ans/10219040/draw_polygon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# draw_polygon\n",
    "Jalankan kode pada sel-sel di bawah ini sehingga identitas Anda tertampilkan dengan benar dan gambarkan sebuah poligon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10219040 Ridwan Muhammad Syahrul https://github.com/RidwanMSyahrul\n"
     ]
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(\"<script>Jupyter.notebook.kernel.restart()</script>\")\n",
    "\n",
    "import sys, os\n",
    "sys.path.insert(1, os.path.join(sys.path[0], '../..'))\n",
    "\n",
    "from src.student import Students as stu\n",
    "nim = 10219040\n",
    "print(nim, end=' ')\n",
    "print(stu[nim]['name'], end=' ')\n",
    "print(\"https://github.com/\", stu[nim]['github'], sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## polygon\n",
    "Poligon digambarkan dengan titik-titik $(x_1, y_1)$, $(x_2, y_2)$, $\\dots$, $(x_N, y_N)$, bila terdapat $N$ buah titik."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "R = 10\n",
    "N = 6\n",
    "dq = 2 * math.pi / N\n",
    "q0 = 0\n",
    "\n",
    "points = []\n",
    "\n",
    "for i in range(N):\n",
    "    q = i * dq + q0\n",
    "    x = R * math.cos(q)\n",
    "    y = R * math.sin(q)\n",
    "\n",
    "    p = [x, y]\n",
    "    points.append(p)\n",
    "    \n",
    "line = plt.Polygon(\n",
    "    points, closed=True,\n",
    "    fill=None,\n",
    "    edgecolor='r', lw=2\n",
    ")\n",
    "plt.gca().add_patch(line)\n",
    "plt.axis('scaled')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotating hexagonal\n",
    "Heksagonal hasil dari kode sebelumnya dapat digambarkan dengan sisi miringnya menjadi searah dengan arah vertikal seperti di bawah ini."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "R = 10\n",
    "N = 6\n",
    "dq = 2 * math.pi / N\n",
    "q0 = math.pi/2\n",
    "\n",
    "points = []\n",
    "\n",
    "for i in range(N):\n",
    "    q = i * dq + q0\n",
    "    x = R * math.cos(q)\n",
    "    y = R * math.sin(q)\n",
    "\n",
    "    p = [x, y]\n",
    "    points.append(p)\n",
    "    \n",
    "line = plt.Polygon(\n",
    "    points, closed=True,\n",
    "    fill=None,\n",
    "    edgecolor='r', lw=2\n",
    ")\n",
    "plt.gca().add_patch(line)\n",
    "plt.axis('scaled')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modifikasi kode di atas agar diperoleh gambar berikut ini.\n",
    "\n",
    "![](hexagonal_vertical.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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