diff --git a/stepushovgs/.gitignore b/stepushovgs/.gitignore
index 09b1f43..b4f7484 100644
--- a/stepushovgs/.gitignore
+++ b/stepushovgs/.gitignore
@@ -253,4 +253,10 @@ go.work.sum
 # .idea/
 .vscode/
 
-!go.mod
\ No newline at end of file
+!go.mod
+
+#################################
+########### Obsidian ############
+#################################
+
+.obsidian/
\ No newline at end of file
diff --git a/stepushovgs/data-structures/source/docs/img/delete.pdf b/stepushovgs/data-structures/docs/img/delete.pdf
similarity index 100%
rename from stepushovgs/data-structures/source/docs/img/delete.pdf
rename to stepushovgs/data-structures/docs/img/delete.pdf
diff --git a/stepushovgs/data-structures/source/docs/img/insert.pdf b/stepushovgs/data-structures/docs/img/insert.pdf
similarity index 100%
rename from stepushovgs/data-structures/source/docs/img/insert.pdf
rename to stepushovgs/data-structures/docs/img/insert.pdf
diff --git a/stepushovgs/data-structures/source/docs/img/search.pdf b/stepushovgs/data-structures/docs/img/search.pdf
similarity index 100%
rename from stepushovgs/data-structures/source/docs/img/search.pdf
rename to stepushovgs/data-structures/docs/img/search.pdf
diff --git a/stepushovgs/data-structures/docs/src/main.ipynb b/stepushovgs/data-structures/docs/src/main.ipynb
new file mode 100644
index 0000000..56e1992
--- /dev/null
+++ b/stepushovgs/data-structures/docs/src/main.ipynb
@@ -0,0 +1,754 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e631810e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "12fa3ed1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CMU Serif\n",
+    "plt.rcParams['font.family'] = 'CMU Serif'\n",
+    "plt.rcParams['mathtext.fontset'] = 'cm'\n",
+    "plt.rcParams['font.size'] = 14\n",
+    "plt.rcParams['axes.titlesize'] = 16\n",
+    "plt.rcParams['axes.labelsize'] = 15\n",
+    "plt.rcParams['xtick.labelsize'] = 13\n",
+    "plt.rcParams['ytick.labelsize'] = 13\n",
+    "plt.rcParams['legend.fontsize'] = 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c691c40e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Structure</th>\n",
+       "      <th>Mode</th>\n",
+       "      <th>Operation</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Вставка</td>\n",
+       "      <td>0.193393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Поиск</td>\n",
+       "      <td>0.023572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Удаление</td>\n",
+       "      <td>0.013566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Вставка</td>\n",
+       "      <td>0.193837</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Поиск</td>\n",
+       "      <td>0.023969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>373</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Поиск</td>\n",
+       "      <td>0.055234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Удаление</td>\n",
+       "      <td>0.068269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>375</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Вставка (среднее)</td>\n",
+       "      <td>0.882379</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Поиск (среднее)</td>\n",
+       "      <td>0.056078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>377</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Удаление (среднее)</td>\n",
+       "      <td>0.068386</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>378 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Structure             Mode           Operation      Time\n",
+       "0            Связный список        Случайный             Вставка  0.193393\n",
+       "1            Связный список        Случайный               Поиск  0.023572\n",
+       "2            Связный список        Случайный            Удаление  0.013566\n",
+       "3            Связный список        Случайный             Вставка  0.193837\n",
+       "4            Связный список        Случайный               Поиск  0.023969\n",
+       "..                      ...              ...                 ...       ...\n",
+       "373  Бинарное дерево поиска  Отсортированный               Поиск  0.055234\n",
+       "374  Бинарное дерево поиска  Отсортированный            Удаление  0.068269\n",
+       "375  Бинарное дерево поиска  Отсортированный   Вставка (среднее)  0.882379\n",
+       "376  Бинарное дерево поиска  Отсортированный     Поиск (среднее)  0.056078\n",
+       "377  Бинарное дерево поиска  Отсортированный  Удаление (среднее)  0.068386\n",
+       "\n",
+       "[378 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "csv_path = \"../../source/results/benchmarks.csv\"\n",
+    "\n",
+    "data = pd.read_csv(csv_path)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "a3737f45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.023409), np.float64(0.192404), np.float64(0.014352))"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Получение данных для Связного списка\n",
+    "\n",
+    "ll_random_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_random_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ll_random_search  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_random_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ll_random_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_random_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "ll_random_search_average, ll_random_insert_average, ll_random_delete_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "5434d260",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.033107), np.float64(0.193206), np.float64(0.022902))"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Получение данных для Связного списка\n",
+    "\n",
+    "ll_sorted_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_sorted_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ll_sorted_search  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_sorted_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ll_sorted_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_sorted_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "3deed9a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.0), np.float64(0.003041), np.float64(5e-05))"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Получение данных для хеш таблицы\n",
+    "ht_random_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_random_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_random_search  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_random_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_random_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_random_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_random_delete_average, ht_random_insert_average, ht_random_search_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "490e5c46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.000147), np.float64(0.002862), np.float64(0.0))"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Получение данных для хеш таблицы\n",
+    "ht_sorted_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_sorted_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_sorted_search  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_sorted_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_sorted_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_sorted_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "9d7274ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.000454), np.float64(0.005367), np.float64(0.000334))"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Получение данных для дерева\n",
+    "bst_random_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_random_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_random_search  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_random_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_random_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_random_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_random_delete_average, bst_random_insert_average, bst_random_search_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "92a545c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.068386), np.float64(0.882379), np.float64(0.056078))"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Получение данных для дерева\n",
+    "bst_sorted_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_sorted_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_sorted_search  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_sorted_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_sorted_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_sorted_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "b2e93d6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# countUsers           = 10_000\n",
+    "# countRepeat          = 10\n",
+    "# countRandomSearch    = 200\n",
+    "# countNotExitstSearch = 100\n",
+    "# countDeletes         = 500\n",
+    "\n",
+    "countUsers           = 20_000\n",
+    "countRepeat          = 20\n",
+    "countRandomSearch    = 1000\n",
+    "countNotExitstSearch = 500\n",
+    "countDeletes         = 1000\n",
+    "\n",
+    "ll_col = 'blue'\n",
+    "ht_col = 'orange'\n",
+    "bst_col = 'green'\n",
+    "\n",
+    "iterations = range(countRepeat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "208784a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4IOD2nJwc9VrROCDByF8cBBkvOhYv8RgzfuIxZnzFdEMZ3RLTDWVkzPiJl5+f73gMIiIiIiJXJGL1dWHRmTfS/7feXtnn6OvKYmmCZ5991nT7O++8Y/ofa8Vifdp//vOfUqNGjYDXueuuu+S2224zjYht0aKFZGZmSu3atSUaB0CoA8SL1gFXNOMxZvzEY8z4iumGMrolphvKyJjxE894PQAiIiIiouqkyiViMzIy1O9Dhw6Zbten/uv3H+lzPvvsM1m4cKF8/vnnUqtWrXLfEw4sMNIDa9Qee+yxAfdjtCx+rHAwEq2DLhwAxXM8xoyfeIwZXzHdUEa3xHRDGRkzPuJFsx6JiIiIiCKpyvVksf6r1+v1r7OqQyIUjGu5GteCbdKkScjPWbJkiXz00UcqCYvnYo3Zffv2qfu6desWsASBntA9fPhwRMpIRERERERERERE7lLlErEpKSnSp08fWbNmjen21atXS8uWLaVjx462zxs8eLDtc/B6vXv3Nl2ca+bMmfLaa6/5R7F+9dVXsn37dvU3Rsd26dLF9Drr169Xo2LtRsMSERERERERERERVbtELGA91lmzZklRUZH/tunTp8tDDz2kpr6tXLlSjVydN2+e//7x48er/40XcMBzcDtGvcKuXbvk2muvlc6dO8vbb78tb775pkrITps2Ta0VC9dff70MHDjQ/xp4DhK3WEc2KSkpSjVARERERERERERE8aTKrRELAwYMkAkTJsgdd9whnTp1UqNYhw8fLpdccom6H0sJbNy40b+cACC5isQqEq8Y0ZqdnS2tWrWScePG+R8zevRo+frrr9WPEUa66uuNXXTRRfLGG2/I+++/r+L8+eef8uqrr8qZZ54ZtfITERERERERERFRfKmSiVgYOnSo+rHTs2dPyc3NDbgdSxrgJxgsQVARjLi94oorwny3RERERERERERERNVsaQIiIiIiIiIiIiKieMJELBEREREREREREZHDmIglIiIiIiIiIiIichgTsUREREREREREREQOYyKWiIiIiIiIiIiIyGFMxBIRERERERERERE5jIlYIiIiIiIiIiIiIocxEUtERERERERERETkMCZiiYiIiIiIiIiIiBzGRCwRERERERERERGRw5iIJSIiIiIiIiIiInIYE7FEREREREREREREDmMiloiIiIiIiIiIiMhhTMQSEREREREREREROYyJWCIiIiIiIiIiIiKHMRFLRERERERERERE5DAmYomIiIiIiIiIiIgcxkQsERERERERERERkcOYiCUiIiIiIiIiIiJyGBOxRERERERERERERA5jIpaIiIiIiIiIiIjIYUzEEhERERERERERETks0ekARESxVFxSLPM3zpec7TmSeSBTTm11qngTvLF+W0RERERERETkMkzEElHc+uiPj+TmL2+WrLws6V67uyzNWypNazeVZ854RoYdNSzWb69aY4KbiIiIiIiIKDxMxBJR3CZhR8wcIT7xSYJhFZateVvV7bNGzmIytpoluJn8JSIiIiIiouqMa8QSVRFIMn238TuZv2G++o3/4020yojXRaIQSVgr/bZbvrzF0fjx+lnqCe4teVtMt+sJbtzvVNzWz7SW094+TZ5c9KT6jf+dikdEREREREQUaRwRS1QFuGEKfTTLuGDTgoBEoTUZuzlvs3pc/9b9Ixo7nkeLVpTg9ohHJbiHdhoa0diuG92MxP2O+SLbc0QkU6ThqSIOjvyNxUhjt8QkIiIiIiIy4ohYqvIj/eJ5dGEsRxhGs16jXcbs/OyIPi5U8T5aNJwEd7yMbo66zR9J8Set5LvPTpP5y55Uv/E/bo+XkcZuiUlERERERGTFRGw1F+1kmlsOnuN9Cn006zUWZWyS3iSij6vqn2W0kr+xSHDHIvkbsxNBmz+Sj+YMl9Yrt8ppW0We3CPqN/7H7ZFOxsbixIFbYrpJvJ8sJSIiIiKKJCZiq7FoJtPcdPAczXqNRZIp2vUaizL2bdlXmtdurqbK28HtLWq3UI+LFDeMFo1FgjtWo5ujfiKopFg+mne1jMgW2VJkvmtrkajbcb9atqCanjhwS0w3zSLhSGMiIiIiovBwjVgHrdu9TtKL0v3/p9VIk0ZpjeRQ8SHZvHdzwOPb1WvnT4gVFhWa7muY2lDSa6bL3sK9snP/Tvlq7Vdy/RfXq/v0ZBMOKpEIGj5zuLxw1gsypN0QdXurOq0kMSFRJSr2H95vet36KfWlTnId2Xdon2zft910Xw1vDWmR0UIdxCGWftCK34Ulheq3ftsNX9wgx2Ye619vD6+J1z5w+IBk5WeZXhePaV2ntfp7Q+6GgIPEpulNVWy8ZjgxPR6PtK3bVv2N+kU9G6Hu8RnkFubKrv27TPelJKWo5NEHKz6QkbNG+utVj4l6RZLylXNfkQGtB5ie2yClgWQkZ0j+wXzZUbDDdF9yYrI0q91M/b1291rTffr7NDKW0+iX7F9U4lCX5E2Slhktg9YhYiI2thVsMzo8rqJ61df4xOdmrcPGaY0ltUaq7DmwR3Yf2G26D7fj/qKSItmYu9H03kMpI7bPvIN5klOQY1uHPp9P1u1ZF1CH+va9bd82KThU4L/9rj53qe0Wn6M1Jn7fe+q9/m0Hr4vXN0Iit2ZiTfV+8L6M8Hnjc8d+iv3VrpxwsORgQDk37Nkg0lpU/aEejcJtI37c8mNA8rfIV2QqJ5K/s/+cLSOOHiElvhJZv2d90DqsqI1omtZUfcaoa/31rZqkNVGPw/aO9gP7MvYL7B9GobYR2K+NEBPlsMJtiIl2Eu2lXR2G00a89/t7pjb2UIn2WLs2FtrUbSMJngRVFpQp3DaiePu3cv2WXf4aNe+bmhu27JJjV78n3sxe6v+6tepKvVr11GdmTURX1Eas3bPWtO3Y7SPYdmYsnyEnNz855Dosr434dfuvIcVEEu+EJieYnpuZmim1a9YOu42w7iPBYs5dO1c61u9o+x1Y2TYC69He9MVNkrUvS45NO1aW71sujVIbycT+E+XaHteqx23au0kOFx82PRffRfhOqkwbgTpG4hlthD9mWiO579T75OKuF5v6EUa1kmqp795w2ghjPwRrNxf7igP6Ied2PLfcOqxsG7F/n7mdIiIiIiKqLpiIddD4eeMlKSXJ/3//Vv1lbK+xKgl4y1e3BDz+0ws/Vb+n/jhVVu1aZbrvtpNvkwFtBsj3m76Xl5a8JPPWz/PfZ5cMGTt3rHzR5gt10PzuBe+qg8LXfn5NFmctNj3uiuOvkPM7ny+/bPtFHvvhMdN9beu0lWfOfEaN4NMTL3q8TYWbTI/N3pctYz4Zow6aYMRRI+TS4y6VNbvXyN3f3G16bP1a9eXN899Uf9//7f2y64A5KfrowEfVbXjNcGLiIHH232erv59c+KSsyzUfkN/Z+07p07KPfLvhW3l92eum+05seqLc3fdu0+eixzTW79ivxkrfVn1NyaFru18rZ3c8W5ZkLZEpP04xvW6n+p3kycFPqr/tPvMx3caY/reLCTNXzpR5G+aZkl1ICsM939wTkAR44vQnpHODzvLxnx/LJ6s+8d+Obc+uXo3x9FGbM1fMVH8b3dv3Xjmp+Uny33X/lbd/e9t0X+8WvWV8n/EqiWUsqzXpHayMSD4gafLc4udMtyPZPum0SSrBa1eH04ZOU4muN395U37Y/IPpvrGnjJX3V7yvkhJ6TCRtjsk8Rm3zuvH/HS8HiszJs6eHPK0Sn7NWzpIv1nxhug+J6itPuFIluO74+g7bckL2weyAcuoJvS/XfCnTl0833RduG6EngY32Fe0LiImkIhKxSN7avW44bUSz9GblJmKRzBn79Vj1NxIxSAYimff1uq9Njwu1jZizeo4kJyRJYclhUyLNKNmbpOpyxooZcnrb0+Wmk25S79Fa1lDbiG/WfaPaUB1i7jy8M2gbC++PeF8lz15e8rIs27bM9NhQ2ogFG7+VbYZcqW2bVywyZt5DUr9uJ/X/hcdeKKO7jJY/d/4pE7+daHpsRW1Ev1b9Qtovn1r0lP9kEtRKrCUz/zZT/T3p+0lhtRFpSWkhxXz3t3cDnnvjiTfK4HaDw24jrPtIsJj4bF5c8qLpNpz8evHsFyvVRuC5Ly95yR9Fj7mtYJv88/N/qpMFuMDcw/MfNrXJ8ED/B1QiOtw2AknSn7N/9pfNH3PfNpUwzdmfIxP7TVT9iJeXvmx67vGNj5cHBzwYchuBhKqxHwL7i/ebvy/njlXb5rNnPuv/H5+RUWXbiHRf2UluIiIiIqLqxOOzDk+gI5aXlycZGRmybP0ySa8d+RGxn/31mVw0+yL/fRit1aN2D1mSt8R0EIQDJ4xkOtIRsdN/ny6jPxptiqePtDHGmzp4qpzb6dyIjIhFAjHcmEc6IhaJrQFvDagwpl6vkRgRi7J2fLqFbCl9P9aYSPE0Takn31y2yHR178qOiP101ady69xbKyzje8PeUwmpSIyIxXvr9/pJsq0wN2jMZil1ZePYHCk4XBCxEbGA0YIYRffVmq9kzZY1Uq9BPZV0R10e6Wg364hYazkxQgzl/H3f7/5yNqlVR1bdvEnty5EaEWtsCxCzW3o3+SX/F9Pn+cHfPojIiFi9jfjqp8ny0JI3ZHuRT7U9S/OWSqNEkXt7XC5Deo73PzcSI2I37F4rX3zYQ67PKtt+Tkg/QZblL5MS0UbGvtC0jgy54H8iCd6IjIhFG3vudK1d0WN2TeuqPks9prUtONIRsdO/v09Gz3u44jbvlOvk3B63RWRE7JB3h4Td3h3piFiMlKwo5swRMyM6Itb6fWkX84vRX0RsRCzquv8bJ0t26f5tF7NFan1Zf9t22Zq/NSIjYlX781Y//0kS23Y2vZlsvGWj2pePdESsXdtzXPpxar801itOUIw8ZqQjI2K7tuoqe/fuldq1awe8X6q4jxqNuispKZEdO3ZIw4YNJSEhOquhRTumG8rImPETjzHjJx5jxldMN5TRDTHzwuhjcUSsg9rWa2v7AeCgQ0+o2DGOPrLCwTwO9o30pQn0Kdg6PM4Yp7x1G3Fwl1bPPFrJ/7zUhgHxkhOSA+Id16hLQLlwcFdeWfVkS0BMy3sNGrPJcbavrx8828HBHX6sMFI2lJjWetUhAYSfYGzroaRYnskUGbExMCbGbsGzmR7piOcaErGh1KGeAMKPsb5CKSPqv7w6RAIIP3ZwsG4qa0mxvNDIW0EZEwSlQ6IFP3aQACpvW0ICyFZJsZxRu5bsqN9YGjbMlASbutQTS3aQAMKPHSSA/O/JUk6omVDTVM7nG3olPSlF/Y0EGn7shNpG4LMf/9XNsnV/2ZT2RE+i//NE5Oap9eWCzheUu+2G1UZs/kiu2/W6XNPaJ/MPJEhOokhmusiptUrEu+t1kYIzRVoMMz0XiVH82KmwjTi8Wa5LzZXGTURuzhHJKvL428AWiSJPZ4oMS80VKc4SadA/5Dosb/u2JoRQnzUSagQ8zq4+kcwKprw2oklTvPeHK27z2g8LiImkXbjtLG5DIhFJfW1bMcfDb9w/6thRppNARuG2Ea1rt5DmSV7ZehhT2APLqLbXJK8M6zRUvImB9S2VaCPUPjJvfIXlxGjbYOUMt434dt08fxI22Ge5uWCXLNjwrfRvOyjo64bTRuD7yzhzxS4mkr6Y7dC/dX/Vl7ATahuxeKt55Dx4Pd6A7dV4AqC8Ogy3jchLNJ8coyoIn/2O+SLbceIkU6ThqUH7MdU6ZrS5oYxu2X7cUEY3xXQDfpbOcEu9uiVmiJiIrYaifcGcvrVEmidqF4yxGz6tDp4TtcdF9GJLKfVNCaaAmKn1I3qxJWvC+UgfF5KcBTKsxi6Z5U8yld3VXE8y1dilHieNypJM1aleo11GE1xlfunNIvuzRLzdRVYsFUlpKtL9mYBkYXUsJ75G9ES+9bJk+v9PN9AeF7EvM9Sn+MTrEemXIrLDK9Kw2HDlx6W3iDQbGrkvuQPaSM9haSJDU0XmHxBD8hfJH/PjqutFyfq27h/avtk6QttOgleeOeMZGTFzeJBtxydPn/F0ucnJsGPuWijPNChWFx4Lvr0Wq8dFbB+JQTmzs74N/XHlJGLDimmzTMmRPK5Kfl9S9RHN795Yxoz2AV4syuiWckY7phvK6KaYbkgwueWzjHbduqVe3RIzDEzEOil/nYjHMPopMU2kViMRTOXcHzjtWNJLR3zs3ypSbF6aQJIbiiSlixzaK33rNpXe6XXUFGgcsB8s0Q7bMQ6lbZJ2cIkp0Hic5K8VSW0lkpCoJSqKLBe4qFlfpEYdkcP7RArNSxMIRoClthDvwR3yakORG0pngyJOM2+h/CY+waTUTK+o+717fxdJKx15hdfEa2NNvQPmKYXi8ZY9bt8GEZ/lis61moo3oYa8lFkit2SZY27y+mRHsQhW3n0VB+won94wYq3GtNIRNwWbRUrX4iyrw0YiWKPwUK7IQct6nokpKpHcKhEjCrWEc0JpzD1JPllbOnO0e02RvpKl1au/DhuI1MgQOZwvUmiedizeZJGU0hHOxufo8D4NSabFhT7Z7S2UerV9cmKyIcm05xeRFMPos4QkkdSWwesQMRG7cKfI4bKlCbwlxapez9soUtODEYVlZUSZMWv0CT1xZ1eHtRqLJKaKHNojctA87VjdjvuxBmCBYVgo3ruhjD8W+iTXrozYPg/niRTm2Nch3ty+wKUJyrbvbSJFhqUJsr8SWaJdSAZbkNeHfconsn+LyILhIif+S6T91aV1uE57fVMdNhfx1tTeD96XUVKGSHIDbT/F/mpTTiQMCxIPSn1rOfF5NcKOu1urR1MdhtlG7PxRJXfnNNWSv6sPi6R6iqRtkk8ae0XurScyBMnfLbNFWo4QwUWu9gVOOw65jdjyiVZ/ftaUIep3s8jGGSINTta2WW8Nbb/A/mEUahthSJ9pyV+f7EgokYYlhuSvCl2i7WNoJ9Fe2tVhiG1E38ZdpHdd7aJkeltQP0F7LPaN1knaGqz+NhbS2ohgpC6+7IvNSxOE0kYgEfja6Q8aLhBmbmcbop097UHxFmww1GFdkZr1tM/MmogOoY3AtvqfJiKT94hsLy5rC1I9IhPqiwxLORzYblVUh+W1EXv/UDE/bCLylCUmttdb62r7j+SuMLd36rPJFEmqXak2YlhqsTpB8vBukfwSc8xxdUXORUy0HWhDbL4Dw20j2vh2S/0EkV0lge0sFrYo9olsLBJpgh5YwSaR0vWPy+qwifpOCqeNaLP/D/X9v670pZp6zTFhB2IW5ah+hBw0L00g3lpa5zTENgLfg72TRa1rvLMY9SpSy1Os2h7EU/0Qr0hfj2F7t63DSrYRcXCxrk8//VQWLFgg7du3l7Vr10q3bt1k9OiyJZnsLFy4UD744APp3LmzZGVlSd26deWWWwLX9I0pHPgsGFH6/WBopfG9hdv7zor8AVCsYkY7cRftMrqlnNGO6YYyui1mvCeY3PJZRrtu3VKvbokZJiZinfTLeJHUsot1qRE+R40VObRLGzVm1V+7EI/8OVUkz3yxLjnqNpFGA0Ryvhfv6pfkg4YFsqT0OOSXgz75tEgk2aONfoMeKQXiXYZ1BD0ivd7VkgBrXhPZZZlO2O4KkRbna0mkleaLdamD7R7PqIPCM1JFFtQUWX5IS/xmJG2SZfmiDrg+bSrSrSauDDJTZFvpxTuQ9Gl7qci+NSK/mC+yoQ6sTtEuxCO/3x+YFD3uUXXbOTX2SPfm5pizSkRmF4j8qyESTLki/xsjUkO7WJc6SDxVuxCP/PFk4AH50XeKNOwjsv1bkbXmi3VJ/RPFm95ens0sS/sgEYKYe5N98vdtIgd8Im83FvGumqqVVdfhWpFmZ4vsXiLyh/lCPFK7k8gJ2sW6bD/ztmNMSaaTa/nkoGySmqXTdP2MdasfqJ+kXYhHfr0nMFF4/BMiGZ1FtnysJc90h7R6RVJiyh6R8XXLypicINI5CVODS0dtImZpotjv2HtFGpwksu2/IuvMF9ORzN4ix4zXEt3GsmJ7N5SxV7Ayokw7fxRZZb4Qj9Q5VuS4SSK+Ivs6PHmalhRd96ZIjn6xLp+5vpDDLNmkPlO/n28TaXuFlsjHvmq5EI90f1pLfG6eJbLVfCEeaT5UpP2VWoJr2R225cRo0UJPttTyWcqpJ66yvxTZYL4QT9htxAEtCTwkVWRFipb8TUzaJ0fVR+JQy5kpG97T9kkkb+1eN9Q24s+nTHeZ6tMIj6vVTKTnC1oyEInZbPOFeEJuI7LmiCQki5ReoAsxE8Vyogr3b5yuxWlyukinm0QKtwWWNcQ2wpuzQD5onipLSnNViLlFdsrcXJG0BJGpDUR6NEgR77KyC3pJn/e15Nmal0V2my/WFWobMSR3jizo2l2W71ghB4sK1b65OE/Em9pcPjt+kHTNnSOydE7Zc1tfKNJ6tEjenyK/mS/WVWEbcdxk1dE8J03k7FQkDX1yyLNJatT1Sb0ETFH3iCy9VaQe1mo1bMGJtUT6lLZ/KyeF10bgPYnIBWki51timrbXnQu1/cOo040iTQZXoo3wiez5WSV4EXO3XUw8p/cMkRWTzE9FErZn6QW8wmgjTjq4Uy6rLfJUrkjrRJEnG5S1s9hj8kpE7tol0rdVf5HlDwcm0bs+oNV7GG3ESfu3ysv4XtyqdTlvqeuTE1PKYsK7+SJ962SqfoSsNl+sS+odL9L1wZDbCO+BrfJBE5ElhSJv5In8p0CkY+J+uaNBWbweOAG14S2RNhdqN/w8VjtRZ1TZNqKoel+s64cffpBHH31UJVb1C/4NHTpUrV02atQo2+esW7dOLrvsMvn1118lOTlZ3XbzzTfLY489JnfeeadUCYZZE4G0BUkiPmsiFjGjfYAXizK6pZzRjumGMropphsSTG75LKNdt26pV7fEjKdErFMjBf744w95+eWX1WP27Nkjhw4dknvvvVcSE8uqIjs7WyZPniwdOnSQwsJC2b59u9x///2SmpoaXiFwoGu4WJcayQJIHOIALpjOt9qPiIXMPirR1STrc6nhEXlot8iGwx5pmipS6BN5fI82Cq5JwmGRVhdqo9IwOgmQOMJBuzXhAXWPC3xP+pqImX1FkhtLk8JtalTW4kKP7Pa0lDcbLVejSNVIv2Qc9L9ZtjFjJAuktQ98XYx203W533ZErOQuV39ixJAx5t/SlsuUTMPowpYjRZqVXlTHfxSPxPXt9qPd9APZOl3M9yGBkrdKzksT+apAq9ftxbjYSUt1sZMGXp9MzRQ5ClXSeaxWr/46LM1+1+sRWFaM1NLZfebJTbVRVaWjDJGyy09oKTVKlpcmuTxafRjrVh/tpuv2iP2IWGh+vpbA1239VGTnIsMIXK1e63uXy4nJ2nRzBYmBo++yH+0GjU8TqWu+mI5/O8NnbywrGsN5/bTEmG0Z8brNtO2suEAkrZ19HXoS7etQ39aQ1G75N+1vJGuyPjc9LCAmRsDpywRgX7Ub7QYtRog0Hhw4IhYwalN/T5Zywj5PE0n27SyLif0E+yU0OUOk/kmWOgyzjUA5F11kSv7mJKRJvRJtpUY/fb9HXdq9bqhtBLb90nhgSTEb3l/pPpJcur20GiXS9GzzY0JtI5CUQmKvdHQzYhaJYb+CE54SaVJ64SmMiAXEDnjdENuIzL7SRAr8bSzagvap2M+zpW6CqNubyH6R458q2y/17bT9tfYjYkNsI5CqbFhSrK5Mv3vnbvn3qZ2lb5tB4sUIxqJ9ljosXYO1dmeb9ruCNgIjXUvbHVRLPa9HdiW0lHoly9UIYNVRKcwu+x6xq8Nw2wgkzLPnqLjWmP7tFSMkuyGpejhwRCzgvYTTRhjagoRgMTGyFDMHgn0HQhhthAcXzso+WZ7K3aNGvt66s+y7BGlKjIh9ull98aLtqd3BfkRsmG2EZ+ePUrJV2zfxCT29xyM9istiwhTMdkhtJlL3eG2bMdVhrfDaiJ0/SpM9F6l94a/St/9XUYrcmueRxl6f1g9Bt6rt5eb91FqHlW0j8nEm2pKkrkYmTJggI0eO9Cdh4dJLL5W77roraCL2kUcekTPOOMOfhNWfM3DgQLnpppukVq0w14fCCPAkwywSfPZoU5AsP2gZdW7cLjHTxrp/JtXRTtJsmxswa8KDEySG/9X+hpPEmb20m2o21NpRjAAvOWh+3cR0bSYTToIc1i7Y6If9He0CvsctMRN8aJd8wWNie8YI++KDIocsI/dx8I1R56qOMFOs7AKN2nMzLAd4xnil/bYlN4vUOzHwAM9fhznaiSO7OsTskyLL6PBdP4VWxqzP1MAC8+vW1sprW4dJ2olsVVbLCSH0aZbcVE45S09iNT5DpGiv5XW9ZcctdnWI9gsj4XGC0DiTKWdhaOXEZ55xTGAdYvvFdmxXh/7tu1jk4I7KxUTdGmaZ+b8n0E9C22bo//np2zdmN6DfFhCvdEaPNR766zgJruoQr+sL7Ffgex4zHIotMwT82/ch7aSdXUw9lnUfaXaONtMDMxSsfY5w2ogd3wWZQWUTE9+Fqg5LAmdnhtNGZH0R2qwtvS3w12FuYL8tlDZC7SM3lL+PLLnR3Bbguxbfbfjet85MCaWNwPaG1wwnpt6/xawtDH6y9tsqaiP2Lg+/jVV1mKb1ye3qsKI2IreSMdVxc+PgdVheG4Gyltu2l7Z5DfpgbpOlDjO0XAJmqIXaRoTT9qDva50hpX8HhtNGhBMT+yVmK1mPH2rUC6+NsGl/PKoP4TPHxACOpkO07wTr4BF/HYbYRlSqnbVZ4q4ybcQBy3ZV3RKxTo0UyM3NlbPOOkt++uknadBA63hMnTpVbrzxRnnppZfU/4cPH5bBgwfLjBkz5JhjjlG3zZ49W0aMGCFz5hhGIoUiva1Ius1FRdD50KcY29GTaHYwIqX0QjUYBXeaGgXnKV0z0SOn1jIk0/A4Yxy9cbeDLzH82MGGjVErC0ao1z6plkd2eJOlYbGn7FxRz+dFMsxXnFbQqSyvrP7pxxaG9xoY09DQIDlk9/r6dFI7+ALUD/CsO3FKcxkiW+W0FF9pvSZLZoperx7twBsHjHZnT/BloyeB7ASrB0x1UGffwCPFHmy/hmRHj2ft67aiOgR0sPVOtl5fodQr6r/cOqxblgSywqhDa1lLt59yy4g6TaitNWp2UP/lbUv6Fy9YR3WaYhrKqTe6+lRrO/iS07/orPCFY3xPpnKKlHhqmsuJ/aRG6TaCKeX4sX3dENuI1NYiv44vXR5BK5cPX8z+cpZus80vsG8TrCpqI7Dtm+LZrLppt4+gs6V3uCrTRnS8Tvt89alC+gULkbRDgsbu7HRFdVje9o1lVgq3WdrYGjIu1bAuLZKUhVmBa5li+lIwIbYRqLmT0tvLjrqGK3yqxEBpcsAKnb9w29lQ95HytpnKtBEVtXf4PNNKl1SwkxRmGxFqOdHJKrcOw2sjzhn0msyaM1wtGbLusEfqFier380Sfdp60YNe0fYRffkIO+G0EamtZUjmeJnl2+K/qN1WS8zzGrbQTnghLvoSdkJtI0rbHnxf/qa+L7F2s1cy0yzfly2Gh1iHYbYRvup7sa4DBw7I/PnzVfLUqE2bNvLXX3+p/mzbtoF19eWXX8q4ceMCnoMr8y5atEglZMOydppIGr6jStXtqrWlOABa/a/Ax3e9X/uNA1/rAQ6eh+fvWmK6GSc7avosB/b6LI3c37W/j75DJCFVW07IOhsMJ9gyT9FmL2z6IHBb7HBNwMETYqb4csqPidlROFmE52I2jRHaF8xCU8/5d+ABIU5iGMqvxys7+ekTObBF5Ld7tf3E/0CvSJf7ykZdWQ/6cCK5zjHae0RdGFlmjgXGLJX1VeCMjKZniTQ4USR/tRbXCPsoTq6A9TPHMjils26CxlSJkNnaaxuh3cLMFFj/VuCSR5hpg++PnEVqcICf/vlUVE7U3d4VgcvUYJYIZpnsWRYwK0oyjhZpNVI74W8sqyWmqpZgMTH7ZMt/Ar9fMRAAyQq7/QbbEvq2GPG/d6VtPK8cDIyH2Wx6gmDdNO1g3wjbPvYBzHBAot6owSlaQgMJC8wAtIlpC/tI3W4i6e1Fdi/VZhAahdNGYDZTqDGRJGpzsZactHvdUNsIvFaoMVEn6Fviewdrge7+2fyYUNoIJKsM+7Ft24NldYxtAWa7ND9PS6pZyxpKG4HklmGpnpBiwjF3ackzJKutS05V1EboJ2ItMQO2WWMbC+gf4wftxPp3w2sj9AEM4cZEGVFWwMzOcNoIlZzbUnGb9+cU8yAKwGeKzzacNiKctgdL/llnSKHf2fH68NqIcGICZitZlxlrc1F4bYRNzGRfbmBMzLxEu4XlybCdGqEPGE4bYRMz0a6d3fpZWTuLGV/WpHNl2oh9ls+huiVinRop8Nxzz0nXrl39SVi45JJLpFGjRnLPPfdI8+bNVQIWB716EhbOO+88ufzyy1VH95RTTpGYsyQpbS+YY3ncEcNOheH4eiLE2EAHS4QcCRwsqpGiZQkms9KDPDwuUnBwWpokwEGkuV5Lt0WUNdJD2KNZt7Go11hsP6Fu+5HcR2JRTsM2G/TyR5HcZqMdzwh1hykk+uL5jRxcPN/Q+S23jY3gBcKizi37SAzLOezMD2Xokptk/q7ssgvM1W8uXiz541BbMGzBCBma6rO5qJ3HsbYgqt+XcQCJ1qKiooBZVmlp2snwVatWBSRiCwoK1Eyv8p4TLBF78OBB9aPLy9MSiyVtLpUS46wtHFiWlIh400TaXRX4QrgPmp0XOIobI7Vwf73upu+GEkmQQk8dSfNtNvc51Eh7vT9dQ3tuo9NFMk8NHO2G+1JaB74nnHTEfWpUtTlmgaehJPu2m0cyGWMisYDn1mxk87oJZWXFc9QoGgN18J7gf11jvAT/Csk+bUQaljGyrcPz7Ue74f7ax4ikWE7QYFmb1S+WE1O9cW35lno9La9bW3vd1HaBZcVIH/09We9DInD92/7yBI2JUYEBdegte93WFwfWIUbE4n6M+M84tux2JGWR2K0oJj7zhgPtR7vhdTO6iaRaTmbo27enlvn9+mNqr10iHpuYpaP30zra1GHp9uvz2O83iKe270EiDXpb4mmfJWb5FIvXHE/tZ3odXhrYZ0+qV1qHvUTqlA2yMG3fNTK192QTE7OL8Ns0EhHbe3Iz7bl1jhdJ62Bfh6G0EdieQ43ZsF/pc732rxtqG4HXChrTpv3B6HY8FyMc63a3r8Py2oj92aG1Pca2ACNi8VzELrcOg7QRe34LP6YWWHttjGBveCi8NgInPUJp80zteumIWNVGNwu/jahsTNUmlb5W8xHhtREq+VdezNLXx/Ow3JipDku3pXDaiHDanlotgn8HhtNGhBMTz20x0n5EbDhtREDMBDngqSupvs3mmHW6lX4HHqXNlLWrw1DbCJs277BdO4uZWHodGmdw6SrRRpTkYUTsZJv3aPPy4qKRAnjMiSeap+zUr19fdWznzp2rkq14jPX1vV6vtGzZUo2IrRKJ2Fgm06KVCIlVwifaSYJo161bEmmx2keiXc5YbLOx2kcAdYgOu+wQaYhpKKa0aPVP3kWTW/aRGJfT22yo9NsxX3Zsz5GGjTIlIQptgXfpzdLPk1WWGC1v5Hh1bQuqMSyLBcYlsYz/6/cf6XN0kyZNkgceeCDg9px9Xik0LgMjSK7qU7ZtttF844UGrffjwCNfxHOcSMoQ/whOHOTtTWgvJZ6kslk3GOmU1AvrBZU+dVc5r4tRUvuD3OcTydshUtLJNmaxp0bwmGqd8cLgZcXr+vtFlvsPN9Eu4FLKNp56ex1E8r3h16Hdfd6eIimDKy6jF6ParN+LmPpfECRmSfDPHO/f26PicqI+rOWsqA7FOEracB8+o1A+S3zmeT6b18VJBn0Es/W+INt3ODF377N53eIK9hvL9h0kns+TYI6XeJLIDv11bfo6+cZ93hrXsn0Hi1l6MVJ/TDxul3FadYh1aLd9hxNzb5h1GKyNCCcm9pNw6tBu+z7YILR9xNQWHDqydrZSMfHUneW8bgVthGpjB4fZxgKmcuvTucNtI46tZMyK6rCcNqK4dWhte0kbmzYPbYM+TT/ENiKctmdXXvDvwHDaiHBi+tsfa9ww24ggMUus/QJP13JiRqOdtXvd8NuI/H2h9/ET3TRSAIncAQMM62UaHofHAB7TqVOnch8T8miDkhL1E3kekROeEfl+pBZHDbTWz/iVJtNOeNp8ViiCsUsa9BWfL0dKGmQ6FEPKzgb2mSXy861Ssj+rrIyYln3CFO1+J2LjdZucKyU5C8S3faeUNGpQNp3TqbJGs25jVa9R3X5iuY9EeT+JxTYbs31Ea1d9Pp9DbWup+r21EUmlyTvzqApD8g6Pc+h9OF9Ot+wjbimnu9oCR/d/h+mzvbB/G+n/W2+v7HN0mE122223mfqoLVq0kMzMTKldO8gyH0fihKtM+xveeWbxz2UHPyfMFGncuPrGbNBf5M/tpu+HsnilI7XUlP/+kT3pEu16dUs5YxHTDWV0Q8xY7CPcL+OrzXNDvboppohpdn61S8Q6OVIAv62P0R8XzmNCHm2Qk6Mu9uWIGr1EuswUWfuqlBzcXXbGDwsZt7tSu9+f4Y/8ARBGGqPjr9YudBLKceIiKdm7XPbm5omvTm1JwDQmxHWofLoSX2fZWxPlzJCEneb1uRyLGa26jWW9RrOMMdpHor6fxGibjet9pN1UkZWTg4+qaDdexMEyR6WcbtlH3FJOF7UF+fmhXwyhqsnI0NbnxcVijfQT+vr9R/ocXc2aNdWPFbZFR7bHlhgpPdM/UhqnPnAQm4CTvU6NlI5mTNRZ96mGta5LyuLpU4O7TxFJNFwwsTrWq1vKGYuYbiijG2LGYh/hfhlfbZ4b6tVNMUXrW1XbRKyTIwXwOLvn47ZwHhPz0Qa6hueLHKWNRPFs3ymZjRpIgj4SxUE4qEQ9oXxRO6hs2FA8OTnRjRmLckY5ZtzXa4z2EddsP/FcRmw7yG2Ujhz3n0nVR443L13cPR7K6YZ9xC3ljFHMaMcLZ8RBVYNZXVjySp89pUOyHjp06GA7K6tJkyZhPSemor1ET7RjxvsyVm4rZyxiuqGMbogZi32E+2V8tXluqFc3xQxDoptGCuC39TH640J5DK5eXSVGG5iDqCsSejw7JEG/unYU4IArKuVjzLiLF/WYMdpHXFG38V5GnE3FxQ52zBfP9hxJcHqNz1iV0y37iFvKGaOY0YwXzXqMtJSUFOnTp4+sWbPGdPvq1avV9Qg6duxo+7zBgwfbPgev17t3b6lyorWmd6xixuoAL9r16pZyxiKmG8rohphuSjDF+2cZq7p1Q726KWaIqs47icJIAXRurY/RHxfOY4iIyCX0L/BGp2q/q8hZVCKqviZOnCizZs1S10TQTZ8+XR566CGV0F65cqV069ZN5s2b579//Pjx6n/jsgx4Dm7Xr4lAUeaW7we3lJOoOu0j3C+dw7olN46IdXKkAB6zaNEi02M2b96sRruedtpp/sdgzVcjjJDduHGjnH766REpIxERERG5Ey4cO2HCBLnjjjvUBWJxodrhw4fLJZdc4r8ILfqd+/bpV2EW6dy5s7z55psq8dqlSxfJzs6WVq1aybhx42JYEiIiIiKq9olYfaTA7bffrjqo+oWzrCMFLrzwQpkyZYoMGjRI3Y+O6TnnnKNGCqSnp9uOFLj++utVJ3bLli3SvHlz/2Muv/xyadeunfp/1KhR8vjjj8uSJUukR48e6raPP/5YevXqJQMHDoxJfRARERFR/Bg6dKj6sdOzZ0/Jzc0NuB0DFfBDRERERNVXoptGCuAiEl988YU88sgj6jHo5OI1XnrpJdMFIObOnatGxS5cuFCNhsWo2dmzZ0e5FoiIiIiIiIiIiCheVMlErJMjBY4++mhT4tUORsu+8MILYb5jIiIiIiIiIiIiompysS4iIiIiIiIiIiKieMNELBEREREREREREZHDmIglIiIiIiIiIiIichgTsUREREREREREREQOYyKWiIiIiIiIiIiIyGFMxBIRERERERERERE5jIlYIiIiIiIiIiIiIocxEUtERERERERERETksESnA7iRz+dTv/Py8qISr6SkRPLz8yU5OVkSEhLiLh5jxk88xoyvmG4oo1tiuqGMjBk/8fT+ld7foqrZR+W2z5iMWbXiMWb8xGPM+IrphjK6IWZeGP1TJmIdgA8aWrRoEeu3QkRERBS3/a2MjIxYv41qhX1UIiIiotj2Tz0+DidwJOuelZUl6enp4vF4HI+HzDs61Js3b5batWvHXTzGjJ94jBlfMd1QRrfEdEMZGTN+4qHrik5u06ZNozaiIl5Es4/KbZ8xGbNqxWPM+InHmPEV0w1ldENMXxj9U46IdQAqvXnz5lGPiw0rWht0LOIxZvzEY8z4iumGMrolphvKyJjxEY8jYatPH5XbPmMyZtWKx5jxE48x4yumG8oY7zEzQuyfchgBERERERERERERkcOYiCUiIiIiIiIiIiJyGBOxcaBmzZoyceJE9Tse4zFm/MRjzPiK6YYyuiWmG8rImPETj6oHbvuMyZhVKx5jxk88xoyvmG4oo5tihoIX6yIiIiIiIiIiIiJyGEfEEhERERERERERETmMiVgiIiIiIiIiIiIihzERS0REREREREREROSwRKcDEFHVdfDgQcnPz5d9+/ZJcnKypKenS0pKing8Hoknu3btUmXFktjGZbFTU1Olbt26MX1vRCRy+PBh1Rbhp0aNGv62KCEhfs4X5+bmyv79+wPaIVw8IDMzM6bvjYioKmH/lP1ToqrADf1TYB81+nixrmru008/lQULFkj79u1l7dq10q1bNxk9erSjMbdv3y533nmnDB482PFYcOjQIXnhhRdUA7hlyxZVTj2+Uw3uRx99JDk5OSr2//73P+nXr59cd911Ei1//fWX3HvvvTJz5kzHYqAuW7Ro4f8fXygXXHCBvPTSS441uGhu8Prr16+XZs2aSUlJiZx55ply1FFHiVOwrTz++OO29z3xxBNy++23Rzzm559/LqtXr1YHDLt371b1fOWVV4qT3n77bVm4cKF07NhR7SPnnnuunHHGGVHb9xH7gw8+kM6dO0tWVpY6gLjlllscjQl5eXnyyCOPSEZGhtx9992OxcO2+8Ybb8jmzZtlx44d8ueff8pVV10lF154oWMx4eOPP1b1iQO1X375RTp06CDjx4+XxMTEqLTjO3fulFGjRsl///vfI4pXUcykpCQpKiry/z9w4EB59dVXpW3bto7FhPfff19tu61bt1b768knn6x+Ih0P7V6w75Drr79enn/++YjHhO+//15++ukn8Xq9al9BQuO222474gOI8mJ+8cUX8uGHH8rRRx8tW7duleOOO04uueSSI4pH1U8s+qfR7qNGu39aFfqo8do/jUUflf3TyHFD/9QtfVT2T53pn8aij8r+afk4IrYa++GHH+TRRx9VO6l+hnjo0KFqA0bDFGloYNEo4Avsrbfekv79+0s0oDNy6aWXSvPmzdX/X3/9tdq53nvvvYh8wVjdd999snz5ctXRxZkvdHabNGmiOryR+OKuSHFxsYwZM0bFdhK+VB577DHp3r276mx27dpVGjVq5GhMdAratWunPlMYPny42n5nzZrlWMwDBw6oRtdYnziQeeWVV+Tmm2+OeLw5c+aoDohxW8EX3GuvveZYZ/fZZ5+Vf//736ou8YWG8uHLpXbt2tKrVy/H9/1169bJZZddJr/++qv6IgXULbYvfBk6EXPjxo3yr3/9S2rVqiXTpk074oPQiuLhMzz11FPliiuuUP+vWLFCTjjhBPU+0Ol0IubLL78s77zzjuro4uCzsLBQWrVqpTqfTz/9tCMxrVCva9asqVSscGIizrBhw9T+ig5Sy5YtHY/58MMPq4MWbEeADiC+VxYvXhzxeEgsIHGB7dXomWeeUQdqlVFRTNyPA+5bb73V1D5NmDBBld2JmJ988olq+/AdihFd+kEL2oWRI0dWKiZVP9Hun8aqjxrt/mms+6jx3D+NRR+V/VP2TyMVMx76qOyfOtM/jUUflf3TEGFELFVPAwcO9E2ZMsV024cffujr2LGj47Gx6UybNs3xOIWFhb569er5Jk+ebLr9xBNP9HXq1MmRmDfffLOvZcuWvn379vlva9Soke/cc8/1RcNzzz3nu+KKK3z9+vVzNM769euj8hnq3n33XbVtlpSU+G977bXXfB999JGjcZ944omA2x566CHfqlWrHIk3cuRIX3Z2tum2vLw833nnnedIvPz8fF9KSopv0qRJptvHjh3rGzJkSFT2/csvv9x30003mW5bunSpLyMjw7d//35HYhq1atXKN3HixCOOU148bLs33nhjwGednp7uO3TokCMxn3rqKV+DBg18GzZs8N920kkn+bp06XLE8YLFNJoxY4ZqD1G/kRIsZiQ/v1BiLliwwFenTh1fQUGB/7bZs2f7Xn31VUfi2bVDiPXdd98dcbxgMceNG+dbtGhRwGP79+/vSEy07dhWrrnmmoDvNKe+r6lqimX/NFp91Fj0T2PdR43X/mms+qjsn0aWG/qnbumjsn/qTP80Fn1U9k+Di6/FLVwEZ2Xmz58fMCy+TZs2atoQzgDGA5wVx1lTTJ+xlhNn+ZyAs3h4bf1sCYbL4+zeKaecIk77+eef1cgGfXRFPMHZ57POOsu0vhfO3GK6mZOMZ9v0qRCNGzdWU6ScgLV0MEIG637pli1bpkZ0OAFnvbGmT8OGDU23Y1rdN998o0bJOO3LL7+0bYv27t0rixYtkniANaEw3ctaRkxJtbZPkYIz4BjthBEGgJFBGzZsiEpbhDPxKBdGrsQjTAfF2XKs86U7//zzHRsVZG2HMIoD06cwgsUpaIswksP4XYkpt/r2FGnYPxDLri1atWqVik3xj/1T5/qnseyjxnP/NFZ9VPZP2T+NFDf1Udk/rf59VPZPNVyaoJpCRxadQL0jpktLS1O/sVEd6dolVQHKZ7dzoPyYHhANGJLft29fx6d8YUoH1i7B2lu///67RMMff/yhph3gYAJD+jGNBZ00JxpAlAnTgxAP07DwGaLBveGGG8RJmAqlw5SoF198UU3vcPLLDJ2QTp06qY49OgqIN2XKFEfi6VOt0AEywglBlBdfpk7uKwUFBWp9qPLaIkz9qO6WLFkScBu24fr16wd8sTsFU5Qw/auy03ZChW0HUxUnTpyo1naLBqzVNHXqVKlXr55a2wxTi8aNG+dILOwr//d//yfXXHONqlP8v23bNnUAjmm/xjYjUqyvef/996s6dhLKh3W9sP/jszzvvPPkySef9E+7daJjHawt0r9vcGBI8Y390+j1T6PVR43n/mks+6jsn7J/Gilu6aOyfxr5/mks+qjsn2qYiK2m9uzZo35bF8PW/9fvj0c4w4rFnd99911H42B9ESwAjjMoWN/Ium5KpKEDhgWxowUdTTRI+jpUOHDCWfg6deqotdwiCWdI9TPTWNdM7xQNGDBAnS136gvNChfVwIUXnHT88cerUQ24EAHOXjZt2lTmzZtnOrMZSccee6waoYL1fYx+++03/1UwneTWtgijAHBgetdddzl+FefZs2fL3Llz1UVZZsyY4fjVS9988011wBvNK8LiQB9tkR4TiQV0nJxYJw+jx3Al7u+++07Gjh2rRnkBDsJvvPFG1RY7Ceup4UBYP0h1Cs70Yz0xtEVYC++hhx5SbbBT2w++O3CQH6u2iKoGt34nRLN/Gu0+ajz3T6tKH5X908hzc1sUr31U9k+d7Z9Gq4/K/qmGSxNUU3qjqmfydfr/1tvjBTpmODt9xx13yD/+8Q9HY6GhxyLkOCvUpUsXdREGp6DBxRWFcZYtWtABM555Qsdk0KBBlV7UvTz61SZx5st4ZhqdTjS+mMroNFxk4qmnnlJldBKmy+AsIjomOCuMjh46v//5z38cO4v5+uuvq4tJYKqV/sWCgwdw+qIabm2LcDXjc845R3VynaZfLRoH971791btklNwth8dzGiPWEOZjB1rdM5wltyJqYt6e4QDRL2Tq7dHGIHg5LRiwEgkp9sh/eABIw4wwgvbD678i6ur4wIbTkEcTEvXO7uYQojRJNFoi6hqcOt3QjT7p9Hso8Z7/7Qq9FHZP3WGW9uieO2jsn/qfP80Wn1U9k81TMRWUxkZGeq3tRE4ePCg6f54g05Yjx491Pop0YJpK507d1Ydayc6Y1jfa+nSpVE5MK8IzkThiw5rCkX6TBS0bt3adDumzODMH65g6DScrcVUKHTwnYJOHa68iNETOGN6zz33yMqVK9VZOKw1hi8eJ+AqzdOnT5fnnntO/WA6EuJDixYtxElubItwRhrlRaczmmflcZCIDuDVV18dcFY3Uh1AHDCNHj1aqkJbhAM3rCkZaeW1R0imVPaqtKHA9Kcff/xRHfw6DSOe0B6dffbZcu2116ppmLjyL6Yw653PSEPCBmv/YdQKRnh9++23cu6550alLaKqwY3fCbHqnzrdR3VD/7Qq9FHZP3WGW9uieOyjsn/qfP80mn1U9k81XJqgmsLZIJxpRCfJSD/j2KFDB4k3OEuCReyxODhgEelGjRpFNAbqD8P/cXbv4osv9t+OdUMw5QKdlu7du0c0JhqFTZs2mc70Y6oQyofbsFA2LiAQSejIHnPMMWpaBaY+WDsn+hm5SGnfvr0624SOppF+NjoaHQWMFjGeXXQCtg9MDzSercWXKaZbYMQK7sc6Z07A6+NHhy8zdIoivY9YYa0t1Ktb2qJPP/1UrQuIddUw2gIjSlAHOJsbSdgH0RbhIhoYYWVsi3DAhPYo0hdNwRpj6Dwb2yLchjLiNmxfToz0QucIFybAmXFrW2RtMyIB0zCxX8aiPUI7hI6200vdIHmANQ979eplOniYOXOmStxg+0Gn1wmoW6wlaby4EMocjeQzxR77p870T2PRR3VD/7Qq9FHZP3WG2/qn8dxHZf80em2R031U9k/LMBFbTWFH7dOnj1ro3Gj16tXSsmVLx664GcsvFnSScNZEh0W6jY1/JODsFqbsYF0UYycXVxjFFxo62pGGKyHix2jMmDFqetTkyZPFCahLvL51O8GXNxbvj/QUNMTDiAp9HS7jGkY4I411pJyGK/5aF+yPNHxR2o1IQfmPOuooadCggSNxP/jgA7W+zVVXXWUaYWG9CqZTMOLBri1CO4VpSvECZ6IxMsY4ZRJTloztUqSgc4nRDBitYmzn9KsdOzFy5uSTT1Y/Rpj2is/WqbYI0OEzHqTpbRE6Zk61DZjmZdceIYFkrYPq1g7pU12DjY5DXTu1DhcSN9hPjAdLaItwdVwuTeAO7J860z+NRR/VDf3TqtBHZf/UOW7pn8Z7H5X9U+f7p9Fqi9g/LcOlCaoxrE+CYfrGs8OY/oH1jJxcmFu/4pz1ynNOwZkRrDGEs0AYTo4frJOCL9JIwxmRIUOGmL7EsIYIFrfHItlYXDoa0Eg5Wb9YYwdrmeELVIcvMwzTf/bZZx2JiS9MLByvTytDGXFRBKxTpV/N0Em4Kq51wf5Iw5cyvigxwsAIZ21x9V0chDoBZxE/+eQT07QknOnH1I9o7Pv4UsMoGeOUQbRFuF2/Om2kY1ofE6n9JVi8tWvXyoQJE9ToCr0devXVV2XhwoVH/AVuFxMdEUzbMa6XhPr97LPP1NVFjftupGI63RYFi4mLwBivXIyOPLZptEVHOoojWExMz0SH0zh9DjExUudI2vmK6tWJdsguJpIHGK32yiuvBBxA4AAKibJIx4Q5c+aYLlT0+eefq3UJjSMQKP7Fqn8a7T5qNPunVaWPGo/901j3Udk/PXJu6J+6pY/K/qkz/dPyYjrVFrF/Wj6PL55XqnYBfLmhc4Kr22HdHfzGWj9OwNmZN954Q3WI8CWG4fpY26Nnz54BZ8wjBdNJMGUIZ4Ks0FHDekORhp0SCzqjgcfUAKyPNXz4cLn88ssdP4D45ZdfVN2ingsKCtRZTHyhYVpEpGH9IJQTZ6UwTQefKxZ3P+mkk8QpuMIvDlratWunvmCwMDemtkTD3/72N9XZfPLJJx2Ng4sQTJ061T8dCE0sOkYYDYBOsBOwts7777+vvnSys7NVvLvvvjtiZ/hC2fdxIIj7cDYT7wGjDdCZqOw+U1FMjLBAPWOKJL7MMR0KnzHWysOInUjHQ9tqtx4ULoaAEVFOlBH7JvZRfVod2gd0bjH9tbIdwFDbcTwO9Yor4OJvtEUYWVKZtr6imNhHcD86gGj3sA4gvseO5ArSoZRz2bJlaj1HbDu4Ui2+azCyozLbbKj1ijYWF1vAKKEjVVFMJMFwkISkEEaRIVmE/RLfnZW9Gm5FMbE/IhGFzxTf22iDHnjgAUlPTz/i8lL1Es3+aSz6qLHon8ayjxrv/dNY9lHZP608N/RP3dJHZf/Umf5pqDEj2Udl/zQ0TMRSyPAlio4fzpSgIcCmo5/pi/TaM26F+kW9okHCj362j/VLbtv3K4qJ/3EQir/1+/GD51TmPVXFMsYypv44HJwdaVtUlctZXeO5KSZRKLhtOov9U6qK3NA/DSWmm/pR7J9W/ZhuKPXoPGYAAAmrSURBVGMkMBFLRERERERERERE5DCuEUtERERERERERETkMCZiiYiIiIiIiIiIiBzGRCwRERERERERERGRw5iIJSIiIiIiIiIiInIYE7FEREREREREREREDmMiloiIiIiIiIiIiMhhTMQSEREREREREREROSzR6QBERPFg+fLlcuedd8rvv/8umzdvlsTERBk0aJAkJyebHldSUiLff/+97NmzRzIyMuTEE0+Uiy++WP0QEREREUUK+6dERNWPx+fz+WL9JoiIqouVK1fKMcccI71791YdWjv33XefPPzww/Liiy/KP//5z6i/RyIiIiJyD/ZPiYiqDy5NQEQUhpSUFPUbIw6C8Xq96netWrWi9r6IiIiIyJ3YPyUiqj6YiCUiIiIiIiIiIiJyGBOxRERERERERERERA7jxbqIiKLk0KFD8uSTT0pWVpY0atRIdu3apX7ffvvtkpSUpB7z9ttvy7///W+ZO3euWufrjDPOkKKiIvn555+lZcuWMmnSJElPT5cNGzZImzZtZMSIEWpNsMWLF8ucOXPkzDPPVBdg+Omnn+SLL74Q4zLg33zzjbz11lvqeYcPH1bxx40bJ23btlX340IPV1xxhXp/zZo1U+915syZkpCQIH/88Yd069ZN7r//fklNTTWVa+HChfLEE09I586dpaCgQPbv36/+r1u3rqxYsUKmTZsmzz33nHrsjTfeKFdeeaVs3LhRlfW9995T5RozZozcdttt8tlnn6nb8N4R7+9//7vcdddd8tRTT6nbUQ9nn322ul2/wMSBAwfk8ccfl1WrVkn79u3VBSpyc3PV+2/evLl63Pjx41W9EREREVEZ9k/ZPyWiKMPFuoiIKDTr169Hz9HXr1+/oI+ZOHGiesy0adP8txUVFfnOPPNM3+OPP2567OTJk31nnXWWul/3119/qee/8cYb/tsKCwt9bdu29V1wwQX+9zF06FD//d988416ztdff+2/rVu3bv6/33nnHd/JJ5/sy8/P99+2atUq9Zq///676X3279/fV6dOHd9TTz3lv/3QoUO+008/Xb3GgQMH/LfPnTvX17hxY9/GjRv9tz388MO+wYMHm8rZu3dvX69evUy34TXxnu+55x7T7atXr1a3v/baa6bbH3vsMXU77jcaMmSIr1WrVqqOjJo3bx7w2kRERETxhv1T9k+JqPrg0gRERFEwdepU+fXXX2Xs2LGm2zHaYOnSpfL000/7b9NHH3g8Hv9tNWvWlC5dush3333nv+20007z/60/1niRhgEDBqjfmzdvlquvvlomTpwoaWlp/vs7duwow4YNk3/84x/+kQm4kEOrVq3UWXuMADC+J5z1//HHH+WRRx5Rtx08eFAuu+wyueiii9SoAR1iYcTEDz/84L8N70svl7Wc1gtL6P/rF5WATZs2qREH1sfn5OTIV199Jb169VJ1ZITnl3fRCiIiIiI3Y/+U/VMiij4mYomIouD555+X7t27q2lU1s5Yz549/VOjgkGncf78+fLoo4+q/9ERbdeuXbnP6dq1q/r92muvqelRmBJmdfLJJ8tvv/1m6kCDtdMI6Gjj5/XXX1f/f/3117J161b1/o0yMzOlRYsW8r///U8ioaSkRJX7mmuuCbgPHXf87N69OyKxiIiIiNyC/dPKY/+UiCqLp2KIiByGta6w5pQ+AsCqfv366n501urVq+e//fPPP5dt27apzuS3334rs2fPln79+qn7GjdurNbbKg9GAwDWrcKIBONrG2Prj+nfv3+FZcF6XVira8+ePbJy5Up/h3fdunWmx51wwgkB8TBqYPLkyVKZ0RpXXXWVimtVq1YteeaZZ9TaXuis6/VDRERERMGxf6ph/5SIoo2JWCIih+FiBmC8MIH1IgnGx+mw6D8uEgD5+fkyZMgQOeecc+Tuu+8OOz5i48c4nay82BXB6+ijJ0aNGiWDBg2q8DmYHoaLEhjhQgflQQcc7xujNew6unD55ZerC0dMnz5dXWgBF1E49thj1QURiIiIiCgQ+6ca9k+JKNq4NAERkcMaNmyopkPt2LHD9n6sI4X78RMMrqh6/fXXyz333KOu2BoOXLVWj2MX2/iYiqxZs0Z1WOvUqeOfWoY1vuzgyrdHAtPVXnnlFdNaYMF06tRJ1e++ffvUCAVcPRfvkYiIiIgCsX9aOeyfEtGRYiKWiMhhODuPM+E//fRTQOcPFxTAWlW4gIB1NIDdNKfyOpbB4Iw81voyXpxAh+lSbdq0kcGDB5tux9l66wgJXLRhxYoVct1116n/Bw4cKB06dFAXPrDasmVLheuKVeTFF19UIxKs65bZwcUk3njjDfnoo4/U+l9EREREFBz7p5XD/ikRHSkmYomIwjwLbvxtZ//+/QGPmTBhgnTu3FldGdYIHTmsV3XfffeVe6a+uLhY/vWvf6k1s4YOHRr0fRUWFgbcd/TRR6uOIN4D1s7SLV68WD755BOZMWNGwBVjMSXM2FHF6+KKupiOhivpAq74iulWuCrsnDlzTM+dNGmSWjfLWCZrufT/g92Oq+XiCrkVPf7tt99WoxKeeOIJOeWUU0x1Fu6UNiIiIqLqhv1T9k+JqPrgGrFERCHAmXZMu/r111/9ncRTTz1VdV4xPQleeukl1XFcsGCB+h+P/+yzz2T06NGq04aLBqADiL/RYd2+fbvqhKKjqF8FFld8RecRcAYdU63QcV6yZImayvT999+rCyHocJEETAVDHLjzzjvl//7v/1RnuFevXv7H3XDDDeoqtuh84vkY6YDOMa50i2lTdtPVsI7VHXfcoUYr4MIHWP/rlltuUf/rsDbWjz/+qDrR6DDjAggYqYDOMKar4Yq3KBPeP26/6aab5Nprr5X169fLtGnT/B1VdEqxPtfHH3/sL/+sWbNUR/Xee++VBx98UN577z1/Wf7+97+rdb8wHQ4XjQBcOEIfRYHXwEUk8Np4DaxbVrt27YhuE0RERESxxP4p+6dEVP14fMFW5yYiIlfCBRjQgd6wYYNUdeggGzveRERERBR/2D8lonjBpQmIiKjaYieXiIiIiKoS9k+JqDxMxBIRkQmmhNmt5UVEREREFAvsnxJRvGAiloiI/OuMnXXWWWodMawP1rt3b7UOFhERERFRLLB/SkTxhmvEEhERERERERERETmMI2KJiIiIiIiIiIiIHMZELBEREREREREREZHDmIglIiIiIiIiIiIichgTsUREREREREREREQOYyKWiIiIiIiIiIiIyGFMxBIRERERERERERE5jIlYIiIiIiIiIiIiIocxEUtEREREREREREQkzvp/FuQZOglkpZoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Создание двух графиков рядом\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# ============= Левый график: случайные данные =============\n",
+    "ax1.set_title(\"Вставка случайных данных\")\n",
+    "ax1.set_ylabel('Время, с')\n",
+    "ax1.set_xlabel('Повторения')\n",
+    "ax1.set_xticks(iterations)\n",
+    "\n",
+    "# Связный список\n",
+    "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n",
+    "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# Хеш таблица\n",
+    "\n",
+    "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n",
+    "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# Дерево\n",
+    "\n",
+    "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n",
+    "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.legend(loc='best')\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ============= Правый график: отсортированные данные =============\n",
+    "ax2.set_title(\"Вставка отсортированных данных\")\n",
+    "ax2.set_ylabel('Время, с')\n",
+    "ax2.set_xlabel('Повторения')\n",
+    "ax2.set_xticks(iterations)\n",
+    "# ax2.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "# Связный список\n",
+    "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n",
+    "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.5)\n",
+    "\n",
+    "# Хеш таблица\n",
+    "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n",
+    "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.5)\n",
+    "\n",
+    "# Дерево\n",
+    "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n",
+    "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.5)\n",
+    "\n",
+    "ax2.legend(loc='best')\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Общая настройка\n",
+    "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n",
+    "             fontsize=14)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../img/insert.pdf',\n",
+    "            format='pdf',\n",
+    "            dpi=300,\n",
+    "            bbox_inches='tight',  \n",
+    "            pad_inches=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "7de42c9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Создание двух графиков рядом\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# ============= Левый график: случайные данные =============\n",
+    "ax1.set_title(\"Поиск в случайных данных\")\n",
+    "ax1.set_ylabel('Время, с')\n",
+    "ax1.set_xlabel('Повторения')\n",
+    "ax1.set_xticks(iterations)\n",
+    "# ax1.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n",
+    "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n",
+    "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n",
+    "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ============= Правый график: отсортированные данные =============\n",
+    "ax2.set_title(\"Поиск в отсортированных данных\")\n",
+    "ax2.set_ylabel('Время, с')\n",
+    "ax2.set_xlabel('Повторения')\n",
+    "ax2.set_xticks(iterations)\n",
+    "# ax2.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n",
+    "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n",
+    "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n",
+    "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.legend()\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Общий заголовок\n",
+    "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../img/search.pdf', \n",
+    "            format='pdf',\n",
+    "            dpi=300,\n",
+    "            bbox_inches='tight',  # обрезает лишние поля\n",
+    "            pad_inches=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "0fd42f30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Создание двух графиков рядом\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# ============= Левый график: случайные данные =============\n",
+    "ax1.set_title(\"Удаление в случайных данных\")\n",
+    "ax1.set_ylabel('Время, с')\n",
+    "ax1.set_xlabel('Повторения')\n",
+    "ax1.set_xticks(iterations)\n",
+    "# ax1.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n",
+    "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n",
+    "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n",
+    "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ============= Правый график: отсортированные данные =============\n",
+    "ax2.set_title(\"Удаление в отсортированных данных\")\n",
+    "ax2.set_ylabel('Время, с')\n",
+    "ax2.set_xlabel('Повторения')\n",
+    "ax2.set_xticks(iterations)\n",
+    "# ax2.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n",
+    "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n",
+    "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n",
+    "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.legend()\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Общий заголовок\n",
+    "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../img/delete.pdf', \n",
+    "            format='pdf',\n",
+    "            dpi=300,\n",
+    "            bbox_inches='tight', \n",
+    "            pad_inches=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9eca6493",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/stepushovgs/data-structures/docs/Отчёт.md b/stepushovgs/data-structures/docs/Отчёт.md
new file mode 100644
index 0000000..3227081
--- /dev/null
+++ b/stepushovgs/data-structures/docs/Отчёт.md
@@ -0,0 +1,122 @@
+## Практические графики
+### Информация о тестировании
+- Общее число записей: 20000
+- Каждый замер повторялся: 20 раз
+- Количество существующих записей для случайного поиска: 1000
+- Количество несуществующих записей для поиска: 500
+- Количество элементов для удаления: 1000
+
+![[insert.pdf]]
+**Тестирование вставки (рис. 1)**
+
+![[search.pdf]]
+**Тестирование поиска (рис. 2)**
+
+![[delete.pdf]]
+**Тестирование удаления (рис. 3)**
+## Анализ результатов
+
+### Как порядок входных данных влияет на скорость вставки в BST (деградация до O(n) на отсортированных данных)?
+
+По определению, при вставке отсортированных данных, структура бинарного дерева поиска вырождается в связный список. 
+Для визуализации этого в тесте выводятся высота и количество элементов в дереве:
+Для случайных данных вывод выглядит примерно так:
+```
+Высота дерева: 28, элементов: 8634
+```
+Для сортированных данных же:
+```
+Высота дерева: 8634, элементов: 8634
+```
+Заметим, что при случайных данных скорость вставки в бинарное дерево почти лишь немного уступает по скорости хеш-таблице. При сортированных данных из-за рекурсивной реализации вставки бинарное дерево проигрывает связному списку(который имеет линейную сложность вставки)
+
+### Почему хеш-таблица почти не чувствительна к порядку.
+Хеш-таблица не чувствительна к порядку данных, так как использует для распределения элементов хеш значения данных (сложность операции одинакова для любых однотипных данных) и после производит вставку в связный список(в моей реализации проходит по списку и вставляет данные в конец). Поэтому хеш-таблица ни на одном из этапов не сравнивает данные, следовательно их порядок не влияет на скорость. 
+
+### Почему связный список всегда медленен при поиске.
+Операция поиска в связном списке имеет линейную сложность $O(n)$ не зависимо от порядка данных, что можно видеть на графике (см. рис. 2). Для бинарного дерева поиска эта сложность в лучшем случае $O(\log(N))$, а в худшем $O(N)$. Для хеш-таблицы сложность вставки $O(1)$, с хорошей хеш-функцией и низким заполнением.
+
+### Как удаление работает в каждой структуре.
+#### Связный список
+Находим элемент перед удаляем элементом, и заменяем его поле `next`  на `next.next`, то есть теперь он указывает на элемент, который идёт после удаляемого элемента 
+``` Go
+current := ll.head
+
+for current.next != nil {
+	if current.next.data.Name == targetName {
+		current.next = current.next.next
+		return true
+	}
+	current = current.next
+}
+```
+
+#### Бинарное дерево поиска
+После того, как мы нашли узел, который необходимо удалить, у нас возможны три случая.
+
+Случай 1: У удаляемого узла нет правого ребенка.
+В этом случае мы просто перемещаем левого ребенка (3) на место удаляемого узла(5). В результате дерево будет выглядеть так:
+```
+Удаляем элемент со значением 5
+ДО УДАЛЕНИЯ:                          ПОСЛЕ УДАЛЕНИЯ:
+
+       [8]                                  [8]
+      /   \                                /   \
+    [5]   [10]                           [3]   [10]
+    /                                   / \
+  [3]                                [1]   [4]
+  / \
+[1] [4]
+```
+
+
+Случай 2: У удаляемого узла есть только правый ребенок, у которого, в свою очередь нет левого ребенка.
+В этом случае нам надо переместить правого ребенка(8) удаляемого узла (5) на его место.
+```
+Удаляем элемент со значением 5
+До удаления:                           После удаления:
+
+       [10]                                 [10]
+      /    \                               /    \
+    [5]    [12]                          [8]    [12]
+    / \                                  / \
+  [1] [8]                              [1] [9]
+        \
+        [9]
+```
+
+
+Случай 3: У удаляемого узла есть первый ребенок, у которого есть левый ребенок.
+В этом случае место удаляемого узла занимает крайний левый ребенок правого ребенка удаляемого узла.
+Давайте посмотрим, почему это так. Мы знаем о поддереве, начинающемся с удаляемого узла следующее:
+
+- Все значения справа от него больше или равны значению самого узла.
+- Наименьшее значение правого поддерева — крайнее левое.
+
+Мы должны поместить на место удаляемого узел со значением, меньшим или равным любому узлу справа от него. Для этого нам необходимо найти наименьшее значение в правом поддереве. Поэтому мы берем крайний левый узел правого поддерева.
+
+```
+Удаляем элемент со значением 5
+До удаления:                          После удаления:
+
+         [10]                                 [10]
+        /    \                               /    \
+      [5]    [12]                          [7]    [12]
+      / \                                  / \
+    [1] [9]                              [1] [9]
+        /                                    /
+      [7]                                  [8]
+        \
+        [8]
+```
+
+#### Хеш-таблица
+Находим индекс элемента в таблица, далее производим удаление элемента в связном списке, который соответствует этому индексу.
+
+
+# Вывод
+Мы реализовали и протестировали три различные структуры хранения данных: связный список, бинарное дерево поиска и хеш-таблица. Сравнили скорость операций вставки, удаления и поиска для каждой структуры. 
+Если не важен порядок хранения и извлечения данных, то хеш-таблица лучший выбор для быстрых вставки, удаления и поиска. 
+Если нужно хранить данные с возможностью быстрого отсортированного обхода, то стоит выбрать бинарное дерево поиска. 
+Если нужно хранить данные в порядке поступления(например очередь), то стоит выбрать связный список.
+
diff --git a/stepushovgs/data-structures/source/docs/report.md b/stepushovgs/data-structures/source/docs/report.md
deleted file mode 100644
index e69de29..0000000
diff --git a/stepushovgs/data-structures/source/docs/src/main.ipynb b/stepushovgs/data-structures/source/docs/src/main.ipynb
deleted file mode 100644
index dbea58c..0000000
--- a/stepushovgs/data-structures/source/docs/src/main.ipynb
+++ /dev/null
@@ -1,754 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "e631810e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "12fa3ed1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# CMU Serif\n",
-    "plt.rcParams['font.family'] = 'CMU Serif'\n",
-    "plt.rcParams['mathtext.fontset'] = 'cm'\n",
-    "plt.rcParams['font.size'] = 14\n",
-    "plt.rcParams['axes.titlesize'] = 16\n",
-    "plt.rcParams['axes.labelsize'] = 15\n",
-    "plt.rcParams['xtick.labelsize'] = 13\n",
-    "plt.rcParams['ytick.labelsize'] = 13\n",
-    "plt.rcParams['legend.fontsize'] = 12"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "c691c40e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Structure</th>\n",
-       "      <th>Mode</th>\n",
-       "      <th>Operation</th>\n",
-       "      <th>Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Вставка</td>\n",
-       "      <td>0.047666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Поиск</td>\n",
-       "      <td>0.002502</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Удаление</td>\n",
-       "      <td>0.004514</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Вставка</td>\n",
-       "      <td>0.048082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Поиск</td>\n",
-       "      <td>0.003015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>193</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Поиск</td>\n",
-       "      <td>0.007098</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>194</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Удаление</td>\n",
-       "      <td>0.026717</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>195</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Вставка (среднее)</td>\n",
-       "      <td>0.317356</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>196</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Поиск (среднее)</td>\n",
-       "      <td>0.007994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>197</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Удаление (среднее)</td>\n",
-       "      <td>0.025775</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>198 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  Structure             Mode           Operation      Time\n",
-       "0            Связный список        Случайный             Вставка  0.047666\n",
-       "1            Связный список        Случайный               Поиск  0.002502\n",
-       "2            Связный список        Случайный            Удаление  0.004514\n",
-       "3            Связный список        Случайный             Вставка  0.048082\n",
-       "4            Связный список        Случайный               Поиск  0.003015\n",
-       "..                      ...              ...                 ...       ...\n",
-       "193  Бинарное дерево поиска  Отсортированный               Поиск  0.007098\n",
-       "194  Бинарное дерево поиска  Отсортированный            Удаление  0.026717\n",
-       "195  Бинарное дерево поиска  Отсортированный   Вставка (среднее)  0.317356\n",
-       "196  Бинарное дерево поиска  Отсортированный     Поиск (среднее)  0.007994\n",
-       "197  Бинарное дерево поиска  Отсортированный  Удаление (среднее)  0.025775\n",
-       "\n",
-       "[198 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "csv_path = \"../../results/benchmarks.csv\"\n",
-    "\n",
-    "data = pd.read_csv(csv_path)\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "a3737f45",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.002936), np.float64(0.047986), np.float64(0.004647))"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Получение данных для Связного списка\n",
-    "\n",
-    "ll_random_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_random_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ll_random_search  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_random_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ll_random_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_random_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "ll_random_search_average, ll_random_insert_average, ll_random_delete_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "5434d260",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.003821), np.float64(0.048337), np.float64(0.005586))"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Получение данных для Связного списка\n",
-    "\n",
-    "ll_sorted_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_sorted_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ll_sorted_search  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_sorted_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ll_sorted_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_sorted_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "3deed9a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.0), np.float64(0.002009), np.float64(0.0))"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Получение данных для хеш таблицы\n",
-    "ht_random_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_random_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_random_search  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_random_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_random_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_random_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_random_delete_average, ht_random_insert_average, ht_random_search_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "490e5c46",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.00015), np.float64(0.001666), np.float64(0.0))"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Получение данных для хеш таблицы\n",
-    "ht_sorted_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_sorted_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_sorted_search  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_sorted_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_sorted_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_sorted_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "9d7274ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.000101), np.float64(0.002153), np.float64(0.0))"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Получение данных для дерева\n",
-    "bst_random_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_random_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_random_search  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_random_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_random_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_random_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_random_delete_average, bst_random_insert_average, bst_random_search_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "92a545c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.025775), np.float64(0.317356), np.float64(0.007994))"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Получение данных для дерева\n",
-    "bst_sorted_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_sorted_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_sorted_search  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_sorted_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_sorted_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_sorted_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b2e93d6e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# countUsers           = 10_000\n",
-    "# countRepeat          = 10\n",
-    "# countRandomSearch    = 200\n",
-    "# countNotExitstSearch = 100\n",
-    "# countDeletes         = 500\n",
-    "\n",
-    "countUsers           = 20_000\n",
-    "countRepeat          = 20\n",
-    "countRandomSearch    = 1000\n",
-    "countNotExitstSearch = 500\n",
-    "countDeletes         = 1000\n",
-    "\n",
-    "ll_col = 'blue'\n",
-    "ht_col = 'orange'\n",
-    "bst_col = 'green'\n",
-    "\n",
-    "iterations = range(countRepeat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "208784a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Создание двух графиков рядом\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
-    "\n",
-    "# ============= Левый график: случайные данные =============\n",
-    "ax1.set_title(\"Вставка случайных данных\")\n",
-    "ax1.set_ylabel('Время, с')\n",
-    "ax1.set_xlabel('Повторения')\n",
-    "ax1.set_xticks(iterations)\n",
-    "\n",
-    "# Связный список\n",
-    "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n",
-    "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.7)\n",
-    "\n",
-    "# Хеш таблица\n",
-    "\n",
-    "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n",
-    "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.7)\n",
-    "\n",
-    "# Дерево\n",
-    "\n",
-    "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n",
-    "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.legend(loc='best')\n",
-    "ax1.grid(True, alpha=0.3)\n",
-    "\n",
-    "# ============= Правый график: отсортированные данные =============\n",
-    "ax2.set_title(\"Вставка отсортированных данных\")\n",
-    "ax2.set_ylabel('Время, с')\n",
-    "ax2.set_xlabel('Повторения')\n",
-    "ax2.set_xticks(iterations)\n",
-    "# ax2.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "# Связный список\n",
-    "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n",
-    "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.5)\n",
-    "\n",
-    "# Хеш таблица\n",
-    "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n",
-    "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.5)\n",
-    "\n",
-    "# Дерево\n",
-    "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n",
-    "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.5)\n",
-    "\n",
-    "ax2.legend(loc='best')\n",
-    "ax2.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Общая настройка\n",
-    "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n",
-    "             fontsize=14)\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('../img/insert.pdf',\n",
-    "            format='pdf',\n",
-    "            dpi=300,\n",
-    "            bbox_inches='tight',  \n",
-    "            pad_inches=0.1)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "7de42c9d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Создание двух графиков рядом\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
-    "\n",
-    "# ============= Левый график: случайные данные =============\n",
-    "ax1.set_title(\"Поиск в случайных данных\")\n",
-    "ax1.set_ylabel('Время, с')\n",
-    "ax1.set_xlabel('Повторения')\n",
-    "ax1.set_xticks(iterations)\n",
-    "# ax1.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n",
-    "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n",
-    "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n",
-    "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.legend()\n",
-    "ax1.grid(True, alpha=0.3)\n",
-    "\n",
-    "# ============= Правый график: отсортированные данные =============\n",
-    "ax2.set_title(\"Поиск в отсортированных данных\")\n",
-    "ax2.set_ylabel('Время, с')\n",
-    "ax2.set_xlabel('Повторения')\n",
-    "ax2.set_xticks(iterations)\n",
-    "# ax2.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n",
-    "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n",
-    "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n",
-    "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.legend()\n",
-    "ax2.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Общий заголовок\n",
-    "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('../img/search.pdf', \n",
-    "            format='pdf',\n",
-    "            dpi=300,\n",
-    "            bbox_inches='tight',  # обрезает лишние поля\n",
-    "            pad_inches=0.1)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "0fd42f30",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Создание двух графиков рядом\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
-    "\n",
-    "# ============= Левый график: случайные данные =============\n",
-    "ax1.set_title(\"Удаление в случайных данных\")\n",
-    "ax1.set_ylabel('Время, с')\n",
-    "ax1.set_xlabel('Повторения')\n",
-    "ax1.set_xticks(iterations)\n",
-    "# ax1.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n",
-    "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n",
-    "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n",
-    "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.legend()\n",
-    "ax1.grid(True, alpha=0.3)\n",
-    "\n",
-    "# ============= Правый график: отсортированные данные =============\n",
-    "ax2.set_title(\"Удаление в отсортированных данных\")\n",
-    "ax2.set_ylabel('Время, с')\n",
-    "ax2.set_xlabel('Повторения')\n",
-    "ax2.set_xticks(iterations)\n",
-    "# ax2.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n",
-    "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n",
-    "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n",
-    "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.legend()\n",
-    "ax2.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Общий заголовок\n",
-    "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('../img/delete.pdf', \n",
-    "            format='pdf',\n",
-    "            dpi=300,\n",
-    "            bbox_inches='tight', \n",
-    "            pad_inches=0.1)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9eca6493",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.13.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/stepushovgs/data-structures/source/results/benchmarks.csv b/stepushovgs/data-structures/source/results/benchmarks.csv
index dcb6c95..ae7cd89 100644
--- a/stepushovgs/data-structures/source/results/benchmarks.csv
+++ b/stepushovgs/data-structures/source/results/benchmarks.csv
@@ -1,379 +1,379 @@
 Structure,Mode,Operation,Time
-Связный список,Случайный,Вставка,0.193393
-Связный список,Случайный,Поиск,0.023572
+Связный список,Случайный,Вставка,0.194844
+Связный список,Случайный,Поиск,0.023535
+Связный список,Случайный,Удаление,0.015070
+Связный список,Случайный,Вставка,0.195476
+Связный список,Случайный,Поиск,0.024576
+Связный список,Случайный,Удаление,0.013662
+Связный список,Случайный,Вставка,0.196501
+Связный список,Случайный,Поиск,0.027028
+Связный список,Случайный,Удаление,0.018409
+Связный список,Случайный,Вставка,0.194328
+Связный список,Случайный,Поиск,0.024307
+Связный список,Случайный,Удаление,0.014566
+Связный список,Случайный,Вставка,0.192402
+Связный список,Случайный,Поиск,0.024709
+Связный список,Случайный,Удаление,0.012597
+Связный список,Случайный,Вставка,0.195884
+Связный список,Случайный,Поиск,0.024688
+Связный список,Случайный,Удаление,0.014169
+Связный список,Случайный,Вставка,0.193099
+Связный список,Случайный,Поиск,0.024969
+Связный список,Случайный,Удаление,0.014594
+Связный список,Случайный,Вставка,0.190199
+Связный список,Случайный,Поиск,0.024101
+Связный список,Случайный,Удаление,0.014087
+Связный список,Случайный,Вставка,0.194119
+Связный список,Случайный,Поиск,0.024697
+Связный список,Случайный,Удаление,0.015095
+Связный список,Случайный,Вставка,0.192847
+Связный список,Случайный,Поиск,0.024285
+Связный список,Случайный,Удаление,0.014059
+Связный список,Случайный,Вставка,0.191560
+Связный список,Случайный,Поиск,0.024087
+Связный список,Случайный,Удаление,0.013595
+Связный список,Случайный,Вставка,0.192786
+Связный список,Случайный,Поиск,0.023233
+Связный список,Случайный,Удаление,0.014077
+Связный список,Случайный,Вставка,0.197807
+Связный список,Случайный,Поиск,0.024156
+Связный список,Случайный,Удаление,0.015051
+Связный список,Случайный,Вставка,0.191335
+Связный список,Случайный,Поиск,0.024676
+Связный список,Случайный,Удаление,0.013941
+Связный список,Случайный,Вставка,0.195091
+Связный список,Случайный,Поиск,0.024660
 Связный список,Случайный,Удаление,0.013566
-Связный список,Случайный,Вставка,0.193837
-Связный список,Случайный,Поиск,0.023969
-Связный список,Случайный,Удаление,0.013580
-Связный список,Случайный,Вставка,0.190963
-Связный список,Случайный,Поиск,0.023944
-Связный список,Случайный,Удаление,0.013505
-Связный список,Случайный,Вставка,0.194440
-Связный список,Случайный,Поиск,0.022179
-Связный список,Случайный,Удаление,0.015040
-Связный список,Случайный,Вставка,0.191873
-Связный список,Случайный,Поиск,0.023604
-Связный список,Случайный,Удаление,0.014556
-Связный список,Случайный,Вставка,0.191788
-Связный список,Случайный,Поиск,0.023570
-Связный список,Случайный,Удаление,0.013037
-Связный список,Случайный,Вставка,0.192362
-Связный список,Случайный,Поиск,0.023050
-Связный список,Случайный,Удаление,0.013997
-Связный список,Случайный,Вставка,0.191223
-Связный список,Случайный,Поиск,0.023778
-Связный список,Случайный,Удаление,0.013582
-Связный список,Случайный,Вставка,0.191326
-Связный список,Случайный,Поиск,0.023035
-Связный список,Случайный,Удаление,0.013183
-Связный список,Случайный,Вставка,0.191919
-Связный список,Случайный,Поиск,0.023584
-Связный список,Случайный,Удаление,0.013988
-Связный список,Случайный,Вставка,0.190009
-Связный список,Случайный,Поиск,0.022013
-Связный список,Случайный,Удаление,0.014679
-Связный список,Случайный,Вставка,0.191010
-Связный список,Случайный,Поиск,0.024724
-Связный список,Случайный,Удаление,0.015116
-Связный список,Случайный,Вставка,0.192876
-Связный список,Случайный,Поиск,0.023690
-Связный список,Случайный,Удаление,0.021114
-Связный список,Случайный,Вставка,0.202786
-Связный список,Случайный,Поиск,0.023466
-Связный список,Случайный,Удаление,0.014063
-Связный список,Случайный,Вставка,0.191339
-Связный список,Случайный,Поиск,0.022909
-Связный список,Случайный,Удаление,0.014661
-Связный список,Случайный,Вставка,0.189898
-Связный список,Случайный,Поиск,0.023056
-Связный список,Случайный,Удаление,0.013592
-Связный список,Случайный,Вставка,0.195372
-Связный список,Случайный,Поиск,0.023198
-Связный список,Случайный,Удаление,0.013819
-Связный список,Случайный,Вставка,0.191713
-Связный список,Случайный,Поиск,0.023542
-Связный список,Случайный,Удаление,0.012883
-Связный список,Случайный,Вставка,0.190514
-Связный список,Случайный,Поиск,0.023745
-Связный список,Случайный,Удаление,0.015012
-Связный список,Случайный,Вставка,0.189447
-Связный список,Случайный,Поиск,0.023544
-Связный список,Случайный,Удаление,0.014063
-Связный список,Случайный,Вставка (среднее),0.192404
-Связный список,Случайный,Поиск (среднее),0.023409
-Связный список,Случайный,Удаление (среднее),0.014352
-Связный список,Отсортированный,Вставка,0.193078
-Связный список,Отсортированный,Поиск,0.033527
-Связный список,Отсортированный,Удаление,0.022597
-Связный список,Отсортированный,Вставка,0.190167
-Связный список,Отсортированный,Поиск,0.032657
-Связный список,Отсортированный,Удаление,0.022773
-Связный список,Отсортированный,Вставка,0.191660
-Связный список,Отсортированный,Поиск,0.034012
-Связный список,Отсортированный,Удаление,0.022709
-Связный список,Отсортированный,Вставка,0.191970
-Связный список,Отсортированный,Поиск,0.032553
-Связный список,Отсортированный,Удаление,0.022828
-Связный список,Отсортированный,Вставка,0.194583
-Связный список,Отсортированный,Поиск,0.034762
-Связный список,Отсортированный,Удаление,0.022843
-Связный список,Отсортированный,Вставка,0.192641
+Связный список,Случайный,Вставка,0.196118
+Связный список,Случайный,Поиск,0.024149
+Связный список,Случайный,Удаление,0.014061
+Связный список,Случайный,Вставка,0.192031
+Связный список,Случайный,Поиск,0.024358
+Связный список,Случайный,Удаление,0.013602
+Связный список,Случайный,Вставка,0.192075
+Связный список,Случайный,Поиск,0.024104
+Связный список,Случайный,Удаление,0.013106
+Связный список,Случайный,Вставка,0.192208
+Связный список,Случайный,Поиск,0.023780
+Связный список,Случайный,Удаление,0.014568
+Связный список,Случайный,Вставка,0.193080
+Связный список,Случайный,Поиск,0.024706
+Связный список,Случайный,Удаление,0.013573
+Связный список,Случайный,Вставка (среднее),0.193690
+Связный список,Случайный,Поиск (среднее),0.024440
+Связный список,Случайный,Удаление (среднее),0.014272
+Связный список,Отсортированный,Вставка,0.193077
+Связный список,Отсортированный,Поиск,0.033712
+Связный список,Отсортированный,Удаление,0.023180
+Связный список,Отсортированный,Вставка,0.191460
 Связный список,Отсортированный,Поиск,0.033173
-Связный список,Отсортированный,Удаление,0.022690
-Связный список,Отсортированный,Вставка,0.194789
-Связный список,Отсортированный,Поиск,0.033526
-Связный список,Отсортированный,Удаление,0.022985
-Связный список,Отсортированный,Вставка,0.191488
-Связный список,Отсортированный,Поиск,0.032518
-Связный список,Отсортированный,Удаление,0.023373
-Связный список,Отсортированный,Вставка,0.203929
-Связный список,Отсортированный,Поиск,0.034133
-Связный список,Отсортированный,Удаление,0.023423
-Связный список,Отсортированный,Вставка,0.193756
-Связный список,Отсортированный,Поиск,0.033023
-Связный список,Отсортированный,Удаление,0.022845
-Связный список,Отсортированный,Вставка,0.195554
-Связный список,Отсортированный,Поиск,0.032817
-Связный список,Отсортированный,Удаление,0.023482
-Связный список,Отсортированный,Вставка,0.193150
-Связный список,Отсортированный,Поиск,0.033271
-Связный список,Отсортированный,Удаление,0.022587
-Связный список,Отсортированный,Вставка,0.192696
-Связный список,Отсортированный,Поиск,0.033454
-Связный список,Отсортированный,Удаление,0.022527
-Связный список,Отсортированный,Вставка,0.192065
-Связный список,Отсортированный,Поиск,0.033382
-Связный список,Отсортированный,Удаление,0.022786
-Связный список,Отсортированный,Вставка,0.191702
-Связный список,Отсортированный,Поиск,0.031664
-Связный список,Отсортированный,Удаление,0.023819
-Связный список,Отсортированный,Вставка,0.194042
-Связный список,Отсортированный,Поиск,0.032380
-Связный список,Отсортированный,Удаление,0.023181
-Связный список,Отсортированный,Вставка,0.190397
-Связный список,Отсортированный,Поиск,0.033168
-Связный список,Отсортированный,Удаление,0.022893
-Связный список,Отсортированный,Вставка,0.191636
-Связный список,Отсортированный,Поиск,0.032367
-Связный список,Отсортированный,Удаление,0.022448
-Связный список,Отсортированный,Вставка,0.193131
-Связный список,Отсортированный,Поиск,0.032905
-Связный список,Отсортированный,Удаление,0.022932
-Связный список,Отсортированный,Вставка,0.191676
-Связный список,Отсортированный,Поиск,0.032851
-Связный список,Отсортированный,Удаление,0.022315
-Связный список,Отсортированный,Вставка (среднее),0.193206
-Связный список,Отсортированный,Поиск (среднее),0.033107
-Связный список,Отсортированный,Удаление (среднее),0.022902
-Хеш таблица,Случайный,Вставка,0.002119
+Связный список,Отсортированный,Удаление,0.024734
+Связный список,Отсортированный,Вставка,0.193809
+Связный список,Отсортированный,Поиск,0.034748
+Связный список,Отсортированный,Удаление,0.022083
+Связный список,Отсортированный,Вставка,0.198505
+Связный список,Отсортированный,Поиск,0.034239
+Связный список,Отсортированный,Удаление,0.022935
+Связный список,Отсортированный,Вставка,0.192402
+Связный список,Отсортированный,Поиск,0.036666
+Связный список,Отсортированный,Удаление,0.022155
+Связный список,Отсортированный,Вставка,0.198979
+Связный список,Отсортированный,Поиск,0.033534
+Связный список,Отсортированный,Удаление,0.024246
+Связный список,Отсортированный,Вставка,0.196372
+Связный список,Отсортированный,Поиск,0.033731
+Связный список,Отсортированный,Удаление,0.023694
+Связный список,Отсортированный,Вставка,0.209801
+Связный список,Отсортированный,Поиск,0.057158
+Связный список,Отсортированный,Удаление,0.027851
+Связный список,Отсортированный,Вставка,0.194425
+Связный список,Отсортированный,Поиск,0.034020
+Связный список,Отсортированный,Удаление,0.021955
+Связный список,Отсортированный,Вставка,0.195977
+Связный список,Отсортированный,Поиск,0.033676
+Связный список,Отсортированный,Удаление,0.022606
+Связный список,Отсортированный,Вставка,0.191872
+Связный список,Отсортированный,Поиск,0.032348
+Связный список,Отсортированный,Удаление,0.023074
+Связный список,Отсортированный,Вставка,0.195098
+Связный список,Отсортированный,Поиск,0.032630
+Связный список,Отсортированный,Удаление,0.023753
+Связный список,Отсортированный,Вставка,0.194327
+Связный список,Отсортированный,Поиск,0.032106
+Связный список,Отсортированный,Удаление,0.023585
+Связный список,Отсортированный,Вставка,0.190991
+Связный список,Отсортированный,Поиск,0.031614
+Связный список,Отсортированный,Удаление,0.022668
+Связный список,Отсортированный,Вставка,0.193774
+Связный список,Отсортированный,Поиск,0.033153
+Связный список,Отсортированный,Удаление,0.022583
+Связный список,Отсортированный,Вставка,0.206674
+Связный список,Отсортированный,Поиск,0.033185
+Связный список,Отсортированный,Удаление,0.023165
+Связный список,Отсортированный,Вставка,0.191183
+Связный список,Отсортированный,Поиск,0.032613
+Связный список,Отсортированный,Удаление,0.022630
+Связный список,Отсортированный,Вставка,0.190937
+Связный список,Отсортированный,Поиск,0.033334
+Связный список,Отсортированный,Удаление,0.022092
+Связный список,Отсортированный,Вставка,0.191349
+Связный список,Отсортированный,Поиск,0.036153
+Связный список,Отсортированный,Удаление,0.022576
+Связный список,Отсортированный,Вставка,0.195836
+Связный список,Отсортированный,Поиск,0.032643
+Связный список,Отсортированный,Удаление,0.022599
+Связный список,Отсортированный,Вставка (среднее),0.195342
+Связный список,Отсортированный,Поиск (среднее),0.034722
+Связный список,Отсортированный,Удаление (среднее),0.023208
+Хеш таблица,Случайный,Вставка,0.002008
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.003507
+Хеш таблица,Случайный,Вставка,0.004030
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002563
+Хеш таблица,Случайный,Вставка,0.002006
+Хеш таблица,Случайный,Поиск,0.001004
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.002157
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.003023
+Хеш таблица,Случайный,Вставка,0.003016
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.003513
+Хеш таблица,Случайный,Вставка,0.002017
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002516
+Хеш таблица,Случайный,Вставка,0.003028
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-Хеш таблица,Случайный,Вставка,0.003369
-Хеш таблица,Случайный,Поиск,0.001002
-Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002091
+Хеш таблица,Случайный,Вставка,0.002514
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-Хеш таблица,Случайный,Вставка,0.003654
+Хеш таблица,Случайный,Вставка,0.002955
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-Хеш таблица,Случайный,Вставка,0.003102
+Хеш таблица,Случайный,Вставка,0.002034
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-Хеш таблица,Случайный,Вставка,0.003008
+Хеш таблица,Случайный,Вставка,0.002006
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-Хеш таблица,Случайный,Вставка,0.003997
+Хеш таблица,Случайный,Вставка,0.004015
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-Хеш таблица,Случайный,Вставка,0.001580
+Хеш таблица,Случайный,Вставка,0.002255
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-Хеш таблица,Случайный,Вставка,0.005478
+Хеш таблица,Случайный,Вставка,0.003012
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+Хеш таблица,Случайный,Вставка,0.002006
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-Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Поиск,0.001009
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-Хеш таблица,Случайный,Вставка,0.003410
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-Хеш таблица,Случайный,Вставка,0.002158
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-Хеш таблица,Случайный,Вставка (среднее),0.003041
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-Хеш таблица,Отсортированный,Вставка,0.003208
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-Хеш таблица,Отсортированный,Вставка,0.003095
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-Хеш таблица,Отсортированный,Вставка,0.002048
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-Хеш таблица,Отсортированный,Вставка,0.003991
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-Хеш таблица,Отсортированный,Вставка,0.003505
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-Хеш таблица,Отсортированный,Вставка,0.003013
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-Хеш таблица,Отсортированный,Вставка,0.003011
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-Хеш таблица,Отсортированный,Вставка,0.002003
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-Хеш таблица,Отсортированный,Вставка,0.004485
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-Бинарное дерево поиска,Случайный,Вставка,0.004626
-Бинарное дерево поиска,Случайный,Поиск,0.000000
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+Хеш таблица,Отсортированный,Вставка (среднее),0.001951
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+Хеш таблица,Отсортированный,Удаление (среднее),0.000083
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-Бинарное дерево поиска,Случайный,Вставка,0.006171
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-Бинарное дерево поиска,Случайный,Вставка,0.006885
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-Бинарное дерево поиска,Случайный,Вставка,0.006402
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-Бинарное дерево поиска,Случайный,Вставка,0.004521
-Бинарное дерево поиска,Случайный,Поиск,0.001055
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-Бинарное дерево поиска,Случайный,Вставка,0.005170
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-Бинарное дерево поиска,Случайный,Вставка,0.005615
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+Бинарное дерево поиска,Случайный,Вставка,0.006937
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-Бинарное дерево поиска,Случайный,Вставка,0.005535
+Бинарное дерево поиска,Случайный,Вставка,0.007520
+Бинарное дерево поиска,Случайный,Поиск,0.000000
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-Бинарное дерево поиска,Случайный,Вставка,0.005566
-Бинарное дерево поиска,Случайный,Поиск,0.001004
-Бинарное дерево поиска,Случайный,Удаление,0.000000
-Бинарное дерево поиска,Случайный,Вставка,0.004022
-Бинарное дерево поиска,Случайный,Поиск,0.001010
-Бинарное дерево поиска,Случайный,Удаление,0.000000
-Бинарное дерево поиска,Случайный,Вставка,0.004053
-Бинарное дерево поиска,Случайный,Поиск,0.001079
-Бинарное дерево поиска,Случайный,Удаление,0.000000
-Бинарное дерево поиска,Случайный,Вставка,0.004512
+Бинарное дерево поиска,Случайный,Вставка,0.007068
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.001003
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-Бинарное дерево поиска,Случайный,Вставка (среднее),0.005367
-Бинарное дерево поиска,Случайный,Поиск (среднее),0.000334
-Бинарное дерево поиска,Случайный,Удаление (среднее),0.000454
-Бинарное дерево поиска,Отсортированный,Вставка,0.901140
-Бинарное дерево поиска,Отсортированный,Поиск,0.055198
-Бинарное дерево поиска,Отсортированный,Удаление,0.068970
-Бинарное дерево поиска,Отсортированный,Вставка,0.901221
-Бинарное дерево поиска,Отсортированный,Поиск,0.055432
-Бинарное дерево поиска,Отсортированный,Удаление,0.067588
-Бинарное дерево поиска,Отсортированный,Вставка,0.875497
-Бинарное дерево поиска,Отсортированный,Поиск,0.055297
-Бинарное дерево поиска,Отсортированный,Удаление,0.068742
-Бинарное дерево поиска,Отсортированный,Вставка,0.894420
-Бинарное дерево поиска,Отсортированный,Поиск,0.054808
-Бинарное дерево поиска,Отсортированный,Удаление,0.068112
-Бинарное дерево поиска,Отсортированный,Вставка,0.879010
-Бинарное дерево поиска,Отсортированный,Поиск,0.054503
-Бинарное дерево поиска,Отсортированный,Удаление,0.068600
-Бинарное дерево поиска,Отсортированный,Вставка,0.873702
-Бинарное дерево поиска,Отсортированный,Поиск,0.055239
-Бинарное дерево поиска,Отсортированный,Удаление,0.067484
-Бинарное дерево поиска,Отсортированный,Вставка,0.889047
-Бинарное дерево поиска,Отсортированный,Поиск,0.066586
-Бинарное дерево поиска,Отсортированный,Удаление,0.071171
-Бинарное дерево поиска,Отсортированный,Вставка,0.881977
-Бинарное дерево поиска,Отсортированный,Поиск,0.059766
-Бинарное дерево поиска,Отсортированный,Удаление,0.069783
-Бинарное дерево поиска,Отсортированный,Вставка,0.890244
-Бинарное дерево поиска,Отсортированный,Поиск,0.054863
-Бинарное дерево поиска,Отсортированный,Удаление,0.069397
-Бинарное дерево поиска,Отсортированный,Вставка,0.871035
-Бинарное дерево поиска,Отсортированный,Поиск,0.055970
-Бинарное дерево поиска,Отсортированный,Удаление,0.065886
-Бинарное дерево поиска,Отсортированный,Вставка,0.873882
-Бинарное дерево поиска,Отсортированный,Поиск,0.056181
-Бинарное дерево поиска,Отсортированный,Удаление,0.069140
-Бинарное дерево поиска,Отсортированный,Вставка,0.932258
-Бинарное дерево поиска,Отсортированный,Поиск,0.055037
-Бинарное дерево поиска,Отсортированный,Удаление,0.066576
-Бинарное дерево поиска,Отсортированный,Вставка,0.875622
-Бинарное дерево поиска,Отсортированный,Поиск,0.055679
-Бинарное дерево поиска,Отсортированный,Удаление,0.068214
-Бинарное дерево поиска,Отсортированный,Вставка,0.871718
-Бинарное дерево поиска,Отсортированный,Поиск,0.055748
-Бинарное дерево поиска,Отсортированный,Удаление,0.067876
-Бинарное дерево поиска,Отсортированный,Вставка,0.872458
-Бинарное дерево поиска,Отсортированный,Поиск,0.054689
-Бинарное дерево поиска,Отсортированный,Удаление,0.068765
-Бинарное дерево поиска,Отсортированный,Вставка,0.871178
-Бинарное дерево поиска,Отсортированный,Поиск,0.054816
-Бинарное дерево поиска,Отсортированный,Удаление,0.067832
-Бинарное дерево поиска,Отсортированный,Вставка,0.876907
-Бинарное дерево поиска,Отсортированный,Поиск,0.054184
-Бинарное дерево поиска,Отсортированный,Удаление,0.068164
-Бинарное дерево поиска,Отсортированный,Вставка,0.868001
-Бинарное дерево поиска,Отсортированный,Поиск,0.054167
-Бинарное дерево поиска,Отсортированный,Удаление,0.068493
-Бинарное дерево поиска,Отсортированный,Вставка,0.879394
-Бинарное дерево поиска,Отсортированный,Поиск,0.058173
-Бинарное дерево поиска,Отсортированный,Удаление,0.068654
-Бинарное дерево поиска,Отсортированный,Вставка,0.868870
-Бинарное дерево поиска,Отсортированный,Поиск,0.055234
-Бинарное дерево поиска,Отсортированный,Удаление,0.068269
-Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.882379
-Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.056078
-Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.068386
+Бинарное дерево поиска,Случайный,Вставка,0.004518
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.003011
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.004291
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.004562
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.001002
+Бинарное дерево поиска,Случайный,Вставка,0.003511
+Бинарное дерево поиска,Случайный,Поиск,0.001003
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка (среднее),0.004803
+Бинарное дерево поиска,Случайный,Поиск (среднее),0.000220
+Бинарное дерево поиска,Случайный,Удаление (среднее),0.000351
+Бинарное дерево поиска,Отсортированный,Вставка,0.900417
+Бинарное дерево поиска,Отсортированный,Поиск,0.052424
+Бинарное дерево поиска,Отсортированный,Удаление,0.061880
+Бинарное дерево поиска,Отсортированный,Вставка,0.889120
+Бинарное дерево поиска,Отсортированный,Поиск,0.053632
+Бинарное дерево поиска,Отсортированный,Удаление,0.062189
+Бинарное дерево поиска,Отсортированный,Вставка,0.881248
+Бинарное дерево поиска,Отсортированный,Поиск,0.055152
+Бинарное дерево поиска,Отсортированный,Удаление,0.061669
+Бинарное дерево поиска,Отсортированный,Вставка,0.885030
+Бинарное дерево поиска,Отсортированный,Поиск,0.055543
+Бинарное дерево поиска,Отсортированный,Удаление,0.062420
+Бинарное дерево поиска,Отсортированный,Вставка,0.891156
+Бинарное дерево поиска,Отсортированный,Поиск,0.056153
+Бинарное дерево поиска,Отсортированный,Удаление,0.062845
+Бинарное дерево поиска,Отсортированный,Вставка,0.891750
+Бинарное дерево поиска,Отсортированный,Поиск,0.058122
+Бинарное дерево поиска,Отсортированный,Удаление,0.062453
+Бинарное дерево поиска,Отсортированный,Вставка,0.888773
+Бинарное дерево поиска,Отсортированный,Поиск,0.055758
+Бинарное дерево поиска,Отсортированный,Удаление,0.061916
+Бинарное дерево поиска,Отсортированный,Вставка,0.888226
+Бинарное дерево поиска,Отсортированный,Поиск,0.051592
+Бинарное дерево поиска,Отсортированный,Удаление,0.063321
+Бинарное дерево поиска,Отсортированный,Вставка,0.880372
+Бинарное дерево поиска,Отсортированный,Поиск,0.053502
+Бинарное дерево поиска,Отсортированный,Удаление,0.063089
+Бинарное дерево поиска,Отсортированный,Вставка,0.889972
+Бинарное дерево поиска,Отсортированный,Поиск,0.055111
+Бинарное дерево поиска,Отсортированный,Удаление,0.062310
+Бинарное дерево поиска,Отсортированный,Вставка,0.882075
+Бинарное дерево поиска,Отсортированный,Поиск,0.055492
+Бинарное дерево поиска,Отсортированный,Удаление,0.062256
+Бинарное дерево поиска,Отсортированный,Вставка,0.884940
+Бинарное дерево поиска,Отсортированный,Поиск,0.056025
+Бинарное дерево поиска,Отсортированный,Удаление,0.062441
+Бинарное дерево поиска,Отсортированный,Вставка,0.881076
+Бинарное дерево поиска,Отсортированный,Поиск,0.051258
+Бинарное дерево поиска,Отсортированный,Удаление,0.061709
+Бинарное дерево поиска,Отсортированный,Вставка,0.882319
+Бинарное дерево поиска,Отсортированный,Поиск,0.053783
+Бинарное дерево поиска,Отсортированный,Удаление,0.061596
+Бинарное дерево поиска,Отсортированный,Вставка,0.890215
+Бинарное дерево поиска,Отсортированный,Поиск,0.051424
+Бинарное дерево поиска,Отсортированный,Удаление,0.063194
+Бинарное дерево поиска,Отсортированный,Вставка,0.886962
+Бинарное дерево поиска,Отсортированный,Поиск,0.054314
+Бинарное дерево поиска,Отсортированный,Удаление,0.062138
+Бинарное дерево поиска,Отсортированный,Вставка,0.887173
+Бинарное дерево поиска,Отсортированный,Поиск,0.055819
+Бинарное дерево поиска,Отсортированный,Удаление,0.064069
+Бинарное дерево поиска,Отсортированный,Вставка,0.884293
+Бинарное дерево поиска,Отсортированный,Поиск,0.052575
+Бинарное дерево поиска,Отсортированный,Удаление,0.063521
+Бинарное дерево поиска,Отсортированный,Вставка,0.888483
+Бинарное дерево поиска,Отсортированный,Поиск,0.053261
+Бинарное дерево поиска,Отсортированный,Удаление,0.062347
+Бинарное дерево поиска,Отсортированный,Вставка,0.889441
+Бинарное дерево поиска,Отсортированный,Поиск,0.050554
+Бинарное дерево поиска,Отсортированный,Удаление,0.062955
+Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.887152
+Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.054075
+Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.062516
diff --git a/stepushovgs/data-structures/source/tests/benchmark/main.go b/stepushovgs/data-structures/source/tests/benchmark/main.go
index b2e804e..5993dbb 100644
--- a/stepushovgs/data-structures/source/tests/benchmark/main.go
+++ b/stepushovgs/data-structures/source/tests/benchmark/main.go
@@ -142,12 +142,19 @@ func testOnesInsert(structure DataStructure, data []ds.MyData) float64 {
 func testOnesSearch(structure DataStructure, data []ds.MyData) float64 {
 	start := time.Now()
 
+	// flag := true
+
 	for _, item := range data {
 		structure.Search(item.Name)
 		// p, ok := structure.Search(item.Name)
-		// fmt.Println(p, item.Phone, ok)
+
+		// if flag {
+		// 	flag = ((p == item.Phone) == ok)
+		// }
 	}
 
+	// fmt.Println(flag)
+
 	return time.Since(start).Seconds()
 }