{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fec6ea4f-082e-463c-89b6-8c68a6e40e60",
   "metadata": {},
   "source": [
    "# Topo - Concatenate two layers stored in geopackages\n",
    "O.Kaufmann, 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d41ffcc-877a-45c4-82c4-dff83278d122",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ba04e7-1e9f-4deb-ac8e-2c48f891b2ce",
   "metadata": {},
   "source": [
    "## Read the layers into geodataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4124d59-fbe8-433e-9008-e6008f7a5d69",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_2 = gpd.read_file('./data_gis/ft2.gpkg', layer='landmarks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be4d87c3-ed26-491b-8867-2c8ac37050d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_3 = gpd.read_file('./data_gis/ft3.gpkg', layer='landmarks')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "029c66d7-7615-4d81-862f-f56c608e226d",
   "metadata": {},
   "source": [
    "## Plot the two layers in separate axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "20f1d9ad-7857-4b20-8e6c-13e83dc3c23d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, figsize=(16,8))\n",
    "gdf_2.plot(ax=ax[0])\n",
    "gdf_3.plot(ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a9b51bb-55ca-4961-8614-412830986c92",
   "metadata": {},
   "source": [
    "## Plot the two layers in the same axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8eb4977-85ad-417e-9b6d-5046aa28c3d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "gdf_2.plot(ax=ax)\n",
    "gdf_3.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2a78f02-8222-459f-9cbd-ef0db6c3fb4c",
   "metadata": {},
   "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>landmark</th>\n",
       "      <th>positioning</th>\n",
       "      <th>year</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L045</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021</td>\n",
       "      <td>POINT (122583.565 125163.235)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  landmark positioning  year                       geometry\n",
       "4     L045         GPS  2021  POINT (122583.565 125163.235)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_2.query('landmark==\"L045\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e405759-7f0d-4033-bda5-dba2576cdbf8",
   "metadata": {},
   "source": [
    "## Clean the second layer and fix the landmark name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b74bd71b-6c7f-4b71-bee8-39ec238f8876",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_3.dropna(axis='index', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "01a86d62-453d-4abb-8a58-5e972b6c265d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_3['landmark'] = gdf_3['landmark'].apply(lambda x: f'L{x}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "43b1f551-2e74-4808-aadd-a563826ee804",
   "metadata": {},
   "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>landmark</th>\n",
       "      <th>positioning</th>\n",
       "      <th>year</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L001</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121184.037 126112.105)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L002</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121219.870 126094.263)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L003</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121203.004 126058.071)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L004</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121167.300 126075.999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L050</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (122599.237 125412.080)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L009</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (122619.580 125809.708)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L090</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121890.166 125914.780)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>L091</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121871.364 125935.615)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L051</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121968.549 125376.939)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>L006</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121258.614 126012.355)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>L007</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121254.530 126054.242)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L005</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121332.472 126030.081)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L008</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121331.031 126075.167)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>L052</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (122217.941 125363.251)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L062</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (122322.165 125356.536)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>L010</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (122021.498 125934.254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>L012</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (122256.704 125920.614)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>L011</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (122141.663 125926.539)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>L013</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121749.332 125951.740)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>L014</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121846.951 125951.865)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>L015</td>\n",
       "      <td>GPS</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (121936.764 125942.185)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>L092</td>\n",
       "      <td>\\nLandmarks in space</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>POINT (122414.240 125913.989)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   landmark           positioning    year                       geometry\n",
       "0      L001                   GPS  2021.0  POINT (121184.037 126112.105)\n",
       "1      L002                   GPS  2021.0  POINT (121219.870 126094.263)\n",
       "2      L003                   GPS  2021.0  POINT (121203.004 126058.071)\n",
       "3      L004                   GPS  2021.0  POINT (121167.300 126075.999)\n",
       "4      L050                   GPS  2021.0  POINT (122599.237 125412.080)\n",
       "5      L009                   GPS  2021.0  POINT (122619.580 125809.708)\n",
       "6      L090                   GPS  2021.0  POINT (121890.166 125914.780)\n",
       "7      L091                   GPS  2021.0  POINT (121871.364 125935.615)\n",
       "8      L051                   GPS  2021.0  POINT (121968.549 125376.939)\n",
       "9      L006                   GPS  2021.0  POINT (121258.614 126012.355)\n",
       "10     L007                   GPS  2021.0  POINT (121254.530 126054.242)\n",
       "11     L005                   GPS  2021.0  POINT (121332.472 126030.081)\n",
       "12     L008                   GPS  2021.0  POINT (121331.031 126075.167)\n",
       "13     L052                   GPS  2021.0  POINT (122217.941 125363.251)\n",
       "14     L062                   GPS  2021.0  POINT (122322.165 125356.536)\n",
       "15     L010                   GPS  2021.0  POINT (122021.498 125934.254)\n",
       "18     L012                   GPS  2021.0  POINT (122256.704 125920.614)\n",
       "19     L011                   GPS  2021.0  POINT (122141.663 125926.539)\n",
       "20     L013                   GPS  2021.0  POINT (121749.332 125951.740)\n",
       "21     L014                   GPS  2021.0  POINT (121846.951 125951.865)\n",
       "22     L015                   GPS  2021.0  POINT (121936.764 125942.185)\n",
       "23     L092  \\nLandmarks in space  2021.0  POINT (122414.240 125913.989)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a95378-953f-44ef-9f9f-9aa048914c6f",
   "metadata": {},
   "source": [
    "## Concatenate the two layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64793c45-47dd-4ae3-a4a1-c320b0b17d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = pd.concat([gdf_2, gdf_3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55dedd3b-0c94-42de-987a-56130523b9ec",
   "metadata": {},
   "source": [
    "## Check the crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6f26f9e5-e766-4554-a5c3-fbd979cf441b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:31370>\n",
       "Name: Belge 1972 / Belgian Lambert 72\n",
       "Axis Info [cartesian]:\n",
       "- X[east]: Easting (metre)\n",
       "- Y[north]: Northing (metre)\n",
       "Area of Use:\n",
       "- name: Belgium - onshore.\n",
       "- bounds: (2.5, 49.5, 6.4, 51.51)\n",
       "Coordinate Operation:\n",
       "- name: Belgian Lambert 72\n",
       "- method: Lambert Conic Conformal (2SP)\n",
       "Datum: Reseau National Belge 1972\n",
       "- Ellipsoid: International 1924\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8eb732-29bc-404d-b04f-f59710ae3800",
   "metadata": {},
   "source": [
    "## Plot the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81435067-d202-45c0-a73c-d394011d6ab4",
   "metadata": {
    "nbsphinx-thumbnail": {
     "tooltip": "This tooltip message was defined in cell metadata"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "gdf.plot(ax=ax)\n",
    "ax.grid('on')"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}