{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Herunterskalierte bioklimatische Indikatoren\n",
    "\n",
    "Dieses Datensatz bietet 76 bioklimatische Indikatoren mit einer Auflösung von 1 × 1 km für Europa, Nordbrasilien und Zentralafrika. Die Indikatoren wurden mithilfe von ERA5 (ERA5-Land)-Reanalysedaten und statistischer Herunterskalierung rekonstruiert. Sie konzentrieren sich auf Biodiversität und Ökosystemdienstleistungen und umfassen Variablen wie Oberflächenenergie, Dürre, Bodenfeuchte, Wind und essenzielle Klimavariablen. Der Datensatz wurde für die Biodiversitätsforschung entwickelt und unterstützt vielfältige Anwendungen durch zeitliche Auflösungen von monatlich bis hin zu mehrjährigen Mittelwerten.\n",
    "\n",
    "**Informationen zum Datensatz:**\n",
    "* Quelle: [Downscaled Bioclimatic Indicators](https://cds.climate.copernicus.eu/datasets/sis-biodiversity-era5-regional?tab=overview)\n",
    "* Author: str.ucture GmbH\n",
    "* Notebook-Version: 1.1 (Aktualisiert: 21. Januar 2025)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Festlegen der Pfade und Arbeitsverzeichnisse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "''' ---- Verzeichnisse hier angeben ---- '''\n",
    "download_folder = r\".\\data\\sis-biodiversity-era5-regional\\download\"\n",
    "working_folder = r\".\\data\\sis-biodiversity-era5-regional\\working\"\n",
    "geotiff_folder = r\".\\data\\sis-biodiversity-era5-regional\\geotiff\"\n",
    "csv_folder = r\".\\data\\sis-biodiversity-era5-regional\\csv\"\n",
    "output_folder = r\".\\data\\sis-biodiversity-era5-regional\\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",
   "metadata": {},
   "source": [
    "## 2. Herunterladen und Entpacken des Datensatzes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Authentifizierung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
   "metadata": {},
   "source": [
    "### 2.2 Definieren die „request“ und laden Sie den Datensatz herunter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definieren der Begrenzungsrahmen-Koordinaten (WGS84-Format)\n",
    "# Das Koordinatenformat lautet: [Norden, Westen, Süden, Osten]\n",
    "bbox_wgs84_deutschland = [56.0, 5.8, 47.2, 15.0]\n",
    "bbox_wgs84_de_standard = [5.7, 47.1, 15.2, 55.2]\n",
    "bbox_wgs84_konstanz = [47.9, 8.9, 47.6, 9.3]\n",
    "bbox_wgs84_konstanz_standard = [9.0, 47.6, 9.3, 47.8]  # [West, South, East, North]\n",
    "\n",
    "# Alternativ können Sie ein Shapefile für eine präzise geografische Filterung verwenden\n",
    "import geopandas as gpd\n",
    "import math\n",
    "\n",
    "# Beispiel: Shapefile von Konstanz laden (WGS84-Projektion)\n",
    "de_shapefile = r\"./shapefiles/de_boundary.shp\"\n",
    "de_gdf = gpd.read_file(de_shapefile)\n",
    "\n",
    "# Extrahieren Sie den Begrenzungsrahmen des Shapefiles\n",
    "de_bounds = de_gdf.total_bounds\n",
    "\n",
    "# Passen Sie den Begrenzungsrahmen an und puffern Sie ihn, um einen etwas größeren\n",
    "de_bounds_adjusted = [(math.floor(de_bounds[0]* 10)/10)-0.1,\n",
    "                      (math.floor(de_bounds[1]* 10)/10)-0.1,\n",
    "                      (math.ceil(de_bounds[2]* 10)/10)+0.1,\n",
    "                      (math.ceil(de_bounds[3]* 10)/10)+0.1]\n",
    "\n",
    "# Ordnen Sie die Koordinaten in das Format: [Nord, West, Süd, Ost] um.\n",
    "bbox_de_bounds_adjusted = [de_bounds_adjusted[3], de_bounds_adjusted[0],\n",
    "                           de_bounds_adjusted[1], de_bounds_adjusted[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Derzeit ist für Region == Europa nur ERA5 verfügbar\n",
    "# Für Region == Zentralafrika ist ERA5 verfügbar\n",
    "# Für Region == Nördliches Brasilien ist ERA5-Land verfügbar\n",
    "\n",
    "## Variablengruppe: Bioklimatische Indikatoren wie in WORLDCLIM\n",
    "# cds.climate.copernicus.eu/datasets/sis-biodiversity-era5-regional?tab=download\n",
    "variable_group = \"bioclimatic_indicators_as_in_worldclim\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition des Datensatzes und der Request-Parameter\n",
    "dataset = \"sis-biodiversity-era5-regional\"\n",
    "request = {\n",
    "    \"region\": [\"europe\"],\n",
    "    \"origin\": \"era5\",\n",
    "    \"variable\": [\n",
    "        \"annual_mean_temperature\",\n",
    "        \"mean_diurnal_range\",\n",
    "        \"isothermality\",\n",
    "        \"temperature_seasonality\",\n",
    "        \"maximum_temperature_of_warmest_month\",\n",
    "        \"minimum_temperature_of_coldest_month\",\n",
    "        \"temperature_annual_range\",\n",
    "        \"mean_temperature_of_wettest_quarter\",\n",
    "        \"mean_temperature_of_driest_quarter\",\n",
    "        \"mean_temperature_of_warmest_quarter\",\n",
    "        \"mean_temperature_of_coldest_quarter\",\n",
    "        \"annual_precipitation\",\n",
    "        \"precipitation_of_wettest_month\",\n",
    "        \"precipitation_of_driest_month\",\n",
    "        \"precipitation_seasonality\",\n",
    "        \"precipitation_of_wettest_quarter\",\n",
    "        \"precipitation_of_driest_quarter\",\n",
    "        \"precipitation_of_warmest_quarter\",\n",
    "        \"precipitation_of_coldest_quarter\"\n",
    "    ],\n",
    "    \"statistic\": [\n",
    "        \"mean\",\n",
    "        \"median\",\n",
    "        \"25th_quartile\",\n",
    "        \"75th_quartile\"\n",
    "    ],\n",
    "    \"version\": [\"1_0\"],\n",
    "    # \"area\": bbox_de_bounds_adjusted,  # Die Definition des Bereichs mit einem Begrenzungsrahmen verursacht Probleme beim Herunterladen\n",
    "    # Fehler: \"Ihre Anfrage ist zu groß, bitte reduzieren Sie Ihre Auswahl\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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}_{variable_group}.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",
   "metadata": {},
   "source": [
    "### 2.3 Extrahieren die ZIP-Datei in Ordner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ordner ist nicht leer. Entpacken überspringen.\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "\n",
    "# Erstelle den Ordnerpfad zum Extrahieren der Zip-Datei(en) basierend auf der Variablengruppe\n",
    "extract_folder = os.path.join(working_folder, variable_group)\n",
    "os.makedirs(extract_folder, exist_ok=True)\n",
    "\n",
    "# Entpacken der ZIP-Datei\n",
    "try:\n",
    "    if not os.listdir(extract_folder):\n",
    "        dataset_filename = f\"{dataset}_{variable_group}.zip\"\n",
    "        dataset_filepath = os.path.join(download_folder, dataset_filename)\n",
    "        \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",
   "metadata": {},
   "source": [
    "## 3. Untersuchen der Metadaten der NetCDF4-Datei"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Erstellen eines DataFrame mit verfügbaren NetCDF-Dateien"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>ds_variable</th>\n",
       "      <th>ds_origin</th>\n",
       "      <th>variable_name</th>\n",
       "      <th>variable_shape</th>\n",
       "      <th>statistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BIO01_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO01</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO01</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BIO01_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO01</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO01</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>median</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BIO01_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO01</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO01</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>q25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BIO01_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO01</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO01</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>q75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BIO02_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO02</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO02</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BIO02_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO02</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO02</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>median</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BIO02_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO02</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO02</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>q25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BIO02_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO02</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO02</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>q75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BIO03_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO03</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO03</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BIO03_era5-to-1km_1979-201...</td>\n",
       "      <td>BIO03</td>\n",
       "      <td>era5-to-1km_1979-2018</td>\n",
       "      <td>BIO03</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "      <td>median</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        filename ds_variable              ds_origin  \\\n",
       "0  BIO01_era5-to-1km_1979-201...       BIO01  era5-to-1km_1979-2018   \n",
       "1  BIO01_era5-to-1km_1979-201...       BIO01  era5-to-1km_1979-2018   \n",
       "2  BIO01_era5-to-1km_1979-201...       BIO01  era5-to-1km_1979-2018   \n",
       "3  BIO01_era5-to-1km_1979-201...       BIO01  era5-to-1km_1979-2018   \n",
       "4  BIO02_era5-to-1km_1979-201...       BIO02  era5-to-1km_1979-2018   \n",
       "5  BIO02_era5-to-1km_1979-201...       BIO02  era5-to-1km_1979-2018   \n",
       "6  BIO02_era5-to-1km_1979-201...       BIO02  era5-to-1km_1979-2018   \n",
       "7  BIO02_era5-to-1km_1979-201...       BIO02  era5-to-1km_1979-2018   \n",
       "8  BIO03_era5-to-1km_1979-201...       BIO03  era5-to-1km_1979-2018   \n",
       "9  BIO03_era5-to-1km_1979-201...       BIO03  era5-to-1km_1979-2018   \n",
       "\n",
       "  variable_name   variable_shape statistic  \n",
       "0         BIO01  (1, 4800, 9600)      mean  \n",
       "1         BIO01  (1, 4800, 9600)    median  \n",
       "2         BIO01  (1, 4800, 9600)       q25  \n",
       "3         BIO01  (1, 4800, 9600)       q75  \n",
       "4         BIO02  (1, 4800, 9600)      mean  \n",
       "5         BIO02  (1, 4800, 9600)    median  \n",
       "6         BIO02  (1, 4800, 9600)       q25  \n",
       "7         BIO02  (1, 4800, 9600)       q75  \n",
       "8         BIO03  (1, 4800, 9600)      mean  \n",
       "9         BIO03  (1, 4800, 9600)    median  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "\n",
    "def meta(filename):\n",
    "    # Überprüfen, ob der Dateiname dem erwarteten Muster entspricht\n",
    "    match = re.search(\n",
    "        r\"(?P<ds_variable>BIO\\d{2})_(?P<ds_origin>[\\w-]+)-(?P<statistic>mean|median|q25|q75)_v(?P<version>\\d+\\.\\d+)\",\n",
    "        filename\n",
    "        )\n",
    "\n",
    "    # Fehler ausgeben, wenn der Dateiname nicht dem erwarteten Schema entspricht\n",
    "    if not match:\n",
    "        match = re.search(\"Der angegebene Dateiname entspricht nicht dem erwarteten Benennungsschema.\")\n",
    "        \n",
    "    # Funktion zum Extrahieren des Variablennamens aus der NetCDF-Datei\n",
    "    def get_nc_variable():\n",
    "        with nc.Dataset(os.path.join(extract_folder, filename), 'r') as nc_dataset:\n",
    "            variable_names = list(nc_dataset.variables.keys())\n",
    "\n",
    "            primary_variable_index = 3  # Adjust this index as needed\n",
    "            if primary_variable_index >= len(variable_names):\n",
    "                raise IndexError(\"Der Index der primären Variablen liegt außerhalb des Bereichs für die NetCDF-Variablen.\")\n",
    "            \n",
    "            primary_variable = variable_names[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(extract_folder, filename),\n",
    "        ds_variable=match.group('ds_variable'),\n",
    "        ds_origin=match.group('ds_origin'),\n",
    "        variable_name=get_nc_variable()[0],\n",
    "        variable_shape=get_nc_variable()[1],\n",
    "        statistic=match.group('statistic')\n",
    "    )\n",
    "\n",
    "# Metadaten für alle NetCDF-Dateien im Ordner extrahieren\n",
    "# Das Dictionary 'nc_files' enthält alle relevanten Metadaten der verfügbaren NetCDF4-Dateien\n",
    "# Dieses Dictionary wird später verwendet, um die Dateien in GeoTiff zu konvertieren\n",
    "nc_files = [meta(f) for f in os.listdir(extract_folder) if f.endswith('.nc')]\n",
    "df_nc_files = pd.DataFrame.from_dict(nc_files)\n",
    "\n",
    "# Pandas-Anzeigeoptionen anpassen\n",
    "pd.options.display.max_colwidth = 30\n",
    "\n",
    "# DataFrame anzeigen, ohne die Spalte 'path' darzustellen\n",
    "df_nc_files.head(10).loc[:, df_nc_files.columns != 'path']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Einzigartige Variablennamen und verfügbare Variablen ausgeben"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  BIO01             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO01']\n",
      "5  BIO02             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO02']\n",
      "9  BIO03             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO03']\n",
      "13 BIO04             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO04']\n",
      "17 BIO05             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO05']\n",
      "21 BIO06             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO06']\n",
      "25 BIO07             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO07']\n",
      "29 BIO08             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO08']\n",
      "33 BIO09             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO09']\n",
      "37 BIO10             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO10']\n",
      "41 BIO11             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO11']\n",
      "45 BIO12             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO12']\n",
      "49 BIO13             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO13']\n",
      "53 BIO14             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO14']\n",
      "57 BIO15             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO15']\n",
      "61 BIO16             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO16']\n",
      "65 BIO17             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO17']\n",
      "69 BIO18             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO18']\n",
      "73 BIO19             : Verfügbare Variablen: ['latitude', 'longitude', 'time', 'BIO19']\n"
     ]
    }
   ],
   "source": [
    "# Variable definieren, um bereits verarbeitete Variablennamen zu speichern und Duplikate zu vermeiden  \n",
    "seen_variables = set()\n",
    "\n",
    "# Alle Variablen in jeder NetCDF-Datei auflisten  \n",
    "for i, nc_file in enumerate(nc_files):\n",
    "    variable_name = nc_file['variable_name']\n",
    "    \n",
    "    # Überspringen, wenn die Variable bereits verarbeitet wurde  \n",
    "    if variable_name in seen_variables:\n",
    "        continue\n",
    "\n",
    "    # NetCDF-Datei im Lesemodus öffnen  \n",
    "    with nc.Dataset(nc_file['path'], mode='r') as nc_dataset:  \n",
    "        # Alle Variablen im aktuellen Datensatz auflisten  \n",
    "        variables_list = list(nc_dataset.variables.keys())  \n",
    "        \n",
    "        # Details der Datei und ihrer Variablen ausgeben  \n",
    "        print(f\"{i + 1:<2} {variable_name:<18}: Verfügbare Variablen: {variables_list}\") \n",
    "    \n",
    "    # Diese Variable als verarbeitet markieren  \n",
    "    seen_variables.add(variable_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Zusammenfassung der Variable 'BIO01':\n"
     ]
    },
    {
     "data": {
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       "      <th>Beschreibung</th>\n",
       "      <th>Bemerkungen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Variablenname</td>\n",
       "      <td>BIO01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Datentyp</td>\n",
       "      <td>float32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Form</td>\n",
       "      <td>(1, 4800, 9600)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Variableninfo</td>\n",
       "      <td>('time', 'latitude', 'long...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Einheiten</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Langer Name</td>\n",
       "      <td>Temperature annual mean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Beschreibung                    Bemerkungen\n",
       "0  Variablenname                          BIO01\n",
       "1       Datentyp                        float32\n",
       "2           Form                (1, 4800, 9600)\n",
       "3  Variableninfo  ('time', 'latitude', 'long...\n",
       "4      Einheiten                              K\n",
       "5    Langer Name        Temperature annual mean"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5. Zusammenfassung der Variable 'BIO02':\n"
     ]
    },
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Variablenname</td>\n",
       "      <td>BIO02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Datentyp</td>\n",
       "      <td>float32</td>\n",
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       "      <td>Form</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Variableninfo</td>\n",
       "      <td>('time', 'latitude', 'long...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Einheiten</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Langer Name</td>\n",
       "      <td>Mean diurnal range (mean o...</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "    Beschreibung                    Bemerkungen\n",
       "0  Variablenname                          BIO02\n",
       "1       Datentyp                        float32\n",
       "2           Form                (1, 4800, 9600)\n",
       "3  Variableninfo  ('time', 'latitude', 'long...\n",
       "4      Einheiten                              K\n",
       "5    Langer Name  Mean diurnal range (mean o..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".... (Ausgabe auf die ersten 2 Variablen gekürzt)\n"
     ]
    }
   ],
   "source": [
    "# Alle Variableninformationen in jeder NetCDF-Datei auflisten  \n",
    "seen_variables = set()\n",
    "\n",
    "# Alle variablen Informationen in jeder NetCDF-Datei auflisten\n",
    "for i, nc_file in enumerate(nc_files):\n",
    "    variable_name = nc_file['variable_name']\n",
    "    \n",
    "    # Überspringen, wenn die Variable bereits verarbeitet wurde\n",
    "    if variable_name in seen_variables:\n",
    "        continue\n",
    "    \n",
    "    # NetCDF-Datei im Lesemodus öffnen\n",
    "    with nc.Dataset(nc_file['path'], mode='r') as nc_dataset:  \n",
    "        # Primärvariable-Daten abrufen  \n",
    "        variable_data = nc_dataset[variable_name]  \n",
    "\n",
    "        # Zusammenfassung der Primärvariable erstellen  \n",
    "        summary = {  \n",
    "            \"Variablenname\": variable_name,  \n",
    "            \"Datentyp\": variable_data.dtype,  \n",
    "            \"Form\": variable_data.shape,  \n",
    "            \"Variableninfo\": f\"{variable_data.dimensions}\",  \n",
    "            \"Einheiten\": getattr(variable_data, \"units\", \"N/A\"),  \n",
    "            \"Langer Name\": getattr(variable_data, \"long_name\", \"N/A\"),  \n",
    "        }  \n",
    "\n",
    "        # Datensatz-Zusammenfassung als DataFrame zur besseren Visualisierung anzeigen  \n",
    "        nc_summary = pd.DataFrame(list(summary.items()), columns=['Beschreibung', 'Bemerkungen'])  \n",
    "        print(f\"{i + 1}. Zusammenfassung der Variable '{variable_name}':\")  \n",
    "        display(nc_summary)  \n",
    "\n",
    "    # Variablenname zur Liste der bereits verarbeiteten Variablen hinzufügen  \n",
    "    seen_variables.add(variable_name)  \n",
    "\n",
    "    # Ausgabe begrenzen  \n",
    "    output_limit = 2  \n",
    "    if len(seen_variables) >= output_limit:  \n",
    "        print(f\".... (Ausgabe auf die ersten {output_limit} Variablen gekürzt)\")  \n",
    "        break  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Exportieren der NetCDF4-Dateien im CSV-Format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "⚠️ <span style=\"background-color: red; color: white; padding: 2px 4px;\">Wichtig</span>: Nach dem Herunterladen der Datensätze definiere die interessierende Variable im folgenden Code. Wenn du beispielsweise an der jährlichen Durchschnittstemperatur interessiert bist, wähle die Variable **BIO01**, die der **Jährlichen Durchschnittstemperatur (BIO01)** entspricht."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ändere hier die ausgewählte Variable\n",
    "selected_variable = \"BIO01\"\n",
    "\n",
    "# Filtere die NetCDF-Dateien basierend auf der ausgewählten Variable\n",
    "nc_files_subset = [nc_file for nc_file in nc_files if nc_file['ds_variable'] == selected_variable]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Define a function to convert NetCDF data to a pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "# Funktion zur Konvertierung von NetCDF-Daten in ein Pandas DataFrame\n",
    "def netcdf_to_dataframe(\n",
    "    nc_file,\n",
    "    bounding_box=None\n",
    "    ):\n",
    "\n",
    "    # Öffne das NetCDF-Dataset im Lesemodus\n",
    "    with xr.open_dataset(nc_file['path']) as nc_dataset:\n",
    "        # Zugriff auf die Variablendaten aus dem Datensatz\n",
    "        variable_data = nc_dataset[nc_file['variable_name']]\n",
    "        \n",
    "        # Sicherstellen, dass die Namen für Breiten- und Längengrad korrekt sind\n",
    "        latitude_name = 'latitude' if 'latitude' in nc_dataset.coords else 'lat'\n",
    "        longitude_name = 'longitude' if 'longitude' in nc_dataset.coords else 'lon'\n",
    "        \n",
    "        # Falls eine Begrenzungsbox angegeben ist, die Daten filtern\n",
    "        if bounding_box:\n",
    "            filtered_data = variable_data.where(\n",
    "                (nc_dataset[latitude_name] >= bounding_box[1]) & (nc_dataset[latitude_name] <= bounding_box[3]) &\n",
    "                (nc_dataset[longitude_name] >= bounding_box[0]) & (nc_dataset[longitude_name] <= bounding_box[2]),\n",
    "                drop=True\n",
    "            )\n",
    "        else:\n",
    "            filtered_data = variable_data\n",
    "\n",
    "        # Umwandlung des xarray-Datensatzes in ein Pandas DataFrame\n",
    "        df = filtered_data.to_dataframe().reset_index().set_index(['time', latitude_name, longitude_name])\n",
    "        \n",
    "        # Benenne die Wertspalte um, sodass der Dateiname als Spaltenname verwendet wird (optional)\n",
    "        df.rename(columns={nc_file['variable_name']: nc_file['filename']}, inplace=True)\n",
    "        \n",
    "        return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Filtern nach Boundary Box, DataFrame erstellen und als CSV-Datei exportieren\n",
    "\n",
    "> Hinweis: **bbox_wgs84_de_standard** wird verwendet, um Daten für Deutschland zu filtern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datei existiert bereits unter .\\data\\sis-biodiversity-era5-regional\\csv\\bioclimatic_indicators_as_in_worldclim\\BIO01.csv.zip. Überspringen den Export.\n",
      "Lesen bestehende CSV-Datei ein...\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>BIO01_era5- to-1km_1979- 2018- mean_v1.0.nc</th>\n",
       "      <th>BIO01_era5- to-1km_1979- 2018- median_v1.0. nc</th>\n",
       "      <th>BIO01_era5- to-1km_1979- 2018- q25_v1.0.nc</th>\n",
       "      <th>BIO01_era5- to-1km_1979- 2018- q75_v1.0.nc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">1999-01-01</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">47.10</th>\n",
       "      <th>5.70</th>\n",
       "      <td>284.57</td>\n",
       "      <td>284.59</td>\n",
       "      <td>284.02</td>\n",
       "      <td>285.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.71</th>\n",
       "      <td>284.57</td>\n",
       "      <td>284.59</td>\n",
       "      <td>284.02</td>\n",
       "      <td>285.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.72</th>\n",
       "      <td>284.55</td>\n",
       "      <td>284.57</td>\n",
       "      <td>284.00</td>\n",
       "      <td>285.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.73</th>\n",
       "      <td>284.54</td>\n",
       "      <td>284.57</td>\n",
       "      <td>283.99</td>\n",
       "      <td>285.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.74</th>\n",
       "      <td>284.51</td>\n",
       "      <td>284.54</td>\n",
       "      <td>283.97</td>\n",
       "      <td>285.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">55.20</th>\n",
       "      <th>15.16</th>\n",
       "      <td>281.95</td>\n",
       "      <td>282.07</td>\n",
       "      <td>281.51</td>\n",
       "      <td>282.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.17</th>\n",
       "      <td>281.94</td>\n",
       "      <td>282.07</td>\n",
       "      <td>281.51</td>\n",
       "      <td>282.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.18</th>\n",
       "      <td>281.93</td>\n",
       "      <td>282.06</td>\n",
       "      <td>281.50</td>\n",
       "      <td>282.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.19</th>\n",
       "      <td>281.93</td>\n",
       "      <td>282.05</td>\n",
       "      <td>281.49</td>\n",
       "      <td>282.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.20</th>\n",
       "      <td>281.92</td>\n",
       "      <td>282.05</td>\n",
       "      <td>281.49</td>\n",
       "      <td>282.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1108080 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               BIO01_era5- to-1km_1979- 2018- mean_v1.0.nc  \\\n",
       "time       latitude longitude                                                \n",
       "1999-01-01 47.10    5.70                              284.57                 \n",
       "                    5.71                              284.57                 \n",
       "                    5.72                              284.55                 \n",
       "                    5.73                              284.54                 \n",
       "                    5.74                              284.51                 \n",
       "...                                                      ...                 \n",
       "           55.20    15.16                             281.95                 \n",
       "                    15.17                             281.94                 \n",
       "                    15.18                             281.93                 \n",
       "                    15.19                             281.93                 \n",
       "                    15.20                             281.92                 \n",
       "\n",
       "                               BIO01_era5- to-1km_1979- 2018- median_v1.0. nc  \\\n",
       "time       latitude longitude                                                   \n",
       "1999-01-01 47.10    5.70                              284.59                    \n",
       "                    5.71                              284.59                    \n",
       "                    5.72                              284.57                    \n",
       "                    5.73                              284.57                    \n",
       "                    5.74                              284.54                    \n",
       "...                                                      ...                    \n",
       "           55.20    15.16                             282.07                    \n",
       "                    15.17                             282.07                    \n",
       "                    15.18                             282.06                    \n",
       "                    15.19                             282.05                    \n",
       "                    15.20                             282.05                    \n",
       "\n",
       "                               BIO01_era5- to-1km_1979- 2018- q25_v1.0.nc  \\\n",
       "time       latitude longitude                                               \n",
       "1999-01-01 47.10    5.70                              284.02                \n",
       "                    5.71                              284.02                \n",
       "                    5.72                              284.00                \n",
       "                    5.73                              283.99                \n",
       "                    5.74                              283.97                \n",
       "...                                                      ...                \n",
       "           55.20    15.16                             281.51                \n",
       "                    15.17                             281.51                \n",
       "                    15.18                             281.50                \n",
       "                    15.19                             281.49                \n",
       "                    15.20                             281.49                \n",
       "\n",
       "                               BIO01_era5- to-1km_1979- 2018- q75_v1.0.nc  \n",
       "time       latitude longitude                                              \n",
       "1999-01-01 47.10    5.70                              285.10               \n",
       "                    5.71                              285.10               \n",
       "                    5.72                              285.08               \n",
       "                    5.73                              285.07               \n",
       "                    5.74                              285.05               \n",
       "...                                                      ...               \n",
       "           55.20    15.16                             282.72               \n",
       "                    15.17                             282.71               \n",
       "                    15.18                             282.70               \n",
       "                    15.19                             282.70               \n",
       "                    15.20                             282.69               \n",
       "\n",
       "[1108080 rows x 4 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "import textwrap\n",
    "\n",
    "# Erstelle einen Ordner für die Teilmengen-CSV-Dateien basierend auf der Variablengruppe\n",
    "subset_csv_folder = os.path.join(csv_folder, variable_group)\n",
    "os.makedirs(subset_csv_folder, exist_ok=True)\n",
    "\n",
    "# Definiere den CSV-Dateinamen und Pfad für die Ausgabe\n",
    "subset_csv_filename = f\"{selected_variable}.csv.zip\"\n",
    "subset_csv_filepath = os.path.join(subset_csv_folder, subset_csv_filename)\n",
    "\n",
    "# Erstelle ein DataFrame und eine CSV-Datei, falls sie noch nicht existiert\n",
    "if not os.path.isfile(subset_csv_filepath):\n",
    "    # Verarbeite NetCDF-Dateien in DataFrames und füge sie zusammen\n",
    "    dataframes = [netcdf_to_dataframe(nc_file=nc_file, bounding_box=bbox_wgs84_de_standard) for nc_file in tqdm(selected_nc_files)]\n",
    "    df_merged = pd.concat(dataframes, axis=1)\n",
    "\n",
    "    # Exportiere das zusammengefügte DataFrame als komprimierte CSV-Datei\n",
    "    df_merged.to_csv(subset_csv_filepath, sep=',', encoding='utf8', compression='zip')\n",
    "else:\n",
    "    print(f\"Datei existiert bereits unter {subset_csv_filepath}. Überspringen den Export.\")\n",
    "    print(\"Lesen bestehende CSV-Datei ein...\")\n",
    "    df_merged = pd.read_csv(subset_csv_filepath).set_index(['time', 'latitude', 'longitude'])\n",
    "\n",
    "# Funktion zum Umbruch der Spaltennamen für bessere Lesbarkeit\n",
    "def wrap_column_names(df, width):\n",
    "    wrapped_columns = {col: \" \".join(textwrap.wrap(col, width)) for col in df.columns}\n",
    "    return df.rename(columns=wrapped_columns)\n",
    "    \n",
    "# Ändere die Pandas-Anzeigeoptionen\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "    \n",
    "# Zeige das DataFrame an\n",
    "df_wrapped = wrap_column_names(df_merged, width=12)\n",
    "df_wrapped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Exportieren der NetCDF4-Datei nach GeoTIFF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Definieren eine Funktion zum Exportieren der NetCDF4-Datei als GeoTIFF-Datei(en)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from rasterio.transform import from_origin\n",
    "import rasterio\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "def main_export_geotiff(\n",
    "    nc_file,\n",
    "    bounding_box=None,\n",
    "    output_directory=None):\n",
    "    \n",
    "    \"\"\"\n",
    "    Parameters:\n",
    "        nc_file (dict): Ein Wörterbuch mit den Schlüsseln 'path' (Dateipfad), 'variable', 'rcp' und 'statistic'.\n",
    "        bounding_box (list): [lon_min, lat_min, lon_max, lat_max] (optional).\n",
    "        output_directory (str): Verzeichnis zum Speichern der Ausgabe-GeoTIFF-Dateien (optional).\n",
    "    \"\"\"\n",
    "    \n",
    "    # Öffnet die NetCDF-Datei\n",
    "    with nc.Dataset(nc_file['path'], 'r') as nc_dataset:\n",
    "        nc_dataset = nc.Dataset(nc_file['path'], 'r')\n",
    "        \n",
    "        lon = nc_dataset['longitude'][:]\n",
    "        lat = nc_dataset['latitude'][:]\n",
    "        \n",
    "        # Falls eine Begrenzungsbox angegeben wurde, filtere die Daten entsprechend\n",
    "        if bounding_box:\n",
    "            lon_min, lat_min, lon_max, lat_max = bounding_box\n",
    "            \n",
    "            indices_lat = np.where((lat >= lat_min) & (lat <= lat_max))[0]\n",
    "            indices_lon = np.where((lon >= lon_min) & (lon <= lon_max))[0]\n",
    "            start_lat, end_lat = indices_lat[0], indices_lat[-1] + 1\n",
    "            start_lon, end_lon = indices_lon[0], indices_lon[-1] + 1\n",
    "        else:\n",
    "            start_lat, end_lat = 0, len(lat)\n",
    "            start_lon, end_lon = 0, len(lon)\n",
    "        \n",
    "        lat = lat[start_lat:end_lat]\n",
    "        lon = lon[start_lon:end_lon]\n",
    "\n",
    "        # Berechnet die räumliche Auflösung und die Rastertransformation\n",
    "        dx = abs(lon[1] - lon[0])\n",
    "        dy = abs(lat[1] - lat[0])\n",
    "        transform = from_origin(lon.min() - dx / 2, lat.min() - dy / 2, dx, -dy)\n",
    "            \n",
    "        # Extrahiere Variablen-Daten\n",
    "        variable_data = nc_dataset.variables[nc_file['variable_name']]\n",
    "        variable_data_subset = variable_data[:]\n",
    "\n",
    "        output_filename = f\"{nc_file['variable_name']}-{nc_file['statistic']}.tif\"\n",
    "        \n",
    "        # Export als GeoTIFF-Dateien\n",
    "        if output_directory:\n",
    "            subset_directory_path = output_directory\n",
    "        else:\n",
    "            subset_directory_path = os.path.join(geotiff_folder, f\"{selected_variable}\")\n",
    "            os.makedirs(subset_directory_path, exist_ok=True)\n",
    "        \n",
    "        for year_index in tqdm(range(variable_data_subset.shape[0]),\n",
    "                            desc=\"Exportieren GeoTIFF-Dateien\"):\n",
    "            \n",
    "            # Definiere den Ausgabe-Dateipfad\n",
    "            output_filepath = os.path.join(subset_directory_path, output_filename)\n",
    "\n",
    "            # Exportiere den aktuellen Zeitschritt als GeoTIFF\n",
    "            with rasterio.open(\n",
    "                output_filepath,\n",
    "                \"w\",\n",
    "                driver=\"GTiff\",\n",
    "                dtype=str(variable_data_subset.dtype),\n",
    "                width=variable_data_subset.shape[2],\n",
    "                height=variable_data_subset.shape[1],\n",
    "                count=1,\n",
    "                crs=\"EPSG:4326\",\n",
    "                nodata=-9999,\n",
    "                transform=transform,\n",
    "            ) as dst:\n",
    "                time_slice_data = variable_data_subset[year_index, :, :]\n",
    "                dst.write(time_slice_data , 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Ausgewählte NetCDF4-Datei(en) in GeoTIFF-Datei(en) exportieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f71562ec01d04b03887c06a98ec5814b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d19bd422d7b4b219be3ecab53cc26b2",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4d2dc40033a49c39371e83bff0239f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49c78e2079e24f80a0bffc5887777684",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    # Exportieren Sie alle NetCDF-Dateien als GeoTIFF-Dateien.\n",
    "    for nc_file in nc_files_subset:\n",
    "        main_export_geotiff(\n",
    "            nc_file=nc_file,\n",
    "            bounding_box=None,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Analyse und Visualisierung Optionen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Definieren eine Funktion zur Erstellung einer Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "\n",
    "def main_plt_plot(\n",
    "        nc_file=nc_file,\n",
    "        bounding_box=None):\n",
    "    \n",
    "    # Öffnet die NetCDF-Datei\n",
    "    nc_dataset = nc.Dataset(nc_file['path'], mode='r')\n",
    "    latitudes = nc_dataset.variables['latitude'][:]\n",
    "    longitudes = nc_dataset.variables['longitude'][:]\n",
    "\n",
    "    # Falls eine Begrenzungsbox angegeben wurde, filtere die Daten entsprechend\n",
    "    if bounding_box:\n",
    "        lat_indices = np.where((latitudes >= bounding_box[1]) & (latitudes <= bounding_box[3]))[0]\n",
    "        lon_indices = np.where((longitudes >= bounding_box[0]) & (longitudes <= bounding_box[2]))[0]\n",
    "\n",
    "        lat_subset = latitudes[lat_indices]\n",
    "        lon_subset = longitudes[lon_indices]\n",
    "    else:\n",
    "        lat_indices = slice(None)\n",
    "        lon_indices = slice(None)\n",
    "\n",
    "        lat_subset = latitudes\n",
    "        lon_subset = longitudes\n",
    "\n",
    "    # Extrahiere Variablen-Daten\n",
    "    variable_name = nc_file['variable_name']\n",
    "    variable_data = nc_dataset.variables[variable_name][..., lat_indices, lon_indices]\n",
    "    var_units = getattr(nc_dataset.variables[variable_name], \"units\", \"N/A\")\n",
    "    var_longname = getattr(nc_dataset.variables[variable_name], \"long_name\", \"N/A\")\n",
    "\n",
    "    # NaN-Werte für Perzentilberechnungen entfernen\n",
    "    band_data_nonan = variable_data[~np.isnan(variable_data)]\n",
    "    vmin = np.nanpercentile(band_data_nonan, 1)\n",
    "    vmax = np.nanpercentile(band_data_nonan, 99)\n",
    "\n",
    "    def dynamic_round(value):\n",
    "        # Bestimmen Sie die Größe des Wertes.\n",
    "        order_of_magnitude = np.floor(np.log10(abs(value)))\n",
    "        \n",
    "        # Verwenden Sie diese Größe, um die Genauigkeit dynamisch zu wählen.\n",
    "        if order_of_magnitude < -2:  # Werte kleiner als 0,01\n",
    "            return round(value, 3)\n",
    "        elif order_of_magnitude < -1:  # Werte zwischen 0,01 und 1\n",
    "            return round(value, 2)\n",
    "        elif order_of_magnitude < 0:  # Werte zwischen 1 und 10\n",
    "            return round(value, 1)\n",
    "        else:  # Werte 10 oder größer\n",
    "            return round(value)\n",
    "    \n",
    "    # Dynamische Rundung auf vmin und vmax anwenden\n",
    "    vmin = dynamic_round(vmin)\n",
    "    vmax = dynamic_round(vmax)\n",
    "\n",
    "    bins = 10\n",
    "    interval = (vmax - vmin) / bins\n",
    "\n",
    "    # Erstellen Sie ein 2D-Netzgitter für die grafische Darstellung.\n",
    "    lon_grid, lat_grid = np.meshgrid(lon_subset, lat_subset)\n",
    "\n",
    "    # Create the plot\n",
    "    fig, ax = plt.subplots(\n",
    "        figsize=(12, 8),\n",
    "        facecolor='#f1f1f1',\n",
    "        edgecolor='k',\n",
    "        subplot_kw={'projection': ccrs.PlateCarree()}\n",
    "    )\n",
    "\n",
    "    # Kartenmerkmale hinzufügen\n",
    "    ax.coastlines(edgecolor='black', linewidth=0.5)\n",
    "    ax.add_feature(cfeature.BORDERS, edgecolor='black', linewidth=0.5)\n",
    "\n",
    "    # Erstellen Sie ein Farbnetzdiagramm mit der angegebenen Farbkarte und den Grenzen.\n",
    "    cmap = plt.get_cmap(\"viridis\", bins)\n",
    "    pcm = ax.pcolormesh(\n",
    "        lon_grid, lat_grid, variable_data[0, :, :],\n",
    "        transform=ccrs.PlateCarree(),\n",
    "        cmap=cmap,\n",
    "        shading='auto',\n",
    "        vmin=vmin,\n",
    "        vmax=vmax\n",
    "    )\n",
    "\n",
    "    # Passe die Kartenausdehnung an die Daten an\n",
    "    ax.set_extent([lon_subset.min(), lon_subset.max(), lat_subset.min(), lat_subset.max()], crs=ccrs.PlateCarree())\n",
    "\n",
    "    # Einen Farbbalken hinzufügen\n",
    "    ticks = np.linspace(vmin, vmax, num=bins + 1)\n",
    "    cbar = plt.colorbar(pcm, ax=ax, orientation='vertical', pad=0.02, ticks=ticks)\n",
    "    cbar.set_label(f\"{var_longname}, ({variable_name}), {var_units}\", fontsize=12)\n",
    "    cbar.ax.tick_params(labelsize=12)\n",
    "    \n",
    "    # Gitterlinien hinzufügen\n",
    "    gl = ax.gridlines(draw_labels=True,\n",
    "                      crs=ccrs.PlateCarree(),\n",
    "                      linewidth=0.8,\n",
    "                      color='gray',\n",
    "                      alpha=0.7,\n",
    "                      linestyle='--')\n",
    "    gl.top_labels = False \n",
    "    gl.right_labels = False\n",
    "    gl.xlabel_style = {'size': 10, 'color': 'black'}\n",
    "    gl.ylabel_style = {'size': 10, 'color': 'black'}\n",
    "    \n",
    "    # Add title and labels\n",
    "    fig.text(0.5, 0.0, 'Longitude', ha='center', fontsize=14)\n",
    "    fig.text(0.04, 0.5, 'Latitude', va='center', rotation='vertical', fontsize=14)\n",
    "    ax.set_aspect(\"equal\")\n",
    "\n",
    "    # Titel und Beschriftungen hinzufügen\n",
    "    ax.set_title(f\"{var_longname} ({variable_name})\", fontsize=14)\n",
    "\n",
    "    # Layout anpassen und das Diagramm anzeigen\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ShaileshShrestha\\anaconda3\\envs\\cds_env\\lib\\site-packages\\numpy\\lib\\_function_base_impl.py:4842: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n",
      "  arr.partition(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    # Beispiel für einen Anwendungsfall\n",
    "    main_plt_plot(nc_file=nc_files_subset[0],\n",
    "                  bounding_box=bbox_wgs84_de_standard\n",
    "    )"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}