diff --git a/agafonovdm/docs/data/2zad/RESULT22.py b/agafonovdm/docs/data/2zad/RESULT22.py
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+++ b/agafonovdm/docs/data/2zad/RESULT22.py
@@ -0,0 +1,363 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Настройка русских шрифтов
+plt.rcParams['font.family'] = 'DejaVu Sans'
+plt.rcParams['axes.unicode_minus'] = False
+
+def load_and_prepare_data(filename='experiment_results.csv'):
+    """Загрузка данных из CSV и подготовка."""
+    df = pd.read_csv(filename, delimiter=',')  # Используем запятую как разделитель
+    
+    # Переименовываем столбцы для удобства
+    df.columns = ['maze_type', 'algorithm', 'avg_time_ms', 'avg_visited_cells', 'avg_path_length']
+    
+    # Преобразование типов
+    numeric_cols = ['avg_time_ms', 'avg_visited_cells', 'avg_path_length']
+    for col in numeric_cols:
+        df[col] = pd.to_numeric(df[col], errors='coerce')
+    
+    # Добавляем столбец с размером лабиринта для анализа
+    def extract_maze_size(maze_name):
+        if 'Small' in maze_name:
+            return 'Small (10x10)'
+        elif 'Medium' in maze_name:
+            return 'Medium (50x50)'
+        elif 'Large' in maze_name:
+            return 'Large (100x100)'
+        elif 'Empty' in maze_name:
+            return 'Empty (30x30)'
+        elif 'No Exit' in maze_name:
+            return 'No Exit (20x20)'
+        return maze_name
+    
+    df['maze_category'] = df['maze_type'].apply(extract_maze_size)
+    
+    return df
+
+def plot_time_comparison(df):
+    """График 1: Сравнение времени выполнения по лабиринтам."""
+    fig, ax = plt.subplots(figsize=(12, 6))
+    
+    maze_types = df['maze_category'].unique()
+    algorithms = df['algorithm'].unique()
+    
+    x = np.arange(len(maze_types))
+    width = 0.2
+    
+    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+    
+    for i, algorithm in enumerate(algorithms):
+        algo_data = df[df['algorithm'] == algorithm]
+        times = []
+        for maze in maze_types:
+            row = algo_data[algo_data['maze_category'] == maze]
+            if not row.empty:
+                times.append(row['avg_time_ms'].values[0])
+            else:
+                times.append(0)
+        
+        bars = ax.bar(x + i*width, times, width, label=algorithm, 
+                      color=colors[i])
+    
+    ax.set_xlabel('Тип лабиринта', fontsize=12)
+    ax.set_ylabel('Время выполнения (мс)', fontsize=12)
+    ax.set_title('Сравнение времени выполнения алгоритмов поиска пути', fontsize=14)
+    ax.set_xticks(x + width * 1.5)
+    ax.set_xticklabels(maze_types, rotation=45, ha='right')
+    ax.legend()
+    ax.grid(True, alpha=0.3, axis='y')
+    
+    # Добавление значений на столбцы
+    for i, algorithm in enumerate(algorithms):
+        algo_data = df[df['algorithm'] == algorithm]
+        for j, maze in enumerate(maze_types):
+            row = algo_data[algo_data['maze_category'] == maze]
+            if not row.empty and row['avg_time_ms'].values[0] > 0:
+                time_val = row['avg_time_ms'].values[0]
+                ax.text(x[j] + i*width, time_val + 0.02, 
+                       f'{time_val:.3f}', ha='center', va='bottom', fontsize=8)
+    
+    plt.tight_layout()
+    plt.savefig('time_comparison.png', dpi=150)
+    plt.show()
+
+def plot_visited_cells(df):
+    """График 2: Количество посещённых клеток."""
+    fig, ax = plt.subplots(figsize=(12, 6))
+    
+    maze_types = df['maze_category'].unique()
+    algorithms = df['algorithm'].unique()
+    
+    x = np.arange(len(maze_types))
+    width = 0.2
+    
+    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+    
+    for i, algorithm in enumerate(algorithms):
+        algo_data = df[df['algorithm'] == algorithm]
+        visited = []
+        for maze in maze_types:
+            row = algo_data[algo_data['maze_category'] == maze]
+            if not row.empty:
+                visited.append(row['avg_visited_cells'].values[0])
+            else:
+                visited.append(0)
+        
+        ax.bar(x + i*width, visited, width, label=algorithm, color=colors[i])
+    
+    ax.set_xlabel('Тип лабиринта', fontsize=12)
+    ax.set_ylabel('Количество посещённых клеток', fontsize=12)
+    ax.set_title('Сравнение количества посещённых клеток', fontsize=14)
+    ax.set_xticks(x + width * 1.5)
+    ax.set_xticklabels(maze_types, rotation=45, ha='right')
+    ax.legend()
+    ax.grid(True, alpha=0.3, axis='y')
+    
+    plt.tight_layout()
+    plt.savefig('visited_cells.png', dpi=150)
+    plt.show()
+
+def plot_path_length(df):
+    """График 3: Длина найденного пути."""
+    fig, ax = plt.subplots(figsize=(12, 6))
+    
+    # Исключаем лабиринты без выхода (где путь = 0)
+    df_filtered = df[df['avg_path_length'] > 0]
+    
+    maze_types = df_filtered['maze_category'].unique()
+    algorithms = df_filtered['algorithm'].unique()
+    
+    x = np.arange(len(maze_types))
+    width = 0.2
+    
+    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+    
+    for i, algorithm in enumerate(algorithms):
+        algo_data = df_filtered[df_filtered['algorithm'] == algorithm]
+        path_lengths = []
+        for maze in maze_types:
+            row = algo_data[algo_data['maze_category'] == maze]
+            if not row.empty:
+                path_lengths.append(row['avg_path_length'].values[0])
+            else:
+                path_lengths.append(0)
+        
+        ax.bar(x + i*width, path_lengths, width, label=algorithm, color=colors[i])
+    
+    ax.set_xlabel('Тип лабиринта', fontsize=12)
+    ax.set_ylabel('Длина пути (количество клеток)', fontsize=12)
+    ax.set_title('Сравнение длины найденного пути', fontsize=14)
+    ax.set_xticks(x + width * 1.5)
+    ax.set_xticklabels(maze_types, rotation=45, ha='right')
+    ax.legend()
+    ax.grid(True, alpha=0.3, axis='y')
+    
+    plt.tight_layout()
+    plt.savefig('path_length.png', dpi=150)
+    plt.show()
+
+def plot_time_per_maze(df):
+    """График 4: Для каждого лабиринта - сравнение алгоритмов по времени."""
+    maze_types = df['maze_category'].unique()
+    algorithms = df['algorithm'].unique()
+    
+    for maze in maze_types:
+        fig, ax = plt.subplots(figsize=(10, 6))
+        
+        maze_data = df[df['maze_category'] == maze]
+        
+        times = []
+        algo_names = []
+        for algo in algorithms:
+            row = maze_data[maze_data['algorithm'] == algo]
+            if not row.empty:
+                times.append(row['avg_time_ms'].values[0])
+                algo_names.append(algo)
+        
+        bars = ax.bar(algo_names, times, 
+                      color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'][:len(algo_names)])
+        
+        ax.set_xlabel('Алгоритм', fontsize=12)
+        ax.set_ylabel('Время выполнения (мс)', fontsize=12)
+        ax.set_title(f'Сравнение алгоритмов на лабиринте: {maze}', fontsize=14)
+        ax.grid(True, alpha=0.3, axis='y')
+        
+        # Добавление значений на столбцы
+        for bar, time_val in zip(bars, times):
+            height = bar.get_height()
+            ax.text(bar.get_x() + bar.get_width()/2., height + 0.02,
+                    f'{time_val:.3f}', ha='center', va='bottom', fontsize=10)
+        
+        plt.tight_layout()
+        # Очищаем имя файла от скобок
+        safe_maze_name = maze.replace('(', '').replace(')', '').replace(' ', '_')
+        plt.savefig(f'time_{safe_maze_name}.png', dpi=150)
+        plt.show()
+
+def plot_visited_per_maze(df):
+    """График 5: Для каждого лабиринта - посещённые клетки."""
+    maze_types = df['maze_category'].unique()
+    
+    for maze in maze_types:
+        fig, ax = plt.subplots(figsize=(10, 6))
+        
+        maze_data = df[df['maze_category'] == maze]
+        
+        visited = maze_data['avg_visited_cells'].values
+        algo_names = maze_data['algorithm'].values
+        
+        bars = ax.bar(algo_names, visited, 
+                      color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'][:len(algo_names)])
+        
+        ax.set_xlabel('Алгоритм', fontsize=12)
+        ax.set_ylabel('Количество посещённых клеток', fontsize=12)
+        ax.set_title(f'Посещённые клетки на лабиринте: {maze}', fontsize=14)
+        ax.grid(True, alpha=0.3, axis='y')
+        
+        # Добавление значений на столбцы
+        for bar, val in zip(bars, visited):
+            height = bar.get_height()
+            ax.text(bar.get_x() + bar.get_width()/2., height + 10,
+                    f'{int(val)}', ha='center', va='bottom', fontsize=10)
+        
+        plt.tight_layout()
+        safe_maze_name = maze.replace('(', '').replace(')', '').replace(' ', '_')
+        plt.savefig(f'visited_{safe_maze_name}.png', dpi=150)
+        plt.show()
+
+def plot_efficiency_ratio(df):
+    """График 6: Эффективность (время на клетку пути)."""
+    fig, ax = plt.subplots(figsize=(12, 6))
+    
+    # Исключаем лабиринты без пути
+    df_filtered = df[(df['avg_path_length'] > 0) & (df['avg_time_ms'] > 0)].copy()
+    df_filtered['efficiency'] = df_filtered['avg_time_ms'] / df_filtered['avg_path_length']
+    
+    maze_types = df_filtered['maze_category'].unique()
+    algorithms = df_filtered['algorithm'].unique()
+    
+    x = np.arange(len(maze_types))
+    width = 0.2
+    
+    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+    
+    for i, algorithm in enumerate(algorithms):
+        algo_data = df_filtered[df_filtered['algorithm'] == algorithm]
+        efficiency = []
+        for maze in maze_types:
+            row = algo_data[algo_data['maze_category'] == maze]
+            if not row.empty:
+                efficiency.append(row['efficiency'].values[0])
+            else:
+                efficiency.append(0)
+        
+        ax.bar(x + i*width, efficiency, width, label=algorithm, color=colors[i])
+    
+    ax.set_xlabel('Тип лабиринта', fontsize=12)
+    ax.set_ylabel('Время на клетку пути (мс/клетку)', fontsize=12)
+    ax.set_title('Эффективность алгоритмов (время на единицу длины пути)', fontsize=14)
+    ax.set_xticks(x + width * 1.5)
+    ax.set_xticklabels(maze_types, rotation=45, ha='right')
+    ax.legend()
+    ax.grid(True, alpha=0.3, axis='y')
+    
+    plt.tight_layout()
+    plt.savefig('efficiency_ratio.png', dpi=150)
+    plt.show()
+
+def plot_path_vs_visited(df):
+    """График 7: Соотношение длины пути и посещённых клеток."""
+    fig, ax = plt.subplots(figsize=(10, 6))
+    
+    algorithms = df['algorithm'].unique()
+    markers = ['o', 's', '^', 'D']
+    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+    
+    for algo, marker, color in zip(algorithms, markers, colors):
+        algo_data = df[df['algorithm'] == algo]
+        # Только лабиринты с путём
+        algo_data = algo_data[algo_data['avg_path_length'] > 0]
+        
+        if not algo_data.empty:
+            plt.scatter(algo_data['avg_visited_cells'], 
+                       algo_data['avg_path_length'],
+                       marker=marker, s=100, label=algo, color=color, alpha=0.7)
+            
+            # Добавляем подписи для каждой точки
+            for _, row in algo_data.iterrows():
+                plt.annotate(row['maze_category'].split()[0], 
+                           (row['avg_visited_cells'], row['avg_path_length']),
+                           xytext=(5, 5), textcoords='offset points', fontsize=8)
+    
+    plt.xlabel('Количество посещённых клеток', fontsize=12)
+    plt.ylabel('Длина пути (клеток)', fontsize=12)
+    plt.title('Соотношение: посещённые клетки vs длина пути', fontsize=14)
+    plt.legend()
+    plt.grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plt.savefig('path_vs_visited.png', dpi=150)
+    plt.show()
+
+def main():
+    """Основная функция: загрузка данных и построение всех графиков."""
+    try:
+        df = load_and_prepare_data('experiment_results.csv')
+        print("Данные успешно загружены")
+        print(f"Найдено {len(df)} записей")
+        print("\nСтруктура данных:")
+        print(df.head())
+        print("\nУникальные типы лабиринтов:")
+        print(df['maze_category'].unique())
+        print("\nУникальные алгоритмы:")
+        print(df['algorithm'].unique())
+        
+        print("\nПостроение графиков...")
+        
+        # Базовые графики
+        plot_time_comparison(df)
+        plot_visited_cells(df)
+        plot_path_length(df)
+        
+        # Детальные графики по каждому лабиринту
+        plot_time_per_maze(df)
+        plot_visited_per_maze(df)
+        
+        # Аналитические графики
+        plot_efficiency_ratio(df)
+        plot_path_vs_visited(df)
+        
+        print("\nВсе графики сохранены в текущей директории:")
+        print("  - time_comparison.png")
+        print("  - visited_cells.png")
+        print("  - path_length.png")
+        print("  - time_{maze}.png (для каждого лабиринта)")
+        print("  - visited_{maze}.png (для каждого лабиринта)")
+        print("  - efficiency_ratio.png")
+        print("  - path_vs_visited.png")
+        
+        # Вывод статистики
+        print("\n=== Краткая статистика ===")
+        for maze in df['maze_category'].unique():
+            print(f"\n{maze}:")
+            maze_data = df[df['maze_category'] == maze]
+            for algo in df['algorithm'].unique():
+                algo_data = maze_data[maze_data['algorithm'] == algo]
+                if not algo_data.empty:
+                    time_val = algo_data['avg_time_ms'].values[0]
+                    visited_val = int(algo_data['avg_visited_cells'].values[0])
+                    path_val = int(algo_data['avg_path_length'].values[0])
+                    print(f"  {algo}: время={time_val:.6f}мс, посещено={visited_val}, путь={path_val}")
+        
+    except FileNotFoundError:
+        print("Ошибка: файл experiment_results.csv не найден")
+        print("Убедитесь, что файл находится в текущей директории")
+    except Exception as e:
+        print(f"Ошибка: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
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