{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Meteocat\n",
    "\n",
    "[Meteocat](https://www.meteo.cat) is the Catalan Meteorological Service. It provides a REST API to access its data. To use the Meteocat API, you first need to have an API key. You can get one by registering at [apidocs.meteocat.gencat.cat/section/informacio-general/plans-i-registre](https://apidocs.meteocat.gencat.cat/section/informacio-general/plans-i-registre). You can choose the \"Accés ciutadà i administració\" plan, which is free, or the paid \"Accés professionals\" plan. In both cases, you need to check the box \"Dades de la XEMA\" (automated weather stations) to get access to the data used in meteostations-geopy. You will then receive by mail the API key.\n",
    "\n",
    "Let us now use the `MeteocatClient` class by instantiating it with the API key and the region of interest (which must be part of Catalonia so that it contains some stations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from meteostations.clients import MeteocatClient\n",
    "\n",
    "API_KEY = \"<your-api-key>\"\n",
    "region = \"Conca de Barberà\"\n",
    "\n",
    "client = MeteocatClient(region, API_KEY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "\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": "3",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>codi</th>\n",
       "      <th>nom</th>\n",
       "      <th>tipus</th>\n",
       "      <th>emplacament</th>\n",
       "      <th>altitud</th>\n",
       "      <th>estats</th>\n",
       "      <th>coordenades.latitud</th>\n",
       "      <th>coordenades.longitud</th>\n",
       "      <th>municipi.codi</th>\n",
       "      <th>municipi.nom</th>\n",
       "      <th>comarca.codi</th>\n",
       "      <th>comarca.nom</th>\n",
       "      <th>provincia.codi</th>\n",
       "      <th>provincia.nom</th>\n",
       "      <th>xarxa.codi</th>\n",
       "      <th>xarxa.nom</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CW</td>\n",
       "      <td>l'Espluga de Francolí</td>\n",
       "      <td>A</td>\n",
       "      <td>Urb. Espluga Parc</td>\n",
       "      <td>446.0</td>\n",
       "      <td>[{'codi': 2, 'dataInici': '1996-02-23T21:00Z',...</td>\n",
       "      <td>41.39241</td>\n",
       "      <td>1.09894</td>\n",
       "      <td>430542</td>\n",
       "      <td>L'Espluga de Francolí</td>\n",
       "      <td>16</td>\n",
       "      <td>Conca de Barberà</td>\n",
       "      <td>43</td>\n",
       "      <td>Tarragona</td>\n",
       "      <td>1</td>\n",
       "      <td>XEMA</td>\n",
       "      <td>POINT (1.09894 41.39241)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>UJ</td>\n",
       "      <td>Santa Coloma de Queralt</td>\n",
       "      <td>A</td>\n",
       "      <td>Partida la Cabana</td>\n",
       "      <td>709.0</td>\n",
       "      <td>[{'codi': 2, 'dataInici': '1996-11-19T20:00Z',...</td>\n",
       "      <td>41.52879</td>\n",
       "      <td>1.36830</td>\n",
       "      <td>431397</td>\n",
       "      <td>Santa Coloma de Queralt</td>\n",
       "      <td>16</td>\n",
       "      <td>Conca de Barberà</td>\n",
       "      <td>43</td>\n",
       "      <td>Tarragona</td>\n",
       "      <td>1</td>\n",
       "      <td>XEMA</td>\n",
       "      <td>POINT (1.36830 41.52879)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>W8</td>\n",
       "      <td>Blancafort</td>\n",
       "      <td>A</td>\n",
       "      <td>Cooperativa vinícola, pl.del Sindicat</td>\n",
       "      <td>438.0</td>\n",
       "      <td>[{'codi': 2, 'dataInici': '2000-01-18T12:00Z',...</td>\n",
       "      <td>41.44237</td>\n",
       "      <td>1.15998</td>\n",
       "      <td>430290</td>\n",
       "      <td>Blancafort</td>\n",
       "      <td>16</td>\n",
       "      <td>Conca de Barberà</td>\n",
       "      <td>43</td>\n",
       "      <td>Tarragona</td>\n",
       "      <td>1</td>\n",
       "      <td>XEMA</td>\n",
       "      <td>POINT (1.15998 41.44237)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    codi                      nom tipus  \\\n",
       "27    CW    l'Espluga de Francolí     A   \n",
       "87    UJ  Santa Coloma de Queralt     A   \n",
       "142   W8               Blancafort     A   \n",
       "\n",
       "                               emplacament  altitud  \\\n",
       "27                       Urb. Espluga Parc    446.0   \n",
       "87                       Partida la Cabana    709.0   \n",
       "142  Cooperativa vinícola, pl.del Sindicat    438.0   \n",
       "\n",
       "                                                estats  coordenades.latitud  \\\n",
       "27   [{'codi': 2, 'dataInici': '1996-02-23T21:00Z',...             41.39241   \n",
       "87   [{'codi': 2, 'dataInici': '1996-11-19T20:00Z',...             41.52879   \n",
       "142  [{'codi': 2, 'dataInici': '2000-01-18T12:00Z',...             41.44237   \n",
       "\n",
       "     coordenades.longitud municipi.codi             municipi.nom  \\\n",
       "27                1.09894        430542    L'Espluga de Francolí   \n",
       "87                1.36830        431397  Santa Coloma de Queralt   \n",
       "142               1.15998        430290               Blancafort   \n",
       "\n",
       "     comarca.codi       comarca.nom  provincia.codi provincia.nom  xarxa.codi  \\\n",
       "27             16  Conca de Barberà              43     Tarragona           1   \n",
       "87             16  Conca de Barberà              43     Tarragona           1   \n",
       "142            16  Conca de Barberà              43     Tarragona           1   \n",
       "\n",
       "    xarxa.nom                  geometry  \n",
       "27       XEMA  POINT (1.09894 41.39241)  \n",
       "87       XEMA  POINT (1.36830 41.52879)  \n",
       "142      XEMA  POINT (1.15998 41.44237)  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.stations_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "\n",
    "Similarly, the `variables_df` property features the available variables and its metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>codi</th>\n",
       "      <th>nom</th>\n",
       "      <th>unitat</th>\n",
       "      <th>acronim</th>\n",
       "      <th>tipus</th>\n",
       "      <th>decimals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Pressió atmosfèrica màxima</td>\n",
       "      <td>hPa</td>\n",
       "      <td>Px</td>\n",
       "      <td>DAT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Pressió atmosfèrica mínima</td>\n",
       "      <td>hPa</td>\n",
       "      <td>Pn</td>\n",
       "      <td>DAT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Humitat relativa màxima</td>\n",
       "      <td>%</td>\n",
       "      <td>HRx</td>\n",
       "      <td>DAT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Temperatura màxima de subsòl a 5 cm</td>\n",
       "      <td>°C</td>\n",
       "      <td>TSUBx5</td>\n",
       "      <td>DAT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Temperatura mínima de subsòl a 5 cm</td>\n",
       "      <td>°C</td>\n",
       "      <td>TSUBn5</td>\n",
       "      <td>DAT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   codi                                  nom unitat acronim tipus  decimals\n",
       "0     1           Pressió atmosfèrica màxima    hPa      Px   DAT         1\n",
       "1     2           Pressió atmosfèrica mínima    hPa      Pn   DAT         1\n",
       "2     3              Humitat relativa màxima      %     HRx   DAT         0\n",
       "3     4  Temperatura màxima de subsòl a 5 cm     °C  TSUBx5   DAT         1\n",
       "4     5  Temperatura mínima de subsòl a 5 cm     °C  TSUBn5   DAT         1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.variables_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "\n",
    "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": "7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>codi</th>\n",
       "      <th>CW</th>\n",
       "      <th>UJ</th>\n",
       "      <th>W8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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+00:00</th>\n",
       "      <td>20.2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13 00:30:00+00:00</th>\n",
       "      <td>19.8</td>\n",
       "      <td>20.5</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13 01:00:00+00:00</th>\n",
       "      <td>19.5</td>\n",
       "      <td>21.2</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13 01:30:00+00:00</th>\n",
       "      <td>20.1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-13 02:00:00+00:00</th>\n",
       "      <td>19.2</td>\n",
       "      <td>21.7</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 21:30:00+00:00</th>\n",
       "      <td>20.6</td>\n",
       "      <td>18.8</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 22:00:00+00:00</th>\n",
       "      <td>19.7</td>\n",
       "      <td>18.7</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 22:30:00+00:00</th>\n",
       "      <td>19.3</td>\n",
       "      <td>17.8</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 23:00:00+00:00</th>\n",
       "      <td>18.8</td>\n",
       "      <td>16.9</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-16 23:30:00+00:00</th>\n",
       "      <td>18.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "codi                         CW    UJ    W8\n",
       "time                                       \n",
       "2021-08-13 00:00:00+00:00  20.2  21.6  20.2\n",
       "2021-08-13 00:30:00+00:00  19.8  20.5  19.6\n",
       "2021-08-13 01:00:00+00:00  19.5  21.2  19.3\n",
       "2021-08-13 01:30:00+00:00  20.1  22.0  19.2\n",
       "2021-08-13 02:00:00+00:00  19.2  21.7  18.9\n",
       "...                         ...   ...   ...\n",
       "2021-08-16 21:30:00+00:00  20.6  18.8  20.6\n",
       "2021-08-16 22:00:00+00:00  19.7  18.7  19.5\n",
       "2021-08-16 22:30:00+00:00  19.3  17.8  19.3\n",
       "2021-08-16 23:00:00+00:00  18.8  16.9  18.7\n",
       "2021-08-16 23:30:00+00:00  18.2  16.2  18.1\n",
       "\n",
       "[192 rows x 3 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": "8",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "\n",
    "The variable argument can be either: - a meteocat variable code (integer or string) or variable name (string) - see the \"CODI_VARIABLE\" and \"NOM_VARIABLE\" columns in [\"Metadades variables meteorològiques\"](https://analisi.transparenciacatalunya.cat/en/Medi-Ambient/Metadades-variables-meteorol-giques/4fb2-n3yi/data) - or an essential climate variable (ECV) following the meteostations-geopy nomenclature (string).\n",
    "\n",
    "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": "9",
   "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": "10",
   "metadata": {},
   "source": [
    "\n",
    "TODO: 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": "11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: ts_gdf = client.get_ts_gdf(variable, start_date, end_date)"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python (meteostations)",
   "language": "python",
   "name": "meteostations"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}