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   "source": [
    "# Introduction to the MetObs-toolkit\n",
    "\n",
    "In this introduction, you will learn the principal components and methods in the MetObs-toolkit. Let's start by importing it.\n",
    "\n",
    "Since this package is under development, it is often relevant to know the precise version of the toolkit."
   ]
  },
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     "text": [
      "1.0.0a13\n"
     ]
    }
   ],
   "source": [
    "import metobs_toolkit\n",
    "\n",
    "#Print out the version of the toolkit\n",
    "print(metobs_toolkit.__version__)\n",
    "import xarray as xr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "## The Dataset class\n",
    "\n",
    "The ``Dataset`` class is for most applications the most important class. It holds all your stations and it's data. Thus a ``Dataset`` is in principal a collection of stations.\n",
    "\n",
    "Since raw data files often include observations from multiple stations, we import our raw data always directly into a ``Dataset``. We use the ``Dataset.import_data_from_file()`` method, to import the raw data into a Dataset. \n",
    "\n",
    "A key component for importing raw data, is a description of what your data represents and how it is formatted. This is done by providing a **template file**, that describes how your raw data is structured. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "### Importing your raw data\n",
    "\n",
    "As an example we will import a demo file of raw observations. In order to do that we need to :\n",
    "\n",
    "* Create a template file for our raw data file. The ``build_template_prompt()`` function will guide you in this process. It will ask questions, once you answered them a template file is created. It will also propose some code that you use to import your data\n",
    "* Create a ``Dataset`` instance \n",
    "* Add the raw data into the ``Dataset``."
   ]
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    "# Specify the path to your raw data file (we use the demo file as example)\n",
    "path_to_datafile=metobs_toolkit.demo_datafile\n",
    "\n",
    "# We will also use a metadata file\n",
    "path_to_metadatafile=metobs_toolkit.demo_metadatafile"
   ]
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    "%%script true\n",
    "\n",
    "#Create a template for these data files\n",
    "metobs_toolkit.build_template_prompt()"
   ]
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    "#specify the path to the templatefile that was created\n",
    "path_to_templatefile=metobs_toolkit.demo_template #demo file as example!!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "Now that we have the datafiles and the templatefile, we create an empty ``Dataset``, and import the data into it."
   ]
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     "text": [
      "Luchtdruk is present in the datafile, but not found in the template! This column will be ignored.\n",
      "Neerslagintensiteit is present in the datafile, but not found in the template! This column will be ignored.\n",
      "Neerslagsom is present in the datafile, but not found in the template! This column will be ignored.\n",
      "Rukwind is present in the datafile, but not found in the template! This column will be ignored.\n",
      "Luchtdruk_Zeeniveau is present in the datafile, but not found in the template! This column will be ignored.\n",
      "Globe Temperatuur is present in the datafile, but not found in the template! This column will be ignored.\n",
      "The following columns are present in the data file, but not in the template! They are skipped!\n",
      " ['Luchtdruk', 'Luchtdruk_Zeeniveau', 'Globe Temperatuur', 'Neerslagsom', 'Rukwind', 'Neerslagintensiteit']\n",
      "The following columns are found in the metadata, but not in the template and are therefore ignored: \n",
      "['stad', 'benaming', 'sponsor', 'Network']\n"
     ]
    }
   ],
   "source": [
    "dataset = metobs_toolkit.Dataset() #Create a new dataset object\n",
    "\n",
    "#Load the data\n",
    "dataset.import_data_from_file(\n",
    "                    template_file=path_to_templatefile, #The template file\n",
    "                    input_data_file=path_to_datafile, #The data file\n",
    "                    input_metadata_file=path_to_metadatafile, #The metadata file\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "As can be seen in the printed logs, there is a lot going on when importing the data. That is because tests are applied on your data to check for gaps, and mismatches between data and metadata. \n",
    "\n",
    "We can now inspect the ´dataset´ further."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "## The attributes\n",
    "\n",
    "The attributes are holding the data of the dataset. Here we present some attributes that can be useful to inspect.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "All classes in the MetObs-toolkit have a ``get_info()`` methods that prints out an overview of its content.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "* ``Dataset.obstypes`` : A collection of ``Obstypes`` that are known. These observationtypes describe a measurable quantity, and its corresponding units."
   ]
  },
  {
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   "execution_count": 6,
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       "{'temp': Obstype(id=temp_degree_Celsius),\n",
       " 'humidity': Obstype(id=humidity_percent),\n",
       " 'radiation_temp': Obstype(id=radiation_temp_degree_Celsius),\n",
       " 'pressure': Obstype(id=pressure_hectopascal),\n",
       " 'pressure_at_sea_level': Obstype(id=pressure_at_sea_level_hectopascal),\n",
       " 'precip': Obstype(id=precip_millimeter / meter ** 2),\n",
       " 'precip_sum': Obstype(id=precip_sum_millimeter / meter ** 2),\n",
       " 'wind_speed': Obstype(id=wind_speed_meter / second),\n",
       " 'wind_gust': Obstype(id=wind_gust_meter / second),\n",
       " 'wind_direction': Obstype(id=wind_direction_degree)}"
      ]
     },
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   "source": [
    "dataset.obstypes"
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      "text/plain": [
       "['humidity', 'temp', 'wind_direction', 'wind_speed']"
      ]
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   ],
   "source": [
    "#Note! The known obstypes are NOT the obstypes for which there are observations.\n",
    "#To get the obstypes for which there are observations, use:\n",
    "dataset.present_observations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15",
   "metadata": {},
   "source": [
    "* ``Dataset.template``: A template class, that is automatically set up by using the template file. This is only used when data is imported from a file. It has no further use."
   ]
  },
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     "text": [
      "================================================================================\n",
      "                            General info of Template                            \n",
      "================================================================================\n",
      "\n",
      "\n",
      "--- Data obstypes map ---\n",
      "\n",
      "  -temp: Temperatuur\n",
      "    -raw data in degC\n",
      "    -description: 2mT passive\n",
      "  -humidity: Vochtigheid\n",
      "    -raw data in percent\n",
      "    -description: 2m relative humidity passive\n",
      "  -wind_speed: Windsnelheid\n",
      "    -raw data in km/h\n",
      "    -description: Average 2m  10-min windspeed\n",
      "  -wind_direction: Windrichting\n",
      "    -raw data in degrees\n",
      "    -description: Average 2m  10-min windspeed, north is zero in CW direction...\n",
      "\n",
      "--- Data extra mapping info ---\n",
      "\n",
      "  -name column (data) <---> Vlinder\n",
      "\n",
      "--- Data timestamp map ---\n",
      "\n",
      "  -datetimecolumn <---> None\n",
      "  -time_column <---> Tijd (UTC)\n",
      "  -date_column <---> Datum\n",
      "  -fmt <---> %Y-%m-%d %H:%M:%S\n",
      "  -Timezone <---> UTC\n",
      "\n",
      "--- Metadata map ---\n",
      "\n",
      "  -name <---> Vlinder\n",
      "  -lat <---> lat\n",
      "  -lon <---> lon\n",
      "  -school <---> school\n",
      "\n"
     ]
    }
   ],
   "source": [
    "template = dataset.template\n",
    "\n",
    "template.get_info() # Prints out how the template maps raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17",
   "metadata": {},
   "source": [
    "* ``dataset.df``: A pandas DataFrame holding all the observation records."
   ]
  },
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TISM Bree</td>\n",
       "      <td>POINT (5.65677 51.13625)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder19</th>\n",
       "      <td>50.841454</td>\n",
       "      <td>4.363672</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UGent</td>\n",
       "      <td>POINT (4.36367 50.84145)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder20</th>\n",
       "      <td>50.847027</td>\n",
       "      <td>4.357971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UGent</td>\n",
       "      <td>POINT (4.35797 50.84703)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder21</th>\n",
       "      <td>51.260387</td>\n",
       "      <td>2.991917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zeelyceum</td>\n",
       "      <td>POINT (2.99192 51.26039)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder22</th>\n",
       "      <td>50.989502</td>\n",
       "      <td>2.856220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>‘t Saam</td>\n",
       "      <td>POINT (2.85622 50.9895)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder23</th>\n",
       "      <td>51.260578</td>\n",
       "      <td>3.580151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Richtpunt Eeklo</td>\n",
       "      <td>POINT (3.58015 51.26058)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder24</th>\n",
       "      <td>51.167015</td>\n",
       "      <td>3.572062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OLV ten Doorn</td>\n",
       "      <td>POINT (3.57206 51.16702)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder25</th>\n",
       "      <td>51.154720</td>\n",
       "      <td>3.708611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Einstein Atheneum</td>\n",
       "      <td>POINT (3.70861 51.15472)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder26</th>\n",
       "      <td>51.161758</td>\n",
       "      <td>4.997653</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sint Dimpna</td>\n",
       "      <td>POINT (4.99765 51.16176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder27</th>\n",
       "      <td>51.058098</td>\n",
       "      <td>3.728067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sec. Kunstinstituut</td>\n",
       "      <td>POINT (3.72807 51.0581)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlinder28</th>\n",
       "      <td>51.035294</td>\n",
       "      <td>3.769741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GO! Ath.</td>\n",
       "      <td>POINT (3.76974 51.03529)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 lat       lon  altitude  LCZ                         school  \\\n",
       "name                                                                           \n",
       "vlinder01  50.980438  3.815763       NaN  NaN                          UGent   \n",
       "vlinder02  51.022381  3.709695       NaN  NaN                          UGent   \n",
       "vlinder03  51.324581  4.952109       NaN  NaN                    Heilig Graf   \n",
       "vlinder04  51.335522  4.934732       NaN  NaN                    Heilig Graf   \n",
       "vlinder05  51.052654  3.675183       NaN  NaN                   Sint-Barbara   \n",
       "vlinder06  51.027100  4.516300       NaN  NaN                         BimSem   \n",
       "vlinder07  51.030888  4.478445       NaN  NaN                            PTS   \n",
       "vlinder08  51.028130  4.477398       NaN  NaN                            TSM   \n",
       "vlinder09  50.927166  4.075722       NaN  NaN                            SMI   \n",
       "vlinder10  50.935555  4.041389       NaN  NaN                            SMI   \n",
       "vlinder11  51.222424  4.381726       NaN  NaN               Sint-Annacollege   \n",
       "vlinder12  51.216476  4.423440       NaN  NaN                          UGent   \n",
       "vlinder13  51.212212  4.398065       NaN  NaN                          UGent   \n",
       "vlinder14  51.350616  4.315013       NaN  NaN                          UGent   \n",
       "vlinder15  50.935299  4.192600       NaN  NaN                  Sint-Martinus   \n",
       "vlinder16  51.266850  4.293436       NaN  NaN                   Sint-Maarten   \n",
       "vlinder17  51.065269  5.613458       NaN  NaN  Sint-Augustinusinstituut Bree   \n",
       "vlinder18  51.136246  5.656769       NaN  NaN                      TISM Bree   \n",
       "vlinder19  50.841454  4.363672       NaN  NaN                          UGent   \n",
       "vlinder20  50.847027  4.357971       NaN  NaN                          UGent   \n",
       "vlinder21  51.260387  2.991917       NaN  NaN                      Zeelyceum   \n",
       "vlinder22  50.989502  2.856220       NaN  NaN                        ‘t Saam   \n",
       "vlinder23  51.260578  3.580151       NaN  NaN                Richtpunt Eeklo   \n",
       "vlinder24  51.167015  3.572062       NaN  NaN                  OLV ten Doorn   \n",
       "vlinder25  51.154720  3.708611       NaN  NaN              Einstein Atheneum   \n",
       "vlinder26  51.161758  4.997653       NaN  NaN                    Sint Dimpna   \n",
       "vlinder27  51.058098  3.728067       NaN  NaN            Sec. Kunstinstituut   \n",
       "vlinder28  51.035294  3.769741       NaN  NaN                       GO! Ath.   \n",
       "\n",
       "                           geometry  \n",
       "name                                 \n",
       "vlinder01  POINT (3.81576 50.98044)  \n",
       "vlinder02   POINT (3.7097 51.02238)  \n",
       "vlinder03  POINT (4.95211 51.32458)  \n",
       "vlinder04  POINT (4.93473 51.33552)  \n",
       "vlinder05  POINT (3.67518 51.05265)  \n",
       "vlinder06    POINT (4.5163 51.0271)  \n",
       "vlinder07  POINT (4.47845 51.03089)  \n",
       "vlinder08   POINT (4.4774 51.02813)  \n",
       "vlinder09  POINT (4.07572 50.92717)  \n",
       "vlinder10  POINT (4.04139 50.93555)  \n",
       "vlinder11  POINT (4.38173 51.22242)  \n",
       "vlinder12  POINT (4.42344 51.21648)  \n",
       "vlinder13  POINT (4.39807 51.21221)  \n",
       "vlinder14  POINT (4.31501 51.35062)  \n",
       "vlinder15    POINT (4.1926 50.9353)  \n",
       "vlinder16  POINT (4.29344 51.26685)  \n",
       "vlinder17  POINT (5.61346 51.06527)  \n",
       "vlinder18  POINT (5.65677 51.13625)  \n",
       "vlinder19  POINT (4.36367 50.84145)  \n",
       "vlinder20  POINT (4.35797 50.84703)  \n",
       "vlinder21  POINT (2.99192 51.26039)  \n",
       "vlinder22   POINT (2.85622 50.9895)  \n",
       "vlinder23  POINT (3.58015 51.26058)  \n",
       "vlinder24  POINT (3.57206 51.16702)  \n",
       "vlinder25  POINT (3.70861 51.15472)  \n",
       "vlinder26  POINT (4.99765 51.16176)  \n",
       "vlinder27   POINT (3.72807 51.0581)  \n",
       "vlinder28  POINT (3.76974 51.03529)  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.metadf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21",
   "metadata": {},
   "source": [
    "## Station class\n",
    "\n",
    "The stationclass is a representatio of a station. A station holds the following:\n",
    "\n",
    "* ``Station.sensordata``: Timeseries of an observation type. A station can hold multiple sensordata, one for each sensor. \n",
    "* ``Station.site``: Each station has a ´Site´ attribute, that holds the information on the location of the station. Metadata related to the station is also stored here. \n",
    "* ``Station.modeldata``: In addition to the observations, modeldata timeseries representing the station can be stored. In pracktice, if one would download ERA5 data (using the MetObs-toolkit), the timeseries are stored as modeldata in the Station.\n",
    "\n",
    "\n",
    "To select a station, one can use the *name* of the station, which is assumed to be unique for each station."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "All the methods and attributes that are present in the ``Dataset`` are also applicable on the ``Station``! Thus if your script works on Dataset-level, it also works on station-level. \n",
    "\n",
    "\n",
    "Only the ``Dataset.sync_records()``, ``Dataset.buddy_check()``, and trivial Dataset-only methods (i.g. ``Dataset.get_station()``) are not defined for Stations.\n",
    "</div>"
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      "================================================================================\n",
      "                            General info of Station                             \n",
      "================================================================================\n",
      "\n",
      "\n",
      "--- Observational info ---\n",
      "\n",
      "Station instance with:\n",
      "  -humidity:\n",
      "    -humidity observations in percent\n",
      "    -from 2022-09-01 00:00:00+00:00 -> 2022-09-15 23:55:00+00:00\n",
      "    -At a resolution of 0 days 00:05:00\n",
      "    -No outliers present.\n",
      "    -2 gaps present, a total of 3 missing timestamps.\n",
      "      -label counts: \n",
      "        -gap: 3\n",
      "  -temp:\n",
      "    -temp observations in degree_Celsius\n",
      "    -from 2022-09-01 00:00:00+00:00 -> 2022-09-15 23:55:00+00:00\n",
      "    -At a resolution of 0 days 00:05:00\n",
      "    -No outliers present.\n",
      "    -2 gaps present, a total of 3 missing timestamps.\n",
      "      -label counts: \n",
      "        -gap: 3\n",
      "  -wind_direction:\n",
      "    -wind_direction observations in degree\n",
      "    -from 2022-09-01 00:00:00+00:00 -> 2022-09-15 23:55:00+00:00\n",
      "    -At a resolution of 0 days 00:05:00\n",
      "    -No outliers present.\n",
      "    -2 gaps present, a total of 3 missing timestamps.\n",
      "      -label counts: \n",
      "        -gap: 3\n",
      "  -wind_speed:\n",
      "    -wind_speed observations in meter / second\n",
      "    -from 2022-09-01 00:00:00+00:00 -> 2022-09-15 23:55:00+00:00\n",
      "    -At a resolution of 0 days 00:05:00\n",
      "    -No outliers present.\n",
      "    -2 gaps present, a total of 3 missing timestamps.\n",
      "      -label counts: \n",
      "        -gap: 3\n",
      "\n",
      "--- Metadata info ---\n",
      "\n",
      "  -Coordinates (51.022379, 3.709695) (latitude, longitude)\n",
      "  -Altitude is unknown\n",
      "  -LCZ is unknown\n",
      "  -Land cover fractions are unknown\n",
      "  -Extra metadata from the metadata file:\n",
      "    -school: UGent\n",
      "\n",
      "--- Modeldata info ---\n",
      "\n",
      "  -Station instance without model data.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Select a station\n",
    "your_station = dataset.get_station('vlinder02')\n",
    "\n",
    "#Print out some details\n",
    "your_station.get_info()"
   ]
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      "================================================================================\n",
      "                              General Info of Site                              \n",
      "================================================================================\n",
      "\n",
      "Site of vlinder02:\n",
      "  -Coordinates (51.022379, 3.709695) (latitude, longitude)\n",
      "  -Altitude is unknown\n",
      "  -LCZ is unknown\n",
      "  -Land cover fractions are unknown\n",
      "  -Extra metadata from the metadata file:\n",
      "    -school: UGent\n",
      "\n"
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    "# Inspecting the attributes of the station\n",
    "\n",
    "#Print out info on the Site of the station:\n",
    "your_station.site.get_info()"
   ]
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      "temp data of station vlinder02.\n"
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       "                                               value label\n",
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       "                          temp             19.400000    ok\n",
       "                          wind_direction   25.000000    ok\n",
       "                          wind_speed        0.194444    ok\n",
       "2022-09-01 00:05:00+00:00 humidity         62.000000    ok\n",
       "...                                              ...   ...\n",
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       "                          wind_direction  295.000000    ok\n",
       "                          wind_speed        0.000000    ok\n",
       "\n",
       "[17280 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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    }
   ],
   "source": [
    "# All observational data is stored as SensorData\n",
    "\n",
    "print(your_station.get_sensor('temp'))\n",
    "\n",
    "# More convenient is to use the pandas dataframe representations,\n",
    "# similar as with the Dataset\n",
    "\n",
    "your_station.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "26",
   "metadata": {
    "execution": {
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    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
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       "\n",
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       "                    --jp-layout-color3: #383838;\n",
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       "\n",
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       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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       "            "
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>altitude</th>\n",
       "      <th>LCZ</th>\n",
       "      <th>school</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",
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       "      <th>vlinder02</th>\n",
       "      <td>51.022381</td>\n",
       "      <td>3.709695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UGent</td>\n",
       "      <td>POINT (3.7097 51.02238)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                 lat       lon  altitude  LCZ school                 geometry\n",
       "name                                                                         \n",
       "vlinder02  51.022381  3.709695       NaN  NaN  UGent  POINT (3.7097 51.02238)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    }
   ],
   "source": [
    "#Or the metadata for this singel station\n",
    "your_station.metadf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27",
   "metadata": {},
   "source": [
    "## Plotting timeseries\n",
    "\n",
    "Plotting the timeseries can be simply done by using the ``make_plot()`` method, on a ``Dataset`` or a ``Station``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28",
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     "shell.execute_reply": "2025-05-14T11:44:47.012190Z"
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   "outputs": [
    {
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      "text/plain": [
       "<Axes: title={'center': 'temp data.'}, xlabel='Timestamps (in UTC)', ylabel='temp (degree_Celsius)'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.make_plot(obstype='temp', #Which observation type to plot. (See dataset.present_observations)\n",
    "                  colorby='station', #if 'station', each station will be a different color\n",
    "                  show_outliers=True,\n",
    "                  show_gaps=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-14T11:44:47.015069Z",
     "iopub.status.busy": "2025-05-14T11:44:47.014742Z",
     "iopub.status.idle": "2025-05-14T11:44:47.395828Z",
     "shell.execute_reply": "2025-05-14T11:44:47.394972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .geemap-dark {\n",
       "                    --jp-widgets-color: white;\n",
       "                    --jp-widgets-label-color: white;\n",
       "                    --jp-ui-font-color1: white;\n",
       "                    --jp-layout-color2: #454545;\n",
       "                    background-color: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-dark .jupyter-button {\n",
       "                    --jp-layout-color3: #383838;\n",
       "                }\n",
       "\n",
       "                .geemap-colab {\n",
       "                    background-color: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "\n",
       "                .geemap-colab .jupyter-button {\n",
       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
       "                }\n",
       "            </style>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'humidity data for station vlinder02'}, xlabel='Timestamps (in UTC)', ylabel='humidity (percent)'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We can also plot a single station\n",
    "your_station.make_plot(obstype='humidity',\n",
    "                       colorby='label') #If 'label', the colors are based on the status/label of an observation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30",
   "metadata": {},
   "source": [
    "## Common usecases\n",
    "\n",
    "Here a collection of common usecases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31",
   "metadata": {},
   "source": [
    "### Resampling time resolution\n",
    "\n",
    "It is common to change or alter the time resolution of your observations. This is often applied when:\n",
    "\n",
    "* the data amount is to big, and the present time resolution is not required for the analysis.\n",
    "* sensor do not have the same time resolution. (i.g. temperature is measured every 5 minutes, but precipitation is measured each hour.)\n",
    "* Observations are not synchronized over multiple stations. This is a special case of resampling, since there is also a synchronization required.\n",
    "\n",
    "It is recommended to set the target time resolution, in the beginning of your pipeline! \n",
    "\n",
    "In the MetObs-toolkit you can resample by using the ``resample()`` method on a ``Dataset`` or ``Station``. By doing so, the toolkit will construct a set of target timestamps (in the new resolution), and will map the raw timestamps to the new target timestamps. There is no interpolation applied! \n",
    "\n",
    "In order to construct the mapping of the old timestamps to the target timestamps, a tolerance is used. The nearest timestamp is tested if it is within the tolerance of the target timestamp. If this test is not successful, no record could be assigned to the target timestamp and thus a gap is created. Thus by increasing the *shift_tolerance*, the resampling method will have more mapped timestamps thus less gaps but at the cost of less accurate timestamps."
   ]
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      "WARNING:<metobs_toolkit>:Luchtdruk is present in the datafile, but not found in the template! This column will be ignored.\n",
      "WARNING:<metobs_toolkit>:Neerslagintensiteit is present in the datafile, but not found in the template! This column will be ignored.\n",
      "WARNING:<metobs_toolkit>:Neerslagsom is present in the datafile, but not found in the template! This column will be ignored.\n",
      "WARNING:<metobs_toolkit>:Rukwind is present in the datafile, but not found in the template! This column will be ignored.\n",
      "WARNING:<metobs_toolkit>:Luchtdruk_Zeeniveau is present in the datafile, but not found in the template! This column will be ignored.\n",
      "WARNING:<metobs_toolkit>:Globe Temperatuur is present in the datafile, but not found in the template! This column will be ignored.\n",
      "WARNING:<metobs_toolkit>:The following columns are present in the data file, but not in the template! They are skipped!\n",
      " ['Luchtdruk', 'Luchtdruk_Zeeniveau', 'Globe Temperatuur', 'Neerslagsom', 'Rukwind', 'Neerslagintensiteit']\n",
      "WARNING:<metobs_toolkit>:The following columns are found in the metadata, but not in the template and are therefore ignored: \n",
      "['stad', 'benaming', 'sponsor', 'Network']\n",
      "WARNING:<metobs_toolkit>:The present gaps are removed, new gaps are constructed for temp data of station vlinder02..\n",
      "WARNING:<metobs_toolkit>:The present gaps are removed, new gaps are constructed for wind_direction data of station vlinder02..\n",
      "WARNING:<metobs_toolkit>:The present gaps are removed, new gaps are constructed for wind_speed data of station vlinder02..\n",
      "WARNING:<metobs_toolkit>:The present gaps are removed, new gaps are constructed for humidity data of station vlinder02..\n"
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    "hourly_dataset = metobs_toolkit.Dataset()\n",
    "#Load the data (raw data has 5 min resolution)\n",
    "hourly_dataset.import_data_from_file(\n",
    "                    template_file=path_to_templatefile, #The template file\n",
    "                    input_data_file=path_to_datafile, #The data file\n",
    "                    input_metadata_file=path_to_metadatafile, #The metadata file\n",
    "                    )\n",
    "#Resample to 1 hour resolution\n",
    "hourly_dataset.resample(target_freq='1h', #Target frequency is set to 1 hour\n",
    "                        obstype=None, #if None, all present observations are resampled\n",
    "                        shift_tolerance='4min', #The maximum shift allow for a timestamp\n",
    "                        origin_simplify_tolerance='3min') # The maximum shift for the origin, to get a simplified origin\n",
    "\n",
    "# You can verify that the resolution is hourly by inspecting the df attribute\n",
    "hourly_dataset.df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33",
   "metadata": {},
   "source": [
    "### Dataframe of one observationtype\n",
    "\n",
    "The ``Dataset.df`` and ``Station.df`` returns a pandas dataframe with a so called Multi-Index. That is because the combination of [´timestamp´, ´observationtype´, 'stationname´] defines an observation, thus the use of the Multi-Index. \n",
    "\n",
    "We are aware that working with Multi-Indexed dataframes can be challenging, thus an example on how to convert a multiindex dataframe to a regular-indexed dataframe. \n",
    "\n",
    "Be aware that removing (or reducing) the Multi-Index, is always a subsetting or approximation."
   ]
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   "source": [
    "#Subset to only temperatures (=subsetting)\n",
    "\n",
    "temperatures = dataset.df.xs(key='temp', \n",
    "                             level='obstype', #the level of the index ('datetime', 'name' or 'obstype')\n",
    "                             drop_level=True)\n",
    "\n",
    "#You can see that the index now only has 2-levels:\n",
    "temperatures"
   ]
  },
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       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">value</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th>vlinder01</th>\n",
       "      <th>vlinder02</th>\n",
       "      <th>vlinder03</th>\n",
       "      <th>vlinder04</th>\n",
       "      <th>vlinder05</th>\n",
       "      <th>vlinder06</th>\n",
       "      <th>vlinder07</th>\n",
       "      <th>vlinder08</th>\n",
       "      <th>vlinder09</th>\n",
       "      <th>vlinder10</th>\n",
       "      <th>...</th>\n",
       "      <th>vlinder19</th>\n",
       "      <th>vlinder20</th>\n",
       "      <th>vlinder21</th>\n",
       "      <th>vlinder22</th>\n",
       "      <th>vlinder23</th>\n",
       "      <th>vlinder24</th>\n",
       "      <th>vlinder25</th>\n",
       "      <th>vlinder26</th>\n",
       "      <th>vlinder27</th>\n",
       "      <th>vlinder28</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>2022-09-01 00:00:00+00:00</th>\n",
       "      <td>18.799999</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>15.9</td>\n",
       "      <td>21.1</td>\n",
       "      <td>17.700001</td>\n",
       "      <td>18.1</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01 00:05:00+00:00</th>\n",
       "      <td>18.799999</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>15.8</td>\n",
       "      <td>21.1</td>\n",
       "      <td>17.700001</td>\n",
       "      <td>18.1</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01 00:10:00+00:00</th>\n",
       "      <td>18.799999</td>\n",
       "      <td>19.299999</td>\n",
       "      <td>16.799999</td>\n",
       "      <td>15.8</td>\n",
       "      <td>21.1</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01 00:15:00+00:00</th>\n",
       "      <td>18.700001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>16.700001</td>\n",
       "      <td>15.6</td>\n",
       "      <td>21.1</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01 00:20:00+00:00</th>\n",
       "      <td>18.700001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>15.4</td>\n",
       "      <td>21.1</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>18.1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>17.700001</td>\n",
       "      <td>18.799999</td>\n",
       "      <td>...</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:35:00+00:00</th>\n",
       "      <td>13.200000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>9.1</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>13.4</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:40:00+00:00</th>\n",
       "      <td>13.100000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>9.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>13.4</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:45:00+00:00</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>9.8</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.3</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:50:00+00:00</th>\n",
       "      <td>12.900000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>13.3</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:55:00+00:00</th>\n",
       "      <td>12.900000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>10.2</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>13.2</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.100000</td>\n",
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       "      <td>ok</td>\n",
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       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4320 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               value                                  \\\n",
       "name                       vlinder01  vlinder02  vlinder03 vlinder04   \n",
       "datetime                                                               \n",
       "2022-09-01 00:00:00+00:00  18.799999  19.400000  17.000000      15.9   \n",
       "2022-09-01 00:05:00+00:00  18.799999  19.400000  16.900000      15.8   \n",
       "2022-09-01 00:10:00+00:00  18.799999  19.299999  16.799999      15.8   \n",
       "2022-09-01 00:15:00+00:00  18.700001  19.200001  16.700001      15.6   \n",
       "2022-09-01 00:20:00+00:00  18.700001  19.200001  16.600000      15.4   \n",
       "...                              ...        ...        ...       ...   \n",
       "2022-09-15 23:35:00+00:00  13.200000  13.300000  12.200000       9.1   \n",
       "2022-09-15 23:40:00+00:00  13.100000  13.200000  12.200000       9.6   \n",
       "2022-09-15 23:45:00+00:00  13.000000  13.100000  12.200000       9.8   \n",
       "2022-09-15 23:50:00+00:00  12.900000  13.000000  12.300000      10.0   \n",
       "2022-09-15 23:55:00+00:00  12.900000  12.900000  12.400000      10.2   \n",
       "\n",
       "                                                                     \\\n",
       "name                      vlinder05  vlinder06 vlinder07  vlinder08   \n",
       "datetime                                                              \n",
       "2022-09-01 00:00:00+00:00      21.1  17.700001      18.1  19.200001   \n",
       "2022-09-01 00:05:00+00:00      21.1  17.700001      18.1  19.100000   \n",
       "2022-09-01 00:10:00+00:00      21.1  17.600000      18.0  19.100000   \n",
       "2022-09-01 00:15:00+00:00      21.1  17.500000      18.0  19.000000   \n",
       "2022-09-01 00:20:00+00:00      21.1  17.500000      18.1  19.000000   \n",
       "...                             ...        ...       ...        ...   \n",
       "2022-09-15 23:35:00+00:00      17.4  13.200000      13.4  14.400000   \n",
       "2022-09-15 23:40:00+00:00      17.4  13.100000      13.4  14.300000   \n",
       "2022-09-15 23:45:00+00:00      17.4  13.000000      13.3  14.300000   \n",
       "2022-09-15 23:50:00+00:00      17.4  13.100000      13.3  14.200000   \n",
       "2022-09-15 23:55:00+00:00      17.4  13.100000      13.2  14.200000   \n",
       "\n",
       "                                                 ...     label            \\\n",
       "name                       vlinder09  vlinder10  ... vlinder19 vlinder20   \n",
       "datetime                                         ...                       \n",
       "2022-09-01 00:00:00+00:00  18.000000  19.100000  ...        ok        ok   \n",
       "2022-09-01 00:05:00+00:00  18.000000  19.000000  ...        ok        ok   \n",
       "2022-09-01 00:10:00+00:00  17.900000  18.900000  ...        ok        ok   \n",
       "2022-09-01 00:15:00+00:00  17.799999  18.900000  ...        ok        ok   \n",
       "2022-09-01 00:20:00+00:00  17.700001  18.799999  ...        ok        ok   \n",
       "...                              ...        ...  ...       ...       ...   \n",
       "2022-09-15 23:35:00+00:00  13.200000  14.300000  ...        ok        ok   \n",
       "2022-09-15 23:40:00+00:00  13.100000  14.200000  ...        ok        ok   \n",
       "2022-09-15 23:45:00+00:00  13.000000  14.200000  ...        ok        ok   \n",
       "2022-09-15 23:50:00+00:00  13.000000  14.200000  ...        ok        ok   \n",
       "2022-09-15 23:55:00+00:00  13.000000  14.100000  ...        ok        ok   \n",
       "\n",
       "                                                                             \\\n",
       "name                      vlinder21 vlinder22 vlinder23 vlinder24 vlinder25   \n",
       "datetime                                                                      \n",
       "2022-09-01 00:00:00+00:00        ok        ok        ok        ok        ok   \n",
       "2022-09-01 00:05:00+00:00        ok        ok        ok        ok        ok   \n",
       "2022-09-01 00:10:00+00:00        ok        ok        ok        ok        ok   \n",
       "2022-09-01 00:15:00+00:00        ok        ok        ok        ok        ok   \n",
       "2022-09-01 00:20:00+00:00        ok        ok        ok        ok        ok   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "2022-09-15 23:35:00+00:00        ok        ok        ok        ok        ok   \n",
       "2022-09-15 23:40:00+00:00        ok        ok        ok        ok        ok   \n",
       "2022-09-15 23:45:00+00:00        ok        ok        ok        ok        ok   \n",
       "2022-09-15 23:50:00+00:00        ok        ok        ok        ok        ok   \n",
       "2022-09-15 23:55:00+00:00        ok        ok        ok        ok        ok   \n",
       "\n",
       "                                                         \n",
       "name                      vlinder26 vlinder27 vlinder28  \n",
       "datetime                                                 \n",
       "2022-09-01 00:00:00+00:00        ok        ok        ok  \n",
       "2022-09-01 00:05:00+00:00        ok        ok        ok  \n",
       "2022-09-01 00:10:00+00:00        ok        ok        ok  \n",
       "2022-09-01 00:15:00+00:00        ok        ok        ok  \n",
       "2022-09-01 00:20:00+00:00        ok        ok        ok  \n",
       "...                             ...       ...       ...  \n",
       "2022-09-15 23:35:00+00:00        ok        ok        ok  \n",
       "2022-09-15 23:40:00+00:00        ok        ok        ok  \n",
       "2022-09-15 23:45:00+00:00        ok        ok        ok  \n",
       "2022-09-15 23:50:00+00:00        ok        ok        ok  \n",
       "2022-09-15 23:55:00+00:00        ok        ok        ok  \n",
       "\n",
       "[4320 rows x 56 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If we assume that all the temperature observations over all the stations have the same\n",
    "#set of timestamps (typical after resampling! ), we can create a dataframe with all stations represented by columns.\n",
    "\n",
    "temperatures_wide = (dataset.df\n",
    "                    #first subset to temperatures\n",
    "                    .xs(key='temp', \n",
    "                            level='obstype', #the level of the index ('datetime', 'name' or 'obstype')\n",
    "                            drop_level=True)\n",
    "                    #Convert a index level to columns (unstacking)\n",
    "                    .unstack(level='name'))\n",
    "temperatures_wide\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-14T11:44:57.062870Z",
     "iopub.status.busy": "2025-05-14T11:44:57.062501Z",
     "iopub.status.idle": "2025-05-14T11:44:57.089993Z",
     "shell.execute_reply": "2025-05-14T11:44:57.088608Z"
    }
   },
   "outputs": [
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       "      <td>18.1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>17.700001</td>\n",
       "      <td>18.799999</td>\n",
       "      <td>...</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.299999</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>19.299999</td>\n",
       "      <td>17.799999</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:35:00+00:00</th>\n",
       "      <td>13.200000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>9.1</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>13.4</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>13.9</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>13.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:40:00+00:00</th>\n",
       "      <td>13.100000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>9.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>13.4</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>13.9</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>13.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:45:00+00:00</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>9.8</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.3</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>13.8</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>13.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:50:00+00:00</th>\n",
       "      <td>12.900000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>13.3</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.9</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>13.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15 23:55:00+00:00</th>\n",
       "      <td>12.900000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>10.2</td>\n",
       "      <td>17.4</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>13.2</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>13.9</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>14.300000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4320 rows × 28 columns</p>\n",
       "</div>"
      ],
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       "name                       vlinder01  vlinder02  vlinder03  vlinder04  \\\n",
       "datetime                                                                \n",
       "2022-09-01 00:00:00+00:00  18.799999  19.400000  17.000000       15.9   \n",
       "2022-09-01 00:05:00+00:00  18.799999  19.400000  16.900000       15.8   \n",
       "2022-09-01 00:10:00+00:00  18.799999  19.299999  16.799999       15.8   \n",
       "2022-09-01 00:15:00+00:00  18.700001  19.200001  16.700001       15.6   \n",
       "2022-09-01 00:20:00+00:00  18.700001  19.200001  16.600000       15.4   \n",
       "...                              ...        ...        ...        ...   \n",
       "2022-09-15 23:35:00+00:00  13.200000  13.300000  12.200000        9.1   \n",
       "2022-09-15 23:40:00+00:00  13.100000  13.200000  12.200000        9.6   \n",
       "2022-09-15 23:45:00+00:00  13.000000  13.100000  12.200000        9.8   \n",
       "2022-09-15 23:50:00+00:00  12.900000  13.000000  12.300000       10.0   \n",
       "2022-09-15 23:55:00+00:00  12.900000  12.900000  12.400000       10.2   \n",
       "\n",
       "name                       vlinder05  vlinder06  vlinder07  vlinder08  \\\n",
       "datetime                                                                \n",
       "2022-09-01 00:00:00+00:00       21.1  17.700001       18.1  19.200001   \n",
       "2022-09-01 00:05:00+00:00       21.1  17.700001       18.1  19.100000   \n",
       "2022-09-01 00:10:00+00:00       21.1  17.600000       18.0  19.100000   \n",
       "2022-09-01 00:15:00+00:00       21.1  17.500000       18.0  19.000000   \n",
       "2022-09-01 00:20:00+00:00       21.1  17.500000       18.1  19.000000   \n",
       "...                              ...        ...        ...        ...   \n",
       "2022-09-15 23:35:00+00:00       17.4  13.200000       13.4  14.400000   \n",
       "2022-09-15 23:40:00+00:00       17.4  13.100000       13.4  14.300000   \n",
       "2022-09-15 23:45:00+00:00       17.4  13.000000       13.3  14.300000   \n",
       "2022-09-15 23:50:00+00:00       17.4  13.100000       13.3  14.200000   \n",
       "2022-09-15 23:55:00+00:00       17.4  13.100000       13.2  14.200000   \n",
       "\n",
       "name                       vlinder09  vlinder10  ...  vlinder19  vlinder20  \\\n",
       "datetime                                         ...                         \n",
       "2022-09-01 00:00:00+00:00  18.000000  19.100000  ...  18.700001  19.400000   \n",
       "2022-09-01 00:05:00+00:00  18.000000  19.000000  ...  18.600000  19.400000   \n",
       "2022-09-01 00:10:00+00:00  17.900000  18.900000  ...  18.600000  19.299999   \n",
       "2022-09-01 00:15:00+00:00  17.799999  18.900000  ...  18.500000  19.299999   \n",
       "2022-09-01 00:20:00+00:00  17.700001  18.799999  ...  18.500000  19.200001   \n",
       "...                              ...        ...  ...        ...        ...   \n",
       "2022-09-15 23:35:00+00:00  13.200000  14.300000  ...  14.500000  15.000000   \n",
       "2022-09-15 23:40:00+00:00  13.100000  14.200000  ...  14.500000  15.000000   \n",
       "2022-09-15 23:45:00+00:00  13.000000  14.200000  ...  14.400000  14.900000   \n",
       "2022-09-15 23:50:00+00:00  13.000000  14.200000  ...  14.300000  14.900000   \n",
       "2022-09-15 23:55:00+00:00  13.000000  14.100000  ...  14.300000  14.800000   \n",
       "\n",
       "name                       vlinder21  vlinder22  vlinder23  vlinder24  \\\n",
       "datetime                                                                \n",
       "2022-09-01 00:00:00+00:00  19.299999  18.799999       18.0  18.200001   \n",
       "2022-09-01 00:05:00+00:00  19.299999  18.799999       18.0  18.200001   \n",
       "2022-09-01 00:10:00+00:00  19.200001  18.700001       18.0  18.100000   \n",
       "2022-09-01 00:15:00+00:00  19.200001  18.600000       18.0  18.000000   \n",
       "2022-09-01 00:20:00+00:00  19.200001  18.299999       18.0  17.900000   \n",
       "...                              ...        ...        ...        ...   \n",
       "2022-09-15 23:35:00+00:00  15.700000  12.100000       13.9  11.700000   \n",
       "2022-09-15 23:40:00+00:00  15.700000  12.100000       13.9  11.600000   \n",
       "2022-09-15 23:45:00+00:00  15.600000  12.100000       13.8  11.400000   \n",
       "2022-09-15 23:50:00+00:00  15.700000  12.000000       13.9  11.300000   \n",
       "2022-09-15 23:55:00+00:00  15.600000  11.900000       13.9  11.100000   \n",
       "\n",
       "name                       vlinder25  vlinder26  vlinder27  vlinder28  \n",
       "datetime                                                               \n",
       "2022-09-01 00:00:00+00:00  18.900000  17.900000  19.600000  17.799999  \n",
       "2022-09-01 00:05:00+00:00  18.500000  17.700001  19.600000  17.799999  \n",
       "2022-09-01 00:10:00+00:00  18.299999  17.500000  19.500000  17.700001  \n",
       "2022-09-01 00:15:00+00:00  18.200001  17.299999  19.400000  17.799999  \n",
       "2022-09-01 00:20:00+00:00  18.100000  17.100000  19.299999  17.799999  \n",
       "...                              ...        ...        ...        ...  \n",
       "2022-09-15 23:35:00+00:00  14.200000  13.400000  14.500000  13.400000  \n",
       "2022-09-15 23:40:00+00:00  14.200000  13.400000  14.500000  13.300000  \n",
       "2022-09-15 23:45:00+00:00  14.200000  13.400000  14.400000  13.200000  \n",
       "2022-09-15 23:50:00+00:00  14.200000  13.400000  14.400000  13.200000  \n",
       "2022-09-15 23:55:00+00:00  14.100000  13.300000  14.300000  13.000000  \n",
       "\n",
       "[4320 rows x 28 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if you are only interested in the value, you can select them:\n",
    "temperatures_wide['value']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54fb9443",
   "metadata": {},
   "source": [
    "### Exporting data\n",
    "\n",
    "The MetObs-toolkit provides several methods to export your processed data in different formats, each serving different purposes:\n",
    "\n",
    "**For data analysis and interoperability:**\n",
    "- `to_parquet()` and `to_csv()` export the observation data as flat tables, ideal for analysis in other tools\n",
    "- `to_xr()` converts the dataset to an xarray Dataset.\n",
    "\n",
    "These methods can be applied on a `Dataset` and on a `Station`.\n",
    "\n",
    "**For preserving the complete MetObs-toolkit structure:**\n",
    "- `save_dataset_to_pkl()` saves the entire Dataset object including all metadata, QC flags, and internal structures\n",
    "\n",
    "The key difference is that `save_dataset_to_pkl()` preserves the complete MetObs-toolkit Dataset structure and can be reloaded with `load_dataset_from_pkl()`, while the other methods export only the observation data in standard formats for external use.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "db2eaba0",
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   "outputs": [
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       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "  line-height: 1.6;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "  margin-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-obj-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-group-box-contents > label {\n",
       "  color: var(--xr-font-color2);\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item > input,\n",
       ".xr-group-box-contents > input,\n",
       ".xr-array-wrap > input {\n",
       "  display: block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item > input + label,\n",
       ".xr-var-item > input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label,\n",
       ".xr-var-item > input:enabled + label,\n",
       ".xr-array-wrap > input:enabled + label,\n",
       ".xr-group-box-contents > input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item > input:focus-visible + label,\n",
       ".xr-var-item > input:focus-visible + label,\n",
       ".xr-array-wrap > input:focus-visible + label,\n",
       ".xr-group-box-contents > input:focus-visible + label {\n",
       "  outline: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item > input:enabled + label:hover,\n",
       ".xr-var-item > input:enabled + label:hover,\n",
       ".xr-array-wrap > input:enabled + label:hover,\n",
       ".xr-group-box-contents > input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "  white-space: nowrap;\n",
       "}\n",
       "\n",
       ".xr-section-summary > em {\n",
       "  font-weight: normal;\n",
       "}\n",
       "\n",
       ".xr-span-grid {\n",
       "  grid-column-end: -1;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.6em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details,\n",
       ".xr-group-box-contents > label {\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  grid-column: 1 / -1;\n",
       "  margin-top: 4px;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in ~ .xr-section-details {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-children {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 100%;\n",
       "  grid-column: 1 / -1;\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 30px auto;\n",
       "}\n",
       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 26px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > label::before {\n",
       "  content: \"📂\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label::before {\n",
       "  content: \"📁\";\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked + label {\n",
       "  padding-bottom: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-contents > input + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-group-box-ellipsis {\n",
       "  font-size: 1.4em;\n",
       "  font-weight: 900;\n",
       "  color: var(--xr-font-color2);\n",
       "  letter-spacing: 0.15em;\n",
       "  cursor: default;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 8MB\n",
       "Dimensions:         (name: 28, kind: 2, datetime: 4320)\n",
       "Coordinates:\n",
       "  * name            (name) &lt;U9 1kB &#x27;vlinder01&#x27; &#x27;vlinder02&#x27; ... &#x27;vlinder28&#x27;\n",
       "    lat             (name) float64 224B 50.98 51.02 51.32 ... 51.16 51.06 51.04\n",
       "    lon             (name) float64 224B 3.816 3.71 4.952 ... 4.998 3.728 3.77\n",
       "    school          (name) &lt;U29 3kB &#x27;UGent&#x27; &#x27;UGent&#x27; ... &#x27;GO! Ath.&#x27;\n",
       "  * kind            (kind) &lt;U5 40B &#x27;obs&#x27; &#x27;label&#x27;\n",
       "  * datetime        (datetime) datetime64[ns] 35kB 2022-09-01 ... 2022-09-15T...\n",
       "    altitude        float64 8B nan\n",
       "    LCZ             float64 8B nan\n",
       "Data variables:\n",
       "    temp            (name, kind, datetime) float64 2MB 18.8 18.8 ... 0.0 0.0\n",
       "    wind_direction  (name, kind, datetime) float64 2MB 65.0 75.0 ... 0.0 0.0\n",
       "    wind_speed      (name, kind, datetime) float64 2MB 1.556 1.528 ... 0.0 0.0\n",
       "    humidity        (name, kind, datetime) float64 2MB 65.0 65.0 ... 0.0 0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-20f3f24b-a9b0-4c25-908c-f48dcff045bf' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-20f3f24b-a9b0-4c25-908c-f48dcff045bf' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>name</span>: 28</li><li><span class='xr-has-index'>kind</span>: 2</li><li><span class='xr-has-index'>datetime</span>: 4320</li></ul></div></li><li class='xr-section-item'><input id='section-f26e26cf-5d96-46fc-897c-39e57d9fe2ff' class='xr-section-summary-in' type='checkbox' checked /><label for='section-f26e26cf-5d96-46fc-897c-39e57d9fe2ff' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>name</span></div><div class='xr-var-dims'>(name)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;vlinder01&#x27; ... &#x27;vlinder28&#x27;</div><input id='attrs-e5fa3d5d-aacd-4697-af1a-f427d316c353' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5fa3d5d-aacd-4697-af1a-f427d316c353' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5247e166-43f1-4446-a20a-d0904fa518bc' class='xr-var-data-in' type='checkbox'><label for='data-5247e166-43f1-4446-a20a-d0904fa518bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;vlinder01&#x27;, &#x27;vlinder02&#x27;, &#x27;vlinder03&#x27;, &#x27;vlinder04&#x27;, &#x27;vlinder05&#x27;,\n",
       "       &#x27;vlinder06&#x27;, &#x27;vlinder07&#x27;, &#x27;vlinder08&#x27;, &#x27;vlinder09&#x27;, &#x27;vlinder10&#x27;,\n",
       "       &#x27;vlinder11&#x27;, &#x27;vlinder12&#x27;, &#x27;vlinder13&#x27;, &#x27;vlinder14&#x27;, &#x27;vlinder15&#x27;,\n",
       "       &#x27;vlinder16&#x27;, &#x27;vlinder17&#x27;, &#x27;vlinder18&#x27;, &#x27;vlinder19&#x27;, &#x27;vlinder20&#x27;,\n",
       "       &#x27;vlinder21&#x27;, &#x27;vlinder22&#x27;, &#x27;vlinder23&#x27;, &#x27;vlinder24&#x27;, &#x27;vlinder25&#x27;,\n",
       "       &#x27;vlinder26&#x27;, &#x27;vlinder27&#x27;, &#x27;vlinder28&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(name)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>50.98 51.02 51.32 ... 51.06 51.04</div><input id='attrs-625554f4-8c4c-458d-942b-497c3fee6678' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-625554f4-8c4c-458d-942b-497c3fee6678' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d147bc8-0515-4932-bb4b-8d98e7816fd7' class='xr-var-data-in' type='checkbox'><label for='data-3d147bc8-0515-4932-bb4b-8d98e7816fd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([50.980438, 51.022379, 51.324583, 51.335522, 51.052655, 51.0271  ,\n",
       "       51.030889, 51.02813 , 50.927167, 50.935556, 51.222422, 51.216477,\n",
       "       51.212211, 51.350618, 50.9353  , 51.26685 , 51.065269, 51.136244,\n",
       "       50.841455, 50.847025, 51.260389, 50.989501, 51.260578, 51.167015,\n",
       "       51.15472 , 51.16176 , 51.058099, 51.035293])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(name)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.816 3.71 4.952 ... 3.728 3.77</div><input id='attrs-cdc0fe49-7251-4747-85f5-4358906d914a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdc0fe49-7251-4747-85f5-4358906d914a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23869899-03e8-45a7-b037-0a744e9f562a' class='xr-var-data-in' type='checkbox'><label for='data-23869899-03e8-45a7-b037-0a744e9f562a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3.815763, 3.709695, 4.952109, 4.934732, 3.675183, 4.5163  ,\n",
       "       4.478445, 4.477398, 4.075722, 4.041389, 4.381726, 4.42344 ,\n",
       "       4.398065, 4.315013, 4.1926  , 4.293436, 5.613458, 5.656769,\n",
       "       4.363672, 4.357971, 2.991917, 2.85622 , 3.580151, 3.572062,\n",
       "       3.708611, 4.997653, 3.728067, 3.769741])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>school</span></div><div class='xr-var-dims'>(name)</div><div class='xr-var-dtype'>&lt;U29</div><div class='xr-var-preview xr-preview'>&#x27;UGent&#x27; &#x27;UGent&#x27; ... &#x27;GO! Ath.&#x27;</div><input id='attrs-858cb746-2ada-42a4-9511-ec67d4655ca3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-858cb746-2ada-42a4-9511-ec67d4655ca3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ecdc8ec-c303-4618-9bce-e5d4ed3d3e41' class='xr-var-data-in' type='checkbox'><label for='data-7ecdc8ec-c303-4618-9bce-e5d4ed3d3e41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;UGent&#x27;, &#x27;UGent&#x27;, &#x27;Heilig Graf&#x27;, &#x27;Heilig Graf&#x27;, &#x27;Sint-Barbara&#x27;,\n",
       "       &#x27;BimSem&#x27;, &#x27;PTS&#x27;, &#x27;TSM&#x27;, &#x27;SMI&#x27;, &#x27;SMI&#x27;, &#x27;Sint-Annacollege&#x27;, &#x27;UGent&#x27;,\n",
       "       &#x27;UGent&#x27;, &#x27;UGent&#x27;, &#x27;Sint-Martinus&#x27;, &#x27;Sint-Maarten&#x27;,\n",
       "       &#x27;Sint-Augustinusinstituut Bree&#x27;, &#x27;TISM Bree&#x27;, &#x27;UGent&#x27;, &#x27;UGent&#x27;,\n",
       "       &#x27;Zeelyceum&#x27;, &#x27;‘t Saam&#x27;, &#x27;Richtpunt Eeklo&#x27;, &#x27;OLV ten Doorn&#x27;,\n",
       "       &#x27;Einstein Atheneum&#x27;, &#x27;Sint Dimpna&#x27;, &#x27;Sec. Kunstinstituut&#x27;,\n",
       "       &#x27;GO! Ath.&#x27;], dtype=&#x27;&lt;U29&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>kind</span></div><div class='xr-var-dims'>(kind)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;obs&#x27; &#x27;label&#x27;</div><input id='attrs-8d675087-c19b-4ec3-84ac-be025bb39ca2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d675087-c19b-4ec3-84ac-be025bb39ca2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0e643e7-f71f-407f-804f-7c0e736b1ebc' class='xr-var-data-in' type='checkbox'><label for='data-e0e643e7-f71f-407f-804f-7c0e736b1ebc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;obs&#x27;, &#x27;label&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>datetime</span></div><div class='xr-var-dims'>(datetime)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-09-01 ... 2022-09-15T23:55:00</div><input id='attrs-bce3cc4a-e301-435c-8214-9540e1490541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bce3cc4a-e301-435c-8214-9540e1490541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f444958-167c-4e5c-999a-1dbf4747b482' class='xr-var-data-in' type='checkbox'><label for='data-3f444958-167c-4e5c-999a-1dbf4747b482' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>timezone :</span></dt><dd>UTC</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-09-01T00:00:00.000000000&#x27;, &#x27;2022-09-01T00:05:00.000000000&#x27;,\n",
       "       &#x27;2022-09-01T00:10:00.000000000&#x27;, ..., &#x27;2022-09-15T23:45:00.000000000&#x27;,\n",
       "       &#x27;2022-09-15T23:50:00.000000000&#x27;, &#x27;2022-09-15T23:55:00.000000000&#x27;],\n",
       "      shape=(4320,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-242a49b8-4a40-4d7f-b7d9-71c83c85afac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-242a49b8-4a40-4d7f-b7d9-71c83c85afac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-667c988f-6b80-4deb-a264-32129e9f487f' class='xr-var-data-in' type='checkbox'><label for='data-667c988f-6b80-4deb-a264-32129e9f487f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(nan)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LCZ</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-3b325bd5-3370-4a87-a3a3-3faabcfdc236' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b325bd5-3370-4a87-a3a3-3faabcfdc236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d048e0d1-d485-49d6-9904-16069efde740' class='xr-var-data-in' type='checkbox'><label for='data-d048e0d1-d485-49d6-9904-16069efde740' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(nan)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62344463-6baf-4828-beff-35e53b280fb1' class='xr-section-summary-in' type='checkbox' checked /><label for='section-62344463-6baf-4828-beff-35e53b280fb1' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(name, kind, datetime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.8 18.8 18.8 18.7 ... 0.0 0.0 0.0</div><input id='attrs-e0d9c7d0-704f-4d73-950b-874ae9459772' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0d9c7d0-704f-4d73-950b-874ae9459772' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9cd6084-1f33-4dbd-90bc-77f971e229a0' class='xr-var-data-in' type='checkbox'><label for='data-f9cd6084-1f33-4dbd-90bc-77f971e229a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>obstype_name :</span></dt><dd>temp</dd><dt><span>obstype_desc :</span></dt><dd>2m - temperature</dd><dt><span>obstype_unit :</span></dt><dd>degree_Celsius</dd><dt><span>MetObs toolkit version :</span></dt><dd>1.0.0a13</dd><dt><span>QC checks :</span></dt><dd>[&#x27;duplicated_timestamp&#x27;]</dd><dt><span>GF methods :</span></dt><dd>[]</dd><dt><span>Label:ok :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>array([[[18.79999924, 18.79999924, 18.79999924, ..., 13.        ,\n",
       "         12.89999962, 12.89999962],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       [[19.39999962, 19.39999962, 19.29999924, ..., 13.10000038,\n",
       "         13.        , 12.89999962],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       [[17.        , 16.89999962, 16.79999924, ..., 12.19999981,\n",
       "         12.30000019, 12.39999962],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[17.89999962, 17.70000076, 17.5       , ..., 13.39999962,\n",
       "         13.39999962, 13.30000019],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       [[19.60000038, 19.60000038, 19.5       , ..., 14.39999962,\n",
       "         14.39999962, 14.30000019],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       [[17.79999924, 17.79999924, 17.70000076, ..., 13.19999981,\n",
       "         13.19999981, 13.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]]], shape=(28, 2, 4320))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_direction</span></div><div class='xr-var-dims'>(name, kind, datetime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>65.0 75.0 75.0 85.0 ... 0.0 0.0 0.0</div><input id='attrs-6b60940e-b471-4d88-b37c-080bf1e50fc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b60940e-b471-4d88-b37c-080bf1e50fc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e3511f1-e8f2-40cc-8228-82eb6c276959' class='xr-var-data-in' type='checkbox'><label for='data-7e3511f1-e8f2-40cc-8228-82eb6c276959' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>obstype_name :</span></dt><dd>wind_direction</dd><dt><span>obstype_desc :</span></dt><dd>wind direction</dd><dt><span>obstype_unit :</span></dt><dd>degree</dd><dt><span>MetObs toolkit version :</span></dt><dd>1.0.0a13</dd><dt><span>QC checks :</span></dt><dd>[&#x27;duplicated_timestamp&#x27;]</dd><dt><span>GF methods :</span></dt><dd>[]</dd><dt><span>Label:ok :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>array([[[ 65.,  75.,  75., ..., 295., 295., 295.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]],\n",
       "\n",
       "       [[ 25.,  85.,  45., ..., 295., 295., 295.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]],\n",
       "\n",
       "       [[115., 115., 115., ...,  95.,  85.,  85.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[355.,  25.,  15., ...,   5.,   5.,   5.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]],\n",
       "\n",
       "       [[335., 335., 335., ...,  85.,  85.,  85.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]],\n",
       "\n",
       "       [[115., 115.,  35., ..., 275., 275., 285.],\n",
       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]]], shape=(28, 2, 4320))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(name, kind, datetime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.556 1.528 1.417 ... 0.0 0.0 0.0</div><input id='attrs-7917e609-3603-4809-8e8f-ea3bec49a67d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7917e609-3603-4809-8e8f-ea3bec49a67d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9f32d5d-30d4-46f5-b670-bcc62261f7cb' class='xr-var-data-in' type='checkbox'><label for='data-d9f32d5d-30d4-46f5-b670-bcc62261f7cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>obstype_name :</span></dt><dd>wind_speed</dd><dt><span>obstype_desc :</span></dt><dd>wind speed</dd><dt><span>obstype_unit :</span></dt><dd>meter / second</dd><dt><span>MetObs toolkit version :</span></dt><dd>1.0.0a13</dd><dt><span>QC checks :</span></dt><dd>[&#x27;duplicated_timestamp&#x27;]</dd><dt><span>GF methods :</span></dt><dd>[]</dd><dt><span>Label:ok :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>array([[[1.55555558, 1.52777791, 1.41666675, ..., 0.08333334,\n",
       "         0.30555558, 0.11111112],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ]],\n",
       "\n",
       "       [[0.19444445, 0.11111112, 0.25      , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ]],\n",
       "\n",
       "       [[0.33333337, 0.30555558, 0.27777779, ..., 0.16666669,\n",
       "         0.02777778, 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.88888896, 0.97222227, 0.72222221, ..., 0.05555556,\n",
       "         0.        , 0.02777778],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ]],\n",
       "\n",
       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ]],\n",
       "\n",
       "       [[0.        , 0.        , 0.25      , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ]]], shape=(28, 2, 4320))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>humidity</span></div><div class='xr-var-dims'>(name, kind, datetime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>65.0 65.0 65.0 65.0 ... 0.0 0.0 0.0</div><input id='attrs-af2f1184-d9f2-4b8a-8b83-6b436c644f59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af2f1184-d9f2-4b8a-8b83-6b436c644f59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d12d1c9-eb43-45bb-8c40-89999bc8382a' class='xr-var-data-in' type='checkbox'><label for='data-6d12d1c9-eb43-45bb-8c40-89999bc8382a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>obstype_name :</span></dt><dd>humidity</dd><dt><span>obstype_desc :</span></dt><dd>2m - relative humidity</dd><dt><span>obstype_unit :</span></dt><dd>percent</dd><dt><span>MetObs toolkit version :</span></dt><dd>1.0.0a13</dd><dt><span>QC checks :</span></dt><dd>[&#x27;duplicated_timestamp&#x27;]</dd><dt><span>GF methods :</span></dt><dd>[]</dd><dt><span>Label:ok :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>array([[[65., 65., 65., ..., 87., 86., 86.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[62., 62., 62., ..., 83., 83., 83.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[65., 65., 65., ..., 78., 78., 77.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[61., 61., 62., ..., 77., 78., 77.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[60., 60., 61., ..., 71., 71., 71.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[69., 69., 69., ..., 77., 77., 77.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]]], shape=(28, 2, 4320))</pre></div></li></ul></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 8MB\n",
       "Dimensions:         (name: 28, kind: 2, datetime: 4320)\n",
       "Coordinates:\n",
       "  * name            (name) <U9 1kB 'vlinder01' 'vlinder02' ... 'vlinder28'\n",
       "    lat             (name) float64 224B 50.98 51.02 51.32 ... 51.16 51.06 51.04\n",
       "    lon             (name) float64 224B 3.816 3.71 4.952 ... 4.998 3.728 3.77\n",
       "    school          (name) <U29 3kB 'UGent' 'UGent' ... 'GO! Ath.'\n",
       "  * kind            (kind) <U5 40B 'obs' 'label'\n",
       "  * datetime        (datetime) datetime64[ns] 35kB 2022-09-01 ... 2022-09-15T...\n",
       "    altitude        float64 8B nan\n",
       "    LCZ             float64 8B nan\n",
       "Data variables:\n",
       "    temp            (name, kind, datetime) float64 2MB 18.8 18.8 ... 0.0 0.0\n",
       "    wind_direction  (name, kind, datetime) float64 2MB 65.0 75.0 ... 0.0 0.0\n",
       "    wind_speed      (name, kind, datetime) float64 2MB 1.556 1.528 ... 0.0 0.0\n",
       "    humidity        (name, kind, datetime) float64 2MB 65.0 65.0 ... 0.0 0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the entire dataset to an xarray Dataset\n",
    "xr_dataset = dataset.to_xr()\n",
    "xr_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37",
   "metadata": {},
   "source": [
    "### Quality control\n",
    "\n",
    "For more details, refer to the [Quality Control Example Notebook](qc_example.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38",
   "metadata": {},
   "source": [
    "### Extracting data from Google Earth Engine\n",
    "\n",
    "For an introduction to extracting data for GEE, we refer to the [Using Google Earth Engine demo](gee_example.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39",
   "metadata": {},
   "source": [
    "### Filling gaps\n",
    "\n",
    "For an introduction to filling gaps, we refer to the [Filling gaps demo](filling_example.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40",
   "metadata": {},
   "source": [
    "### Analysis \n",
    "\n",
    "For an introduction to analyzing your dataset, we refer to the [Analysis demo](analysis_example.ipynb)."
   ]
  }
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