{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "62332b33",
   "metadata": {
    "id": "62332b33"
   },
   "source": [
    "# Analyze LUCAS data\n",
    "\n",
    "## Install\n",
    "\n",
    "### Note for Google Colab\n",
    "\n",
    "In the case that this notebook is run in [Google Colab](https://colab.research.google.com/), additional steps are needed. Google Colab comes with GDAL 2.2. We have to update this library to version 3.0. It will takes a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6CDxArKFAwID",
   "metadata": {
    "id": "6CDxArKFAwID"
   },
   "outputs": [],
   "source": [
    "!add-apt-repository --yes ppa:ubuntugis/ubuntugis-unstable\n",
    "!apt update\n",
    "!apt install libgdal26 libsqlite3-mod-spatialite \n",
    "!pip install gdal==3.0.4\n",
    "print(\"INSTALLATION COMPLETED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8cc48f4-44af-4032-bb3a-3318ee201c83",
   "metadata": {},
   "source": [
    "### Install st_lucas package\n",
    "\n",
    "First of all, let's install [st_lucas](https://pypi.org/project/st-lucas/) Python package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dPyAK3ZGA3rg",
   "metadata": {
    "id": "dPyAK3ZGA3rg"
   },
   "outputs": [],
   "source": [
    "!pip install st_lucas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fhApDe8HA6v-",
   "metadata": {
    "id": "fhApDe8HA6v-"
   },
   "source": [
    "Now **we have to restart runtime**: `Runtime -> Restart runtime` (on Google Colab) or ``Kernel -> Restart`` (on JupyterLab).\n",
    "\n",
    "From `st_lucas` package let's import `LucasRequest`, `LucasIO` and `LucasClassAggregate`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a252f4a",
   "metadata": {
    "id": "7a252f4a"
   },
   "outputs": [],
   "source": [
    "from st_lucas import LucasRequest, LucasIO, LucasClassAggregate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f4b71fd",
   "metadata": {
    "id": "9f4b71fd"
   },
   "source": [
    "## Download sample LUCAS points\n",
    "\n",
    "### Define request\n",
    "\n",
    "Request LUCAS points located in the Czech Republic and surveyed in 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd98872f",
   "metadata": {
    "id": "cd98872f"
   },
   "outputs": [],
   "source": [
    "request = LucasRequest()\n",
    "request.countries = ['CZ']\n",
    "request.years = [2018]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361ae58a",
   "metadata": {
    "id": "361ae58a"
   },
   "source": [
    "### Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "360341c4",
   "metadata": {
    "id": "360341c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-25 19:52:50,928 - LUCAS - INFO - io.download - Download process successfuly finished. Size of downloaded data: 27387kb\n"
     ]
    }
   ],
   "source": [
    "lucasio = LucasIO()\n",
    "lucasio.download(request)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10c2199",
   "metadata": {
    "id": "c10c2199"
   },
   "source": [
    "### Inspect downloaded LUCAS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "076d9698",
   "metadata": {
    "id": "076d9698"
   },
   "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>point_id</th>\n",
       "      <th>lc1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48462894</td>\n",
       "      <td>A30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46142892</td>\n",
       "      <td>C10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48042992</td>\n",
       "      <td>A30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46142990</td>\n",
       "      <td>A30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46582898</td>\n",
       "      <td>A22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5708</th>\n",
       "      <td>46482884</td>\n",
       "      <td>A12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5709</th>\n",
       "      <td>47402970</td>\n",
       "      <td>A11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5710</th>\n",
       "      <td>46682906</td>\n",
       "      <td>G11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5711</th>\n",
       "      <td>47303016</td>\n",
       "      <td>G11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5712</th>\n",
       "      <td>45623046</td>\n",
       "      <td>A11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5713 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      point_id  lc1\n",
       "0     48462894  A30\n",
       "1     46142892  C10\n",
       "2     48042992  A30\n",
       "3     46142990  A30\n",
       "4     46582898  A22\n",
       "...        ...  ...\n",
       "5708  46482884  A12\n",
       "5709  47402970  A11\n",
       "5710  46682906  G11\n",
       "5711  47303016  G11\n",
       "5712  45623046  A11\n",
       "\n",
       "[5713 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = lucasio.to_geopandas()\n",
    "df[[\"point_id\",\"lc1\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1elilCmBfyts",
   "metadata": {
    "id": "1elilCmBfyts"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "point_id         5713\n",
       "nuts0            5713\n",
       "nuts1            5713\n",
       "nuts2            5713\n",
       "nuts3            5713\n",
       "                 ... \n",
       "crop_residues    5713\n",
       "transect            0\n",
       "ex_ante          5713\n",
       "survey_year      5713\n",
       "geometry         5713\n",
       "Length: 116, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f052fe",
   "metadata": {
    "id": "f9f052fe"
   },
   "source": [
    "### Show statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6c1b36e",
   "metadata": {
    "id": "b6c1b36e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5713\n",
       "unique      57\n",
       "top        E20\n",
       "freq       990\n",
       "Name: lc1, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"lc1\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "612c8e73",
   "metadata": {
    "id": "612c8e73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='lc1_l1'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['lc1_l1'] = df['lc1'].str.slice(0,1)\n",
    "df['lc1_l1'].value_counts().plot.pie(figsize=(10, 10), autopct=\"%.1f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030d6a4c",
   "metadata": {
    "id": "030d6a4c"
   },
   "source": [
    "## Class aggregation\n",
    "\n",
    "LUCAS points are by default obtained with information corresponding to LUCAS land cover level 3. With the `LucasClassAggregate` method, you can get information about level 2 or level 1. New column will be added at the end of attribute table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc01d224",
   "metadata": {
    "id": "cc01d224"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-25 19:53:18,974 - LUCAS - INFO - io.download - Download process successfuly finished. Size of downloaded data: 27387kb\n"
     ]
    }
   ],
   "source": [
    "from st_lucas import LucasRequest, LucasIO, LucasClassAggregate\n",
    "request = LucasRequest()\n",
    "request.countries = ['CZ']\n",
    "request.years = [2018]\n",
    "lucasio = LucasIO()\n",
    "lucasio.download(request)\n",
    "\n",
    "lc1_to_level1 = {\n",
    "    \"A00\": [\"A11\", \"A12\", \"A13\", \"A21\", \"A22\", \"A30\"],\n",
    "    \"B00\": [\"B11\", \"B12\", \"B13\", \"B14\", \"B15\", \"B16\", \"B17\", \"B18\", \"B19\", \"B21\", \"B22\", \"B23\", \"B31\", \"B32\", \"B33\", \"B34\", \"B35\", \"B36\", \"B37\", \"B41\", \"B42\", \"B43\", \"B44\", \"B45\", \"B51\", \"B52\", \"B53\", \"B54\", \"B55\", \"B71\", \"B72\", \"B73\", \"B74\", \"B75\", \"B76\", \"B77\", \"B81\", \"B82\", \"B83\", \"B84\"],\n",
    "    \"Bx1\": [\"Bx1\"],\n",
    "    \"Bx2\": [\"Bx2\"],\n",
    "    \"C00\": [\"C10\", \"C21\", \"C22\", \"C23\", \"C31\", \"C32\", \"C33\"],\n",
    "    \"D00\": [\"D10\", \"D20\"],\n",
    "    \"E00\": [\"E10\", \"E20\", \"E30\"],\n",
    "    \"F00\": [\"F10\", \"F20\", \"F30\", \"F40\"],\n",
    "    \"G00\": [\"G11\", \"G12\", \"G21\", \"G22\", \"G30\", \"G40\", \"G50\"],\n",
    "    \"H00\": [\"H11\", \"H12\", \"H21\", \"H22\", \"H23\"]\n",
    "}\n",
    "\n",
    "lucasaggr = LucasClassAggregate(lucasio.data, mappings=lc1_to_level1)\n",
    "lucasaggr.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0644183e",
   "metadata": {
    "id": "0644183e"
   },
   "source": [
    "Check the appearance of the new column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7520e894",
   "metadata": {
    "id": "7520e894"
   },
   "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>lc1_h</th>\n",
       "      <th>lc1_a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A30</td>\n",
       "      <td>A00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C10</td>\n",
       "      <td>C00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A30</td>\n",
       "      <td>A00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A30</td>\n",
       "      <td>A00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A22</td>\n",
       "      <td>A00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lc1_h lc1_a\n",
       "0   A30   A00\n",
       "1   C10   C00\n",
       "2   A30   A00\n",
       "3   A30   A00\n",
       "4   A22   A00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = lucasio.to_geopandas()\n",
    "df[[\"lc1_h\", \"lc1_a\"]].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "utility-swaziland",
   "metadata": {
    "id": "utility-swaziland"
   },
   "source": [
    "## Nomenclature translation\n",
    "\n",
    "Translate the obtained data into CLC2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8cb579ff",
   "metadata": {
    "id": "8cb579ff"
   },
   "outputs": [],
   "source": [
    "from st_lucas import LucasClassTranslate\n",
    "\n",
    "lucastrans = LucasClassTranslate(lucasio.data)\n",
    "lucastrans.set_translations(\"CLC2\")\n",
    "lucastrans.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a5954db",
   "metadata": {
    "id": "5a5954db"
   },
   "source": [
    "Check the appearance of the new column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33aea10b",
   "metadata": {
    "id": "33aea10b"
   },
   "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>lc1_h</th>\n",
       "      <th>lu1_h</th>\n",
       "      <th>clc2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A30</td>\n",
       "      <td>U319</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C10</td>\n",
       "      <td>U120</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A30</td>\n",
       "      <td>U319</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A30</td>\n",
       "      <td>U319</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A22</td>\n",
       "      <td>U312</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A22</td>\n",
       "      <td>U312</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A30</td>\n",
       "      <td>U319</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B73</td>\n",
       "      <td>U113</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B71</td>\n",
       "      <td>U113</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B73</td>\n",
       "      <td>U113</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lc1_h lu1_h clc2\n",
       "0   A30  U319   99\n",
       "1   C10  U120   31\n",
       "2   A30  U319   99\n",
       "3   A30  U319   99\n",
       "4   A22  U312   12\n",
       "5   A22  U312   12\n",
       "6   A30  U319   99\n",
       "7   B73  U113   22\n",
       "8   B71  U113   22\n",
       "9   B73  U113   22"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = lucasio.to_geopandas()\n",
    "df[[\"lc1_h\", \"lu1_h\", \"clc2\"]].head(10)"
   ]
  }
 ],
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