ans/10219052/draw_polygon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cca11c7c",
   "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": 1,
   "id": "4e2d555c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10219052 Tiara Novis Saputri https://github.com/tiaranovis\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 = 10219052\n",
    "print(nim, end=' ')\n",
    "print(stu[nim]['name'], end=' ')\n",
    "print(\"https://github.com/\", stu[nim]['github'], sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1c0d218",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d42d14",
   "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": 3,
   "id": "86684f45",
   "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",
   "id": "a4fd87e5",
   "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": 4,
   "id": "40a29a42",
   "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(points, closed=True,fill=None,edgecolor='r', lw=2)\n",
    "plt.gca().add_patch(line)\n",
    "plt.axis('scaled')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9461815c",
   "metadata": {},
   "source": [
    "Modifikasi kode di atas agar diperoleh gambar berikut ini.\n",
    "\n",
    "![](hexagonal_vertical.png)"
   ]
  }
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