solve_of_npy_exc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "#ex 6\n",
    "x = np.zeros(10)\n",
    "x[4] = 1\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ex 8\n",
    "x = x[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ex9\n",
    "y = np.arange(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.reshape(3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ex19\n",
    "z = np.zeros((8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(8):\n",
    "    for j in range(8):\n",
    "        z[i, j] = (i + j) % 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 1., 0., 1., 0., 1.],\n",
       "       [1., 0., 1., 0., 1., 0., 1., 0.],\n",
       "       [0., 1., 0., 1., 0., 1., 0., 1.],\n",
       "       [1., 0., 1., 0., 1., 0., 1., 0.],\n",
       "       [0., 1., 0., 1., 0., 1., 0., 1.],\n",
       "       [1., 0., 1., 0., 1., 0., 1., 0.],\n",
       "       [0., 1., 0., 1., 0., 1., 0., 1.],\n",
       "       [1., 0., 1., 0., 1., 0., 1., 0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ex 44\n",
    "def cl(x = []):\n",
    "    r = np.sqrt(x[0]**2 + x[1]**2)\n",
    "    phi = np.arctan2(x[1], x[0])\n",
    "    return(r, phi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = np.random.random((10, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08114714, 0.72084297],\n",
       "       [0.82237575, 0.08052429],\n",
       "       [0.64050894, 0.41393432],\n",
       "       [0.73394363, 0.88129665],\n",
       "       [0.49427563, 0.67739401],\n",
       "       [0.47505867, 0.19482877],\n",
       "       [0.67315597, 0.48273769],\n",
       "       [0.61467662, 0.63113347],\n",
       "       [0.09736915, 0.50570405],\n",
       "       [0.19313131, 0.04021563]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "polar = list(map(cl, w))\n",
    "# это и есть массив с полярными координатами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.7253960641251871, 1.458695707839564),\n",
       " (0.8263086847081814, 0.09760551621734125),\n",
       " (0.7626226638645981, 0.5737404634870041),\n",
       " (1.146890152857148, 0.8763730355001323),\n",
       " (0.838552946758145, 0.9404323934359191),\n",
       " (0.5134578796294916, 0.38919583102310207),\n",
       " (0.8283565914718134, 0.6221277349587008),\n",
       " (0.8809976211646742, 0.7986071508985804),\n",
       " (0.5149925613881334, 1.3805823328905769),\n",
       " (0.197273920372294, 0.20529586670857075)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Зовут меня Паша, 5 букв в имени, а 5 число четное, значит буду рисовать 𝑟 = 𝑎 + cos𝜙\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "phi = np.arange(0, 2*np.pi, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = [(1 + np.cos(_))*np.cos(_) for _ in phi]\n",
    "y1 = [(1 + np.cos(_))*np.sin(_) for _ in phi]\n",
    "x3 = [(3 + np.cos(_))*np.cos(_) for _ in phi]\n",
    "y3 = [(3 + np.cos(_))*np.sin(_) for _ in phi]\n",
    "x5 = [(5 + np.cos(_))*np.cos(_) for _ in phi]\n",
    "y5 = [(5 + np.cos(_))*np.sin(_) for _ in phi]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x1, y1)\n",
    "plt.plot(x3, y3)\n",
    "plt.plot(x5, y5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}