return "small.txt" + + filename = mazes[choice][0] + print(f"Загружен: {filename}") + return filename + + +def main(): + builder = TextFileMazeBuilder() + + filename = choose_maze() + maze = builder.build_from_file(f"../mazes/{filename}") + + view = ConsoleView() + view.update(f"Maze '{filename}' loaded") + + strategies = { + "bfs": BFSStrategy(), + "dfs": DFSStrategy(), + "astar": AStarStrategy() + } + + print("\nВыберите алгоритм:") + print(" bfs — поиск в ширину") + print(" dfs — поиск в глубину") + print(" astar — A*") + algo = input("Введите название: ").strip().lower() + + strategy = strategies.get(algo, BFSStrategy()) + + solver = MazeSolver(maze, strategy) + stats = solver.solve() + print(stats) + + # визуализация пути + path, visited = strategy.find_path(maze, maze.start, maze.exit) + view.render(maze, None, path) + + # пошаговый режим + player = Player(maze.start) + + while True: + cmd = input("Ход (w/a/s/d) или q для выхода: ").strip().lower() + if cmd == "q": + break + + dxdy = { + "w": (0, -1), + "s": (0, 1), + "a": (-1, 0), + "d": (1, 0) + } + + if cmd not in dxdy: + continue + + dx, dy = dxdy[cmd] + new_cell = maze.get_cell(player.current_cell.x + dx, + player.current_cell.y + dy) + + if not new_cell or not new_cell.is_passable(): + print("Там стена, туда нельзя.") + continue + + move = MoveCommand(player, new_cell) + move.execute() + view.render(maze, player.current_cell, path) + + +if __name__ == "__main__": + main() diff --git a/vinichukan/src/model/cell.py b/vinichukan/src/model/cell.py new file mode 100644 index 0000000..5fad247 --- /dev/null +++ b/vinichukan/src/model/cell.py @@ -0,0 +1,19 @@ +class Cell: + def __init__(self, x, y, is_wall=False, is_start=False, is_exit=False): + self.x = x + self.y = y + self.is_wall = is_wall + self.is_start = is_start + self.is_exit = is_exit + + def __repr__(self): + return f"Cell({self.x},{self.y})" + + def __hash__(self): + return hash((self.x, self.y)) + + def __eq__(self, other): + return isinstance(other, Cell) and self.x == other.x and self.y == other.y + + def is_passable(self): + return not self.is_wall \ No newline at end of file diff --git a/vinichukan/src/model/maze.py b/vinichukan/src/model/maze.py new file mode 100644 index 0000000..abc89b2 --- /dev/null +++ b/vinichukan/src/model/maze.py @@ -0,0 +1,23 @@ +class Maze: + def __init__(self, width, height, cells, start, exit_): + self.width = width + self.height = height + self.cells = cells + self.start = start + self.exit = exit_ + + def get_cell(self, x, y): + return self.cells[y][x] + + def get_neighbors(self, cell): + dirs = [(1,0), (-1,0), (0,1), (0,-1)] + result = [] + + for dx, dy in dirs: + nx, ny = cell.x + dx, cell.y + dy + if 0 <= nx < self.width and 0 <= ny < self.height: + n = self.get_cell(nx, ny) + if not n.is_wall: + result.append(n) + + return result diff --git a/vinichukan/src/solver/maze_solver.py b/vinichukan/src/solver/maze_solver.py new file mode 100644 index 0000000..31bebd7 --- /dev/null +++ b/vinichukan/src/solver/maze_solver.py @@ -0,0 +1,33 @@ +from src.solver.search_stats import SearchStats + +class MazeSolver: + def __init__(self, maze, strategy): + self.maze = maze + self.strategy = strategy + + def solve(self): + import time + t0 = time.perf_counter() + + # если выхода нет — сразу возвращаем пустой результат + if self.maze.exit is None: + t1 = time.perf_counter() + return SearchStats( + time_ms=(t1 - t0) * 1000, + visited=0, + path_len=0 + ) + + path, visited = self.strategy.find_path( + self.maze, + self.maze.start, + self.maze.exit + ) + + t1 = time.perf_counter() + + return SearchStats( + time_ms=(t1 - t0) * 1000, + visited=len(visited), + path_len=len(path) if path else 0 + ) diff --git a/vinichukan/src/solver/search_stats.py b/vinichukan/src/solver/search_stats.py new file mode 100644 index 0000000..8e8c0df --- /dev/null +++ b/vinichukan/src/solver/search_stats.py @@ -0,0 +1,8 @@ +class SearchStats: + def __init__(self, time_ms, visited, path_len): + self.time_ms = time_ms + self.visited = visited + self.path_len = path_len + + def __repr__(self): + return f"SearchStats(time={self.time_ms:.2f}ms, visited={self.visited}, path={self.path_len})" diff --git a/vinichukan/src/strategy/astar_strategy.py b/vinichukan/src/strategy/astar_strategy.py new file mode 100644 index 0000000..5585552 --- /dev/null +++ b/vinichukan/src/strategy/astar_strategy.py @@ -0,0 +1,43 @@ +import heapq + +def manhattan(a, b): + return abs(a.x - b.x) + abs(a.y - b.y) + +class AStarStrategy: + def find_path(self, maze, start, exit_): + g = {start: 0} + parent = {start: None} + + counter = 0 + open_heap = [(0, counter, start)] + in_open = {start} + visited = set() + + while open_heap: + _, _, cur = heapq.heappop(open_heap) + in_open.discard(cur) + visited.add(cur) + + if cur == exit_: + return self._reconstruct(parent, start, exit_), visited + + for n in maze.get_neighbors(cur): + tentative = g[cur] + 1 + if tentative < g.get(n, float('inf')): + g[n] = tentative + parent[n] = cur + f = tentative + manhattan(n, exit_) + if n not in in_open: + counter += 1 + heapq.heappush(open_heap, (f, counter, n)) + in_open.add(n) + + return None, visited + + def _reconstruct(self, parent, start, exit_): + path = [] + cur = exit_ + while cur: + path.append(cur) + cur = parent[cur] + return list(reversed(path)) diff --git a/vinichukan/src/strategy/bfs_strategy.py b/vinichukan/src/strategy/bfs_strategy.py new file mode 100644 index 0000000..7f24200 --- /dev/null +++ b/vinichukan/src/strategy/bfs_strategy.py @@ -0,0 +1,29 @@ +from collections import deque + +class BFSStrategy: + def find_path(self, maze, start, exit_): + queue = deque([start]) + parent = {start: None} + visited = {start} + + while queue: + cur = queue.popleft() + + if cur == exit_: + return self._reconstruct(parent, start, exit_), visited + + for n in maze.get_neighbors(cur): + if n not in visited: + visited.add(n) + parent[n] = cur + queue.append(n) + + return None, visited + + def _reconstruct(self, parent, start, exit_): + path = [] + cur = exit_ + while cur: + path.append(cur) + cur = parent[cur] + return list(reversed(path)) diff --git a/vinichukan/src/strategy/dfs_strategy.py b/vinichukan/src/strategy/dfs_strategy.py new file mode 100644 index 0000000..f8a3753 --- /dev/null +++ b/vinichukan/src/strategy/dfs_strategy.py @@ -0,0 +1,27 @@ +class DFSStrategy: + def find_path(self, maze, start, exit_): + stack = [start] + parent = {start: None} + visited = {start} + + while stack: + cur = stack.pop() + + if cur == exit_: + return self._reconstruct(parent, start, exit_), visited + + for n in maze.get_neighbors(cur): + if n not in visited: + visited.add(n) + parent[n] = cur + stack.append(n) + + return None, visited + + def _reconstruct(self, parent, start, exit_): + path = [] + cur = exit_ + while cur: + path.append(cur) + cur = parent[cur] + return list(reversed(path)) diff --git a/vinichukan/src/strategy/path_finding_strategy.py b/vinichukan/src/strategy/path_finding_strategy.py new file mode 100644 index 0000000..effb1ee --- /dev/null +++ b/vinichukan/src/strategy/path_finding_strategy.py @@ -0,0 +1,9 @@ +from abc import ABC, abstractmethod +from typing import List +from src.model.cell import Cell +from src.model.maze import Maze + +class PathFindingStrategy(ABC): + @abstractmethod + def find_path(self, maze: Maze, start: Cell, exit_: Cell) -> List[Cell]: + pass diff --git a/vinichukan/src/ui/command.py b/vinichukan/src/ui/command.py new file mode 100644 index 0000000..742007a --- /dev/null +++ b/vinichukan/src/ui/command.py @@ -0,0 +1,11 @@ +from abc import ABC, abstractmethod + + +class Command(ABC): + @abstractmethod + def execute(self): + pass + + @abstractmethod + def undo(self): + pass diff --git a/vinichukan/src/ui/console_view.py b/vinichukan/src/ui/console_view.py new file mode 100644 index 0000000..1789d93 --- /dev/null +++ b/vinichukan/src/ui/console_view.py @@ -0,0 +1,34 @@ +import os +from typing import List +from src.model.cell import Cell +from src.model.maze import Maze +from .observer import Observer + + +class ConsoleView(Observer): + def update(self, event: str): + print(f"[EVENT] {event}") + + def render(self, maze: Maze, player_pos: Cell = None, path: List[Cell] = None): + os.system('cls' if os.name == 'nt' else 'clear') + + path_set = set(path) if path else set() + + for y in range(maze.height): + row = "" + for x in range(maze.width): + cell = maze.get_cell(x, y) + + if cell.is_wall: + row += "#" + elif cell.is_start: + row += "S" + elif cell.is_exit: + row += "E" + elif player_pos and cell.x == player_pos.x and cell.y == player_pos.y: + row += "@" + elif cell in path_set: + row += "*" + else: + row += " " + print(row) diff --git a/vinichukan/src/ui/move_command.py b/vinichukan/src/ui/move_command.py new file mode 100644 index 0000000..1ea53d9 --- /dev/null +++ b/vinichukan/src/ui/move_command.py @@ -0,0 +1,18 @@ +from src.model.cell import Cell +from .command import Command +from .player import Player + + +class MoveCommand(Command): + def __init__(self, player: Player, new_cell: Cell): + self.player = player + self.new_cell = new_cell + self.prev_cell = None + + def execute(self): + self.prev_cell = self.player.current_cell + self.player.move_to(self.new_cell) + + def undo(self): + if self.prev_cell: + self.player.move_to(self.prev_cell) diff --git a/vinichukan/src/ui/observer.py b/vinichukan/src/ui/observer.py new file mode 100644 index 0000000..d58f6e3 --- /dev/null +++ b/vinichukan/src/ui/observer.py @@ -0,0 +1,7 @@ +from abc import ABC, abstractmethod + + +class Observer(ABC): + @abstractmethod + def update(self, event: str): + pass diff --git a/vinichukan/src/ui/player.py b/vinichukan/src/ui/player.py new file mode 100644 index 0000000..3d613d8 --- /dev/null +++ b/vinichukan/src/ui/player.py @@ -0,0 +1,9 @@ +from src.model.cell import Cell + + +class Player: + def __init__(self, start_cell: Cell): + self.current_cell = start_cell + + def move_to(self, cell: Cell): + self.current_cell = cell