{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 16 Extracting Interfaces from Geological Maps\n",
    "\n",
    "Geological Maps nowadays are usually available in a vector format consisting of connected polygons. These polygons consist of single vertices and hence, boundaries between younger and older stratigraphic units can be used for modeling. However, these specific vertices need to be kept while removing all unnecessary ones. The following will introduce how that works in GemGIS. \n",
    "\n",
    "<img src=\"../images/tutorial16_cover.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set File Paths and download Tutorial Data\n",
    "\n",
    "If you downloaded the latest `GemGIS` version from the Github repository, append the path so that the package can be imported successfully. Otherwise, it is recommended to install `GemGIS` via `pip install gemgis` and import `GemGIS` using `import gemgis as gg`. In addition, the file path to the folder where the data is being stored is set. The tutorial data is downloaded using Pooch (https://www.fatiando.org/pooch/latest/index.html) and stored in the specified folder. Use `pip install pooch` if Pooch is not installed on your system yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-17T11:11:44.835933Z",
     "start_time": "2021-03-17T11:11:42.598151Z"
    }
   },
   "outputs": [],
   "source": [
    "import gemgis as gg\n",
    "\n",
    "file_path ='data/16_extracting_interfaces_from_geological_maps/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-17T11:11:44.913801Z",
     "start_time": "2021-03-17T11:11:44.884672Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading file '16_extracting_interfaces_from_geological_maps.zip' from 'https://rwth-aachen.sciebo.de/s/AfXRsZywYDbUF34/download?path=%2F16_extracting_interfaces_from_geological_maps.zip' to 'C:\\Users\\ale93371\\Documents\\gemgis\\docs\\getting_started\\tutorial\\data\\16_extracting_interfaces_from_geological_maps'.\n"
     ]
    }
   ],
   "source": [
    "gg.download_gemgis_data.download_tutorial_data(filename=\"16_extracting_interfaces_from_geological_maps.zip\", dirpath=file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data\n",
    "\n",
    "A simple geological map will be used to demonstrate the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:34:41.978556Z",
     "start_time": "2020-12-06T16:34:36.350088Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>formation</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>Sand1</td>\n",
       "      <td>POLYGON ((0.25633 264.86215, 10.59347 276.7337...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Ton</td>\n",
       "      <td>POLYGON ((0.25633 264.86215, 0.18819 495.78721...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>Sand2</td>\n",
       "      <td>POLYGON ((0.18819 495.78721, 0.24897 1068.7595...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>Sand2</td>\n",
       "      <td>POLYGON ((511.67477 1068.85246, 971.69794 1068...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id formation                                           geometry\n",
       "0  None     Sand1  POLYGON ((0.25633 264.86215, 10.59347 276.7337...\n",
       "1  None       Ton  POLYGON ((0.25633 264.86215, 0.18819 495.78721...\n",
       "2  None     Sand2  POLYGON ((0.18819 495.78721, 0.24897 1068.7595...\n",
       "3  None     Sand2  POLYGON ((511.67477 1068.85246, 971.69794 1068..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "gmap = gpd.read_file(file_path + 'interfaces_polygons.shp')\n",
    "gmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:34:42.343553Z",
     "start_time": "2020-12-06T16:34:41.985074Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "gmap.plot(column='formation', aspect='equal', legend=True)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting the GeoDataFrame by stratigraphic age\n",
    "\n",
    "The probably most important step of this workflow is to sort the stratigraphic units according to their age. This can be done using the function ``sort_by_stratigraphy(..)`` and by providing a list with the formations in stratigraphic order from young to old.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:34:42.391072Z",
     "start_time": "2020-12-06T16:34:42.348556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>formation</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>Sand2</td>\n",
       "      <td>POLYGON ((0.18819 495.78721, 0.24897 1068.7595...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Sand2</td>\n",
       "      <td>POLYGON ((511.67477 1068.85246, 971.69794 1068...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>Ton</td>\n",
       "      <td>POLYGON ((0.25633 264.86215, 0.18819 495.78721...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>Sand1</td>\n",
       "      <td>POLYGON ((0.25633 264.86215, 10.59347 276.7337...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id formation                                           geometry\n",
       "0  None     Sand2  POLYGON ((0.18819 495.78721, 0.24897 1068.7595...\n",
       "1  None     Sand2  POLYGON ((511.67477 1068.85246, 971.69794 1068...\n",
       "2  None       Ton  POLYGON ((0.25633 264.86215, 0.18819 495.78721...\n",
       "3  None     Sand1  POLYGON ((0.25633 264.86215, 10.59347 276.7337..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stratigraphy = ['Sand2', 'Ton', 'Sand1']\n",
    "\n",
    "gmap_sorted = gg.vector.sort_by_stratigraphy(gdf=gmap,\n",
    "                                             stratigraphy=stratigraphy)\n",
    "\n",
    "gmap_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Intersections from Polygons\n",
    "\n",
    "The functionality is mainly based on the intersections between Shapely Polygons which will be demonstrated below. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intersections between two polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:34:42.438208Z",
     "start_time": "2020-12-06T16:34:42.394480Z"
    }
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<MULTILINESTRING ((0.256 264.862, 10.593 276.734), (10.593 276.734, 17.135 2...>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection = gg.vector.intersection_polygon_polygon(polygon1=gmap_sorted.loc[3].geometry,\n",
    "                                                      polygon2=gmap_sorted.loc[2].geometry)\n",
    "intersection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intersections between a polygon and polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:34:42.502422Z",
     "start_time": "2020-12-06T16:34:42.489220Z"
    }
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<POLYGON ((0.256 264.862, 0.188 495.787, 8.841 504.142, 41.093 546.423, 71.7...>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmap_sorted.loc[2].geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:34:42.518226Z",
     "start_time": "2020-12-06T16:34:42.505224Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<POLYGON ((511.675 1068.852, 971.698 1068.8, 971.738 832.976, 959.372 800.02...>,\n",
       " <POLYGON ((0.188 495.787, 0.249 1068.76, 278.524 1068.772, 265.602 1045.513,...>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_list=[gmap_sorted.loc[1].geometry, gmap_sorted.loc[0].geometry]\n",
    "poly_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:35:42.303554Z",
     "start_time": "2020-12-06T16:35:42.282988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MULTILINESTRING ((511.675 1068.852, 526.375 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MULTILINESTRING ((0.188 495.787, 8.841 504.142...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  MULTILINESTRING ((511.675 1068.852, 526.375 10...\n",
       "1  MULTILINESTRING ((0.188 495.787, 8.841 504.142..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection = gg.vector.intersections_polygon_polygons(polygon1=gmap_sorted.loc[2].geometry,\n",
    "                                                        polygons2=poly_list)\n",
    "intersection_gdf = gpd.GeoDataFrame(geometry=intersection)\n",
    "intersection_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:35:43.529978Z",
     "start_time": "2020-12-06T16:35:43.293795Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intersection_gdf.plot()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intersections between polygons and polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:35:46.393486Z",
     "start_time": "2020-12-06T16:35:46.376121Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((0.256 264.862, 0.188 495.787, 8.841 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  POLYGON ((0.256 264.862, 0.188 495.787, 8.841 ..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf1 = gpd.GeoDataFrame(geometry=[gmap_sorted.loc[2].geometry])\n",
    "gdf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:35:47.394428Z",
     "start_time": "2020-12-06T16:35:47.365255Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((511.675 1068.852, 971.698 1068.800, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POLYGON ((0.188 495.787, 0.249 1068.760, 278.5...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  POLYGON ((511.675 1068.852, 971.698 1068.800, ...\n",
       "1  POLYGON ((0.188 495.787, 0.249 1068.760, 278.5..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf2 = gpd.GeoDataFrame(geometry=[gmap_sorted.loc[1].geometry, gmap_sorted.loc[0].geometry])\n",
    "gdf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:35:48.308616Z",
     "start_time": "2020-12-06T16:35:48.276533Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MULTILINESTRING ((511.675 1068.852, 526.375 10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MULTILINESTRING ((0.188 495.787, 8.841 504.142...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry\n",
       "0  MULTILINESTRING ((511.675 1068.852, 526.375 10...\n",
       "1  MULTILINESTRING ((0.188 495.787, 8.841 504.142..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection = gg.vector.intersections_polygons_polygons(polygons1=gdf1,\n",
    "                                                         polygons2=gdf2)\n",
    "\n",
    "intersection_gdf = gpd.GeoDataFrame(geometry=intersection)\n",
    "intersection_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:35:49.364609Z",
     "start_time": "2020-12-06T16:35:49.084541Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intersection_gdf.plot()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Interfaces from Geological Map\n",
    "\n",
    "The final step is to use the entire GeoDataFrame for the extraction of the intersections. The resulting GeoDataFrame only contains interfaces of formations that also have a base. Therefore, interfaces of ``Sand1`` are missing as this formation represents the basement of the stratigraphy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:35:50.281792Z",
     "start_time": "2020-12-06T16:35:50.161744Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>formation</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>Sand2</td>\n",
       "      <td>MULTILINESTRING ((278.52378 1068.77171, 265.60...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Sand2</td>\n",
       "      <td>MULTILINESTRING ((971.73811 832.97587, 959.372...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>Ton</td>\n",
       "      <td>MULTILINESTRING ((972.08890 529.79176, 966.073...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id formation                                           geometry\n",
       "0  None     Sand2  MULTILINESTRING ((278.52378 1068.77171, 265.60...\n",
       "1  None     Sand2  MULTILINESTRING ((971.73811 832.97587, 959.372...\n",
       "2  None       Ton  MULTILINESTRING ((972.08890 529.79176, 966.073..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection = gg.vector.extract_xy_from_polygon_intersections(gdf=gmap_sorted)\n",
    "\n",
    "intersection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When plotting the data, it can be seen that there are three different boundaries. First, there is the base of ``Ton`` and then twice the base of ``Sand2``. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-06T16:35:51.499708Z",
     "start_time": "2020-12-06T16:35:51.172541Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intersection.plot(column='formation', aspect='equal', legend=True)\n",
    "plt.grid()"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.0"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}