{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "23ebf1ed",
   "metadata": {},
   "source": [
    "# Seewassertemperatur aus Satellitendaten abgeleitet\n",
    "\n",
    "Dieses Datenset liefert tägliche Werte der Oberflächenwassertemperatur von Seen (LSWT) am Vormittag, abgeleitet aus Satellitendaten, zusammen mit zugehöriger Unsicherheit und Qualitätsstufen. Die Daten, die von den ATSR- und AVHRR-Sensoren stammen, wurden zur Konsistenzanpassung bias-korrigiert und können aufgrund fehlender Beobachtungen Lücken enthalten. LSWT ist eine essenzielle Klimavariable, die für das Verständnis der Seeökologie, hydrologischer Prozesse und großräumiger Klimawechselwirkungen von entscheidender Bedeutung ist. Die Datenentwicklung wurde durch das UK NERC GloboLakes-Projekt unterstützt, und zukünftige Verbesserungen erfolgen im Rahmen der ESA Climate Change Initiative.\n",
    "\n",
    "**Schnellnavigation:**\n",
    "* [Herunterladen und Entpacken des Datensatzes](#herunterladen-und-entpacken-des-datensatzes)\n",
    "* [NetCDF4-Dateien zu einer einzigen NetCDF4-Datei zusammenführen](#netcdf4-dateien-zu-einer-einzigen-netcdf4-datei-zusammenfuhren)\n",
    "* [Untersuchen der Metadaten der netCDF4-Datei](#untersuchen-der-metadaten-der-netcdf4-datei)\n",
    "* [Exportieren der Zeitreihe im CSV-Format](#exportieren-der-zeitreihe-im-csv-format)\n",
    "* [Analyse und Visualisierung Optionen](#analyse-und-visualisierung-optionen)\n",
    "* [Exportieren der NetCDF4-Datei nach GeoTIFF](#exportieren-der-netcdf4-datei-nach-geotiff)\n",
    "* [Zusätzliche Visualisierung mit einem Kalenderdiagramm](#zusatzliche-visualisierung-mit-einem-kalenderdiagramm)\n",
    "\n",
    "**Information on Dataset:**\n",
    "* Quelle: <a href=\"https://cds.climate.copernicus.eu/datasets/satellite-lake-water-temperature?tab=overview\" target=\"_blank\">Satellite Lake Water Temperature</a>\n",
    "* Author: T. Tewes (City of Konstanz)\n",
    "* Notebook Version: 1.3 (Updated: January 17, 2025)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dda6192",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Laden Sie bitte über den unten stehenden Link eine Kopie dieses Notebooks herunter, um es lokal auf Ihrem System auszuprobieren:\n",
    "\n",
    "<div class=\"download-button\"> <a href=\"/CoKLIMAx-II/jupyter_execute/notebooks/kurs2_einfache_cds/02-satellite-lake-water-temperature.ipynb\" download>⇩ Satellite Lake Water Temperature</a> </div>\n",
    "\n",
    "Öffnen Sie es nach dem Download in Jupyter Notebook und beginnen Sie mit der schrittweisen Ausführung des Codes.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dacce046",
   "metadata": {},
   "source": [
    "## 1. Festlegen der Pfade und Arbeitsverzeichnisse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a74e11ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "''' ---- Verzeichnisse hier angeben ---- '''\n",
    "download_folder = r\".\\data\\satellite-lake-water-temperature\\download\"\n",
    "working_folder = r\".\\data\\satellite-lake-water-temperature\\working\"\n",
    "geotiff_folder = r\".\\data\\satellite-lake-water-temperature\\geotiff\"\n",
    "csv_folder = r\".\\data\\satellite-lake-water-temperature\\csv\"\n",
    "output_folder = r\".\\data\\satellite-lake-water-temperature\\output\"\n",
    "''' ----- Ende der Angaben ---- '''\n",
    "\n",
    "os.makedirs(download_folder, exist_ok=True)\n",
    "os.makedirs(working_folder, exist_ok=True)\n",
    "os.makedirs(geotiff_folder, exist_ok=True)\n",
    "os.makedirs(csv_folder, exist_ok=True)\n",
    "os.makedirs(output_folder, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c4fbb35",
   "metadata": {},
   "source": [
    "## 2. Herunterladen und Entpacken des Datensatzes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9519e01",
   "metadata": {},
   "source": [
    "### 2.1 Authentifizierung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "507616bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cdsapi\n",
    "\n",
    "def main():\n",
    "    # API-Key für die Authentifizierung\n",
    "    api_key = \"fdae60fd-35d4-436f-825c-c63fedab94a4\"\n",
    "    api_url = \"https://cds.climate.copernicus.eu/api\"\n",
    "\n",
    "    # Erstellung des CDS-API-Clients\n",
    "    client = cdsapi.Client(url=api_url, key=api_key)\n",
    "    return client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233eefdd",
   "metadata": {},
   "source": [
    "### 2.2 Definieren Sie die „request“ und laden Sie den Datensatz herunter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c163b0fd",
   "metadata": {},
   "source": [
    "Definieren Sie zusätzliche Anfragefelder, um sicherzustellen, dass die Anfrage innerhalb der Dateigrößenbeschränkung bleibt. Bei der Arbeit mit Geodaten oder APIs, die Karten- oder Satellitenbilder zurückgeben, kann die Begrenzung des geografischen Interessengebiets verhindern, dass Anfragen zu groß werden und die Datei- oder Verarbeitungsgrenzen überschreiten. Begrenzungsrahmen (Bounding Boxes) werden verwendet, um das geografische Gebiet für solche Anfragen festzulegen.\n",
    "\n",
    "Die untenstehenden Koordinaten wurden mit dem Tool <a href=\"https://str-ucture.github.io/bbox-extractor/\" target=\"_blank\">BBox Extractor</a> ermittelt.\n",
    "\n",
    "*BBox Extractor ist ein webbasiertes Tool, das Benutzern hilft, interaktiv Begrenzungsrahmen-Koordinaten im WGS84-Format (Breite/Länge) auszuwählen und zu generieren. Dies ist besonders nützlich für APIs oder Datensätze, die eine Eingabe eines geografischen Gebiets erfordern*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "657e4886",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definieren der Begrenzungsrahmen-Koordinaten (WGS84-Format) für die Region Bodensee.\n",
    "# Das Koordinatenformat lautet: [Norden, Westen, Süden, Osten]\n",
    "bbox_wgs84_constance = [48.0, 8.7, 47.3, 9.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a942975f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geben Sie das Jahr von Interesse für die Datenanforderung an.\n",
    "# Die entsprechende Datenversion hängt vom Jahr ab.\n",
    "year = 2007\n",
    "\n",
    "# Bestimmen Sie die Datenversion basierend auf dem Jahr:\n",
    "# Version \"4_5_1\" wird für Jahre bis 2020 verwendet, und \"4_5_2\" für spätere Jahre.\n",
    "if 1900 <= year <= 2100:  # Überprüfen Sie den gültigen Jahresbereich für Robustheit.\n",
    "    version = \"4_5_1\" if year <= 2020 else \"4_5_2\"\n",
    "else:\n",
    "    raise ValueError(f\"Ungültiges Jahr: {year}. Bitte geben Sie ein Jahr zwischen 1900 und 2100 an.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a199e44d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition des Datensatzes und der Request-Parameter\n",
    "dataset = \"satellite-lake-water-temperature\"\n",
    "request = {\n",
    "    \"variable\": \"all\",\n",
    "    \"year\": [f\"{year}\"],\n",
    "    \"month\": [\n",
    "        \"01\", \"02\", \"03\",\n",
    "        \"04\", \"05\", \"06\",\n",
    "        \"07\", \"08\", \"09\",\n",
    "        \"10\", \"11\", \"12\"\n",
    "    ],\n",
    "    \"day\": [\n",
    "        \"01\", \"02\", \"03\",\n",
    "        \"04\", \"05\", \"06\",\n",
    "        \"07\", \"08\", \"09\",\n",
    "        \"10\", \"11\", \"12\",\n",
    "        \"13\", \"14\", \"15\",\n",
    "        \"16\", \"17\", \"18\",\n",
    "        \"19\", \"20\", \"21\",\n",
    "        \"22\", \"23\", \"24\",\n",
    "        \"25\", \"26\", \"27\",\n",
    "        \"28\", \"29\", \"30\",\n",
    "        \"31\"\n",
    "    ],\n",
    "    \"version\": version,\n",
    "    \"area\": bbox_wgs84_constance\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cff16f16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datensatz bereits heruntergeladen.\n"
     ]
    }
   ],
   "source": [
    "# Führen Sie es aus, um den Datensatz herunterzuladen:\n",
    "def main_retrieve():\n",
    "    dataset_filename = f\"{dataset}_{request['year'][0]}.zip\"\n",
    "    dataset_filepath = os.path.join(download_folder, dataset_filename)\n",
    "    \n",
    "    # Den Datensatz nur herunterladen, wenn er noch nicht heruntergeladen wurde\n",
    "    if not os.path.isfile(dataset_filepath):\n",
    "        # Rufen Sie den CDS-Client nur auf, wenn der Datensatz noch nicht heruntergeladen wurde.\n",
    "        client = main()\n",
    "        # Den Datensatz mit den definierten Anforderungsparametern herunterladen\n",
    "        client.retrieve(dataset, request, dataset_filepath)\n",
    "    else:\n",
    "        print(\"Datensatz bereits heruntergeladen.\")\n",
    "        \n",
    "if __name__ == \"__main__\":\n",
    "    main_retrieve()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df293df1",
   "metadata": {},
   "source": [
    "### 2.3 Extrahieren Sie die ZIP-Dateien in Ordner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90fd0d19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ordner ist nicht leer. Entpacken überspringen.\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "\n",
    "# Erstellen des Dateinamens und des Dateipfads für die ZIP-Datei des Datensatzes\n",
    "dataset_filename = f\"{dataset}_{year}.zip\"\n",
    "dataset_filepath = os.path.join(download_folder, dataset_filename)\n",
    "\n",
    "# Erstellen Sie einen Ordner zum Extrahieren der ZIP-Datei basierend auf dem ausgewählten Jahr\n",
    "extract_folder = os.path.join(working_folder, str(year))\n",
    "\n",
    "# Entpacken der ZIP-Datei\n",
    "try:\n",
    "    os.makedirs(extract_folder, exist_ok=True)\n",
    "    \n",
    "    if not os.listdir(extract_folder):\n",
    "        # Versuchen Sie, die ZIP-Datei zu öffnen und zu extrahieren\n",
    "        with zipfile.ZipFile(dataset_filepath, 'r') as zip_ref:\n",
    "            zip_ref.extractall(extract_folder)\n",
    "            print(f\"Dateien erfolgreich extrahiert nach: {extract_folder}\")\n",
    "    else:\n",
    "        print(\"Ordner ist nicht leer. Entpacken überspringen.\")\n",
    "except FileNotFoundError:\n",
    "    print(f\"Fehler: Die Datei {dataset_filepath} wurde nicht gefunden.\")\n",
    "except zipfile.BadZipFile:\n",
    "    print(f\"Fehler: Die Datei {dataset_filepath} ist keine gültige ZIP-Datei.\")\n",
    "except Exception as e:\n",
    "    print(f\"Ein unerwarteter Fehler ist aufgetreten: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d6d514",
   "metadata": {},
   "source": [
    "## 3. NetCDF4-Dateien zu einer einzigen NetCDF4-Datei zusammenführen\n",
    "\n",
    "Viele **jährliche Datensätze** werden als tägliche NetCDF4-Dateien bereitgestellt, wobei jede Datei einen Tag des Jahres repräsentiert *(365 Dateien für normale Jahre, 366 Dateien für Schaltjahre)*. Die Verwaltung dieser zahlreichen Dateien kann mühsam sein, insbesondere beim Datenzugriff oder bei der Visualisierung.\n",
    "\n",
    "Um Arbeitsabläufe zu vereinfachen und die Effizienz der Datenverarbeitung zu verbessern, werden alle täglichen Datensätze eines bestimmten Jahres in einer **einzigen jährlichen NetCDF4-Datei** zusammengeführt. Diese Konsolidierung erleichtert die spätere Datenverarbeitung, beispielsweise für die Visualisierung oder statistische Auswertungen.\n",
    "\n",
    "> Wichtig: Tägliche Datensätze können spärliche oder fehlende Daten enthalten. Daher kann die zusammengeführte NetCDF4-Datei, die zusammengeführte GeoTIFF-Datei oder einzelne GeoTIFF-Dateien leere Zeitpunkte enthalten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00e34cfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zusammengeführte NetCDF-Datei für das Jahr 2007 existiert bereits. Überspringe Zusammenführung.\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "# Definiere den Dateipfad für die zusammengeführte NetCDF-Datei (1 Datei pro Jahr)\n",
    "nc_filepath_merged = os.path.join(output_folder,f\"{dataset}_{year}.nc\")\n",
    "\n",
    "# Überprüfe, ob die zusammengeführte Datei bereits existiert\n",
    "if not os.path.isfile(nc_filepath_merged):\n",
    "    # Liste alle NetCDF-Dateien im Extrakt-Ordner auf\n",
    "    filename_list = os.listdir(extract_folder)\n",
    "\n",
    "    if not filename_list:\n",
    "        print(f\"Keine NetCDF-Dateien im Ordner {extract_folder} gefunden.\")\n",
    "    else:\n",
    "        try:\n",
    "            # Öffne und verknüpfe alle NetCDF-Dateien entlang der 'time'-Dimension\n",
    "            datasets = [xr.open_dataset(os.path.join(extract_folder, f)) for f in filename_list]\n",
    "            merged_dataset = xr.concat(datasets, dim='time')\n",
    "            \n",
    "            # Speichere den zusammengeführten Datensatz in der neuen NetCDF-Datei\n",
    "            merged_dataset.to_netcdf(nc_filepath_merged)\n",
    "            print(f\"Neue NetCDF4-Datei erstellt unter {nc_filepath_merged} für das Jahr {year}\")\n",
    "        \n",
    "        except Exception as e:\n",
    "            print(f\"Fehler bei der Dateiverarbeitung: {e}\")\n",
    "else:\n",
    "    print(f\"Zusammengeführte NetCDF-Datei für das Jahr {year} existiert bereits. Überspringe Zusammenführung.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b517ae",
   "metadata": {},
   "source": [
    "## 4. Untersuchen der Metadaten der NetCDF4-Datei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2dc9c968",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verfügbare Variablen: ['lake_surface_water_temperature', 'lswt_uncertainty', 'lswt_quality_level', 'lswt_obs_instr', 'lswt_flag_bias_correction', 'lakeid_CCI', 'lakeid_GloboLakes', 'lat', 'lon', 'time']\n"
     ]
    }
   ],
   "source": [
    "import netCDF4 as nc\n",
    "\n",
    "# Definieren Sie den Dateipfad für den zusammengeführten netCDF-Datensatz\n",
    "nc_filename = f\"satellite-lake-water-temperature_{year}.nc\"\n",
    "nc_filepath = os.path.join(output_folder, nc_filename)\n",
    "\n",
    "# Öffnen der NetCDF-Datei im Lesemodus\n",
    "nc_dataset = nc.Dataset(nc_filepath_merged, mode=\"r\")\n",
    "\n",
    "# Auflisten aller Variablen im Datensatz\n",
    "variables_list = nc_dataset.variables.keys()\n",
    "print(f\"Verfügbare Variablen: {list(variables_list)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b6a6922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beschreibung</th>\n",
       "      <th>Bemerkungen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Variablename</td>\n",
       "      <td>lake_surface_water_temperature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Datentyp</td>\n",
       "      <td>int16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Form</td>\n",
       "      <td>(365, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Variableinfo</td>\n",
       "      <td>lake_surface_water_temperature(time, lat, lon)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Einheiten</td>\n",
       "      <td>kelvin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Langer Name</td>\n",
       "      <td>lake surface skin water temperature</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Beschreibung                                     Bemerkungen\n",
       "0  Variablename                  lake_surface_water_temperature\n",
       "1      Datentyp                                           int16\n",
       "2          Form                                   (365, 14, 24)\n",
       "3  Variableinfo  lake_surface_water_temperature(time, lat, lon)\n",
       "4     Einheiten                                          kelvin\n",
       "5   Langer Name             lake surface skin water temperature"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Variablennamen aus vorhandenen Variablen definieren und Variablendaten lesen\n",
    "variable_name = 'lake_surface_water_temperature'\n",
    "variable_data = nc_dataset[variable_name]\n",
    "\n",
    "# Erstellen einer Zusammenfassung der Hauptvariablen\n",
    "summary = {\n",
    "    \"Variablename\": variable_name,\n",
    "    \"Datentyp\": variable_data.dtype,\n",
    "    \"Form\": variable_data.shape,\n",
    "    \"Variableinfo\": f\"{variable_name}({', '.join(variable_data.dimensions)})\",\n",
    "    \"Einheiten\": getattr(variable_data, \"units\", \"N/A\"),\n",
    "    \"Langer Name\": getattr(variable_data, \"long_name\", \"N/A\"),\n",
    "}\n",
    "\n",
    "# Anzeigen der Zusammenfassung des Datensatzes als DataFrame zur besseren Visualisierung\n",
    "nc_summary = pd.DataFrame(list(summary.items()), columns=['Beschreibung', 'Bemerkungen'])\n",
    "\n",
    "# Anzeigen des Zusammenfassungs-DataFrames\n",
    "nc_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0bbc8e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nc_variablen</th>\n",
       "      <th>einheit</th>\n",
       "      <th>form</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lake_surface_water_temperature</td>\n",
       "      <td>kelvin</td>\n",
       "      <td>(365, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lswt_uncertainty</td>\n",
       "      <td>kelvin</td>\n",
       "      <td>(365, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lswt_quality_level</td>\n",
       "      <td>N/A</td>\n",
       "      <td>(365, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lswt_obs_instr</td>\n",
       "      <td>N/A</td>\n",
       "      <td>(365, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lswt_flag_bias_correction</td>\n",
       "      <td>N/A</td>\n",
       "      <td>(365, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lakeid_CCI</td>\n",
       "      <td>1</td>\n",
       "      <td>(365, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lakeid_GloboLakes</td>\n",
       "      <td>1</td>\n",
       "      <td>(365, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lat</td>\n",
       "      <td>degrees_north</td>\n",
       "      <td>(14,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lon</td>\n",
       "      <td>degrees_east</td>\n",
       "      <td>(24,)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>time</td>\n",
       "      <td>seconds since 1970-01-01</td>\n",
       "      <td>(365,)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     nc_variablen                   einheit           form\n",
       "0  lake_surface_water_temperature                    kelvin  (365, 14, 24)\n",
       "1                lswt_uncertainty                    kelvin  (365, 14, 24)\n",
       "2              lswt_quality_level                       N/A  (365, 14, 24)\n",
       "3                  lswt_obs_instr                       N/A  (365, 14, 24)\n",
       "4       lswt_flag_bias_correction                       N/A  (365, 14, 24)\n",
       "5                      lakeid_CCI                         1  (365, 14, 24)\n",
       "6               lakeid_GloboLakes                         1  (365, 14, 24)\n",
       "7                             lat             degrees_north          (14,)\n",
       "8                             lon              degrees_east          (24,)\n",
       "9                            time  seconds since 1970-01-01         (365,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drucken Sie eine Zusammenfassung aller Variablen des Datensatzes\n",
    "rows = []\n",
    "for variable in variables_list:\n",
    "    try:\n",
    "        var_obj = nc_dataset.variables[variable]\n",
    "        unit = getattr(var_obj, 'units', 'N/A')\n",
    "        shape = var_obj.shape\n",
    "        rows.append({\n",
    "            \"nc_variablen\": variable,\n",
    "            \"einheit\": unit,\n",
    "            \"form\": shape\n",
    "        })\n",
    "    except Exception as e:\n",
    "        print(f\"Fehler bei der Verarbeitung der Variable {variable}: {e}\")\n",
    "\n",
    "# Erstelle ein DataFrame\n",
    "df = pd.DataFrame(rows)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd664fe",
   "metadata": {},
   "source": [
    "## 5. Exportieren der Zeitreihe im CSV-Format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6844c20",
   "metadata": {},
   "source": [
    "### 5.1 Definieren Sie eine Funktion zur Berechnung des Tagesdurchschnitts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a135acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Funktion zur Konvertierung von NetCDF-Daten in ein Pandas DataFrame\n",
    "def netcdf_to_dataframe(nc_file):\n",
    "    \"\"\"\n",
    "    Konvertiert eine NetCDF-Datei mit Daten zur Wassertemperatur der Seeoberfläche (LSWT)\n",
    "    in ein Pandas DataFrame mit berechneten Statistiken.\n",
    "\n",
    "    Parameter:\n",
    "    nc_file (str): Pfad zur NetCDF-Datei.\n",
    "\n",
    "    Rückgabe:\n",
    "    pd.DataFrame: Ein DataFrame mit Zeit, mittlerer Temperatur, Standardabweichung, \n",
    "                  Unsicherheit, mittlerer Qualitätsstufe und Anzahl der Nicht-Null-Pixel.\n",
    "    \"\"\"\n",
    "    # Öffne das NetCDF-Dataset im Lesemodus\n",
    "    with nc.Dataset(nc_file, \"r\") as nc_dataset:\n",
    "        # Extrahiere und dekodiere die Zeitvariable unter Berücksichtigung des Kalenders und der Einheiten\n",
    "        time_var = nc_dataset.variables[\"time\"]\n",
    "        time_units = time_var.units\n",
    "        time_calendar = getattr(time_var, \"calendar\", \"standard\")\n",
    "        cftime = nc.num2date(time_var[:], units=time_units, calendar=time_calendar)\n",
    "        datetime_cftime = [t.strftime(\"%Y-%m-%d %H:%M:%S\") for t in cftime]\n",
    "\n",
    "        # Extrahiere Temperaturdaten und zugehörige Einheiten\n",
    "        temperature_data = nc_dataset.variables[\"lake_surface_water_temperature\"][:]\n",
    "        temperature_data_units = nc_dataset.variables[\"lake_surface_water_temperature\"].units\n",
    "\n",
    "        # Berechnen Sie die Durchschnittstemperatur und die Standardabweichung für jeden Zeitschritt.\n",
    "        # Gemittelt über die räumlichen Dimensionen\n",
    "        temperature_mean_list = np.nanmean(temperature_data, axis=(1, 2))\n",
    "        temperature_std_list = np.nanstd(temperature_data, axis=(1, 2))\n",
    "        \n",
    "        # Zähle die Anzahl gültiger (nicht-NaN) Pixel für jeden Zeitschritt\n",
    "        nonzero_count_list = np.count_nonzero(~np.isnan(temperature_data), axis=(1,2))\n",
    "\n",
    "        # Extrahiere Unsicherheitsdaten und deren Einheiten\n",
    "        lswt_uncertainty = nc_dataset.variables[\"lswt_uncertainty\"][:]\n",
    "        lswt_uncertainty_units = nc_dataset.variables[\"lswt_uncertainty\"].units\n",
    "\n",
    "        # Berechnen Sie die Unsicherheit der Durchschnittstemperatur\n",
    "        lswt_uncertainty_squared = np.nanmean(lswt_uncertainty**2, axis=(1, 2))\n",
    "        lswt_mean_uncertainty = np.sqrt(lswt_uncertainty_squared)\n",
    "        \n",
    "        # Extrahiere und berechne den mittleren Qualitätswert für jeden Zeitschritt\n",
    "        lswt_quality_level = nc_dataset.variables[\"lswt_quality_level\"][:]\n",
    "        lswt_quality_level_mean_list = np.nanmean(lswt_quality_level, axis=(1, 2))\n",
    "\n",
    "        # Erstelle ein DataFrame mit den berechneten Statistiken\n",
    "        df = pd.DataFrame(\n",
    "            {\n",
    "                \"Time\": datetime_cftime,\n",
    "                f\"Mittlere Temperatur ({temperature_data_units[0].capitalize()})\": temperature_mean_list,\n",
    "                \"Standardabweichung\": temperature_std_list,\n",
    "                f\"Unsicherheit ({lswt_uncertainty_units[0].capitalize()})\": lswt_mean_uncertainty,\n",
    "                \"Mittlere Qualitätsstufe\": lswt_quality_level_mean_list,\n",
    "                \"Nicht-Null-Anzahl\":nonzero_count_list,\n",
    "            }\n",
    "        )\n",
    "        return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342fe1a3",
   "metadata": {},
   "source": [
    "### 5.2 Erstellen des DataFrames für Jahresdaten und Export als CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "281ac465",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datei existiert bereits unter .\\data\\satellite-lake-water-temperature\\csv\\satellite-lake-water-temperature_daily-mean_2007.csv.\n",
      "Überspringen den Export.\n",
      "Lesen bestehende CSV-Datei ein...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Mittlere Temperatur (K)</th>\n",
       "      <th>Standardabweichung</th>\n",
       "      <th>Unsicherheit (K)</th>\n",
       "      <th>Mittlere Qualitätsstufe</th>\n",
       "      <th>Nicht-Null-Anzahl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-01-02 12:00:00</td>\n",
       "      <td>278.46</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.43</td>\n",
       "      <td>4.86</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-01-05 12:00:00</td>\n",
       "      <td>275.98</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.29</td>\n",
       "      <td>3.60</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-01-06 12:00:00</td>\n",
       "      <td>278.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-01-11 12:00:00</td>\n",
       "      <td>277.96</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.18</td>\n",
       "      <td>3.14</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-01-14 12:00:00</td>\n",
       "      <td>278.68</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.19</td>\n",
       "      <td>4.30</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Time  Mittlere Temperatur (K)  Standardabweichung  \\\n",
       "0  2007-01-02 12:00:00                   278.46                0.11   \n",
       "1  2007-01-05 12:00:00                   275.98                0.58   \n",
       "2  2007-01-06 12:00:00                   278.64                0.00   \n",
       "3  2007-01-11 12:00:00                   277.96                1.43   \n",
       "4  2007-01-14 12:00:00                   278.68                0.76   \n",
       "\n",
       "   Unsicherheit (K)  Mittlere Qualitätsstufe  Nicht-Null-Anzahl  \n",
       "0              0.43                     4.86               7.00  \n",
       "1              0.29                     3.60               5.00  \n",
       "2              0.34                     4.00               1.00  \n",
       "3              0.18                     3.14               7.00  \n",
       "4              0.19                     4.30              20.00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Definieren den CSV-Dateinamen und den Dateipfad für die Ausgabe\n",
    "csv_filename = f\"{dataset}_daily-mean_{year}.csv\"\n",
    "csv_filepath = os.path.join(csv_folder, csv_filename)\n",
    "\n",
    "# Exportieren das DataFrame als CSV, falls es noch nicht existiert\n",
    "if not os.path.isfile(csv_filepath):\n",
    "    dataframe = netcdf_to_dataframe(nc_filepath_merged)\n",
    "    filtered_dataframe = dataframe.dropna().reset_index(drop=True)\n",
    "\n",
    "    filtered_dataframe.to_csv(csv_filepath, sep=\",\", encoding='utf8', index=False)\n",
    "    print(f\"Gefilterte Daten erfolgreich exportiert nach {csv_filepath}\")\n",
    "else:\n",
    "    print(f\"Datei existiert bereits unter {csv_filepath}.\\nÜberspringen den Export.\")\n",
    "    print(\"Lesen bestehende CSV-Datei ein...\")\n",
    "    filtered_dataframe = pd.read_csv(csv_filepath)\n",
    "\n",
    "# Ändere die Pandas-Anzeigeoptionen\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "\n",
    "# Zeige das DataFrame an\n",
    "filtered_dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e67c307",
   "metadata": {},
   "source": [
    "## 6. Analyse und Visualisierung Optionen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4056c789",
   "metadata": {},
   "source": [
    "### 6.1 Visualisierung des Tagesdurchschnitts mit Liniendiagramm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef382804",
   "metadata": {},
   "source": [
    "> Hinweis: Aufgrund der begrenzten täglichen/monatlichen Daten für den Datensatz 2023 funktionieren **Plots** nicht richtig."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f757c022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import DateFormatter, MonthLocator \n",
    "import matplotlib.ticker as ticker\n",
    "import math\n",
    "\n",
    "# Bereite das DataFrame für die Darstellung vor\n",
    "filtered_df = filtered_dataframe.copy()\n",
    "\n",
    "# Konvertiere 'Time' in das Datetime-Format und extrahiere sowie formatiere die 'Date'-Spalte\n",
    "filtered_df['Time'] = pd.to_datetime(filtered_df['Time'])\n",
    "filtered_df['Date'] = filtered_df['Time'].dt.date\n",
    "\n",
    "# Bestimme den Wertebereich für die y-Achse, gerundet auf die nächste Zehnerstelle\n",
    "vmax = math.ceil(filtered_df['Mittlere Temperatur (K)'].max() / 10) * 10\n",
    "vmin = math.floor(filtered_df['Mittlere Temperatur (K)'].min() / 10) * 10\n",
    "\n",
    "# Erstelle die Figur und Achsen\n",
    "fig, ax = plt.subplots(figsize=(12, 6), facecolor='#f1f1f1', edgecolor='k')\n",
    "\n",
    "# Plotten die mittlere Temperatur\n",
    "ax.plot(filtered_df['Date'],\n",
    "        filtered_df['Mittlere Temperatur (K)'],\n",
    "        marker='o',\n",
    "        markersize=4.5,\n",
    "        linestyle='--',\n",
    "        color='#1877F2',\n",
    "        label=\"Oberflächenwassertemperatur\",\n",
    "        )\n",
    "\n",
    "# Formatieren der x-Achse für bessere Lesbarkeit\n",
    "ax.xaxis.set_major_locator(MonthLocator())\n",
    "ax.xaxis.set_major_formatter(DateFormatter('%b'))\n",
    "ax.tick_params(axis='x', which='major', length=4, direction='inout', width=2)\n",
    "ax.tick_params(axis='x', which='minor', length=3, direction='inout')\n",
    "\n",
    "# Setzen der y-Achsen-Grenzen\n",
    "ax.set_ylim(vmin, vmax)\n",
    "\n",
    "# Setzen der Achsenbeschriftungen und Titel des Diagramms\n",
    "ax.set_xlabel('Monate', fontsize=12)\n",
    "ax.set_ylabel('Temperatur (K)', fontsize=12)\n",
    "ax.set_title(f'Oberflächenwassertemperatur des Bodensees, {year}', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Hinzufügen eines Rasters zum Diagramm und Formatierung der y-Achse\n",
    "ax.grid(visible=True, color='#b0b0b0', linestyle='--', linewidth=0.8, alpha=0.6)\n",
    "ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.2f'))\n",
    "\n",
    "# Hinzufügen einer Beschreibung und Quelleninformation\n",
    "plt.figtext(\n",
    "    0.4,\n",
    "    -0.05,\n",
    "    (\n",
    "        'Beschreibung: Oberflächenwassertemperatur des Bodensees, ermittelt aus Satellitendaten des CDS.\\n'\n",
    "        'Quelle: Copernicus Climate Change Service, Climate Data Store, (2020): Oberflächenwassertemperatur von Seen '\n",
    "        'von 1995 bis heute, abgeleitet aus Satellitenbeobachtungen. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). '\n",
    "        'DOI: 10.24381/cds.5714c668 (Zugriff am 22-01-2025)'\n",
    "    ),\n",
    "    ha='left',\n",
    "    va='center',\n",
    "    fontsize=9,\n",
    "    wrap=True,\n",
    "    backgroundcolor='w',\n",
    ")\n",
    "\n",
    "ax.legend(loc='upper left')\n",
    "\n",
    "# Layout anpassen und das Diagramm anzeigen\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f80848e",
   "metadata": {},
   "source": [
    "### 6.2 Visualisierung des monatlichen Durchschnitts mit Liniendiagramm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb0d4ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Mittlere Temperatur (K)</th>\n",
       "      <th>Standardabweichung</th>\n",
       "      <th>Unsicherheit (K)</th>\n",
       "      <th>Mittlere Qualitätsstufe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>277.77</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.31</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>277.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.23</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-03-01</td>\n",
       "      <td>279.02</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.28</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-04-01</td>\n",
       "      <td>285.22</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>288.30</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.42</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Mittlere Temperatur (K)  Standardabweichung  Unsicherheit (K)  \\\n",
       "0 2007-01-01                   277.77                0.63              0.31   \n",
       "1 2007-02-01                   277.36                0.35              0.23   \n",
       "2 2007-03-01                   279.02                1.16              0.28   \n",
       "3 2007-04-01                   285.22                1.27              0.27   \n",
       "4 2007-05-01                   288.30                1.75              0.42   \n",
       "\n",
       "   Mittlere Qualitätsstufe  \n",
       "0                     4.16  \n",
       "1                     4.14  \n",
       "2                     3.70  \n",
       "3                     3.41  \n",
       "4                     3.28  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bereite das DataFrame für die Darstellung vor, indem monatliche Aggregation und Fehlerfortpflanzung durchgeführt werden\n",
    "\n",
    "# Gruppiere nach monatlichen Perioden anhand der 'Date'-Spalte\n",
    "filtered_df_monthly = (\n",
    "    filtered_df.groupby(pd.PeriodIndex(filtered_df[\"Date\"], freq=\"M\"))[[  \n",
    "        \"Mittlere Temperatur (K)\",  \n",
    "        \"Standardabweichung\",  \n",
    "        \"Unsicherheit (K)\",  \n",
    "        \"Mittlere Qualitätsstufe\"  \n",
    "    ]]  \n",
    "    .agg({  \n",
    "        \"Mittlere Temperatur (K)\": \"mean\",  \n",
    "        \"Standardabweichung\": lambda x: (x**2).mean()**0.5,  \n",
    "        \"Unsicherheit (K)\": lambda x: (x**2).mean()**0.5,  \n",
    "        \"Mittlere Qualitätsstufe\": \"mean\"  \n",
    "    })  \n",
    "    .reset_index()  \n",
    ")\n",
    "\n",
    "# Konvertiere den PeriodIndex zurück in Zeitstempel für eine konsistente Datumsverarbeitung\n",
    "filtered_df_monthly['Date'] = filtered_df_monthly['Date'].dt.to_timestamp()\n",
    "\n",
    "# Zeige das DataFrame an\n",
    "filtered_df_monthly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17f46c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Erstelle die Grafik und Achsen\n",
    "fig, ax = plt.subplots(figsize=(12, 6), facecolor='#f1f1f1', edgecolor='k')\n",
    "\n",
    "# Plotte die monatliche mittlere Temperatur mit Fehlerbalken\n",
    "ax.errorbar(\n",
    "    filtered_df_monthly['Date'],\n",
    "    filtered_df_monthly['Mittlere Temperatur (K)'],\n",
    "    yerr=filtered_df_monthly['Standardabweichung'],\n",
    "    fmt='o--',\n",
    "    label='Mittlere Temperatur ± Unsicherheit',\n",
    "    capsize=4,\n",
    "    elinewidth=1.5,\n",
    "    capthick=1.5,\n",
    ")\n",
    "\n",
    "# Formatieren der x-Achse für bessere Lesbarkeit\n",
    "ax.xaxis.set_major_locator(MonthLocator())\n",
    "ax.xaxis.set_major_formatter(DateFormatter('%b'))\n",
    "ax.tick_params(axis='x', which='major', length=4, direction='inout', width=2)\n",
    "ax.tick_params(axis='x', which='minor', length=3, direction='inout')\n",
    "\n",
    "# Setzen der y-Achsen-Grenzen\n",
    "ax.set_ylim(vmin, vmax)\n",
    "\n",
    "# Setzen der Achsenbeschriftungen und Titel des Diagramms\n",
    "ax.set_xlabel('Monate', fontsize=12)\n",
    "ax.set_ylabel('Temperatur (K)', fontsize=12)\n",
    "ax.set_title(f'Monatliche durchschnittliche Oberflächenwassertemperatur des Bodensees, {year}', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Hinzufügen eines Rasters zum Diagramm und Formatierung der y-Achse\n",
    "ax.grid(visible=True, color='#b0b0b0', linestyle='--', linewidth=0.8, alpha=0.6)\n",
    "ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.2f'))\n",
    "\n",
    "# Hinzufügen einer Beschreibung und Quelleninformation\n",
    "plt.figtext(\n",
    "    0.4,\n",
    "    -0.05,\n",
    "    (\n",
    "        'Beschreibung: Oberflächenwassertemperatur des Bodensees, ermittelt aus Satellitendaten des CDS.\\n'\n",
    "        'Quelle: Copernicus Climate Change Service, Climate Data Store, (2020): Oberflächenwassertemperatur von Seen '\n",
    "        'von 1995 bis heute, abgeleitet aus Satellitenbeobachtungen. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). '\n",
    "        'DOI: 10.24381/cds.5714c668 (Zugriff am 22-01-2025)'\n",
    "    ),\n",
    "    ha='left',\n",
    "    va='center',\n",
    "    fontsize=9,\n",
    "    wrap=True,\n",
    "    backgroundcolor='w',\n",
    ")\n",
    "\n",
    "ax.legend(loc='upper left')\n",
    "\n",
    "# Layout anpassen und das Diagramm anzeigen\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98916fb7",
   "metadata": {},
   "source": [
    "### 6.3 Visualisierung des monatlichen Durchschnitts mit Balkendiagramm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7904e0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Erstellen die Figur und Achsen\n",
    "fig, ax = plt.subplots(figsize=(12, 6), facecolor='#f1f1f1', edgecolor='k')\n",
    "\n",
    "# Plotten die monatliche mittlere Temperatur als Balkendiagramm\n",
    "ax.bar(\n",
    "    filtered_df_monthly['Date'],\n",
    "    filtered_df_monthly['Mittlere Temperatur (K)'],\n",
    "    yerr=filtered_df_monthly['Standardabweichung'],\n",
    "    color='skyblue',\n",
    "    alpha=0.7,\n",
    "    width=25,\n",
    "    label='Mittlere Temperatur ± Unsicherheit',\n",
    "    capsize=5,\n",
    "    error_kw=dict(ecolor='black', lw=1.5),\n",
    ")\n",
    "\n",
    "# Formatieren der x-Achse für bessere Lesbarkeit\n",
    "ax.xaxis.set_major_locator(MonthLocator())\n",
    "ax.xaxis.set_major_formatter(DateFormatter('%b'))\n",
    "ax.tick_params(axis='x', which='major', length=4, direction='inout', width=2)\n",
    "ax.tick_params(axis='x', which='minor', length=3, direction='inout')\n",
    "\n",
    "# Setzen die Grenzen der y-Achse\n",
    "ax.set_ylim(vmin, vmax)\n",
    "\n",
    "# Setzen der Achsenbeschriftungen und Titel des Diagramms\n",
    "ax.set_xlabel('Monate', fontsize=12)\n",
    "ax.set_ylabel('Temperatur (K)', fontsize=12)\n",
    "ax.set_title(f'Monatliche durchschnittliche Oberflächenwassertemperatur des Bodensees, {year}', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Hinzufügen eines Rasters zum Diagramm und Formatierung der y-Achse\n",
    "ax.grid(visible=True, color='#b0b0b0', linestyle='--', linewidth=0.8, alpha=0.6)\n",
    "ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.2f'))\n",
    "\n",
    "# Hinzufügen einer Beschreibung und Quelleninformation\n",
    "plt.figtext(\n",
    "    0.4,\n",
    "    -0.05,\n",
    "    (\n",
    "        'Beschreibung: Oberflächenwassertemperatur des Bodensees, ermittelt aus Satellitendaten des CDS.\\n'\n",
    "        'Quelle: Copernicus Climate Change Service, Climate Data Store, (2020): Oberflächenwassertemperatur von Seen '\n",
    "        'von 1995 bis heute, abgeleitet aus Satellitenbeobachtungen. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). '\n",
    "        'DOI: 10.24381/cds.5714c668 (Zugriff am 22-01-2025)'\n",
    "    ),\n",
    "    ha='left',\n",
    "    va='center',\n",
    "    fontsize=9,\n",
    "    wrap=True,\n",
    "    backgroundcolor='w',\n",
    ")\n",
    "\n",
    "ax.legend(loc='upper left')\n",
    "\n",
    "# Layout anpassen und das Diagramm anzeigen\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7e72a5",
   "metadata": {},
   "source": [
    "## 7. Exportieren der NetCDF4-Datei nach GeoTIFF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5188f58",
   "metadata": {},
   "source": [
    "### 7.1 Exportieren Sie den jährlichen Datensatz als eine GeoTIFF-Datei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e417565f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'lake_surface_water_temperature_2007_merged.tif' existiert bereits. Export überspringen.\n"
     ]
    }
   ],
   "source": [
    "import rasterio  \n",
    "from rasterio.transform import from_origin  \n",
    "import netCDF4 as nc  \n",
    "from tqdm.notebook import tqdm  \n",
    "\n",
    "def main_export_geotiff(nc_file):  \n",
    "    # NetCDF-Datei öffnen und Variable auslesen  \n",
    "    nc_dataset = nc.Dataset(nc_file, mode='r')  \n",
    "    temperature_data = nc_dataset[variable_name]  \n",
    "\n",
    "    # Zeitvariable extrahieren und in ein lesbares Datumsformat konvertieren  \n",
    "    time_var = nc_dataset.variables['time']  \n",
    "    time_units = nc_dataset.variables['time'].units  \n",
    "    time_calendar = getattr(time_var, \"calendar\", \"standard\")  \n",
    "    cftime = nc.num2date(time_var[:], units=time_units, calendar=time_calendar)  \n",
    "\n",
    "    # Räumliche Auflösung berechnen und Rastertransformation definieren  \n",
    "    lat = nc_dataset['lat'][:]  \n",
    "    lon = nc_dataset['lon'][:]  \n",
    "\n",
    "    pixelgröße_lat = (lat.max() - lat.min()) / (len(lat) - 1)  \n",
    "    pixelgröße_lon = (lon.max() - lon.min()) / (len(lon) - 1)  \n",
    "    transform = from_origin(lon.min() - pixelgröße_lon / 2,  \n",
    "                            lat.min() - pixelgröße_lat / 2,  \n",
    "                            pixelgröße_lon,  \n",
    "                            -pixelgröße_lat  \n",
    "                            )  \n",
    "\n",
    "    # Ausgabepfad für das zusammengeführte GeoTIFF definieren  \n",
    "    output_filename = f\"{variable_name}_{year}_merged.tif\"  \n",
    "    output_folder = os.path.join(geotiff_folder, \"merged_geotiff\")  \n",
    "    os.makedirs(output_folder, exist_ok=True)  \n",
    "    output_filepath = os.path.join(output_folder, output_filename)  \n",
    "\n",
    "    if not os.path.isfile(output_filepath):  \n",
    "        # GeoTIFF-Datei mit mehreren Bändern (je eines pro Zeitschritt) erstellen  \n",
    "        with rasterio.open(  \n",
    "            output_filepath,  \n",
    "            \"w\",  \n",
    "            driver=\"GTiff\",  \n",
    "            dtype=str(temperature_data.dtype),  \n",
    "            width=temperature_data.shape[2],  \n",
    "            height=temperature_data.shape[1],  \n",
    "            count=temperature_data.shape[0],  \n",
    "            crs=\"EPSG:4326\",  \n",
    "            nodata=-9999,  \n",
    "            transform=transform,        \n",
    "        ) as dst:  \n",
    "            # Jeden Zeitschritt als separates Band schreiben  \n",
    "            for day_index in tqdm(range(temperature_data.shape[0]),  \n",
    "                                  desc=f\"Zusammengeführte GeoTIFF-Datei für {year} exportieren\"):  \n",
    "                band_data = temperature_data[day_index, :, :]  \n",
    "                dt = cftime[day_index]  \n",
    "                band_desc = f\"{dt.year:04d}-{dt.month:02d}-{dt.day:02d}\"  \n",
    "\n",
    "                # Daten für das aktuelle Band schreiben und Band annotieren  \n",
    "                dst.write(band_data, day_index + 1)  \n",
    "                dst.set_band_description(day_index + 1, band_desc)  \n",
    "    else:  \n",
    "        print(f\"'{output_filename}' existiert bereits. Export überspringen.\")  \n",
    "\n",
    "# Parameter für die Verarbeitung der NetCDF-Datei definieren  \n",
    "nc_filename = f\"{dataset}_{year}.nc\"  \n",
    "nc_filepath_merged = os.path.join(output_folder, nc_filename)  \n",
    "variable_name = 'lake_surface_water_temperature'  \n",
    "\n",
    "# Exportprozess ausführen  \n",
    "main_export_geotiff(nc_filepath_merged)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b8b5ed",
   "metadata": {},
   "source": [
    "### 7.2 Export des Jahresdatensatzes als einzelne GeoTIFF-Dateien  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fc21670",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ordner ist nicht leer. Export überspringen.\n"
     ]
    }
   ],
   "source": [
    "import rasterio  \n",
    "from rasterio.transform import from_origin  \n",
    "import netCDF4 as nc  \n",
    "from tqdm.notebook import tqdm  \n",
    "\n",
    "def main_export_individual_geotiff(nc_file):  \n",
    "    # NetCDF-Datensatz öffnen und Variablendaten auslesen  \n",
    "    nc_dataset = nc.Dataset(nc_file, mode='r')  \n",
    "    temperature_data = nc_dataset[variable_name]  \n",
    "\n",
    "    # Zeitvariable extrahieren und in ein lesbares Datumsformat konvertieren  \n",
    "    time_var = nc_dataset.variables['time']  \n",
    "    time_units = nc_dataset.variables['time'].units  \n",
    "    time_calendar = getattr(time_var, \"calendar\", \"standard\")  \n",
    "    cftime = nc.num2date(time_var[:], units=time_units, calendar=time_calendar)  \n",
    "\n",
    "    # Räumliche Auflösung berechnen und Rastertransformation definieren  \n",
    "    lat = nc_dataset['lat'][:]  \n",
    "    lon = nc_dataset['lon'][:]  \n",
    "\n",
    "    pixelgröße_lat = (lat.max() - lat.min()) / (len(lat) - 1)  \n",
    "    pixelgröße_lon = (lon.max() - lon.min()) / (len(lon) - 1)  \n",
    "    transform = from_origin(lon.min() - pixelgröße_lon / 2,  \n",
    "                            lat.min() - pixelgröße_lat / 2,  \n",
    "                            pixelgröße_lon,  \n",
    "                            -pixelgröße_lat  \n",
    "                            )  \n",
    "\n",
    "    # Ausgabeordner für einzelne GeoTIFF-Dateien des ausgewählten Jahres definieren und erstellen  \n",
    "    year_folder = os.path.join(geotiff_folder, f\"{year}_individual_geotiff\")  \n",
    "    os.makedirs(year_folder, exist_ok=True)  \n",
    "\n",
    "    if len(os.listdir(year_folder)) == 0:  \n",
    "        # Einzelne GeoTIFF-Dateien mit täglichen Zeitschritten erstellen  \n",
    "        for day_index in tqdm(range(temperature_data.shape[0]), desc=f\"GeoTIFF-Dateien für {year} exportieren\"):  \n",
    "            dt = cftime[day_index]  \n",
    "            band_desc = f\"{dt.year:04d}-{dt.month:02d}-{dt.day:02d}\"  \n",
    "\n",
    "            output_filename = f\"{variable_name}_{band_desc}.tif\"  \n",
    "            output_filepath = os.path.join(year_folder, output_filename)  \n",
    "\n",
    "            band_data = temperature_data[day_index, :, :]  \n",
    "\n",
    "            # GeoTIFF-Datei mit einem einzelnen Band für jeden Zeitschritt erstellen  \n",
    "            with rasterio.open(  \n",
    "                output_filepath,  \n",
    "                \"w\",  \n",
    "                driver=\"GTiff\",  \n",
    "                dtype=str(band_data.dtype),  \n",
    "                width=band_data.shape[1],  \n",
    "                height=band_data.shape[0],  \n",
    "                count=1,  \n",
    "                crs=\"EPSG:4326\",  \n",
    "                nodata=-9999,  \n",
    "                transform=transform,        \n",
    "            ) as dst:  \n",
    "                # Daten für das aktuelle Band schreiben und Band annotieren  \n",
    "                dst.write(band_data, 1)  \n",
    "                dst.set_band_description(1, band_desc)  \n",
    "    else:  \n",
    "        print(f\"Ordner ist nicht leer. Export überspringen.\")  \n",
    "\n",
    "# Parameter für die Verarbeitung der NetCDF-Datei definieren  \n",
    "nc_filename_merged = f\"{dataset}_{year}.nc\"  \n",
    "nc_filepath_merged = os.path.join(output_folder, nc_filename)  \n",
    "variable_name = 'lake_surface_water_temperature'  \n",
    "\n",
    "# Exportprozess ausführen  \n",
    "main_export_individual_geotiff(nc_filepath_merged)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f069558",
   "metadata": {},
   "source": [
    "## 8. Zusätzliche Visualisierung mit einem Kalenderdiagramm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "39bdccad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>year</th>\n",
       "      <th>variable_name</th>\n",
       "      <th>variable_shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>satellite-lake-water-temperature_1995.nc</td>\n",
       "      <td>1995</td>\n",
       "      <td>lake_surface_water_temperature</td>\n",
       "      <td>(201, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>satellite-lake-water-temperature_1996.nc</td>\n",
       "      <td>1996</td>\n",
       "      <td>lake_surface_water_temperature</td>\n",
       "      <td>(180, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>satellite-lake-water-temperature_1997.nc</td>\n",
       "      <td>1997</td>\n",
       "      <td>lake_surface_water_temperature</td>\n",
       "      <td>(361, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>satellite-lake-water-temperature_1998.nc</td>\n",
       "      <td>1998</td>\n",
       "      <td>lake_surface_water_temperature</td>\n",
       "      <td>(360, 14, 24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>satellite-lake-water-temperature_1999.nc</td>\n",
       "      <td>1999</td>\n",
       "      <td>lake_surface_water_temperature</td>\n",
       "      <td>(352, 14, 24)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   filename  year  \\\n",
       "0  satellite-lake-water-temperature_1995.nc  1995   \n",
       "1  satellite-lake-water-temperature_1996.nc  1996   \n",
       "2  satellite-lake-water-temperature_1997.nc  1997   \n",
       "3  satellite-lake-water-temperature_1998.nc  1998   \n",
       "4  satellite-lake-water-temperature_1999.nc  1999   \n",
       "\n",
       "                    variable_name variable_shape  \n",
       "0  lake_surface_water_temperature  (201, 14, 24)  \n",
       "1  lake_surface_water_temperature  (180, 14, 24)  \n",
       "2  lake_surface_water_temperature  (361, 14, 24)  \n",
       "3  lake_surface_water_temperature  (360, 14, 24)  \n",
       "4  lake_surface_water_temperature  (352, 14, 24)  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re  \n",
    "import pandas as pd  \n",
    "\n",
    "# Funktion definieren, um Metadaten aus einem NetCDF-Dateinamen zu extrahieren  \n",
    "def meta(filename):  \n",
    "    match = re.search(r\"(.+?)_(\\d{4})\\.nc\", filename)  \n",
    "    if not match:  \n",
    "        raise ValueError(\"Der angegebene Dateiname entspricht nicht dem erwarteten Namensschema.\")  \n",
    "\n",
    "    def get_nc_variable():  \n",
    "        with nc.Dataset(os.path.join(output_folder, filename), 'r') as nc_dataset:  \n",
    "            nc_variable_name_list = nc_dataset.variables.keys()  \n",
    "\n",
    "            primary_variable_index = 0  \n",
    "            primary_variable = [*nc_variable_name_list][primary_variable_index]  \n",
    "            primary_variable_shape = np.shape(nc_dataset[primary_variable])  \n",
    "\n",
    "            return primary_variable, primary_variable_shape  \n",
    "\n",
    "    # Metadaten als Dictionary zurückgeben  \n",
    "    return dict(  \n",
    "        filename=filename,  \n",
    "        path=os.path.join(output_folder, filename),  \n",
    "        year=match.group(2),  \n",
    "        variable_name=get_nc_variable()[0],  \n",
    "        variable_shape=get_nc_variable()[1],  \n",
    "    )  \n",
    "\n",
    "# Alle NetCDF-Dateien im Ausgabeordner auflisten, verarbeiten und nach Jahr sortieren\n",
    "nc_files = [meta(f) for f in os.listdir(output_folder) if f.endswith('.nc')]\n",
    "nc_files = sorted(nc_files, key=lambda x: x['year'])\n",
    "\n",
    "df_nc_files = pd.DataFrame.from_dict(nc_files)\n",
    "\n",
    "# DataFrame anzeigen, ohne den Pfad anzuzeigen  \n",
    "df_nc_files.head().loc[:, df_nc_files.columns != 'path']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d9294b18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keine NetCDF-Datei für das angegebene Jahr gefunden.\n",
      "Das neueste verfügbare Jahr wird ausgewählt.\n",
      "Es werden Daten aus dem Jahr 2020 verwendet.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import calmap\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import math as ma\n",
    "\n",
    "def plot_calendarplot(year=None):\n",
    "    # NetCDF-Datei für das angegebene Jahr filtern\n",
    "    nc_file = next((item for item in nc_files if item['year'] == str(year)), None)\n",
    "\n",
    "    if not nc_file:\n",
    "        print(f\"Keine NetCDF-Datei für das angegebene Jahr gefunden.\")\n",
    "        print(\"Das neueste verfügbare Jahr wird ausgewählt.\")\n",
    "        if nc_files:\n",
    "            nc_file = nc_files[-1]  # Das neueste verfügbare Jahr auswählen\n",
    "            print(f\"Es werden Daten aus dem Jahr {nc_file['year']} verwendet.\")\n",
    "        else:\n",
    "            print(\"Keine NetCDF-Dateien verfügbar.\")\n",
    "            return\n",
    "\n",
    "    # Die NetCDF-Datei öffnen und die angegebene Variable verarbeiten\n",
    "    with xr.open_dataset(nc_file['path']) as nc_dataset:\n",
    "        # Variablendaten als DataFrame extrahieren\n",
    "        variable_data = nc_dataset[nc_file['variable_name']]\n",
    "        df = variable_data.to_dataframe().reset_index().dropna().reset_index(drop=True)\n",
    "\n",
    "        # 'time' in das Datetime-Format konvertieren und 'day_of_year' sowie 'date' ableiten\n",
    "        df['time'] = pd.to_datetime(df['time'])\n",
    "        df['day_of_year'] = df['time'].dt.dayofyear\n",
    "        df['date'] = df['time'].dt.year.astype(str) + '-' + df['day_of_year'].astype(str).str.zfill(3)\n",
    "        df['date'] = pd.to_datetime(df['date'], format='%Y-%j').dt.strftime('%Y-%m-%d')\n",
    "\n",
    "        # Nach 'date' gruppieren und den täglichen Mittelwert für die Variable berechnen\n",
    "        df2 = df.groupby('date')[variable_name].mean()\n",
    "\n",
    "    # Sicherstellen, dass der Index des Ergebnisses ein datetime-Format hat\n",
    "    df2.index = pd.to_datetime(df2.index)\n",
    "\n",
    "    # Umrechnung in °C\n",
    "    df2 = df2-273.15\n",
    "\n",
    "    # Kalender-Heatmap der mittleren Temperaturwerte plotten\n",
    "    fig, axs = calmap.calendarplot(df2,\n",
    "                                fig_kws={'figsize': (12, 8)},\n",
    "                                yearlabel_kws={'color': 'black', 'fontsize': 22},\n",
    "                                subplot_kws={'title': f'Kalender-Heatmap von {variable_name} für {year}'},\n",
    "                                cmap='turbo',\n",
    "                                fillcolor='#efefef',\n",
    "                                daylabels='MTWTFSS',\n",
    "                                linecolor='#ffffff',\n",
    "                                dayticks=True,\n",
    "                                )\n",
    "\n",
    "    # # Gitternetzlinien entfernen\n",
    "    for ax in axs.flatten():\n",
    "        ax.grid(False) \n",
    "\n",
    "    # Farbleiste rechts neben dem Diagramm hinzufügen\n",
    "    cax = fig.add_axes([1.005, 0.38, 0.02, 0.2])\n",
    "    vmin = ma.floor(df2.min() // 5 * 5)\n",
    "    vmax = ma.ceil(df2.max() // 5 * 5)\n",
    "\n",
    "    sm = plt.cm.ScalarMappable(cmap='turbo', norm=plt.Normalize(vmin=vmin, vmax=vmax))\n",
    "    cbar = fig.colorbar(sm, cax=cax)\n",
    "\n",
    "    # Ticks auf der Farbleiste anpassen\n",
    "    tick_interval = 5\n",
    "    ticks = np.arange(vmin, vmax + 1, tick_interval)\n",
    "    cbar.set_ticks(ticks)  # Ticks setzen\n",
    "    cbar.set_ticklabels(ticks)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Kalender-Heatmap für das angegebene Jahr plotten\n",
    "    plot_calendarplot(year=1990)\n",
    "    plot_calendarplot(year=2005)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cds_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
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