0_outline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_c = [-0.15, -0.1, -0.05, 0, 0.05, 0.1, 0.15]\n",
    "sum_oil = [13.14605, 15.5679, 18.1933, 21.0043, 23.9502, 26.7845, 29.2950]\n",
    "mob_oil = [9.2591, 10.9966, 12.8832, 14.9129, 17.0494, 19.1419, 21.0213]\n",
    "\n",
    "swat_init_sum = [20.6446] * len(p_c)\n",
    "swat_init_mob = [14.6002] * len(p_c)\n",
    "\n",
    "plt.plot(p_c, sum_oil, label='Всей нефти', color='blue')\n",
    "plt.scatter(p_c, sum_oil, color='blue', s=20)\n",
    "plt.plot(p_c, mob_oil, label='Подвижной нефти', color='red')\n",
    "plt.scatter(p_c, mob_oil, color='red', s=20)\n",
    "plt.plot(p_c, swat_init_sum, '--', label='+SWATINIT (в EQUIL: Pc=0)', color='blue', linewidth=0.7)\n",
    "plt.plot(p_c, swat_init_mob, '--', label='+SWATINIT (в EQUIL: Pc=0)', color='red', linewidth=0.7)\n",
    "plt.xlabel('Капиллярное давление на ВНК, бар')\n",
    "plt.ylabel('Начальные запасы нефти, млн. ст.м3')\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kruskal(\n",
    "    num_nodes: int, edges: list[tuple[int, int, int]]\n",
    ") -> list[tuple[int, int, int]]:\n",
    "    \"\"\"\n",
    "    >>> kruskal(4, [(0, 1, 3), (1, 2, 5), (2, 3, 1)])\n",
    "    [(2, 3, 1), (0, 1, 3), (1, 2, 5)]\n",
    "    >>> kruskal(4, [(0, 1, 3), (1, 2, 5), (2, 3, 1), (0, 2, 1), (0, 3, 2)])\n",
    "    [(2, 3, 1), (0, 2, 1), (0, 1, 3)]\n",
    "    >>> kruskal(4, [(0, 1, 3), (1, 2, 5), (2, 3, 1), (0, 2, 1), (0, 3, 2),\n",
    "    ... (2, 1, 1)])\n",
    "    [(2, 3, 1), (0, 2, 1), (2, 1, 1)]\n",
    "    \"\"\"\n",
    "    edges = sorted(edges, key=lambda edge: edge[2])\n",
    "\n",
    "    parent = list(range(num_nodes))\n",
    "\n",
    "    def find_parent(i):\n",
    "        if i != parent[i]:\n",
    "            parent[i] = find_parent(parent[i])\n",
    "        return parent[i]\n",
    "\n",
    "    minimum_spanning_tree_cost = 0\n",
    "    minimum_spanning_tree = []\n",
    "\n",
    "    for edge in edges:\n",
    "        parent_a = find_parent(edge[0])\n",
    "        parent_b = find_parent(edge[1])\n",
    "        if parent_a != parent_b:\n",
    "            minimum_spanning_tree_cost += edge[2]\n",
    "            minimum_spanning_tree.append(edge)\n",
    "            parent[parent_a] = parent_b\n",
    "\n",
    "    return minimum_spanning_tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 3, 1), (0, 1, 3), (1, 2, 5)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kruskal(4, [(0, 1, 3), (1, 2, 5), (2, 3, 1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 2, 1),\n",
       " (6, 7, 1),\n",
       " (0, 5, 2),\n",
       " (3, 8, 2),\n",
       " (6, 9, 2),\n",
       " (13, 14, 2),\n",
       " (5, 6, 3),\n",
       " (3, 4, 3),\n",
       " (2, 8, 3),\n",
       " (8, 9, 3),\n",
       " (11, 14, 3),\n",
       " (9, 14, 3),\n",
       " (5, 10, 4),\n",
       " (7, 12, 4)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_kruskal = kruskal(\n",
    "    15,\n",
    "    [(0, 5, 2), (0, 4, 6), (0, 1, 8), (4, 5, 5), (5, 11, 6), (5, 10, 4), (5, 6, 3), (4, 10, 8), (6, 10, 4), (4, 6, 7),\n",
    "    (3, 4, 3), (1, 7, 5), (3, 6, 4), (1, 3, 4), (1, 2, 1), (2, 3, 9), (2, 8, 3), (3, 8, 2), (6, 7, 1), (7, 8, 5),\n",
    "    (8, 9, 3), (7, 9, 3), (6, 9, 2), (6, 13, 9), (10, 14, 7), (11, 14, 3), (9, 11, 6), (10, 13, 6), (9, 14, 3),\n",
    "    (13, 14, 2), (9, 13, 5), (7, 12, 4), (8, 12, 6), (12, 13, 4)]\n",
    ")\n",
    "save_kruskal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 3, 1),\n",
       " (7, 8, 1),\n",
       " (1, 6, 2),\n",
       " (4, 9, 2),\n",
       " (7, 10, 2),\n",
       " (14, 15, 2),\n",
       " (6, 7, 3),\n",
       " (4, 5, 3),\n",
       " (3, 9, 3),\n",
       " (9, 10, 3),\n",
       " (12, 15, 3),\n",
       " (10, 15, 3),\n",
       " (6, 11, 4),\n",
       " (8, 13, 4)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(i+1, j+1, k) for (i, j, k) in save_kruskal]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://disk.yandex.ru/d/HeQUisnwV0K5EQ/\\%D0\\%92\\%D1\\%82\\%D0\\%BE\\%D1\\%80\\%D0\\%BE\\%D0\\%B9\\%20\\%D0\\%BA\\%D1\\%83\\%D1\\%80\\%D1\\%81\\%20\\%D0\\%BC\\%D0\\%B0\\%D0\\%B3\\%D0\\%B8\\%D1\\%81\\%D1\\%82\\%D1\\%80\\%D0\\%B0\\%D1\\%82\\%D1\\%83\\%D1\\%80\\%D1\\%8B/\\%D0\\%93\\%D0\\%B8\\%D0\\%B4\\%D1\\%80\\%D0\\%BE\\%D0\\%B4\\%D0\\%B8\\%D0\\%BD\\%D0\\%B0\\%D0\\%BC\\%D0\\%B8\\%D1\\%87\\%D0\\%B5\\%D1\\%81\\%D0\\%BA\\%D0\\%BE\\%D0\\%B5\\%20\\%D0\\%BC\\%D0\\%BE\\%D0\\%B4\\%D0\\%B5\\%D0\\%BB\\%D0\\%B8\\%D1\\%80\\%D0\\%BE\\%D0\\%B2\\%D0\\%B0\\%D0\\%BD\\%D0\\%B8\\%D0\\%B5/2022_09_21_\\%D0\\%93\\%D0\\%94\\%D0\\%9C_\\%D0\\%BF\\%D1\\%80\\%D0\\%B0\\%D0\\%BA\\%D1\\%82\\%D0\\%B8\\%D0\\%BA\\%D0\\%B02.mp4\n"
     ]
    }
   ],
   "source": [
    "from urllib.parse import quote\n",
    "initial_link = 'https://disk.yandex.ru/d/HeQUisnwV0K5EQ/Второй%20курс%20магистратуры/Гидродинамическое%20моделирование/2022_09_21_ГДМ_практика2.mp4'\n",
    "initial_link = initial_link.replace('%20', ' ')\n",
    "encoded_link = quote(initial_link)\n",
    "encoded_link = encoded_link.replace('%', '\\%')\n",
    "encoded_link = encoded_link.replace('https\\%3A','https:')\n",
    "print(encoded_link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Исходная ссылка:\n",
      "https://docs.yandex.ru/docs/view?url=ya-disk-public%3A%2F%2FWicxck3PRHBvGgXBKaNry%2FRtiluR1m6cR6Bi14xdyyNQDBM0D9VXsHr3EpLffWemDqZvSgIch5AN9ddz7ydViQ%3D%3D%3A%2FВторой%20курс%20магистратуры%2FГидродинамическое%20моделирование%2FЗадания%2FНевное_решение_уравнения_фильтрации.pdf&name=Невное_решение_уравнения_фильтрации.pdf\n",
      "Закодированная ссылка для XeLaTeX:\n",
      "https://docs.yandex.ru/docs/view?url=ya-disk-public\\%3A\\%2F\\%2FWicxck3PRHBvGgXBKaNry\\%2FRtiluR1m6cR6Bi14xdyyNQDBM0D9VXsHr3EpLffWemDqZvSgIch5AN9ddz7ydViQ\\%3D\\%3D\\%3A\\%2F\\%D0\\%92\\%D1\\%82\\%D0\\%BE\\%D1\\%80\\%D0\\%BE\\%D0\\%B9\\%20\\%D0\\%BA\\%D1\\%83\\%D1\\%80\\%D1\\%81\\%20\\%D0\\%BC\\%D0\\%B0\\%D0\\%B3\\%D0\\%B8\\%D1\\%81\\%D1\\%82\\%D1\\%80\\%D0\\%B0\\%D1\\%82\\%D1\\%83\\%D1\\%80\\%D1\\%8B\\%2F\\%D0\\%93\\%D0\\%B8\\%D0\\%B4\\%D1\\%80\\%D0\\%BE\\%D0\\%B4\\%D0\\%B8\\%D0\\%BD\\%D0\\%B0\\%D0\\%BC\\%D0\\%B8\\%D1\\%87\\%D0\\%B5\\%D1\\%81\\%D0\\%BA\\%D0\\%BE\\%D0\\%B5\\%20\\%D0\\%BC\\%D0\\%BE\\%D0\\%B4\\%D0\\%B5\\%D0\\%BB\\%D0\\%B8\\%D1\\%80\\%D0\\%BE\\%D0\\%B2\\%D0\\%B0\\%D0\\%BD\\%D0\\%B8\\%D0\\%B5\\%2F\\%D0\\%97\\%D0\\%B0\\%D0\\%B4\\%D0\\%B0\\%D0\\%BD\\%D0\\%B8\\%D1\\%8F\\%2F\\%D0\\%9D\\%D0\\%B5\\%D0\\%B2\\%D0\\%BD\\%D0\\%BE\\%D0\\%B5_\\%D1\\%80\\%D0\\%B5\\%D1\\%88\\%D0\\%B5\\%D0\\%BD\\%D0\\%B8\\%D0\\%B5_\\%D1\\%83\\%D1\\%80\\%D0\\%B0\\%D0\\%B2\\%D0\\%BD\\%D0\\%B5\\%D0\\%BD\\%D0\\%B8\\%D1\\%8F_\\%D1\\%84\\%D0\\%B8\\%D0\\%BB\\%D1\\%8C\\%D1\\%82\\%D1\\%80\\%D0\\%B0\\%D1\\%86\\%D0\\%B8\\%D0\\%B8.pdf&name=\\%D0\\%9D\\%D0\\%B5\\%D0\\%B2\\%D0\\%BD\\%D0\\%BE\\%D0\\%B5_\\%D1\\%80\\%D0\\%B5\\%D1\\%88\\%D0\\%B5\\%D0\\%BD\\%D0\\%B8\\%D0\\%B5_\\%D1\\%83\\%D1\\%80\\%D0\\%B0\\%D0\\%B2\\%D0\\%BD\\%D0\\%B5\\%D0\\%BD\\%D0\\%B8\\%D1\\%8F_\\%D1\\%84\\%D0\\%B8\\%D0\\%BB\\%D1\\%8C\\%D1\\%82\\%D1\\%80\\%D0\\%B0\\%D1\\%86\\%D0\\%B8\\%D0\\%B8.pdf\n"
     ]
    }
   ],
   "source": [
    "initial_link = 'https://docs.yandex.ru/docs/view?url=ya-disk-public%3A%2F%2FWicxck3PRHBvGgXBKaNry%2FRtiluR1m6cR6Bi14xdyyNQDBM0D9VXsHr3EpLffWemDqZvSgIch5AN9ddz7ydViQ%3D%3D%3A%2FВторой%20курс%20магистратуры%2FГидродинамическое%20моделирование%2FЗадания%2FНевное_решение_уравнения_фильтрации.pdf&name=Невное_решение_уравнения_фильтрации.pdf'\n",
    "encoded_link = initial_link.replace('%', '%')\n",
    "print('Исходная ссылка:')\n",
    "print(encoded_link)\n",
    "encoded_link = encoded_link.replace('Второй', quote('Второй'))\n",
    "encoded_link = encoded_link.replace('курс', quote('курс'))\n",
    "encoded_link = encoded_link.replace('магистратуры', quote('магистратуры'))\n",
    "encoded_link = encoded_link.replace('Гидродинамическое', quote('Гидродинамическое'))\n",
    "encoded_link = encoded_link.replace('моделирование', quote('моделирование'))\n",
    "encoded_link = encoded_link.replace('Задания', quote('Задания'))\n",
    "encoded_link = encoded_link.replace('Невное_решение_уравнения_фильтрации', quote('Невное_решение_уравнения_фильтрации'))\n",
    "encoded_link = encoded_link.replace('%', '\\%')\n",
    "print('Закодированная ссылка для XeLaTeX:')\n",
    "print(encoded_link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moviepy - Building video 2023_01_12_ГДМ_консультация+экзамен.mp4.\n",
      "MoviePy - Writing audio in 2023_01_12_ГДМ_консультация+экзаменTEMP_MPY_wvf_snd.mp3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                      \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MoviePy - Done.\n",
      "Moviepy - Writing video 2023_01_12_ГДМ_консультация+экзамен.mp4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moviepy - Done !\n",
      "Moviepy - video ready 2023_01_12_ГДМ_консультация+экзамен.mp4\n"
     ]
    }
   ],
   "source": [
    "import moviepy.editor as mov_ed\n",
    "import os\n",
    "\n",
    "recording_1 = mov_ed.VideoFileClip(os.path.join('videos', '2023_01_12_ГДМ_консультации.mp4'))\n",
    "recording_2 = mov_ed.VideoFileClip(os.path.join('videos', '2023_01_12_ГДМ_экзамен.mp4'))\n",
    "\n",
    "recording_1 = recording_1.subclip(t_start=0, t_end='00:04:41.00')\n",
    "\n",
    "#final_recording = mov_ed.concatenate_videoclips([recording_1, recording_2])\n",
    "recording_1.write_videofile('2023_01_12_ГДМ_консультация+экзамен.mp4')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e35407101947112fb25cd4070636ac985004b794245e42b89ebe2c1cd3755b1b"
  },
  "kernelspec": {
   "display_name": "Python 3.10.5 ('venv': venv)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
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  "orig_nbformat": 4
 },
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}