904 lines
171 KiB
Plaintext
904 lines
171 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6dc3ad27",
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"metadata": {},
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"source": [
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"# Отчёт: Поиск выхода из лабиринта (объектно-ориентированная реализация с паттернами)\n",
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"\n",
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"- Описание задачи и выбранных паттернов (с диаграммой классов из Mermaid).\n",
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"- Результаты экспериментов (таблицы, графики).\n",
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"- Анализ эффективности алгоритмов и применимости паттернов.\n",
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"- Выводы: как ООП и паттерны помогли сделать код гибким и расширяемым. Что было бы сложно изменить без них."
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]
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},
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{
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"cell_type": "markdown",
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"id": "59b2f0d3",
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"metadata": {},
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"source": [
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"## 1. Описание задачи и выбранных паттернов\n",
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"\n",
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"Необходимо создать программу с применением паттернов ООП для решения задачи поиска из лабиринта. Для этого, подготовим схему из Mermaid, отражающую связи между объектами:\n",
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"\n",
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"```mermaid\n",
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"classDiagram\n",
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" class Maze {\n",
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" -Cell[] cells\n",
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" -int width, height\n",
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" -Cell start\n",
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" -Cell exit\n",
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" +getCell(x,y): Cell\n",
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" +getNeighbors(cell): List~Cell~\n",
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" }\n",
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" \n",
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" class Cell {\n",
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" -int x, y\n",
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" -bool isWall\n",
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" -bool isStart\n",
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" -bool isExit\n",
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" +isPassable(): bool\n",
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" }\n",
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" \n",
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" class MazeBuilder {\n",
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" <<interface>>\n",
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" +buildFromFile(filename): Maze\n",
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" }\n",
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" \n",
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" class TextFileMazeBuilder {\n",
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" +buildFromFile(filename): Maze\n",
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" }\n",
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" \n",
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" class PathFindingStrategy {\n",
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" <<interface>>\n",
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" +findPath(maze, start, exit): List~Cell~\n",
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" }\n",
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" \n",
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" class BFS\n",
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" class DFS\n",
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" class AStar\n",
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" \n",
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" class SearchStats {\n",
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" +timeMs: float\n",
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" +visitedCells: int\n",
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" +pathLength: int\n",
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" }\n",
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" \n",
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" class MazeSolver {\n",
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" -Maze maze\n",
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" -PathFindingStrategy strategy\n",
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" +setStrategy(strategy)\n",
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" +solve(): SearchStats\n",
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" }\n",
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" \n",
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" class Observer {\n",
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" <<interface>>\n",
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" +update(event)\n",
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" }\n",
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" \n",
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" class ConsoleView {\n",
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" +update(event)\n",
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" +render(maze, player, path)\n",
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" }\n",
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" \n",
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" MazeBuilder <|.. TextFileMazeBuilder\n",
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" MazeBuilder --> Maze : creates\n",
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" PathFindingStrategy <|.. BFS\n",
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" PathFindingStrategy <|.. DFS\n",
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" PathFindingStrategy <|.. AStarStrategy\n",
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" MazeSolver --> PathFindingStrategy : uses\n",
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" MazeSolver --> Maze : uses\n",
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" Observer <|.. ConsoleView\n",
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" MazeSolver --> Observer : notifies\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e268fdf0",
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"metadata": {},
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"source": [
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"Builder — позволяет отделить создание сложного объекта от его представления. \n",
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"Strategy — позволяет инкапсулировать разные алгоритмы поиска пути так, чтобы их можно было подставлять динамически. \n",
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"Observer — позволяет обеспечить реакцию отображения на изменения состояния без жёсткой привязки. \n",
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"\n",
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"Код можно найти в репозитории: http://31.128.43.79:3000/musinaa/2026-rff_mp"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6ffa70d6",
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"metadata": {},
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"source": [
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"# 2. Практическая часть\n",
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"# 2.0 Подготовим окружение"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d457dda4",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import os\n",
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"sys.path.insert(0, os.path.abspath( '../'))\n",
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"\n",
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"from task2.mazeBuilder import TextFileMazeBuilder\n",
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"from task2.tester import Tester"
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]
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},
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{
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"cell_type": "markdown",
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"id": "888f0e3c",
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"metadata": {},
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"source": [
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"## 2.1 Данные для анализа\n",
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"\n",
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"В папке `mazeExamples` лежит несколько лабиринтов разных размеров: 5x5, 10x10, 50x50, 100x100. Для каждого лабиринта будем искать путь с помощью всех доступных алгоритмов поиска: BFS, DFS и A*. Измерения будем проводить 10 раз, а затем усреднённое значение записывать."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "22ac68eb",
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"metadata": {},
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"outputs": [],
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"source": [
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"builder = TextFileMazeBuilder()\n",
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"tester = Tester(builder, \"docs/data/task2/results.csv\")\n",
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"tester.setTestingDirectory(\"task2/mazeExamples\")\n",
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"tester.test()\n",
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"tester.saveCSV()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "27441b5f",
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"metadata": {},
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"source": [
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"## 2.2 Данные по лабиринтам и графики"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "702c1844",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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" Лабиринт 5x5\n"
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]
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" <th>Посещённые клетки</th>\n",
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" <tr>\n",
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" <th>BFS</th>\n",
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" <td>5x5</td>\n",
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" <td>0.075332</td>\n",
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" <td>8</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>DFS</th>\n",
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" <td>5x5</td>\n",
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" <td>0.018403</td>\n",
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" <td>8</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AStar</th>\n",
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" <td>5x5</td>\n",
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" <td>0.022371</td>\n",
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" <td>8</td>\n",
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" <td>7</td>\n",
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" Лабиринт Время (мс) Посещённые клетки Длинна пути\n",
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"Алгоритм \n",
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"BFS 5x5 0.075332 8 7\n",
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"DFS 5x5 0.018403 8 7\n",
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"AStar 5x5 0.022371 8 7"
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"\n",
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" Лабиринт 10x10\n"
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" <td>10x10</td>\n",
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" <td>0.403814</td>\n",
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" <td>53</td>\n",
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" <td>23</td>\n",
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" <tr>\n",
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" <th>DFS</th>\n",
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" <td>10x10</td>\n",
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" <td>0.079438</td>\n",
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" <td>35</td>\n",
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" <td>31</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AStar</th>\n",
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" <td>10x10</td>\n",
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" <td>0.067146</td>\n",
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" <td>23</td>\n",
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" <td>23</td>\n",
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"BFS 10x10 0.403814 53 23\n",
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"DFS 10x10 0.079438 35 31\n",
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"AStar 10x10 0.067146 23 23"
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" Лабиринт 50x50\n"
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|
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|
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" <th>DFS</th>\n",
|
||
" <td>50x50</td>\n",
|
||
" <td>2.066407</td>\n",
|
||
" <td>995</td>\n",
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||
" <td>435</td>\n",
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" <tr>\n",
|
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" <th>AStar</th>\n",
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||
" <td>50x50</td>\n",
|
||
" <td>2.081632</td>\n",
|
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" <td>496</td>\n",
|
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" <td>427</td>\n",
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|
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|
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"DFS 50x50 2.066407 995 435\n",
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|
||
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|
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|
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" </thead>\n",
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|
||
" <tr>\n",
|
||
" <th>BFS</th>\n",
|
||
" <td>100x100</td>\n",
|
||
" <td>17.143568</td>\n",
|
||
" <td>2495</td>\n",
|
||
" <td>1171</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DFS</th>\n",
|
||
" <td>100x100</td>\n",
|
||
" <td>8.430860</td>\n",
|
||
" <td>3219</td>\n",
|
||
" <td>1243</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AStar</th>\n",
|
||
" <td>100x100</td>\n",
|
||
" <td>4.951790</td>\n",
|
||
" <td>1286</td>\n",
|
||
" <td>1171</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
|
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|
||
"Алгоритм \n",
|
||
"BFS 100x100 17.143568 2495 1171\n",
|
||
"DFS 100x100 8.430860 3219 1243\n",
|
||
"AStar 100x100 4.951790 1286 1171"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"df = pd.read_csv(\"data/task2/results.csv\")\n",
|
||
"for name in [\"5x5\", \"10x10\", \"50x50\", \"100x100\"]:\n",
|
||
" print(f\"\\n Лабиринт {name}\")\n",
|
||
" display(df[df[\"Лабиринт\"] == name].set_index(\"Алгоритм\"))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c7a1b48e",
|
||
"metadata": {},
|
||
"source": [
|
||
"Как видно из таблиц, A* рекордно низкое врея выполнения, а так же хорошая длинна пути, часто совпадающая с медленными алгоритмами вроде BFS. Примечательно, что у A* всегда посещает меньше клеток, чем остальные алгоритмы, сильнее всего это заметно на большом лабиринте в 100 клеток, где разрыв от BFS: примерно в 2 раза меньше посещённых клеток, с DFS и вовсе почти в 3 раза.\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "d5078321",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" Лабиринт maze_25x25_wo_exit\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Лабиринт</th>\n",
|
||
" <th>Время (мс)</th>\n",
|
||
" <th>Посещённые клетки</th>\n",
|
||
" <th>Длинна пути</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Алгоритм</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>BFS</th>\n",
|
||
" <td>maze_25x25_wo_exit</td>\n",
|
||
" <td>1.968229</td>\n",
|
||
" <td>338</td>\n",
|
||
" <td>-1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DFS</th>\n",
|
||
" <td>maze_25x25_wo_exit</td>\n",
|
||
" <td>0.719102</td>\n",
|
||
" <td>338</td>\n",
|
||
" <td>-1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AStar</th>\n",
|
||
" <td>maze_25x25_wo_exit</td>\n",
|
||
" <td>1.011797</td>\n",
|
||
" <td>338</td>\n",
|
||
" <td>-1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Лабиринт Время (мс) Посещённые клетки Длинна пути\n",
|
||
"Алгоритм \n",
|
||
"BFS maze_25x25_wo_exit 1.968229 338 -1\n",
|
||
"DFS maze_25x25_wo_exit 0.719102 338 -1\n",
|
||
"AStar maze_25x25_wo_exit 1.011797 338 -1"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" Лабиринт maze_25x25_empty\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Лабиринт</th>\n",
|
||
" <th>Время (мс)</th>\n",
|
||
" <th>Посещённые клетки</th>\n",
|
||
" <th>Длинна пути</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Алгоритм</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>BFS</th>\n",
|
||
" <td>maze_25x25_empty</td>\n",
|
||
" <td>4.574538</td>\n",
|
||
" <td>625</td>\n",
|
||
" <td>49</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DFS</th>\n",
|
||
" <td>maze_25x25_empty</td>\n",
|
||
" <td>0.903779</td>\n",
|
||
" <td>625</td>\n",
|
||
" <td>337</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AStar</th>\n",
|
||
" <td>maze_25x25_empty</td>\n",
|
||
" <td>0.217635</td>\n",
|
||
" <td>49</td>\n",
|
||
" <td>49</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Лабиринт Время (мс) Посещённые клетки Длинна пути\n",
|
||
"Алгоритм \n",
|
||
"BFS maze_25x25_empty 4.574538 625 49\n",
|
||
"DFS maze_25x25_empty 0.903779 625 337\n",
|
||
"AStar maze_25x25_empty 0.217635 49 49"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"tester.setTestingDirectory(\"task2/mazeExamplesSpeical\")\n",
|
||
"tester.writefile = \"../docs/data/task2/results2.csv\"\n",
|
||
"tester.test()\n",
|
||
"tester.saveCSV()\n",
|
||
"\n",
|
||
"df = pd.read_csv(\"data/task2/results2.csv\")\n",
|
||
"for name in [\"maze_25x25_wo_exit\", \"maze_25x25_empty\"]:\n",
|
||
" print(f\"\\n Лабиринт {name}\")\n",
|
||
" display(df[df[\"Лабиринт\"] == name].set_index(\"Алгоритм\"))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "41192153",
|
||
"metadata": {},
|
||
"source": [
|
||
"Из выходных данных видно, что A* всегда быстро находит путь в пустом лабиринте, в реальных системах это очень большой плюс. BFS и BFS, из-за своей особенности пытается найти кратчайший путь и ему приходится оббегать весь лабиринт в поисках оптимального пути. \n",
|
||
"\n",
|
||
"Для того, чтобы оценить различия в алгоритмах на обычных лабиринтах нагляднее, построем серию графиков."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "43409471",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1500x400 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"df = pd.read_csv(\"data/task2/results.csv\")\n",
|
||
"\n",
|
||
"size_order = [\"5x5\", \"10x10\", \"50x50\", \"100x100\"]\n",
|
||
"algo_order = [\"BFS\", \"DFS\", \"AStar\"]\n",
|
||
"colors = {\"BFS\": \"blue\", \"DFS\": \"green\", \"AStar\": \"orange\"}\n",
|
||
"\n",
|
||
"x = range(len(size_order))\n",
|
||
"fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
|
||
"\n",
|
||
"params = [\"Время (мс)\", \"Посещённые клетки\", \"Длинна пути\"]\n",
|
||
"titles = [\"Время выполнения (мс)\", \"Посещённые клетки\", \"Длина пути\"]\n",
|
||
"\n",
|
||
"for i, (param, title) in enumerate(zip(params, titles)):\n",
|
||
" ax = axes[i]\n",
|
||
" for algo in algo_order:\n",
|
||
" values = []\n",
|
||
" for s in size_order:\n",
|
||
" val = df[(df[\"Алгоритм\"] == algo) & (df[\"Лабиринт\"] == s)][param].values\n",
|
||
" if len(val) > 0:\n",
|
||
" values.append(val[0])\n",
|
||
" else:\n",
|
||
" values.append(None)\n",
|
||
" ax.plot(x, values, marker='o', label=algo, color=colors[algo], linewidth=2, markersize=6)\n",
|
||
" ax.set_xticks(x)\n",
|
||
" ax.set_xticklabels(size_order)\n",
|
||
" ax.set_title(title)\n",
|
||
" ax.set_xlabel(\"Размер лабиринта\")\n",
|
||
" if i == 0:\n",
|
||
" ax.set_ylabel(\"Значение\")\n",
|
||
" ax.grid(True, linestyle='--', alpha=0.7)\n",
|
||
"\n",
|
||
"# Общая легенда сверху\n",
|
||
"handles, labels = axes[0].get_legend_handles_labels()\n",
|
||
"fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.05), ncol=3)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c573b32b",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3. Применение паттерна Observer\n",
|
||
"\n",
|
||
"Для того, чтобы продемонстрировать работу этого паттерна необходимо иметь возможность очищать вывод, но в Jupyter такой возможности нет. В терминале всё работает корректно."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "898e8536",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[H\u001b[2J\u001b[48;5;9m\u001b[38;5;7mS\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;12m\u001b[38;5;7mE\u001b[0m\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from task2.consoleView import ConsoleView\n",
|
||
"from task2.mazeSolver import MazeSolver\n",
|
||
"from task2.strategyObjects.AStar import AStar\n",
|
||
"\n",
|
||
"maze = builder.buildFromFile(\"../task2/mazeExamples/25x25.txt\")\n",
|
||
"console = ConsoleView(maze)\n",
|
||
"solver = MazeSolver(AStar(), maze)\n",
|
||
"solver.attach(console)\n",
|
||
"console.render()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "aa5a4c8c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[H\u001b[2J\u001b[48;5;9m\u001b[38;5;7mS\u001b[0m \u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;196m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;161m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\n",
|
||
"\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\n",
|
||
"\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\n",
|
||
"\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\n",
|
||
"\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;91m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\n",
|
||
"\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
"\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \n",
|
||
" \u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;126m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m \u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;56m\u001b[38;5;10m+\u001b[0m\u001b[48;5;7m\u001b[38;5;7m#\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;21m\u001b[38;5;10m+\u001b[0m\u001b[48;5;12m\u001b[38;5;7mE\u001b[0m\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# При нахождении решения рендер должен произойти автоматически\n",
|
||
"_ = solver.solve()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "484b4ef6",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4. Применимость паттернов\n",
|
||
"После выполнения работы можно оценить, насколько для этого помогли паттерны ООП. \n",
|
||
"Самый полезный паттерн - это конечно Strategy. Он позволил без лишней мороки провести замеры серийно, не используя каждый алгоритм вручную. \n",
|
||
" \n",
|
||
"Вторым по значимости я бы назвал Observer, он позволил сделать рендер лабиринта проще, однако сильнее всего оценить его получилось бы совместно с паттерном Commad и игроком с передвижениями. Его я не успел реализовать.\n",
|
||
" \n",
|
||
"Третий - Builder. В этой работе его преимущества оценить трудно из-за того, что использовался только один формат файла - plaintext. Однако, если понадобится искать пути в больших лабиринтах, то их придётся хрвнить в другом формате, например бинарном, и тогда преимщества паттерна Builder станут очевидны. \n",
|
||
" \n",
|
||
"Так же я создал класс-оркестратор MazeSolver, который помог перенести почти весь код тестов в одно место и удобно эти тесты использовать. Этот паттерн я так же считаю полезным. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d4026233",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5. Вывод\n",
|
||
"Применение объектно-ориентированного подхода и паттернов проектирования позволило разделить ответственность между независимыми модулями и обеспечить простоту внесения изменений. Если бы логика поиска пути, построения лабиринта и визуализации была сосредоточена в одном классе или реализована процедурно, любое расширение требовало бы переписывания значительной части кода."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.14.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|