program_lab9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Спецификация к Лабораторной работе №9 \n",
    "\n",
    "# Поиск строки или столбца матрицы по условию. \n",
    "# Обработка исключений.\n",
    "\n",
    "# (Вариант 22)\n",
    "\n",
    "## Абстракция А0\n",
    "### 1. Постановка задачи.\n",
    "#### Задание: \n",
    "Написать программу обработки матрицы в соответствии с условием. \n",
    "\n",
    "#### Условие: \n",
    "Проанализировать матрицу. \n",
    "\n",
    "Проверить, все ли столбцы матрицы упорядочены по неубыванию.\n",
    "\n",
    "Если не все, сообщить номер последнего неупорядоченного."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Уточненная постановка задачи.\n",
    "Дана матрица $A$, состоящая из n строк и m столбцов. \n",
    "\n",
    "Проанализировать столбцы с номерами $i \\in \\{1, ..., m\\}$,\n",
    "\n",
    "выполняется ли условие $A_{1i} \\leq A_{2i} \\leq  ... \\leq A_{ni} (*)$\n",
    "\n",
    "Если не для всех, сообщить наибольший номер $N$ столбца, \n",
    "\n",
    "для которого $(*)$ не выполняется."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Пример с иллюстрацией."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "import numpy\n",
    "import pylab \n",
    "%matplotlib inline\n",
    "pylab.style.use('seaborn-whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 84  73 -20 -62 -47]\n",
      " [ 13  40 -96  16 -47]\n",
      " [ 51 -31  66 -43  39]\n",
      " [-94  70 -12 -13 -15]]\n"
     ]
    }
   ],
   "source": [
    "A1 = numpy.random.randint(-100, 100, size=(4,5))\n",
    "numpy.savetxt('A_Lab9_1.txt', A1, fmt='%5.0f')\n",
    "print(A1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 0 1 1]\n",
      " [0 0 0 0 0]\n",
      " [1 0 1 0 0]] \n",
      "\n",
      " (3, 5)\n"
     ]
    }
   ],
   "source": [
    "A2 = numpy.random.randint(0, 2, size=(3,5))\n",
    "numpy.savetxt('A_Lab9_2.txt', A2, fmt='%5.0f')\n",
    "print(A2, '\\n\\n', A2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False,  True, False, False],\n",
       "       [False,  True,  True,  True,  True],\n",
       "       [ True, False,  True, False, False]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A2 == numpy.sort(A2, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 3, 1, 1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.sum(A2 == numpy.sort(A2, axis=0), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False,  True,  True])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.sum(A2 == numpy.sort(A2, axis=0), axis=0) < A2.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(numpy.sum(A2 == numpy.sort(A2, axis=0), axis=0) < A2.shape[0]) != 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.nonzero(numpy.sum(A2 == numpy.sort(A2, axis=0), axis=0) < A2.shape[0])[0][-1] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0]\n",
      " [0 2]\n",
      " [2 4]] \n",
      "\n",
      " (3, 2)\n"
     ]
    }
   ],
   "source": [
    "A3 = numpy.random.randint(0, 5, size=(3,2))\n",
    "numpy.savetxt('A_Lab9_3.txt', A3, fmt='%5.0f')\n",
    "print(A3, '\\n\\n', A3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4 -9 -3 -9 -6 -8 -6]\n",
      " [-4 -1 -1 -6 -1 -8 -7]\n",
      " [-8 -8 -8 -1 -8 -2 -6]] \n",
      "\n",
      " (3, 7)\n"
     ]
    }
   ],
   "source": [
    "A4 = numpy.random.randint(-10, 0, size=(3,7))\n",
    "numpy.savetxt('A_Lab9_4.txt', A4, fmt='%5.0f')\n",
    "print(A4, '\\n\\n', A4.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4.65686407  4.04533509 -3.30944422  3.04703298 -2.82836498 -1.33122366\n",
      "  -3.73740085 -3.81033447 -1.26949406  4.76996665]\n",
      " [ 1.7333839  -1.78020585  3.62076868 -1.8891421   2.21228912  3.43417954\n",
      "   2.83782822  1.70289398 -1.8955828   3.05625326]\n",
      " [ 2.75286188 -4.36364877 -3.17314753  0.3313719   3.52666878 -4.240443\n",
      "  -3.88176302  4.44243148  0.30038683  1.13223991]] \n",
      "\n",
      " (3, 10)\n"
     ]
    }
   ],
   "source": [
    "A5 = numpy.random.uniform(-5, 5, size=(3,10))\n",
    "numpy.savetxt('A_Lab9_5.txt', A5, fmt='%1.6f')\n",
    "print(A5, '\\n\\n', A5.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Лаб.9\n",
      "==========================================================================================\n",
      "Текстовый файл с элементами матрицы A: A_Lab9_5.txt\n",
      "Текстовый файл с элементами матрицы A и номером столбца N: AN_Lab9_5.txt\n",
      "==========================================================================================\n",
      "Исходная матрица A: \n",
      "\n",
      "[[ 4.656864  4.045335 -3.309444  3.047033 -2.828365 -1.331224 -3.737401\n",
      "  -3.810334 -1.269494  4.769967]\n",
      " [ 1.733384 -1.780206  3.620769 -1.889142  2.212289  3.43418   2.837828\n",
      "   1.702894 -1.895583  3.056253]\n",
      " [ 2.752862 -4.363649 -3.173148  0.331372  3.526669 -4.240443 -3.881763\n",
      "   4.442431  0.300387  1.13224 ]]\n",
      "==========================================================================================\n",
      "Наибольший номер неупорядоченного столбца N (по неубыванию): \n",
      "\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "print(20*' ' + 'Лаб.9\\n' + 90*'=')\n",
    "\n",
    "fin = str(input('Текстовый файл с элементами матрицы A: '))\n",
    "fout = str(input('Текстовый файл с элементами матрицы A и номером столбца N: '))\n",
    "\n",
    "print(90*'=' + '\\n' + 'Исходная матрица A: \\n')\n",
    "A = numpy.loadtxt(fin, dtype='float')\n",
    "print(A)\n",
    "\n",
    "if sum(numpy.sum(A == numpy.sort(A, axis=0), axis=0) < A.shape[0]) != 0:\n",
    "    N = numpy.nonzero(numpy.sum(A == numpy.sort(A, axis=0), axis=0) < A.shape[0])[0][-1] + 1\n",
    "\n",
    "    print(90*'=' + '\\n' + 'Наибольший номер неупорядоченного столбца N (по неубыванию): \\n')\n",
    "    print(N)\n",
    "else:\n",
    "    N = 0\n",
    "    print(90*'=' + '\\n' + 'Все столбцы матрицы сортированы по неубыванию')\n",
    "    \n",
    "with open(fout, 'w') as f:\n",
    "    for i in range(len(A)):\n",
    "        f.write(\"{}\\n\".format(A[i]))\n",
    "    f.write(\"{}\\n\".format(N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Лаб.9\n",
      "==========================================================================================\n",
      "Текстовый файл с элементами матрицы A: A_Lab9_5.txt\n",
      "Текстовый файл с элементами матрицы A и номером столбца N: AN_Lab9_5.txt\n",
      "==========================================================================================\n",
      "Исходная матрица A: \n",
      "\n",
      "[0.656864, 4.045335, -3.309444, 3.047033, -2.828365, -1.331224, -3.737401, -3.810334, -1.269494, 4.769967]\n",
      "[0.733384, -1.780206, 3.620769, -1.889142, 2.212289, 3.43418, 2.837828, 1.702894, -1.895583, 3.056253]\n",
      "[0.752862, -4.363649, -3.173148, 0.331372, 3.526669, -4.240443, -3.881763, 4.442431, 0.300387, 1.13224]\n",
      "==========================================================================================\n",
      "Наибольший номер неупорядоченного столбца N (по неубыванию): \n",
      "\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "# python only\n",
    "print(20*' ' + 'Лаб.9\\n' + 90*'=')\n",
    "\n",
    "fin = str(input('Текстовый файл с элементами матрицы A: '))\n",
    "fout = str(input('Текстовый файл с элементами матрицы A и номером столбца N: '))\n",
    "\n",
    "print(90*'=' + '\\n' + 'Исходная матрица A: \\n')\n",
    "\n",
    "with open(fin, 'r') as f:\n",
    "    A = f.readlines()\n",
    "A = [x[1:-1].split() for x in A]\n",
    "A = [[float(x) for x in A[i]] for i in range(len(A))]\n",
    "[print(A[i]) for i in range(len(A))]\n",
    "\n",
    "N = 0\n",
    "\n",
    "for j in range(len(A[0])):\n",
    "    for i in range(len(A)-1):\n",
    "        if A[i][j] > A[i+1][j]:\n",
    "            N = j + 1\n",
    "if N == 0:\n",
    "    print(90*'=' + '\\n' + 'Все столбцы матрицы сортированы по неубыванию')\n",
    "    \n",
    "print(90*'=' + '\\n' + 'Наибольший номер неупорядоченного столбца N (по неубыванию): \\n')\n",
    "print(N)    \n",
    "    \n",
    "with open(fout, 'w') as f:\n",
    "    for i in range(len(A)):\n",
    "        f.write(\"{}\\n\".format(A[i]))\n",
    "    f.write(\"{}\\n\".format(N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.figure(figsize=(10,5))\n",
    "col = ['steelblue', 'darkred']\n",
    "s = numpy.sum(A == numpy.sort(A, axis=0), axis=0) < len(A)\n",
    "for j in range(len(A[0])):\n",
    "    pylab.plot(range(len(A)), [A[i][j] for i in range(len(A))], \n",
    "               '-o', c=col[int(s[j])], \n",
    "               label='j = ' + str(j+1))\n",
    "\n",
    "pylab.suptitle('N = ' + str(N), fontsize=15)\n",
    "pylab.xlabel('i', fontsize=15) \n",
    "pylab.ylabel(r'$A_{ij}$', fontsize=15)\n",
    "pylab.xticks(range(-1, len(A)+1), range(len(A)+2));\n",
    "pylab.legend(title='Столбцы матрицы A', \n",
    "             fontsize=15, shadow=True, \n",
    "             bbox_to_anchor=(1.5,1.0));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Таблица данных \n",
    "\n",
    "| Класс | Имя | Описание (смысл, диапазон, точность) | Тип | Структура | Формат в/в |\n",
    "| :-: | :-: | :-: | :-: | :-: | :-: |\n",
    "| входные данные | fin | название текстового файла с элементами матрицы А | символ | строка | \"+XX.txt\" |\n",
    "| входные данные | fout | название текстового файла с элементами матрицы А | символ | строка | \"+XX.txt\" |\n",
    "| входные данные | A | вводимые из текстового файла числа исходной матрицы | вещ | двумерный массив | +XX (:5:6) |\n",
    "| выходные данные | N | выводимый в текстовый файл наибольший номер неупорядоченного столбца | цел | простая переменная | +XX (:5) |\n",
    "| промежуточные данные | i | индекс текущего элемента, 1≤i≤50 | цел | простая переменная | --- |\n",
    "| промежуточные данные | j | индекс текущего элемента, 1≤j≤50 | цел | простая переменная | --- |\n",
    "| промежуточные данные | m | массив максимальных значений для каждого столбца | вещ | одномерный массив | --- |\n",
    "| промежуточные данные | c | массив для учета количества элементов, нарушающих порядок неубывания | цел | одномерный массив | --- |\n",
    "\n",
    "_! в Delphi выход из цикла с 1 до 20 с шагом +1 происходит при значении 21, из цикла с 20 до 1 – при 0_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Входная форма\n",
    "- обр 1.1 Текстовый файл с элементами матрицы A: $<fin>$       \n",
    "- обр 1.2 Текстовый файл с элементами матрицы A и номером столбца N: $<fout>$\n",
    "- обр 2.1 Исходная матрица A:\n",
    "- обр 2.2 $ < A[11] >  < A[12] >  …  < A[1m] > \\\\ ... \\\\ < A[n1] >  < A[n2] >  …  < A[nm] >$\n",
    "\n",
    "\n",
    "### 6. Выходная форма\n",
    "\n",
    "- обр 3 Лаб. 9\n",
    "- обр 4.1 Текстовый файл с элементами матрицы A: $<fin>$       \n",
    "- обр 4.2 Текстовый файл с элементами матрицы A и номером столбца N:: $<fout>$\n",
    "- обр 5.1 Исходная матрица A:\n",
    "- обр 5.2 $ < A[11] >  < A[12] >  …  < A[1m] > \\\\ ... \\\\ < A[n1] >  < A[n2] >  …  < A[nm] >$\n",
    "- обр 6.1 Наибольший номер неупорядоченного столбца N (по неубыванию): $ < N > $\n",
    "- обр 6.2 Все столбцы матрицы сортированы по неубыванию\n",
    "\n",
    "### 7. Аномалии\n",
    "\n",
    "-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Функциональные тесты\n",
    "\n",
    "| Исходные данные | - | - |Результаты | - | Тест |\n",
    "| :-: | :-: | :-: | :-: | :-: | :-: |\n",
    "| fin | fout | A | N | Сообщение |№ |\n",
    "| A_Lab9_1.txt | AN_Lab9_1.txt | `[[ 84  73 -20 -62 -47] [ 13  40 -96  16 -47] [ 51 -31  66 -43  39] [-94  70 -12 -13 -15]]` | `5` | - | 1 | \n",
    "| - | - | - | - | - | - |\n",
    "| A_Lab9_2.txt | AN_Lab9_2.txt | `[[1 1 0 1 1] [0 0 0 0 0] [1 0 1 0 0]]` | `5` | - | 2 |\n",
    "| - | - | - | - | - | - |\n",
    "| A_Lab9_3.txt | AN_Lab9_3.txt | `[[0 0] [0 2] [2 4]]` | `0` | Все столбцы матрицы сортированы по неубыванию | 3 |\n",
    "| - | - | - | - | - | - |\n",
    "| A_Lab9_4.txt | AN_Lab9_4.txt | `[[-4 -9 -3 -9 -6 -8 -6] [-4 -1 -1 -6 -1 -8 -7] [-8 -8 -8 -1 -8 -2 -6]]` | `7` | - | 4 |\n",
    "| - | - | - | - | - | - |\n",
    "| A_Lab9_5.txt | AN_Lab9_5.txt | `[[ 4.65686407  4.04533509 -3.30944422  3.04703298 -2.82836498 -1.33122366 -3.73740085 -3.81033447 -1.26949406  4.76996665] [ 1.7333839  -1.78020585  3.62076868 -1.8891421   2.21228912  3.43417954 2.83782822  1.70289398 -1.8955828   3.05625326] [ 2.75286188 -4.36364877 -3.17314753  0.3313719   3.52666878 -4.240443 -3.88176302  4.44243148  0.30038683  1.13223991]]` | `10` | - | 5 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| № теста | Входные  данные | - | - | Ожидаемый результат | - | Смысл теста |\n",
    "| :-: | :-: | :-: | :-: | :-: | :-: | :-: |\n",
    "| - | - | - | - | - | - | - |\n",
    "| 1 | fin = 'A_Lab9_1.txt' | fout = 'AN_Lab9_1.txt' |  A = `[[ 84  73 -20 -62 -47] [ 13  40 -96  16 -47] [ 51 -31  66 -43  39] [-94  70 -12 -13 -15]]` | N = `5` | - | Подтвердить правильность расчетов в случае широкого спектра положительных и отрицательных чисел |\n",
    "| - | - | - | - | - | - | - |\n",
    "| 2 | fin = 'A_Lab9_2.txt' | fout = 'AN_Lab9_2.txt' |  A = `[[1 1 0 1 1] [0 0 0 0 0] [1 0 1 0 0]]` | N = `5` | - | Протестировать простейший бинарный случай |\n",
    "| - | - | - | - | - | - | - |\n",
    "| 3 | fin = 'A_Lab9_3.txt' | fout = 'AN_Lab9_3.txt' |  A = `[[0 0] [0 2] [2 4]]` | N = `0` | Все столбцы матрицы сортированы по неубыванию | Протестировать массив в узком диапазоне неотрицательных чисел |\n",
    "| - | - | - | - | - | - | - |\n",
    "| 4 | fin = 'A_Lab9_4.txt' | fout = 'AN_Lab9_4.txt' |  A = `[[-4 -9 -3 -9 -6 -8 -6] [-4 -1 -1 -6 -1 -8 -7] [-8 -8 -8 -1 -8 -2 -6]]` | N = `7` | - | Протестировать массив в узком диапазоне положительных и отрицательных чисел |\n",
    "| - | - | - | - | - | - | - |\n",
    "| 5 | fin = 'A_Lab9_5.txt' | fout = 'AN_Lab9_5.txt' |  A = `[[ 4.65686407  4.04533509 -3.30944422  3.04703298 -2.82836498 -1.33122366 -3.73740085 -3.81033447 -1.26949406  4.76996665] [ 1.7333839  -1.78020585  3.62076868 -1.8891421   2.21228912  3.43417954 2.83782822  1.70289398 -1.8955828   3.05625326] [ 2.75286188 -4.36364877 -3.17314753  0.3313719   3.52666878 -4.240443 -3.88176302  4.44243148  0.30038683  1.13223991]]` | N = `10` | - | Подтвердить правильность расчетов в случае действительных чисел |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| - | Результаты | № теста |\n",
    "| :-: | :-: | :-: |\n",
    "| - | - | - |\n",
    "| N | Максимальная вычислительная нагрузка = 15 сравнений | 1 |\n",
    "| - | Соответствует ожидаемым числовым значениям | - |\n",
    "| N | Максимальная вычислительная нагрузка = 10 сравнений | 2 |\n",
    "| - | Соответствует ожидаемым числовым значениям, легко подтверждается визуально | - |\n",
    "| N | Максимальная вычислительная нагрузка = 3 сравнения  | 3 |\n",
    "| - | Нет неупорядоченных столбцов | - |\n",
    "| N | Максимальная вычислительная нагрузка = 14 сравнений | 4 |\n",
    "| - | Соответствует ожидаемым числовым значениям | - |\n",
    "| N | Максимальная вычислительная нагрузка = 20 сравнений | 5 |\n",
    "| - | Соответствует ожидаемым числовым значениям | - |\n",
    "| - | - | - |\n",
    "| Сообщение | - | 1 |\n",
    "| Сообщение | - | 2 |\n",
    "| Сообщение | Все столбцы матрицы сортированы по неубыванию | 3 |\n",
    "| Сообщение | - | 4 |\n",
    "| Сообщение | - | 5 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Метод\n",
    "Отделим ввод-вывод от обработки данных и разделим задачу на три подзадачи:\n",
    "\n",
    "1. _Подзадача А 0.1._ \n",
    "\n",
    "   Запросить названия текстовых файлов ввода-вывода, \n",
    "   \n",
    "   считать из текстового файла ввода исходные данные (обр.1-2), \n",
    "   \n",
    "   затем вывести их на экран (обр.3-5) для визуального подтверждения.\n",
    "\n",
    "2. _Подзадача А 0.2._ \n",
    "\n",
    "   Решение поставленной задачи: \n",
    "\n",
    "   на основе введенных исходных данных (матрицы `A`) определить \n",
    "   \n",
    "   номер последнего неупорядоченного по неубыванию столбца.\n",
    "    \n",
    "3. _Подзадача А 0.3._ \n",
    "\n",
    "   Ввести полученные результаты (`N`) на экран (обр.6.1, 6.2) и \n",
    "   \n",
    "   записать в файл вывода исходный массив и полученный результат.\n",
    "   \n",
    "---\n",
    "\n",
    "Ввод-вывод тривиален и представляет собой чисто техническую задачу. \n",
    "\n",
    "Для осуществления процесса ввода-вывода и обработки массива потребуются промежуточные переменные: \n",
    "\n",
    "- `i` – индекс текущей строки матрицы, \n",
    "\n",
    "- `j` – индекс текущего столбца матрицы,\n",
    "\n",
    "- `m` - массив максимальный значений для каждого столбца,\n",
    "\n",
    "- `c` - массив для учета количества элементов, нарушающих порядок неубывания\n",
    "\n",
    "Их следует добавить в таблицу данных. \n",
    "\n",
    "Алгоритм необходим только для обработка входных данных и расчета результата. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Алгоритм\n",
    "\n",
    "[Sample diagrams](http://blockdiag.com/en/blockdiag/examples.html#sample-diagrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting block_diagram\n"
     ]
    }
   ],
   "source": [
    "%%file block_diagram\n",
    "\n",
    "blockdiag {\n",
    "    orientation = portrait;\n",
    "    class start_end [shape = ellipse, fontsize = 15, color = lightskyblue];\n",
    "    class input_out [shape = flowchart.input, fontsize = 15, width = 700, color = lightcyan];\n",
    "    class main_box [shape = box, width = 600, height = 150, fontsize = 18];\n",
    "    \n",
    "    Начало -> \n",
    "    \"Вывод заголовка по обр.3 на экран\" -> \n",
    "    \"Ввод имени файла по обр.1.1\" -> \"Ввод имени файла по обр.1.2\" ->\n",
    "    \"Вывод пояснения по обр.2.1\" -> \"Ввод А из файла по обр.2.2\" ->\n",
    "    \"Вывод имени файла по обр.4.1 на экран\" -> \"Вывод имени файла по обр.4.2 на экран\" ->\n",
    "    \"Вывод пояснения по обр.5.1 на экран\" -> \"Вывод А на экран и в файл по обр.5.2\" ->\n",
    "    \"Определение наибольшего номера\\n неупорядоченного по неубыванию\\n столбца N\" ->\n",
    "    \"Вывод N или сообщения на экран и в файл по обр.6.1,6.2\" ->\n",
    "    Конец;\n",
    "\n",
    "    Начало, Конец [class = \"start_end\"];\n",
    "    \"Ввод имени файла по обр.1.1\", \"Ввод имени файла по обр.1.2\" [class = \"input_out\"];\n",
    "    \"Вывод пояснения по обр.2.1\", \"Ввод А из файла по обр.2.2\" [class = \"input_out\"];\n",
    "    \"Вывод имени файла по обр.4.1 на экран\", \"Вывод имени файла по обр.4.2 на экран\" [class = \"input_out\"];\n",
    "    \"Вывод пояснения по обр.5.1 на экран\", \"Вывод А на экран и в файл по обр.5.2\" [class = \"input_out\"]; \n",
    "    \"Вывод заголовка по обр.3 на экран\", \"Вывод N или сообщения на экран и в файл по обр.6.1,6.2\" [class = \"input_out\"];\n",
    "    \"Определение наибольшего номера\\n неупорядоченного по неубыванию\\n столбца N\" [class = \"main_box\"];\n",
    "    \n",
    "    \"Вывод А на экран и в файл по обр.5.2\" -> \n",
    "    \"Определение наибольшего номера\\n неупорядоченного по неубыванию\\n столбца N\" [label = 'Вх. А', fontsize = 18];\n",
    "    \"Определение наибольшего номера\\n неупорядоченного по неубыванию\\n столбца N\" ->\n",
    "    \"Вывод N или сообщения на экран и в файл по обр.6.1,6.2\" [label = 'Вых. N', fontsize = 18];\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "!blockdiag block_diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"block_diagram.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Программа на <i>Delphi</i>. \n",
    "\n",
    "__Диалоговый вариант {ввод названий файлов и данных из текстового файла, вывод на экран и в текстовый файл}__\n",
    "\n",
    "Синтаксис:\n",
    "\n",
    "[Pascal Tutorial](https://www.tutorialspoint.com/pascal/index.htm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "program Lab9; //сохранить как lab9.dpr (DPR – Delphi PRoject)  \n",
    "{$APPTYPE CONSOLE}\n",
    "Uses Windows; // \n",
    "\n",
    "    var\n",
    "        i, j, N: integer;\n",
    "        fin, fout: string;\n",
    "        tfin, tfout: TextFile;\n",
    "        A: array [0..1000, 0..1000] of real;\n",
    "\n",
    "    begin\n",
    "        setConsoleCP(1251); // \n",
    "        setConsoleOutputCP(1251); // \n",
    "\n",
    "        {A0.1, A0.3 - ввод-вывод входных данных}\n",
    "        writeln('Лаб.9' :40); {вывод заголовка c переходом на следующую строку по обр.3}\n",
    "        \n",
    "        {ввод-вывод строковой переменной - имени файла с входными данными по обр.1.1, 4.1}\n",
    "        writeln('Текстовый файл с элементами матрицы A: '); \n",
    "        readln(fin);\n",
    "        writeln(fin);\n",
    "\n",
    "        {ввод-вывод строковой переменной - имени файла с выходными данными по обр.1.2, 4.2}\n",
    "        writeln('Текстовый файл с элементами матрицы A и номером столбца N: '); \n",
    "        readln(fout); \n",
    "        writeln(fout);\n",
    "        \n",
    "        {ввод-вывод исходного массивa A по обр.2.1, 2.2, 5.1, 5.2}\n",
    "        writeln('Исходная матрица A: '); {вывод пояснения с переходом на следующую строку}\n",
    "        \n",
    "        AssignFile(tfin, fin); reset(tfin); {открыть файл для чтения} \n",
    "        AssignFile(tfout, fout); rewrite(tfout); {открыть файл для записи}\n",
    "            \n",
    "        i :=1;\n",
    "        while not eof(tfin) do\n",
    "        begin\n",
    "            j := 1;\n",
    "            while not eof(tfin) do\n",
    "            begin\n",
    "                read(tfin, A[i,j]);\n",
    "                write(A[i,j], ' '); {вывод элемента на экран}\n",
    "                write(tfout, A[i,j]:5:6); {вывод элемента в файл}\n",
    "                j := j + 1;\n",
    "            end;\n",
    "            readln; writeln;\n",
    "            writeln(tfout);\n",
    "            i := i + 1;\n",
    "        end;\n",
    "            \n",
    "        CloseFile(tfin); {закрыть файл для чтения} \n",
    "   \n",
    "        for i:=1 to 80 do \n",
    "            write('='); {отделим визуально чертой и строкой введенные и выводимые значения}\n",
    "        writeln;\n",
    "        \n",
    "        N := 5; {заглушка для теста 1, 2}             \n",
    "        // N := 0; writeln('Все столбцы матрицы сортированы по неубыванию'); {заглушка для теста 3}\n",
    "        // N := 7; {заглушка для теста 4}\n",
    "        // N := 10; {заглушка для теста 5}    \n",
    "                    \n",
    "        writeln('Наибольший номер неупорядоченного столбца по неубыванию N:', N:2);\n",
    "        {вывод переменной и переход на следующую строку на экране по обр.6.1,6.2}             \n",
    "        writeln(tfout, N:2); \n",
    "        {вывод переменной и переход на следующую строку в файле по обр.6.1,6.2}\n",
    "        \n",
    "        CloseFile(tfout); {закрыть файл для записи}\n",
    "        \n",
    "    end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Раскрытие абстракции А0.2\n",
    "_Поскольку условие всей задачи совпадает (за исключением необходимости ввода-вывода) с условием выделенной подзадачи,_\n",
    "\n",
    "_вместо полной спецификации с пунктами 1-11 выполнено просто дополнение к пунктам 9-11._\n",
    "\n",
    "### 9. Метод\n",
    "Пусть\n",
    "\n",
    "- $i$ – номер текущей строки,\n",
    "\n",
    "- $j$ – номер текущего столбца,\n",
    "\n",
    "- $A[i][j]$ – соответствующий элемент исходной матрицы A,\n",
    "\n",
    "- $N$ - наибольший номер неупорядоченного по неубыванию столбца.\n",
    "  \n",
    "В циклах по каждой $i$-ой строке по каждому $j$-му столбцу выполняются следующие действия:\n",
    "\n",
    "- проверяется выполнение условия: $\\exists i: 0<i<n+1 \\land A_{ij} > A_{i+1j}$;\n",
    "\n",
    "- если выражение верно хотя бы для одного $i$, то \n",
    "\n",
    "  - столбец $j$ не упорядочен по неубыванию и \n",
    "  \n",
    "  - значение номера $N$ необходимо обновить. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Алгоритм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting block_diagram2\n"
     ]
    }
   ],
   "source": [
    "%%file block_diagram2\n",
    "\n",
    "blockdiag {\n",
    "    orientation = portrait;\n",
    "    \n",
    "    class yes [thick, label = \"Да\", color = blue, textcolor = blue];\n",
    "    class no [thick, label = \"Нет\", color = red, textcolor = red];\n",
    "    class start_end [shape = ellipse, fontsize = 18, color = lightskyblue];\n",
    "    class input_out [shape = flowchart.input, fontsize = 18, \n",
    "                     width = 400, color = lightcyan];\n",
    "    class loop_in [shape = flowchart.loopin, fontsize = 18, \n",
    "                   width = 200, color = lightsteelblue];\n",
    "    class loop_out [shape = flowchart.loopout, fontsize = 18, \n",
    "                    width = 200, color = lightsteelblue];\n",
    "    class condition [shape = flowchart.condition, fontsize = 18, \n",
    "                     width = 250, height = 100, color = lightsteelblue];\n",
    "    class command_box [shape = roundedbox, fontsize = 18, width = 300];\n",
    "    \n",
    "    Начало -> \"N := 0\" -> \"i := 1; +1; n\" -> \n",
    "    \"j := 1; +1; n\" -> \"i = 1\" -> \"m[j] := A[1,j]; c[j] := 0\" -> \"j = n\";\n",
    "    \"i = 1\" -> \"A[i,j] >= m[j]\" -> \"m[j] := A[i,j]\" -> \"c[j] > 0\" ->\n",
    "    \"j = n\" -> \"i = n\" -> Конец; \n",
    "    \"A[i,j] >= m[j]\" -> \"c[j] := c[j] + 1\" -> \n",
    "    \"c[j] > 0\" -> \"N := j\" -> \"j = n\";\n",
    "    \n",
    "    Начало, Конец [class = \"start_end\"];\n",
    "    \"i := 1; +1; n\", \"j := 1; +1; n\" [class = \"loop_in\"];\n",
    "    \"i = n\", \"j = n\" [class = \"loop_out\"];\n",
    "    \"i = 1\", \"A[i,j] >= m[j]\", \"c[j] > 0\" [class = \"condition\"];\n",
    "    \"m[j] := A[1,j]; c[j] := 0\", \"m[j] := A[i,j]\" [class = \"command_box\"];\n",
    "    \"N := 0\", \"c[j] := c[j] + 1\", \"N := j\" [class = \"command_box\"];\n",
    "    \n",
    "    \"i = 1\" -> \"m[j] := A[1,j]; c[j] := 0\"  [class = 'yes'];\n",
    "    \"i = 1\" -> \"A[i,j] >= m[j]\" [class = 'no'];\n",
    "    \"A[i,j] >= m[j]\" -> \"m[j] := A[i,j]\"  [class = 'yes'];\n",
    "    \"A[i,j] >= m[j]\" -> \"c[j] := c[j] + 1\" [class = 'no'];\n",
    "    \"c[j] > 0\" -> \"N := j\" [class = 'yes'];\n",
    "    \"c[j] > 0\" -> \"j = n\" [class = 'no'];\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "!blockdiag block_diagram2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"block_diagram2.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Программа на <i>Delphi</i>.\n",
    "#### Программный код раскрытия абстракции."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i := 1; N := 0;\n",
    "            \n",
    "while not eof(tfin) do\n",
    "begin\n",
    "    j := 1; \n",
    "    while not eof(tfin) do\n",
    "    begin\n",
    "        read(tfin, A[i,j]);\n",
    "        write(A[i,j]:5:6, ' ');\n",
    "        write(tfout, A[i,j]:5:6);\n",
    "        \n",
    "        if i = 1 then \n",
    "        begin\n",
    "            m[j] := A[1,j];\n",
    "            c[j] := 0;\n",
    "        end\n",
    "        else \n",
    "            if A[i,j] >= m[j] then\n",
    "                m[j] := A[i,j]\n",
    "            else c[j] := c[j] + 1; \n",
    "                \n",
    "        if c[j] > 0 then N := j;\n",
    "        j := j + 1;\n",
    "        end;\n",
    "        \n",
    "    readln; writeln; writeln(tfout);\n",
    "    i := i + 1;\n",
    "end;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Вариант программы для чтения и записи текстовых файлов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "program Lab9; //сохранить как lab9.dpr (DPR – Delphi PRoject)  \n",
    "{$APPTYPE CONSOLE}\n",
    "Uses Windows; // для русификации \n",
    "\n",
    "    var\n",
    "    i, j: integer;\n",
    "    N: integer;\n",
    "    fin, fout: string;\n",
    "    tfin, tfout: TextFile;    \n",
    "    A: array [0..1000, 0..1000] of real;    \n",
    "    m: array [0..1000] of real; \n",
    "    c: array [0..1000] of integer; \n",
    "            \n",
    "    begin\n",
    "        setConsoleCP(1251); // для ввода  русификация и \n",
    "        setConsoleOutputCP(1251); // для вывода \n",
    "\n",
    "        {A0.1, A0.3 - ввод-вывод входных данных}\n",
    "        writeln('Лаб.9' :40); {вывод заголовка c переходом на следующую строку по обр.3}\n",
    "        writeln;\n",
    "        \n",
    "        {ввод-вывод строковой переменной - имени файла с входными данными по обр.1.1, 4.1}\n",
    "        writeln('Текстовый файл с элементами матрицы A: '); \n",
    "        readln(fin);\n",
    "        writeln(fin);\n",
    "\n",
    "        {ввод-вывод строковой переменной - имени файла с выходными данными по обр.1.2, 4.2}\n",
    "        writeln('Текстовый файл с элементами матрицы A и номером столбца N: '); \n",
    "        readln(fout); \n",
    "        writeln(fout);\n",
    "        \n",
    "        AssignFile(tfin, fin); reset(tfin); {открыть файл для чтения} \n",
    "        AssignFile(tfout, fout); rewrite(tfout); {открыть файл для записи}\n",
    "    \n",
    "        writeln;\n",
    "        {ввод-вывод исходной матрицы A по обр.2.1, 2.2, 5.1, 5.2}    \n",
    "        writeln('Исходная матрица A: '); \n",
    "        {вывод пояснения с переходом на следующую строку}\n",
    "            \n",
    "        i := 1; N := 0;\n",
    "            \n",
    "        while not eof(tfin) do\n",
    "        begin\n",
    "            j := 1; \n",
    "            while not eof(tfin) do\n",
    "            begin\n",
    "                read(tfin, A[i,j]);\n",
    "                write(A[i,j]:5:6, ' ');\n",
    "                write(tfout, A[i,j]:5:6);\n",
    "            \n",
    "                if i = 1 then \n",
    "                begin\n",
    "                    m[j] := A[1,j];\n",
    "                    c[j] := 0;\n",
    "                end\n",
    "                else \n",
    "                    if A[i,j] >= m[j] then\n",
    "                        m[j] := A[i,j]\n",
    "                    else c[j] := c[j] + 1; \n",
    "                \n",
    "                if c[j] > 0 then N := j;\n",
    "                j := j + 1;\n",
    "            end;\n",
    "        \n",
    "            readln; writeln; writeln(tfout);                \n",
    "            i := i + 1;\n",
    "        end;\n",
    "\n",
    "        writeln; writeln(tfout);\n",
    "    \n",
    "        {вывод пояснения с переходом на следующую строку}\n",
    "        if N = 0 then writeln('Все столбцы матрицы сортированы по неубыванию');            \n",
    "        writeln('Наибольший номер неупорядоченного столбца по неубыванию N:', N:2);\n",
    "        {вывод переменной и переход на следующую строку на экране по обр.6.1,6.2}             \n",
    "        writeln(tfout, N:2); \n",
    "        {вывод переменной и переход на следующую строку в файле по обр.6.1,6.2}\n",
    "    end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Вариант программы с именами файлов, заданными в качестве параметров."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "program Lab9; //сохранить как lab9.dpr (DPR – Delphi PRoject)  \n",
    "{$APPTYPE CONSOLE}\n",
    "Uses Windows; // для русификации \n",
    "\n",
    "    var\n",
    "    i, j: integer;\n",
    "    N: integer;\n",
    "    A: array [0..1000, 0..1000] of real;    \n",
    "    m: array [0..1000] of real; \n",
    "    c: array [0..1000] of integer; \n",
    "            \n",
    "    begin\n",
    "        setConsoleCP(1251); // для ввода  русификация и \n",
    "        setConsoleOutputCP(1251); // для вывода \n",
    "\n",
    "        {A0.1, A0.3 - ввод-вывод входных данных}\n",
    "        writeln('Лаб.9' :40); {вывод заголовка c переходом на следующую строку по обр.3}\n",
    "        writeln;\n",
    "        \n",
    "        if (ParamCount<2) then \n",
    "        begin \n",
    "        writeln('CritError: Не заданы параметры программы’);\n",
    "        readln: exit;\n",
    "        end;\n",
    "        \n",
    "        AssignFile(tfin, ParamStr(1)); reset(tfin); {открыть файл для чтения} \n",
    "        AssignFile(tfout, ParamStr(2)); rewrite(tfout); {открыть файл для записи}\n",
    "    \n",
    "        writeln;\n",
    "        {ввод-вывод исходной матрицы A по обр.2.1, 2.2, 5.1, 5.2}    \n",
    "        writeln('Исходная матрица A: '); \n",
    "        {вывод пояснения с переходом на следующую строку}\n",
    "            \n",
    "        i := 1; N := 0;\n",
    "            \n",
    "        while not eof(tfin) do\n",
    "        begin\n",
    "            j := 1; \n",
    "            while not eof(tfin) do\n",
    "            begin\n",
    "                read(tfin, A[i,j]);\n",
    "                write(A[i,j]:5:6, ' ');\n",
    "                write(tfout, A[i,j]:5:6);\n",
    "            \n",
    "                if i = 1 then \n",
    "                begin\n",
    "                    m[j] := A[1,j];\n",
    "                    c[j] := 0;\n",
    "                end\n",
    "                else \n",
    "                    if A[i,j] >= m[j] then\n",
    "                        m[j] := A[i,j]\n",
    "                    else c[j] := c[j] + 1; \n",
    "                \n",
    "                if c[j] > 0 then N := j;\n",
    "                j := j + 1;\n",
    "            end;\n",
    "        \n",
    "            readln; writeln; writeln(tfout);                \n",
    "            i := i + 1;\n",
    "        end;\n",
    "\n",
    "        writeln; writeln(tfout);\n",
    "                \n",
    "        {вывод пояснения с переходом на следующую строку}\n",
    "        if N = 0 then writeln('Все столбцы матрицы сортированы по неубыванию');            \n",
    "        writeln('Наибольший номер неупорядоченного столбца по неубыванию N:', N:2);\n",
    "        {вывод переменной и переход на следующую строку на экране по обр.6.1,6.2}             \n",
    "        writeln(tfout, N:2); \n",
    "        {вывод переменной и переход на следующую строку в файле по обр.6.1,6.2}          \n",
    "    \n",
    "    end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Вариант программы для онлайн-компилятора.\n",
    "[Compile and Execute Pascal Online](http://www.compileonline.com/compile_pascal_online.php)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "// main.pas\n",
    "Program Lab9;\n",
    "\n",
    "var\n",
    "    i, j: integer;\n",
    "    N: integer;\n",
    "    A: array [0..1000, 0..1000] of real;    \n",
    "    m: array [0..1000] of real; \n",
    "    c: array [0..1000] of integer;\n",
    "        \n",
    "begin\n",
    "\n",
    "    {A0.1, A0.3 - ввод-вывод входных данных}\n",
    "    writeln('Лаб.9' :40); {вывод заголовка c переходом на следующую строку по обр.3}\n",
    "    \n",
    "    writeln;\n",
    "    {ввод-вывод исходной матрицы A по обр.2.1, 2.2, 5.1, 5.2}    \n",
    "    writeln('Исходная матрица A: '); \n",
    "    {вывод пояснения с переходом на следующую строку}\n",
    "            \n",
    "    i := 1; N := 0;\n",
    "            \n",
    "    while not eoln do\n",
    "    begin\n",
    "        j := 1; \n",
    "        while not eoln do\n",
    "        begin\n",
    "            read(A[i,j]);\n",
    "            write(A[i,j]:5:6, ' ');\n",
    "            \n",
    "            if i = 1 then \n",
    "            begin\n",
    "                m[j] := A[1,j];\n",
    "                c[j] := 0;\n",
    "            end\n",
    "            else \n",
    "                if A[i,j] >= m[j] then\n",
    "                    m[j] := A[i,j]\n",
    "                else c[j] := c[j] + 1; \n",
    "                \n",
    "            if c[j] > 0 then N := j;\n",
    "            j := j + 1;\n",
    "        end;\n",
    "        \n",
    "        readln; writeln;\n",
    "        i := i + 1;\n",
    "    end;\n",
    "\n",
    "    writeln;\n",
    "    \n",
    "    {вывод пояснения с переходом на следующую строку}\n",
    "    if N = 0 then writeln('Все столбцы матрицы сортированы по неубыванию');            \n",
    "    writeln('Наибольший номер неупорядоченного столбца по неубыванию N:', N:2);\n",
    "    {вывод переменной и переход на следующую строку на экране по обр.6.1,6.2}             \n",
    "    \n",
    "end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "// STDIN\n",
    "-4 -9 -3 -9 -6 -8 -6\n",
    "-4 -1 -1 -6 -1 -8 -7\n",
    "-8 -8 -8 -1 -8 -2 -6\n"
   ]
  }
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