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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "code",
  5.    "execution_count": 17,
  6.    "metadata": {},
  7.    "outputs": [],
  8.    "source": [
  9.     "import numpy as np"
  10.    ]
  11.   },
  12.   {
  13.    "cell_type": "code",
  14.    "execution_count": 18,
  15.    "metadata": {},
  16.    "outputs": [
  17.     {
  18.      "data": {
  19.       "text/plain": [
  20.        "array([0., 0., 0., 0., 1., 0., 0., 0., 0., 0.])"
  21.       ]
  22.      },
  23.      "execution_count": 18,
  24.      "metadata": {},
  25.      "output_type": "execute_result"
  26.     }
  27.    ],
  28.    "source": [
  29.     "#6\n",
  30.     "V1 = np.zeros(10)\n",
  31.     "V1[4] = 1\n",
  32.     "V1"
  33.    ]
  34.   },
  35.   {
  36.    "cell_type": "code",
  37.    "execution_count": 19,
  38.    "metadata": {},
  39.    "outputs": [
  40.     {
  41.      "data": {
  42.       "text/plain": [
  43.        "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
  44.       ]
  45.      },
  46.      "execution_count": 19,
  47.      "metadata": {},
  48.      "output_type": "execute_result"
  49.     }
  50.    ],
  51.    "source": [
  52.     "#8\n",
  53.     "V2 = np.arange(10)\n",
  54.     "V2 = myVector2[::-1]\n",
  55.     "V2"
  56.    ]
  57.   },
  58.   {
  59.    "cell_type": "code",
  60.    "execution_count": 20,
  61.    "metadata": {},
  62.    "outputs": [
  63.     {
  64.      "data": {
  65.       "text/plain": [
  66.        "array([[0, 1, 2],\n",
  67.        "       [3, 4, 5],\n",
  68.        "       [6, 7, 8]])"
  69.       ]
  70.      },
  71.      "execution_count": 20,
  72.      "metadata": {},
  73.      "output_type": "execute_result"
  74.     }
  75.    ],
  76.    "source": [
  77.     "#9\n",
  78.     "M = np.reshape(np.arange(9), (3, 3))\n",
  79.     "M"
  80.    ]
  81.   },
  82.   {
  83.    "cell_type": "code",
  84.    "execution_count": 21,
  85.    "metadata": {},
  86.    "outputs": [
  87.     {
  88.      "data": {
  89.       "text/plain": [
  90.        "array([[1., 0., 1., 0., 1., 0., 1., 0.],\n",
  91.        "       [0., 1., 0., 1., 0., 1., 0., 1.],\n",
  92.        "       [1., 0., 1., 0., 1., 0., 1., 0.],\n",
  93.        "       [0., 1., 0., 1., 0., 1., 0., 1.],\n",
  94.        "       [1., 0., 1., 0., 1., 0., 1., 0.],\n",
  95.        "       [0., 1., 0., 1., 0., 1., 0., 1.],\n",
  96.        "       [1., 0., 1., 0., 1., 0., 1., 0.],\n",
  97.        "       [0., 1., 0., 1., 0., 1., 0., 1.]])"
  98.       ]
  99.      },
  100.      "execution_count": 21,
  101.      "metadata": {},
  102.      "output_type": "execute_result"
  103.     }
  104.    ],
  105.    "source": [
  106.     "#19\n",
  107.     "M2 = np.zeros((8,8))\n",
  108.     "for i in range(0, 8):\n",
  109.     "    for j in range(0, 8):\n",
  110.     "        if (i + j) % 2 == 0:\n",
  111.     "            M2[i][j] = 1\n",
  112.     "M2"
  113.    ]
  114.   },
  115.   {
  116.    "cell_type": "code",
  117.    "execution_count": 22,
  118.    "metadata": {},
  119.    "outputs": [
  120.     {
  121.      "data": {
  122.       "text/plain": [
  123.        "array([[ 75.95393341,   0.15865526],\n",
  124.        "       [ 48.83646179,   0.82884906],\n",
  125.        "       [ 75.95393341,   0.15865526],\n",
  126.        "       [109.71326264,   0.74026754],\n",
  127.        "       [ 92.35799911,   0.34229561],\n",
  128.        "       [ 93.30058949,   1.23209824],\n",
  129.        "       [100.56838469,   0.72911963],\n",
  130.        "       [ 57.80138407,   0.92037491],\n",
  131.        "       [ 48.27007354,   1.3408919 ],\n",
  132.        "       [ 66.85057965,   0.60459859]])"
  133.       ]
  134.      },
  135.      "execution_count": 22,
  136.      "metadata": {},
  137.      "output_type": "execute_result"
  138.     }
  139.    ],
  140.    "source": [
  141.     "#44\n",
  142.     "def DecToPol(x):\n",
  143.     "    rho = np.sqrt(x[0]**2 + x[1]**2)\n",
  144.     "    phi = np.arctan2(x[1],x[0])\n",
  145.     "    return (rho, phi)\n",
  146.     "M3 = np.random.randint(100, size=(10, 2))\n",
  147.     "NewM = []\n",
  148.     "for i in M3:\n",
  149.     "    NewM.append(DecToPol(i))\n",
  150.     "NewM = np.array(NewM)\n",
  151.     "NewM"
  152.    ]
  153.   },
  154.   {
  155.    "cell_type": "code",
  156.    "execution_count": 28,
  157.    "metadata": {},
  158.    "outputs": [
  159.     {
  160.      "data": {
  161.       "image/png": 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\n",
  162.       "text/plain": [
  163.        "<Figure size 432x288 with 1 Axes>"
  164.       ]
  165.      },
  166.      "metadata": {
  167.       "needs_background": "light"
  168.      },
  169.      "output_type": "display_data"
  170.     }
  171.    ],
  172.    "source": [
  173.     "# Задание № 4\n",
  174.     "from matplotlib import pyplot as plt\n",
  175.     "a = np.random.randint(3)\n",
  176.     "phi = np.linspace(0.1, 100, 1000)\n",
  177.     "r = a * np.sin(phi) / phi\n",
  178.     "x = r * np.cos(phi)\n",
  179.     "y = r * np.sin(phi)\n",
  180.     "plt.figure()\n",
  181.     "plt.plot(x, y)\n",
  182.     "plt.show()"
  183.    ]
  184.   },
  185.   {
  186.    "cell_type": "code",
  187.    "execution_count": null,
  188.    "metadata": {},
  189.    "outputs": [],
  190.    "source": []
  191.   },
  192.   {
  193.    "cell_type": "code",
  194.    "execution_count": null,
  195.    "metadata": {},
  196.    "outputs": [],
  197.    "source": []
  198.   },
  199.   {
  200.    "cell_type": "code",
  201.    "execution_count": null,
  202.    "metadata": {},
  203.    "outputs": [],
  204.    "source": []
  205.   },
  206.   {
  207.    "cell_type": "code",
  208.    "execution_count": null,
  209.    "metadata": {},
  210.    "outputs": [],
  211.    "source": []
  212.   }
  213.  ],
  214.  "metadata": {
  215.   "kernelspec": {
  216.    "display_name": "Python 3",
  217.    "language": "python",
  218.    "name": "python3"
  219.   },
  220.   "language_info": {
  221.    "codemirror_mode": {
  222.     "name": "ipython",
  223.     "version": 3
  224.    },
  225.    "file_extension": ".py",
  226.    "mimetype": "text/x-python",
  227.    "name": "python",
  228.    "nbconvert_exporter": "python",
  229.    "pygments_lexer": "ipython3",
  230.    "version": "3.6.7"
  231.   }
  232.  },
  233.  "nbformat": 4,
  234.  "nbformat_minor": 2
  235. }
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