diff --git a/KuznetsovYuM/docs/data/2-nd-exercise/plots.py b/KuznetsovYuM/docs/data/2-nd-exercise/plots.py
new file mode 100644
index 0000000..9d52b66
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+++ b/KuznetsovYuM/docs/data/2-nd-exercise/plots.py
@@ -0,0 +1,376 @@
+import sys
+import csv
+from collections import deque
+import heapq
+import time
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+class Tile:
+    def __init__(self, x, y):
+        self._x = x
+        self._y = y
+        self._wall = False
+        self._start = False
+        self._exit = False
+    
+    @property
+    def x(self):
+        return self._x
+    
+    @property
+    def y(self):
+        return self._y
+    
+    @property
+    def is_wall(self):
+        return self._wall
+    
+    @is_wall.setter
+    def is_wall(self, v):
+        self._wall = v
+    
+    @property
+    def is_start(self):
+        return self._start
+    
+    @is_start.setter
+    def is_start(self, v):
+        self._start = v
+    
+    @property
+    def is_exit(self):
+        return self._exit
+    
+    @is_exit.setter
+    def is_exit(self, v):
+        self._exit = v
+    
+    def passable(self):
+        return not self._wall
+
+
+class Maze:
+    def __init__(self, w, h):
+        self._w = w
+        self._h = h
+        self._cells = [[Tile(x, y) for x in range(w)] for y in range(h)]
+        self._start = None
+        self._exit = None
+    
+    @property
+    def width(self):
+        return self._w
+    
+    @property
+    def height(self):
+        return self._h
+    
+    @property
+    def start(self):
+        return self._start
+    
+    @property
+    def exit(self):
+        return self._exit
+    
+    def get_cell(self, x, y):
+        if 0 <= x < self._w and 0 <= y < self._h:
+            return self._cells[y][x]
+        return None
+    
+    def set_cell(self, x, y, kind):
+        c = self.get_cell(x, y)
+        if not c:
+            return
+        if kind == 'wall':
+            c.is_wall = True
+        elif kind == 'start':
+            if self._start:
+                self._start.is_start = False
+            c.is_start = True
+            c.is_wall = False
+            self._start = c
+        elif kind == 'exit':
+            if self._exit:
+                self._exit.is_exit = False
+            c.is_exit = True
+            c.is_wall = False
+            self._exit = c
+        elif kind == 'path':
+            c.is_wall = False
+    
+    def neighbours(self, cell):
+        res = []
+        for dx, dy in [(0,-1),(0,1),(-1,0),(1,0)]:
+            nx, ny = cell.x + dx, cell.y + dy
+            nb = self.get_cell(nx, ny)
+            if nb and nb.passable():
+                res.append(nb)
+        return res
+
+
+class MazeLoader:
+    def load(self, fname):
+        raise NotImplementedError
+
+
+class TextMazeLoader(MazeLoader):
+    def load(self, fname):
+        with open(fname, 'r') as f:
+            lines = [ln.rstrip('\n') for ln in f.readlines()]
+        h = len(lines)
+        w = max(len(ln) for ln in lines) if h else 0
+        cntS = 0
+        cntE = 0
+        m = Maze(w, h)
+        for y, ln in enumerate(lines):
+            for x, ch in enumerate(ln):
+                if ch == '#':
+                    m.set_cell(x, y, 'wall')
+                elif ch == 'S':
+                    m.set_cell(x, y, 'start')
+                    cntS += 1
+                elif ch == 'E':
+                    m.set_cell(x, y, 'exit')
+                    cntE += 1
+                else:
+                    m.set_cell(x, y, 'path')
+        if cntS != 1 or cntE != 1:
+            raise ValueError(f"Bad maze: S={cntS}, E={cntE}")
+        return m
+
+
+class PathFinder:
+    def find(self, maze, start, goal):
+        raise NotImplementedError
+    
+    def _reconstruct(self, parent, start, goal):
+        path = []
+        cur = goal
+        while cur:
+            path.append(cur)
+            cur = parent.get(cur)
+        path.reverse()
+        return path
+    
+    def visited_count(self):
+        return getattr(self, '_vis', 0)
+
+
+class BFS(PathFinder):
+    def find(self, maze, start, goal):
+        q = deque([start])
+        parent = {start: None}
+        visited = {start}
+        while q:
+            cur = q.popleft()
+            if cur == goal:
+                self._vis = len(visited)
+                return self._reconstruct(parent, start, goal)
+            for nb in maze.neighbours(cur):
+                if nb not in visited:
+                    visited.add(nb)
+                    parent[nb] = cur
+                    q.append(nb)
+        self._vis = len(visited)
+        return []
+
+
+class DFS(PathFinder):
+    def find(self, maze, start, goal):
+        stack = [start]
+        parent = {start: None}
+        visited = {start}
+        while stack:
+            cur = stack.pop()
+            if cur == goal:
+                self._vis = len(visited)
+                return self._reconstruct(parent, start, goal)
+            for nb in maze.neighbours(cur):
+                if nb not in visited:
+                    visited.add(nb)
+                    parent[nb] = cur
+                    stack.append(nb)
+        self._vis = len(visited)
+        return []
+
+
+class AStar(PathFinder):
+    def _h(self, cell, goal):
+        return abs(cell.x - goal.x) + abs(cell.y - goal.y)
+    
+    def find(self, maze, start, goal):
+        heap = []
+        idx = 0
+        start_f = self._h(start, goal)
+        heapq.heappush(heap, (start_f, idx, start))
+        idx += 1
+        parent = {}
+        g = {start: 0}
+        f = {start: start_f}
+        visited = set()
+        while heap:
+            cur_f, _, cur = heapq.heappop(heap)
+            visited.add(cur)
+            if cur == goal:
+                self._vis = len(visited)
+                return self._reconstruct(parent, start, goal)
+            if cur_f > f.get(cur, float('inf')):
+                continue
+            for nb in maze.neighbours(cur):
+                new_g = g[cur] + 1
+                if new_g < g.get(nb, float('inf')):
+                    parent[nb] = cur
+                    g[nb] = new_g
+                    new_f = new_g + self._h(nb, goal)
+                    f[nb] = new_f
+                    heapq.heappush(heap, (new_f, idx, nb))
+                    idx += 1
+        self._vis = len(visited)
+        return []
+
+
+class Solver:
+    def __init__(self, maze):
+        self._maze = maze
+        self._algo = None
+    
+    def set_algo(self, algo):
+        self._algo = algo
+    
+    def run(self):
+        if not self._algo:
+            return None
+        t0 = time.perf_counter()
+        path = self._algo.find(self._maze, self._maze.start, self._maze.exit)
+        t1 = time.perf_counter()
+        return {
+            'time_ms': (t1 - t0) * 1000,
+            'visited': self._algo.visited_count(),
+            'path_len': len(path)
+        }
+
+
+def benchmark(maze_file, algorithm, runs=5):
+    loader = TextMazeLoader()
+    maze = loader.load(maze_file)
+    total_t = 0.0
+    total_v = 0
+    total_l = 0
+    for _ in range(runs):
+        s = Solver(maze)
+        s.set_algo(algorithm)
+        stats = s.run()
+        if stats:
+            total_t += stats['time_ms']
+            total_v += stats['visited']
+            total_l += stats['path_len']
+    return {
+        'time_ms': total_t / runs,
+        'visited_cells': total_v / runs,
+        'path_length': total_l / runs
+    }
+
+
+def create_plots(results):
+    mazes = sorted(set(r['maze'] for r in results))
+    algos = ['BFS', 'DFS', 'AStar']
+    
+    fig, axes = plt.subplots(1, 3, figsize=(15,5))
+    x = np.arange(len(mazes))
+    width = 0.25
+    
+    for i, algo in enumerate(algos):
+        times = []
+        for m in mazes:
+            val = next((r['time_ms'] for r in results if r['maze'] == m and r['strategy'] == algo), 0)
+            times.append(val)
+        axes[0].bar(x + i*width, times, width, label=algo)
+    axes[0].set_title('Execution time (ms)')
+    axes[0].set_xticks(x + width)
+    axes[0].set_xticklabels(mazes, rotation=45, ha='right')
+    axes[0].legend()
+    axes[0].grid(alpha=0.3)
+    
+    for i, algo in enumerate(algos):
+        visited = []
+        for m in mazes:
+            val = next((r['visited_cells'] for r in results if r['maze'] == m and r['strategy'] == algo), 0)
+            visited.append(val)
+        axes[1].bar(x + i*width, visited, width, label=algo)
+    axes[1].set_title('Visited cells')
+    axes[1].set_xticks(x + width)
+    axes[1].set_xticklabels(mazes, rotation=45, ha='right')
+    axes[1].legend()
+    axes[1].grid(alpha=0.3)
+    
+    for i, algo in enumerate(algos):
+        lengths = []
+        for m in mazes:
+            val = next((r['path_length'] for r in results if r['maze'] == m and r['strategy'] == algo), 0)
+            lengths.append(val)
+        axes[2].bar(x + i*width, lengths, width, label=algo)
+    axes[2].set_title('Path length')
+    axes[2].set_xticks(x + width)
+    axes[2].set_xticklabels(mazes, rotation=45, ha='right')
+    axes[2].legend()
+    axes[2].grid(alpha=0.3)
+    
+    plt.tight_layout()
+    plt.savefig('performance_comparison_2-nd-exercise.png', dpi=150, bbox_inches='tight')
+    plt.show()
+
+
+if __name__ == "__main__":
+    test_mazes = [
+        ("maze1.txt", "Small 10x6"),
+        ("maze10x10.txt", "Medium 10x10"),
+        ("maze20x20.txt", "Large 20x20"),
+        ("maze_empty.txt", "Empty 15x15"),
+        ("maze_no_exit.txt", "No exit 10x10")
+    ]
+    
+    algorithms = [
+        ("BFS", BFS()),
+        ("DFS", DFS()),
+        ("AStar", AStar())
+    ]
+    
+    all_results = []
+    for fname, label in test_mazes:
+        print(f"Testing {label}...")
+        for name, algo in algorithms:
+            try:
+                stat = benchmark(fname, algo, runs=3)
+                all_results.append({
+                    'maze': label,
+                    'strategy': name,
+                    'time_ms': stat['time_ms'],
+                    'visited_cells': stat['visited_cells'],
+                    'path_length': stat['path_length']
+                })
+                print(f"  {name}: time={stat['time_ms']:.3f}ms, visited={stat['visited_cells']:.0f}, length={stat['path_length']:.0f}")
+            except Exception as e:
+                print(f"  {name}: ERROR - {e}")
+                all_results.append({
+                    'maze': label,
+                    'strategy': name,
+                    'time_ms': -1,
+                    'visited_cells': -1,
+                    'path_length': -1
+                })
+    
+    good = [r for r in all_results if r['time_ms'] >= 0]
+    
+    with open('experiment_results_2-nd-exercise.csv', 'w', newline='', encoding='utf-8') as f:
+        writer = csv.DictWriter(f, fieldnames=['maze', 'strategy', 'time_ms', 'visited_cells', 'path_length'])
+        writer.writeheader()
+        writer.writerows(good)
+    
+    if good:
+        create_plots(good)
+    
+    print("\nResults saved to experiment_results_2-nd-exercise.csv")
+    print("Plot saved to performance_comparison_2-nd-exercise.png")
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