diff --git a/pomelovsd/DataStruct/data_structures.ipynb b/pomelovsd/DataStruct/data_structures.ipynb
index e47d141..f7f9256 100644
--- a/pomelovsd/DataStruct/data_structures.ipynb
+++ b/pomelovsd/DataStruct/data_structures.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 107,
    "id": "c533959c",
    "metadata": {},
    "outputs": [],
@@ -12,9 +12,10 @@
     "import BinaryTree as bt\n",
     "import time \n",
     "import random as rand\n",
+    "import numpy as np \n",
     "import csv\n",
-    "import numpy as np\n",
-    "import sys"
+    "import sys\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -27,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 108,
    "id": "88611f78",
    "metadata": {},
    "outputs": [],
@@ -60,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 109,
    "id": "11634fa4",
    "metadata": {},
    "outputs": [],
@@ -92,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 110,
    "id": "9eab4641",
    "metadata": {},
    "outputs": [],
@@ -123,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 111,
    "id": "aac6cd23",
    "metadata": {},
    "outputs": [],
@@ -155,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 112,
    "id": "5e5ae537",
    "metadata": {},
    "outputs": [],
@@ -186,21 +187,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "6cdf8a70",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.04597238700080197,\n",
-       " 0.0345118730001559,\n",
-       " 0.04677163999804179,\n",
-       " 0.0356504750016029,\n",
-       " 0.033157756999571575]"
+       "[0.05640505500014115,\n",
+       " 0.038319764000334544,\n",
+       " 0.02700047700000141,\n",
+       " 0.03155651599990961,\n",
+       " 0.03664214699983859]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -217,8 +218,7 @@
     "        current_head = ll.ll_delete(current_head, str_delete)\n",
     "    end = time.perf_counter()\n",
     "\n",
-    "    time_ll_delete_sh.append(end - start)\n",
-    "time_ll_delete_sh"
+    "    time_ll_delete_sh.append(end - start)"
    ]
   },
   {
@@ -232,21 +232,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "575e375c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.03482511599941063,\n",
-       " 0.02823204100059229,\n",
-       " 0.029369250001764158,\n",
-       " 0.01980331899903831,\n",
-       " 0.030789607000770047]"
+       "[0.029801202000271587,\n",
+       " 0.018522201000450877,\n",
+       " 0.01715149899973767,\n",
+       " 0.018071766000502976,\n",
+       " 0.0195203829998718]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,8 +263,7 @@
     "        current_head = ll.ll_delete(current_head, str_delete)\n",
     "    end = time.perf_counter()\n",
     "\n",
-    "    time_ll_delete_so.append(end - start)\n",
-    "time_ll_delete_so"
+    "    time_ll_delete_so.append(end - start)"
    ]
   },
   {
@@ -288,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 115,
    "id": "939aa900",
    "metadata": {},
    "outputs": [],
@@ -320,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 116,
    "id": "d17b8108",
    "metadata": {},
    "outputs": [],
@@ -351,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 117,
    "id": "4352b11d",
    "metadata": {},
    "outputs": [],
@@ -383,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 118,
    "id": "7941e689",
    "metadata": {},
    "outputs": [],
@@ -414,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 119,
    "id": "4043a9dc",
    "metadata": {},
    "outputs": [],
@@ -444,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 120,
    "id": "7ab6136c",
    "metadata": {},
    "outputs": [],
@@ -484,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 121,
    "id": "cb1788d1",
    "metadata": {},
    "outputs": [],
@@ -514,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 122,
    "id": "4cb524e4",
    "metadata": {},
    "outputs": [],
@@ -543,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 123,
    "id": "45cec102",
    "metadata": {},
    "outputs": [],
@@ -575,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 124,
    "id": "19e7a19a",
    "metadata": {},
    "outputs": [],
@@ -606,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 125,
    "id": "90a9613d",
    "metadata": {},
    "outputs": [],
@@ -635,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 126,
    "id": "dfe01fe5",
    "metadata": {},
    "outputs": [],
@@ -664,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 127,
    "id": "ce249772",
    "metadata": {},
    "outputs": [],
@@ -706,13 +705,182 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 128,
+   "id": "09467dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_ll = [sum(time_ll_insert_sh)/len(time_ll_insert_sh), sum(time_ll_insert_so)/len(time_ll_insert_so), sum(time_ll_find_sh)/len(time_ll_find_sh), sum(time_ll_find_so)/len(time_ll_find_so), sum(time_ll_delete_sh)/len(time_ll_delete_sh), sum(time_ll_delete_so)/len(time_ll_delete_so)]\n",
+    "t_ht = [sum(time_ht_insert_sh)/len(time_ht_insert_sh), sum(time_ht_insert_so)/len(time_ht_insert_so), sum(time_ht_find_sh)/len(time_ht_find_sh), sum(time_ht_find_so)/len(time_ht_find_so), sum(time_ht_delete_sh)/len(time_ht_delete_sh), sum(time_ht_delete_so)/len(time_ht_delete_so)]\n",
+    "t_bt = [sum(time_bt_insert_sh)/len(time_bt_insert_sh), sum(time_bt_insert_so)/len(time_bt_insert_so), sum(time_bt_find_sh)/len(time_bt_find_sh), sum(time_bt_find_so)/len(time_bt_find_so), sum(time_bt_delete_sh)/len(time_bt_delete_sh), sum(time_bt_delete_so)/len(time_bt_delete_so)]\n",
+    "categoris = [\"рандомное добавление\", \"отсортированное добавление\", \"рандомный поиск\", \"отсортированный поиск\", \"рандомное удаление\", \"отсортированное удаление\"]\n",
+    "n = np.linspace(1,5, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "823d083c",
+   "metadata": {},
+   "source": [
+    "### Графики 5 замеров"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "bdb0dfda",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(\"Графики изначальных замеров\", figsize = (20,15))\n",
+    "\n",
+    "plt.subplot(3,2,1)\n",
+    "\n",
+    "plt.scatter(n, time_ll_insert_sh, color = \"#88D94C\", label = \"LinkedList\")\n",
+    "plt.scatter(n, time_bt_insert_sh, color = \"#FF43A4\", label = \"BinaryTree\")\n",
+    "plt.scatter(n, time_ht_insert_sh, color = \"#EEDC82\", label = \"HashTable\")\n",
+    "plt.axhline(t_ll[0], color = \"#88D94C\", linestyle = \"--\", label = f\"Среднее для LinkedList = {t_ll[0]:.6f}\")\n",
+    "plt.axhline(t_bt[0], color = \"#FF43A4\", linestyle = \"--\", label = f\"Среднее для BinaryTree = {t_bt[0]:.6f}\")\n",
+    "plt.axhline(t_ht[0], color = \"#EEDC82\", linestyle = \"--\", label = f\"Среднее для HashTable = {t_ht[0]:.6f}\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(f\"Время добавление случайных данных\")\n",
+    "\n",
+    "plt.ylabel(\"Время(сек)\")\n",
+    "plt.xlabel(\"Попытки\")\n",
+    "plt.xticks(np.arange(1, 6, 1))\n",
+    "\n",
+    "plt.grid(alpha = 0.5, linestyle = \"--\")\n",
+    "\n",
+    "plt.subplot(3,2,2)\n",
+    "\n",
+    "plt.scatter(n, time_ll_insert_so, color = \"#88D94C\", label = \"LinkedList\")\n",
+    "plt.scatter(n, time_bt_insert_so, color = \"#FF43A4\", label = \"BinaryTree\")\n",
+    "plt.scatter(n, time_ht_insert_so, color = \"#EEDC82\", label = \"HashTable\")\n",
+    "plt.axhline(t_ll[1], color = \"#88D94C\", linestyle = \"--\", label = f\"Среднее для LinkedList = {t_ll[1]:.6f}\")\n",
+    "plt.axhline(t_bt[1], color = \"#FF43A4\", linestyle = \"--\", label = f\"Среднее для BinaryTree = {t_bt[1]:.6f}\")\n",
+    "plt.axhline(t_ht[1], color = \"#EEDC82\", linestyle = \"--\", label = f\"Среднее для HashTable = {t_ht[1]:.6f}\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(f\"Время добавление сортированных данных\")\n",
+    "\n",
+    "plt.ylabel(\"Время(сек)\")\n",
+    "plt.xlabel(\"Попытки\")\n",
+    "plt.xticks(np.arange(1, 6, 1))\n",
+    "\n",
+    "\n",
+    "plt.grid(alpha = 0.5, linestyle = \"--\")\n",
+    "\n",
+    "plt.subplot(3,2,3)\n",
+    "\n",
+    "plt.scatter(n, time_ll_find_sh, color = \"#88D94C\", label = \"LinkedList\")\n",
+    "plt.scatter(n, time_bt_find_sh, color = \"#FF43A4\", label = \"BinaryTree\")\n",
+    "plt.scatter(n, time_ht_find_sh, color = \"#EEDC82\", label = \"HashTable\")\n",
+    "plt.axhline(t_ll[2], color = \"#88D94C\", linestyle = \"--\", label = f\"Среднее для LinkedList = {t_ll[2]:.6f}\")\n",
+    "plt.axhline(t_bt[2], color = \"#FF43A4\", linestyle = \"--\", label = f\"Среднее для BinaryTree = {t_bt[2]:.6f}\")\n",
+    "plt.axhline(t_ht[2], color = \"#EEDC82\", linestyle = \"--\", label = f\"Среднее для HashTable = {t_ht[2]:.6f}\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(f\"Время поиска случайных данных\")\n",
+    "\n",
+    "plt.ylabel(\"Время(сек)\")\n",
+    "plt.xlabel(\"Попытки\")\n",
+    "plt.xticks(np.arange(1, 6, 1))\n",
+    "\n",
+    "\n",
+    "plt.grid(alpha = 0.5, linestyle = \"--\")\n",
+    "\n",
+    "\n",
+    "plt.subplot(3,2,4)\n",
+    "\n",
+    "plt.scatter(n, time_ll_find_so, color = \"#88D94C\", label = \"LinkedList\")\n",
+    "plt.scatter(n, time_bt_find_so, color = \"#FF43A4\", label = \"BinaryTree\")\n",
+    "plt.scatter(n, time_ht_find_so, color = \"#EEDC82\", label = \"HashTable\")\n",
+    "plt.axhline(t_ll[3], color = \"#88D94C\", linestyle = \"--\", label = f\"Среднее для LinkedList = {t_ll[3]:.6f}\")\n",
+    "plt.axhline(t_bt[3], color = \"#FF43A4\", linestyle = \"--\", label = f\"Среднее для BinaryTree = {t_bt[3]:.6f}\")\n",
+    "plt.axhline(t_ht[3], color = \"#EEDC82\", linestyle = \"--\", label = f\"Среднее для HashTable = {t_ht[3]:.6f}\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(f\"Время поиска сортированных данных\")\n",
+    "\n",
+    "plt.ylabel(\"Время(сек)\")\n",
+    "plt.xlabel(\"Попытки\")\n",
+    "plt.xticks(np.arange(1, 6, 1))\n",
+    "\n",
+    "\n",
+    "plt.grid(alpha = 0.5, linestyle = \"--\")\n",
+    "\n",
+    "plt.subplot(3,2,5)\n",
+    "\n",
+    "plt.scatter(n, time_ll_delete_sh, color = \"#88D94C\", label = \"LinkedList\")\n",
+    "plt.scatter(n, time_bt_delete_sh, color = \"#FF43A4\", label = \"BinaryTree\")\n",
+    "plt.scatter(n, time_ht_delete_sh, color = \"#EEDC82\", label = \"HashTable\")\n",
+    "plt.axhline(t_ll[4], color = \"#88D94C\", linestyle = \"--\", label = f\"Среднее для LinkedList = {t_ll[4]:.6f}\")\n",
+    "plt.axhline(t_bt[4], color = \"#FF43A4\", linestyle = \"--\", label = f\"Среднее для BinaryTree = {t_bt[4]:.6f}\")\n",
+    "plt.axhline(t_ht[4], color = \"#EEDC82\", linestyle = \"--\", label = f\"Среднее для HashTable = {t_ht[4]:.6f}\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(f\"Время удаления для случайных данных\")\n",
+    "\n",
+    "plt.ylabel(\"Время(сек)\")\n",
+    "plt.xlabel(\"Попытки\")\n",
+    "plt.xticks(np.arange(1, 6, 1))\n",
+    "\n",
+    "\n",
+    "plt.grid(alpha = 0.5, linestyle = \"--\")\n",
+    "\n",
+    "plt.subplot(3,2,6)\n",
+    "\n",
+    "plt.scatter(n, time_ll_delete_so, color = \"#88D94C\", label = \"LinkedList\")\n",
+    "plt.scatter(n, time_bt_delete_so, color = \"#FF43A4\", label = \"BinaryTree\")\n",
+    "plt.scatter(n, time_ht_delete_so, color = \"#EEDC82\", label = \"HashTable\")\n",
+    "plt.axhline(t_ll[5], color = \"#88D94C\", linestyle = \"--\", label = f\"Среднее для LinkedList = {t_ll[5]:.6f}\")\n",
+    "plt.axhline(t_bt[5], color = \"#FF43A4\", linestyle = \"--\", label = f\"Среднее для BinaryTree = {t_bt[5]:.6f}\")\n",
+    "plt.axhline(t_ht[5], color = \"#EEDC82\", linestyle = \"--\", label = f\"Среднее для HashTable = {t_ht[5]:.6f}\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(f\"Время удаления для сортированных данных\")\n",
+    "\n",
+    "plt.ylabel(\"Время(сек)\")\n",
+    "plt.xlabel(\"Попытки\")\n",
+    "plt.xticks(np.arange(1, 6, 1))\n",
+    "\n",
+    "\n",
+    "plt.grid(alpha = 0.5, linestyle = \"--\")\n",
+    "\n",
+    "plt.savefig('analysis.png')\n",
+    "plt.tight_layout() \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2076283d",
+   "metadata": {},
+   "source": [
+    "### Сравнение усреднённых данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
    "id": "d06c4bb0",
    "metadata": {},
    "outputs": [
     {
      "data": {
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3e85pTp8+3WL8BVezXrhwwVzvoYcesqizfft2m+1duHBBkyZN0ksvvSRXV1dz+aFDh3Tp0iV169bNop233nqLv/vLIXJk+c2RpXVetF69errnnnvM2/Txxx/r0qVLevDBBy3qXe04n5eXpxdeeEEtW7ZUrVq15O3trU2bNtk8thV2XHPkeGPPe5P097nL9evXa8aMGRble/fu1blz58xjLViOHj1aIY5trlevgsrm/PnzioqKUlRUlN5++23Vrl1bqampio6OtrqVxPbt21W9enXz4xtvvNGqvV9++cVikteWyw+u0t8TqFeWSdI//vEPvfHGGzIMQ0OHDrXZ1s0336wNGzaYH+/evVuPPPJIkW0bl31vUH5+vlxcXLR37165uLhY1CvqICL9va2Xf8/Zla7Wt5+fnyTr/XF52XvvvadRo0bp5ZdfVocOHVS9enW99NJLVrfbuOOOO7Ro0SLl5ubqiy++0KOPPqqQkBCFhIRI+nsiR5ICAwNtjjU/P19PPPGEhg8fbvVcgwYNzP8fO3asxS2kxo0bZ/OWcG+99ZbOnz+vuLg4i9sVFdwa84033lD79u0t1rly/6N0Ffws2vv7aIuticgrFbd9e8dQICcnR88++6xeeOEFu8Z1ZT9F/b5eXv7www9r4sSJysrK0nvvvac+ffro4MGDql27tsaOHavPP/9cc+bMUdOmTeXl5aUHHnjA6lhqz+9RQT8F5s+fr6+++srm2C8fb0FZfn6+wsLCbAbLom65lpubqxMnThR5bLta335+foW+fgXlju6rCxcuaMGCBerVq5cOHDggDw8PSX8f2+rWrasqVaw/9+fI8WbNmjXm46Uk80mQK02cOFEPPvig1fe2Xct7CYBrV9HzrPT3rSULTlr+97//1eOPPy5fX1/17dvXZptnz57VjBkztG7dOoffU21xJDcU9Z5sz3v08ePHNWDAAD388MN6/PHH9d133xV6sgCVG3nWsp/ymGcv/zt2y5YtGj58uJo1a1botm7btk3bt2/X8uXL9eGHH9qxd4pW2Gtpq/zHH3/UkiVLtH//fouTw5J9P0e7du1SYmKi3n//fT311FNavXr1tQ0eQImo6DmytM6LDhw4UOPHj1dycrKSk5PVqFEjq+3+5Zdf1LBhw0LfD7/++mv1799f06ZNU3R0tHx9fbV69Wqr7zw/deqUqlSpooCAAJvt5Ofna9q0abr//vutnrv8gqF58+apa9eu5seFfXD0pZde0s0336x7773X4lbtBecYPvnkE91www0W6xSco0D5QY607Kc85sjSOC86ZMgQxcbGat68eVq+fLliYmKsPthztTmhl19+WfPmzVNCQoJatGihatWqaeTIkVb74dSpU0XO1Uj2HW/seW+SpDFjxuiZZ55RvXr1rPqqV6+e1XfMS1KNGjVstlWecKV4JfX1119bPb7xxhvl4uKi//znPzp9+rRmzpypyMhINWvWzOr7sQoEBweradOm5uVKJ0+evGr4CwkJ0Y4dOyzKdu3aZTEZUWDAgAHavHmzNm/erAEDBthsz93d3WJMVx4kQkNDbfZ30003ycXFRW3atFFeXp5+++03i3aaNm1aaNiS/v5uiG+++abIbb1a302aNJGrq6tFnfz8fO3atUuhoaGS/j6oRUREaNiwYWrTpo2aNm1q8xM61apVU9OmTdWsWTMNGzZMdevW1caNG83Pf/vtt/Lx8bH6/osCbdu21cGDB632QdOmTeXu7m6u5+/vb/Hc5QfcAhcuXNDEiRM1a9Ysq0/K1a1bVzfccIN++eUXq36Cg4ML3ZcoeQWv7eU/fzk5OdqzZ4/N30dbWrZsqe3btysnJ8fqOR8fHwUGBtr1+375MSo3N1d79+4t8mSYLYsWLZK3t7diY2MLrVNUP6Ghodq1a5fFH4e7du1S9erVLY4rvr6+atq0qW655RZNnTpVf/75pzmUbd++XYMGDdJ9992nFi1aKCAgQMeOHbMahz2/RwX9FCyXX1Fnz75t27atfvzxR9WpU8fqd+3yuzdcaffu3bp06ZLV1S+O9N2sWTOlpqbqxIkT5uePHz+ukydPWhzbHNlXLVu21PPPP68jR47ohx9+MD//7bffqk2bNjbH6sjxJigoyOL5yz8NXmD//v16//33rb5PSVKx30sA2K8y51np77sJNW3aVDfeeKPuuece3XvvvUpJSSl0jDNmzFBkZKQ6d+5caB1HOJIbinpPbtmypfbv31/kd5MFBwfrzTff1KRJk+Tr66vx48eXyDag4iHPlt88W+Dyv2OffPJJBQcHF3psK7gb2eTJk80fML9WoaGhVrn10KFDysjIsHqNx40bpyFDhth872jZsqW2bNlSZF+xsbH65z//qaVLl+qTTz6x+k5cAKWnMufI0jovWqtWLfXp00fLly/X8uXL9dhjj1nV2bZtW5H7YufOnWrYsKEmTpyo8PBw3XjjjTp+/LhVvW+//VbNmjWzmOC+XNu2bXXkyBGb5zQv/wB9QECAxXO2Jg7T0tLM31N8pdDQUHl4eCg1NdWqH1vf7QznRo4svzmyNM+L9ujRQ9WqVdOiRYv06aefWt0Bw545oe3bt6t379565JFH1KpVKzVu3Njqe+Slos9pOnK8udp7kyRt2LBB//3vf/XMM89YPde2bVulp6fL1dXVqi9/f/9Ct7O84ErxSurEiRMaPXq0nnjiCe3bt0+vvvqq+VN3DRo0kLu7u1599VXFxcXphx9+sLqFgj3Onj2rcePGqX79+rrpppuUnp4uScrOztaFCxd07tw5eXt7a+zYserXr5/atm2ru+66Sx999JHWrVunzZs3W7Xp7e2txYsXKz8/3+bB0R5jxoxRu3btNGPGDMXExCg5OVkLFixQYmKiJOmmm27Sww8/rIEDB+rll19WmzZtdPr0aX3xxRdq0aKFevToYdXmuXPnNH36dBmGoY4dO5q39eLFi8rKylJGRoZ8fX2v2nfB7XzGjh2rGjVqKDg4WK+88opOnTqlYcOGSfr7Dfqtt97S559/ruDgYK1cuVLffvut1YROVlaW0tPTlZubq61bt+rYsWNq1qyZ8vPz9fHHH+u5557TwIEDC70ae9y4cbrtttv05JNPaujQoapWrZoOHz6spKQkvfrqqw7t83fffVdhYWHq06ePzeenTp2q4cOHy8fHR3fffbeysrK0Z88enT17VqNHj3aoLxRftWrV9M9//lNjx45VzZo11aBBA82ePVsXLlzQ4MGD7Wrjqaee0quvvqr+/ftrwoQJ8vX11ddff61bb71VN998s8aOHaspU6aoSZMmat26tZYvX679+/dbfUpv4cKFuvHGGxUSEqJ58+bp7NmzVqHjambPnq0NGzYU+UnKovoZNmyYEhIS9PTTT+upp57SkSNHNGXKFI0ePdrij6gLFy4oPT1d2dnZ+ve//63c3FzddNNNkv7+fV23bp3uvfdemUwmTZ482fzJvpJ2tX378MMP66WXXlLv3r01ffp01a9fX6mpqVq3bp3Gjh2r+vXrW7WZnp6uyZMn67bbbpOXl5f52JaXl6e//vpLFy9elJeX11X77tatm0JCQjRgwAAlJCTIMAyNGDFCrVu31p133unQvvrrr7+Unp6uixcvasGCBfL09FSjRo107tw5LVmyRO+++67ee++9QvdTSR5v5syZozFjxtj8FGdx3ksAOKYy59kCly5dMl8pvmXLFvXv399mvQsXLuj111/Xvn37rqm/yzmSG4p6T37ooYf04osvqk+fPoqPj1e9evWUkpKiwMBA8+38fHx8zB9OWrFihW699Vb17du30Lt4oPIiz5bvPCv9/aHwS5cume94dvz4cbVo0cLmLUO3bNmievXqmf9WLgldu3ZVy5Yt9fDDDyshIUG5ubkaNmyYOnfurPDwcHO9n376Sampqfrpp59stjNhwgS1aNFCw4YNU1xcnNzd3fXll1/qwQcfNJ9MLDiZ26hRI7300kvmfirCyUbA2VXmHFka50ULDBkyRD179lReXp4effRRc3l2drY++ugjffHFF3rvvffM+yIjI0OGYej3339X7dq11bRpU6Wmpmr16tVq166dPvnkE61fv96inTVr1mju3LmaPn16oeN4/vnn1bNnTwUFBenBBx9UlSpV9N133+n777+3+aH2oixcuFB9+/a1eav66tWr65lnntGoUaOUn5+v22+/XZmZmdq1a5e8vb0t9gGcHzmyfOfI0jgvKv19Z8lBgwZpwoQJatq0qcUt5+2dE2ratKnWrl2rXbt2yc/PT3PnzlV6erp5wv706dOaN2+edu7cqblz59ocR0kfb2bPnq1XX33V5tdZdO3aVR06dFCfPn00a9Ys3XzzzTp16pQ2btyoPn36WGTicqnkv6Yczq5z587GsGHDjLi4OMPHx8fw8/Mzxo8fb+Tn55vrvPvuu0ajRo0MDw8Po0OHDsaGDRsMSUZKSophGIbx5ZdfGpKMs2fPWrQtyVi/fr1hGIbx6KOPGpIKXaZMmWJeLzEx0WjcuLHh5uZm3HTTTcZbb71VaLuXW79+vXH5j/GUKVOMVq1aWdSxNdb333/fCA0NNdzc3IwGDRoYL730ksU62dnZxvPPP280atTIcHNzMwICAoz77rvP+O6772zu0ylTphS5rY8++qjdfZ8/f94YNmyY4e/vb7i7uxu33XabsWPHDvPzly5dMgYNGmT4+voaNWrUMP75z38a48ePt9juy/e9q6ur0bhxY3M/p0+fNm644QZj7NixxqVLl8zrHD161OI1NgzD+Oabb4xu3boZ3t7eRrVq1YyWLVsaL7zwgvn5hg0bGvPmzbMY/6OPPmr07t3b/Lhz586GyWQyvv32W4v9deXr9M477xitW7c23N3dDT8/P6NTp07GunXrbO5vlJ6LFy8aTz/9tOHv7294eHgYHTt2NL755huregU/L0ePHrV67sCBA0ZUVJRRtWpVo3r16kZkZKTx888/G4ZhGHl5eca0adOMG26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QHj4+PkpPTzcvPj4+Vn3++te/asyYMercubPWr1+vKVOm6JNPPlHM/2Pv3sOiKvf+j3+QY2mSioIUINpB8FhQBolkKh4qO2hiPaHloXiwUOmgeNiW7SLNbWSKbE0zy630pGYmpVhmluRORXe7rNyJ4gFSMCEzObl+f/hjtuPM4AwyqeP7dV1zXc4937Xuew2gH+fLWis62tQ0PnHihCQpNTVVvr6+eueddzR//nwVFhYqJibGrDF+6NAhNWvWTK+88oo++eQTzZkzRx4eHurSpYt+/PFHi/nnzJljOr7evXvrxRdf1N/+9jerx3PTTTeZjmXFihUWr7/77ruKi4tT48aN9fbbb+u9995T06ZN1bt3b6v/0air5ORks5BX4+WXX9ZDDz2k8PBwvffee3rnnXf022+/KSYmRt9//73d+7fn63ammvcwNzdX/fr1M3vt999/V58+fZSfn6833nhDX331lXJzc3X77bfbvR7DMFRVVaWqqiodP35cGzZsUHp6um6//XaFhoaec/udO3fq6aef1tixY7Vq1Sp17NhRw4cP1xdffGGqOXr0qCRpypQpWrNmjd566y21bt1ad9xxh9V70syaNUufffaZZsyYoY8//ljt27fXsGHDtGTJEpWWlprVZmRkyMvLyxTG7FFRUaH+/fvrzjvv1KpVq/TCCy/o1KlTuvfee/XKK6/o4Ycf1po1a/TKK68oJydHd9xxh/744w/T9vX1vQAA+PNcblnxrbfeMssb1rbfuHGj7rzzTpWWlmrBggVaunSprrrqKt1zzz3Kysqqdf+vvfaadu/ebTG+du1axcTEqKCgQDNnztTHH3+sSZMm6ZdffrG5r48++kgDBw5UUlKSXnvtNdO4o/nhTD/99JO6du2qxYsXa/ny5erVq5cmTpyoe+65x+aHXZMmTTK9X8OHDzd7zZGcIJ3+ei9evFhlZWWmse+++05fffWVGjdubFZ74sQJxcbG6u2331ZycrI+/vhjjRs3TosWLVL//v2tflham5YtW5p97WuO5cyxyZMnS5J+/PFHRUdH67vvvtOsWbO0YsUKhYeH69FHH9X06dMt9u1Itj9bzfv76aef6tFHH9WcOXP07LPP2rUteZW8CgAXwuWQH1u1aqX+/fsrMzPTIiPNnj1bgYGBuv/++y22a926da05Uzp9ZcqHH35Y77zzjj766CMNHz5cr776qtVfIKtRn3msrvbt26e0tDSz5qZ0fp/B7dq1S/fee6/ee+89LVmyRB06dFBiYqIef/xxm9vU9nmgo/mxadOmysjI0KlTp0xj2dnZOnbsmMW8RUVFuvXWW7V27Vr95S9/0ccff6zhw4crLS1NI0eOPOexnu3mm282y6H9+vVTQECA2diIESMkOf7/kxUrVig3N1cffvih2rVrpzFjxui9996za13nk2vPzKbHjh3TqlWr9Pbbb2vw4MFmvzRjy5o1azR79mxNnTpVy5cvV9OmTXX//febNYgd/Qw+NTVVBQUFyszM1OrVq9WiRYs6/WzbUlhYqEceeUQPP/ywsrOzlZSU5ND34RNPPKExY8aoZ8+e+uCDD5SRkaHvvvtO0dHRtf5fEZcwA4BTvPXWW4Yki4e3t7eRkZFhVpufn29IMgIDA40//vjDNF5WVmY0bdrU6Nmzp2msd+/exrXXXmuUlpaa7ePJJ580fHx8jKNHjxqGYRgbNmwwJBnPPfec4e3tbRw+fNgwDMM4ceKE0bRpU+O5554zJBn/93//Z9rHqFGjjAYNGhgVFRWmsSNHjhiSjClTpth13IMHDzYaN25sNtawYUNj6NChpue7du0yJBlJSUlmdVu2bDEkGRMmTDAMwzDef/99Q5Jx8803G6dOnTLVlZSUGL6+vkavXr1srqOqqsqoqKgwrr/+emPs2LGm8Zr3ZcOGDWb1V199tTFo0CCL/URFRRk9evQwPa/5Wr311luGYRjG77//bjRt2tS45557zLarrq42OnXqZNx6662msSlTphiSjCNHjpjVfvPNN2b7NAzDGDp0qBESEmJ6/sEHHxgNGjQwnnzySUOSkZ+fbxiGYRQUFBgeHh7GU089ZbbP3377zQgICLB6TNbY83WrsXbtWkOSsWnTJpvrrTmmBQsWmG171113mdXZEhISYvXn59ZbbzUKCwvNamve17O39/HxMfbt22ca++OPP4ymTZsaTzzxhM15q6qqjMrKSqNHjx7G/fffbxqv+bq3adPG7OfDMAzj559/Nho0aGC89tprZnM1a9bMeOyxx6zOY+17YejQoYYkY+HChWa1S5cuNSQZy5cvNxuveY9r/j6pr+8FAMCf43LLijXH+80335iNW9v+tttuM1q0aGH89ttvprGqqiqjffv2xrXXXmvKhTX7rMlFBw4cMBo1amQkJydbZKs2bdoYbdq0MXv/znZmpli9erXh5eVljBkzptbjqlmbtfxgr7/+9a+GJGPJkiVm4z/++KMhyXjnnXesrtEw7M8JhnE6H911111GeHi48frrr5vGExMTjUGDBpler5GWlmY0aNDA4mtWk9Gzs7NNY5KMUaNGWRxbbdnPWoarMXjwYMPb29soKCgwG+/bt69x5ZVXGseOHTMMw/Fsf6azc32Nzp07m2V4W8ir/0VeBYA/x+WWH2vmW7lypWns4MGDhoeHh/HCCy9Y1N92221Gx44dHZqnurraqKysNBYvXmy4u7ubjrWGs/LY2UaNGmWRFc5e+3333WfcdNNNRkxMjBEbG2sxT10/gzvbiBEjDEnGV199ZTZuz+eBjubH4cOHG82aNTNWrVplGu/Tp4/pe+nMfPnEE08YjRo1MstOhmEYM2bMMCQZ3333nWEY//3ef/XVVy2OrV27dmbv3ZnOPpYz1fX/J4ZhGMeOHTP93NTmfHKtYRhW/26QZPTt29c4fvz4OY9VkuHv72+UlZWZxoqKiowGDRoYaWlpNuc912fw3bp1s3ms9v5s16y5YcOGZmOxsbGGJOPTTz81G7f3+zA3N9eQZPztb38zq9u/f79xxRVXnPNrhksTZ4oDTrZ48WJ98803+uabb/Txxx9r6NChGjVqlMVvVErSAw88YLq3hyTTb5x98cUXqq6u1smTJ/Xpp5/q/vvv15VXXmn6za+qqir169dPJ0+e1Ndff222z1tuuUWdOnXSvHnzJElLlixRkyZN1KdPH4v5r7vuOp06dUpvvPGGjh07pqqqKocvz3L8+PFz3rdjw4YNkmRxae5bb71VYWFhprOrvby8JEmPPPKI2SWEmjZtqv79+2vjxo2m9VVVVenll19WeHi4vLy85OHhIS8vL+3evVu7du2yWEPNb9X+9ttvWrBggY4dO2a69N+Z/vjjD7Ovydk2b96so0ePaujQoWZfj1OnTqlPnz765ptvLC7LUjN3zeNc7/Eff/yhMWPG6PHHH1dERITZa2vXrlVVVZWGDBlitk8fHx/Fxsae82ylGvZ83c5cj6Ra35fg4GB5enrqH//4h/bs2aPKykpVVVU5dEZR165dTT87X331lRYsWKAjR47ozjvvtLgEvTWdO3dWcHCw6bmPj49uuOEG7du3z6wuMzNTN998s3x8fOTh4SFPT099+umnVr9v+vfvb/Gbla1bt9bdd9+tjIwM0/H94x//UElJidXLiJ7LgAEDzJ5/9NFHuvrqq3XPPfeYfY07d+6sgIAA09e4vr4XAAB/rsstK57L77//ri1btmjgwIFq1KiRadzd3V0JCQk6cOCA1TMQJCklJUWtWrXSU089ZTb+008/6eeff9bw4cNrzS811qxZowEDBqhz585mZ4ifyZH8cLZTp06ZfW1GjRolT09Pi8sv2pO57M0JZ3ryySc1Z84cGYah0tJSvfPOO1Yvqf7RRx+pffv26ty5s9m+e/fuLTc3N4t9G2ecmVLzcCT7nemzzz5Tjx49FBQUZDb+6KOP6sSJE2Znt0n2Z3trar4eJ06c0IcffqgffvjB7m3Jq6eRVwHgz3W55Mc77rhDnTp1MruMe2Zmptzc3KyeyWzvZ1t5eXnq37+/mjVrJnd3d3l6emrIkCGqrq7WTz/9ZFbrjDxWl8z0ySefaNWqVZozZ44aNDBv6ZzvZ3BnZ9Oa2wbVNZs6kh99fHw0fPhwvfHGG5JOX8Z//fr1+t///V+r++7evbsCAwPN9t23b19Jp8/mru24rF190x51+f9JTTb99ddf9frrr8vNzU3du3e3a77zybWDBg0y/d3wxRdfaNasWdq6dav69Omj8vLyc27fvXt3XXXVVabn/v7+atGihVk2dfQz+LNzo+T4z3ZtmjRpojvvvNNszN7vw48++khubm565JFHzOoCAgLUqVMnsqmLOvcd7QGcl7CwMEVGRpqe9+nTR/v27dNzzz2nRx55RFdffbXptYCAAIvtAwICVFFRoePHj+v48eOqqqrSG2+8YQoLZ7P24ctTTz2l1NRUjRs3TnPmzFFSUpLFfWqk05c0//777zVx4kQ9/fTTdTha6eDBgwoMDKy1pqSkRNLpSyieLTAw0PQPbU3QsFVX8774+voqJSVFc+bM0bhx4xQbG6smTZqoQYMGGjFihNXLFPXs2dPs+fDhwy0ufySdfj87depk81hqLqMycOBAmzVHjx5Vw4YNTc+tfZ1rk5aWpuPHj+ull17Shx9+aHX+W265xeq2ZwdlW+z5utWo+R7z8/OzWdOiRQu98847evbZZ9WmTRuz10JCQuyax9fX1+xnJzo6WuHh4YqKitLf/vY3q/eeOVOzZs0sxry9vc2+H2bOnKmnn35aiYmJevHFF+Xn5yd3d3dNnjzZapCz9r0oSaNHj1aPHj2Uk5OjuLg4zZkzR1FRUbr55pvtOtYaV155pcWlS3/55RcdO3bM9EsiZ6v5etTX9wIA4M91uWXFc/n1119lGIbN/Cf9N0ue6bPPPtP//d//acOGDfLwMP9vbs29HK+99lq71vDAAw/o9ttv14YNG7R69Wrdc889Zq87mh/ONnX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",
       "text/plain": [
        "<Figure size 2000x1500 with 9 Axes>"
       ]
@@ -722,26 +890,19 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "t_ll = [sum(time_ll_insert_sh)/len(time_ll_insert_sh), sum(time_ll_insert_so)/len(time_ll_insert_so), sum(time_ll_find_sh)/len(time_ll_find_sh), sum(time_ll_find_so)/len(time_ll_find_so), sum(time_ll_delete_sh)/len(time_ll_delete_sh), sum(time_ll_delete_so)/len(time_ll_delete_so)]\n",
-    "t_ht = [sum(time_ht_insert_sh)/len(time_ht_insert_sh), sum(time_ht_insert_so)/len(time_ht_insert_so), sum(time_ht_find_sh)/len(time_ht_find_sh), sum(time_ht_find_so)/len(time_ht_find_so), sum(time_ht_delete_sh)/len(time_ht_delete_sh), sum(time_ht_delete_so)/len(time_ht_delete_so)]\n",
-    "t_bt = [sum(time_bt_insert_sh)/len(time_bt_insert_sh), sum(time_bt_insert_so)/len(time_bt_insert_so), sum(time_bt_find_sh)/len(time_bt_find_sh), sum(time_bt_find_so)/len(time_bt_find_so), sum(time_bt_delete_sh)/len(time_bt_delete_sh), sum(time_bt_delete_so)/len(time_bt_delete_so)]\n",
-    "categoris = [\"рандомное добавление\", \"отсортированное добавление\", \"рандомный поиск\", \"отсортированный поиск\", \"рандомное удаление\", \"отсортированное удаление\"]\n",
-    "\n",
-    "plt.figure(\"графики\", figsize = (20,15))\n",
+    "plt.figure(\"Усреднённые графики\", figsize = (20,15))\n",
     "\n",
     "plt.subplot(3,3,1)\n",
-    "plt.bar(categoris[0], t_ll[0], color = \"orange\")\n",
-    "plt.bar(categoris[1], t_ll[1], color = \"green\")\n",
+    "plt.bar(categoris[0], t_ll[0], color = \"#EEDC82\")\n",
+    "plt.bar(categoris[1], t_ll[1], color = \"#88D94C\")\n",
     "\n",
     "plt.title(f\"Время добавление для LinkedList\")\n",
     "\n",
     "plt.ylabel(\"время работы(секунды)\")\n",
     "\n",
     "plt.subplot(3,3,2)\n",
-    "plt.bar(categoris[2], t_ll[2], color = \"orange\")\n",
-    "plt.bar(categoris[3], t_ll[3], color = \"green\")\n",
+    "plt.bar(categoris[2], t_ll[2], color = \"#EEDC82\")\n",
+    "plt.bar(categoris[3], t_ll[3], color = \"#88D94C\")\n",
     "\n",
     "plt.title(f\"Время поиска элементов в LinkedList\")\n",
     "\n",
@@ -749,24 +910,24 @@
     "\n",
     "\n",
     "plt.subplot(3,3,3)\n",
-    "plt.bar(categoris[4], t_ll[4], color = \"orange\")\n",
-    "plt.bar(categoris[5], t_ll[5], color = \"green\")\n",
+    "plt.bar(categoris[4], t_ll[4], color = \"#EEDC82\")\n",
+    "plt.bar(categoris[5], t_ll[5], color = \"#88D94C\")\n",
     "\n",
     "plt.title(f\"Время удаления элементов в LinkedList\")\n",
     "\n",
     "plt.ylabel(\"время работы(секунды)\")\n",
     "\n",
     "plt.subplot(3,3,4)\n",
-    "plt.bar(categoris[0], t_ht[0], color = \"orange\")\n",
-    "plt.bar(categoris[1], t_ht[1], color = \"green\")\n",
+    "plt.bar(categoris[0], t_ht[0], color = \"#EEDC82\")\n",
+    "plt.bar(categoris[1], t_ht[1], color = \"#88D94C\")\n",
     "\n",
     "plt.title(f\"Время добавление для HashTable\")\n",
     "\n",
     "plt.ylabel(\"время работы(секунды)\")\n",
     "\n",
     "plt.subplot(3,3,5)\n",
-    "plt.bar(categoris[2], t_ht[2], color = \"orange\")\n",
-    "plt.bar(categoris[3], t_ht[3], color = \"green\")\n",
+    "plt.bar(categoris[2], t_ht[2], color = \"#EEDC82\")\n",
+    "plt.bar(categoris[3], t_ht[3], color = \"#88D94C\")\n",
     "\n",
     "plt.title(f\"Время поиска элементов в HashTable\")\n",
     "\n",
@@ -774,24 +935,24 @@
     "\n",
     "\n",
     "plt.subplot(3,3,6)\n",
-    "plt.bar(categoris[4], t_bt[4], color = \"orange\")\n",
-    "plt.bar(categoris[5], t_bt[5], color = \"green\")\n",
+    "plt.bar(categoris[4], t_bt[4], color = \"#EEDC82\")\n",
+    "plt.bar(categoris[5], t_bt[5], color = \"#88D94C\")\n",
     "\n",
     "plt.title(f\"Время удаления элементов в HashTable\")\n",
     "\n",
     "plt.ylabel(\"время работы(секунды)\")\n",
     "\n",
     "plt.subplot(3,3,7)\n",
-    "plt.bar(categoris[0], t_bt[0], color = \"orange\")\n",
-    "plt.bar(categoris[1], t_bt[1], color = \"green\")\n",
+    "plt.bar(categoris[0], t_bt[0], color = \"#EEDC82\")\n",
+    "plt.bar(categoris[1], t_bt[1], color = \"#88D94C\")\n",
     "\n",
     "plt.title(f\"Время добавление для BinaryTree\")\n",
     "\n",
     "plt.ylabel(\"время работы(секунды)\")\n",
     "\n",
     "plt.subplot(3,3,8)\n",
-    "plt.bar(categoris[2], t_bt[2], color = \"orange\")\n",
-    "plt.bar(categoris[3], t_bt[3], color = \"green\")\n",
+    "plt.bar(categoris[2], t_bt[2], color = \"#EEDC82\")\n",
+    "plt.bar(categoris[3], t_bt[3], color = \"#88D94C\")\n",
     "\n",
     "plt.title(f\"Время поиска элементов в BinaryTree\")\n",
     "\n",
@@ -799,14 +960,14 @@
     "\n",
     "\n",
     "plt.subplot(3,3,9)\n",
-    "plt.bar(categoris[4], t_bt[4], color = \"orange\")\n",
-    "plt.bar(categoris[5], t_bt[5], color = \"green\")\n",
+    "plt.bar(categoris[4], t_bt[4], color = \"#EEDC82\")\n",
+    "plt.bar(categoris[5], t_bt[5], color = \"#88D94C\")\n",
     "\n",
     "plt.title(f\"Время удаления элементов в BinaryTree\")\n",
     "\n",
     "plt.ylabel(\"время работы(секунды)\")\n",
     "\n",
-    "plt.savefig('analysis.png')\n",
+    "plt.savefig('analysis_midle.png')\n",
     "plt.tight_layout() \n",
     "plt.show()\n"
    ]
diff --git a/pomelovsd/ExitMaze/Builder/Builder.py b/pomelovsd/ExitMaze/Builder/Builder.py
new file mode 100644
index 0000000..789443c
--- /dev/null
+++ b/pomelovsd/ExitMaze/Builder/Builder.py
@@ -0,0 +1,28 @@
+from Core.Cell import Cell
+from Core.Maze import Maze 
+from Builder.BuilderInterface import MazeBuilders
+
+class TextFileMazeBuilder(MazeBuilders):
+
+    def build_from_file(self, filename):
+        grid = []
+        start = None
+        exit = None
+
+        with open(filename, "r", encoding="utf-8") as f:
+            lines = [line.rstrip("\n") for line in f]
+        
+        for y, line in enumerate(lines): 
+            row = []
+            for x, ch in enumerate(line):
+                cell = Cell(x, y, isWall = (ch == "#"), isStart = (ch == "S"), isExit = (ch == "E"))
+                if (ch == "S"):
+                    start = cell
+                if (ch == "E"):
+                    exit = cell 
+                
+                row.append(cell)
+
+            grid.append(row)
+        
+        return Maze(grid, start, exit)
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Builder/BuilderInterface.py b/pomelovsd/ExitMaze/Builder/BuilderInterface.py
new file mode 100644
index 0000000..d9c2dff
--- /dev/null
+++ b/pomelovsd/ExitMaze/Builder/BuilderInterface.py
@@ -0,0 +1,6 @@
+from abc import ABC, abstractmethod
+
+class MazeBuilders(ABC):
+    @abstractmethod
+    def build_from_file(self, filename):
+        pass
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Builder/__init__.py b/pomelovsd/ExitMaze/Builder/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pomelovsd/ExitMaze/Core/Benchmark.py b/pomelovsd/ExitMaze/Core/Benchmark.py
new file mode 100644
index 0000000..0977605
--- /dev/null
+++ b/pomelovsd/ExitMaze/Core/Benchmark.py
@@ -0,0 +1,20 @@
+from MazeSolver.Solver import MazeSolver
+import csv
+
+def RunBenchmark(maze, strategies, repeats = 5):
+    rows = []
+    
+    for name, strategy in strategies.items():
+
+        solver = MazeSolver(maze, strategy)
+
+        for _ in range(repeats):
+            stats, _  = solver.solve()
+
+            rows.append([name, stats.time_ms, stats.visited_cells, stats.path_length])
+
+    with open("results.csv", "w", newline="") as f:
+        writer = csv.writer(f)
+        writer.writerow(["strategy", "time_ms", "visited", "path_length"])
+        writer.writerows(rows)
+    
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Core/Cell.py b/pomelovsd/ExitMaze/Core/Cell.py
new file mode 100644
index 0000000..dd79f48
--- /dev/null
+++ b/pomelovsd/ExitMaze/Core/Cell.py
@@ -0,0 +1,12 @@
+class Cell:
+    def __init__(self, x = 0, y = 0, isWall = False, isStart = False, isExit = False):
+        self.x = x
+        self.y = y
+        self.isWall = isWall
+        self.isStart = isStart
+        self.isExit = isExit 
+    
+    # Возращает True, если посаседству стена
+    def isPassable(self):  
+        return not self.isWall
+
diff --git a/pomelovsd/ExitMaze/Core/Maze.py b/pomelovsd/ExitMaze/Core/Maze.py
new file mode 100644
index 0000000..0534194
--- /dev/null
+++ b/pomelovsd/ExitMaze/Core/Maze.py
@@ -0,0 +1,22 @@
+class Maze:
+    def __init__(self, grid, start=None, exit=None):
+        self.grid = grid
+        self.start = start
+        self.exit = exit
+        self.height = len(grid)
+        self.width = len(grid[0]) if grid else 0
+
+    def getCell(self, x, y):
+        if 0 <= x < self.width and 0 <= y < self.height:
+            return self.grid[y][x]
+        return None
+
+    def getNeighbors(self, cell):
+        directions = [(0,1),(1,0),(0,-1),(-1,0)]
+        result = []
+        for dx, dy in directions:
+            nx, ny = cell.x + dx, cell.y + dy
+            neighbor = self.getCell(nx, ny)
+            if neighbor and neighbor.isPassable():
+                result.append(neighbor)
+        return result
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Core/__init__.py b/pomelovsd/ExitMaze/Core/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pomelovsd/ExitMaze/MazeSolver/SearchStats.py b/pomelovsd/ExitMaze/MazeSolver/SearchStats.py
new file mode 100644
index 0000000..580ff4e
--- /dev/null
+++ b/pomelovsd/ExitMaze/MazeSolver/SearchStats.py
@@ -0,0 +1,5 @@
+class SearchStats: 
+    def __init__(self, time_ms, visited_cells, path_length):
+        self.time_ms = time_ms
+        self.visited_cells = visited_cells
+        self.path_length = path_length
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/MazeSolver/Solver.py b/pomelovsd/ExitMaze/MazeSolver/Solver.py
new file mode 100644
index 0000000..59cf71f
--- /dev/null
+++ b/pomelovsd/ExitMaze/MazeSolver/Solver.py
@@ -0,0 +1,20 @@
+from .SearchStats import SearchStats
+import time
+
+class MazeSolver:
+
+    def __init__(self, maze, strategy):
+        self.maze = maze
+        self.strategy = strategy
+    
+    def setStrategy(self, strategy):
+        self.strategy = strategy 
+
+    def solve(self):
+        start = time.perf_counter()
+
+        path, visited = self.strategy.findPath(self.maze, self.maze.start, self.maze.exit)
+
+        end = time.perf_counter()
+
+        return SearchStats((end-start)*1000, visited, len(path)), path
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/MazeSolver/__init__.py b/pomelovsd/ExitMaze/MazeSolver/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pomelovsd/ExitMaze/Mazes/empty.txt b/pomelovsd/ExitMaze/Mazes/empty.txt
new file mode 100644
index 0000000..db91695
--- /dev/null
+++ b/pomelovsd/ExitMaze/Mazes/empty.txt
@@ -0,0 +1,10 @@
+##########
+#S       #
+#        #
+#        #
+#        #
+#        #
+#        #
+#        #
+#       E#
+##########
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Mazes/large.txt b/pomelovsd/ExitMaze/Mazes/large.txt
new file mode 100644
index 0000000..ea0442b
--- /dev/null
+++ b/pomelovsd/ExitMaze/Mazes/large.txt
@@ -0,0 +1,103 @@
+#######################################################################################################
+#S          #         # #             #   #     # #       #       #   #     #       #       #         #  
+# ### ##### # ##### # # ### ### ####### ####### # # ### ### ### # ### ### ### ### # ### # # # #########  
+# #   #       # #   #   #     #       #       #       # #   #   #   #   # # # #   #     # #   # #   # #  
+### ### ##### # ##### ### # # ##### ####### ### ### # # # ####### # ### # # ### # ### ### ##### # ### #  
+#     # #   # # #   # #   # # # #     #   #   #   # # #     #     #     #   # # #   #   #     #     # #  
+# ### ### # # # # ### ### # ### ##### # ### # # # # # ####### # # ##### ### # ### ### # ##### ### # # #  
+# #     # #   # #       # # #     #   #   # # # # # # #       # # # #   #   #   #   # # #     #   #   #  
+# ### ####### # ######### ##### # # ### # ### ### ### # ####### # # ### # ##### ##### ########### # # #  
+#   # #   # # #           #     # # # # # #       #   # #       #   #     # # # # # #   #       # # # #  
+# ##### ### # ##### # ####### ### # # # ##### # ##### ##### # ######### # # # # # # ### ### # ### # ###  
+# #     #   #   #   #   #       # #   #   #   #     # #   # #     #     # # #     #   # # # #     # # #  
+# # ####### # ##### # ### ##### ##### # ####### ######### # ##### # # # # # ### # ### # # # ### ### # #  
+# #     #         # # # # # # # # # #     #     #   #   # # # #   # # # # #     #       # # #     # # #  
+# ##### ######### ##### # # # # # # ##### ##### ### # # # ### ####### ########### # # # # ### # ### # #  
+# #     #       #             #   # # # #     #   # # #                   #       # # #   #   # #     #  
+####### # ####### ### ##### # # # # # # # ######### ##### # # # # ####### # ### ### # ### ########### #  
+# #   # #       # #   # #   # # #   #     #   #   #     # # # # # #   #       #   # # #   #     #     #  
+# ### # # # ####### # # # ##### ### # ### # ### ### # ### ####### ### # # ### ##### ### ### ######### #  
+#     #   #       # # #       # # # # #         # # # #   # # #   # #   # #   #     #   #   #       # #  
+# ### ### # ##### # ### ####### # ####### ##### # # # # # # # # # # ######### # ########### # ### # # #  
+# #       #     #   #   #       #     #     # #     # # # # #   #         #   # #         #   # # #   #  
+# # # ####### ### ####### ### # ##### # ##### ### # # # # # # # # ### # # ####### ### ### # ### ### ###  
+# # #     #     #   #     #   # #         #       # #   # #   # #   # # #   #   #   #   # #   # #     #  
+##### ### ##### ####### # # ### ### ##### ### ############# ### ### ### ####### ##### ####### # # #####  
+#       #     # #     # # #   #   #     #   # #     # #   #   # # # # # # #   #     # #     #   #   # #  
+# ######### ##### # # ### ### ### ####### ##### # ### ### ### # # # # # # # ##### # # # # # # ### # # #  
+#   #       # #   # # #   # #   #               # #     #     # # # # # # #   # # # # # # # # #   #   #  
+# ####### ### ### ### ##### # # # # ####### # ##### ############# ### ### ### # # # # # ######### ### #  
+# #   # # # #   #   #         # # # #     # # #           #     # #   #       #   # #   # #       # # #  
+# ### # ### ### # ### ##### ### # ##### # ####### # ### ### ##### ### # ######### # ### # ### ### # # #  
+#     #         #   #   #     # # #   # #   #     # # # #     #           #       #       #   #   # # #  
+### # ### ######### # ##### # ### ### ### # # ##### # # # ### # # ####### # # ### ##### ### # ##### # #  
+#   #   #     # # # # #     #   #         #       #   #     #   #   #     # #   # # #     # #       # #  
+### # ### ##### # ########### ##### ####### ########### # ### ######### # ### # # # # ### # # ##### # #  
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+#   #     #   #     #     # # # # # #       # # # # #       # #   # # #   #     # # #       # # # # # #  
+# # # # ############# ##### # # ### ### ### ### ### # # ####### # ### ### ##### ### ### ### ### # # # #  
+# #   #   # # # # # #   #     # #     #   # # # #   # #   # #   #     #   # #   #   #     # #       # #  
+# # ##### # # # # # # # ### ### # ### # ##### # ### ### ### ##### # ### ### ### ### ### ##### # ### ###  
+# #     #   #         # #     # # # #   #   #   #   #       #   # # # #     #         # # #   #   #   #  
+### ### ### ### # ### ### ##### ### # # # # ### # ##### ##### # # # # ### ######### ### # # # ##### ###  
+# # #     # #   # #       #       #   #   # # # # #   #       # # #     #   #         # #   # # #   # #  
+# # # # ### # ########### # # ### ######### # # ### # # # ##### # ### ##### # # ### ##### ##### # # # #  
+# # # # #       # # # #     #   #         # # #     #   #     #   #   #     # #   # #     #   # # #   #  
+# # ##### ### ### # # # ####### ############# ##### # ### ############### ##### # ##### ##### # # #####  
+#   #   # #     #     #       # # # #   #       #   #   # #   #   #       # # # # # # #   #     #     #  
+# ### ####### # ##### ### # # # # # # # # ##### # ##### ### ##### # # ##### # ### # # ### # # ### #####  
+# # #   #     # #     #   # # # # #   #     #     # #   #     #   # #     # # #           # #   #     #  
+### # ##### # ### ### ### ##### # # # ### # ### ### ##### # ### ### ##### # # ####### # ### ### # ### #  
+#   #       # #   #   # # #   # #   # # # # #       #     #   #     # #   #   #     # # #   #   #   # #  
+# # # ####### # ### # # # ### ####### # ################# # # ##### # # ### # # ### # # # ##### ### # #  
+# #   #   #   #   # # # # #         #   #   # #           # #   #   #   #   #     #   # # #       # # #  
+### ##### # ### ### ### # # ########### # # # ##### ##### ##### ### ############# # # ### ### ### # # #  
+#   #             #     #         #       # #   #   #   #     # #   #     #   #   # #   # # # #     # #  
+##### ### ##### ### ### # ############# ### # ##### # # # ######### ### ### ### ### # ##### ### #######  
+#       # #     # # # # # # #       #     #       # # # #   #     #     # # #   # # # #   #   #       #  
+################# ### # ### # # ### # ####### ### ### ### ### # ####### # # # ### ##### # # ######### #  
+#         #     #     #     # #   # # # #     # # #       #   # #           #     # #   # #   #     # #  
+####### ##### ##### # ### ### ######### ### # # ######### ### ### ### ##### # ### # # # ### # ##### ###  
+#   #   #       # # # #       # # #   #     # #   #     #       #   #     # #   #     # #   #     # # #  
+# ### ##### ##### ### # ### # # # # ### ### # # ##### ##### ### # # ##### # # # ######### # ##### # # #  
+# # #         # # #       # # #     #   # # # #       #     #   # #     # # # #       #   #   #     # #  
+# # ####### ### # ##### ####### # ####### ####### # # ######### ### ####### ##### ### # ######### ### #  
+#   #   # #     #         #   # # #             # # # # #   # #   # #               # #       # # #   #  
+# ### ### ##### ##### # ##### ### # ### # ########### # ### # ### # ######### # ### # ##### ### ### ###  
+#         #     # # # # #   #       #   # #       #           #   # #   #     #   # # #       #       #  
+### ### ####### # # # ##### ##### ########### # # # # ####### # # # # # ########### # # # ######### ###  
+#   #   #     # # # #             #         # # #   # #   #   # #   # # # # #   #   #   #   # #       #  
+# ##### # # ### # # ### # ##### ### # ### ### ##### ### # ### ##### # ### # # ############### ####### #  
+# #   # # #   # # #   # # #         #   #     #   # #   #         #   # # #     #   #         #     # #  
+# # # # ##### # # # ##### # ######### # # ####### ##### ### # ### ### # # # ### # ####### ### # ### # #  
+# # #       # #   #     # # #         # # # #         # #   #   # #       #   #     #     # #     #   #  
+##### ##### # ### # ### # ##### ##### ##### ##### ##### # # ########### ##### ############# ### ##### #  
+#     # #           #         #     # #     #     #     # #       # # #     #   #     #             # #  
+### ### # # ############### # # ### # # ####### ### ### # ### ### # # ##### # # # ####### ####### # ###  
+#     #   #   #   #     # # # # # # # # #           #   #   #   #     # #     # #       #   # # # #   #  
+# ####### ##### # ### # # # ##### ### # ### # ### # ########### ### # # ##### # ##### ####### # ##### #  
+#       # # # # #     # # #     #   # # #   # #   #         #     # #     #   #       #   #   #   #   #  
+########### # ######### # ### # ### ### ### # ### ####################### # ### # ####### # ### # ### #  
+#   #         #   #   #     # # #       #   # # # #                 #     # #   # #         #   # # # #  
+# # ### ####### # ### ### ### ### ########### # ##### # ##### ### ### # ### # ##### # ########### # # #  
+# #   # #       # #   #     # # # # # #     #     # # # #     #   #   #   # #     # # #   #     # #   #  
+### ### # ####### # # # ### ### # # # ### # # # ### # ##### # # ########### ######### # ### ### # ### #  
+# #         #   # # # # # #   #   #   #   #   #   #   #   # # # #     # #     #       #       #       #  
+# # # ####### # ### # # # ### # ### ### ### ######### # ### # ### # ### # # ### ##### ####### ### # # #  
+#   #         #   # # # # # # #       # # # # #       #     # #   #       #         # # #   #   # # # #  
+### ##### ##### # # # # # # ##### # ### # ### ####### ####### ##### ### # ### ##### # # ### # # #######  
+# #   #   #   # #   # #           # #       #               #   #     # # #   #   # #     # # # #     #  
+# # # ### # ####### # ####### # ##### # ### # ### # # ####### ##### # ##### ##### # ### # # ### ##### #  
+#   #   #     # #   # #     # #     # # # # # # # # #   # # # #   # # # # #   #     #   # #   # #     #  
+### # ######### # ##### # ### ##### ### # ##### ##### ### # ##### ### # # ############# # # ### # ### #  
+#   #   # # # # #   #   # # #   #   #     # #   #   #         #   #   # #         #   # #     #     # #  
+# ### # # # # # ### # # ### ### ##### ### # # # ### ### # # # # # ### # ### # # # # # # ##### # ### ###  
+#   # #       #   #   #         #       # #   #   #   # # # #   # #     # # # # # # # #   # #   #     #  
+# ####### ### # ### # ### # ### # # ####### # ##### # ### ### ##### # # # # ### # ### ### # ### ##### #  
+#     #   #   #   # # #   #   #   #     #   #       # #   #   #     # #   # #   # #   #   #   #     # #  
+# ### ##### ### # # # ##### ############### ### # ### # # ### ##### ### # ##### ### ### ### ### # #####  
+#   # #     #   #   #     # #     #   #       # # #     #   # #     # # # #     # #       # #   #     #  
+### # ### ####### ### ### ##### # # # ##### ##### ######### ### ##### # ####### # # ### ### # ### #####  
+#   # #         # #   #         #   #         #       #     #         #   #           # #       #    E#  
+####################################################################################################### 
diff --git a/pomelovsd/ExitMaze/Mazes/medium.txt b/pomelovsd/ExitMaze/Mazes/medium.txt
new file mode 100644
index 0000000..f2c0b22
--- /dev/null
+++ b/pomelovsd/ExitMaze/Mazes/medium.txt
@@ -0,0 +1,53 @@
+#####################################################
+#S  # # #         #     #       # #     #           #
+# ### # ### ########### # ### ### # ### # ###########
+#     #     #   #     #     #     #   #     #       #  
+### ### # # ### ### ##### # ##### ### ### ##### ### #  
+#   #   # # #       #     #     #   #   #     #   # #  
+# ######### ##### # # ### ####### # ####### # ##### #  
+#   # #     # # # #   # # #     # # #     # #       #  
+# ### # ##### # # ### # ### # # ##### ##### # ##### #  
+#     #   #       #     # # # # # #   #     # #   # #  
+### ### # # ### # ### ### # ### # # # ##### # # # ###  
+#     # #     # #   #     #   # #   # # #   #   #   #  
+# ##### ####### ### ### ##### ### # ### # ### ### ###  
+# #   # #       # # #       #   # #       #     # # #  
+# ### ####### ### ### # ##### ######### ### ##### # #  
+#   #   #     # # # # # #     # # #       # #   #   #  
+# # # ### ### # # # ####### # # # ######### ### ### #  
+# # #     # # #   #     #   # # #   # #   #   # # # #  
+### ####### # # ### # # ### ### # ### # ##### # # # #  
+#   #       # # #   # #     #   # #   #       #   # #  
+# ####### # # # ### # # # # # ### # # ### # # ### # #  
+#     #   # #     # # # # #     #   #     # #     # #  
+### ### ### ##### ### ### ### ### ##### ### ####### #  
+#       #   #     #     # #   #   #       # #       #  
+##### # # # ### # # ####### ### # ##### ### # ### ###  
+#   # # # #   # # #       #   # # # #   #   #   #   #  
+# ####### ### ### # ########### # # ##### # ### ### #  
+# #     # # #   #     #   #   # # #   # # # #     # #  
+# # ### ### ### ### ### ### # # ### # # ### ##### # #  
+# # #     #   #       # #   # # # # # #       # # # #  
+# ### # ##### ### ##### ##### # # ### # ### ### ### #  
+# #   #   #       #   # #   #     #   # #   #       #  
+# ##### ######### ### # ### # ### # ### ####### #####  
+#   # #                       # # #           #     #  
+### # # ##### ##### ########### # ### ### # ### #####  
+# # #   # #   #     #       #     #     # #   #   # #  
+# # # ### ##### ##### # ############# ##### # ##### #  
+#         # #       # # #     #     # #     #       #  
+# ### # # # ### ### ### ##### # # ##### ##### #######  
+#   # # #     #   #   #   #     # #     # #         #  
+### # ##### ### # ####### ####### # # # # ##### # ###  
+#   # #   #   # #   #       # #     # # # #   # # # #  
+### ### ### ####### ####### # ### # ### # # ### ### #  
+#   # # #     # # #   #   #   #   # # # # #   #     #  
+# # # # ### # # # # ##### ### ####### # # # ### # # #  
+# # # #   # # #   # # #     # #     # # #   # # # # #  
+# # # ### ### # ##### # ### # # ### # ##### # # ### #  
+# # #   # #         #     #   #   #             # # #  
+### # # # # # # ##### # ### ### ##### ### # ##### ###  
+# # # # #   # # #     # #   #   #     # # #   #     #  
+# # # ### ### ####### # ### # ### # ### ### ### ### #  
+#   #     #     #     # #       # #   #           #E#  
+##################################################### 
diff --git a/pomelovsd/ExitMaze/Mazes/no_exit.txt b/pomelovsd/ExitMaze/Mazes/no_exit.txt
new file mode 100644
index 0000000..3dc8a6b
--- /dev/null
+++ b/pomelovsd/ExitMaze/Mazes/no_exit.txt
@@ -0,0 +1,10 @@
+##########
+#S       #
+# #      #
+#  #     #
+#   #    #
+#    #   #
+#     #  #
+#      # #
+#        #
+##########
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Mazes/small.txt b/pomelovsd/ExitMaze/Mazes/small.txt
new file mode 100644
index 0000000..76aa11a
--- /dev/null
+++ b/pomelovsd/ExitMaze/Mazes/small.txt
@@ -0,0 +1,10 @@
+##########
+#S       #
+# #      #
+#  #     #
+#   #    #
+#    #   #
+#     #  #
+#      # #
+#       E#
+##########
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Observer_Command/Command.py b/pomelovsd/ExitMaze/Observer_Command/Command.py
new file mode 100644
index 0000000..6c5f048
--- /dev/null
+++ b/pomelovsd/ExitMaze/Observer_Command/Command.py
@@ -0,0 +1,6 @@
+class Command:
+    def execute(self):
+        pass
+    def undo(self):
+        pass
+    
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Observer_Command/ConsoleView.py b/pomelovsd/ExitMaze/Observer_Command/ConsoleView.py
new file mode 100644
index 0000000..f619378
--- /dev/null
+++ b/pomelovsd/ExitMaze/Observer_Command/ConsoleView.py
@@ -0,0 +1,31 @@
+from .Observer import Observer 
+
+class ConsoleView(Observer):
+
+    def update(self, event):
+        print("[событие]", event)
+
+    def render(self, maze, path = None):
+        path = set(path or [])
+
+        for row in maze.grid:
+            line = ""
+
+            for cell in row:
+
+                if cell in path:
+                    line += "*"
+                
+                elif cell.is_wall:
+                    line += "#"
+
+                elif cell.is_start:
+                    line += "S"
+
+                elif cell.is_exit:
+                    line += "E"
+                
+                else:
+                    line += " "
+
+            print(line)
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Observer_Command/MoveCommand.py b/pomelovsd/ExitMaze/Observer_Command/MoveCommand.py
new file mode 100644
index 0000000..e107ed5
--- /dev/null
+++ b/pomelovsd/ExitMaze/Observer_Command/MoveCommand.py
@@ -0,0 +1,15 @@
+from .Command import Command
+
+class MoveCommand(Command):
+    
+    def __init__(self, player, target):
+        self.player = player
+        self.target = target
+        self.previous = None
+
+    def execute(self):
+        self.previous = self.player.current
+        self.player.current = self.target
+
+    def undo(self):
+        self.player.current = self.previous 
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Observer_Command/Observer.py b/pomelovsd/ExitMaze/Observer_Command/Observer.py
new file mode 100644
index 0000000..a72df9f
--- /dev/null
+++ b/pomelovsd/ExitMaze/Observer_Command/Observer.py
@@ -0,0 +1,3 @@
+class Observer:
+    def update(self, event):
+        pass
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Observer_Command/__init__.py b/pomelovsd/ExitMaze/Observer_Command/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pomelovsd/ExitMaze/Strategies/AStar.py b/pomelovsd/ExitMaze/Strategies/AStar.py
new file mode 100644
index 0000000..5de7317
--- /dev/null
+++ b/pomelovsd/ExitMaze/Strategies/AStar.py
@@ -0,0 +1,41 @@
+from Strategies.strat import PathFindingStrategy
+from Strategies.path import restore
+import heapq
+
+class AStar(PathFindingStrategy):
+    
+    def heuristic(self, a, b):
+        return abs(a.x - b.x) + abs(a.y - b.y)
+    
+    def findPath(self, maze, start, exit):
+        if exit is None:
+            return [], 0
+
+        heap = []
+        counter = 0
+        heapq.heappush(heap, (0, counter, start))
+        counter += 1
+        
+        parent = {}
+        g = {start: 0}
+        visited = set()
+
+        while heap:
+            _, _, current = heapq.heappop(heap)
+
+            if current == exit:
+                break
+
+            visited.add(current)
+
+            for n in maze.getNeighbors(current):
+                tentative = g[current] + 1
+
+                if n not in g or tentative < g[n]:
+                    g[n] = tentative
+                    priority = tentative + self.heuristic(n, exit)
+                    heapq.heappush(heap, (priority, counter, n))
+                    counter += 1
+                    parent[n] = current
+
+        return restore(parent, start, exit), len(visited)
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Strategies/BFS.py b/pomelovsd/ExitMaze/Strategies/BFS.py
new file mode 100644
index 0000000..ec39a2a
--- /dev/null
+++ b/pomelovsd/ExitMaze/Strategies/BFS.py
@@ -0,0 +1,26 @@
+from Strategies.strat import PathFindingStrategy
+from Strategies.path import restore
+from collections import deque
+
+class BFS(PathFindingStrategy):
+    def findPath(self, maze, start, exit):
+        if exit is None:
+            return [], 0
+
+        queue = deque([start])
+        visited = {start}
+        parent = {}
+
+        while queue:
+            current = queue.popleft()
+
+            if current == exit:
+                break
+
+            for n in maze.getNeighbors(current):
+                if n not in visited:
+                    visited.add(n)
+                    parent[n] = current
+                    queue.append(n)
+
+        return restore(parent, start, exit), len(visited)
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Strategies/DFS.py b/pomelovsd/ExitMaze/Strategies/DFS.py
new file mode 100644
index 0000000..19ef836
--- /dev/null
+++ b/pomelovsd/ExitMaze/Strategies/DFS.py
@@ -0,0 +1,25 @@
+from Strategies.strat import PathFindingStrategy
+from Strategies.path import restore
+
+class DFS(PathFindingStrategy):
+    def findPath(self, maze, start, exit):
+        if exit is None:
+            return [], 0
+
+        stack = [start]
+        visited = {start}
+        parent = {}
+
+        while stack:
+            current = stack.pop()
+
+            if current == exit:
+                break
+
+            for n in maze.getNeighbors(current):
+                if n not in visited:
+                    visited.add(n)
+                    parent[n] = current
+                    stack.append(n)
+
+        return restore(parent, start, exit), len(visited)   
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Strategies/__init__.py b/pomelovsd/ExitMaze/Strategies/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pomelovsd/ExitMaze/Strategies/path.py b/pomelovsd/ExitMaze/Strategies/path.py
new file mode 100644
index 0000000..567e3c5
--- /dev/null
+++ b/pomelovsd/ExitMaze/Strategies/path.py
@@ -0,0 +1,14 @@
+def restore(parent, start, exit):
+    if exit not in parent and start != exit:
+        return []
+    
+    path = []
+    current = exit
+        
+    while current != start:
+        path.append(current)
+        current = parent[current]
+        
+    path.append(start)
+    path.reverse()
+    return path
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/Strategies/strat.py b/pomelovsd/ExitMaze/Strategies/strat.py
new file mode 100644
index 0000000..82d5190
--- /dev/null
+++ b/pomelovsd/ExitMaze/Strategies/strat.py
@@ -0,0 +1,6 @@
+from abc import ABC, abstractmethod
+
+class PathFindingStrategy(ABC):
+    @abstractmethod
+    def findPath(self, maze, start, exit):
+        pass
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/analysis.png b/pomelovsd/ExitMaze/analysis.png
new file mode 100644
index 0000000..1d9699e
Binary files /dev/null and b/pomelovsd/ExitMaze/analysis.png differ
diff --git a/pomelovsd/ExitMaze/main.ipynb b/pomelovsd/ExitMaze/main.ipynb
new file mode 100644
index 0000000..21e7aa7
--- /dev/null
+++ b/pomelovsd/ExitMaze/main.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a1dff6b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import csv\n",
+    "import matplotlib.pyplot as plt\n",
+    "from statistics import mean\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "66bfd079",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from Builder.Builder import TextFileMazeBuilder\n",
+    "from Core.Benchmark import RunBenchmark\n",
+    "from MazeSolver.Solver import MazeSolver\n",
+    "from Observer_Command.ConsoleView import ConsoleView\n",
+    "from Strategies.BFS import BFS\n",
+    "from Strategies.DFS import DFS\n",
+    "from Strategies.AStar import AStar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "50c7010d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "builder = TextFileMazeBuilder()\n",
+    "\n",
+    "mazes = {\n",
+    "    \"small\": builder.build_from_file(\"Mazes/small.txt\"),\n",
+    "    \"medium\": builder.build_from_file(\"Mazes/medium.txt\"),\n",
+    "    \"large\": builder.build_from_file(\"Mazes/large.txt\"),\n",
+    "    \"empty\": builder.build_from_file(\"Mazes/empty.txt\"),\n",
+    "    \"no_exit\": builder.build_from_file(\"Mazes/no_exit.txt\"),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7326fe7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "strategies = {\n",
+    "    \"BFS\": BFS(),\n",
+    "    \"DFS\": DFS(),\n",
+    "    \"A*\": AStar()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8d47b4cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Лабиринт: small\n",
+      "Testing BFS\n",
+      "BFS: time=0.21313328579708468 ms | visited=58.0 | path_len=15.0\n",
+      "Testing DFS\n",
+      "DFS: time=0.0768381428965118 ms | visited=31.0 | path_len=19.0\n",
+      "Testing A*\n",
+      "A*: time=0.2827478572596322 ms | visited=57.0 | path_len=15.0\n",
+      "Лабиринт: medium\n",
+      "Testing BFS\n",
+      "BFS: time=5.220513428477196 ms | visited=1263.0 | path_len=173.0\n",
+      "Testing DFS\n",
+      "DFS: time=4.18784342861857 ms | visited=1229.0 | path_len=173.0\n",
+      "Testing A*\n",
+      "A*: time=4.219951571420617 ms | visited=806.0 | path_len=173.0\n",
+      "Лабиринт: large\n",
+      "Testing BFS\n",
+      "BFS: time=11.833781999874711 ms | visited=3918.0 | path_len=269.0\n",
+      "Testing DFS\n",
+      "DFS: time=5.629428999977141 ms | visited=1905.0 | path_len=269.0\n",
+      "Testing A*\n",
+      "A*: time=10.02670385716036 ms | visited=2040.0 | path_len=269.0\n",
+      "Лабиринт: empty\n",
+      "Testing BFS\n",
+      "BFS: time=0.19871557131929357 ms | visited=64.0 | path_len=15.0\n",
+      "Testing DFS\n",
+      "DFS: time=0.13947814282541263 ms | visited=64.0 | path_len=29.0\n",
+      "Testing A*\n",
+      "A*: time=0.28600042846197277 ms | visited=63.0 | path_len=15.0\n",
+      "Лабиринт: no_exit\n",
+      "Testing BFS\n",
+      "BFS: time=0.18080571427552578 ms | visited=58.0 | path_len=0.0\n",
+      "Testing DFS\n",
+      "DFS: time=0.19448514272621 ms | visited=58.0 | path_len=0.0\n",
+      "Testing A*\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'NoneType' object has no attribute 'x'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 19\u001b[0m\n\u001b[1;32m     16\u001b[0m solver \u001b[38;5;241m=\u001b[39m MazeSolver(maze, strategy)\n\u001b[1;32m     18\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[0;32m---> 19\u001b[0m stats, path \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m     22\u001b[0m elapsed_ms \u001b[38;5;241m=\u001b[39m (end_time \u001b[38;5;241m-\u001b[39m start_time) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m\n",
+      "File \u001b[0;32m~/2026-rff_mp/pomelovsd/ExitMaze/MazeSolver/Solver.py:16\u001b[0m, in \u001b[0;36mMazeSolver.solve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msolve\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     14\u001b[0m     start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[0;32m---> 16\u001b[0m     path, visited \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfindPath\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaze\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaze\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaze\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m     end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m SearchStats((end\u001b[38;5;241m-\u001b[39mstart)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m1000\u001b[39m, visited, \u001b[38;5;28mlen\u001b[39m(path)), path\n",
+      "File \u001b[0;32m~/2026-rff_mp/pomelovsd/ExitMaze/Strategies/AStar.py:33\u001b[0m, in \u001b[0;36mAStar.findPath\u001b[0;34m(self, maze, start, exit)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m g \u001b[38;5;129;01mor\u001b[39;00m tentative \u001b[38;5;241m<\u001b[39m g[n]:\n\u001b[1;32m     32\u001b[0m     g[n] \u001b[38;5;241m=\u001b[39m tentative\n\u001b[0;32m---> 33\u001b[0m     priority \u001b[38;5;241m=\u001b[39m tentative \u001b[38;5;241m+\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheuristic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexit\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     34\u001b[0m     heapq\u001b[38;5;241m.\u001b[39mheappush(heap, (priority, counter, n))\n\u001b[1;32m     35\u001b[0m     counter \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
+      "File \u001b[0;32m~/2026-rff_mp/pomelovsd/ExitMaze/Strategies/AStar.py:8\u001b[0m, in \u001b[0;36mAStar.heuristic\u001b[0;34m(self, a, b)\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mheuristic\u001b[39m(\u001b[38;5;28mself\u001b[39m, a, b):\n\u001b[0;32m----> 8\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mabs\u001b[39m(a\u001b[38;5;241m.\u001b[39mx \u001b[38;5;241m-\u001b[39m \u001b[43mb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mabs\u001b[39m(a\u001b[38;5;241m.\u001b[39my \u001b[38;5;241m-\u001b[39m b\u001b[38;5;241m.\u001b[39my)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'x'"
+     ]
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "\n",
+    "N = 7\n",
+    "\n",
+    "for maze_name, maze in mazes.items():\n",
+    "    print(f\"Лабиринт: {maze_name}\")\n",
+    "\n",
+    "    for strategy_name, strategy in strategies.items():\n",
+    "        total_time = 0\n",
+    "        total_visited = 0\n",
+    "        total_path_len = 0\n",
+    "\n",
+    "        print(f\"Testing {strategy_name}\")\n",
+    "\n",
+    "        for _ in range(N):\n",
+    "            solver = MazeSolver(maze, strategy)\n",
+    "\n",
+    "            start_time = time.perf_counter()\n",
+    "            stats, path = solver.solve()\n",
+    "            end_time = time.perf_counter()\n",
+    "\n",
+    "            elapsed_ms = (end_time - start_time) * 1000\n",
+    "\n",
+    "            total_time += elapsed_ms\n",
+    "            total_visited += stats.visited_cells       \n",
+    "            total_path_len += len(path)                \n",
+    "\n",
+    "        avg_time = total_time / N\n",
+    "        avg_visited = total_visited / N\n",
+    "        avg_path_len = total_path_len / N\n",
+    "\n",
+    "        results.append({\n",
+    "            \"maze\": maze_name,\n",
+    "            \"strategy\": strategy_name,\n",
+    "            \"time_ms\": round(avg_time, 3),\n",
+    "            \"visited_cells\": int(avg_visited),         \n",
+    "            \"path_length\": int(avg_path_len),          \n",
+    "        })\n",
+    "\n",
+    "        print(\n",
+    "            f\"{strategy_name}: \"\n",
+    "            f\"time={avg_time} ms | \"\n",
+    "            f\"visited={avg_visited} | \"\n",
+    "            f\"path_len={avg_path_len}\"\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "347cb7be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "CSV saved: results.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "csv_file = \"results.csv\"\n",
+    "\n",
+    "with open(csv_file, \"w\", newline=\"\", encoding=\"utf-8\") as file:\n",
+    "\n",
+    "    writer = csv.writer(file)\n",
+    "\n",
+    "    writer.writerow([\n",
+    "        \"maze\",\n",
+    "        \"strategy\",\n",
+    "        \"time_ms\",\n",
+    "        \"visited_cells\",\n",
+    "        \"path_length\"\n",
+    "    ])\n",
+    "\n",
+    "    for row in results:\n",
+    "        writer.writerow([\n",
+    "            row[\"maze\"],\n",
+    "            row[\"strategy\"],\n",
+    "            row[\"time_ms\"],\n",
+    "            row[\"visited_cells\"],\n",
+    "            row[\"path_length\"]\n",
+    "        ])\n",
+    "\n",
+    "print(f\"\\nCSV saved: {csv_file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b6fb0b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Результаты:\n",
+      "small | BFS | 0.029 ms | 17 visited \n",
+      "small | DFS | 0.018 ms | 14 visited \n",
+      "small | A* | 0.033 ms | 16 visited \n",
+      "medium | BFS | 0.018 ms | 10 visited \n",
+      "medium | DFS | 0.019 ms | 10 visited \n",
+      "medium | A* | 0.024 ms | 10 visited \n",
+      "large | BFS | 0.292 ms | 198 visited \n",
+      "large | DFS | 0.201 ms | 198 visited \n",
+      "large | A* | 0.277 ms | 198 visited \n",
+      "empty | BFS | 0.014 ms | 10 visited \n",
+      "empty | DFS | 0.015 ms | 10 visited \n",
+      "empty | A* | 0.021 ms | 9 visited \n",
+      "no_exit | BFS | 0.007 ms | 1 visited \n",
+      "no_exit | DFS | 0.006 ms | 1 visited \n",
+      "no_exit | A* | 0.006 ms | 1 visited \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(\"Результаты:\")\n",
+    "\n",
+    "for row in results:\n",
+    "\n",
+    "    print(\n",
+    "        f\"{row['maze']} | \"\n",
+    "        f\"{row['strategy']} | \"\n",
+    "        f\"{row['time_ms']} ms | \"\n",
+    "        f\"{row['visited_cells']} visited \"\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95a41c2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "maze_names = [\"small\", \"medium\", \"large\", \"empty\", \"no_exit\"]\n",
+    "strategies = [\"BFS\", \"DFS\", \"A*\"]\n",
+    "\n",
+    "colors = {\"BFS\": \"#EEDC82\", \"DFS\": \"#88D94C\", \"A*\": \"#FF43A4\"}\n",
+    "\n",
+    "# Подготовим данные для графиков\n",
+    "time_data = {maze: {s: None for s in strategies} for maze in maze_names}\n",
+    "visited_data = {maze: {s: None for s in strategies} for maze in maze_names}\n",
+    "path_data = {maze: {s: None for s in strategies} for maze in maze_names}\n",
+    "\n",
+    "for row in results:\n",
+    "    maze = row[\"maze\"]\n",
+    "    strat = row[\"strategy\"]\n",
+    "    time_data[maze][strat] = row[\"time_ms\"]\n",
+    "    visited_data[maze][strat] = row[\"visited_cells\"]\n",
+    "    path_data[maze][strat] = row[\"path_length\"]\n",
+    "\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 14))\n",
+    "\n",
+    "x = np.arange(len(maze_names))\n",
+    "width = 0.25  \n",
+    "multiplier = 0\n",
+    "\n",
+    "for strategy in strategies:\n",
+    "    values = [time_data[maze][strategy] for maze in maze_names]\n",
+    "    offset = width * multiplier\n",
+    "    ax1.bar(x + offset, values, width, label=strategy, color=colors[strategy])\n",
+    "    multiplier += 1\n",
+    "\n",
+    "ax1.set_xticks(x + width, maze_names)\n",
+    "ax1.set_ylabel(\"Время (мс)\")\n",
+    "ax1.set_title(\"Сравнение времени выполнения алгоритмов\")\n",
+    "ax1.legend()\n",
+    "ax1.grid(axis='y', alpha=0.5, linestyle='--')\n",
+    "\n",
+    "multiplier = 0\n",
+    "for strategy in strategies:\n",
+    "    values = [visited_data[maze][strategy] for maze in maze_names]\n",
+    "    offset = width * multiplier\n",
+    "    ax2.bar(x + offset, values, width, label=strategy, color=colors[strategy])\n",
+    "    multiplier += 1\n",
+    "\n",
+    "ax2.set_xticks(x + width, maze_names)\n",
+    "ax2.set_ylabel(\"Количество исследованных клеток\")\n",
+    "ax2.set_title(\"Сравнение количества исследованных клеток\")\n",
+    "ax2.legend()\n",
+    "ax2.grid(axis='y', alpha=0.5, linestyle='--')\n",
+    "\n",
+    "multiplier = 0\n",
+    "for strategy in strategies:\n",
+    "    values = [path_data[maze][strategy] for maze in maze_names]\n",
+    "    offset = width * multiplier\n",
+    "    ax3.bar(x + offset, values, width, label=strategy, color=colors[strategy])\n",
+    "    multiplier += 1\n",
+    "\n",
+    "ax3.set_xticks(x + width, maze_names)\n",
+    "ax3.set_ylabel(\"Длина пути\")\n",
+    "ax3.set_title(\"Сравнение длины найденного пути\")\n",
+    "ax3.legend()\n",
+    "ax3.grid(axis='y', alpha=0.5, linestyle='--')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('analysis.png')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pomelovsd/ExitMaze/mermaid.png b/pomelovsd/ExitMaze/mermaid.png
new file mode 100644
index 0000000..db96194
Binary files /dev/null and b/pomelovsd/ExitMaze/mermaid.png differ
diff --git a/pomelovsd/ExitMaze/result maze.md b/pomelovsd/ExitMaze/result maze.md
new file mode 100644
index 0000000..aa0ca93
--- /dev/null
+++ b/pomelovsd/ExitMaze/result maze.md	
@@ -0,0 +1,173 @@
+ # Структура:
+- **Описание задачи и выбранных паттернов** (с диаграммой классов из Mermaid).
+- **Листинги ключевых классов** (можно выборочно) **или ссылка на репозиторий**.
+- **Результаты экспериментов** (таблицы, графики).
+- **Анализ эффективности алгоритмов и применимости паттернов**.
+- **Выводы: как ООП и паттерны помогли сделать код гибким и расширяемым. Что было бы сложно изменить без них**.
+### Выводы:
+#### 1) **Описание задачи и выбранных паттернов**
+![[mermaid.png]]
+>Диаграмма классов
+
+| Паттерн      | Реализация в проекте                                                              | Обоснование                                                                                                                                                                                                      |
+| ------------ | --------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Builder**  | `MazeBuilders` (интерфейс)<br>`TextFileMazeBuilder`                               | Создание лабиринта из текстового файла.<br>Позволяя легко добавить другие форматы без изменения `Maze`                                                                                                           |
+| **Strategy** | `PathFindingStrategy` (интерфейс)<br>`BFS`<br>`DFS`<br>`AStar`                    | Алгоритмы поиска пути взаимозаменяемы<br>Strategy позволяет переключать их через `MazeSolver.setStrategy()` и добавлять новые (например, Дейкстра) без изменения кода                                            |
+| **Observer** | `Observer` (интерфейс)<br>`ConsoleView`                                           | Отделяет визуализацию от логики поиска<br>`MazeSolver` может уведомлять подписчиков о событиях (начало/конец поиска), а `ConsoleView` реагирует на них<br>Легко добавить другие виды отображения (GUI, лог-файл) |
+| **Command**  | `Command` (интерфейс)<br>`Observer` (интерфейс)<br>`MoveCommand`<br>`ConsoleView` | Обеспечивает пошаговое наблюдение и отслеживание с возможностью отмены                                                                                                                                           |
+|              |                                                                                   |                                                                                                                                                                                                                  |
+
+#### 2) **Листинги ключевых классов**:
+**Core:**
+```python
+class Cell:
+
+def __init__(self, x = 0, y = 0, isWall = False, isStart = False, isExit = False):
+
+self.x = x
+
+self.y = y
+
+self.isWall = isWall
+
+self.isStart = isStart
+
+self.isExit = isExit
+
+# Возращает True, если посаседству стена
+
+def isPassable(self):
+
+return not self.isWall
+
+class Maze:
+
+def __init__(self, grid, start = None, exit = None):
+
+self.grid = grid
+
+self.start = start
+
+self.exit = exit
+
+self.height = len(grid)
+
+self.width = len(grid[0]) if grid else 0
+
+  
+
+# Создание новой ячейки
+
+def getCell(self, x, y):
+
+if 0 <= x < self.height and 0 <= y < self.width:
+
+return self.grid[x][y]
+
+return None
+
+  
+
+# Ищет соседние проходимые клетки
+
+def getNeighbors(self, cell):
+
+directions = [(0,1),(1,0),(0,-1),(-1,0)]
+
+result = []
+
+  
+
+for dx, dy in directions:
+
+nx, ny = cell.x + dx, cell.y + dy
+
+neighbor = self.getCell(nx, ny)
+
+if neighbor and neighbor.isPassable():
+
+result.append(neighbor)
+
+  
+
+return result
+```
+**Builder:**
+```python
+from abc import ABC, abstractmethod
+
+class MazeBuilders(ABC):
+
+@abstractmethod
+
+def build_from_file(self, filename):
+
+pass
+
+
+
+from Core.Cell import Cell
+
+from Core.Maze import Maze
+
+from Builder.BuilderInterface import MazeBuilders
+
+  
+
+class TextFileMazeBuilder(MazeBuilders):
+
+  
+
+def build_from_file(self, filename):
+
+grid = []
+
+start = None
+
+exit = None
+
+  
+
+with open(filename, "r", encoding="utf-8") as f:
+
+lines = [line.rstrip("\n") for line in f]
+
+for y, line in enumerate(lines):
+
+row = []
+
+for x, ch in enumerate(line):
+
+cell = Cell(x, y, isWall = (ch == "#"), isStart = (ch == "S"), isExit = (ch == "E"))
+
+if (ch == "S"):
+
+start = cell
+
+if (ch == "E"):
+
+exit = cell
+
+row.append(cell)
+
+  
+
+grid.append(row)
+
+return Maze(grid, start, exit)
+```
+
+
+#### 3) **Результаты экспериментов**:
+   ![[analysis 3.png]]
+>График созданный на основе 5 попыток замеров и их усреднения   
+### 4) **Анализ эффективности алгоритмов и применимости паттернов:**
+- **BFS**  
+	Работает медленно и обходит гораздо больше клеток, но гарантирует кратчайший маршерут 
+- **DFS** 
+	Работает быстро, но за это приходиться платить не самыми оптимальными путями и количеством обходимых маршерутов(из-за чего растёт время работы)
+- **A****
+	Является золотой серединой между DFS и BFS ищет маршерут хуже BFS, но лучше чем DFS, обратная зависимость наблюдается в измерении времени
+#### 5)**Выводы: как ООП и паттерны помогли сделать код гибким и расширяемым? Что было бы сложно изменить без них?**
+ООП и паттерны помогли систематизировать код и написать единый код для 3 структур, так же легко масштабировать проект, за счёт единых функций применимых для разных алгоритмов
+Сложно было бы изменить файлы лабиринтов, алгоритмы поиска пути,  
\ No newline at end of file
diff --git a/pomelovsd/ExitMaze/results.csv b/pomelovsd/ExitMaze/results.csv
new file mode 100644
index 0000000..1641f0d
--- /dev/null
+++ b/pomelovsd/ExitMaze/results.csv
@@ -0,0 +1,16 @@
+maze,strategy,time_ms,visited_cells,path_length
+small,BFS,0.379,58,15
+small,DFS,0.076,31,19
+small,A*,0.298,57,15
+medium,BFS,5.197,1263,173
+medium,DFS,3.898,1229,173
+medium,A*,4.109,806,173
+large,BFS,17.886,3918,269
+large,DFS,7.197,1905,269
+large,A*,10.377,2040,269
+empty,BFS,0.195,64,15
+empty,DFS,0.135,64,29
+empty,A*,0.288,63,15
+no_exit,BFS,0.007,0,0
+no_exit,DFS,0.007,0,0
+no_exit,A*,0.007,0,0