{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 224,
   "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": 225,
   "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": 226,
   "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>0.047621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Связный список</td>\n",
       "      <td>Случайный</td>\n",
       "      <td>Поиск</td>\n",
       "      <td>0.003505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Связный список</td>\n",
       "      <td>Случайный</td>\n",
       "      <td>Удаление</td>\n",
       "      <td>0.005502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Связный список</td>\n",
       "      <td>Случайный</td>\n",
       "      <td>Вставка</td>\n",
       "      <td>0.048707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Связный список</td>\n",
       "      <td>Случайный</td>\n",
       "      <td>Поиск</td>\n",
       "      <td>0.003505</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>0.007040</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>0.013611</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>0.303134</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>0.007562</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>0.013591</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  0.047621\n",
       "1            Связный список        Случайный               Поиск  0.003505\n",
       "2            Связный список        Случайный            Удаление  0.005502\n",
       "3            Связный список        Случайный             Вставка  0.048707\n",
       "4            Связный список        Случайный               Поиск  0.003505\n",
       "..                      ...              ...                 ...       ...\n",
       "193  Бинарное дерево поиска  Отсортированный               Поиск  0.007040\n",
       "194  Бинарное дерево поиска  Отсортированный            Удаление  0.013611\n",
       "195  Бинарное дерево поиска  Отсортированный   Вставка (среднее)  0.303134\n",
       "196  Бинарное дерево поиска  Отсортированный     Поиск (среднее)  0.007562\n",
       "197  Бинарное дерево поиска  Отсортированный  Удаление (среднее)  0.013591\n",
       "\n",
       "[198 rows x 4 columns]"
      ]
     },
     "execution_count": 226,
     "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": 227,
   "id": "a3737f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.003486), np.float64(0.048099), np.float64(0.006181))"
      ]
     },
     "execution_count": 227,
     "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": 228,
   "id": "5434d260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.003512), np.float64(0.048028), np.float64(0.005869))"
      ]
     },
     "execution_count": 228,
     "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": 229,
   "id": "3deed9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.0), np.float64(0.002007), np.float64(0.0))"
      ]
     },
     "execution_count": 229,
     "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": 230,
   "id": "490e5c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.00015), np.float64(0.001794), np.float64(5e-05))"
      ]
     },
     "execution_count": 230,
     "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": 231,
   "id": "9d7274ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.013959), np.float64(0.301662), np.float64(0.007347))"
      ]
     },
     "execution_count": 231,
     "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": 232,
   "id": "92a545c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.013591), np.float64(0.303134), np.float64(0.007562))"
      ]
     },
     "execution_count": 232,
     "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": 233,
   "id": "b2e93d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "countUsers           = 10_000\n",
    "countRepeat          = 10\n",
    "countRandomSearch    = 200\n",
    "countNotExitstSearch = 100\n",
    "countDeletes         = 500\n",
    "\n",
    "\n",
    "ll_col = 'blue'\n",
    "ht_col = 'orange'\n",
    "bst_col = 'green'\n",
    "\n",
    "iterations = range(countRepeat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "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": 235,
   "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": 236,
   "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": []
  }
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