{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Agrometeo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from meteostations.clients import agrometeo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "The core object of agrometeo-geopy is the `AgrometeoClient`, which can be instantiated providing a Nominatim query (of a Swiss region) as the `region` argument as in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = agrometeo.AgrometeoClient(region=\"Canton de Genève\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "Alternatively, the `region` argument may also be provided as:\n",
    "\n",
    "* A list-like with the west, south, east and north bounds\n",
    "* A geopandas geo-series or geo-data frame\n",
    "* A geometric object, e.g., shapely geometry\n",
    "* A filename or URL, a file-like object opened in binary ('rb') mode, or a Path object that will be passed to `geopandas.read_file`.\n",
    "\n",
    "Once instantiated, the `stations_gdf` property features the stations within the region, with its locations and other attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>lat_dec</th>\n",
       "      <th>long_dec</th>\n",
       "      <th>lat_ch</th>\n",
       "      <th>long_ch</th>\n",
       "      <th>altitude</th>\n",
       "      <th>interval</th>\n",
       "      <th>phone</th>\n",
       "      <th>email</th>\n",
       "      <th>...</th>\n",
       "      <th>preview_until</th>\n",
       "      <th>data_until</th>\n",
       "      <th>type_id</th>\n",
       "      <th>owner_id</th>\n",
       "      <th>active</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>replacement_id</th>\n",
       "      <th>report_url</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>44</td>\n",
       "      <td>DARDAGNY</td>\n",
       "      <td>46.1920109</td>\n",
       "      <td>5.9808399</td>\n",
       "      <td>487462.0</td>\n",
       "      <td>116662.0</td>\n",
       "      <td>488</td>\n",
       "      <td>10</td>\n",
       "      <td>079 332 38 36</td>\n",
       "      <td>domaine.les.hutins@bluewin.ch</td>\n",
       "      <td>...</td>\n",
       "      <td>2023-04-30T16:00:00.000000Z</td>\n",
       "      <td>2023-04-24T01:50:00.000000Z</td>\n",
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       "      <td>True</td>\n",
       "      <td>2020-12-14T13:17:18.000000Z</td>\n",
       "      <td>2023-04-24T00:01:05.000000Z</td>\n",
       "      <td>45.0</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (487462.000 116662.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>45</td>\n",
       "      <td>LA-PLAINE</td>\n",
       "      <td>46.1828330</td>\n",
       "      <td>6.0067971</td>\n",
       "      <td>489447.0</td>\n",
       "      <td>115605.0</td>\n",
       "      <td>360</td>\n",
       "      <td>10</td>\n",
       "      <td>079 332 32 73</td>\n",
       "      <td>gegedudu@bluewin.ch</td>\n",
       "      <td>...</td>\n",
       "      <td>2023-04-30T16:00:00.000000Z</td>\n",
       "      <td>2023-04-24T01:50:00.000000Z</td>\n",
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       "      <td>True</td>\n",
       "      <td>2020-12-14T13:17:22.000000Z</td>\n",
       "      <td>2023-04-24T00:01:10.000000Z</td>\n",
       "      <td>44.0</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (489447.000 115605.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>46</td>\n",
       "      <td>SATIGNY</td>\n",
       "      <td>46.2206561</td>\n",
       "      <td>6.0369114</td>\n",
       "      <td>491847.0</td>\n",
       "      <td>119767.0</td>\n",
       "      <td>442</td>\n",
       "      <td>10</td>\n",
       "      <td>079 332 36 56</td>\n",
       "      <td>info@champvigny.ch</td>\n",
       "      <td>...</td>\n",
       "      <td>2023-04-30T16:00:00.000000Z</td>\n",
       "      <td>2023-04-24T01:50:00.000000Z</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-12-14T13:17:25.000000Z</td>\n",
       "      <td>2023-04-24T00:01:16.000000Z</td>\n",
       "      <td>47.0</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (491847.000 119767.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>47</td>\n",
       "      <td>PEISSY</td>\n",
       "      <td>46.2052881</td>\n",
       "      <td>6.0130878</td>\n",
       "      <td>489978.0</td>\n",
       "      <td>118092.0</td>\n",
       "      <td>450</td>\n",
       "      <td>10</td>\n",
       "      <td>079 332 32 81</td>\n",
       "      <td>lesvallieres@bluewin.ch</td>\n",
       "      <td>...</td>\n",
       "      <td>2023-04-30T16:00:00.000000Z</td>\n",
       "      <td>2023-04-24T01:50:00.000000Z</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-12-14T13:17:24.000000Z</td>\n",
       "      <td>2023-04-24T00:01:14.000000Z</td>\n",
       "      <td>46.0</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (489978.000 118092.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>48</td>\n",
       "      <td>ANIERES</td>\n",
       "      <td>46.2711303</td>\n",
       "      <td>6.2229249</td>\n",
       "      <td>506285.0</td>\n",
       "      <td>125138.0</td>\n",
       "      <td>412</td>\n",
       "      <td>10</td>\n",
       "      <td>079 332 38 89</td>\n",
       "      <td>vinsvillard@bluewin.ch</td>\n",
       "      <td>...</td>\n",
       "      <td>2023-04-30T16:00:00.000000Z</td>\n",
       "      <td>2023-04-24T01:50:00.000000Z</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>True</td>\n",
       "      <td>2020-12-14T13:17:14.000000Z</td>\n",
       "      <td>2023-04-24T00:00:59.000000Z</td>\n",
       "      <td>71.0</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (506285.000 125138.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id       name     lat_dec   long_dec    lat_ch   long_ch  altitude  \\\n",
       "29  44   DARDAGNY  46.1920109  5.9808399  487462.0  116662.0       488   \n",
       "30  45  LA-PLAINE  46.1828330  6.0067971  489447.0  115605.0       360   \n",
       "31  46    SATIGNY  46.2206561  6.0369114  491847.0  119767.0       442   \n",
       "32  47     PEISSY  46.2052881  6.0130878  489978.0  118092.0       450   \n",
       "33  48    ANIERES  46.2711303  6.2229249  506285.0  125138.0       412   \n",
       "\n",
       "    interval          phone                          email  ...  \\\n",
       "29        10  079 332 38 36  domaine.les.hutins@bluewin.ch  ...   \n",
       "30        10  079 332 32 73            gegedudu@bluewin.ch  ...   \n",
       "31        10  079 332 36 56             info@champvigny.ch  ...   \n",
       "32        10  079 332 32 81        lesvallieres@bluewin.ch  ...   \n",
       "33        10  079 332 38 89         vinsvillard@bluewin.ch  ...   \n",
       "\n",
       "                  preview_until                   data_until type_id  \\\n",
       "29  2023-04-30T16:00:00.000000Z  2023-04-24T01:50:00.000000Z       3   \n",
       "30  2023-04-30T16:00:00.000000Z  2023-04-24T01:50:00.000000Z       3   \n",
       "31  2023-04-30T16:00:00.000000Z  2023-04-24T01:50:00.000000Z       3   \n",
       "32  2023-04-30T16:00:00.000000Z  2023-04-24T01:50:00.000000Z       3   \n",
       "33  2023-04-30T16:00:00.000000Z  2023-04-24T01:50:00.000000Z       3   \n",
       "\n",
       "    owner_id  active                   created_at  \\\n",
       "29        26    True  2020-12-14T13:17:18.000000Z   \n",
       "30        26    True  2020-12-14T13:17:22.000000Z   \n",
       "31        27    True  2020-12-14T13:17:25.000000Z   \n",
       "32        27    True  2020-12-14T13:17:24.000000Z   \n",
       "33        28    True  2020-12-14T13:17:14.000000Z   \n",
       "\n",
       "                     updated_at replacement_id  report_url  \\\n",
       "29  2023-04-24T00:01:05.000000Z           45.0        None   \n",
       "30  2023-04-24T00:01:10.000000Z           44.0        None   \n",
       "31  2023-04-24T00:01:16.000000Z           47.0        None   \n",
       "32  2023-04-24T00:01:14.000000Z           46.0        None   \n",
       "33  2023-04-24T00:00:59.000000Z           71.0        None   \n",
       "\n",
       "                         geometry  \n",
       "29  POINT (487462.000 116662.000)  \n",
       "30  POINT (489447.000 115605.000)  \n",
       "31  POINT (491847.000 119767.000)  \n",
       "32  POINT (489978.000 118092.000)  \n",
       "33  POINT (506285.000 125138.000)  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.stations_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "Similarly, the `variables_df` property features the available variables and its metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>42</td>\n",
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       "      <td>2021-03-09T00:00:56.000000Z</td>\n",
       "      <td>2023-04-22T00:00:35.000000Z</td>\n",
       "      <td>Blattnässe III</td>\n",
       "      <td>Leaf wetnessIII</td>\n",
       "      <td>Humectage du feuillage III</td>\n",
       "      <td>Bagnatura fogliare III</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>2021-09-11T00:02:00.000000Z</td>\n",
       "      <td>2023-04-22T00:00:35.000000Z</td>\n",
       "      <td>Spannung interne Lithiumbatterie</td>\n",
       "      <td>Voltage of internal lithium battery</td>\n",
       "      <td>Voltage batterie interne</td>\n",
       "      <td>Tensione batteria al litio interna</td>\n",
       "      <td>Volt</td>\n",
       "      <td>Volt</td>\n",
       "      <td>Volt</td>\n",
       "      <td>Volt</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>#AA0000</td>\n",
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       "      <td>2023-04-22T00:00:36.000000Z</td>\n",
       "      <td>Temperatur 2m über dem Boden</td>\n",
       "      <td>Temperature 2m above ground</td>\n",
       "      <td>Température à +2m</td>\n",
       "      <td>Temperatura a 2m dal suolo</td>\n",
       "      <td>°C</td>\n",
       "      <td>°C</td>\n",
       "      <td>°C</td>\n",
       "      <td>°C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>#606CB2</td>\n",
       "      <td>avg</td>\n",
       "      <td>Area</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>[{'agg': 'avg', 'label': {'de': 'Rel. Luftfeuc...</td>\n",
       "      <td>2020-12-14T13:17:02.000000Z</td>\n",
       "      <td>2023-04-22T00:00:36.000000Z</td>\n",
       "      <td>relative Luftfeuchtigkeit</td>\n",
       "      <td>Relative humidity</td>\n",
       "      <td>Humidité relative</td>\n",
       "      <td>Umiditá relativa</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>#000080</td>\n",
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       "      <td>Bar</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>[{'agg': 'sum', 'label': {'de': 'Niederschlag ...</td>\n",
       "      <td>2020-12-14T13:17:04.000000Z</td>\n",
       "      <td>2023-04-22T00:00:36.000000Z</td>\n",
       "      <td>Niederschläge</td>\n",
       "      <td>Precipitation</td>\n",
       "      <td>Précipitations</td>\n",
       "      <td>Precipitazioni</td>\n",
       "      <td>mm oder Liter/m2</td>\n",
       "      <td>mm ou litres/m2</td>\n",
       "      <td>mm ou litres/m2</td>\n",
       "      <td>mm ou litres/m2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "   id    color default_agg series_style  decimals  order  \\\n",
       "0  42  #167687         sum          Bar       NaN      0   \n",
       "1  43     None         avg         Line       NaN      0   \n",
       "2   1  #AA0000         avg         Line       1.0      1   \n",
       "3   4  #606CB2         avg         Area       NaN      2   \n",
       "4   6  #000080         sum          Bar       1.0      3   \n",
       "\n",
       "                                        aggregations  \\\n",
       "0                                                 []   \n",
       "1                                                 []   \n",
       "2  [{'agg': 'avg', 'label': {'de': 'Temperatur Du...   \n",
       "3  [{'agg': 'avg', 'label': {'de': 'Rel. Luftfeuc...   \n",
       "4  [{'agg': 'sum', 'label': {'de': 'Niederschlag ...   \n",
       "\n",
       "                    created_at                   updated_at  \\\n",
       "0  2021-03-09T00:00:56.000000Z  2023-04-22T00:00:35.000000Z   \n",
       "1  2021-09-11T00:02:00.000000Z  2023-04-22T00:00:35.000000Z   \n",
       "2  2020-12-14T13:17:02.000000Z  2023-04-22T00:00:36.000000Z   \n",
       "3  2020-12-14T13:17:02.000000Z  2023-04-22T00:00:36.000000Z   \n",
       "4  2020-12-14T13:17:04.000000Z  2023-04-22T00:00:36.000000Z   \n",
       "\n",
       "                            name.de                              name.en  \\\n",
       "0                    Blattnässe III                      Leaf wetnessIII   \n",
       "1  Spannung interne Lithiumbatterie  Voltage of internal lithium battery   \n",
       "2      Temperatur 2m über dem Boden          Temperature 2m above ground   \n",
       "3         relative Luftfeuchtigkeit                    Relative humidity   \n",
       "4                     Niederschläge                        Precipitation   \n",
       "\n",
       "                      name.fr                             name.it  \\\n",
       "0  Humectage du feuillage III              Bagnatura fogliare III   \n",
       "1    Voltage batterie interne  Tensione batteria al litio interna   \n",
       "2           Température à +2m          Temperatura a 2m dal suolo   \n",
       "3           Humidité relative                    Umiditá relativa   \n",
       "4              Précipitations                      Precipitazioni   \n",
       "\n",
       "            unit.de          unit.en          unit.fr          unit.it  \\\n",
       "0                 ?                ?                ?                ?   \n",
       "1              Volt             Volt             Volt             Volt   \n",
       "2                °C               °C               °C               °C   \n",
       "3                 %                %                %                %   \n",
       "4  mm oder Liter/m2  mm ou litres/m2  mm ou litres/m2  mm ou litres/m2   \n",
       "\n",
       "   actions.update  actions.delete  \n",
       "0           False           False  \n",
       "1           False           False  \n",
       "2           False           False  \n",
       "3           False           False  \n",
       "4           False           False  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.variables_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {},
   "source": [
    "Given a variable and a time range, the time seres data frame of measurements in the corresponding stations can be obtained with the `get_ts_df` method as in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9",
   "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>name</th>\n",
       "      <th>DARDAGNY</th>\n",
       "      <th>LA-PLAINE</th>\n",
       "      <th>SATIGNY</th>\n",
       "      <th>PEISSY</th>\n",
       "      <th>ANIERES</th>\n",
       "      <th>LULLY</th>\n",
       "      <th>LULLIER</th>\n",
       "      <th>BERNEX</th>\n",
       "      <th>TROINEX</th>\n",
       "      <th>MEINIER</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-08-13 00:00:00</th>\n",
       "      <td>19.3</td>\n",
       "      <td>17.8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>17.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>20.3</td>\n",
       "      <td>18.6</td>\n",
       "      <td>19.4</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13 00:10:00</th>\n",
       "      <td>19.6</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.4</td>\n",
       "      <td>17.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>19.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>19.1</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13 00:20:00</th>\n",
       "      <td>19.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>17.6</td>\n",
       "      <td>19.4</td>\n",
       "      <td>18.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>19.2</td>\n",
       "      <td>24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13 00:30:00</th>\n",
       "      <td>18.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>17.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>18.3</td>\n",
       "      <td>19.1</td>\n",
       "      <td>18.6</td>\n",
       "      <td>18.9</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13 00:40:00</th>\n",
       "      <td>18.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>17.6</td>\n",
       "      <td>19.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>18.5</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 23:10:00</th>\n",
       "      <td>17.5</td>\n",
       "      <td>17.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.9</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 23:20:00</th>\n",
       "      <td>17.4</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 23:30:00</th>\n",
       "      <td>17.2</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.8</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 23:40:00</th>\n",
       "      <td>17.2</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.1</td>\n",
       "      <td>18.1</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 23:50:00</th>\n",
       "      <td>17.1</td>\n",
       "      <td>17.8</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>18.1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>576 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name                 DARDAGNY  LA-PLAINE  SATIGNY  PEISSY  ANIERES  LULLY  \\\n",
       "time                                                                        \n",
       "2021-08-13 00:00:00      19.3       17.8     18.5    17.9     20.6   18.4   \n",
       "2021-08-13 00:10:00      19.6       17.9     18.4    17.7     20.0   18.3   \n",
       "2021-08-13 00:20:00      19.0       17.7     18.2    17.6     19.4   18.4   \n",
       "2021-08-13 00:30:00      18.3       18.0     18.1    17.4     19.1   18.3   \n",
       "2021-08-13 00:40:00      18.7       18.0     18.1    17.6     19.1   18.0   \n",
       "...                       ...        ...      ...     ...      ...    ...   \n",
       "2021-08-16 23:10:00      17.5       17.8     17.3    16.9     17.9   17.6   \n",
       "2021-08-16 23:20:00      17.4       17.9     17.4    17.1     17.9   17.6   \n",
       "2021-08-16 23:30:00      17.2       17.9     17.5    17.3     17.8   17.6   \n",
       "2021-08-16 23:40:00      17.2       17.9     17.7    17.1     17.7   17.4   \n",
       "2021-08-16 23:50:00      17.1       17.8     17.5    17.1     17.7   17.3   \n",
       "\n",
       "name                 LULLIER  BERNEX  TROINEX  MEINIER  \n",
       "time                                                    \n",
       "2021-08-13 00:00:00     20.3    18.6     19.4     25.8  \n",
       "2021-08-13 00:10:00     19.6    18.7     19.1     28.6  \n",
       "2021-08-13 00:20:00     19.1    18.7     19.2     24.1  \n",
       "2021-08-13 00:30:00     19.1    18.6     18.9     22.5  \n",
       "2021-08-13 00:40:00     19.0    18.7     18.5     21.5  \n",
       "...                      ...     ...      ...      ...  \n",
       "2021-08-16 23:10:00     17.3    17.2     17.9     22.2  \n",
       "2021-08-16 23:20:00     17.3    17.2     18.0     22.0  \n",
       "2021-08-16 23:30:00     17.3    17.3     18.0     21.7  \n",
       "2021-08-16 23:40:00     17.2    17.1     18.1     21.9  \n",
       "2021-08-16 23:50:00     17.1    17.1     18.1     22.0  \n",
       "\n",
       "[576 rows x 10 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable = \"temperature\"\n",
    "start_date = \"2021-08-13\"\n",
    "end_date = \"2021-08-16\"\n",
    "\n",
    "ts_df = client.get_ts_df(variable, start_date, end_date)\n",
    "ts_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "The `variable` argument can be either:\n",
    "- an agrometeo variable code (integer or string) - see the `variables_df` property above\n",
    "- an essential climate variable (ECV) following the meteostations-geopy nomenclature (string)\n",
    "- an agrometeo variable name (string) - see the `variables_df` property above"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "The returned object is a pandas data frame upon which we can operate using any of its supported functions, e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='time'>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_df.resample(\"H\").mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13",
   "metadata": {},
   "source": [
    "We can also use the `get_ts_gdf` method to include the locations of the stations along with the time series of measurements as in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14",
   "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>time</th>\n",
       "      <th>2021-08-13 00:00:00</th>\n",
       "      <th>2021-08-13 00:10:00</th>\n",
       "      <th>2021-08-13 00:20:00</th>\n",
       "      <th>2021-08-13 00:30:00</th>\n",
       "      <th>2021-08-13 00:40:00</th>\n",
       "      <th>2021-08-13 00:50:00</th>\n",
       "      <th>2021-08-13 01:00:00</th>\n",
       "      <th>2021-08-13 01:10:00</th>\n",
       "      <th>2021-08-13 01:20:00</th>\n",
       "      <th>2021-08-13 01:30:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2021-08-16 22:30:00</th>\n",
       "      <th>2021-08-16 22:40:00</th>\n",
       "      <th>2021-08-16 22:50:00</th>\n",
       "      <th>2021-08-16 23:00:00</th>\n",
       "      <th>2021-08-16 23:10:00</th>\n",
       "      <th>2021-08-16 23:20:00</th>\n",
       "      <th>2021-08-16 23:30:00</th>\n",
       "      <th>2021-08-16 23:40:00</th>\n",
       "      <th>2021-08-16 23:50:00</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DARDAGNY</th>\n",
       "      <td>19.3</td>\n",
       "      <td>19.6</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>18.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>...</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.1</td>\n",
       "      <td>POINT (487462.000 116662.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA-PLAINE</th>\n",
       "      <td>17.8</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.2</td>\n",
       "      <td>...</td>\n",
       "      <td>16.9</td>\n",
       "      <td>16.9</td>\n",
       "      <td>16.8</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.8</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>POINT (489447.000 115605.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SATIGNY</th>\n",
       "      <td>18.5</td>\n",
       "      <td>18.4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>18.1</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.5</td>\n",
       "      <td>POINT (491847.000 119767.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEISSY</th>\n",
       "      <td>17.9</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>POINT (489978.000 118092.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANIERES</th>\n",
       "      <td>20.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>19.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.8</td>\n",
       "      <td>...</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>POINT (506285.000 125138.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LULLY</th>\n",
       "      <td>18.4</td>\n",
       "      <td>18.3</td>\n",
       "      <td>18.4</td>\n",
       "      <td>18.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>...</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.3</td>\n",
       "      <td>POINT (494821.000 113788.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LULLIER</th>\n",
       "      <td>20.3</td>\n",
       "      <td>19.6</td>\n",
       "      <td>19.1</td>\n",
       "      <td>19.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>19.1</td>\n",
       "      <td>19.2</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.1</td>\n",
       "      <td>POINT (508630.000 119651.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BERNEX</th>\n",
       "      <td>18.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>18.7</td>\n",
       "      <td>18.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>18.7</td>\n",
       "      <td>18.8</td>\n",
       "      <td>19.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>...</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>POINT (493900.000 114060.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TROINEX</th>\n",
       "      <td>19.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>19.2</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>18.4</td>\n",
       "      <td>18.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>18.1</td>\n",
       "      <td>...</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.6</td>\n",
       "      <td>17.8</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>18.1</td>\n",
       "      <td>POINT (500520.000 112865.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEINIER</th>\n",
       "      <td>25.8</td>\n",
       "      <td>28.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>21.5</td>\n",
       "      <td>19.8</td>\n",
       "      <td>19.8</td>\n",
       "      <td>20.7</td>\n",
       "      <td>20.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>21.1</td>\n",
       "      <td>21.2</td>\n",
       "      <td>22.7</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>POINT (506270.000 122120.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 577 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "time       2021-08-13 00:00:00  2021-08-13 00:10:00  2021-08-13 00:20:00  \\\n",
       "name                                                                       \n",
       "DARDAGNY                  19.3                 19.6                 19.0   \n",
       "LA-PLAINE                 17.8                 17.9                 17.7   \n",
       "SATIGNY                   18.5                 18.4                 18.2   \n",
       "PEISSY                    17.9                 17.7                 17.6   \n",
       "ANIERES                   20.6                 20.0                 19.4   \n",
       "LULLY                     18.4                 18.3                 18.4   \n",
       "LULLIER                   20.3                 19.6                 19.1   \n",
       "BERNEX                    18.6                 18.7                 18.7   \n",
       "TROINEX                   19.4                 19.1                 19.2   \n",
       "MEINIER                   25.8                 28.6                 24.1   \n",
       "\n",
       "time       2021-08-13 00:30:00  2021-08-13 00:40:00  2021-08-13 00:50:00  \\\n",
       "name                                                                       \n",
       "DARDAGNY                  18.3                 18.7                 18.2   \n",
       "LA-PLAINE                 18.0                 18.0                 17.9   \n",
       "SATIGNY                   18.1                 18.1                 17.9   \n",
       "PEISSY                    17.4                 17.6                 17.7   \n",
       "ANIERES                   19.1                 19.1                 19.0   \n",
       "LULLY                     18.3                 18.0                 18.2   \n",
       "LULLIER                   19.1                 19.0                 18.9   \n",
       "BERNEX                    18.6                 18.7                 18.7   \n",
       "TROINEX                   18.9                 18.5                 18.4   \n",
       "MEINIER                   22.5                 21.5                 19.8   \n",
       "\n",
       "time       2021-08-13 01:00:00  2021-08-13 01:10:00  2021-08-13 01:20:00  \\\n",
       "name                                                                       \n",
       "DARDAGNY                  17.9                 17.6                 17.5   \n",
       "LA-PLAINE                 17.9                 17.7                 17.6   \n",
       "SATIGNY                   17.9                 17.9                 18.0   \n",
       "PEISSY                    17.6                 17.5                 17.2   \n",
       "ANIERES                   18.9                 18.9                 18.9   \n",
       "LULLY                     18.2                 18.0                 17.8   \n",
       "LULLIER                   19.1                 19.2                 18.9   \n",
       "BERNEX                    18.8                 19.1                 18.8   \n",
       "TROINEX                   18.4                 18.6                 18.4   \n",
       "MEINIER                   19.8                 20.7                 20.4   \n",
       "\n",
       "time       2021-08-13 01:30:00  ...  2021-08-16 22:30:00  2021-08-16 22:40:00  \\\n",
       "name                            ...                                             \n",
       "DARDAGNY                  17.4  ...                 17.5                 17.3   \n",
       "LA-PLAINE                 17.2  ...                 16.9                 16.9   \n",
       "SATIGNY                   18.7  ...                 17.6                 17.5   \n",
       "PEISSY                    17.0  ...                 17.1                 17.2   \n",
       "ANIERES                   18.8  ...                 17.7                 17.7   \n",
       "LULLY                     17.7  ...                 17.5                 17.5   \n",
       "LULLIER                   18.7  ...                 17.5                 17.6   \n",
       "BERNEX                    18.5  ...                 17.4                 17.3   \n",
       "TROINEX                   18.1  ...                 17.9                 17.6   \n",
       "MEINIER                   19.6  ...                 21.1                 21.2   \n",
       "\n",
       "time       2021-08-16 22:50:00  2021-08-16 23:00:00  2021-08-16 23:10:00  \\\n",
       "name                                                                       \n",
       "DARDAGNY                  17.4                 17.5                 17.5   \n",
       "LA-PLAINE                 16.8                 17.4                 17.8   \n",
       "SATIGNY                   17.6                 17.6                 17.3   \n",
       "PEISSY                    17.3                 17.1                 16.9   \n",
       "ANIERES                   17.9                 17.9                 17.9   \n",
       "LULLY                     17.6                 17.6                 17.6   \n",
       "LULLIER                   17.5                 17.3                 17.3   \n",
       "BERNEX                    17.2                 17.2                 17.2   \n",
       "TROINEX                   17.6                 17.8                 17.9   \n",
       "MEINIER                   22.7                 22.8                 22.2   \n",
       "\n",
       "time       2021-08-16 23:20:00  2021-08-16 23:30:00  2021-08-16 23:40:00  \\\n",
       "name                                                                       \n",
       "DARDAGNY                  17.4                 17.2                 17.2   \n",
       "LA-PLAINE                 17.9                 17.9                 17.9   \n",
       "SATIGNY                   17.4                 17.5                 17.7   \n",
       "PEISSY                    17.1                 17.3                 17.1   \n",
       "ANIERES                   17.9                 17.8                 17.7   \n",
       "LULLY                     17.6                 17.6                 17.4   \n",
       "LULLIER                   17.3                 17.3                 17.2   \n",
       "BERNEX                    17.2                 17.3                 17.1   \n",
       "TROINEX                   18.0                 18.0                 18.1   \n",
       "MEINIER                   22.0                 21.7                 21.9   \n",
       "\n",
       "time       2021-08-16 23:50:00                       geometry  \n",
       "name                                                           \n",
       "DARDAGNY                  17.1  POINT (487462.000 116662.000)  \n",
       "LA-PLAINE                 17.8  POINT (489447.000 115605.000)  \n",
       "SATIGNY                   17.5  POINT (491847.000 119767.000)  \n",
       "PEISSY                    17.1  POINT (489978.000 118092.000)  \n",
       "ANIERES                   17.7  POINT (506285.000 125138.000)  \n",
       "LULLY                     17.3  POINT (494821.000 113788.000)  \n",
       "LULLIER                   17.1  POINT (508630.000 119651.000)  \n",
       "BERNEX                    17.1  POINT (493900.000 114060.000)  \n",
       "TROINEX                   18.1  POINT (500520.000 112865.000)  \n",
       "MEINIER                   22.0  POINT (506270.000 122120.000)  \n",
       "\n",
       "[10 rows x 577 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_gdf = client.get_ts_gdf(variable, start_date, end_date)\n",
    "ts_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15",
   "metadata": {},
   "source": [
    "TODO: We can then use the `plot_temperature_map` method to plot a map of the temperature measurements at a given point in time provided as the `dt` argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO:\n",
    "# plot_temperature_map(\n",
    "#     ts_gdf,\n",
    "#     dt=datetime(year=2021, month=8, day=15, hour=21),\n",
    "#     plot_kws=dict(edgecolors=\"black\"),\n",
    "# )"
   ]
  }
 ],
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