numpy_stl_examples/numpy_stl_example_03.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import spatial\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = np.array([\n",
    "    [0,0],\n",
    "    [-2,0],\n",
    "    [-2,2],\n",
    "    [0,1.5],\n",
    "    [2,2],\n",
    "    [2,0]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1],\n",
       "       [2, 4],\n",
       "       [5, 1],\n",
       "       [5, 4]], dtype=int32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hull = spatial.ConvexHull(points)\n",
    "hull.simplices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWWUlEQVR4nO3df5BdZX3H8feHJcCqDBvIimSJJLZMBIsSeieiYRR/JZGxJKKdBq0GxUm1UmvbSScpHXCgHaKZ8VdFMaMp0MHgiCHGGgzRSGmrwWxIIAJGY1TIEs1K2KhlB/Lj2z/u2Xiy3Lv37O65dzdPPq+ZOznnec6553tPzn727Dln91FEYGZm6TphrAswM7PmctCbmSXOQW9mljgHvZlZ4hz0ZmaJO3GsC6hl0qRJMXXq1LEuw8zsmLFly5bfRERnrb5xGfRTp06lu7t7rMswMztmSPplvT5fujEzS5yD3swscQ56M7PEOejNzBLnoDczS1zDp24kTQFuB84EAlgREZ8ZtIyAzwCXAc8AV0XEg1nfQuCfs0X/JSJuK6/8P1iztYfl63fwZF8/kzvaWTxnOvNndDVjU2ZmpWp2fhV5vPIg8A8R8aCkU4EtkjZExKO5Zd4KnJu9Xg18AXi1pNOB64EK1W8SWyStjYinS/sEVHfS0tXb6T9wCICevn6Wrt4O4LA3s3GtFfnVMOgjYg+wJ5v+naTHgC4gH/TzgNuj+jePN0nqkHQWcCmwISL2AUjaAMwFVpVSfWb5+h30HzjEk//+EQ4/s58TJ54FwMI72pjx0o4yN2VmVqqtj/fx7MFDHHx6Dye84DQmv++z9B84xPL1O0oL+mFdo5c0FZgBPDCoqwt4Ije/O2ur117rvRdJ6pbU3dvbO5yyeLKvH4DDz+zn8IH+I+3PHjw0rPcxM2u1gZw6fKCfw8/sP9I+kGtlKPybsZJeBHwd+GhE/La0CjIRsQJYAVCpVIY1GsrkjnZ6+vqPnMm/5F3LAOjqaOe+JW8suVIzs/LMWraRnr5+fvWVJUe1T+5oL20bhc7oJU2gGvJ3RMTqGov0AFNy82dnbfXaS7V4znTaJ7Qd1dY+oY3Fc6aXvSkzs1K1Ir8aBn32RM2Xgcci4pN1FlsLvFdVFwP7s2v764HZkiZKmgjMztpKNX9GFzddcQEnn1jdWV0d7dx0xQW+EWtm414r8qvIpZtZwHuA7ZK2ZW3/BLwUICJuAdZRfbRyJ9XHK9+X9e2TdCOwOVvvhoEbs2WbP6PryI1XX64xs2NJs/OryFM3/wOowTIBfLhO30pg5YiqMzOzUfNvxpqZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolrOPCIpJXA24C9EfEnNfoXA+/Ovd95QGc2utQvgN8Bh4CDEVEpq3AzMyumyBn9rcDcep0RsTwiLoyIC4GlwH8NGi7wDVm/Q97MbAw0DPqIuB8oOs7rlcCqUVVkZmalKu0avaQXUD3z/3quOYB7JW2RtKjB+oskdUvq7u3tLassM7PjXpk3Y/8M+N9Bl20uiYiLgLcCH5b0unorR8SKiKhERKWzs7PEsszMjm9lBv0CBl22iYie7N+9wN3AzBK3Z2ZmBZQS9JJOA14PfCPX9kJJpw5MA7OBH5WxPTMzK67I45WrgEuBSZJ2A9cDEwAi4pZssbcD90bE/+VWPRO4W9LAdr4SEd8ur3QzMyuiYdBHxJUFlrmV6mOY+bZdwKtGWpiZmZXDvxlrZpY4B72ZWeIc9GZmiXPQm5klzkFvZpY4B72ZWeIc9GZmiXPQm5klzkFvZpY4B72ZWeIc9GZmiXPQm5klzkFvZpY4B72ZWeIc9GZmiXPQm5klrmHQS1opaa+kmsMASrpU0n5J27LXdbm+uZJ2SNopaUmZhZu10pqtPcxatpFpS77FrGUbWbO1Z6xLMius4QhTVEeO+hxw+xDL/HdEvC3fIKkNuBl4C7Ab2CxpbUQ8OsJazcbEmq09LF29nf4DhwDo6etn6ertAMyf0TWWpZkV0vCMPiLuB/aN4L1nAjsjYldEPAfcCcwbwfuYjanl63ccCfkB/QcOsXz9jjGqyGx4yrpG/xpJD0m6R9IrsrYu4IncMruztpokLZLULam7t7e3pLLMRu/Jvv5htZuNN2UE/YPAORHxKuDfgDUjeZOIWBERlYiodHZ2llCWWTkmd7QPq91svBl10EfEbyPi99n0OmCCpElADzAlt+jZWZvZMWXxnOm0T2g7qq19QhuL50wfo4rMhqfIzdghSXoJ8OuICEkzqX7zeAroA86VNI1qwC8A3jXa7Zm12sAN1+Xrd/BkXz+TO9pZPGe6b8TaMaNh0EtaBVwKTJK0G7gemAAQEbcA7wQ+JOkg0A8siIgADkq6BlgPtAErI+KRpnwKsyabP6PLwW7HrIZBHxFXNuj/HNXHL2v1rQPWjaw0MzMrg38z1swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLXMOglrZS0V9KP6vS/W9LDkrZL+r6kV+X6fpG1b5PUXWbhZmZWTJEz+luBuUP0/xx4fURcANwIrBjU/4aIuDAiKiMr0czMRqPICFP3S5o6RP/3c7ObqA4CbmZm40TZ1+ivBu7JzQdwr6QtkhYNtaKkRZK6JXX39vaWXJaZ2fGr4Rl9UZLeQDXoL8k1XxIRPZJeDGyQ9OOIuL/W+hGxguyyT6VSibLqMjM73pVyRi/plcCXgHkR8dRAe0T0ZP/uBe4GZpaxPTMzK27UQS/ppcBq4D0R8ZNc+wslnTowDcwGaj65Y2ZmzdPw0o2kVcClwCRJu4HrgQkAEXELcB1wBvB5SQAHsydszgTuztpOBL4SEd9uwmcwM7MhFHnq5soG/R8APlCjfRfwquevYWZmreTfjDUzS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBJXKOglrZS0V1LNoQBV9VlJOyU9LOmiXN9CST/NXgvLKtzMzIopekZ/KzB3iP63Audmr0XAFwAknU516MFXUx0Y/HpJE0darJmZDV+hoI+I+4F9QywyD7g9qjYBHZLOAuYAGyJiX0Q8DWxg6G8YZmZWsrKu0XcBT+Tmd2dt9dqfR9IiSd2Sunt7e0sqy8zMxs3N2IhYERGViKh0dnaOdTlmZskoK+h7gCm5+bOztnrtZmbWImUF/VrgvdnTNxcD+yNiD7AemC1pYnYTdnbWZmZmLXJikYUkrQIuBSZJ2k31SZoJABFxC7AOuAzYCTwDvC/r2yfpRmBz9lY3RMRQN3XNzKxkhYI+Iq5s0B/Ah+v0rQRWDr80MzMrw7i5GWtmZs3hoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEFQp6SXMl7ZC0U9KSGv2fkrQte/1EUl+u71Cub22JtZuZWQENR5iS1AbcDLwF2A1slrQ2Ih4dWCYi/i63/N8AM3Jv0R8RF5ZWsZmZDUuRM/qZwM6I2BURzwF3AvOGWP5KYFUZxZmZ2egVCfou4Inc/O6s7XkknQNMAzbmmk+R1C1pk6T59TYiaVG2XHdvb2+BsszMrIiyb8YuAO6KiEO5tnMiogK8C/i0pD+qtWJErIiISkRUOjs7Sy7LzOz4VSToe4Apufmzs7ZaFjDosk1E9GT/7gLu4+jr92Zm1mRFgn4zcK6kaZJOohrmz3t6RtLLgYnAD3JtEyWdnE1PAmYBjw5e18zMmqfhUzcRcVDSNcB6oA1YGRGPSLoB6I6IgdBfANwZEZFb/Tzgi5IOU/2msiz/tI6ZmTVfw6AHiIh1wLpBbdcNmv9YjfW+D1wwivrMzGyU/JuxZmaJc9CbmSXOQW9mljgHvZlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4hz0ZmaJc9CbmSXOQW9mlrhCQS9prqQdknZKWlKj/ypJvZK2Za8P5PoWSvpp9lpYZvFmZtZYw4FHJLUBNwNvAXYDmyWtrTFS1Fcj4ppB654OXA9UgAC2ZOs+XUr1ZmbWUJEz+pnAzojYFRHPAXcC8wq+/xxgQ0Tsy8J9AzB3ZKWamdlIFAn6LuCJ3PzurG2wd0h6WNJdkqYMc10kLZLULam7t7e3QFlmZlZEWTdjvwlMjYhXUj1rv224bxARKyKiEhGVzs7OksoyM7MiQd8DTMnNn521HRERT0XEs9nsl4A/LbqumZk1V5Gg3wycK2mapJOABcDa/AKSzsrNXg48lk2vB2ZLmihpIjA7azMzsxZp+NRNRByUdA3VgG4DVkbEI5JuALojYi3wEUmXAweBfcBV2br7JN1I9ZsFwA0Rsa8Jn8PMzOpoGPQAEbEOWDeo7brc9FJgaZ11VwIrR1GjmZmNgn8z1swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS1yhoJc0V9IOSTslLanR//eSHpX0sKTvSjon13dI0rbstXbwumZm1lwNR5iS1AbcDLwF2A1slrQ2Ih7NLbYVqETEM5I+BHwC+Iusrz8iLiy3bDMzK6rIGf1MYGdE7IqI54A7gXn5BSLiexHxTDa7CTi73DLNzGykigR9F/BEbn531lbP1cA9uflTJHVL2iRpfr2VJC3Kluvu7e0tUJaZmRVRaHDwoiT9JVABXp9rPicieiS9DNgoaXtE/GzwuhGxAlgBUKlUosy6zMyOZ0XO6HuAKbn5s7O2o0h6M3AtcHlEPDvQHhE92b+7gPuAGaOo18zMhqlI0G8GzpU0TdJJwALgqKdnJM0Avkg15Pfm2idKOjmbngTMAvI3cc3MrMkaXrqJiIOSrgHWA23Ayoh4RNINQHdErAWWAy8CviYJ4PGIuBw4D/iipMNUv6ksG/S0jpmZNVmha/QRsQ5YN6jtutz0m+us933ggtEUaGZmo+PfjDUzS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBJXKOglzZW0Q9JOSUtq9J8s6atZ/wOSpub6lmbtOyTNKbH2o6zZ2sPWx/vYtOspZi3byJqtzxvW1mzE1mztYdayjUxb8i0fX1a6ZudXw6CX1AbcDLwVOB+4UtL5gxa7Gng6Iv4Y+BTw8Wzd86mOMfsKYC7w+ez9SrVmaw9LV2/n2YOHAOjp62fp6u3+YrRSDBxfPX39BD6+rFytyK8iQwnOBHZGxC4ASXcC8zh6kO95wMey6buAz6k6eOw84M6IeBb4uaSd2fv9oJzyq5av30H/gUMcfHoPhw/086uvVH/oWHhHGzNe2lHmpuw4tPXxviNfhHk+vqwMA8fXc3t3ccKEdgD6Dxxi+fodzJ/RVco2ily66QKeyM3vztpqLhMRB4H9wBkF1wVA0iJJ3ZK6e3t7i1WfebKvH4ATXnDakR0F1PziNBuueseRjy8rw8BxdMKEdk54wWlH2gdyrQyFBgdvhYhYAawAqFQqMZx1J3e009PXz+T3ffao9q6Odu5b8sbyirTj0qxlG+mp8UXn48vKUO/4mtzRXmPpkSlyRt8DTMnNn5211VxG0onAacBTBdcdtcVzptM+4ehL/+0T2lg8Z3rZm7LjkI8va6ZWHF9Fgn4zcK6kaZJOonpzde2gZdYCC7PpdwIbIyKy9gXZUznTgHOBH5ZT+h/Mn9HFTVdcQFdHO6J6pnXTFReUdn3Ljm8+vqyZWnF8qZrHDRaSLgM+DbQBKyPiXyXdAHRHxFpJpwD/AcwA9gELcjdvrwXeDxwEPhoR9zTaXqVSie7u7hF+JDOz44+kLRFRqdlXJOhbzUFvZjY8QwW9fzPWzCxxDnozs8Q56M3MEuegNzNL3Li8GSupF/jlCFefBPymxHLK4rqGx3UNj+sanhTrOiciOmt1jMugHw1J3fXuPI8l1zU8rmt4XNfwHG91+dKNmVniHPRmZolLMehXjHUBdbiu4XFdw+O6hue4qiu5a/RmZna0FM/ozcwsx0FvZpa4Yz7oJS2X9GNJD0u6W1JHneWGHOC8CXX9uaRHJB2WVPdxKUm/kLRd0jZJTf9LbsOoq9X763RJGyT9NPt3Yp3lDmX7apukwX8uu8x6hvz82Z/e/mrW/4Ckqc2qZZh1XSWpN7ePPtCCmlZK2ivpR3X6JemzWc0PS7qo2TUVrOtSSftz++q6FtU1RdL3JD2afS3+bY1lyt1nEXFMv4DZwInZ9MeBj9dYpg34GfAy4CTgIeD8Jtd1HjAduA+oDLHcL4BJLdxfDesao/31CWBJNr2k1v9j1vf7Fuyjhp8f+Gvglmx6AfDVcVLXVcDnWnU8Zdt8HXAR8KM6/ZcB9wACLgYeGCd1XQr8Zyv3Vbbds4CLsulTgZ/U+H8sdZ8d82f0EXFvVMepBdhEdRSrwY4McB4RzwEDA5w3s67HImJHM7cxEgXravn+yt7/tmz6NmB+k7c3lCKfP1/vXcCbJGkc1NVyEXE/1XEo6pkH3B5Vm4AOSWeNg7rGRETsiYgHs+nfAY/x/LG0S91nx3zQD/J+qt8FBys8SPkYCOBeSVskLRrrYjJjsb/OjIg92fSvgDPrLHdKNoj8Jknzm1RLkc9/ZJnsRGM/cEaT6hlOXQDvyH7cv0vSlBr9rTaev/5eI+khSfdIekWrN55d8psBPDCoq9R9Nm4GBx+KpO8AL6nRdW1EfCNb5lqqo1jdMZ7qKuCSiOiR9GJgg6QfZ2ciY11X6YaqKz8TESGp3nO/52T762XARknbI+JnZdd6DPsmsCoinpX0V1R/6vAI5rU9SPV4+r2qo+itoTrcaUtIehHwdaoj7/22mds6JoI+It48VL+kq4C3AW+K7ALXIE0ZpLxRXQXfoyf7d6+ku6n+eD6qoC+hrpbvL0m/lnRWROzJfkTdW+c9BvbXLkn3UT0bKjvoi3z+gWV2SzoROA14quQ6hl1XRORr+BLVex9jrSnH02jlwzUi1kn6vKRJEdH0P3YmaQLVkL8jIlbXWKTUfXbMX7qRNBf4R+DyiHimzmJFBjhvOUkvlHTqwDTVG8s1nxBosbHYX/kB5hcCz/vJQ9JESSdn05OAWcCjTailyOfP1/tOYGOdk4yW1jXoOu7lVK//jrW1wHuzJ0kuBvbnLtONGUkvGbivImkm1Txs9jdrsm1+GXgsIj5ZZ7Fy91mr7ziX/QJ2Ur2WtS17DTwJMRlYl1vuMqp3t39G9RJGs+t6O9Xras8CvwbWD66L6tMTD2WvR8ZLXWO0v84Avgv8FPgOcHrWXgG+lE2/Ftie7a/twNVNrOd5nx+4geoJBcApwNey4++HwMuavY8K1nVTdiw9BHwPeHkLaloF7AEOZMfW1cAHgQ9m/QJuzmrezhBPobW4rmty+2oT8NoW1XUJ1XtzD+dy67Jm7jP/CQQzs8Qd85duzMxsaA56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBL3/xO5YCFrD1NlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(points[:,0], points[:,1], 'o')\n",
    "for simplex in hull.simplices:\n",
    "    plt.plot(points[simplex, 0], points[simplex, 1], 'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "faces = np.array([\n",
    "    [0, 1],\n",
    "    [1, 2],\n",
    "    [2, 3],\n",
    "    [3, 4],\n",
    "    [4, 5],\n",
    "    [5, 0]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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(points[:,0], points[:,1], 'o')\n",
    "for simplex in faces:\n",
    "    plt.plot(points[simplex, 0], points[simplex, 1], 'k-')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit",
   "language": "python",
   "name": "python385jvsc74a57bd031f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}