standardcalculation_sage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"color:##3636ff;\">Спецификация к типовому расчету</h1>\n",
    "# \"Процедуры, функции, модули\"\n",
    "## по дисциплине «Программирование»\n",
    "# 1 семестр\n",
    "# (Вариант 22)\n",
    "## Абстракция А0\n",
    "### 1. Постановка задачи.\n",
    "#### Задание:\n",
    "Написать программу обработки двух матриц в соответствии с условием.\n",
    "\n",
    "#### Условие:\n",
    "Дана матрица  $A$  из  $N$  строк и  $N$  столбцов.\n",
    "\n",
    "Кроме матрицы  $A$  дана матрица  $B$  такого же размера.\n",
    "\n",
    "Если каждый элемент матрицы  $A$  больше соответствующего элемента матрицы  $B$,\n",
    "\n",
    "присвоить элементам  $C_1,C_2,...,C_N$  значения по правилу:\n",
    "\n",
    "$\\displaystyle{C_i = \\sum_{k=1}^{N} (A_{ik} + B_{ik}) \\ (*)}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Уточненная постановка задачи.\n",
    "Дана две квадратные матрицы $A$ и $B$, состоящие из N строк и N столбцов. \n",
    "\n",
    "Сравнить соответствующие элементы матриц $A$ и $B$.\n",
    "\n",
    "Если $A_{ij} > B_{ij}, \\forall i, j: 0 < i < N+1, 0 < j < N+1$, то\n",
    "\n",
    "сформирмировать одномерный массив $C_1, C_2, ..., C_N$  по правилу $(*)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Пример с иллюстрацией."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[True, True, True, True, True],\n",
       " [True, True, True, True, True],\n",
       " [True, True, True, True, True],\n",
       " [True, True, True, True, True],\n",
       " [True, True, True, True, True]]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = random_matrix(ZZ,5,x=0,y=100)\n",
    "B = random_matrix(ZZ,5,x=-100,y=0)\n",
    "with open('AB_SC_0.txt', 'w') as f:\n",
    "    for i in range(A.nrows()):\n",
    "        f.write('[' + str(A[i]).replace(',','')[1:-1] + ']\\n')\n",
    "    for i in range(B.nrows()):\n",
    "        f.write('[' + str(B[i]).replace(',','')[1:-1] + ']\\n')\n",
    "C = [[A[i,j] > B[i,j] for i in range(A.nrows())] for j in range(A.ncols())]\n",
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Лаб.12 Типовой Расчет \n",
      "==================================================\n",
      "Input Text File: 'AB_SC_0.txt'\n",
      "Output Text File: 'ABC_SC_0.txt'\n",
      "\n",
      "Исходная матрица A: \n",
      "==================================================\n",
      "\n",
      "[41 82 20 64 15]\n",
      "[42 19  3 30 92]\n",
      "[39 43 53 30 45]\n",
      "[26 86 38 18 54]\n",
      "[41 75 88 37 29]\n",
      "\n",
      "Исходная матрица B: \n",
      "==================================================\n",
      "\n",
      "[-11 -78 -66 -95 -78]\n",
      "[-69  -5 -51 -68 -88]\n",
      "[-60 -56 -17 -10 -99]\n",
      "[-12 -30 -76 -32 -44]\n",
      "[ -7 -77 -44 -10  -3]\n",
      "\n",
      "Сформированный массив C: \n",
      "==================================================\n",
      "\n",
      "(-106, -95, -32, 28, 129)\n"
     ]
    }
   ],
   "source": [
    "def read_txt(fin):\n",
    "    with open(fin, 'r') as f:\n",
    "        Y = f.readlines()\n",
    "    Y = [x[1:-2].split() for x in Y]\n",
    "    Y = [[int(x) for x in Y[i]] for i in range(len(Y))]\n",
    "    return Matrix(Y[:int(len(Y)/2)]), Matrix(Y[int(len(Y)/2):])\n",
    "\n",
    "print(20*' ' + 'Лаб.12 Типовой Расчет \\n' + 50*'=')\n",
    "\n",
    "fin = input('Input Text File: ')\n",
    "fout = input('Output Text File: ')\n",
    "\n",
    "A, B = read_txt(fin)\n",
    "\n",
    "print('\\nИсходная матрица A: \\n' + 50*'=' + '\\n')\n",
    "print(A)\n",
    "print('\\nИсходная матрица B: \\n' + 50*'=' + '\\n')\n",
    "print(B)\n",
    "\n",
    "if (A.nrows() == A.ncols() == B.nrows() == B.ncols()) and (A.nrows <> 0):\n",
    "    if A > B:\n",
    "        C = sum(A.T) + sum(B.T)\n",
    "        print('\\nСформированный массив C: \\n' + 50*'=' + '\\n')\n",
    "        print(C)\n",
    "    else:\n",
    "        print('Не все элементы матрицы A больше соответствующих элементов матрицы B')\n",
    "else:\n",
    "    print('Некорректное число строк или столбцов')\n",
    "    \n",
    "def write_txt(fout, A, B, C):\n",
    "    with open(fout, 'w') as f:\n",
    "        for i in range(A.nrows()):\n",
    "            f.write('[' + str(A[i]).replace(',','')[1:-1] + ']\\n')\n",
    "        for i in range(B.nrows()):\n",
    "            f.write('[' + str(B[i]).replace(',','')[1:-1] + ']\\n')\n",
    "        f.write('[' + str(C).replace(',','')[1:-1] + ']')\n",
    "        \n",
    "write_txt(fout, A, B, C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "Graphics object consisting of 20 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = [[(j+1, A[i,j]) for j in range(5)] for i in range(5)]\n",
    "b = [[(j+1, B[i,j]) for j in range(5)] for i in range(5)]\n",
    "g = Graphics()\n",
    "for i in range(5):\n",
    "    p1 = points(a[i], color=hue(3*i/15), alpha=0.7)\n",
    "    p2 = line2d(a[i], color=hue(3*i/15), legend_label='A{}'.format(i+1))\n",
    "    p3 = points(b[i], color=hue((3*i+1)/15), alpha=0.7)\n",
    "    p4 = line2d(b[i], color=hue((3*i+1)/15), legend_label='B{}'.format(i+1))\n",
    "    g = g + p1 + p2 + p3 + p4\n",
    "g.set_legend_options(bbox_to_anchor=(1.1, 0.1, 0.11, 0.7))    \n",
    "g.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "Graphics object consisting of 6 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = [(i+1, sum(A.T)[i]) for i in range(5)]\n",
    "b = [(i+1, sum(B.T)[i]) for i in range(5)]\n",
    "c = [(i+1, C[i]) for i in range(5)]\n",
    "g = Graphics()\n",
    "p1 = points(a, color=hue(0.3), alpha=0.7)\n",
    "p2 = line2d(a, color=hue(0.3), legend_label='A')\n",
    "p3 = points(b, color=hue(0.6), alpha=0.7)\n",
    "p4 = line2d(b, color=hue(0.6), legend_label='B')\n",
    "p5 = points(c, color=hue(0.9), alpha=0.7)\n",
    "p6 = line2d(c, color=hue(0.9), legend_label='C')\n",
    "g = g + p1 + p2 + p3 + p4 + p5 + p6\n",
    "g.set_legend_options(bbox_to_anchor=(1.1, 0.1, 0.11, 0.7)) \n",
    "g.show()"
   ]
  },
  {
   "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 массива B | символ | строка | \"+XX.txt\" |\n",
    "| входные данные | fout | название текстового файла с элементами массива B | символ | строка | \"+XX.txt\" |\n",
    "| входные данные | A, B | исходные матрицы | цел | двумерные массивы | +XX (:5) |\n",
    "| выходные данные | C | выводимый в текстовый файл сформированный массив | цел | одномерный массив | +XX (:5) |\n",
    "| промежуточные данные | i | индекс текущей строки, 1≤i≤50 | цел | простая переменная | --- |\n",
    "| промежуточные данные | j | индекс текущего столбца, 1≤j≤50 | цел | простая переменная | --- |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Входная форма\n",
    "- обр 1.1 Исходная матрица A:\n",
    "- обр 1.2 $ < A[11] >  < A[12] >  …  < A[1n] > \\\\ ... \\\\ < A[n1] >  < A[n2] >  …  < A[nn] >$    \n",
    "- обр 2.1 Исходная матрица B:\n",
    "- обр 2.2 $ < B[11] >  < B[12] >  …  < B[1n] > \\\\ ... \\\\ < B[n1] >  < B[n2] >  …  < B[nn] >$\n",
    "\n",
    "### 6. Выходная форма\n",
    "- обр 3 Лаб. 12 Типовой расчет\n",
    "- обр 4.1 Исходная матрица A:\n",
    "- обр 4.2 $ < A[11] >  < A[12] >  …  < A[1n] > \\\\ ... \\\\ < A[n1] >  < A[n2] >  …  < A[nn] >$    \n",
    "- обр 5.1 Исходная матрица B:\n",
    "- обр 5.2 $ < B[11] >  < B[12] >  …  < B[1n] > \\\\ ... \\\\ < B[n1] >  < B[n2] >  …  < B[nn] >$\n",
    "- обр 6 Некорректное число строк или столбцов\n",
    "- обр 7.1 Сформированный массив C:\n",
    "- обр 7.2 $ < C[1] >  < C[2] >  …  < C[n] > $\n",
    "\n",
    "### 7. Аномалии\n",
    "\n",
    "| N | Описание | Условие возникновения | Реакция |\n",
    "| :-: | :-: | :-: | :-: |\n",
    "| 1 | Не входных данных | $A[i,j] = 0 \\ \\& \\ B[i,j] = 0 \\ \\forall i,j$ | Обр 6 |\n",
    "| 2 | Число строк A (или B) не равно числу столбцов | $i \\neq j$ | Обр 6 |\n",
    "\n",
    "### 8. Функциональные тесты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "A1, B1 = read_txt('AB_SC_1.txt')\n",
    "A2, B2 = read_txt('AB_SC_2.txt')\n",
    "A3, B3 = read_txt('AB_SC_3.txt')\n",
    "A4, B4 = read_txt('AB_SC_4.txt')\n",
    "A5, B5 = read_txt('AB_SC_5.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"notruncate\">\n",
       "<table  class=\"table_form\">\n",
       "<tbody>\n",
       "<tr>\n",
       "<th>Inputs</th>\n",
       "<th>-</th>\n",
       "<th>-</th>\n",
       "<th>-</th>\n",
       "<th>Results</th>\n",
       "<th>-</th>\n",
       "<th>Test</th>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>fin(+.txt)</td>\n",
       "<td>fout(+.txt)</td>\n",
       "<td>A</td>\n",
       "<td>B</td>\n",
       "<td>C</td>\n",
       "<td>Message</td>\n",
       "<td>N</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>AB_SC_1</td>\n",
       "<td>ABC_SC_1</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "38 & 74 & 64 & 76 & 99 \\\\\n",
       "53 & 98 & 15 & 33 & 45 \\\\\n",
       "91 & 49 & 97 & 84 & 90 \\\\\n",
       "22 & 48 & 83 & 0 & 63 \\\\\n",
       "99 & 52 & 33 & 99 & 48\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "-76 & -98 & -99 & -98 & -82 \\\\\n",
       "-16 & -22 & -65 & -83 & -75 \\\\\n",
       "-25 & -76 & -66 & -8 & -2 \\\\\n",
       "-70 & -45 & -74 & -70 & -43 \\\\\n",
       "-37 & -84 & -64 & -3 & -94\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(-102,\\,-17,\\,234,\\,-86,\\,49\\right)</script></td>\n",
       "<td>-</td>\n",
       "<td><script type=\"math/tex\">1</script></td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>AB_SC_2</td>\n",
       "<td>ABC_SC_2</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "2 & 1 & 1 & 2 & 1 \\\\\n",
       "2 & 2 & 2 & 1 & 1 \\\\\n",
       "1 & 1 & 1 & 1 & 1 \\\\\n",
       "1 & 1 & 1 & 1 & 2 \\\\\n",
       "1 & 2 & 1 & 1 & 2\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "-1 & -1 & -1 & -1 & -1 \\\\\n",
       "-1 & -1 & -1 & -2 & -1 \\\\\n",
       "-2 & -1 & -2 & -2 & -1 \\\\\n",
       "-1 & -1 & -2 & -2 & -2 \\\\\n",
       "-1 & -2 & -1 & -2 & -2\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(2,\\,2,\\,-3,\\,-2,\\,-1\\right)</script></td>\n",
       "<td>-</td>\n",
       "<td><script type=\"math/tex\">2</script></td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>AB_SC_3</td>\n",
       "<td>ABC_SC_3</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "1 & 1 & 0 & 0 & 0 \\\\\n",
       "0 & 0 & 0 & 1 & 1 \\\\\n",
       "0 & 0 & 0 & 0 & 0 \\\\\n",
       "0 & 1 & 1 & 0 & 0 \\\\\n",
       "1 & 1 & 0 & 0 & 1\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "0 & 0 & 1 & 1 & 0 \\\\\n",
       "1 & 1 & 1 & 0 & 0 \\\\\n",
       "0 & 1 & 0 & 1 & 0 \\\\\n",
       "1 & 1 & 0 & 0 & 0 \\\\\n",
       "0 & 0 & 1 & 1 & 1\n",
       "\\end{array}\\right)</script></td>\n",
       "<td>-</td>\n",
       "<td><script type=\"math/tex\">\\exists (i,j): A[i,j] \\leq B[i,j]</script></td>\n",
       "<td><script type=\"math/tex\">3</script></td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>AB_SC_4</td>\n",
       "<td>ABC_SC_4</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "7 & 6 & 9 & 7 & 4 \\\\\n",
       "6 & 9 & 2 & 1 & 2 \\\\\n",
       "3 & 1 & 2 & 7 & 5 \\\\\n",
       "3 & 4 & 8 & 3 & 1 \\\\\n",
       "3 & 6 & 2 & 1 & 2\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "-9 & -8 & -2 & -9 & -10 \\\\\n",
       "-9 & -6 & -3 & -7 & -8 \\\\\n",
       "-10 & -10 & -1 & -7 & -9 \\\\\n",
       "-6 & -7 & -3 & -9 & -8 \\\\\n",
       "-2 & -9 & -8 & -7 & -5\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(-5,\\,-13,\\,-19,\\,-14,\\,-17\\right)</script></td>\n",
       "<td>-</td>\n",
       "<td><script type=\"math/tex\">4</script></td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>AB_SC_5</td>\n",
       "<td>ABC_SC_5</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "5 & 1 & 8 & 2 & 8 \\\\\n",
       "4 & 3 & 6 & 0 & 8 \\\\\n",
       "0 & 0 & 3 & 8 & 5 \\\\\n",
       "5 & 3 & 1 & 2 & 3 \\\\\n",
       "1 & 3 & 9 & 8 & 0\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "5 & 0 & 7 & 6 & 7 \\\\\n",
       "7 & 3 & 6 & 2 & 5 \\\\\n",
       "1 & 6 & 6 & 1 & 4 \\\\\n",
       "2 & 4 & 8 & 4 & 4 \\\\\n",
       "4 & 5 & 3 & 0 & 0\n",
       "\\end{array}\\right)</script></td>\n",
       "<td>-</td>\n",
       "<td><script type=\"math/tex\">\\exists (i,j): A[i,j] \\leq B[i,j]</script></td>\n",
       "<td><script type=\"math/tex\">5</script></td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Inputs          -                                                 -                                                                                                   -                                                                  Results                             -                    Test\n",
       "+------------+-------------+--------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------+---------------------------+-------------------------------------+------+\n",
       "  fin(+.txt)   fout(+.txt)                                            A                                                                                                   B                                                                     C                             Message                  N\n",
       "   AB_SC_1      ABC_SC_1     [38 74 64 76 99]\n",
       "[53 98 15 33 45]\n",
       "[91 49 97 84 90]\n",
       "[22 48 83  0 63]\n",
       "[99 52 33 99 48]   [-76 -98 -99 -98 -82]\n",
       "[-16 -22 -65 -83 -75]\n",
       "[-25 -76 -66  -8  -2]\n",
       "[-70 -45 -74 -70 -43]\n",
       "[-37 -84 -64  -3 -94]   (-102, -17, 234, -86, 49)                    -                     1\n",
       "   AB_SC_2      ABC_SC_2                 [2 1 1 2 1]\n",
       "[2 2 2 1 1]\n",
       "[1 1 1 1 1]\n",
       "[1 1 1 1 2]\n",
       "[1 2 1 1 2]                            [-1 -1 -1 -1 -1]\n",
       "[-1 -1 -1 -2 -1]\n",
       "[-2 -1 -2 -2 -1]\n",
       "[-1 -1 -2 -2 -2]\n",
       "[-1 -2 -1 -2 -2]                   (2, 2, -3, -2, -1)                        -                     2\n",
       "   AB_SC_3      ABC_SC_3                 [1 1 0 0 0]\n",
       "[0 0 0 1 1]\n",
       "[0 0 0 0 0]\n",
       "[0 1 1 0 0]\n",
       "[1 1 0 0 1]                                         [0 0 1 1 0]\n",
       "[1 1 1 0 0]\n",
       "[0 1 0 1 0]\n",
       "[1 1 0 0 0]\n",
       "[0 0 1 1 1]                                        -               $\\exists (i,j): A[i,j] \\leq B[i,j]$    3\n",
       "   AB_SC_4      ABC_SC_4                 [7 6 9 7 4]\n",
       "[6 9 2 1 2]\n",
       "[3 1 2 7 5]\n",
       "[3 4 8 3 1]\n",
       "[3 6 2 1 2]                [ -9  -8  -2  -9 -10]\n",
       "[ -9  -6  -3  -7  -8]\n",
       "[-10 -10  -1  -7  -9]\n",
       "[ -6  -7  -3  -9  -8]\n",
       "[ -2  -9  -8  -7  -5]   (-5, -13, -19, -14, -17)                     -                     4\n",
       "   AB_SC_5      ABC_SC_5                 [5 1 8 2 8]\n",
       "[4 3 6 0 8]\n",
       "[0 0 3 8 5]\n",
       "[5 3 1 2 3]\n",
       "[1 3 9 8 0]                                         [5 0 7 6 7]\n",
       "[7 3 6 2 5]\n",
       "[1 6 6 1 4]\n",
       "[2 4 8 4 4]\n",
       "[4 5 3 0 0]                                        -               $\\exists (i,j): A[i,j] \\leq B[i,j]$    5"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = [['Inputs', '-', '-', '-', 'Results', '-', 'Test'],\n",
    "        ['fin(+.txt)', 'fout(+.txt)', 'A', 'B', 'C', 'Message', 'N'],\n",
    "        ['AB_SC_1', 'ABC_SC_1', A1, B1, sum(A1.T) + sum(B1.T), '-', 1],\n",
    "        ['AB_SC_2', 'ABC_SC_2', A2, B2, sum(A2.T) + sum(B2.T), '-', 2],\n",
    "        ['AB_SC_3', 'ABC_SC_3', A3, B3, '-', \n",
    "         '$\\exists (i,j): A[i,j] \\leq B[i,j]$', 3],\n",
    "        ['AB_SC_4', 'ABC_SC_4', A4, B4, sum(A4.T) + sum(B4.T), '-', 4],\n",
    "        ['AB_SC_5', 'ABC_SC_5', A5, B5, '-', \n",
    "         '$\\exists (i,j): A[i,j] \\leq B[i,j]$', 5]]\n",
    "table(rows, header_row=True, align='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-106, -95, -32, 28, 129]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sumAB(A, B):\n",
    "    return [sum([A[i,j] + B[i,j] for j in range(A.nrows())]) for i in range(A.nrows())]\n",
    "\n",
    "C = sumAB(A, B)\n",
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"notruncate\">\n",
       "<table  class=\"table_form\">\n",
       "<tbody>\n",
       "<tr>\n",
       "<th>Test</th>\n",
       "<th>Inputs</th>\n",
       "<th>-</th>\n",
       "<th>-</th>\n",
       "<th>-</th>\n",
       "<th>Expected Results</th>\n",
       "<th>-</th>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>N</td>\n",
       "<td>fin(+.txt)</td>\n",
       "<td>fout(+.txt)</td>\n",
       "<td>A</td>\n",
       "<td>B</td>\n",
       "<td>C</td>\n",
       "<td>Message</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td><script type=\"math/tex\">1</script></td>\n",
       "<td>AB_SC_1</td>\n",
       "<td>ABC_SC_1</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "38 & 74 & 64 & 76 & 99 \\\\\n",
       "53 & 98 & 15 & 33 & 45 \\\\\n",
       "91 & 49 & 97 & 84 & 90 \\\\\n",
       "22 & 48 & 83 & 0 & 63 \\\\\n",
       "99 & 52 & 33 & 99 & 48\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "-76 & -98 & -99 & -98 & -82 \\\\\n",
       "-16 & -22 & -65 & -83 & -75 \\\\\n",
       "-25 & -76 & -66 & -8 & -2 \\\\\n",
       "-70 & -45 & -74 & -70 & -43 \\\\\n",
       "-37 & -84 & -64 & -3 & -94\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left[-102, -17, 234, -86, 49\\right]</script></td>\n",
       "<td>-</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td><script type=\"math/tex\">2</script></td>\n",
       "<td>AB_SC_2</td>\n",
       "<td>ABC_SC_2</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "2 & 1 & 1 & 2 & 1 \\\\\n",
       "2 & 2 & 2 & 1 & 1 \\\\\n",
       "1 & 1 & 1 & 1 & 1 \\\\\n",
       "1 & 1 & 1 & 1 & 2 \\\\\n",
       "1 & 2 & 1 & 1 & 2\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "-1 & -1 & -1 & -1 & -1 \\\\\n",
       "-1 & -1 & -1 & -2 & -1 \\\\\n",
       "-2 & -1 & -2 & -2 & -1 \\\\\n",
       "-1 & -1 & -2 & -2 & -2 \\\\\n",
       "-1 & -2 & -1 & -2 & -2\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left[2, 2, -3, -2, -1\\right]</script></td>\n",
       "<td>-</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td><script type=\"math/tex\">3</script></td>\n",
       "<td>AB_SC_3</td>\n",
       "<td>ABC_SC_3</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "1 & 1 & 0 & 0 & 0 \\\\\n",
       "0 & 0 & 0 & 1 & 1 \\\\\n",
       "0 & 0 & 0 & 0 & 0 \\\\\n",
       "0 & 1 & 1 & 0 & 0 \\\\\n",
       "1 & 1 & 0 & 0 & 1\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "0 & 0 & 1 & 1 & 0 \\\\\n",
       "1 & 1 & 1 & 0 & 0 \\\\\n",
       "0 & 1 & 0 & 1 & 0 \\\\\n",
       "1 & 1 & 0 & 0 & 0 \\\\\n",
       "0 & 0 & 1 & 1 & 1\n",
       "\\end{array}\\right)</script></td>\n",
       "<td>-</td>\n",
       "<td><script type=\"math/tex\">\\exists (i,j): A[i,j] \\leq B[i,j]</script></td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td><script type=\"math/tex\">4</script></td>\n",
       "<td>AB_SC_4</td>\n",
       "<td>ABC_SC_4</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "7 & 6 & 9 & 7 & 4 \\\\\n",
       "6 & 9 & 2 & 1 & 2 \\\\\n",
       "3 & 1 & 2 & 7 & 5 \\\\\n",
       "3 & 4 & 8 & 3 & 1 \\\\\n",
       "3 & 6 & 2 & 1 & 2\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "-9 & -8 & -2 & -9 & -10 \\\\\n",
       "-9 & -6 & -3 & -7 & -8 \\\\\n",
       "-10 & -10 & -1 & -7 & -9 \\\\\n",
       "-6 & -7 & -3 & -9 & -8 \\\\\n",
       "-2 & -9 & -8 & -7 & -5\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left[-5, -13, -19, -14, -17\\right]</script></td>\n",
       "<td>-</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td><script type=\"math/tex\">5</script></td>\n",
       "<td>AB_SC_5</td>\n",
       "<td>ABC_SC_5</td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "5 & 1 & 8 & 2 & 8 \\\\\n",
       "4 & 3 & 6 & 0 & 8 \\\\\n",
       "0 & 0 & 3 & 8 & 5 \\\\\n",
       "5 & 3 & 1 & 2 & 3 \\\\\n",
       "1 & 3 & 9 & 8 & 0\n",
       "\\end{array}\\right)</script></td>\n",
       "<td><script type=\"math/tex\">\\left(\\begin{array}{rrrrr}\n",
       "5 & 0 & 7 & 6 & 7 \\\\\n",
       "7 & 3 & 6 & 2 & 5 \\\\\n",
       "1 & 6 & 6 & 1 & 4 \\\\\n",
       "2 & 4 & 8 & 4 & 4 \\\\\n",
       "4 & 5 & 3 & 0 & 0\n",
       "\\end{array}\\right)</script></td>\n",
       "<td>-</td>\n",
       "<td><script type=\"math/tex\">\\exists (i,j): A[i,j] \\leq B[i,j]</script></td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Test     Inputs          -                                                 -                                                                                                   -                                                             Expected Results                         -\n",
       "+------+------------+-------------+--------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------+---------------------------+-------------------------------------+\n",
       "   N     fin(+.txt)   fout(+.txt)                                            A                                                                                                   B                                                                     C                             Message\n",
       "   1      AB_SC_1      ABC_SC_1     [38 74 64 76 99]\n",
       "[53 98 15 33 45]\n",
       "[91 49 97 84 90]\n",
       "[22 48 83  0 63]\n",
       "[99 52 33 99 48]   [-76 -98 -99 -98 -82]\n",
       "[-16 -22 -65 -83 -75]\n",
       "[-25 -76 -66  -8  -2]\n",
       "[-70 -45 -74 -70 -43]\n",
       "[-37 -84 -64  -3 -94]   [-102, -17, 234, -86, 49]                    -\n",
       "   2      AB_SC_2      ABC_SC_2                 [2 1 1 2 1]\n",
       "[2 2 2 1 1]\n",
       "[1 1 1 1 1]\n",
       "[1 1 1 1 2]\n",
       "[1 2 1 1 2]                            [-1 -1 -1 -1 -1]\n",
       "[-1 -1 -1 -2 -1]\n",
       "[-2 -1 -2 -2 -1]\n",
       "[-1 -1 -2 -2 -2]\n",
       "[-1 -2 -1 -2 -2]                   [2, 2, -3, -2, -1]                        -\n",
       "   3      AB_SC_3      ABC_SC_3                 [1 1 0 0 0]\n",
       "[0 0 0 1 1]\n",
       "[0 0 0 0 0]\n",
       "[0 1 1 0 0]\n",
       "[1 1 0 0 1]                                         [0 0 1 1 0]\n",
       "[1 1 1 0 0]\n",
       "[0 1 0 1 0]\n",
       "[1 1 0 0 0]\n",
       "[0 0 1 1 1]                                        -               $\\exists (i,j): A[i,j] \\leq B[i,j]$\n",
       "   4      AB_SC_4      ABC_SC_4                 [7 6 9 7 4]\n",
       "[6 9 2 1 2]\n",
       "[3 1 2 7 5]\n",
       "[3 4 8 3 1]\n",
       "[3 6 2 1 2]                [ -9  -8  -2  -9 -10]\n",
       "[ -9  -6  -3  -7  -8]\n",
       "[-10 -10  -1  -7  -9]\n",
       "[ -6  -7  -3  -9  -8]\n",
       "[ -2  -9  -8  -7  -5]   [-5, -13, -19, -14, -17]                     -\n",
       "   5      AB_SC_5      ABC_SC_5                 [5 1 8 2 8]\n",
       "[4 3 6 0 8]\n",
       "[0 0 3 8 5]\n",
       "[5 3 1 2 3]\n",
       "[1 3 9 8 0]                                         [5 0 7 6 7]\n",
       "[7 3 6 2 5]\n",
       "[1 6 6 1 4]\n",
       "[2 4 8 4 4]\n",
       "[4 5 3 0 0]                                        -               $\\exists (i,j): A[i,j] \\leq B[i,j]$"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = [['Test', 'Inputs', '-', '-', '-', 'Expected Results', '-'],\n",
    "        ['N', 'fin(+.txt)', 'fout(+.txt)', 'A', 'B', 'C', 'Message'],\n",
    "        [1, 'AB_SC_1', 'ABC_SC_1', A1, B1, sumAB(A1, B1), '-'],\n",
    "        [2, 'AB_SC_2', 'ABC_SC_2', A2, B2, sumAB(A2, B2), '-'],\n",
    "        [3, 'AB_SC_3', 'ABC_SC_3', A3, B3, '-', \n",
    "         '$\\exists (i,j): A[i,j] \\leq B[i,j]$'],\n",
    "        [4, 'AB_SC_4', 'ABC_SC_4', A4, B4, sumAB(A4, B4), '-'],\n",
    "        [5, 'AB_SC_5', 'ABC_SC_5', A5, B5, '-', \n",
    "         '$\\exists (i,j): A[i,j] \\leq B[i,j]$']]\n",
    "table(rows, header_row=True, align='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| - | Результаты | № теста |\n",
    "| :-: | :-: | :-: |\n",
    "| - | - | - |\n",
    "| C | Максимальная вычислительная нагрузка = 25 (сравнения) + 5 (суммирование) | 1 |\n",
    "| - | Соответствует ожидаемым числовым значениям | - |\n",
    "| C | Максимальная вычислительная нагрузка = 25 (сравнения) + 5 (суммирование) | 2 |\n",
    "| - | Соответствует ожидаемым числовым значениям | - |\n",
    "| C | - | 3 |\n",
    "| - | Задача не решалась, массивы не соответствуют условиям | - |\n",
    "| C | Максимальная вычислительная нагрузка = 25 (сравнения) + 5 (суммирование) | 4 |\n",
    "| - | Соответствует ожидаемым числовым значениям | - |\n",
    "| C | - | 5 |\n",
    "| - | Задача не решалась, массивы не соответствуют условиям | - |\n",
    "| - | - | - |\n",
    "| Сообщение | - | 1 |\n",
    "| Сообщение | - | 2 |\n",
    "| Сообщение | Не все элементы матрицы A больше соответствующих элементов матрицы B | 3 |\n",
    "| Сообщение | - | 4 |\n",
    "| Сообщение | Не все элементы матрицы A больше соответствующих элементов матрицы B | 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, считать из текстового файла ввода исходные данные (обр.1-2), затем вывести их на экран (обр.3-5) для визуального подтверждения.\n",
    "\n",
    "    Вывести сообщение о наличии некорректных данных, если задача не решена (обр.6).\n",
    "\n",
    "2. _Подзадача А 0.2._ Решение поставленной задачи: \n",
    "\n",
    "   - 2.1. На основе введенных исходных данных ($A, B$) определить, каждый ли элемент матрицы $A$ больше соответствующего элемента матрицы $B$.\n",
    "\n",
    "   - 2.2. Если условие 2.1 выполнено, то сформировать новый массив $C$ по правилу $(*)$.\n",
    "   \n",
    "3. _Подзадача А 0.3._ \n",
    "\n",
    "   Ввести полученные результаты ($C$) на экран (обр.7.1, 7.2) и записать в файл вывода исходные матрицы и полученный результат. \n",
    "\n",
    "Ввод-вывод тривиален и представляет собой чисто техническую задачу. \n",
    "\n",
    "Для осуществления процесса ввода-вывода и обработки матриц потребуются промежуточные переменные: \n",
    "\n",
    "- `i` – индекс строки текущего элемента матрицы, \n",
    "\n",
    "- `j` - индекс столбца текущего элемента матрицы.\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": 252,
   "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 = 600, color = lightgray];\n",
    "    class main_box [shape = box, width = 500, height = 150, fontsize = 20];\n",
    "    \n",
    "    Начало -> \n",
    "    \"Вывод заголовка по обр.4 на экран\" -> \"Ввод имен файлов по обр.1.1,1.2\" ->\n",
    "    \"Вывод имен файлов по обр.5.1,5.2 на экран\" ->\n",
    "    \"Функция 1. \\nСохранение исходных данных \\nв виде матрицы X\" ->\n",
    "    \"Функция 2. \\nВывод на экран и в файл \\nматриц A,B\" ->   \n",
    "    \"\\nСравнение \\nэлементов матриц A,B \\nФормирование \\nмассива C\" ->\n",
    "    \"Функция 3. \\nВывод на экран и в файл \\nA, B, C\" ->\n",
    "    Конец;\n",
    "\n",
    "    Начало, Конец [class = \"start_end\"];\n",
    "    \"Вывод заголовка по обр.4 на экран\", \"Ввод имен файлов по обр.1.1,1.2\" [class = \"input_out\"];\n",
    "    \"Вывод имен файлов по обр.5.1,5.2 на экран\" [class = \"input_out\"];\n",
    "    \"Функция 1. \\nСохранение исходных данных \\nв виде матрицы X\" [class = \"main_box\"];\n",
    "    \"Функция 2. \\nВывод на экран и в файл \\nматриц A,B\" [class = \"main_box\"];\n",
    "    \"\\nСравнение \\nэлементов матриц A,B \\nФормирование \\nмассива C\" [class = \"main_box\"];\n",
    "    \"Функция 3. \\nВывод на экран и в файл \\nA, B, C\" [class = \"main_box\"];\n",
    "    \n",
    "    \"Вывод имен файлов по обр.5.1,5.2 на экран\" -> \n",
    "    \"Функция 1. \\nСохранение исходных данных \\nв виде матрицы X\" [label = 'Вх. A,B', fontsize = 18, textcolor = blue];\n",
    "    \"Функция 3. \\nВывод на экран и в файл \\nA, B, C\" ->\n",
    "    Конец [label = 'Вых. C', fontsize = 18, textcolor = red];\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "!blockdiag block_diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"block_diagram.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Программа на <i style=\"color:##3636ff;\">R</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",
    "[R Tutorial](https://www.tutorialspoint.com/r/index.htm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%r\n",
    "read_in_txt <- function(fin) {\n",
    "  X <- read.csv(fin, header=F, sep=' ')\n",
    "  X[,1] <- as.integer(gsub(\"\\\\[\", \"\", X[,1]))\n",
    "  X[,length(X)] <- as.integer(gsub(\"\\\\]\", \"\", X[,length(X)]))\n",
    "  colnames(X) <- c(1:length(X))\n",
    "  return(X)\n",
    "}\n",
    "\n",
    "print_out_matrix <- function(m){\n",
    "        write.table(format(m, justify=\"centre\"), \n",
    "                    row.names=F, col.names=F, quote=F)\n",
    "}\n",
    "\n",
    "write_out_txt <- function(fout, X, C) {\n",
    "    s <- vector(length=2*length(X)+1)\n",
    "    for (i in 1:(2*length(X)+1)) {\n",
    "        s[i] <- paste('[', toString(rbind(X, C)[i,]))\n",
    "        s[i] <- paste(s[i], ']')\n",
    "        s[i] <- gsub(\",\", \"\", s[i])\n",
    "    }\n",
    "    writeLines(s, fout)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Лаб.12 Типовой Расчет \n",
      "\n",
      "\n",
      "\n",
      "Текстовый файл с элементами исходных матриц: \n",
      "[1] \"AB_SC_0.txt\"\n",
      "\n",
      "Текстовый файл с элементами исходных матриц и результатами: \n",
      "[1] \"ABC_SC_0.txt\"\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "************************************************************ \n",
      "Исходная матрица A: \n",
      "************************************************************ \n",
      "41 82 20 64 15\n",
      "42 19  3 30 92\n",
      "39 43 53 30 45\n",
      "26 86 38 18 54\n",
      "41 75 88 37 29\n",
      "************************************************************ \n",
      "Исходная матрица B: \n",
      "************************************************************ \n",
      "-11 -78 -66 -95 -78\n",
      "-69  -5 -51 -68 -88\n",
      "-60 -56 -17 -10 -99\n",
      "-12 -30 -76 -32 -44\n",
      " -7 -77 -44 -10  -3\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "************************************************************ \n",
      "Сформированный массив C: \n",
      "************************************************************ \n",
      "-106  -95  -32   28  129\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%r\n",
    "cat(strrep(' ', 20), 'Лаб.12 Типовой Расчет \\n') \n",
    "fin <- 'AB_SC_0.txt'\n",
    "fout <- 'ABC_SC_0.txt'\n",
    "\n",
    "cat('\\nТекстовый файл с элементами исходных матриц: \\n')\n",
    "print(fin)\n",
    "cat('\\nТекстовый файл с элементами исходных матриц и результатами: \\n')\n",
    "print(fout)\n",
    "\n",
    "X <- read_in_txt(fin)\n",
    "A <- X[1:length(X), 1:length(X)]\n",
    "B <- X[(length(X)+1):(2*length(X)),1:length(X)]\n",
    "rownames(B) <- c(1:length(X))\n",
    "\n",
    "cat(strrep('*', 60), '\\nИсходная матрица A: \\n')\n",
    "cat(strrep('*', 60), '\\n')\n",
    "print_out_matrix(A)\n",
    "cat(strrep('*', 60), '\\nИсходная матрица B: \\n')\n",
    "cat(strrep('*', 60), '\\n')\n",
    "print_out_matrix(B)\n",
    "\n",
    "if ((sum(is.na(A)) != 0) | (sum(is.na(B)) != 0)) {\n",
    "    print('Некорректное число строк или столбцов')\n",
    "} else {\n",
    "    if (sum(A > B) == length(A) ** 2) {\n",
    "        C <- rowSums(A) + rowSums(B)\n",
    "        cat(strrep('*', 60), '\\nСформированный массив C: \\n')\n",
    "        cat(strrep('*', 60), '\\n')\n",
    "        print_out_matrix(t(C))\n",
    "    } else {\n",
    "        print('Не все элементы матрицы A больше соответствующих элементов матрицы B')\n",
    "    }\n",
    "}\n",
    "\n",
    "write_out_txt(fout, X, C)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SageMath 8.4",
   "language": "",
   "name": "sagemath"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
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}