{
"cells": [
{
"cell_type": "markdown",
"id": "8df41e3d",
"metadata": {},
"source": [
"### Лабораторная работа разработана Т.Ф. Долгих к.ф.-м.н., доц. кафедры ВМ и МФ мехмата ЮФУ 👩🏫.\n",
"\n",
"-----------------------\n",
"\n",
"\n",
"# Внимание!\n",
"\n",
"Перед началом работы переименуйте файл 👩💻\n",
"\n",
"Для этого выберите **File $\\to$ Rename...** и измените **NameStud** на свою фамилию, записанную латинскими буквами.\n"
]
},
{
"cell_type": "markdown",
"id": "16a55aae",
"metadata": {},
"source": [
"-----------------------\n",
"\n",
"## Задание 1 💥 \n",
"\n",
"Описать функцию, моделирующую стандартную функцию `divmod()`. \n",
"У функции второй параметр сделать по умолчанию, равным двум.\n",
"\n",
"*Указание.* Используйте разные вызовы функции: позиционный, по имени, с использованием значений по умолчанию."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ee16c85",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "dc4e606f",
"metadata": {},
"source": [
"## Задание 2 💥\n",
"\n",
"Дано действительное $x$. Вычислить приближенное значение бесконечной суммы:\n",
"$$\n",
"\\frac{1}{2} \\ln \\left( \\frac{1 + \\frac{1}{x}}{1 - \\frac{1}{x}} \\right) = \\frac{1}{x} + \\frac{1}{3x^3} + \\frac{1}{5x^5} + \\ldots\n",
"\\quad (|x| > 1).\n",
"$$\n",
"\n",
"На печать выдавать\n",
"1) значение суммы, посчитанное с заданной точностью $\\varepsilon$ с использованием рекуррентного соотношения для нахождения члена ряда $y_i$;\n",
"\n",
"2) количество членов суммирования, понадобившееся для достижения точности $\\varepsilon$;\n",
"\n",
"3) результат вычисления функции с помощью функции `ln()`.\n",
"\n",
"*Указание 1.* Используйте рекуррентные соотношения.\n",
"\n",
"*Указание 2.* Для приближенного вычисления суммы используйте функцию с параметрами `x` и `eps` (`eps` — значение по умолчанию).\n",
"\n",
"*Указание 3.* Примеры оформления **диалога с пользователем**\n",
"\n",
"```python\n",
"x >>> 0.3\n",
"eps = 1e-5 (n - нет, enter - да) >>>\n",
"sum = 0.30952\n",
"n = 4\n",
"Проверка\n",
"f(x) = 0.30952\n",
"```\n",
"---------------------\n",
"\n",
"```python\n",
"x >>> 0.7\n",
"eps = 1e-5 (n - нет, enter - да) >>> n\n",
"Задайте число цифр после запятой >>> 8\n",
"eps = 1e-8\n",
"sum = 0.86730052\n",
"n = 21\n",
"Проверка\n",
"f(x) = 0.86730053\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bd7317db",
"metadata": {},
"outputs": [],
"source": []
},
{
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"
}
},
"cell_type": "markdown",
"id": "f9c5e929",
"metadata": {},
"source": [
"## Задание 3 💥\n",
"\n",
"Даны натуральные числа $m$ и $n$. \n",
"Описать функцию печати таблицы Пифагора размера $m \\times n$ со значениями по умолчанию $m=3$ (количество строк) и $n=2$ (количество столбцов).\n",
"\n",
"*Указание.* Используйте разные вызовы функции: позиционный, по имени, с использованием значений по умолчанию.\n",
"\n",
"**Пример таблицы Пифагора для $5 \\times 5$**\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38666cae",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}