mipt_labs/lab1/1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f6b71dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3832d7ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
     ]
    }
   ],
   "source": [
    "print(style.available)\n",
    "# style.use('seaborn-dark')\n",
    "# style.use('classic')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89775777",
   "metadata": {},
   "source": [
    "# Упражнение 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eaa9906a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = lambda x: np.log(np.exp(1/(1 + np.sin(x))) / (1.25 + 1 / (x**15))) / np.log(1 + x ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "634c6aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3864779350529028"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a68e773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4268448523147221"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43c74d8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.023469121119570484"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9251dfa",
   "metadata": {},
   "source": [
    "# Упражнение 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d08e6177",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-10, 10.01, 0.01)\n",
    "plt.plot(x, x ** 2 - x - 6)\n",
    "plt.xlabel(r'$x$')\n",
    "plt.ylabel(r'$x^2 - x - 6$')\n",
    "plt.title(r'$x^2 - x - 6$')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "520b7715",
   "metadata": {},
   "source": [
    "# Упражнение 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a7e1ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_3 = lambda x: np.log(x ** 2 + 1) / np.log(1 + np.tan(1 / 1 + np.sin(x) ** 2)) * np.exp(-np.abs(x) / 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34e1873b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-fefc875d1c35>:1: RuntimeWarning: overflow encountered in exp\n",
      "  y = lambda x: np.log(np.exp(1/(1 + np.sin(x))) / (1.25 + 1 / (x**15))) / np.log(1 + x ** 2)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEJCAYAAACpATGzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAltElEQVR4nO3de5zcdX3v8dd79pJ7SELCkpDEBOQioiJGQFFcQYSqp9jHqaeIWFQqbb239oiop8qxWDy11tpWaqwXbBHKQ/QhKl4AWbEqci8QAgYJhJBAEnJjN5u9zHzOH/ObZLKZ3Wx2f7+Znf29nw/jzPzmN9/f50sy+9nv9aeIwMzMLG2FRgdgZmaTkxOMmZllwgnGzMwy4QRjZmaZcIIxM7NMOMGYmVkmmiLBSFoi6VZJqyWtkvTB5Pg8STdJWpM8zq36zKWSHpX0iKSzGxe9mVk+qRnWwUhaCCyMiHskzQLuBt4MvAPYGhFXSPooMDciLpF0PHANcDKwCLgZOCYiig2pgJlZDrU2OoDRiIiNwMbk+XOSVgNHAOcCnclpVwFdwCXJ8Wsjog9YK+lRysnm1yNdZ/78+bFs2bIMajDx9fT0MGPGjEaH0TCuv+vv+o+t/nffffeWiFhQ672mSDDVJC0DXgr8BuhIkg8RsVHSYclpRwC3V31sfXJsRMuWLeOuu+5KN+Am0dXVRWdnZ6PDaBjX3/V3/TvH9FlJTwz3XlMlGEkzgeuBD0XETknDnlrjWM2+QEkXAxcDdHR00NXVlUKkzae7uzu3dQfX3/V3/bOof9MkGEltlJPL1RHxneTwM5IWJq2XhcCm5Ph6YEnVxxcDG2qVGxErgZUAK1asiLz+FuPf4Fx/17+z0WE0TFb1b5ZZZAK+CqyOiM9XvXUDcGHy/ELge1XHz5M0RdJy4GjgjnrFa2ZmzdOCOQ14O/CApPuSYx8DrgCuk3QRsA54C0BErJJ0HfAQMAi81zPIzMzqqykSTET8F7XHVQDOHOYzlwOXZxaUmZmNqCm6yMzMrPk4wZiZWSacYMwytn1XP5//6SOs2rCj0aGY1ZUTjFnGdvYO8sWfPcrDG59rdChmdeUEY5axynrgUhPs+2eWJicYs4wVCuUM4/xieeMEY5axglswllNOMGYZKyR9ZCXnF8sZJxizjHkMxvLKCcYsY5UWTDPc3M8sTU4wZhmr7HHkLjLLGycYs4y5BWN55QRjljEP8lteOcGYZUzJt8yD/JY3TjBmGdvbRdbgQMzqzAnGLGNeaGl55QRjljGPwVheOcGY1YlbMJY3TjBmGau0YMzyxgnGLGN7xmDcR2Y54wRjljGPwVheOcGYZcybXVpepZZgJM2Q1JJWeWaThSQkbxVj+TPmBCOpIOl8ST+UtAl4GHha0ipJfyfp6PTCNGtuBcldZJY742nB3AocBVwKHB4RSyJiAfBq4HbgCkkXpBCjWdN50ad+wudv+u2e1wW5i8zyp3Ucn31dRAxUH5BUjIgW4Hrgeklt44rOrEnt6i/uM2tMuAVj+TPmFszQ5JLQKM4xm/RKEXumJ0N5oD9whrF8SXsWmb9BlnsRQUR5cL+iIHmzS8udEbvIJC0dZTnbI2LnkM8eB5wLHEE58WwAboiI1WMJ1KxZVLrCWgrVCcYLLS1/DjQGcxXl5DDSXhcBfAP4ZuWApEuAtwLXAnckhxcD10i6NiKuGGvAZhNdZTC/uovMs8gsj0ZMMBHx2jGWexHwwhqTAD4PrAKcYGzSKiaZpLqLTJ5FZjk0qjEYSV84yHJLwKIaxxcm75lNWlGri6wgL7S03BntNOVuSd8HzouIHkmvBz4ZEacNc/6HgFskrQGeTI4tBZ4PvH88AZtNdLW6yIT3IrP8GVWCiYhPSDof6JLUB/QAHx3h/B9LOgY4mfIgv4D1wJ0RURxLoJK+BrwJ2BQRJyTHPgW8G9icnPaxiLgxee9Syl11ReADEfGTsVzX7GDtTTD7ziJzF5nlzagSjKQzKf8g76HczXVRRDwy0mciokR5Rf/Qst4ZEV8fQ6zfAP6ZqskEiX+IiM8NucbxwHnACyl31d0s6ZixJjezg1FKOoH3HYOR5/Bb7ox2HczHgb+OiE7gD4H/lHTGGK952Vg+FBG3AVtHefq5wLUR0RcRa4FHKbemzDJXaam07DOLzJtdWv6MtovsjKrnD0j6Pcrbwbyy1vmSHqD2oksBHWOIcyTvk/THwF3AhyNiG+VuuerW0/rkmFnm9nSRFYZ0kXl6i+XMeBZaXlT1/tCFlocBZwPbhhYJ/OqgoxzelcCnKSezTwN/D7yL2ut2av76KOli4GKAjo4Ourq6UgyveXR3d+e27pBu/bf3lTPJo2vW0NX3OAD9/X1s2LiRrq7RNsLry3//rn8W9R/PQsvK8f0WWgI/AGZGxH1DPySpawxx1hQRz1SV+5XkulBusSypOnUx5Z0EapWxElgJsGLFiujs7EwrvKbS1dVFXusO6db/mZ274dZbOO7YY+k8pfw72LTbf8ZhHYfS2fmSVK6RNv/9u/5Z1D/thZZKPnfRCGWef5BlDn8xaWFEbExe/gHwYPL8BuBbycLORcDR7N1RwCxTNVfyFzwGY/kznu369yHpf0REZrdglnQN0AnMl7Qe+CTQKelEyq2ox4E/BYiIVZKuAx4CBoH3egaZ1UtlJX/1NOXydv1OMJYvqSUY4HLg+ymWt4+IeGuNw18d4fzLk5jM6qqSRwpDNrt0erG8SbPFMdKGmGa54c0uzcrSTDD++pgxTBeZN7u0HMpszMQsryotFQ1pwXiQ3/LGCcYsZZVE0uKFlpZzaSaYZw58itnkV2nBuIvM8i61BBMRZ6VVllkz2zsGs/eYd1O2PBpzglH1VrHjOMdssqm1XX9LwbPILH/G04K5VdL7h+5XJqld0hmSrgIuHF94Zs0nanSRFQpi0BnGcmY8Cy3Pobyx5DWSlgPbgalAC/BTyvdpuW+8AZo1m+Ke3ZT3HmstiJITjOXMmBNMROwGvgR8SVIbMB/ojYjtKcVm1pRqdpFJDHoameVMKlvFRMQAsPGAJ5rlQAwzBlN0C8ZyZtwJRtJU4D3Aqyiv5v8v4MqkhWOWO8WkoTI0wfQNer9Vy5c0WjDfBJ4D/il5/Vbg34G3pFC2WdMp1RiDaSmIohswljNpJJhjI6L6Lkq3SvrvFMo1a0rDTVMuegzGciaNhZb3Sjq18kLSKcAvUyjXrCnVmqZcTjANCsisQdJowZwC/LGkdcnrpcBqSQ8AEREvTuEaZk2j1kr+FrkFY/mTRoI5J4UyzCaNvWMwnkVm+ZZGgpkREQ9VH5DUGRFdKZRt1nSG7yJzgrF8SWMM5jpJl6hsmqR/Av42hXLNmlLNLrKC9qzwN8uLNBLMKcAS4FfAncAG4LQUyjVrSsUa94NpKfh+MJY/aSSYAaAXmEZ5L7K1EeGvkuVWpQWzT4LxVjGWQ2kkmDspJ5iXU17N/1ZJ306hXLOmVEkwrdUJpsXTlC1/0hjkvygi7kqePw2cK+ntKZRr1pT2jsHs24LxNGXLmzRaMHdLukDSXwMk94d5JIVyzZrS3hbM3q+XZ5FZHqWRYL4EvILyHmRQ3pfsX1Io16wp7WnBDN2LzAnGciaVlfwRcZKkewEiYpuk9hTKNWtKlVlk1S2YVk9TthxKZRaZpBbKW/UjaQHgzmbLrcEaLZiCWzCWQ2kkmC8C3wUOk3Q55fvBfCaFcs2aUqnGGEyrE4zl0Li7yCLiakl3A2cCAt4cEavHHZlZk6q0YFqqZpEVJEpRvtulqo6bTWZp3TL5YUlPArsjwrfts1yrtGBaWvZdyQ/lCQCtLU4wlg9j7iKTVJB0vqQfStoEPAxslLRK0t9JOjq9MM2aR60WTCXBDLqbzHJkPGMwtwJHAZcCh0fEkog4DHg1cDtwhaQLUojRrKmUhtmLrPo9szwYTxfZ6yJiYOjBiNgKXA9cL6ltHOWbNaXB4v4JprWqi8wsL8bcgqmVXCokvfNA55hNVpX1LtXb9Ve2jXGCsTxJY5pyLZelXaCkr0naJOnBqmPzJN0kaU3yOLfqvUslPSrpEUlnpx2P2XCKpRI
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(list(filter(lambda x: np.sin(x) != 0, np.arange(0.5, 10.01, 0.01))))\n",
    "plt.plot(x, y(x))\n",
    "plt.xlabel(r'$x$')\n",
    "plt.ylabel(r'$\\log_{1 + \\tan(\\dfrac {1}{1 + \\sin^2 x})}(x^2 + 1)\\exp(-\\dfrac{|x|}{10})$')\n",
    "# plt.title(r'$x^2 - x - 6$')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8e734fe",
   "metadata": {},
   "source": [
    "# Упражнение 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a81816d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x**2\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.xkcd():\n",
    "    func = input()\n",
    "    x = np.arange(0.5, 10.01, 0.01)\n",
    "    y = eval(func)\n",
    "    plt.plot(x, y)\n",
    "    plt.xlabel(r'$x$')\n",
    "    plt.ylabel(r'Your function')\n",
    "    plt.title(func)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e59d402",
   "metadata": {},
   "source": [
    "# Упражнение 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0296ba29",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 2, 3, 4, 5, 6]\n",
    "y = [0.99, 0.49, 0.35, 0.253, 0.18, 0.12]\n",
    "p_1, _v = np.polyfit(x, y, deg=1, cov=True)\n",
    "p_1 = np.poly1d(p_1)\n",
    "p_2, _v = np.polyfit(x, y, deg=2, cov=True)\n",
    "p_2 = np.poly1d(p_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d875c453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x, y, yerr=0.1)\n",
    "plt.plot(x, p_1(x))\n",
    "plt.xlabel(r'$x$')\n",
    "plt.title('1-degree polynom approximation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "337683be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x, y, yerr=0.1)\n",
    "plt.plot(x, p_2(x))\n",
    "plt.xlabel(r'$x$')\n",
    "plt.title('2-degree polynom approximation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e769d4",
   "metadata": {},
   "source": [
    "# Упражнение 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "58e718dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def w(x, a=2, b=0.5, n_max=1000):\n",
    "    return np.sum(np.array([b ** n * np.cos(a ** n *np.pi * x) for n in range(0, n_max)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5553d023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_w(a=2, b=0.5, x0=-10, x1=10, n_max=1000, step=None):\n",
    "    x = np.arange(x0, x1, step or (x1 - x0) / 4000)\n",
    "    plt.plot(x, [w(x_i, a, b, n_max) for x_i in x])\n",
    "    plt.xlabel(r'$x$')\n",
    "    plt.ylabel(r'$w(x)=\\sum _{n=0}^{\\infty }b^{n}\\cos(a^{n}\\pi x)$')\n",
    "    plt.title(r'Функция Вейерштрасса')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "plot_w()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2e44c687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_w(x0=0.5, x1=0.7, step=0.005, n_max=4000, b=0.2, a=1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e81c5db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}