{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d56db611-251f-48d1-b892-646704862e24",
   "metadata": {},
   "source": [
    "# Import and display symbols used for reporting field work\n",
    "O. Kaufmann, 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a32e4fa-ca6c-4a8f-99cb-dd0f07bdab6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from geometron.plot import symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6bb131e-cf6f-42fd-992a-9c97998c673e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'marker': Path(array([[-6.24790884,  7.01052446],\n",
       "        [ 6.33508116, -5.57246554],\n",
       "        [ 6.33508116, -5.57246554],\n",
       "        [ 6.33508116, -5.57246554],\n",
       "        [ 6.33508116,  7.01052446],\n",
       "        [-6.24790884, -5.57246454],\n",
       "        [-6.24790884, -5.57246454],\n",
       "        [-6.24790884, -5.57246454],\n",
       "        [-1.48190354,  0.71902746],\n",
       "        [-1.48190354,  0.71902746],\n",
       "        [-1.48190354,  0.31459787],\n",
       "        [-1.32107315, -0.07368104],\n",
       "        [-1.03509824, -0.35965595],\n",
       "        [-0.74912334, -0.64563085],\n",
       "        [-0.36084443, -0.80646124],\n",
       "        [ 0.04358516, -0.80646124],\n",
       "        [ 0.44801475, -0.80646124],\n",
       "        [ 0.83629366, -0.64563085],\n",
       "        [ 1.12226857, -0.35965595],\n",
       "        [ 1.40824347, -0.07368104],\n",
       "        [ 1.56907386,  0.31459787],\n",
       "        [ 1.56907386,  0.71902746],\n",
       "        [ 1.56907386,  0.71902746],\n",
       "        [ 1.56907386,  1.12345705],\n",
       "        [ 1.40824347,  1.51173596],\n",
       "        [ 1.12226857,  1.79771086],\n",
       "        [ 0.83629366,  2.08368577],\n",
       "        [ 0.44801475,  2.24451616],\n",
       "        [ 0.04358516,  2.24451616],\n",
       "        [-0.36084443,  2.24451616],\n",
       "        [-0.74912334,  2.08368577],\n",
       "        [-1.03509824,  1.79771086],\n",
       "        [-1.32107315,  1.51173596],\n",
       "        [-1.48190354,  1.12345705],\n",
       "        [-1.48190354,  0.71902746]]), array([1, 4, 4, 4, 1, 4, 4, 4, 1, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "        2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], dtype=uint8)),\n",
       " 'alpha': 1.0,\n",
       " 'markerfacecolor': 'none',\n",
       " 'markeredgecolor': 'black',\n",
       " 'markersize': 8}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols['stake']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3859286",
   "metadata": {
    "nbsphinx-thumbnail": {
     "tooltip": "This tooltip message was defined in cell metadata"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.text(0, 1, 'landmark', va='bottom', ha='center')\n",
    "ax.plot(0, 0, **symbols['landmark'])\n",
    "ax.text(10, 1, 'station', va='bottom', ha='center')\n",
    "ax.plot(10, 0, **symbols['station'])\n",
    "ax.text(20, 1, 'stake', va='bottom', ha='center')\n",
    "ax.plot(20, 0, **symbols['stake'])\n",
    "ax.text(30, 1, 'start', va='bottom', ha='center')\n",
    "ax.plot(30, 0, **symbols['start'])\n",
    "ax.text(40, 1, 'end', va='bottom', ha='center')\n",
    "ax.plot(40, 0, **symbols['end'])\n",
    "ax.set_xlim(-5, 45)\n",
    "ax.set_ylim(-5, 5)\n",
    "ax.axis('off');"
   ]
  }
 ],
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