program_lab5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Спецификация к Лабораторной работе №5 \"Экстремум\" \n",
    "# (Вариант 22)\n",
    "## Абстракция А0\n",
    "### 1. Постановка задачи.\n",
    "#### Задание: \n",
    "Написать программу обработки одномерного массива в соответствии с условием.\n",
    "\n",
    "#### Условие: \n",
    "Для заданного массива $\\{A_1, A_2, …, A_n\\}$ определить наименьшее значение сумм $A_i + A_{i-1}$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Уточненная постановка задачи.\n",
    "Дан одномерный вещественный массив A из n элементов.\n",
    "\n",
    "Найти $\\displaystyle{\\min_i(A_i + A_{i-1}), \\ i \\in \\{2, ..., n\\} }$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Пример с иллюстрацией."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Лаб.5\n",
      "==========================================================================================\n",
      "Текстовый файл с элементами массива A: A_Lab5_4.txt\n",
      "Текстовый файл с элементами массива A и минимальной суммой соседних элементов: AMIN_Lab5_4.txt\n",
      "==========================================================================================\n",
      "Массив A: \n",
      " 101.5\n",
      "212.6\n",
      "-3.08\n",
      "-15.876\n",
      "-104.2\n",
      "12.81\n",
      "33.17\n",
      "1.9\n",
      "-564.22\n",
      "371.4\n",
      "178.23\n",
      "-468.11\n",
      "982.36\n",
      "19.2\n",
      "-908.1\n",
      "-45.5\n",
      "-22.345\n",
      "342.6\n",
      "825.33\n",
      "-567.4\n",
      "==========================================================================================\n",
      "Минимальная сумма соседних элементов массива A: -953.6\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab \n",
    "import numpy\n",
    "%matplotlib inline\n",
    "pylab.style.use('seaborn-whitegrid')\n",
    "\n",
    "# A_Lab5_1.txt, A_Lab5_2.txt, A_Lab5_3.txt, A_Lab5_4.txt, A_Lab5_5.txt\n",
    "# AMIN_Lab5_1.txt, AMIN_Lab5_2.txt, AMIN_Lab5_3.txt, AMIN_Lab5_4.txt, AMIN_Lab5_5.txt\n",
    "\n",
    "print(20*' ' + 'Лаб.5\\n' + 90*'=')\n",
    "\n",
    "fin = str(input('Текстовый файл с элементами массива A: '))\n",
    "fout = str(input('Текстовый файл с элементами массива A и минимальной суммой соседних элементов: '))\n",
    "\n",
    "with open(fin, 'r') as f:\n",
    "    A = [float(x) for x in f.readlines()]\n",
    "n = len(A)\n",
    "MIN = 10**10\n",
    "MIN_ID = 10**5\n",
    "\n",
    "print(90*'=' + '\\n' + 'Массив A: \\n', A[0])\n",
    "for i in range(1, n):\n",
    "    print(A[i])\n",
    "    if (A[i] + A[i-1] < MIN):\n",
    "        MIN = A[i] + A[i-1]\n",
    "        MIN_ID = i\n",
    "               \n",
    "print(90*'=' + '\\n' + 'Минимальная сумма соседних элементов массива A:', MIN)\n",
    "\n",
    "with open(fout, 'w') as f:\n",
    "    for i in range(0, n):\n",
    "        f.write(\"{}\\n\".format(A[i]))\n",
    "    f.write(\"{}\".format(MIN))\n",
    "    \n",
    "pylab.figure(figsize=(10,10))\n",
    "pylab.scatter(range(n), A)\n",
    "pylab.scatter([MIN_ID - 1, MIN_ID], [A[MIN_ID - 1], A[MIN_ID]],\n",
    "             s=150, facecolors='none', edgecolors='r')\n",
    "\n",
    "pylab.xlabel('i', fontsize=15) \n",
    "pylab.ylabel('A[i]', fontsize=15)\n",
    "pylab.xticks(range(-1, n+1), range(n+2));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[101.5, 212.6, -3.08, -15.876, -104.2, 12.81, 33.17, 1.9, -564.22, 371.4, 178.23, -468.11, 982.36, 19.2, -908.1, -45.5, -22.345, 342.6, 825.33, -567.4]\n"
     ]
    }
   ],
   "source": [
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-953.6"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MIN"
   ]
  },
  {
   "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 А | символ | строка | \"+X.txt\" |\n",
    "| входные данные | fout | название текстового файла с элементами массива А | символ | строка | \"+X.txt\" |\n",
    "| входные данные | A | вводимые из текстового файла числа | вещ | одномерный массив (20) | +XX.XX+ (:5:6) |\n",
    "| выходные данные | MIN | выводимая в текстовый файл минимальная сумма соседних элементов массива | вещ | простая переменная | +XX.XX+ (:5:6) |\n",
    "| промежуточные данные | PRE | предыдущий элемент для $A[i]$ | вещ | простая переменная |  +XX.XX+ (:5:6) |\n",
    "| промежуточные данные | i | индекс текущего элемента, 1≤i≤21 | цел | простая переменная | --- |\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 массива A: $<fin>$       \n",
    "- обр 1.2 Текстовый файл с элементами массива A и минимальной суммой соседних элементов: $<fout>$\n",
    "- обр 2.1 Массив A:\n",
    "- обр 2.2 $ < A[1] > \\\\ < A[2] > \\\\ … \\\\ < A[n] > $\n",
    "\n",
    "### 6. Выходная форма\n",
    "\n",
    "- обр 3 Лаб. 5\n",
    "- обр 4.1 Текстовый файл с элементами массива A: $<fin>$       \n",
    "- обр 4.2 Текстовый файл с элементами массива A и минимальной суммой соседних элементов: $<fout>$\n",
    "- обр 5.1 Массив A:\n",
    "- обр 5.2 $ < A[1] > \\\\ < A[2] > \\\\ … \\\\ < A[n] > $\n",
    "\n",
    "- обр 6 Минимальная сумма соседних элементов массива A: $< MIN >$\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 | MIN | № |\n",
    "| A_Lab5_1.txt | AMIN_Lab5_1.txt | `[11, 20, -35, 100, -56, -71, -89, 90, 33, 25, 16, 67, 112, -107, 64, 38, 22, 45, -93]` | `-160` | 1 | \n",
    "| - | - | - | - | - |\n",
    "| A_Lab5_2.txt | AMIN_Lab5_2.txt | `[0, 0, 0, -1, -1, 0, 0, 0]` | `-2` | 2 |\n",
    "| - | - | - | - | - |\n",
    "| A_Lab5_3.txt | AMIN_Lab5_3.txt | `[10, 8, 6, 4, 2, 0, -1, -3, -5, -7, -9]` | `-16` | 3 |\n",
    "| - | - | - | - | - |\n",
    "| A_Lab5_4.txt | AMIN_Lab5_4.txt | `[101.5, 212.6, -3.08, -15.876, -104.2, 12.81, 33.17, 1.9, -564.22,` | `-953.6` | 4 |\n",
    "| - | - | `371.4, 178.23, -468.11, 982.36, 19.2, -908.1, -45.5, -22.345, 342.6, 825.33, -567.4]`| - | - |\n",
    "| - | - | - | - | - |\n",
    "| A_Lab5_5.txt | AMIN_Lab5_5.txt | `[1000.0, 2000.0, 3000.0, 4000.0, 5000.0, -5000.0, -4000.0, -3000.0, -2000.0, -1000.0]` | `-9000.0` | 5 |\n",
    "\n",
    "| № теста | Входные  данные | - | - | Ожидаемый результат | Смысл теста |\n",
    "| :-: | :-: | :-: | :-: | :-: | :-: |\n",
    "| - | - | - | - | - | - |\n",
    "| 1 | fin = 'A_Lab5_1.txt' | fout = 'AMIN_Lab5_1.txt' |  A = `[11, 20, -35, 100, -56, -71, -89, 90, 33, 25, 16, 67, 112, -107, 64, 38, 22, 45, -93]` | MIN = `-160` | Подтвердить правильность расчетов в случае целочисленного массива |\n",
    "| - | - | - | - | - | - |\n",
    "| 2 | fin = 'A_Lab5_2.txt' | fout = 'AMIN_Lab5_2.txt' |  A = `[0, 0, 0, -1, -1, 0, 0, 0]` | MIN = `-2` | Протестировать простейший случай |\n",
    "| - | - | - | - | - | - |\n",
    "| 3 | fin = 'A_Lab5_3.txt' | fout = 'AMIN_Lab5_3.txt' |  A = `[10, 8, 6, 4, 2, 0, -1, -3, -5, -7, -9]` | MIN = `-16` | Протестировать убывающую последовательность |\n",
    "| - | - | - | - | - | - |\n",
    "| 4 | fin = 'A_Lab5_4.txt' | fout = 'AMIN_Lab5_4.txt' |  A = `[101.5, 212.6, -3.08, -15.876, -104.2, 12.81, 33.17, 1.9, -564.22,` | MIN = `-953.6` | Подтвердить правильность расчетов в случае вещественного массива |\n",
    "| - | - | - | `371.4, 178.23, -468.11, 982.36, 19.2, -908.1, -45.5, -22.345, 342.6, 825.33, -567.4]` | - | - |\n",
    "| - | - | - | - | - | - |\n",
    "| 5 | fin = 'A_Lab5_5.txt' | fout = 'AMIN_Lab5_5.txt' |  A = `[1000.0, 2000.0, 3000.0, 4000.0, 5000.0, -5000.0, -4000.0, -3000.0, -2000.0, -1000.0]` | MIN = `-9000.0` | Протестировать последовательность, состоящую из двух возрастающих |\n",
    "\n",
    "| - | Результаты | № теста |\n",
    "| :-: | :-: | :-: |\n",
    "| - | - | - |\n",
    "| MIN | Максимальная вычислительная нагрузка – 18 сумм соседних элементов. | 1 |\n",
    "| - | Соответствует ожидаемым числовым значениям. | - |\n",
    "| MIN | -2 | 2 |\n",
    "| MIN | Максимальная вычислительная нагрузка – 7 сумм соседних элементов. | 3 |\n",
    "| - | Равно сумме последней пары. | - |\n",
    "| MIN | Максимальная вычислительная нагрузка – 19 сумм соседних элементов. | 4 |\n",
    "| - | Соответствует ожидаемым числовым значениям. | - |\n",
    "| MIN | Максимальная вычислительная нагрузка – 9 сумм соседних элементов.| 5 |\n",
    "| - | Соответствует ожидаемым числовым значениям. | - |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Метод\n",
    "Отделим ввод-вывод от обработки данных и разделим задачу на три подзадачи:\n",
    "\n",
    "1.\t_Подзадача А 0.1._ 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",
    "2.\t_Подзадача А 0.2._ Решение поставленной задачи: на основе введенных исходных данных (`A`) подсчитать минимальную сумму соседних элементов этого массива.\n",
    "3.\t_Подзадача А 0.3._ Ввести полученные результаты (`MIN`) на экран (обр.6) и записать в файл вывода исходный массив и полученный результат.\n",
    "\n",
    "Ввод-вывод тривиален и представляет собой чисто техническую задачу. \n",
    "\n",
    "Для осуществления процесса ввода-вывода массива потребуются промежуточные переменные `i` – индекс текущего элемента массива и предыдущий элемент `PRE` для значения `А[i]`, 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": 85,
   "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 = 18, width = 650, color = lightcyan];\n",
    "    class main_box [shape = box, width = 500, 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",
    "    \"Ввод 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",
    "    \"Подсчет минимальной суммы \\nсоседних элементов массива A \\n(MIN)\" ->\n",
    "    \"Вывод MIN на экран и в файл по обр.6\" ->\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",
    "    \"Ввод 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 MIN на экран и в файл по обр.6\" [class = \"input_out\"];\n",
    "    \"Подсчет минимальной суммы \\nсоседних элементов массива A \\n(MIN)\" [class = \"main_box\"];\n",
    "    \n",
    "    \"Вывод А на экран и в файл по обр.5.2\" -> \n",
    "    \"Подсчет минимальной суммы \\nсоседних элементов массива A \\n(MIN)\" [label = 'Вх. А', fontsize = 18];\n",
    "    \"Подсчет минимальной суммы \\nсоседних элементов массива A \\n(MIN)\" ->\n",
    "    \"Вывод MIN на экран и в файл по обр.6\" [label = 'Вых. MIN', fontsize = 18];\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "!blockdiag block_diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 87,
     "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>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)\n",
    "\n",
    "Проверить программу онлайн без установки программной среды:\n",
    "\n",
    "[Compile and Execute Pascal Online](http://www.compileonline.com/compile_pascal_online.php)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "program Lab5; //сохранить как lab5.dpr (DPR – Delphi PRoject)  \n",
    "{$APPTYPE CONSOLE}\n",
    "Uses Windows; // для русификации (сделать комментарием в онлайн-компиляторе!)\n",
    "\n",
    "    var\n",
    "        i: integer;\n",
    "        PRE, MIN: real;\n",
    "        fin, fout: string;\n",
    "        tfin, tfout: TextFile;\n",
    "        A: array of real; \n",
    "\n",
    "    begin\n",
    "        setConsoleCP(1251); // для ввода  русификация и (сделать строку комментарием в онлайн-компиляторе!)\n",
    "        setConsoleOutputCP(1251); // для вывода (сделать строку комментарием в онлайн-компиляторе!)\n",
    "\n",
    "        {A0.1, A0.3 - ввод-вывод входных данных}\n",
    "        writeln('Лаб.5' :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 массива A:  '); \n",
    "        readln(fin);\n",
    "        writeln(fin);\n",
    "\n",
    "        {ввод-вывод строковой переменной - имени файла с входными данными по обр.1.2, 4.2}\n",
    "        writeln('Текстовый файл с элементами массива A и минимальной суммой соседних элементов: '); \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('Массив 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",
    "        MIN := 10000000000;\n",
    "                \n",
    "        while not eof(tfin) do \n",
    "\n",
    "        begin\n",
    "            SetLength(A,i);\n",
    "            readln(tfin, A[i]); {ввод элемента и переход на следующую строку} \n",
    "            writeln(A[i]:5:6); {вывод элемента и переход на следующую строку на экране}\n",
    "            writeln(tfout, A[i]:5:6); {вывод элемента и переход на следующую строку в файле}\n",
    "            \n",
    "            MIN := -160; {заглушка для теста 1}\n",
    "            // MIN := -2; {заглушка для теста 2}\n",
    "            // MIN := -16; {заглушка для теста 3}\n",
    "            // MIN := -953.6; {заглушка для теста 4}\n",
    "            // MIN := -9000.0; {заглушка для теста 5}\n",
    "\n",
    "            \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",
    "        writeln('Минимальная сумма соседних элементов массива A:', MIN:5:6);\n",
    "        {вывод переменной и переход на следующую строку на экране по обр.6}             \n",
    "        writeln(tfout, MIN:5:6); \n",
    "        {вывод MIN и переход на следующую строку в файле по обр.6}\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._ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Метод\n",
    "Пусть\n",
    "\n",
    "- `i` – номер текущий точки,\n",
    "- `A[i]` – соответствующий элемент исходного массива A,\n",
    "- `PRE` - предыдущий элемент для `A[i]`,\n",
    "- `MIN` - минимальная сумма соседних элементов массива A.\n",
    "\n",
    "Присвоим переменной `MIN` начальное значение: `MIN = 10000000000`.\n",
    "\n",
    "Поиск минимальной суммы осуществляется с помощью цикла.\n",
    "\n",
    "На каждом этапе сумма соседних элементов сравнивается с текущим значением переменной MIN.\n",
    "\n",
    "Для каждого элемента, начиная со второго, предыдущий элемент сохраняется в качестве вспомогательной переменной `PRE`.\n",
    "\n",
    "Если `A[i] + PRE < MIN`, то значение переменной `MIN` заменяется: `MIN = A[i] + PRE`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Алгоритм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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, width = 400, color = lightcyan];\n",
    "    class loop_in [shape = flowchart.loopin, fontsize = 18, width = 300, color = lightsteelblue];\n",
    "    class loop_out [shape = flowchart.loopout, fontsize = 18, width = 300, color = lightsteelblue];\n",
    "    class condition [shape = flowchart.condition, fontsize = 18, width = 400, color = lightsteelblue];\n",
    "    class command_box [shape = roundedbox, fontsize = 18, width = 300];\n",
    "    \n",
    "    Начало -> \"MIN := 10000000000\" -> \"i := 2; +1; n\" -> \n",
    "    \"A[i] + PRE < MIN\" -> \"MIN := A[i] + PRE\" -> \n",
    "    \"PRE = A[i]\" -> \"i = n\" -> Конец;\n",
    "    \"A[i] + PRE < MIN\" -> \"PRE = A[i]\" -> \"i = n\" -> Конец;\n",
    "    \n",
    "    Начало, Конец [class = \"start_end\"];\n",
    "    \"i := 2; +1; n\" [class = \"loop_in\"];\n",
    "    \"i = n\" [class = \"loop_out\"];\n",
    "    \"A[i] + PRE < MIN\" [class = \"condition\"];\n",
    "    \"MIN := 10000000000\", \"MIN := A[i] + PRE\", \"PRE = A[i]\" [class = \"command_box\"];\n",
    "    \n",
    "    \"A[i] + PRE < MIN\" -> \"MIN := A[i] + PRE\" [class = 'yes'];\n",
    "    \"A[i] + PRE < MIN\" -> \"PRE = A[i]\" [class = 'no'];\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "!blockdiag block_diagram2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"block_diagram2.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Программный код"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MIN := 10000000000;\n",
    "        \n",
    "if i > 2 then\n",
    "begin\n",
    "    if (A[i] + PRE < MIN) then MIN := A[i] + PRE;\n",
    "    PRE: = A[i];\n",
    "end;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Получившаяся программа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "program Lab5; //сохранить как lab5.dpr (DPR – Delphi PRoject)  \n",
    "{$APPTYPE CONSOLE}\n",
    "Uses Windows; // для русификации (сделать комментарием в онлайн-компиляторе!)\n",
    "\n",
    "    var\n",
    "        i: integer;\n",
    "        PRE, MIN: real;\n",
    "        fin, fout: string;\n",
    "        tfin, tfout: TextFile;\n",
    "        A: array of real; \n",
    "\n",
    "    begin\n",
    "        setConsoleCP(1251); // для ввода  русификация и (сделать строку комментарием в онлайн-компиляторе!)\n",
    "        setConsoleOutputCP(1251); // для вывода (сделать строку комментарием в онлайн-компиляторе!)\n",
    "\n",
    "        {A0.1, A0.2, A0.3 - ввод-вывод входных данных}\n",
    "        writeln('Лаб.5' :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 массива A:  '); \n",
    "        readln(fin);\n",
    "        writeln(fin);\n",
    "\n",
    "        {ввод-вывод строковой переменной - имени файла с входными данными по обр.1.2, 4.2}\n",
    "        writeln('Текстовый файл с элементами массива A и минимальной суммой соседних элементов: '); \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('Массив 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",
    "        MIN := 10000000000;\n",
    "                \n",
    "        while not eof(tfin) do \n",
    "\n",
    "        begin\n",
    "            SetLength(A,i);\n",
    "            readln(tfin, A[i]); {ввод элемента и переход на следующую строку} \n",
    "            writeln(A[i]:5:6); {вывод элемента и переход на следующую строку на экране}\n",
    "            writeln(tfout, A[i]:5:6); {вывод элемента и переход на следующую строку в файле}\n",
    "            \n",
    "            if i > 1 then\n",
    "            begin\n",
    "                if (A[i] + PRE < MIN) then MIN := A[i] + PRE;\n",
    "                PRE: = A[i];\n",
    "            end;\n",
    "            \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",
    "        writeln('Минимальная сумма соседних элементов массива A:', MIN:5:6);\n",
    "        {вывод переменной и переход на следующую строку на экране по обр.6}             \n",
    "        writeln(tfout, MIN:5:6); \n",
    "        {вывод MIN и переход на следующую строку в файле по обр.6}\n",
    "        \n",
    "        CloseFile(tfout); {закрыть файл для записи}\n",
    "        \n",
    "    end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Та же программа с именами файлов, заданными в качестве параметров._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "program Lab5; //сохранить как lab5.dpr (DPR – Delphi PRoject)  \n",
    "{$APPTYPE CONSOLE}\n",
    "Uses Windows; // для русификации (сделать комментарием в онлайн-компиляторе!)\n",
    "\n",
    "    var\n",
    "        i: integer;\n",
    "        PRE, MIN: real;\n",
    "        fin, fout: string;\n",
    "        tfin, tfout: TextFile;\n",
    "        A: array of real; \n",
    "\n",
    "    begin\n",
    "        setConsoleCP(1251); // для ввода  русификация и (сделать строку комментарием в онлайн-компиляторе!)\n",
    "        setConsoleOutputCP(1251); // для вывода (сделать строку комментарием в онлайн-компиляторе!)\n",
    "        \n",
    "        if (ParamCount<2) then \n",
    "        begin \n",
    "        writeln(‘CritError: Не заданы параметры программы’);\n",
    "        readln: exit;\n",
    "        end; \n",
    "\n",
    "        {A0.1, A0.2, A0.3 - ввод-вывод входных данных}\n",
    "        writeln('Лаб.5' :40); {вывод заголовка c переходом на следующую строку по обр.3}       \n",
    "        \n",
    "        {ввод-вывод исходного массивa A по обр.2.1, 2.2, 5.1, 5.2}\n",
    "        writeln('Массив A: '); {вывод пояснения с переходом на следующую строку}\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",
    "        i := 1;             \n",
    "        MIN := 10000000000;\n",
    "                \n",
    "        while not eof(tfin) do \n",
    "\n",
    "        begin\n",
    "            SetLength(A,i);\n",
    "            readln(tfin, A[i]); {ввод элемента и переход на следующую строку} \n",
    "            writeln(A[i]:5:6); {вывод элемента и переход на следующую строку на экране}\n",
    "            writeln(tfout, A[i]:5:6); {вывод элемента и переход на следующую строку в файле}\n",
    "            \n",
    "            if i > 1 then\n",
    "            begin\n",
    "                if (A[i] + PRE < MIN) then MIN := A[i] + PRE;\n",
    "                PRE: = A[i];\n",
    "            end;\n",
    "            \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",
    "        writeln('Минимальная сумма соседних элементов массива A:', MIN:5:6);\n",
    "        {вывод переменной и переход на следующую строку на экране по обр.6}             \n",
    "        writeln(tfout, MIN:5:6); \n",
    "        {вывод MIN и переход на следующую строку в файле по обр.6}\n",
    "        \n",
    "        CloseFile(tfout); {закрыть файл для записи}\n",
    "        \n",
    "    end."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}