05 - Week/5 - Drawing with Python with Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical: Drawing with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro to Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use Matplotlib you need to import it as follows. Moreover, to extend your jupyter notebook with some extra\n",
    "functionalities for Matplotlib plots you need to use also the magic command `%matplotlib notebook` (In jupyter notebooks\n",
    "magic commands are those that start with a `%` sign).\n",
    "This magic command will make you able to zoom, pan, save plots, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib notebook\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate a simple plot you can use the `plot` method of `pyplot`. This method accepts two parameters,\n",
    "the list of x-values and the list y-values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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XuHtJfn5+Vx9aJC6/e72GnbWnWTynmF69LHQckW4TT6HXAK0vCRgd2/a+XOBSYKOZ7QKmAav0wqgkg4amZh5cW8ElIwfw0UuGh44j0q3iKfQtQJGZjTOzbGA+sOr9ne5+3N3z3L3Q3QuBTcCt7r61WxKLfAC/frWaPUfOsGSuVueS+jotdHdvBBYCzwI7gKfdfbuZ3W9mt3Z3QJELda6xie+tr+TKgkHM+tDQ0HFEul1cf2bO3VcDq9tsu6+DsTO7Hkuk636xZS81x87yT5+5DDOtziX16fotSUl1DU08tL6SqYWDuX5iXug4Ij1ChS4p6aebdnPw5DmWzivW6lzShgpdUs7pc408snEn10/M49rxQ0LHEekxKnRJOT96eReHT9ezZF5x6CgiPUqFLinlRF0Dj/6pilkfGsqUMReFjiPSo1ToklKeeOFdjp9tYMlcrc4l/ajQJWUcO1PP48+/y02XDOfSUQNDxxHpcSp0SRkrn6viVH0ji7U6lzSlQpeUcOjUOZ58aRefuHwkk4bnho4jEoQKXVLCDzbupK6hiUVzikJHEQlGhS6Rd+BEHT/ZtJtPXTWaCfn9Q8cRCUaFLpH38IZKmpqdRbO1Opf0pkKXSKs5dpanXtnL7SUFjBnSN3QckaBU6BJpD62vAOBbsyYGTiISngpdImv34dM8vbWaz187hpGD+oSOIxKcCl0ia8W6CjJ7Gd+YOSF0FJGkoEKXSKo8eIrfvlbDl64rZOiAnNBxRJKCCl0i6cG15eRkZfC1GeNDRxFJGip0iZwd+0/wzJv7+fL0Qob07x06jkjSUKFL5CwvLSc3J5MFN+jcuUhrKnSJlLeqj7PmnQP8zfXjGdg3K3QckaSiQpdIWVZaxqC+WXzl+sLQUUSSjgpdIuPV3UfZUFbL12ZMIDdHq3ORtlToEhnLSsvI65/Nl64bGzqKSFJSoUskvLzzMC9WHuaemRPpm50ZOo5IUlKhS9Jzd5aVljFsQG/++toxoeOIJC0VuiS95ysOsWXXURbOKiInKyN0HJGkpUKXpObuPLCmjFGD+vC5koLQcUSSmgpdktq6HQd5o/o43549kexMPV1Fzkc/IZK0mpudB0rLKRzSl09PGR06jkjSi6vQzewmMyszs0oz+047+5eY2Ttm9qaZrTMzXVcmXfbv299jx/4TLJpTRFaG1h4inen0p8TMMoCHgZuBycAdZja5zbDXgBJ3vxz4FfC/Ex1U0ktTs7O8tJyJQ/tz6xWjQscRiYR4lj1TgUp3r3L3euAp4LbWA9x9g7ufid3dBOj3Y+mS37+xj4qDp1g8p5iMXhY6jkgkxFPoo4C9re5Xx7Z15G7gj+3tMLMFZrbVzLbW1tbGn1LSSmNTMw+uLedDw3O5+dLhoeOIREZCT0ya2Z1ACfAv7e1395XuXuLuJfn5+Yl8aEkhv9lWw67DZ1g6bxK9tDoXiVs876GuAVpfADw6tu0/MbM5wH8DPuLu5xITT9JNfWMzK9ZVcMXogcy5eGjoOCKREs8KfQtQZGbjzCwbmA+saj3AzK4CHgVudfeDiY8p6eLprXupOXaWJfMmYabVucgH0Wmhu3sjsBB4FtgBPO3u283sfjO7NTbsX4D+wC/N7HUzW9XBtxPpUF1DEw+tr6Rk7EXMKMoLHUckcuL6s3XuvhpY3Wbbfa1uz0lwLklDP9u8h/dO1LHsc1dodS5yAfRuDUkKZ+ob+f7GSq6bMITrJmh1LnIhVOiSFH788m4Onapn6bzi0FFEIkuFLsGdrGvg0T/t5CPF+Vw9dnDoOCKRpUKX4P71xV0cPdOg1blIF6nQJajjZxp47Pkq5k4exuWjB4WOIxJpKnQJ6ocvVHGyrpElc7U6F+kqFboEc+R0PU+88C4fu3wEF48YEDqOSOSp0CWYR/+0k7MNTSyeUxQ6ikhKUKFLEAdP1vGjl3fxyStHMXFobug4IilBhS5BfH/DThqanG/P1upcJFFU6NLj9h07y8827+H2q0dTmNcvdByRlKFClx730IZKHGfhrImho4ikFBW69Ki9R87w9Ja9zL9mDKMv6hs6jkhKUaFLj1qxroKMXqbVuUg3UKFLj6mqPcVvtlVz57SxDBuQEzqOSMpRoUuPWbGugt6ZGdwzc0LoKCIpSYUuPaLsvZOsemMfd00vJK9/79BxRFKSCl16xINry+mXncmCG8aHjiKSslTo0u3erjnOH99+j7uvH8dF/bJDxxFJWSp06XbLS8sZ2CeLu28YFzqKSEpToUu3em3PUdb9+SALZoxnQE5W6DgiKU2FLt1qWWk5Q/plc9d1haGjiKQ8Fbp0m81Vh3m+4hBf/8gE+vXODB1HJOWp0KVbuDsPlJYzNLc3d04bGzqOSFpQoUu3eLHyMK+8e4Rv3jiRPtkZoeOIpAUVuiRcy+q8jJEDc5g/tSB0HJG0oUKXhNtQdpDX9hzjW7OL6J2p1blIT1GhS0K5O8tKyxkzuC+fvXp06DgiaUWFLgn17PYDvF1zgkWzi8jK0NNLpCfpJ04SprnZWV5azvj8fnzyqlGh44ikHRW6JMwzb+2n7MBJ7p1TTEYvCx1HJO3EVehmdpOZlZlZpZl9p539vc3sF7H9m82sMOFJJak1NjXzYGk5k4bl8vHLRoSOI5KWOi10M8sAHgZuBiYDd5jZ5DbD7gaOuvtEYDnwz4kOKsntt6/vo+rQaRbPLaaXVuciQcTzfuypQKW7VwGY2VPAbcA7rcbcBvxj7PavgIfMzNzdE5gVgKe37OWx56sS/W2li/Yfr+PSUQP46CXDQkcRSVvxFPooYG+r+9XAtR2NcfdGMzsODAEOtR5kZguABQBjxoy5oMCD+mZRNKz/Bf1b6T7Fw3P56g3jMdPqXCSUHv2LSe6+ElgJUFJSckGr93mXDGfeJcMTmktEJBXE86JoDdD6/dujY9vaHWNmmcBA4HAiAoqISHziKfQtQJGZjTOzbGA+sKrNmFXAl2K3Pwus747z5yIi0rFOT7nEzokvBJ4FMoAn3H27md0PbHX3VcDjwE/MrBI4Qkvpi4hID4rrHLq7rwZWt9l2X6vbdcDtiY0mIiIfhN4pKiKSIlToIiIpQoUuIpIiVOgiIinCQl1daGa1wO4L/Od5tHkXaoRpLsknVeYBmkuy6spcxrp7fns7ghV6V5jZVncvCZ0jETSX5JMq8wDNJVl111x0ykVEJEWo0EVEUkRUC31l6AAJpLkkn1SZB2guyapb5hLJc+giIvKXorpCFxGRNlToIiIpImkL3cyeMLODZvZ2B/vNzL4b+2DqN81sSk9njFccc5lpZsfN7PXY133tjUsGZlZgZhvM7B0z225mi9oZk/THJs55ROK4mFmOmb1iZm/E5vI/2hkTiQ9yj3Mud5lZbavj8jchssbDzDLM7DUze6adfYk/Ju6elF/ADGAK8HYH+28B/ggYMA3YHDpzF+YyE3gmdM445zICmBK7nQuUA5OjdmzinEckjkvsv3P/2O0sYDMwrc2YbwA/iN2eD/widO4uzOUu4KHQWeOczxLgZ+09j7rjmCTtCt3dn6Plb6t35Dbgx95iEzDIzEb0TLoPJo65RIa773f3bbHbJ4EdtHymbGtJf2zinEckxP47n4rdzYp9tb3a4TbgR7HbvwJmWxJ+AGycc4kEMxsNfAz4YQdDEn5MkrbQ49Deh1dH8gcy5sOxXzP/aGaXhA4Tj9iviFfRsopqLVLH5jzzgIgcl9iv9q8DB4FSd+/wmLh7I/D+B7knnTjmAvCZ2Om8X5lZQTv7k8GDwH8BmjvYn/BjEuVCTyXbaPn7DFcA3wN+GzZO58ysP/Br4F53PxE6z4XqZB6ROS7u3uTuV9Lymb9TzezSwJEuWBxz+T1Q6O6XA6X8/1Vu0jCzjwMH3f3VnnzcKBd6PB9eHQnufuL9XzO95dOhsswsL3CsDplZFi0l+G/u/pt2hkTi2HQ2j6gdFwB3PwZsAG5qsytyH+Te0Vzc/bC7n4vd/SFwdQ9Hi8d04FYz2wU8Bcwys5+2GZPwYxLlQl8FfDF2RcU04Li77w8d6kKY2fD3z52Z2VRajktS/rDFcj4O7HD3ZR0MS/pjE888onJczCzfzAbFbvcB5gJ/bjMsEh/kHs9c2rwecystr38kFXf/e3cf7e6FtLzgud7d72wzLOHHJK7PFA3BzH5Oy1UGeWZWDfwDLS+Q4O4/oOUzTm8BKoEzwJfDJO1cHHP5LHCPmTUCZ4H5yfjDFjMd+ALwVuw8J8B/BcZApI5NPPOIynEZAfzIzDJo+Z/O0+7+jEXzg9zjmcu3zexWoJGWudwVLO0H1N3HRG/9FxFJEVE+5SIiIq2o0EVEUoQKXUQkRajQRURShApdRCRFqNBFRFKECl1EJEX8P7mMAOHbkjiHAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [0, 0, 1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before continuing to the next code cell, make sure to close the previous plot otherwise the next `pyplot` command\n",
    "will be overlaid on top of the previous plot. This is also a feature of the magic command.\n",
    "\n",
    "You can use the same method to draw a polygon by providing the coordinates defining the polygon:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 1, 2, 2, 1], [0, 1, 1, 0, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coordinates provided in the previous code cell defines a square. However, this does not look like a square.\n",
    "Does it? To make it look like a square you need to tell `matplotlib` to equalize its axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 1, 2, 2, 1], [0, 1, 1, 0, 0])\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to draw a filled shape you just need to use `fill` instead of `plot`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.fill([1, 1, 2, 2, 1], [0, 1, 1, 0, 0])\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To quickly format the style of your plot you can use format strings like `ro`. Where `r` stands for red and `o`\n",
    "for dots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can overlay plots by executing multiple `pyplot` methods one after the other:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.fill([1, 1, 2, 2, 1], [0, 1, 1, 0, 0], 'g')\n",
    "plt.plot(1.5, 0.5, 'ro')\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to show a legend about your plot, you need to first label each component of your plot and then call the\n",
    "`legend` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.fill([1, 1, 2, 2, 1], [0, 1, 1, 0, 0], label='square')\n",
    "plt.plot(1.5, 0.5, 'ro', label='point')\n",
    "plt.axis('equal')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper Class Plotter\n",
    "\n",
    "The class defined in the following code cell is a helper class I have provided you for the 1st Assignment. This will\n",
    "help you plotting points and polygons. As you can see, this implements the same commands you have seen above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "class Plotter:\n",
    "\n",
    "    def __init__(self):\n",
    "        plt.figure()\n",
    "\n",
    "    def add_polygon(self, xs, ys):\n",
    "        plt.fill(xs, ys, 'lightgray', label='Polygon')\n",
    "\n",
    "    def add_point(self, x, y, kind=None):\n",
    "        if kind == 'outside':\n",
    "            plt.plot(x, y, 'ro', label='Outside')\n",
    "        elif kind == 'boundary':\n",
    "            plt.plot(x, y, 'bo', label='Boundary')\n",
    "        elif kind == 'inside':\n",
    "            plt.plot(x, y, 'go', label='Inside')\n",
    "        else:\n",
    "            plt.plot(x, y, 'ko', label='Unclassified')\n",
    "\n",
    "    def show(self):\n",
    "        handles, labels = plt.gca().get_legend_handles_labels()\n",
    "        by_label = OrderedDict(zip(labels, handles))\n",
    "        plt.legend(by_label.values(), by_label.keys())\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use this class, you first need to create a `Plotter` object. Then, you can draw a polygon by providing its\n",
    "coordinates using the `add_polygon` method. Finally, you need to call the method show."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = Plotter()\n",
    "plotter.add_polygon([1, 1, 2, 2, 1], [0, 1, 1, 0, 0])\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add a point by using the `add_point` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = Plotter()\n",
    "plotter.add_polygon([1, 1, 2, 2, 1], [0, 1, 1, 0, 0])\n",
    "plotter.add_point(1.5, 0.5)\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The added point is considered as an unclassified point. If you want to change the label of this point, you need pass a\n",
    "third argument to the method `add_point`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUTElEQVR4nO3df5BV5Z3n8fdXkIAjyhb0bqZsGKwssoKridt2G3/vahJMqqQmcYmQMStlpMjoxC1TmxizUTH+kfgrcbI6ApqQsSYBk4kuG/HHVtYJyaJgy/gTyhE1SDtaIgRBB6J0vvtHX3qbpum+Dbf70k+/X1Vddc9znnvO9wHqw+nn/IrMRJI09B1W7wIkSbVhoEtSIQx0SSqEgS5JhTDQJakQI+u14wkTJuTkyZPrtXtJGpKeeuqptzOzoad1dQv0yZMn09raWq/dS9KQFBEb97fOKRdJKoSBLkmFMNAlqRB1m0OXNDx98MEHtLW1sWvXrnqXckgbPXo0jY2NHH744VV/x0CXNKja2toYO3YskydPJiLqXc4hKTPZsmULbW1tHHvssVV/r88pl4j4YUS8FRHP72d9RMRfR8SGiHg2Ik7uR92Shpldu3Yxfvx4w7wXEcH48eP7/VtMNXPoS4AZvaw/H5hS+ZkH/E2/KpA07BjmfTuQP6M+Az0zVwJbe+kyE/jb7PAEMC4i/rTflUiSDkot5tCPATZ1WW6rtL3RvWNEzKPjKJ5JkyYd8A6ff77H2R9JQ0B7ezs7d+6saw0NDQ1s3ry5399bvHgxRxxxBF/4whf2at+4cSOf+9znqr5ZcsyYMf3edzUG9bLFzFyUmU2Z2dTQ0OOdq5K0l6XrljL1rqn8yU1/wtS7prJ03dK61XLZZZftE+aHkloE+uvAxC7LjZU2STooS9ct5fKHL2fT9k0kyabtm7j84ctrFuorV67kU5/6FHPmzOGjH/0oc+fOZc9b3L71rW9x8skn09zczDe+8Q0AbrzxRr7//e8DsHbtWlpaWmhpaWHhwoWd22xvb+eaa67hjDPOoLm5mbvvvrsmtVajFoG+HPhi5WqXU4F3MnOf6RZJ6q/rVl7Hzt17T8/s3L2T61ZeV7N9PPPMM9x0002sXbuWV199lccff5wtW7awfPlynnrqKdasWcPXv/71fb43f/58br31VlavXr1X+5IlSzjqqKP47W9/y29+8xt+9KMf8bvf/a5m9fammssWfwo8DkyNiLaIuDQi5kfE/EqXFcArwAZgMfCXA1atpGGlbXtbv9oPRFNTE42NjRx22GGceOKJbNy4kaOPPprRo0czf/58HnjgAY444oi9vrNt2za2bdvGGWecAcDs2bM71/3qV7/iJz/5CS0tLZx11lls3bqVDRs21Kze3vR5UjQzZ/exPoHLa1aRJFU0HtXIpu2bemyvlVGjRnV+HjFiBLt372bkyJGsXLmSxx57jPvvv5+FCxfy0EMPVbW9zOTWW2/lE5/4RM1qrJbPcpF0yFpw1gLGjNz7ipAxI8ew4KwFA7rfd999l3feeYcZM2Zw00038dxzz+21fty4cYwbN45Vq1YBsGzZss515513HosXL+aDDz4A4KWXXuK9994b0Hr38NZ/SYesi6ZdBHTMpbdtb6PxqEYWnLWgs32g7Nixg1mzZvGHP/yBzOQ73/nOPn3uuusuvvzlLwMdIb7H3Llz2bhxI6eddhqZyYQJE/YK/IEUe87oDrampqY80BdceB26NHS1t7dz3HHH1buMuqr2OvT169dz/PHH79UWEU9lZlNP/Z1ykaRCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYXwOnRJdfXyyy/XdHsf+chH+uxz5JFHMn36dNrb25k6dWrnY3F7cu+997J27Vq+973v1bTOgeARuqRhZ8yYMaxevZrW1lZGjRo1qE9EHEgGuqRh7bTTTuPll19m69atzJo1i+bmZs4+++x9bvffsWMHxx9/fOct/du3b+9cbm1tpbm5mZaWFq655hqamjru+9m1axfz5s3jlFNO4dRTT+XXv/410PFExs9+9rPMmDGDKVOm8LWvfa0mYzHQJQ1bu3fv5tFHH2X69OnceOONnHTSSaxZs4YFCxbwpS99aa++Y8eO5cwzz+x8SNfPfvYzZs6cyeGHH878+fP5wQ9+wOrVqxkxYkTndxYuXEhE8OSTT/LjH/+Yyy67rPPFz08//TTLli3jueeeY9myZWzatO9DyPrLQJc07OzcuZOWlhZOP/10Jk6cyCWXXMKqVauYM2cOAOeccw5bt25l+/bte31v7ty53HvvvUDH3PrFF1/Mtm3b2LFjBy0tLQDMmjWrs/+qVas6H607depUJk2axEsvvQTAueee2/mY3mnTprFx48aDHpcnRSUNO3vm0Pvr4x//OK+99horV66kvb2d6dOns23btgOq4UMf+lDn5z2P7T1YHqFLEnD66aezdGnHq+1WrlzJ+PHjOeqoo/bpN2fOHC655BIuvvhioONRumPHjmXNmjVAx1RMT9t86aWX2LRp04A+mMwjdEl1Vc1lhoPhm9/8JvPnz6e5uZkxY8awePHiHvt9/vOfZ8GCBXtNrdx5551cccUVRARnnnlm538E8+bN4ytf+QqnnHIKI0aMYNGiRXsdmdeaj8+VNKiG+uNz77//fn75y19yzz33dLa9++67HHnkkQDccsstvPnmm9xyyy373cZAPT7XI3RJqtJVV13Fo48+yv33379X+8MPP8zNN99Me3s7EydOZNGiRXWpz0CXpCrddtttPbZfeOGFXHjhhYNczb48KSpp0NVrqncoOZA/IwNd0qCKCLZt22ao9yIz2bJlC6NHj+7X95xykTSoIoK3336bzZs317uUuhk1alSffUaPHk1jY2O/tmugSxpUEUFE1LuMuup+5UqtOOUiSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1Ihqgr0iJgRES9GxIaIuLqH9ZMi4rGI+MeIeDYiPl37UiVJvekz0CNiBHAHcD4wDZgdEdO6dfvvwH2Z+THgIuDOWhcqSepdNUfozcCGzHwlM98HlgIzu/VJYM+rPY4G/rl2JUqSqlFNoB8DdH0ddVulravrgb+IiDZgBfBXPW0oIuZFRGtEtA7n5zhI0kCo1UnR2cCSzGwEPg3cGxH7bDszF2VmU2Y2NTQ01GjXkiSoLtBfByZ2WW6stHV1KXAfQGY+DowGJtSiQElSdaoJ9CeBKRFxbESMouOk5/JufV4DzgWIiOPpCHTnVCRpEPUZ6Jm5G7gCeARYT8fVLC9ExA0RcUGl21eByyLiGeCnwCXp0+slaVBV9Tz0zFxBx8nOrm3Xdvm8Dji9tqVJkvrDO0UlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIaoK9IiYEREvRsSGiLh6P31mRcS6iHghIn5S2zIlSX0Z2VeHiBgB3AF8AmgDnoyI5Zm5rkufKcA3gNMz8/cR8a8HqmBJUs+qOUJvBjZk5iuZ+T6wFJjZrc9lwB2Z+XuAzHyrtmVKkvpSTaAfA2zqstxWaevqOOC4iPi/EfFERMzoaUMRMS8iWiOidfPmzQdWsSSpR7U6KToSmAKcA8wGFkfEuO6dMnNRZjZlZlNDQ0ONdi1JguoC/XVgYpflxkpbV23A8sz8IDNfBf6JjoCXJA2SagL9SWBKRBwbEaOAi4Dl3fo8QMfRORExgY4pmFdqV6YkqS99Bnpm7gauAB4B1gP3ZeYLEXFDRFxQ6fYIsCUi1gGPAf8tM7cMVNGSpH31edkiQGauAFZ0a7u2y+cErqr8SJLqwDtFJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoUsWDrz3IJ1d8khP//kQ+ueKTPPjag/UuSeqXqh7OJZXuwdce5Pq117OrfRcAb+x8g+vXXg/AZyZ9po6VSdXzCF0Cbn/+9s4w32NX+y5uf/72OlUk9Z+BLgFv7nyzX+3SochAl4APj/lwv9qlQ5GBLgFXnnAlo0eM3qtt9IjRXHnClXWqSOo/T4pK/P8Tn7c/fztv7nyTD4/5MFeecKUnRDWkGOhSxWcmfcYA15DmlIskFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SClFVoEfEjIh4MSI2RMTVvfT7XERkRDTVrkRJUjX6DPSIGAHcAZwPTANmR8S0HvqNBa4EVte6SElS36o5Qm8GNmTmK5n5PrAUmNlDv28D3wV29bBOkjTAqgn0Y4BNXZbbKm2dIuJkYGJmPtjbhiJiXkS0RkTr5s2b+12sJGn/DvqkaEQcBtwGfLWvvpm5KDObMrOpoaHhYHctSeqimkB/HZjYZbmx0rbHWOAE4B8i4nfAqcByT4xK0uCqJtCfBKZExLERMQq4CFi+Z2VmvpOZEzJzcmZOBp4ALsjM1gGpWJLUoz4DPTN3A1cAjwDrgfsy84WIuCEiLhjoAiVJ1anqJdGZuQJY0a3t2v30Pefgy5Ik9Zd3ikpSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCVBXoETEjIl6MiA0RcXUP66+KiHUR8WxE/Coi/qz2pUqSetNnoEfECOAO4HxgGjA7IqZ16/aPQFNmngj8HLip1oVKknpXzRF6M7AhM1/JzPeBpcDMrh0y87HM/JfK4hNAY23LlCT1pZpAPwbY1GW5rdK2P5cCD/W0IiLmRURrRLRu3ry5+iolSX2q6UnRiPgLoAm4uaf1mbkoM5sys6mhoaGWu5akYW9kFX1eByZ2WW6stO0lIs4DvgmcnZl/qE15kqRqVXOE/iQwJSKOjYhRwEXA8q4dIuJjwELggsx8q/ZlSpL60megZ+Zu4ArgEWA9cF9mvhARN0TEBZVuNwNHAj+LiKcjYvl+NidJGiDVTLmQmSuAFd3aru3y+bwa1yVJ6ifvFJWkQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRBVBXpEzIiIFyNiQ0Rc3cP6D0XEssr61RExueaVSpJ61WegR8QI4A7gfGAaMDsipnXrdinw+8z8t8D3gO/WulBJUu+qOUJvBjZk5iuZ+T6wFJjZrc9M4MeVzz8Hzo2IqF2ZkqS+jKyizzHApi7LbUDL/vpk5u6IeAcYD7zdtVNEzAPmAUyaNOkAS4YTTjjhgL8rSaUa1JOimbkoM5sys6mhoWEwdy1Jxasm0F8HJnZZbqy09dgnIkYCRwNbalGgJKk61QT6k8CUiDg2IkYBFwHLu/VZDvyXyucLgf+TmVm7MiVJfelzDr0yJ34F8AgwAvhhZr4QETcArZm5HLgHuDciNgBb6Qh9SdIgquakKJm5AljRre3aLp93Af+5tqVJkvrDO0UlqRAGuiQVwkCXpEIY6JJUiKjX1YURsRnYeIBfn0C3u1CHAcc8PDjm4eFgxvxnmdnjnZl1C/SDERGtmdlU7zoGk2MeHhzz8DBQY3bKRZIKYaBLUiGGaqAvqncBdeCYhwfHPDwMyJiH5By6JGlfQ/UIXZLUjYEuSYU4ZAM9In4YEW9FxPP7WR8R8deVF1M/GxEnD3aNtVbFmL9QGetzEbEqIk4a7Bprra8xd+l3SkTsjogLB6u2gVLNmCPinIh4OiJeiIhfD2Z9A6GKf9tHR8T/iohnKmOeO9g11lJETIyIxyJiXWU8V/bQp+YZdsgGOrAEmNHL+vOBKZWfecDfDEJNA20JvY/5VeDszPz3wLcp42TSEnof854XlX8XeHQwChoES+hlzBExDrgTuCAzp1PGk0yX0Pvf8+XAusw8CTgHuLXy/oWhajfw1cycBpwKXB4R07r1qXmGHbKBnpkr6Xi2+v7MBP42OzwBjIuIPx2c6gZGX2POzFWZ+fvK4hN0vD1qSKvi7xngr4C/B94a+IoGXhVjngP8IjNfq/Qf8uOuYswJjK28XP7ISt/dg1HbQMjMNzJzbeXzDmA9He9e7qrmGXbIBnoVenp5dfc/sJJdCjxU7yIGWkQcA/w5ZfwGVq3jgH8VEf8QEU9FxBfrXdAg+B/A8cA/A88BV2bmH+tbUm1ExGTgY8DqbqtqnmFVveBCh5aI+I90BPoZ9a5lEHwf+Hpm/rHj4G1YGAn8B+BcYAzweEQ8kZn/VN+yBtSngKeB/wR8BPjfEfGbzNxe16oOUkQcScdvl/91MMYylAO9mpdXFyciTgTuBs7PzOHwIu4mYGklzCcAn46I3Zn5QF2rGlhtwJbMfA94LyJWAicBJQf6XOA7lXcRb4iIV4F/B6ypb1kHLiIOpyPM/y4zf9FDl5pn2FCeclkOfLFypvhU4J3MfKPeRQ2kiJgE/AK4uPCjtU6ZeWxmTs7MycDPgb8sPMwB/idwRkSMjIgjgBY65mBL9hodv5EQEf8GmAq8UteKDkLlXMA9wPrMvG0/3WqeYYfsEXpE/JSOs90TIqINuA44HCAz76LjHaefBjYA/0LH//BDWhVjvhYYD9xZOWLdPdSfUlfFmIvT15gzc31EPAw8C/wRuDsze72s81BXxd/zt4ElEfEcEHRMsw3lR+qeDlwMPBcRT1fargEmwcBlmLf+S1IhhvKUiySpCwNdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFeL/ATpP71NmQjkjAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = Plotter()\n",
    "plotter.add_polygon([1, 1, 2, 2, 1], [0, 1, 1, 0, 0])\n",
    "plotter.add_point(1.5, 0.5, 'inside')\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 26\n",
    "\n",
    "Using the `Plotter` object, draw a trapezoid and 3 points: one inside, one outside and one on the boundary of the\n",
    "trapezoid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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/h/C+vqVmXCId46VmXaNHj2b37t31B1fDIZSSyyVAUYPHxcCVTdZZBvzBzL4OfAz4bHNvZGYLgAVA/S6KH6kZl0jH1DXr8sJNL5re+zQcwnVQdCawxjmXDtwI/NzMzntv59wq51yucy637o7ZfqTZuUjHaTLUslAC/ShwaYPH6bXLGpoH/ArAOfcWkAr0DccAvcY5p/PPRTpBgd6yUAJ9OzDYzAaZWVfgNmBDk3UKgesAzGwYwUBv+cRPH1MzLpHOaW+zLj9pM9CdcwFgEfAysA/4lXNuj5ndZ2Y31a62BPiKmb0LvAB82XnlyEWYqdwi0nkK9OaFdB567Tnlm5osu7fB13uBq8M7NG9SuUX85G9/+1tY3++yyy4DgmWXlq6sTEpKIisri0AgwLBhw3j++edJS0trdt01a9awY8cOHn/88bCOM1Z0pWgUVVVVqRmXSBi0dvpiXS+X999/n65du7Jy5coojy52FOhRpHKLSPiEcnB0/PjxHDx4kH/9619Mnz6d7Oxsxo0bx+7duxutV1payqBBg6iqqgKCe9J1j7dv3052djYjRoyob78LwZ4vc+fOJSsri5EjR/L6668DwVn/jBkzmDx5MoMHD+Zb3/pWmLe8ZQr0KFKgi4RPW3X0QCDA5s2bycrKIi8vj5EjR7J7924efPBB5syZ02jdnj17MnHiRDZu3AhAfn4+M2bMICUlhblz5/L0009TUFDQqO3AE088gZnx3nvv8cILL/ClL32pvhFXQUEB69at47333mPdunUUFRURDQr0KFEzLpHwaqlZV10vl9zcXDIyMpg3bx5bt25l9uzZAHzmM5/h5MmT5x3Pmj9/PqtXrwZg9erVzJ07l1OnTlFaWspVV10FwO23316//tatW+uv8rz88ssZMGAABw4cAOC6666jV69epKamkpmZyZEjR8L/DWiG2udGiZpxiYRfc8266mro7XX11Vdz+PBhtmzZQnV1NcOHD+9wv6W6lrsQWtvdcNEMPUp0dotI+IV6kdH48eNZu3YtEOxZ3rdvXy644ILz1pszZw633357fTvb3r1707NnT95++20gWIpp7j0PHDhAYWEhQ4cO7dT2dJZm6FGgZlziV3WnGUZKqLemW7ZsGXfeeSfZ2dmkpaXx/PPPN7verFmzuOeee5g5c2b9smeffZavfOUrdOnShWuvvZZevXoBcNddd/G1r32NrKwskpOTWbNmTaOZeSy02T43UvzUPre8vJwPP/ww1sMQibhIt89tTrdu3ejSJTzFhvXr1/O73/2On//85/XLysrK6NGjBwAPP/wwx44d47HHHgvL57Wlve1zNUOPApVbRCKnuro6LIH+9a9/nc2bN7NpU+N782zcuJGHHnqIQCDAgAEDWLNmTac/K1IU6BGmcotIZFVXV5OSktLp91mxYkWzy2+99VZuvfXWTr9/NOigaISpGZf4TbTLuF5t1tWR76MCPcI0Oxc/MTNOnToV9VD3WqA75zh58iSpqantep1KLhGm+rn4iZlx4sQJjh+PbvfsLl26tNisK1GlpqaSnp7ertd46zsQZ9SMS/zGzGJyezgzY8iQIeddZOQ3KrlEkMotItHhnFNrDRToEaVAF4kelTcV6BFTXV2tGYNIFJWWlvq+X5ICPULUjEskumpqaigvL4/1MGJKgR4hKreIRJ/fyy4K9AjQ1aEiseH3sosCPQIqKipCbuspIuFTVVVVf9cgP1KgR4Dfd/tEYsnPe8cK9DBTuUUktvw8oVKgh5macYnEVmVlpW//BhXoYabZuUjs+fXvUIEeZn7e3ROJF379O1Sgh5GacYnEB7+eaaZADyO/7uaJxBu/npygQA8jv+7micQjBbp0WHV1te/7SIjEk9LSUs/dyagtIQW6mU02s7+a2UEzu7uFdb5oZnvNbI+Z/TK8w4x/asYlEl9qamqoqKiI9TCiqs07FplZEvAE8O9AMbDdzDY45/Y2WGcw8B3gaufcR2b2fyI14Hjlx907kXhXUlJCjx49Yj2MqAllhj4WOOicO+ScOwfkA9OarPMV4Ann3EcAzrl/hneY8c2vB2BE4p3fmnWFEuiXAEUNHhfXLmtoCDDEzN40s21mNrm5NzKzBWa2w8x2RPsmspHk11OkROKd35p1heugaDIwGJgIzAR+ama9m67knFvlnMt1zuX269cvTB8dezq7RSR++envM5RAPwpc2uBxeu2yhoqBDc65Kufch8ABggHvec45X/3CiCQaP5VDQwn07cBgMxtkZl2B24ANTdb5LcHZOWbWl2AJ5lD4hhm/zp49S1VVVayHISIt8FOzrjYD3TkXABYBLwP7gF855/aY2X1mdlPtai8DJ81sL/A6sNQ5dzJSg44nmp2LxD+/zNLbPG0RwDm3CdjUZNm9Db52wH/U/ucrfvlFEUlkJSUl9OnTJ9bDiDhdKdoJasYlkhjKy8t9cSaaAr0TNDsXSRx++HtVoHeC6uciiUOBLi1SMy6RxOKHZl0K9A5SMy6RxOKHZl0K9A7yw+6biNd4vUyqQO8ANeMSSUxeb9alQO8Av5wCJeI1Xm/WpUDvAM3ORRKXl8suCvR2UjMukcTm5QmZAr2d1IxLJLF5uVmXAr2dNDv3ro2FG5m0aRLZv8lm0qZJbCzcGOshSYR4dZauQG8nr/4i+N3Gwo0s27WMY2eO4XAcO3OMZbuWKdQ9yqsTMwV6O6gZl3c99v5jVFY3PvuhsrqSx95/LEYjkkjy6plqCvR20Ozcu/5x5h/tWi6Jz4t/zwr0dvDqbppA/+7927VcEp8C3cfUjMvbFg9fTGpSaqNlqUmpLB6+OEYjkkjzYrOukO5YJGrG5XVTMqYAwVr6P878g/7d+7N4+OL65eI9NTU1lJeX07Nnz1gPJWwU6CFSucX7pmRMUYD7TGlpqacCXSWXEDjnKCsri/UwRCTMSkpKPLXnrUAPgVdPcRLxu0Ag4KlmXQr0EHjxaLiIBHmpnKpAb4OacYl4m5cmbAr0NqgZl4i3ealZlwK9DZqdi3ifV2bpCvQ2eOUHLSIt88rETYHeCjXjEvEHr5zJpkBvhWbnIv7hhb93BXorvLIbJiJt88LfuwK9BWrGJeIvZWVlCd+sS4HeAjXjEvGXumZdiSykQDezyWb2VzM7aGZ3t7LeLWbmzCw3fEOMDS/sfolI+yR6Hb3NQDezJOAJ4AYgE5hpZpnNrNcTWAy8He5BRptzLuF/sCLSfonerCuUGfpY4KBz7pBz7hyQD0xrZr37ge8DCd/ppry8POFraSLSfonerCuUQL8EKGrwuLh2WT0zGwVc6pxr9RbpZrbAzHaY2Y7jx4+3e7DRotm5iH8lcrm10wdFzawL8CNgSVvrOudWOedynXO5/fr16+xHR4SacYn4WyJP6EIJ9KPApQ0ep9cuq9MTGA5sMbPDwDhgQ6IeGFUzLhF/S+RmXaEE+nZgsJkNMrOuwG3AhronnXOnnXN9nXMDnXMDgW3ATc65HREZcYRpdi4iiZoDbQa6cy4ALAJeBvYBv3LO7TGz+8zspkgPMNoSeXdLRMIjUXMgpJtEO+c2AZuaLLu3hXUndn5YsaFmXCIC/9usKykpKdZDaRddKdpAou5miUj4JeIsXYHeQCL+AEUkMhJxgqdAr6VmXCLSUCI261Kg11IzLhFpKBGbdSnQayXi7pWIRFailWEV6KgZl4g0L9GadSnQUTMuEWleojXrUqCTeLtVIhI9iVSO9X2gqxmXiLQmkfLB94FeWVmpZlwi0qKzZ88mTLMu3we6yi0i0pZEmaX7PtAT5QclIrGTKBM/Xwd6VVVVQh3BFpHYKC8vJxAIxHoYbfJ1oGt2LiKhKisri/UQ2uTrQE+U3SgRib1EmAD6NtDVjEtE2iMRmnX5NtDVjEtE2iMRmnX5NtATYfdJROJLvJdpfRnoasYlIh0R7826fBnoasYlIh0RCATi+r7Dvgx0lVtEpKPiee/ed4GucouIdEY8Twh9F+hqxiUinRHPzbp8F+ianYtIZ8XrLN13gR6vPwgRSRzxOjH0VaCfO3dOzbhEpNPitVmXrwI9Xv9VFZHEE4954qtAV7lFRMJFgR5D1dXVVFRUxHoYIuIR8disyzeBrmZcIhJO8disK6RAN7PJZvZXMztoZnc38/x/mNleM9ttZq+a2YDwD7VzVG4RkXCLt1xpM9DNLAl4ArgByARmmllmk9X+AuQ657KB9cAPwj3QztDVoSISCaWlpXG15x/KDH0scNA5d8g5dw7IB6Y1XME597pzrq5AvQ1ID+8wO0fNuEQkEuKtWVcogX4JUNTgcXHtspbMAzY394SZLTCzHWa24/jx46GPspPibbdIRLwjnvb+w3pQ1MzuAHKB/2zueefcKudcrnMut1+/fuH86Bap3CIikRRPE8ZQAv0ocGmDx+m1yxoxs88C3wVucs6dDc/wOk/NuEQkkuKpWVcogb4dGGxmg8ysK3AbsKHhCmY2EniaYJj/M/zD7DjNzkUk0uJllt5moDvnAsAi4GVgH/Ar59weM7vPzG6qXe0/gR7Ar82swMw2tPB2URcv32gR8a54yZnkUFZyzm0CNjVZdm+Drz8b5nGFhZpxiUg0VFRUEAgESE4OKVIjxtNXiqrcIiLREg954+lAj5fdIBHxPgV6BKkZl4hEUzw06/JsoMfbJbki4m3x0KzL04EuIhJNsS7zejLQa2pqFOgiEnWxrgx4MtArKipiXssSEf+JdbMuTwZ6rHd7RMS/Ylkd8FygqxmXiMRSLCeUngt0NeMSkVg6e/YsZ8/Gpj+h5wJd5RYRibVYVQk8F+gqt4hIrMVqYumpQFczLhGJB3XNuqLNU4Gu2bmIxItY5JGnAl31cxGJFwr0Tqiuro55HwURkTqxaNblmUBXuUVE4kksmnUp0EVEIiTaZWBPBLqacYlIPIp2sy5PBLqacYlIPIp2sy5PBLrObhGReBXNfEr4QFczLhGJZ9HMp4QPdDXjEpF4Fs1mXQkf6Cq3iEi8i9YsPeEDXeUWEYl30Zp4JnSgqxmXiCSCaDXrSuhA1+xcRBJFNPIqoQNd9XMRSRQK9FaoGZeIJJLS0tKIXwCZsIGucouIJBLnHGVlZRH9DAW6iEiURDq3Qgp0M5tsZn81s4Nmdnczz3czs3W1z79tZgPDPtIG1IxLRBJRpJt1tRnoZpYEPAHcAGQCM80ss8lq84CPnHP/BjwKfD/cA22ovLxczbhEJOFEullXcgjrjAUOOucOAZhZPjAN2NtgnWnAstqv1wOPm5m5CP1TVF5eTlJSUiTeWkQkosrLy0lLS4vIe4cS6JcARQ0eFwNXtrSOcy5gZqeBPsCJhiuZ2QJgAUBGRkYHhwz9+/enf//+HX69iIgXRfWgqHNulXMu1zmX269fv2h+tIiI54US6EeBSxs8Tq9d1uw6ZpYM9AJOhmOAIiISmlACfTsw2MwGmVlX4DZgQ5N1NgBfqv3688Brkaqfi4hI89qsodfWxBcBLwNJwHPOuT1mdh+wwzm3AXgW+LmZHQT+RTD0RUQkikI5KIpzbhOwqcmyext8XQl8IbxDExGR9kjYK0VFRKQxBbqIiEco0EVEPEKBLiLiERarswvN7DhwpIMv70uTq1B9QNvsD9pmf+jMNg9wzjV7ZWbMAr0zzGyHcy431uOIJm2zP2ib/SFS26ySi4iIRyjQRUQ8IlEDfVWsBxAD2mZ/0Db7Q0S2OSFr6CIicr5EnaGLiEgTCnQREY+I20A3s+fM7J9m9n4Lz5uZ/aT2xtS7zWxUtMcYbiFs86zabX3PzP5sZjnRHmO4tbXNDdYbY2YBM/t8tMYWKaFss5lNNLMCM9tjZn+K5vgiIYTf7V5m9l9m9m7tNs+N9hjDycwuNbPXzWxv7fYsbmadsGdY3AY6sAaY3MrzNwCDa/9bADwVhTFF2hpa3+YPgWudc1nA/XjjYNIaWt/muhuVfx/4QzQGFAVraGWbzaw38CRwk3PuCrzRyXQNrf+cFwJ7nXM5wETgh7X3X0hUAWCJcy4TGAcsNLPMJuuEPcPiNtCdc28Q7K3ekmnAz1zQNqC3mV0UndFFRlvb7Jz7s3Puo9qH2wjePSqhhfBzBvg68Bvgn5EfUeSFsM23Ay865wpr10/47Q5hmx3Q08wM6FG7biAaY4sE59wx59yu2q9LgX0E773cUNgzLG4DPQTN3by66TfMy+YBm2M9iEgzs0uAm/HGHliohgAXmtkWM9tpZnNiPaAoeBwYBvwdeA9Y7Jyrie2QwsPMBgIjgbebPBX2DAvpBhcSX8zs0wQD/ZpYjyUKfgx82zlXE5y8+UIyMBq4DugOvGVm25xzB2I7rIi6HigAPgNcBvzRzP7bOVcS01F1kpn1ILh3+X+jsS2JHOih3Lzac8wsG3gGuME554cbcecC+bVh3he40cwCzrnfxnRUkVUMnHTOlQPlZvYGkAN4OdDnAg/X3ov4oJl9CFwOvBPbYXWcmaUQDPO1zrkXm1kl7BmWyCWXDcCc2iPF44DTzrljsR5UJJlZBvAiMNvjs7V6zrlBzrmBzrmBwHrgLo+HOcDvgGvMLNnM0oArCdZgvayQ4B4JZvYJYChwKKYj6oTaYwHPAvuccz9qYbWwZ1jcztDN7AWCR7v7mlkxkAekADjnVhK8x+mNwEGgguC/8AkthG2+F+gDPFk7Yw0kepe6ELbZc9raZufcPjP7PbAbqAGecc61elpnvAvh53w/sMbM3gOMYJktkVvqXg3MBt4zs4LaZf8PyIDIZZgu/RcR8YhELrmIiEgDCnQREY9QoIuIeIQCXUTEIxToIiIeoUAXEfEIBbqIiEf8f4bST2EnBcvCAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter = Plotter()\n",
    "plotter.add_polygon([1, 1.2, 1.8, 2, 1], [0, 1, 1, 0, 0])\n",
    "plotter.add_point(1.5, 0.5, 'inside')\n",
    "plotter.add_point(1, 1, 'outside')\n",
    "plotter.add_point(1.5, 1, 'boundary')\n",
    "plotter.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}