07_Sentinel_Boxplot_vergleichsfläche.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e1041601",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasterio.plot import show\n",
    "from rasterio.merge import merge\n",
    "import rasterio as rio\n",
    "from rasterio import features\n",
    "import rioxarray\n",
    "\n",
    "from pathlib import Path\n",
    "import glob\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526e96b7",
   "metadata": {},
   "source": [
    "Zwischenspeichern des Raster Stacks auf der Festplatte. <br>\n",
    "Da alle Raster-Dateien des Arbeitsgebietes die gleiche Ausdehnung haben, können sie einfach mit rasterio aufeinandergestackt werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a769d279",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def make_stack (path, file_string, appendix):\n",
    "    #getall file paths from path\n",
    "    #path = 'C:/Uni Jena/GEO 411/Data/Sentinel-2/NDVI_20m_arbeitsgebiete/Thueringen/'\n",
    "    #file_string = \"ND\"\n",
    "    file_list = glob.glob(path + file_string+'*.tiff')\n",
    "    str_start = -13\n",
    "    str_end = -5\n",
    "\n",
    "    # Read metadata of first file\n",
    "    with rio.open(file_list[0]) as src0:\n",
    "        meta = src0.meta\n",
    "        # mein Proj4 Pfad ist kaputt, deshalb hier der explizite Proj4 String\n",
    "        meta.update({'crs': 'PROJCS[\"ETRS89 / UTM zone 32N\",GEOGCS[\"ETRS89\",DATUM[\"European_Terrestrial_Reference_System_1989\",SPHEROID[\"GRS 1980\",6378137,298.257222101,AUTHORITY[\"EPSG\",\"7019\"]],AUTHORITY[\"EPSG\",\"6258\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.01745329251994328,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4258\"]],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],AUTHORITY[\"EPSG\",\"25832\"],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]'})\n",
    "\n",
    "    # Update meta to reflect the number of layers\n",
    "    meta.update(count = len(file_list))\n",
    "\n",
    "    # Read each layer and write it to stack\n",
    "    with rio.open(path + 'stack_' +appendix + '.tif', 'w', **meta) as dst:\n",
    "        for id, layer in enumerate(file_list, start=1):\n",
    "            with rio.open(layer) as src1:\n",
    "                dst.write_band(id, src1.read(1))\n",
    "                time = pd.to_datetime(layer[str_start:str_end], format='%Y%m%d')\n",
    "                # speichere Zeitstempel in Band descriptions\n",
    "                dst.set_band_description(id, str(time))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "492b478a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reclassify(input_ds):\n",
    "    for i in range(len(input_ds)):\n",
    "        arr = input_ds[i].values\n",
    "        arr[np.where(arr< 5)] = 0\n",
    "        arr[np.where((arr> 5) & (arr <= 25))] = 1\n",
    "        arr[np.where((arr> 25) & (arr <= 45))] = 2\n",
    "        arr[np.where((arr> 45) & (arr <= 65))] = 3\n",
    "        arr[np.where((arr> 65) & (arr <= 85))] = 4\n",
    "        arr[np.where(arr> 85)] = 5\n",
    "        input_ds[i] = arr\n",
    "    return input_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c62d505c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make_stack('C:/Uni Jena/GEO 411/Data/Sentinel-2/NDVI_20m_arbeitsgebiete/Thueringen/', 'ND', 'NDVI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff8b3205",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make_stack('C:/Uni Jena/GEO 411/Data/Sentinel-2/NDWI_20m_arbeitsgebiete/Thueringen/', 'ND', 'NDWI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90cdfe11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make_stack('C:/Uni Jena/GEO 411/Data/Sentinel-2/NDVI_20m_arbeitsgebiete/Thueringen/', 'SCL', 'SCL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a33e56d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make_stack('C:/Uni Jena/GEO 411/Data/Sentinel-2/NDVI_20m_arbeitsgebiete/Unterfranken/', 'ND', 'NDVI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e56bc248",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make_stack('C:/Uni Jena/GEO 411/Data/Sentinel-2/NDWI_20m_arbeitsgebiete/Unterfranken/', 'ND', 'NDWI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "176d74fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make_stack('C:/Uni Jena/GEO 411/Data/Sentinel-2/NDVI_20m_arbeitsgebiete/Unterfranken/', 'SCL', 'SCL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "392a84db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cba1da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndvi_thueringen = rioxarray.open_rasterio('C:/Uni Jena/GEO 411/Data/Sentinel-2/NDVI_20m_arbeitsgebiete/Thueringen/stack_NDVI.tif')\n",
    "ndwi_thueringen = rioxarray.open_rasterio('C:/Uni Jena/GEO 411/Data/Sentinel-2/NDWI_20m_arbeitsgebiete/Thueringen/stack_NDWI.tif')\n",
    "SCL_thueringen = rioxarray.open_rasterio('C:/Uni Jena/GEO 411/Data/Sentinel-2/NDVI_20m_arbeitsgebiete/Thueringen/stack_SCL.tif')\n",
    "\n",
    "ndvi_thueringen = ndvi_thueringen.assign_coords({\"band\": (pd.to_datetime(list(ndvi_thueringen.long_name)))})\n",
    "ndwi_thueringen = ndwi_thueringen.assign_coords({\"band\": (pd.to_datetime(list(ndwi_thueringen.long_name)))})\n",
    "SCL_thueringen = SCL_thueringen.assign_coords({\"band\": (pd.to_datetime(list(SCL_thueringen.long_name)))})\n",
    "\n",
    "ndvi_thueringen.attrs['_FillValue'] = np.nan\n",
    "ndwi_thueringen.attrs['_FillValue'] = np.nan\n",
    "\n",
    "ndvi_thueringen.attrs['long_name'] = None\n",
    "ndwi_thueringen.attrs['long_name'] = None\n",
    "SCL_thueringen.attrs['long_name'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af2047d6",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6fc877e",
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  },
  {
   "cell_type": "markdown",
   "id": "f1b82e30",
   "metadata": {},
   "source": [
    "Das einzige, was bei dem oberen Datensatz nicht so schön ist, ist dass immer alle time stamps aus dem Attribut \"long_name\" angezeigt werden.\n",
    "Ich hatte versucht, mit assin_coords() den einzelnen Arrays eine \"time\" zuzuordnen, aber das hat nur einmal funktioniert und dann wurde es einfach nicht mehr im xarray gespeichert...\n",
    "Zur Not kann man aber immer die Position des bands im Stack mit der Position in dem long_name array gleichsetzen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63ad8f25",
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e22ac48",
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e67d4947",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
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  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c713cb82",
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b6b513",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0858b065",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_cloud = xr.where(((SCL_thueringen == 0) | \n",
    "                 (SCL_thueringen == 1) | \n",
    "                 (SCL_thueringen == 3) | \n",
    "                 (SCL_thueringen == 8) | \n",
    "                 (SCL_thueringen == 9) | \n",
    "                 (SCL_thueringen == 10) | \n",
    "                 (SCL_thueringen == 11) |\n",
    "                 (SCL_thueringen == 255)), True, False)\n",
    "\n",
    "#mask only cloud free\n",
    "mask = xr.where((is_cloud == 0), True, False)\n",
    "ndvi_thueringen = ndvi_thueringen.where(mask, np.nan)\n",
    "ndwi_thueringen = ndwi_thueringen.where(mask, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30adca24",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d406156",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_obs_per_year = mask.resample(band='1Y').sum(dim='band')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11984605",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "11741dfa",
   "metadata": {},
   "source": [
    "### Pfad unten anpassen mit Staunässe shapefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2dc6a341",
   "metadata": {},
   "outputs": [],
   "source": [
    "geodf = gpd.read_file(r'C:\\Uni Jena\\GEO 411\\Data\\Feldgrenzen\\thueringen_stauenaesse_bekannt.shp')\n",
    "trocken = gpd.read_file(r'C:\\Uni Jena\\GEO 411\\Data\\Feldgrenzen\\vergleichsfelder.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "28757337",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get geometries of waterlogged fields\n",
    "geodf = geodf[geodf['cat']=='staunaesse']\n",
    "geodf = geodf.set_crs('epsg:25832', allow_override=True)\n",
    "trocken = trocken.set_crs('epsg:25832', allow_override=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e09ebf9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "stau0 = geodf[geodf['field_id']==0].geometry\n",
    "stau1 = geodf[geodf['field_id']==1].geometry\n",
    "stau2 = geodf[geodf['field_id']==2].geometry\n",
    "stau3 = geodf[geodf['field_id']==3].geometry\n",
    "stau4 = geodf[geodf['field_id']==4].geometry\n",
    "stau5 = geodf[geodf['field_id']==5].geometry\n",
    "stau6 = geodf[geodf['field_id']==6].geometry\n",
    "\n",
    "liste_stau = [stau0, stau1, stau2, stau3, stau4, stau5, stau6]\n",
    "\n",
    "trock0 = trocken[trocken.index==0].geometry\n",
    "trock1 = trocken[trocken.index==1].geometry\n",
    "trock2 = trocken[trocken.index==2].geometry\n",
    "trock3 = trocken[trocken.index==3].geometry\n",
    "trock4 = trocken[trocken.index==4].geometry\n",
    "trock5 = trocken[trocken.index==5].geometry\n",
    "trock6 = trocken[trocken.index==6].geometry\n",
    "trock7 = trocken[trocken.index==7].geometry\n",
    "\n",
    "liste_trock = [trock0, trock1, trock2, trock3, trock4, trock5, trock6, trock7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f038c79f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "liste_stau[1].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "33f7cd6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "liste_trock[1].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d7be471",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95401699",
   "metadata": {},
   "outputs": [],
   "source": [
    "staux = geodf.geometry\n",
    "trockx = trocken.geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6417d92b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#staux.to_file(r'C:\\Uni Jena\\GEO 411\\plots\\Sentinel_Karten\\thueringen_staunaesse_reproj.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc3b3abc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46509b65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80e19d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "liste_stau_ndwi = []\n",
    "liste_stau_ndvi = []\n",
    "for i in range (len(liste_stau)):\n",
    "    res = ndwi_thueringen.rio.clip(liste_stau[i], all_touched=True)\n",
    "    liste_stau_ndwi.append(res)\n",
    "    res1 = ndvi_thueringen.rio.clip(liste_stau[i], all_touched=True)\n",
    "    liste_stau_ndvi.append(res1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf8c5cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "liste_trock_ndwi = []\n",
    "liste_trock_ndvi = []\n",
    "for i in range (len(liste_trock)):\n",
    "    res = ndwi_thueringen.rio.clip(liste_trock[i], all_touched=True)\n",
    "    liste_trock_ndwi.append(res)\n",
    "    res1 = ndvi_thueringen.rio.clip(liste_trock[i], all_touched=True)\n",
    "    liste_trock_ndvi.append(res1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "204acb84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.6658416  -0.6644622  -0.6559356  -0.65491587 -0.6488472  -0.6442532\n",
      " -0.6422703  -0.64081424 -0.6408121  -0.6402798  -0.63920385 -0.63920164\n",
      " -0.6387712  -0.6377171  -0.6369404  -0.63692534 -0.63607025 -0.6353004\n",
      " -0.63444823 -0.6270214 ]\n",
      "[-0.06855278 -0.03258356 -0.02264906 -0.01448838 -0.01213513 -0.0015674\n",
      "  0.00315325  0.00387297  0.00900901  0.01026444  0.01376743  0.01401186\n",
      "  0.01626988  0.01642382  0.01742613  0.01845018  0.01860687  0.02162044\n",
      "  0.02163808  0.02212478]\n",
      "[-0.7060051  -0.69493866 -0.6887912  -0.68773735 -0.6807596  -0.6780735\n",
      " -0.6682742  -0.66116685 -0.6539337  -0.6531165  -0.6513393  -0.6506338\n",
      " -0.6474398  -0.6466587  -0.64455956 -0.64439195 -0.6404701  -0.6378883\n",
      " -0.6350549  -0.6344239 ]\n",
      "[-0.16903006 -0.1507552  -0.13282356 -0.12770745 -0.11687161 -0.11315909\n",
      " -0.11153626 -0.09444445 -0.09023202 -0.08869395 -0.08670348 -0.08360974\n",
      " -0.08354289 -0.07746639 -0.07417037 -0.06526668 -0.05971155 -0.05803093\n",
      " -0.05349713 -0.05137913]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(liste_stau_ndwi[0], return_counts=True)[0][:20])\n",
    "print(np.unique(liste_stau_ndvi[0], return_counts=True)[0][:20])\n",
    "\n",
    "print(np.unique(liste_trock_ndwi[0], return_counts=True)[0][:20])\n",
    "print(np.unique(liste_trock_ndvi[0], return_counts=True)[0][:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "db23c6be",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 22, x: 31)&gt;\n",
       "array([[        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan, -0.27804065,         nan, -0.25698757, -0.2785426 ,\n",
       "        -0.3175853 ,         nan,         nan, -0.39115986,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan, -0.22173351,         nan, -0.31138977,         nan,\n",
       "        -0.38183886,         nan,         nan,         nan, -0.37307692,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "...\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan, -0.2648305 , -0.25057766, -0.2644125 ,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan]], dtype=float32)\n",
       "Coordinates:\n",
       "    band         datetime64[ns] 2017-05-17\n",
       "  * x            (x) float64 6.698e+05 6.698e+05 ... 6.704e+05 6.704e+05\n",
       "  * y            (y) float64 5.626e+06 5.626e+06 ... 5.625e+06 5.625e+06\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    scale_factor:   1.0\n",
       "    add_offset:     0.0\n",
       "    long_name:      None\n",
       "    _FillValue:     nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 22</li><li><span class='xr-has-index'>x</span>: 31</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d647856a-146b-47dc-aad2-7286e63465b6' class='xr-array-in' type='checkbox' checked><label for='section-d647856a-146b-47dc-aad2-7286e63465b6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan, -0.27804065,         nan, -0.25698757, -0.2785426 ,\n",
       "        -0.3175853 ,         nan,         nan, -0.39115986,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan, -0.22173351,         nan, -0.31138977,         nan,\n",
       "        -0.38183886,         nan,         nan,         nan, -0.37307692,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "...\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan, -0.2648305 , -0.25057766, -0.2644125 ,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-6fb346bd-75c7-4bc2-ad82-bfa11e74b4bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6fb346bd-75c7-4bc2-ad82-bfa11e74b4bf' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-05-17</div><input id='attrs-64d55885-d76a-493c-a967-6ad3e65cc6ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64d55885-d76a-493c-a967-6ad3e65cc6ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fd72d3d-cadb-46c1-aee0-b5c51b113f96' class='xr-var-data-in' type='checkbox'><label for='data-6fd72d3d-cadb-46c1-aee0-b5c51b113f96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2017-05-17T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.698e+05 6.698e+05 ... 6.704e+05</div><input id='attrs-a16667b5-43b7-464b-9ef8-2bc8890bc421' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a16667b5-43b7-464b-9ef8-2bc8890bc421' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90a874b8-d9ed-4a94-8abb-e1e9a1b96cae' class='xr-var-data-in' type='checkbox'><label for='data-90a874b8-d9ed-4a94-8abb-e1e9a1b96cae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([669816.056674, 669836.103593, 669856.150512, 669876.197431,\n",
       "       669896.24435 , 669916.29127 , 669936.338189, 669956.385108,\n",
       "       669976.432027, 669996.478946, 670016.525865, 670036.572784,\n",
       "       670056.619703, 670076.666623, 670096.713542, 670116.760461,\n",
       "       670136.80738 , 670156.854299, 670176.901218, 670196.948137,\n",
       "       670216.995056, 670237.041975, 670257.088895, 670277.135814,\n",
       "       670297.182733, 670317.229652, 670337.276571, 670357.32349 ,\n",
       "       670377.370409, 670397.417328, 670417.464247])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.626e+06 5.626e+06 ... 5.625e+06</div><input id='attrs-31c41cec-9b35-48a5-abc0-31180be4d118' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31c41cec-9b35-48a5-abc0-31180be4d118' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e32f84e-c1ef-4b19-aefa-04900720fc99' class='xr-var-data-in' type='checkbox'><label for='data-2e32f84e-c1ef-4b19-aefa-04900720fc99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([5625537.268002, 5625517.221083, 5625497.174164, 5625477.127245,\n",
       "       5625457.080325, 5625437.033406, 5625416.986487, 5625396.939568,\n",
       "       5625376.892649, 5625356.84573 , 5625336.798811, 5625316.751892,\n",
       "       5625296.704973, 5625276.658053, 5625256.611134, 5625236.564215,\n",
       "       5625216.517296, 5625196.470377, 5625176.423458, 5625156.376539,\n",
       "       5625136.32962 , 5625116.282701])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-0237969e-e695-42e1-8974-36443f62f8cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0237969e-e695-42e1-8974-36443f62f8cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-460957bb-1a82-483d-994f-182d09822384' class='xr-var-data-in' type='checkbox'><label for='data-460957bb-1a82-483d-994f-182d09822384' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89 / UTM zone 32N&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;25832&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89 / UTM zone 32N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89 / UTM zone 32N&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;25832&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>669806.0332144563 20.046919116024704 0.0 5625547.291461521 0.0 -20.04691911599643</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-164c6fbf-f32f-4bfc-98bc-13ab90ae548c' class='xr-section-summary-in' type='checkbox'  ><label for='section-164c6fbf-f32f-4bfc-98bc-13ab90ae548c' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba6cac21-53ca-4650-8e90-3e9d6de5140b' class='xr-index-data-in' type='checkbox'/><label for='index-ba6cac21-53ca-4650-8e90-3e9d6de5140b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([669816.0566740143, 669836.1035931304, 669856.1505122464,\n",
       "              669876.1974313624, 669896.2443504784, 669916.2912695945,\n",
       "              669936.3381887105, 669956.3851078264, 669976.4320269425,\n",
       "              669996.4789460585, 670016.5258651746, 670036.5727842906,\n",
       "              670056.6197034066, 670076.6666225226, 670096.7135416387,\n",
       "              670116.7604607547, 670136.8073798707, 670156.8542989867,\n",
       "              670176.9012181028, 670196.9481372188, 670216.9950563348,\n",
       "              670237.0419754508, 670257.0888945669, 670277.1358136829,\n",
       "              670297.1827327989, 670317.2296519149,  670337.276571031,\n",
       "               670357.323490147,  670377.370409263,  670397.417328379,\n",
       "               670417.464247495],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-19357bfc-0974-4760-88e3-6b7f9aa9a077' class='xr-index-data-in' type='checkbox'/><label for='index-19357bfc-0974-4760-88e3-6b7f9aa9a077' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 5625537.268001962,  5625517.221082847,  5625497.174163731,\n",
       "               5625477.127244615,  5625457.080325498,  5625437.033406382,\n",
       "               5625416.986487267,  5625396.939568151,  5625376.892649034,\n",
       "               5625356.845729918,  5625336.798810802,  5625316.751891687,\n",
       "               5625296.704972571,  5625276.658053454,  5625256.611134338,\n",
       "               5625236.564215222,  5625216.517296107,   5625196.47037699,\n",
       "               5625176.423457874,  5625156.376538758,  5625136.329619642,\n",
       "              5625116.2827005265],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a1ba31b4-808f-4993-9719-36204f305716' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a1ba31b4-808f-4993-9719-36204f305716' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>None</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div></li></ul></div></div>"
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       "        -0.38183886,         nan,         nan,         nan, -0.37307692,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "...\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan, -0.2648305 , -0.25057766, -0.2644125 ,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan],\n",
       "       [        nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan,         nan,         nan,         nan,         nan,\n",
       "                nan]], dtype=float32)\n",
       "Coordinates:\n",
       "    band         datetime64[ns] 2017-05-17\n",
       "  * x            (x) float64 6.698e+05 6.698e+05 ... 6.704e+05 6.704e+05\n",
       "  * y            (y) float64 5.626e+06 5.626e+06 ... 5.625e+06 5.625e+06\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    scale_factor:   1.0\n",
       "    add_offset:     0.0\n",
       "    long_name:      None\n",
       "    _FillValue:     nan"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liste_trock_ndwi[0][9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8821bea1",
   "metadata": {},
   "outputs": [
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       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 22, x: 31)&gt;\n",
       "array([[       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 0.2808926 ,        nan, 0.2924428 , 0.3128659 ,\n",
       "        0.33235034,        nan,        nan, 0.3769737 ,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 0.17345925,        nan, 0.33050624,        nan,\n",
       "        0.4884433 ,        nan,        nan,        nan, 0.4363974 ,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "...\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 0.37537798, 0.3405807 , 0.3369863 ,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan]], dtype=float32)\n",
       "Coordinates:\n",
       "    band         datetime64[ns] 2017-05-17\n",
       "  * x            (x) float64 6.698e+05 6.698e+05 ... 6.704e+05 6.704e+05\n",
       "  * y            (y) float64 5.626e+06 5.626e+06 ... 5.625e+06 5.625e+06\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    scale_factor:   1.0\n",
       "    add_offset:     0.0\n",
       "    long_name:      None\n",
       "    _FillValue:     nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 22</li><li><span class='xr-has-index'>x</span>: 31</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1189347f-ee40-4c15-bd39-cafb04323986' class='xr-array-in' type='checkbox' checked><label for='section-1189347f-ee40-4c15-bd39-cafb04323986' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 0.2808926 ,        nan, 0.2924428 , 0.3128659 ,\n",
       "        0.33235034,        nan,        nan, 0.3769737 ,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 0.17345925,        nan, 0.33050624,        nan,\n",
       "        0.4884433 ,        nan,        nan,        nan, 0.4363974 ,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "...\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 0.37537798, 0.3405807 , 0.3369863 ,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-1d53485a-6ea3-436f-901e-4329d80628ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d53485a-6ea3-436f-901e-4329d80628ec' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-05-17</div><input id='attrs-12d739d1-08e9-4240-bc0c-02963acf88ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12d739d1-08e9-4240-bc0c-02963acf88ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-887fc1ec-9c22-454c-9aa4-f3ab469137b6' class='xr-var-data-in' type='checkbox'><label for='data-887fc1ec-9c22-454c-9aa4-f3ab469137b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2017-05-17T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.698e+05 6.698e+05 ... 6.704e+05</div><input id='attrs-e4503535-b442-4ac8-a1f3-6b0cdfb13708' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4503535-b442-4ac8-a1f3-6b0cdfb13708' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-befde92d-9b13-4b88-933d-950ca6aa4852' class='xr-var-data-in' type='checkbox'><label for='data-befde92d-9b13-4b88-933d-950ca6aa4852' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([669816.056674, 669836.103593, 669856.150512, 669876.197431,\n",
       "       669896.24435 , 669916.29127 , 669936.338189, 669956.385108,\n",
       "       669976.432027, 669996.478946, 670016.525865, 670036.572784,\n",
       "       670056.619703, 670076.666623, 670096.713542, 670116.760461,\n",
       "       670136.80738 , 670156.854299, 670176.901218, 670196.948137,\n",
       "       670216.995056, 670237.041975, 670257.088895, 670277.135814,\n",
       "       670297.182733, 670317.229652, 670337.276571, 670357.32349 ,\n",
       "       670377.370409, 670397.417328, 670417.464247])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.626e+06 5.626e+06 ... 5.625e+06</div><input id='attrs-ec485475-57bd-4565-a796-aa7e119b8720' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec485475-57bd-4565-a796-aa7e119b8720' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8ad3dd6-6e3d-4ca4-b29e-1757422032b5' class='xr-var-data-in' type='checkbox'><label for='data-c8ad3dd6-6e3d-4ca4-b29e-1757422032b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([5625537.268002, 5625517.221083, 5625497.174164, 5625477.127245,\n",
       "       5625457.080325, 5625437.033406, 5625416.986487, 5625396.939568,\n",
       "       5625376.892649, 5625356.84573 , 5625336.798811, 5625316.751892,\n",
       "       5625296.704973, 5625276.658053, 5625256.611134, 5625236.564215,\n",
       "       5625216.517296, 5625196.470377, 5625176.423458, 5625156.376539,\n",
       "       5625136.32962 , 5625116.282701])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-020daf89-b7c4-414d-a9a7-ea6234769e00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-020daf89-b7c4-414d-a9a7-ea6234769e00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15b32c50-14ea-4c95-b40c-0e9e713fa7a2' class='xr-var-data-in' type='checkbox'><label for='data-15b32c50-14ea-4c95-b40c-0e9e713fa7a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;ETRS89 / UTM zone 32N&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;25832&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>ETRS89</dd><dt><span>horizontal_datum_name :</span></dt><dd>European Terrestrial Reference System 1989</dd><dt><span>projected_crs_name :</span></dt><dd>ETRS89 / UTM zone 32N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;ETRS89 / UTM zone 32N&quot;,GEOGCS[&quot;ETRS89&quot;,DATUM[&quot;European_Terrestrial_Reference_System_1989&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6258&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4258&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;25832&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>669806.0332144563 20.046919116024704 0.0 5625547.291461521 0.0 -20.04691911599643</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8f33c925-3bbb-4338-a89e-d39679396aae' class='xr-section-summary-in' type='checkbox'  ><label for='section-8f33c925-3bbb-4338-a89e-d39679396aae' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5ef0f1c2-eb5b-4606-a8a2-007416dd06f8' class='xr-index-data-in' type='checkbox'/><label for='index-5ef0f1c2-eb5b-4606-a8a2-007416dd06f8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([669816.0566740143, 669836.1035931304, 669856.1505122464,\n",
       "              669876.1974313624, 669896.2443504784, 669916.2912695945,\n",
       "              669936.3381887105, 669956.3851078264, 669976.4320269425,\n",
       "              669996.4789460585, 670016.5258651746, 670036.5727842906,\n",
       "              670056.6197034066, 670076.6666225226, 670096.7135416387,\n",
       "              670116.7604607547, 670136.8073798707, 670156.8542989867,\n",
       "              670176.9012181028, 670196.9481372188, 670216.9950563348,\n",
       "              670237.0419754508, 670257.0888945669, 670277.1358136829,\n",
       "              670297.1827327989, 670317.2296519149,  670337.276571031,\n",
       "               670357.323490147,  670377.370409263,  670397.417328379,\n",
       "               670417.464247495],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6f2d948a-fca8-4478-8e46-62e1cea7c0e1' class='xr-index-data-in' type='checkbox'/><label for='index-6f2d948a-fca8-4478-8e46-62e1cea7c0e1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 5625537.268001962,  5625517.221082847,  5625497.174163731,\n",
       "               5625477.127244615,  5625457.080325498,  5625437.033406382,\n",
       "               5625416.986487267,  5625396.939568151,  5625376.892649034,\n",
       "               5625356.845729918,  5625336.798810802,  5625316.751891687,\n",
       "               5625296.704972571,  5625276.658053454,  5625256.611134338,\n",
       "               5625236.564215222,  5625216.517296107,   5625196.47037699,\n",
       "               5625176.423457874,  5625156.376538758,  5625136.329619642,\n",
       "              5625116.2827005265],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2b0105c-74fa-4e2e-958b-c4cba15c7f83' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2b0105c-74fa-4e2e-958b-c4cba15c7f83' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>long_name :</span></dt><dd>None</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (y: 22, x: 31)>\n",
       "array([[       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 0.2808926 ,        nan, 0.2924428 , 0.3128659 ,\n",
       "        0.33235034,        nan,        nan, 0.3769737 ,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 0.17345925,        nan, 0.33050624,        nan,\n",
       "        0.4884433 ,        nan,        nan,        nan, 0.4363974 ,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "...\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan, 0.37537798, 0.3405807 , 0.3369863 ,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan,        nan,        nan,        nan,        nan,\n",
       "               nan]], dtype=float32)\n",
       "Coordinates:\n",
       "    band         datetime64[ns] 2017-05-17\n",
       "  * x            (x) float64 6.698e+05 6.698e+05 ... 6.704e+05 6.704e+05\n",
       "  * y            (y) float64 5.626e+06 5.626e+06 ... 5.625e+06 5.625e+06\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    scale_factor:   1.0\n",
       "    add_offset:     0.0\n",
       "    long_name:      None\n",
       "    _FillValue:     nan"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liste_trock_ndvi[0][9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "89bf3e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(liste_stau_ndwi)):\n",
    "    liste_stau_ndwi[i].rio.to_raster(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped\"+str(i)+\"_stau_ndwi.tiff\")\n",
    "    liste_stau_ndvi[i].rio.to_raster(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped\"+str(i)+\"_stau_ndvi.tiff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fe68eaa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(liste_trock_ndwi)):\n",
    "    liste_trock_ndwi[i].rio.to_raster(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped\"+str(i)+\"_trock_ndwi.tiff\")\n",
    "    liste_trock_ndvi[i].rio.to_raster(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped\"+str(i)+\"_trock_ndvi.tiff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16fd5db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "clipped_ndvi = ndvi_thueringen.rio.clip(staux, all_touched=True)\n",
    "clipped_ndwi = ndwi_thueringen.rio.clip(staux, all_touched=True)\n",
    "\n",
    "mask_clip = mask.rio.clip(staux, all_touched=True)\n",
    "\n",
    "clipped_ndvi = clipped_ndvi.where(mask_clip, np.nan)\n",
    "clipped_ndwi = clipped_ndwi.where(mask_clip, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "695d4191",
   "metadata": {},
   "outputs": [],
   "source": [
    "clipped_ndvi.rio.to_raster(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped_staunass_ndvi.tiff\")\n",
    "clipped_ndwi.rio.to_raster(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped_staunass_ndwi.tiff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c6ca3fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "clipped_ndvi_trocken = ndvi_thueringen.rio.clip(trockx, all_touched=True)\n",
    "clipped_ndwi_trocken = ndwi_thueringen.rio.clip(trockx, all_touched=True)\n",
    "\n",
    "mask_trocken = mask.rio.clip(trockx, all_touched=True)\n",
    "\n",
    "clipped_ndvi_trocken = clipped_ndvi_trocken.where(mask_trocken, np.nan)\n",
    "clipped_ndwi_trocken = clipped_ndwi_trocken.where(mask_trocken, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "af9b0948",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clipped_ndvi_trocken.rio.to_raster(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped_trocken_ndvi.tiff\")\n",
    "clipped_ndwi_trocken.rio.to_raster(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped_trocken_ndwi.tiff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b90b9e",
   "metadata": {},
   "source": [
    "## Hier sollte ein Boxplot erstellt werden\n",
    "- für jeden Array einen: clipped0, clipped1 ... clipped0_ndvi ... \n",
    "- clipped_ndwi und clipped_ndvi sind ein subset über alle Polygone mit Staunässe\n",
    "- xr.resample ist eine gute Methode um Zeiträume zusammenzufassen z.B. res = sample.resample(time='1Y').mean(dim='time')\n",
    "- mit der resample mthode kannst du auch \"4Y\" für den gesamten Zeitraum einstellen\n",
    "-Auf diesem zweidimensionalen Array lässt sich ein Boxp,lot erstellen und andere Parameter ableiten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d5b897ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read data if alread y creatred above\n",
    "staunass_ndvi = rioxarray.open_rasterio(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped_staunass_ndvi.tiff\").assign_coords({\"band\": ndvi_thueringen.band})\n",
    "staunass_ndwi = rioxarray.open_rasterio(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped_staunass_ndwi.tiff\").assign_coords({\"band\": ndwi_thueringen.band})\n",
    "\n",
    "trocken_ndvi = rioxarray.open_rasterio(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped_trocken_ndvi.tiff\").assign_coords({\"band\": ndvi_thueringen.band})\n",
    "trocken_ndwi = rioxarray.open_rasterio(\"C:/Uni Jena/GEO 411/Data/Sentinel-2/Boxplots/clipped_trocken_ndwi.tiff\").assign_coords({\"band\": ndwi_thueringen.band})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0d2503e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#staunass_ndvi = staunass_ndvi.where(((staunass_ndvi['band.month'] >=3) & (staunass_ndvi['band.month'] <= 6)), drop=True)\n",
    "#staunass_ndwi = staunass_ndwi.where(((staunass_ndwi['band.month'] >=3) & (staunass_ndwi['band.month'] <= 6)), drop=True)\n",
    "\n",
    "#trocken_ndvi = trocken_ndvi.where(((trocken_ndvi['band.month'] >=3) & (trocken_ndvi['band.month'] <= 6)), drop=True)\n",
    "#trocken_ndwi = trocken_ndwi.where(((trocken_ndwi['band.month'] >=3) & (trocken_ndwi['band.month'] <= 6)), drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44184f7b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cfd10dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r'C:\\Uni Jena\\GEO 411\\Data\\Sentinel-2\\Boxplots\\\\'\n",
    "sndvi_list = glob.glob(path+ '*_stau_ndvi*.tiff')\n",
    "tndvi_list = glob.glob(path+ '*_trock_ndvi*.tiff')\n",
    "sndwi_list = glob.glob(path+ '*_stau_ndwi*.tiff')\n",
    "tndwi_list = glob.glob(path+ '*_trock_ndwi*.tiff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3451c73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped0_trock_ndwi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped1_trock_ndwi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped2_trock_ndwi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped3_trock_ndwi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped4_trock_ndwi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped5_trock_ndwi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped6_trock_ndwi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped7_trock_ndwi.tiff']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tndwi_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e93bea45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped0_trock_ndvi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped1_trock_ndvi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped2_trock_ndvi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped3_trock_ndvi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped4_trock_ndvi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped5_trock_ndvi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped6_trock_ndvi.tiff',\n",
       " 'C:\\\\Uni Jena\\\\GEO 411\\\\Data\\\\Sentinel-2\\\\Boxplots\\\\clipped7_trock_ndvi.tiff']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tndvi_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "707a261e",
   "metadata": {},
   "outputs": [],
   "source": [
    "liste_stau_ndwi = [rioxarray.open_rasterio(i).assign_coords({\"band\": ndwi_thueringen.band}) for i in sndwi_list]\n",
    "liste_stau_ndvi = [rioxarray.open_rasterio(i).assign_coords({\"band\": ndvi_thueringen.band}) for i in sndvi_list]\n",
    "liste_trock_ndwi = [rioxarray.open_rasterio(i).assign_coords({\"band\": ndwi_thueringen.band}) for i in tndwi_list]\n",
    "liste_trock_ndvi = [rioxarray.open_rasterio(i).assign_coords({\"band\": ndvi_thueringen.band}) for i in tndvi_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1730cd0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e063516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "\n",
    "#ndvi mean\n",
    "sb.boxplot([staunass_ndvi.mean(dim=\"band\").values, trocken_ndvi.mean(dim=\"band\").values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ebd951c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.6658416  -0.6644622  -0.6559356  -0.65491587 -0.6488472  -0.6442532\n",
      " -0.6422703  -0.64081424 -0.6408121  -0.6402798  -0.63920385 -0.63920164\n",
      " -0.6387712  -0.6377171  -0.6369404  -0.63692534 -0.63607025 -0.6353004\n",
      " -0.63444823 -0.6270214 ]\n",
      "[-0.06855278 -0.03258356 -0.02264906 -0.01448838 -0.01213513 -0.0015674\n",
      "  0.00315325  0.00387297  0.00900901  0.01026444  0.01376743  0.01401186\n",
      "  0.01626988  0.01642382  0.01742613  0.01845018  0.01860687  0.02162044\n",
      "  0.02163808  0.02212478]\n",
      "[-0.7060051  -0.69493866 -0.6887912  -0.68773735 -0.6807596  -0.6780735\n",
      " -0.6682742  -0.66116685 -0.6539337  -0.6531165  -0.6513393  -0.6506338\n",
      " -0.6474398  -0.6466587  -0.64455956 -0.64439195 -0.6404701  -0.6378883\n",
      " -0.6350549  -0.6344239 ]\n",
      "[-0.16903006 -0.1507552  -0.13282356 -0.12770745 -0.11687161 -0.11315909\n",
      " -0.11153626 -0.09444445 -0.09023202 -0.08869395 -0.08670348 -0.08360974\n",
      " -0.08354289 -0.07746639 -0.07417037 -0.06526668 -0.05971155 -0.05803093\n",
      " -0.05349713 -0.05137913]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(liste_stau_ndwi[0], return_counts=True)[0][:20])\n",
    "print(np.unique(liste_stau_ndvi[0], return_counts=True)[0][:20])\n",
    "print(np.unique(liste_trock_ndwi[0], return_counts=True)[0][:20])\n",
    "print(np.unique(liste_trock_ndvi[0], return_counts=True)[0][:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "32dac763",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funktionen zur Erstellung von Boxplotdaten pro Feld für die einzelnen Lagemaße\n",
    "\n",
    "\n",
    "def get_mean_data(data):\n",
    "\n",
    "    boxplot_data = data.mean(dim=(\"band\"))\n",
    "\n",
    "    return boxplot_data.to_numpy()\n",
    "\n",
    "\n",
    "def get_median_data(data):\n",
    "\n",
    "    boxplot_data = data.median(dim=(\"band\"))\n",
    "\n",
    "    return boxplot_data.to_numpy()\n",
    "\n",
    "\n",
    "def get_25_percentile_data(data):\n",
    "\n",
    "    boxplot_data = data.quantile(q=0.25, dim=(\"band\"))\n",
    "\n",
    "    return boxplot_data.to_numpy()\n",
    "\n",
    "\n",
    "def get_75_percentile_data(data):\n",
    "\n",
    "    boxplot_data = data.quantile(q=0.75, dim=(\"band\"))\n",
    "\n",
    "    return boxplot_data.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "387a17a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "C:\\Users\\apelt\\anaconda3\\envs\\RADOLAN\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1577: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n"
     ]
    }
   ],
   "source": [
    "# Einzelne Felddaten\n",
    "\n",
    "gesamt_list_ndvi = [staunass_ndvi, trocken_ndvi]\n",
    "gesamt_list_ndwi = [staunass_ndwi, trocken_ndwi]\n",
    "\n",
    "mean_trock_list = []\n",
    "median_trock_list = []\n",
    "quantil_25_trock_list = []\n",
    "quantil_75_trock_list = []\n",
    "\n",
    "mean_stau_list = []\n",
    "median_stau_list = []\n",
    "quantil_25_stau_list = []\n",
    "quantil_75_stau_list = []\n",
    "\n",
    "mean_trock_list_ndvi = []\n",
    "median_trock_list_ndvi = []\n",
    "quantil_25_trock_list_ndvi = []\n",
    "quantil_75_trock_list_ndvi = []\n",
    "\n",
    "mean_stau_list_ndvi = []\n",
    "median_stau_list_ndvi = []\n",
    "quantil_25_stau_list_ndvi = []\n",
    "quantil_75_stau_list_ndvi = []\n",
    "\n",
    "\n",
    "########################################\n",
    "mean_gesamt_list_ndvi = []\n",
    "median_gesamt_list_ndvi = []\n",
    "quantil_25_gesamt_list_ndvi = []\n",
    "quantil_75_gesamt_list_ndvi = []\n",
    "\n",
    "mean_gesamt_list_ndwi = []\n",
    "median_gesamt_list_ndwi = []\n",
    "quantil_25_gesamt_list_ndwi = []\n",
    "quantil_75_gesamt_list_ndwi = []\n",
    "\n",
    "\n",
    "for field in liste_stau_ndwi:\n",
    "\n",
    "    # Mean\n",
    "    field_mean = get_mean_data(field)\n",
    "    mean_stau_list.append(field_mean)\n",
    "\n",
    "\n",
    "    # Median\n",
    "    field_median = get_median_data(field)\n",
    "    median_stau_list.append(field_median)\n",
    "\n",
    "\n",
    "    # percentile 5\n",
    "    field_quant_25 = get_25_percentile_data(field)\n",
    "    quantil_25_stau_list.append(field_quant_25)\n",
    "\n",
    "\n",
    "    # percentile 75\n",
    "    field_quant_75 = get_75_percentile_data(field)\n",
    "    quantil_75_stau_list.append(field_quant_75)\n",
    "    \n",
    "\n",
    "for field in liste_trock_ndwi:\n",
    "\n",
    "    # Mean\n",
    "    field_mean = get_mean_data(field)\n",
    "    mean_trock_list.append(field_mean)\n",
    "\n",
    "\n",
    "    # Median\n",
    "    field_median = get_median_data(field)\n",
    "    median_trock_list.append(field_median)\n",
    "\n",
    "\n",
    "    # percentile 5\n",
    "    field_quant_25 = get_25_percentile_data(field)\n",
    "    quantil_25_trock_list.append(field_quant_25)\n",
    "\n",
    "\n",
    "    # percentile 75\n",
    "    field_quant_75 = get_75_percentile_data(field)\n",
    "    quantil_75_trock_list.append(field_quant_75)\n",
    "    \n",
    "\n",
    "for field in liste_stau_ndvi:\n",
    "\n",
    "    # Mean\n",
    "    field_mean = get_mean_data(field)\n",
    "    mean_stau_list_ndvi.append(field_mean)\n",
    "\n",
    "\n",
    "    # Median\n",
    "    field_median = get_median_data(field)\n",
    "    median_stau_list_ndvi.append(field_median)\n",
    "\n",
    "\n",
    "    # percentile 5\n",
    "    field_quant_25 = get_25_percentile_data(field)\n",
    "    quantil_25_stau_list_ndvi.append(field_quant_25)\n",
    "\n",
    "\n",
    "    # percentile 75\n",
    "    field_quant_75 = get_75_percentile_data(field)\n",
    "    quantil_75_stau_list_ndvi.append(field_quant_75)\n",
    "    \n",
    "\n",
    "for field in liste_trock_ndvi:\n",
    "\n",
    "    # Mean\n",
    "    field_mean = get_mean_data(field)\n",
    "    mean_trock_list_ndvi.append(field_mean)\n",
    "\n",
    "\n",
    "    # Median\n",
    "    field_median = get_median_data(field)\n",
    "    median_trock_list_ndvi.append(field_median)\n",
    "\n",
    "\n",
    "    # percentile 5\n",
    "    field_quant_25 = get_25_percentile_data(field)\n",
    "    quantil_25_trock_list_ndvi.append(field_quant_25)\n",
    "\n",
    "\n",
    "    # percentile 75\n",
    "    field_quant_75 = get_75_percentile_data(field)\n",
    "    quantil_75_trock_list_ndvi.append(field_quant_75)\n",
    "    \n",
    "    \n",
    "for field in gesamt_list_ndvi:\n",
    "\n",
    "    # Mean\n",
    "    field_mean = get_mean_data(field)\n",
    "    mean_gesamt_list_ndvi.append(field_mean)\n",
    "\n",
    "\n",
    "    # Median\n",
    "    field_median = get_median_data(field)\n",
    "    median_gesamt_list_ndvi.append(field_median)\n",
    "\n",
    "\n",
    "    # percentile 5\n",
    "    field_quant_25 = get_25_percentile_data(field)\n",
    "    quantil_25_gesamt_list_ndvi.append(field_quant_25)\n",
    "\n",
    "\n",
    "    # percentile 75\n",
    "    field_quant_75 = get_75_percentile_data(field)\n",
    "    quantil_75_gesamt_list_ndvi.append(field_quant_75)\n",
    "    \n",
    "    \n",
    "for field in gesamt_list_ndwi:\n",
    "\n",
    "    # Mean\n",
    "    field_mean = get_mean_data(field)\n",
    "    mean_gesamt_list_ndwi.append(field_mean)\n",
    "\n",
    "\n",
    "    # Median\n",
    "    field_median = get_median_data(field)\n",
    "    median_gesamt_list_ndwi.append(field_median)\n",
    "\n",
    "\n",
    "    # percentile 5\n",
    "    field_quant_25 = get_25_percentile_data(field)\n",
    "    quantil_25_gesamt_list_ndwi.append(field_quant_25)\n",
    "\n",
    "\n",
    "    # percentile 75\n",
    "    field_quant_75 = get_75_percentile_data(field)\n",
    "    quantil_75_gesamt_list_ndwi.append(field_quant_75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "af44d742",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sb.boxplot(array_data)\n",
    "# sb.boxplot(mean_field_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f93fa4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'mean NDVI'), [Text(0, 0, 'staunass'), Text(1, 0, 'trocken')]]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(mean_gesamt_list_ndvi).set( \n",
    "    ylabel='mean NDVI',\n",
    "    xticklabels=['staunass','trocken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fcfe9b47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'mean NDWI'), [Text(0, 0, 'staunass'), Text(1, 0, 'trocken')]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(mean_gesamt_list_ndwi).set( \n",
    "    ylabel='mean NDWI',\n",
    "    xticklabels=['staunass','trocken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7ad5da7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'median NDWI'), [Text(0, 0, 'staunass'), Text(1, 0, 'trocken')]]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(median_gesamt_list_ndwi).set( \n",
    "    ylabel='median NDWI',\n",
    "    xticklabels=['staunass','trocken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "414e3708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'median NDVI'), [Text(0, 0, 'staunass'), Text(1, 0, 'trocken')]]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(median_gesamt_list_ndvi).set( \n",
    "    ylabel='median NDVI',\n",
    "    xticklabels=['staunass','trocken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "19421e43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'quantile 25 NDWI'),\n",
       " [Text(0, 0, 'staunass'), Text(1, 0, 'trocken')]]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(quantil_25_gesamt_list_ndwi).set( \n",
    "    ylabel='quantile 25 NDWI',\n",
    "    xticklabels=['staunass','trocken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4a7d05b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'quantile 25 NDVI'),\n",
       " [Text(0, 0, 'staunass'), Text(1, 0, 'trocken')]]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(quantil_25_gesamt_list_ndvi).set( \n",
    "    ylabel='quantile 25 NDVI',\n",
    "    xticklabels=['staunass','trocken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9f18c108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'quantile 75 NDWI'),\n",
       " [Text(0, 0, 'staunass'), Text(1, 0, 'trocken')]]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(quantil_75_gesamt_list_ndwi).set( \n",
    "    ylabel='quantile 75 NDWI',\n",
    "    xticklabels=['staunass','trocken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "41344538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'quantile 75 NDVI'),\n",
       " [Text(0, 0, 'staunass'), Text(1, 0, 'trocken')]]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(quantil_75_gesamt_list_ndvi).set( \n",
    "    ylabel='quantile 75 NDVI',\n",
    "    xticklabels=['staunass','trocken'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38580ca5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ec27e76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5991969f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa2a4e46",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "16123536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "sb.boxplot(mean_stau_list+mean_trock_list).set( \n",
    "    ylabel='mean NDWI',\n",
    "    xticklabels=['Feld0 (n)','Feld1 (n)', 'Feld2 (n)','Feld3 (n)', 'Feld4 (n)','Feld5 (n)', \n",
    "                 'Feld6 (n)','Feld0 (t)', 'Feld1 (t)','Feld2 (t)', 'Feld3 (t)','Feld4 (5)', \n",
    "                 'Feld5 (t)','Feld6 (t)', 'Feld7 (t)'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e37f65b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "82295731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Op+nwcD/eruIeytOn3E828gjbHhkMBqlWqy19PvJfjo6O0t3d3QZPtT30ydOnnEZ5+uTpU06jcqs0ms/nqdFopG63+40n2x4a5emTp89yX/mNfn9/T+PxeG+WRym5h8roU+4nG/kHEgAAAABZ/oEEAAAAQJYFEgAAAABZFkgAAAAAZFkgAQAAAJBlgQQAAABAlgUSAAAAAFkWSAAAAABkWSABAAAAkGWBBAAAAEDWHwTlYb+zytdAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sb.boxplot(median_stau_list+median_trock_list).set( \n",
    "    ylabel='median NDWI',\n",
    "    xticklabels=['Feld0 (n)','Feld1 (n)', 'Feld2 (n)','Feld3 (n)', 'Feld4 (n)','Feld5 (n)', \n",
    "                 'Feld6 (n)','Feld0 (t)', 'Feld1 (t)','Feld2 (t)', 'Feld3 (t)','Feld4 (5)', \n",
    "                 'Feld5 (t)','Feld6 (t)', 'Feld7 (t)'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "70479a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sb.boxplot(quantil_25_stau_list+quantil_25_trock_list).set( \n",
    "    ylabel='quantile 25 NDWI',\n",
    "    xticklabels=['Feld0 (n)','Feld1 (n)', 'Feld2 (n)','Feld3 (n)', 'Feld4 (n)','Feld5 (n)', \n",
    "                 'Feld6 (n)','Feld0 (t)', 'Feld1 (t)','Feld2 (t)', 'Feld3 (t)','Feld4 (5)', \n",
    "                 'Feld5 (t)','Feld6 (t)', 'Feld7 (t)'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "600fdca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sb.boxplot(quantil_75_stau_list+quantil_75_trock_list).set( \n",
    "    ylabel='quantile 75 NDWI',\n",
    "    xticklabels=['Feld0 (n)','Feld1 (n)', 'Feld2 (n)','Feld3 (n)', 'Feld4 (n)','Feld5 (n)', \n",
    "                 'Feld6 (n)','Feld0 (t)', 'Feld1 (t)','Feld2 (t)', 'Feld3 (t)','Feld4 (5)', \n",
    "                 'Feld5 (t)','Feld6 (t)', 'Feld7 (t)'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3b905ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8b1342d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sb.boxplot(mean_stau_list_ndvi+mean_trock_list_ndvi).set( \n",
    "    ylabel='mean NDVI',\n",
    "    xticklabels=['Feld0 (n)','Feld1 (n)', 'Feld2 (n)','Feld3 (n)', 'Feld4 (n)','Feld5 (n)', \n",
    "                 'Feld6 (n)','Feld0 (t)', 'Feld1 (t)','Feld2 (t)', 'Feld3 (t)','Feld4 (5)', \n",
    "                 'Feld5 (t)','Feld6 (t)', 'Feld7 (t)'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "845d20a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sb.boxplot(median_stau_list_ndvi+median_trock_list_ndvi).set( \n",
    "    ylabel='median NDVI',\n",
    "    xticklabels=['Feld0 (n)','Feld1 (n)', 'Feld2 (n)','Feld3 (n)', 'Feld4 (n)','Feld5 (n)', \n",
    "                 'Feld6 (n)','Feld0 (t)', 'Feld1 (t)','Feld2 (t)', 'Feld3 (t)','Feld4 (5)', \n",
    "                 'Feld5 (t)','Feld6 (t)', 'Feld7 (t)'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5898f369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sb.boxplot(quantil_25_stau_list_ndvi+quantil_25_trock_list_ndvi).set( \n",
    "    ylabel='quantile 25 NDVI',\n",
    "    xticklabels=['Feld0 (n)','Feld1 (n)', 'Feld2 (n)','Feld3 (n)', 'Feld4 (n)','Feld5 (n)', \n",
    "                 'Feld6 (n)','Feld0 (t)', 'Feld1 (t)','Feld2 (t)', 'Feld3 (t)','Feld4 (5)', \n",
    "                 'Feld5 (t)','Feld6 (t)', 'Feld7 (t)'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0d91357b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sb.boxplot(quantil_75_stau_list_ndvi+quantil_75_trock_list_ndvi).set( \n",
    "    ylabel='quantile 75 NDVI',\n",
    "    xticklabels=['Feld0 (n)','Feld1 (n)', 'Feld2 (n)','Feld3 (n)', 'Feld4 (n)','Feld5 (n)', \n",
    "                 'Feld6 (n)','Feld0 (t)', 'Feld1 (t)','Feld2 (t)', 'Feld3 (t)','Feld4 (5)', \n",
    "                 'Feld5 (t)','Feld6 (t)', 'Feld7 (t)'])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c42cb6f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}