Scale-up-Non-Dask.ipynb

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   "source": [
    "# Goal of this notebook is to scale through a cluster to calculate NDVI across 128+ Landsat Scenes (using DASK)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uses rio-xarray pkg"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "(https://landsat.usgs.gov/landsat_acq)\n",
    "Use the tools on this page to determine when the Landsat 7 and Landsat 8 satellites acquire data over your area of interest, and to view the paths that were acquired on any given day. \n",
    "\n",
    "Landsat satellites image the entire Earth every 16 days in an 8-day offset. Landsat 7 acquires data in descending (daytime) node, while Landsat 8 acquires data in both descending and occasional ascending (nighttime) node. "
   ]
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   "source": [
    "The Landsat 8 satellite orbits the the Earth in a sun-synchronous, near-polar orbit, at an altitude of 705 km (438 mi), inclined at 98.2 degrees, and circles the Earth every 99 minutes. The satellite has a 16-day repeat cycle with an equatorial crossing time: 10:00 a.m. +/- 15 minutes.\n",
    "\n",
    "Landsat satellites image the entire Earth every 16 days in an 8-day offset. Landsat 7 acquires data in descending (daytime) node, while Landsat 8 acquires data in both descending and occasional ascending (nighttime) node. \n",
    "\n",
    "Row refers to the latitudinal center line of a frame of imagery. As the satellite moves along its path, the observatory instruments are continuously scanning the terrain below.  These will be squares centered on the orbital path, but tilted clockwise when views on the UTM projection used for the distributed data."
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200608_20200626_01_T1/'\n",
    "redband = url+'LC08_L1TP_227065_20200608_20200626_01_T1_B{}.TIF'.format(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-02-14 21:36:29--  http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200608_20200626_01_T1/LC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF\n",
      "Resolving landsat-pds.s3.amazonaws.com (landsat-pds.s3.amazonaws.com)... 52.218.251.35\n",
      "Connecting to landsat-pds.s3.amazonaws.com (landsat-pds.s3.amazonaws.com)|52.218.251.35|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 59222647 (56M) [image/tiff]\n",
      "Saving to: ‘LC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF.19’\n",
      "\n",
      "LC08_L1TP_227065_20 100%[===================>]  56.48M   109MB/s    in 0.5s    \n",
      "\n",
      "2021-02-14 21:36:30 (109 MB/s) - ‘LC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF.19’ saved [59222647/59222647]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget {redband}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Driver: GTiff/GeoTIFF\n",
      "Files: LC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF\n",
      "Size is 7621, 7761\n",
      "Coordinate System is:\n",
      "PROJCRS[\"WGS 84 / UTM zone 21N\",\n",
      "    BASEGEOGCRS[\"WGS 84\",\n",
      "        DATUM[\"World Geodetic System 1984\",\n",
      "            ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
      "                LENGTHUNIT[\"metre\",1]]],\n",
      "        PRIMEM[\"Greenwich\",0,\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
      "        ID[\"EPSG\",4326]],\n",
      "    CONVERSION[\"UTM zone 21N\",\n",
      "        METHOD[\"Transverse Mercator\",\n",
      "            ID[\"EPSG\",9807]],\n",
      "        PARAMETER[\"Latitude of natural origin\",0,\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433],\n",
      "            ID[\"EPSG\",8801]],\n",
      "        PARAMETER[\"Longitude of natural origin\",-57,\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433],\n",
      "            ID[\"EPSG\",8802]],\n",
      "        PARAMETER[\"Scale factor at natural origin\",0.9996,\n",
      "            SCALEUNIT[\"unity\",1],\n",
      "            ID[\"EPSG\",8805]],\n",
      "        PARAMETER[\"False easting\",500000,\n",
      "            LENGTHUNIT[\"metre\",1],\n",
      "            ID[\"EPSG\",8806]],\n",
      "        PARAMETER[\"False northing\",0,\n",
      "            LENGTHUNIT[\"metre\",1],\n",
      "            ID[\"EPSG\",8807]]],\n",
      "    CS[Cartesian,2],\n",
      "        AXIS[\"(E)\",east,\n",
      "            ORDER[1],\n",
      "            LENGTHUNIT[\"metre\",1]],\n",
      "        AXIS[\"(N)\",north,\n",
      "            ORDER[2],\n",
      "            LENGTHUNIT[\"metre\",1]],\n",
      "    USAGE[\n",
      "        SCOPE[\"unknown\"],\n",
      "        AREA[\"World - N hemisphere - 60°W to 54°W - by country\"],\n",
      "        BBOX[0,-60,84,-54]],\n",
      "    ID[\"EPSG\",32621]]\n",
      "Data axis to CRS axis mapping: 1,2\n",
      "Origin = (573285.000000000000000,-683685.000000000000000)\n",
      "Pixel Size = (30.000000000000000,-30.000000000000000)\n",
      "Metadata:\n",
      "  AREA_OR_POINT=Point\n",
      "Image Structure Metadata:\n",
      "  COMPRESSION=DEFLATE\n",
      "  INTERLEAVE=BAND\n",
      "Corner Coordinates:\n",
      "Upper Left  (  573285.000, -683685.000) ( 56d20'15.33\"W,  6d11' 5.49\"S)\n",
      "Lower Left  (  573285.000, -916515.000) ( 56d20' 4.24\"W,  8d17'27.11\"S)\n",
      "Upper Right (  801915.000, -683685.000) ( 54d16'19.33\"W,  6d10'41.77\"S)\n",
      "Lower Right (  801915.000, -916515.000) ( 54d15'33.74\"W,  8d16'55.22\"S)\n",
      "Center      (  687600.000, -800100.000) ( 55d18' 3.19\"W,  7d14' 6.72\"S)\n",
      "Band 1 Block=512x512 Type=UInt16, ColorInterp=Gray\n"
     ]
    }
   ],
   "source": [
    "!gdalinfo LC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xa\n",
    "red = xa.open_rasterio('LC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'+init=epsg:32621'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7621, 7761)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red.shape[2],red.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "  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 (band: 1, y: 7761, x: 7621)&gt;\n",
       "[59146581 values with dtype=uint16]\n",
       "Coordinates:\n",
       "  * band     (band) int64 1\n",
       "  * y        (y) float64 -6.837e+05 -6.837e+05 ... -9.165e+05 -9.165e+05\n",
       "  * x        (x) float64 5.733e+05 5.733e+05 5.734e+05 ... 8.019e+05 8.019e+05\n",
       "Attributes:\n",
       "    transform:      (30.0, 0.0, 573285.0, 0.0, -30.0, -683685.0)\n",
       "    crs:            +init=epsg:32621\n",
       "    res:            (30.0, 30.0)\n",
       "    is_tiled:       1\n",
       "    nodatavals:     (nan,)\n",
       "    scales:         (1.0,)\n",
       "    offsets:        (0.0,)\n",
       "    AREA_OR_POINT:  Point</pre><div class='xr-wrap' hidden><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'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 7761</li><li><span class='xr-has-index'>x</span>: 7621</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-be279b75-def6-41af-b966-910a88eddfa9' class='xr-array-in' type='checkbox' checked><label for='section-be279b75-def6-41af-b966-910a88eddfa9' 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>...</span></div><div class='xr-array-data'><pre>[59146581 values with dtype=uint16]</pre></div></div></li><li class='xr-section-item'><input id='section-018a5d10-e484-4c5c-8a10-647e04b12097' class='xr-section-summary-in' type='checkbox'  checked><label for='section-018a5d10-e484-4c5c-8a10-647e04b12097' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-3d53be00-c558-437d-a449-7f764f0aa1cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d53be00-c558-437d-a449-7f764f0aa1cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75b6c62e-cfb2-4f73-93fb-26e9e46d3bb9' class='xr-var-data-in' type='checkbox'><label for='data-75b6c62e-cfb2-4f73-93fb-26e9e46d3bb9' 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([1])</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'>-6.837e+05 ... -9.165e+05</div><input id='attrs-e700fc5b-ebf3-4c83-b44a-7b9e3cd9ac78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e700fc5b-ebf3-4c83-b44a-7b9e3cd9ac78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acc7aaeb-7c21-49ab-a53b-44853c36bbf1' class='xr-var-data-in' type='checkbox'><label for='data-acc7aaeb-7c21-49ab-a53b-44853c36bbf1' 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([-683700., -683730., -683760., ..., -916440., -916470., -916500.])</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'>5.733e+05 5.733e+05 ... 8.019e+05</div><input id='attrs-028be7d2-0b9f-481b-b694-dbe8fae04a4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-028be7d2-0b9f-481b-b694-dbe8fae04a4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ef195f1-f917-46fc-89ba-64f229311922' class='xr-var-data-in' type='checkbox'><label for='data-0ef195f1-f917-46fc-89ba-64f229311922' 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([573300., 573330., 573360., ..., 801840., 801870., 801900.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b0293b3-645d-41eb-9771-e9ccd71558c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b0293b3-645d-41eb-9771-e9ccd71558c4' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(30.0, 0.0, 573285.0, 0.0, -30.0, -683685.0)</dd><dt><span>crs :</span></dt><dd>+init=epsg:32621</dd><dt><span>res :</span></dt><dd>(30.0, 30.0)</dd><dt><span>is_tiled :</span></dt><dd>1</dd><dt><span>nodatavals :</span></dt><dd>(nan,)</dd><dt><span>scales :</span></dt><dd>(1.0,)</dd><dt><span>offsets :</span></dt><dd>(0.0,)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Point</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (band: 1, y: 7761, x: 7621)>\n",
       "[59146581 values with dtype=uint16]\n",
       "Coordinates:\n",
       "  * band     (band) int64 1\n",
       "  * y        (y) float64 -6.837e+05 -6.837e+05 ... -9.165e+05 -9.165e+05\n",
       "  * x        (x) float64 5.733e+05 5.733e+05 5.734e+05 ... 8.019e+05 8.019e+05\n",
       "Attributes:\n",
       "    transform:      (30.0, 0.0, 573285.0, 0.0, -30.0, -683685.0)\n",
       "    crs:            +init=epsg:32621\n",
       "    res:            (30.0, 30.0)\n",
       "    is_tiled:       1\n",
       "    nodatavals:     (nan,)\n",
       "    scales:         (1.0,)\n",
       "    offsets:        (0.0,)\n",
       "    AREA_OR_POINT:  Point"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/pyproj/crs/crs.py:280: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  projstring = _prepare_from_string(projparams)\n"
     ]
    }
   ],
   "source": [
    "import rioxarray\n",
    "import xarray\n",
    "\n",
    "#xds = xarray.open_rasterio(\"myfile.tif\")\n",
    "#wgs84_xds = xds.rio.reproject(\"EPSG:4326\")\n",
    "red.rio.to_raster(\"myfile_wgs84.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rasterio\n",
    "from rasterio.plot import show\n",
    "src = rasterio.open(\"myfile_wgs84.tif\")\n",
    "\n",
    "show(src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[573285.0, -916515.0, 801915.0, -683685.0]\n"
     ]
    }
   ],
   "source": [
    "import gdal\n",
    "from gdalconst import GA_ReadOnly\n",
    "\n",
    "data = gdal.Open('LC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF', GA_ReadOnly)\n",
    "geoTransform = data.GetGeoTransform()\n",
    "minx = geoTransform[0]\n",
    "maxy = geoTransform[3]\n",
    "maxx = minx + geoTransform[1] * data.RasterXSize\n",
    "miny = maxy + geoTransform[5] * data.RasterYSize\n",
    "print ([minx, miny, maxx, maxy])\n",
    "data = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EPSG:4326\n",
    "WGS 84 -- WGS84 - World Geodetic System 1984, used in GPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reproject \"EPSG\",32621 to EPSG:4326"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets find the bounding box for the region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<osgeo.gdal.Dataset; proxy of <Swig Object of type 'GDALDatasetShadow *' at 0x7fc788785f30> >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from osgeo import gdal\n",
    "\n",
    "input_raster=gdal.Open('LC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF')\n",
    "output_raster = \"Reproj\" + 'LC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF'\n",
    "gdal.Warp(output_raster,input_raster,dstSRS='EPSG:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-56.33759064732078, -8.290860873734875, -54.2594726602711, -6.178270448812246]\n"
     ]
    }
   ],
   "source": [
    "data = gdal.Open('ReprojLC08_L1TP_227065_20200608_20200626_01_T1_B4.TIF', GA_ReadOnly)\n",
    "geoTransform = data.GetGeoTransform()\n",
    "minx = geoTransform[0]\n",
    "maxy = geoTransform[3]\n",
    "maxx = minx + geoTransform[1] * data.RasterXSize\n",
    "miny = maxy + geoTransform[5] * data.RasterYSize\n",
    "print ([minx, miny, maxx, maxy])\n",
    "data = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have trouble getting the right scene to cover the area you want:\n",
    "\n",
    "Go to https://landsat.usgs.gov/wrs-2-pathrow-latitudelongitude-converter  and enter the lat/long. \n",
    "Then note the path row, and when you get the Landsat data, insure you have the correct path and row, which are listed in the download table.\n",
    "Use https://landsat.usgs.gov/landsat_acq tool, which will show the coverage for each data\n",
    "Path/Row shapefiles and KML: https://www.usgs.gov/land-resources/nli/landsat/landsat-shapefiles-and-kml-files\n",
    "KML file direct download:https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/WRS-2_bound_world_0.kml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get list of images\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "URL = 'https://landsatonaws.com/L8/227/065/'\n",
    "page = requests.get(URL)\n",
    "\n",
    "soup = BeautifulSoup(page.content, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use beautiful soup to extract all the links, and Tier 1 only\n",
    "LevelOnes=[]\n",
    "for link in soup.findAll('a', attrs={'href': re.compile(\"^/L8/227/065\")}):\n",
    "    #print(link.get('href'))\n",
    "    href = link.get('href')\n",
    "    if href.endswith(\"T1\"):\n",
    "        LevelOnes.append(href)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(LevelOnes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Base links\n",
    "LevelOnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "LevelOnes=['/L8/227/065/LC08_L1TP_227065_20201201_20201217_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20201115_20201210_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200928_20201007_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200912_20200920_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200827_20200905_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200827_20200905_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200811_20200822_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200726_20200807_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200710_20200721_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200624_20200707_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200608_20200626_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200608_20200626_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200523_20200607_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200507_20200509_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200405_20200410_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20200217_20200225_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20191215_20201023_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20191215_20191226_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20191129_20191216_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20191028_20191114_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20191012_20191018_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190926_20191017_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190910_20190917_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190825_20190903_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190724_20190801_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190708_20190719_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190622_20190704_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190606_20190619_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190505_20190520_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190505_20190520_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190419_20190423_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20190318_20190325_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20181126_20181210_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20181126_20181210_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20181110_20181127_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20181025_20181114_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20181009_20181029_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180923_20180929_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180923_20180929_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180907_20180912_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180907_20180912_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180822_20180829_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180806_20180815_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180721_20180731_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180705_20180717_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180705_20180717_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180705_20180717_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180619_20180703_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180619_20180703_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180518_20180604_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180502_20180516_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180227_20180308_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20180110_20180119_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20171123_20171206_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20171022_20171107_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20171006_20171023_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170904_20170916_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170904_20170916_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170904_20170916_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170904_20170916_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170819_20170826_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170803_20170812_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170718_20170727_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170718_20170727_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170718_20170727_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170718_20170727_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170702_20170715_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170616_20170629_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170616_20180528_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170616_20170629_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170531_20170615_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170515_20170525_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170413_20170501_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170312_20170317_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20170107_20170311_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20161206_20180528_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20161104_20170318_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20161003_20170320_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160917_20170321_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160901_20170321_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160816_20170322_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160731_20170322_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160715_20170323_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160629_20170323_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160613_20170324_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160528_20170324_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160512_20170324_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160426_20170326_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160410_20170326_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160222_20170329_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20160206_20170330_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20151204_20170401_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20151118_20170402_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20151102_20170402_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20151017_20170403_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20151001_20170403_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150915_20170404_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150830_20170405_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150814_20170406_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150729_20170406_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150713_20170407_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150627_20170407_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150611_20170408_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150526_20170408_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150510_20170409_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150424_20170409_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150408_20170410_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150203_20170413_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20150102_20170415_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20141217_20170416_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20141115_20180528_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20141014_20170418_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140928_20170419_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140912_20170419_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140827_20170420_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140811_20170420_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140726_20170421_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140710_20170421_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140624_20170421_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140608_20170422_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140523_20170422_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140507_20170422_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140421_20170423_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20140320_20170425_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20131011_20170429_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20130909_20170502_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20130824_20170502_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20130808_20170503_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20130723_20170503_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20130707_20170503_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20130621_20170503_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20130605_20170504_01_T1',\n",
    " '/L8/227/065/LC08_L1TP_227065_20130520_20170504_01_T1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Populate the collection of scenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets add Band 4 (Red) only\n",
    "base=\"http://landsat-pds.s3.amazonaws.com/c1\"\n",
    "redlinks=[]\n",
    "baselinks=[]\n",
    "for link in LevelOnes:\n",
    "    #print(link.split('/')[4])\n",
    "    redband = base+link+\"/\"+link.split('/')[4]+'_B{}.TIF'.format(4)\n",
    "    #print(redband)\n",
    "    baselink = base+link+\"/\"+link.split('/')[4]\n",
    "    baselinks.append(baselink)\n",
    "    redlinks.append(redband)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(LevelOnes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20130520_20170504_01_T1/LC08_L1TP_227065_20130520_20170504_01_T1_B4.TIF'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "redband"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'LC08_L1TP_227065_20201201_20201217_01_T1'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baselinks[0].split('/')[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Landsat on AWS:\n",
      "{'driver': 'GTiff', 'dtype': 'uint16', 'nodata': None, 'width': 7621, 'height': 7761, 'count': 1, 'crs': CRS.from_epsg(32621), 'transform': Affine(30.0, 0.0, 573285.0,\n",
      "       0.0, -30.0, -683685.0), 'blockxsize': 512, 'blockysize': 512, 'tiled': True, 'compress': 'deflate', 'interleave': 'band'}\n"
     ]
    }
   ],
   "source": [
    "import rasterio\n",
    "print('Landsat on AWS:')\n",
    "with rasterio.open(redlinks[0]) as src:\n",
    "    print(src.profile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing a non-DASK task which calculates NDVI of each pixel in the large scene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate and display NDVI : Uses the MTL to calculate Landsat scene's image as reflectance values, and then we are ready to compute NDVI.\n",
    "\n",
    "𝑁𝐷𝑉𝐼=𝑁𝐼𝑅−𝑅𝑒𝑑𝑁𝐼𝑅+𝑅𝑒𝑑, highlights areas of healthy vegetation with high NDVI values, which appear as green in the image below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Landsat Level 1 images are delivered in a quantized format. \n",
    "This has to be converted to top-of-atmosphere reflectance using the provided metadata."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Be warned that TOA reflectance with a correction for the sun angle is not done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conversion to TOA Reflectance\n",
    "### Ref - https://www.usgs.gov/core-science-systems/nli/landsat/using-usgs-landsat-level-1-data-product\n",
    "Reflective band DN’s can be converted to TOA reflectance using the rescaling coefficients in the MTL file:  \n",
    "\n",
    "ρλ′=Mρ * Qcal+Aρ\n",
    "\n",
    "where:\n",
    "\n",
    "ρλ'   = TOA planetary reflectance, without correction for solar angle.  Note that ρλ' does not contain a correction for the sun angle.\n",
    "\n",
    "Mρ=Band-specific multiplicative rescaling factor from the metadata (REFLECTANCE_MULT_BAND_x, where x is the band number)\n",
    "\n",
    "Aρ  =Band-specific additive rescaling factor from the metadata (REFLECTANCE_ADD_BAND_x, where x is the band number) \n",
    "\n",
    "Qcal =  Quantized and calibrated standard product pixel values (DN)\n",
    "\n",
    "TOA reflectance with a correction for the sun angle is then:\n",
    "\n",
    "ρλ=ρλ′cos(θSZ)=ρλ′sin(θSE)\n",
    "\n",
    "where:\n",
    "\n",
    "ρλ=  TOA planetary reflectance\n",
    "θSE =  Local sun elevation angle. \n",
    "The scene center sun elevation angle in degrees is provided in the metadata (SUN_ELEVATION).\n",
    "θSZ =Local solar zenith angle;  θSZ = 90° - θSE\n",
    "\n",
    "For more accurate reflectance calculations, per-pixel solar angles could be used instead of the scene center solar angle. \n",
    "While per-pixel solar zenith angles are not provided with the Landsat Level-1 products, tools are provided which allow users to create angle bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask\n",
    "import xarray as xa\n",
    "import os\n",
    "import json\n",
    "import rasterio\n",
    "import requests\n",
    "import numpy as np\n",
    "import rioxarray\n",
    "import xarray\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#xds = xarray.open_rasterio(\"myfile.tif\")\n",
    "#wgs84_xds = xds.rio.reproject(\"EPSG:4326\")\n",
    "\n",
    "def calculate_ndvi_rioxarray(item):\n",
    "    # reclassed count\n",
    "    reclassed=[]\n",
    "    # Get MTL \n",
    "    #https://landsat-pds.s3.amazonaws.com\n",
    "    mtl = item +'_MTL.json'\n",
    "    # A hack to get the path to download the MTL file\n",
    "    mtl = mtl.replace(\"s3://landsat-pds\", \"https://landsat-pds.s3.amazonaws.com\")\n",
    "    if not os.path.exists(mtl):\n",
    "        print(\"Downloading\", mtl)\n",
    "        response = requests.get(mtl)\n",
    "        out_filename = mtl.split('.')[3].split('/')[6]+'.json'\n",
    "        with open(out_filename, 'wb') as f:\n",
    "            f.write(response.content)\n",
    "    # The Landsat Level 1 images are delivered in a quantized format. \n",
    "    # This has to be converted to top-of-atmosphere reflectance using the provided metadata.  \n",
    "    \n",
    "    # One MTL file for a scene - bundled with a list of Band specific TIFF files\n",
    "    with open(out_filename) as f:\n",
    "        metadata = json.load(f)\n",
    "        # M_p for Red\n",
    "        band_number = 4\n",
    "        M_p_red = metadata['L1_METADATA_FILE'] \\\n",
    "                  ['RADIOMETRIC_RESCALING'] \\\n",
    "                  ['REFLECTANCE_MULT_BAND_{}'.format(band_number)]\n",
    "        band_number = 5\n",
    "        M_p_nir = metadata['L1_METADATA_FILE'] \\\n",
    "                  ['RADIOMETRIC_RESCALING'] \\\n",
    "                  ['REFLECTANCE_MULT_BAND_{}'.format(band_number)]\n",
    "        band_number = 4\n",
    "        A_p_red = metadata['L1_METADATA_FILE'] \\\n",
    "                  ['RADIOMETRIC_RESCALING'] \\\n",
    "                  ['REFLECTANCE_ADD_BAND_{}'.format(band_number)]\n",
    "        band_number = 5\n",
    "        A_p_nir = metadata['L1_METADATA_FILE'] \\\n",
    "                  ['RADIOMETRIC_RESCALING'] \\\n",
    "                  ['REFLECTANCE_ADD_BAND_{}'.format(band_number)]\n",
    "    \n",
    "    \n",
    "    # +'_B{}.TIF'.format(4)\n",
    "    redband = item +'_B{}.TIF'.format(4)\n",
    "    nirband = item +'_B{}.TIF'.format(5)\n",
    "    \n",
    "    \n",
    "    # read red and nir\n",
    "    red = rioxarray.open_rasterio(redband, chunks={'band': 1, 'x': 1024, 'y': 1024})\n",
    "    nir = rioxarray.open_rasterio(nirband, chunks={'band': 1, 'x': 1024, 'y': 1024})\n",
    "    \n",
    "    red_toa = M_p_red * red + A_p_red\n",
    "    nir_toa = M_p_nir * nir + A_p_nir\n",
    "    \n",
    "    ndvi = (nir_toa - red_toa) / (nir_toa + red_toa)\n",
    "    \n",
    "    # split\n",
    "    name = item.split('/')[7]\n",
    "    \n",
    "    #save\n",
    "    ndvi.rio.to_raster(\n",
    "        \"./data/vegetation/ndvi/\"+name+\".tiff\",\n",
    "        tiled=True,\n",
    "    )\n",
    "    \n",
    "    # Define class names\n",
    "    ndvi_cat_names = [\n",
    "    \"No Vegetation\",\n",
    "    \"Bare Area\",\n",
    "    \"Low Vegetation\",\n",
    "    \"Moderate Vegetation\",\n",
    "    \"High Vegetation\",\n",
    "    ]\n",
    "\n",
    "    with rasterio.open(\"./data/vegetation/ndvi/\"+name+\".tiff\") as src:    \n",
    "        array = src.read()\n",
    "        profile = src.profile\n",
    "        bins = np.array([-np.inf, 0, 0.1, 0.25, 0.4, np.inf]) \n",
    "        inds = np.digitize(array, bins)\n",
    "        ind64 = np.float64(inds)\n",
    "        unique_elements, counts_elements = np.unique(ind64, return_counts=True)\n",
    "        print(\"Frequency of unique values of the said array:\")\n",
    "        print(np.asarray((unique_elements, counts_elements)))\n",
    "        reclassed = np.asarray((unique_elements, counts_elements))\n",
    "    \n",
    "\n",
    "    with rasterio.open('./data/vegetation/ndvi/reclass_ndvi_'+name+\".tiff\", 'w', **profile) as dst:\n",
    "        dst.write(ind64)\n",
    "    \n",
    "    os.remove(\"./data/vegetation/ndvi/\"+name+\".tiff\")\n",
    "    os.remove('./data/vegetation/ndvi/reclass_ndvi_'+name+\".tiff\")\n",
    "\n",
    "    return {\"tile\":name,\"var\":reclassed}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20201201_20201217_01_T1/LC08_L1TP_227065_20201201_20201217_01_T1'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baselinks[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20201201_20201217_01_T1/LC08_L1TP_227065_20201201_20201217_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.0699000e+04 2.1815834e+07 8.8956250e+06 7.7507970e+06 2.0653626e+07]]\n"
     ]
    }
   ],
   "source": [
    "recl= calculate_ndvi_rioxarray(baselinks[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tile': 'LC08_L1TP_227065_20201201_20201217_01_T1',\n",
       " 'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "         5.0000000e+00],\n",
       "        [3.0699000e+04, 2.1815834e+07, 8.8956250e+06, 7.7507970e+06,\n",
       "         2.0653626e+07]])}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20201201_20201217_01_T1/LC08_L1TP_227065_20201201_20201217_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.0699000e+04 2.1815834e+07 8.8956250e+06 7.7507970e+06 2.0653626e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20201115_20201210_01_T1/LC08_L1TP_227065_20201115_20201210_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.0840000e+04 2.0935594e+07 8.4653550e+06 7.1621340e+06 2.2640268e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200928_20201007_01_T1/LC08_L1TP_227065_20200928_20201007_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.5806000e+04 1.8093655e+07 1.4772200e+06 4.2532630e+06 3.5314247e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200912_20200920_01_T1/LC08_L1TP_227065_20200912_20200920_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.2182000e+04 1.7741209e+07 1.6514840e+06 5.9542690e+06 3.3747437e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200827_20200905_01_T1/LC08_L1TP_227065_20200827_20200905_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.7980500e+05 2.1601746e+07 4.9680980e+06 6.8061740e+06 2.5590758e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200827_20200905_01_T1/LC08_L1TP_227065_20200827_20200905_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.7980500e+05 2.1601746e+07 4.9680980e+06 6.8061740e+06 2.5590758e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200811_20200822_01_T1/LC08_L1TP_227065_20200811_20200822_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.8603000e+04 1.7649810e+07 8.7566300e+05 6.1846740e+06 3.4397831e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200726_20200807_01_T1/LC08_L1TP_227065_20200726_20200807_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0251500e+05 1.7639010e+07 1.9205300e+05 1.5041640e+06 3.9708839e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200710_20200721_01_T1/LC08_L1TP_227065_20200710_20200721_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.4019000e+04 1.7658459e+07 1.3507300e+05 7.2930300e+05 4.0559727e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200624_20200707_01_T1/LC08_L1TP_227065_20200624_20200707_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0076900e+05 1.7651259e+07 1.3643800e+05 3.1043300e+05 4.0947682e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200608_20200626_01_T1/LC08_L1TP_227065_20200608_20200626_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0807300e+05 1.7633159e+07 9.2012000e+04 2.0395200e+05 4.1109385e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200608_20200626_01_T1/LC08_L1TP_227065_20200608_20200626_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0807300e+05 1.7633159e+07 9.2012000e+04 2.0395200e+05 4.1109385e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200523_20200607_01_T1/LC08_L1TP_227065_20200523_20200607_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.5400000e+04 1.7762936e+07 6.6097200e+05 1.0400430e+06 3.9587230e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200507_20200509_01_T1/LC08_L1TP_227065_20200507_20200509_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.6650000e+03 2.3957863e+07 1.8780863e+07 7.9554330e+06 8.4437570e+06]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200405_20200410_01_T1/LC08_L1TP_227065_20200405_20200410_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.6404000e+04 1.7977212e+07 2.6260000e+06 2.9784820e+06 3.5498483e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200217_20200225_01_T1/LC08_L1TP_227065_20200217_20200225_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.3369000e+04 2.0267431e+07 7.9690030e+06 7.8116080e+06 2.3065170e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20191215_20201023_01_T1/LC08_L1TP_227065_20191215_20201023_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.1528000e+04 1.8141730e+07 3.0162450e+06 4.3372920e+06 3.3619786e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20191215_20191226_01_T1/LC08_L1TP_227065_20191215_20191226_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.1528000e+04 1.8141730e+07 3.0162450e+06 4.3372920e+06 3.3619786e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20191129_20191216_01_T1/LC08_L1TP_227065_20191129_20191216_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [4.0520000e+04 1.9402160e+07 6.1426440e+06 6.4811400e+06 2.7157727e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20191028_20191114_01_T1/LC08_L1TP_227065_20191028_20191114_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.8700000e+04 2.0895705e+07 5.5810930e+06 4.7408730e+06 2.7890210e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20191012_20191018_01_T1/LC08_L1TP_227065_20191012_20191018_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.0731000e+04 1.7744511e+07 8.6954400e+05 2.2820980e+06 3.8199697e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190926_20191017_01_T1/LC08_L1TP_227065_20190926_20191017_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.8904000e+04 1.9490196e+07 3.0545360e+06 4.2616430e+06 3.2301302e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190910_20190917_01_T1/LC08_L1TP_227065_20190910_20190917_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.4459000e+04 1.7666452e+07 1.0915010e+06 4.8517430e+06 3.5472426e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190825_20190903_01_T1/LC08_L1TP_227065_20190825_20190903_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.9130000e+04 1.8972080e+07 3.5133030e+06 4.7883810e+06 3.1871297e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190724_20190801_01_T1/LC08_L1TP_227065_20190724_20190801_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0355600e+05 1.7635297e+07 1.3809600e+05 1.0421470e+06 4.0227485e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190708_20190719_01_T1/LC08_L1TP_227065_20190708_20190719_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.2865000e+04 2.0955679e+07 8.9420730e+06 5.8891420e+06 2.3336822e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190622_20190704_01_T1/LC08_L1TP_227065_20190622_20190704_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0534400e+05 1.7635371e+07 6.5012000e+04 1.6757700e+05 4.1173277e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190606_20190619_01_T1/LC08_L1TP_227065_20190606_20190619_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.1840000e+04 2.2354408e+07 6.3423160e+06 3.9641500e+06 2.6403867e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190505_20190520_01_T1/LC08_L1TP_227065_20190505_20190520_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.3374000e+04 1.7850268e+07 1.0312390e+06 1.1802860e+06 3.8991414e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190505_20190520_01_T1/LC08_L1TP_227065_20190505_20190520_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.3374000e+04 1.7850268e+07 1.0312390e+06 1.1802860e+06 3.8991414e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190419_20190423_01_T1/LC08_L1TP_227065_20190419_20190423_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.3990000e+04 1.8251033e+07 5.8035630e+06 7.4844430e+06 2.7583552e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20190318_20190325_01_T1/LC08_L1TP_227065_20190318_20190325_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.4469000e+04 2.0003074e+07 7.6357440e+06 5.3926820e+06 2.6090612e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20181126_20181210_01_T1/LC08_L1TP_227065_20181126_20181210_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.9718000e+04 2.1151591e+07 1.0066808e+07 6.2419730e+06 2.1646491e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20181126_20181210_01_T1/LC08_L1TP_227065_20181126_20181210_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.9718000e+04 2.1151591e+07 1.0066808e+07 6.2419730e+06 2.1646491e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20181110_20181127_01_T1/LC08_L1TP_227065_20181110_20181127_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.2970000e+03 2.1996694e+07 9.7685680e+06 1.1782868e+07 1.5595154e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20181025_20181114_01_T1/LC08_L1TP_227065_20181025_20181114_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.4352000e+04 1.7716306e+07 1.0644100e+06 2.0008100e+06 3.8330703e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20181009_20181029_01_T1/LC08_L1TP_227065_20181009_20181029_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.5040000e+03 2.5532529e+07 1.4032098e+07 6.9750050e+06 1.2604445e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180923_20180929_01_T1/LC08_L1TP_227065_20180923_20180929_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0645000e+04 1.9793368e+07 1.2610098e+07 7.6659100e+06 1.9066560e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180923_20180929_01_T1/LC08_L1TP_227065_20180923_20180929_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0645000e+04 1.9793368e+07 1.2610098e+07 7.6659100e+06 1.9066560e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180907_20180912_01_T1/LC08_L1TP_227065_20180907_20180912_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [4.8076000e+04 1.7980146e+07 1.5370370e+06 2.5406810e+06 3.7040641e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180907_20180912_01_T1/LC08_L1TP_227065_20180907_20180912_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [4.8076000e+04 1.7980146e+07 1.5370370e+06 2.5406810e+06 3.7040641e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180822_20180829_01_T1/LC08_L1TP_227065_20180822_20180829_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.8440000e+04 1.7754004e+07 5.5002100e+05 1.5137760e+06 3.9240340e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180806_20180815_01_T1/LC08_L1TP_227065_20180806_20180815_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.7347000e+04 1.7875578e+07 6.8500300e+05 2.3838130e+06 3.8114840e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180721_20180731_01_T1/LC08_L1TP_227065_20180721_20180731_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.0450000e+04 1.7636800e+07 1.1242500e+05 1.0542470e+06 4.0252659e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180705_20180717_01_T1/LC08_L1TP_227065_20180705_20180717_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0343000e+05 1.7706450e+07 8.7557000e+04 3.9135100e+05 4.0935403e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180705_20180717_01_T1/LC08_L1TP_227065_20180705_20180717_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0343000e+05 1.7706450e+07 8.7557000e+04 3.9135100e+05 4.0935403e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180705_20180717_01_T1/LC08_L1TP_227065_20180705_20180717_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0343000e+05 1.7706450e+07 8.7557000e+04 3.9135100e+05 4.0935403e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180619_20180703_01_T1/LC08_L1TP_227065_20180619_20180703_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.000000e+00 2.000000e+00 3.000000e+00 4.000000e+00 5.000000e+00]\n",
      " [1.014320e+05 1.762800e+07 1.971240e+05 6.018050e+05 4.061822e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180619_20180703_01_T1/LC08_L1TP_227065_20180619_20180703_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.000000e+00 2.000000e+00 3.000000e+00 4.000000e+00 5.000000e+00]\n",
      " [1.014320e+05 1.762800e+07 1.971240e+05 6.018050e+05 4.061822e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180518_20180604_01_T1/LC08_L1TP_227065_20180518_20180604_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.9050000e+04 1.9219452e+07 4.6369270e+06 3.7363760e+06 3.1484776e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180502_20180516_01_T1/LC08_L1TP_227065_20180502_20180516_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.1848000e+04 1.8377677e+07 5.9249540e+06 6.6608890e+06 2.8161213e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180227_20180308_01_T1/LC08_L1TP_227065_20180227_20180308_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.2334000e+04 2.1845045e+07 1.4386932e+07 9.6946460e+06 1.3197624e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20180110_20180119_01_T1/LC08_L1TP_227065_20180110_20180119_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [4.2264000e+04 2.1461123e+07 5.7745330e+06 3.8327950e+06 2.8035866e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20171123_20171206_01_T1/LC08_L1TP_227065_20171123_20171206_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.6490000e+03 1.9425880e+07 4.6485790e+06 7.7474980e+06 2.7398585e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20171022_20171107_01_T1/LC08_L1TP_227065_20171022_20171107_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.6241000e+04 2.5572199e+07 8.8317350e+06 6.0434590e+06 1.8682947e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20171006_20171023_01_T1/LC08_L1TP_227065_20171006_20171023_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.9770000e+03 1.8511667e+07 5.0325690e+06 7.7327420e+06 2.7862626e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170904_20170916_01_T1/LC08_L1TP_227065_20170904_20170916_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.8884000e+04 1.7811319e+07 1.2244910e+06 5.3844110e+06 3.4775086e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170904_20170916_01_T1/LC08_L1TP_227065_20170904_20170916_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.8884000e+04 1.7811319e+07 1.2244910e+06 5.3844110e+06 3.4775086e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170904_20170916_01_T1/LC08_L1TP_227065_20170904_20170916_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.8884000e+04 1.7811319e+07 1.2244910e+06 5.3844110e+06 3.4775086e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170904_20170916_01_T1/LC08_L1TP_227065_20170904_20170916_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.8884000e+04 1.7811319e+07 1.2244910e+06 5.3844110e+06 3.4775086e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170819_20170826_01_T1/LC08_L1TP_227065_20170819_20170826_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.9169000e+04 1.7725133e+07 4.8722900e+05 3.2005010e+06 3.7732159e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170803_20170812_01_T1/LC08_L1TP_227065_20170803_20170812_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.3888000e+04 1.7637079e+07 1.9120400e+05 1.9385530e+06 3.9285857e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170718_20170727_01_T1/LC08_L1TP_227065_20170718_20170727_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.4791000e+04 1.7709592e+07 1.1341300e+05 7.7778600e+05 4.0528609e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170718_20170727_01_T1/LC08_L1TP_227065_20170718_20170727_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.4791000e+04 1.7709592e+07 1.1341300e+05 7.7778600e+05 4.0528609e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170718_20170727_01_T1/LC08_L1TP_227065_20170718_20170727_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.4791000e+04 1.7709592e+07 1.1341300e+05 7.7778600e+05 4.0528609e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170718_20170727_01_T1/LC08_L1TP_227065_20170718_20170727_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.4791000e+04 1.7709592e+07 1.1341300e+05 7.7778600e+05 4.0528609e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170702_20170715_01_T1/LC08_L1TP_227065_20170702_20170715_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.3132000e+04 1.7637408e+07 1.3472300e+05 3.9452700e+05 4.0886791e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170616_20170629_01_T1/LC08_L1TP_227065_20170616_20170629_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.5606000e+04 1.7650118e+07 1.8209700e+05 4.7777800e+05 4.0760982e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170616_20180528_01_T1/LC08_L1TP_227065_20170616_20180528_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.5606000e+04 1.7650125e+07 1.8210300e+05 4.7773700e+05 4.0761010e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170616_20170629_01_T1/LC08_L1TP_227065_20170616_20170629_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.5606000e+04 1.7650118e+07 1.8209700e+05 4.7777800e+05 4.0760982e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170531_20170615_01_T1/LC08_L1TP_227065_20170531_20170615_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.5451000e+04 2.5257767e+07 1.1109772e+07 7.6637630e+06 1.5039828e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170515_20170525_01_T1/LC08_L1TP_227065_20170515_20170525_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.1992000e+04 1.9234691e+07 4.4946200e+06 3.6111580e+06 3.1754120e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170413_20170501_01_T1/LC08_L1TP_227065_20170413_20170501_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.5358000e+04 1.8759506e+07 6.7579430e+06 8.1645630e+06 2.5429211e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170312_20170317_01_T1/LC08_L1TP_227065_20170312_20170317_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.4900000e+02 1.9579474e+07 1.5596784e+07 1.5107902e+07 8.8615720e+06]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20170107_20170311_01_T1/LC08_L1TP_227065_20170107_20170311_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.1610000e+03 1.9968702e+07 1.0863926e+07 1.0115206e+07 1.8191586e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20161206_20180528_01_T1/LC08_L1TP_227065_20161206_20180528_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [4.9753000e+04 1.7657073e+07 1.1055290e+06 2.4233180e+06 3.7910908e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20161104_20170318_01_T1/LC08_L1TP_227065_20161104_20170318_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.1818000e+04 1.7657493e+07 1.5267270e+06 2.5185160e+06 3.7412027e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20161003_20170320_01_T1/LC08_L1TP_227065_20161003_20170320_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.8470000e+04 1.9458426e+07 6.4214330e+06 4.5376650e+06 2.8700587e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160917_20170321_01_T1/LC08_L1TP_227065_20160917_20170321_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.1702000e+04 1.8199687e+07 2.8809100e+06 4.7118850e+06 3.3332397e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160901_20170321_01_T1/LC08_L1TP_227065_20160901_20170321_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.3129000e+04 2.3158246e+07 6.8380690e+06 6.1424600e+06 2.2974677e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160816_20170322_01_T1/LC08_L1TP_227065_20160816_20170322_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.8948000e+04 1.7712232e+07 4.4863100e+05 2.4352670e+06 3.8471503e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160731_20170322_01_T1/LC08_L1TP_227065_20160731_20170322_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.4490000e+04 1.7722731e+07 1.3716300e+05 1.4648520e+06 3.9824955e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160715_20170323_01_T1/LC08_L1TP_227065_20160715_20170323_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.7514000e+04 1.7632999e+07 8.8713000e+04 4.6067500e+05 4.0866680e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160629_20170323_01_T1/LC08_L1TP_227065_20160629_20170323_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.8722000e+04 1.7630251e+07 6.3779000e+04 1.8110200e+05 4.1172727e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160613_20170324_01_T1/LC08_L1TP_227065_20160613_20170324_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.6236000e+04 1.8072910e+07 1.8107920e+06 2.7605490e+06 3.6523704e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160528_20170324_01_T1/LC08_L1TP_227065_20160528_20170324_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [4.8859000e+04 1.7630810e+07 5.3804000e+05 3.1427470e+06 3.7786125e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160512_20170324_01_T1/LC08_L1TP_227065_20160512_20170324_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.3697000e+04 2.5924049e+07 4.3670880e+06 4.6741030e+06 2.4245254e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160426_20170326_01_T1/LC08_L1TP_227065_20160426_20170326_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.5410000e+03 2.0776508e+07 1.3013177e+07 1.0056923e+07 1.5291432e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160410_20170326_01_T1/LC08_L1TP_227065_20160410_20170326_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [4.1910000e+03 2.6359335e+07 1.0781405e+07 6.8950170e+06 1.5106633e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160222_20170329_01_T1/LC08_L1TP_227065_20160222_20170329_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.4290000e+04 1.7747916e+07 1.2159150e+06 1.5928990e+06 3.8525561e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20160206_20170330_01_T1/LC08_L1TP_227065_20160206_20170330_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.6025000e+04 2.1616581e+07 1.1353073e+07 7.0285170e+06 1.9132385e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20151204_20170401_01_T1/LC08_L1TP_227065_20151204_20170401_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.5400000e+02 1.9726420e+07 6.8948240e+06 8.3583290e+06 2.4166854e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20151118_20170402_01_T1/LC08_L1TP_227065_20151118_20170402_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.2100000e+02 1.9463388e+07 7.4734850e+06 1.2834525e+07 1.9375062e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20151102_20170402_01_T1/LC08_L1TP_227065_20151102_20170402_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.5761000e+04 2.0079715e+07 6.2502840e+06 6.0181220e+06 2.6860309e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20151017_20170403_01_T1/LC08_L1TP_227065_20151017_20170403_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.3020000e+04 1.7772264e+07 8.2863900e+05 1.9196480e+06 3.8680620e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20151001_20170403_01_T1/LC08_L1TP_227065_20151001_20170403_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.8220000e+03 2.9642096e+07 5.7575010e+06 6.0761900e+06 1.7667972e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150915_20170404_01_T1/LC08_L1TP_227065_20150915_20170404_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.7720000e+03 2.5094982e+07 3.6494800e+06 4.1950520e+06 2.6277905e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150830_20170405_01_T1/LC08_L1TP_227065_20150830_20170405_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.6814000e+04 1.7749189e+07 3.2174030e+06 6.4857610e+06 3.1657414e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150814_20170406_01_T1/LC08_L1TP_227065_20150814_20170406_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.1210000e+04 1.7715922e+07 1.9703500e+05 1.6801160e+06 3.9539908e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150729_20170406_01_T1/LC08_L1TP_227065_20150729_20170406_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.1932000e+04 1.7637260e+07 9.9895000e+04 5.0848300e+05 4.0809011e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150713_20170407_01_T1/LC08_L1TP_227065_20150713_20170407_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.9348000e+04 1.8356856e+07 1.1291370e+06 1.2448640e+06 3.8326376e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150627_20170407_01_T1/LC08_L1TP_227065_20150627_20170407_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.6366000e+04 2.1406886e+07 5.0153520e+06 5.8906470e+06 2.6817330e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150611_20170408_01_T1/LC08_L1TP_227065_20150611_20170408_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [9.8149000e+04 1.7630502e+07 4.5525000e+04 9.9525000e+04 4.1272880e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150526_20170408_01_T1/LC08_L1TP_227065_20150526_20170408_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.1128000e+04 1.7798601e+07 2.4590110e+06 4.2777730e+06 3.4627678e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150510_20170409_01_T1/LC08_L1TP_227065_20150510_20170409_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.5378400e+05 2.2945937e+07 7.4974510e+06 5.2074120e+06 2.3141997e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150424_20170409_01_T1/LC08_L1TP_227065_20150424_20170409_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.1281000e+04 1.9080304e+07 6.8953440e+06 1.0801441e+07 2.2358211e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150408_20170410_01_T1/LC08_L1TP_227065_20150408_20170410_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.0465000e+04 2.4661152e+07 9.1154780e+06 5.5509910e+06 1.9788495e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150203_20170413_01_T1/LC08_L1TP_227065_20150203_20170413_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.6041000e+04 1.8376558e+07 5.3214000e+06 5.7178720e+06 2.9694710e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20150102_20170415_01_T1/LC08_L1TP_227065_20150102_20170415_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [4.6421000e+04 1.9858718e+07 5.1957120e+06 3.5967440e+06 3.0448986e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20141217_20170416_01_T1/LC08_L1TP_227065_20141217_20170416_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.1283000e+04 1.9266213e+07 1.0524781e+07 9.5576480e+06 1.9786656e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20141115_20180528_01_T1/LC08_L1TP_227065_20141115_20180528_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.2393000e+04 1.8080223e+07 1.7248170e+06 2.2716230e+06 3.7017525e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20141014_20170418_01_T1/LC08_L1TP_227065_20141014_20170418_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.5925000e+04 1.7714180e+07 1.5438850e+06 3.8520260e+06 3.6020565e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140928_20170419_01_T1/LC08_L1TP_227065_20140928_20170419_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0370000e+04 1.8147425e+07 3.0672050e+06 4.6288910e+06 3.3292690e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140912_20170419_01_T1/LC08_L1TP_227065_20140912_20170419_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.0205000e+04 1.7724328e+07 4.6594100e+05 2.9963340e+06 3.7967383e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140827_20170420_01_T1/LC08_L1TP_227065_20140827_20170420_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.3772000e+04 1.7716873e+07 1.6095440e+06 4.4252460e+06 3.5371146e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140811_20170420_01_T1/LC08_L1TP_227065_20140811_20170420_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [7.3784000e+04 1.7668644e+07 6.0952700e+05 2.6259080e+06 3.8168718e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140726_20170421_01_T1/LC08_L1TP_227065_20140726_20170421_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.7378000e+04 1.7665716e+07 3.2923400e+05 1.2412410e+06 3.9823012e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140710_20170421_01_T1/LC08_L1TP_227065_20140710_20170421_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.9535000e+04 1.7711298e+07 6.8261000e+04 4.2581000e+05 4.0949287e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140624_20170421_01_T1/LC08_L1TP_227065_20140624_20170421_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.7811000e+04 1.7650531e+07 8.6230000e+05 3.1272070e+06 3.7438732e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140608_20170422_01_T1/LC08_L1TP_227065_20140608_20170422_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.1413000e+04 1.9862539e+07 5.3411920e+06 4.5981000e+06 2.9360947e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140523_20170422_01_T1/LC08_L1TP_227065_20140523_20170422_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [3.2365970e+06 3.0161586e+07 1.2815225e+07 5.5151530e+06 7.4180200e+06]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140507_20170422_01_T1/LC08_L1TP_227065_20140507_20170422_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [2.6328000e+04 2.1196911e+07 9.1682990e+06 7.2416410e+06 2.1513402e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140421_20170423_01_T1/LC08_L1TP_227065_20140421_20170423_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0016000e+04 2.1249279e+07 1.2576018e+07 8.3560870e+06 1.6955181e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20140320_20170425_01_T1/LC08_L1TP_227065_20140320_20170425_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.2750000e+03 2.1328150e+07 1.2864150e+07 9.9635130e+06 1.4985493e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20131011_20170429_01_T1/LC08_L1TP_227065_20131011_20170429_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.8092000e+04 1.8136805e+07 2.6262470e+06 2.6980510e+06 3.5627386e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20130909_20170502_01_T1/LC08_L1TP_227065_20130909_20170502_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.2247000e+04 1.7799062e+07 1.7542140e+06 4.6081840e+06 3.4932874e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20130824_20170502_01_T1/LC08_L1TP_227065_20130824_20170502_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.6383000e+04 1.7694325e+07 1.0569600e+05 1.8020240e+06 3.9535763e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20130808_20170503_01_T1/LC08_L1TP_227065_20130808_20170503_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [6.7611000e+04 1.8340504e+07 1.4784520e+06 2.1724030e+06 3.7165221e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20130723_20170503_01_T1/LC08_L1TP_227065_20130723_20170503_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [1.0914000e+04 1.9029325e+07 5.0178030e+06 5.6528420e+06 2.9513307e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20130707_20170503_01_T1/LC08_L1TP_227065_20130707_20170503_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [4.1598000e+04 1.7974619e+07 2.9277780e+06 3.5621310e+06 3.4640455e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20130621_20170503_01_T1/LC08_L1TP_227065_20130621_20170503_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.5584000e+04 1.6423239e+07 1.6679400e+05 4.0106300e+05 3.7984561e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20130605_20170504_01_T1/LC08_L1TP_227065_20130605_20170504_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.8988000e+04 1.7856266e+07 3.7402080e+06 4.1626720e+06 2.9243107e+07]]\n",
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20130520_20170504_01_T1/LC08_L1TP_227065_20130520_20170504_01_T1_MTL.json\n",
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [5.0091000e+04 1.7724081e+07 4.7474830e+06 4.3148270e+06 2.8297969e+07]]\n"
     ]
    }
   ],
   "source": [
    "state_datasets = []\n",
    "for item in baselinks:\n",
    "    ds = calculate_ndvi_rioxarray(item)\n",
    "    state_datasets.append(ds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'tile': 'LC08_L1TP_227065_20201201_20201217_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [3.0699000e+04, 2.1815834e+07, 8.8956250e+06, 7.7507970e+06,\n",
       "          2.0653626e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20201115_20201210_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [2.0840000e+04, 2.0935594e+07, 8.4653550e+06, 7.1621340e+06,\n",
       "          2.2640268e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200928_20201007_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [8.5806000e+04, 1.8093655e+07, 1.4772200e+06, 4.2532630e+06,\n",
       "          3.5314247e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200912_20200920_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [5.2182000e+04, 1.7741209e+07, 1.6514840e+06, 5.9542690e+06,\n",
       "          3.3747437e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200827_20200905_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [1.7980500e+05, 2.1601746e+07, 4.9680980e+06, 6.8061740e+06,\n",
       "          2.5590758e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200827_20200905_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [1.7980500e+05, 2.1601746e+07, 4.9680980e+06, 6.8061740e+06,\n",
       "          2.5590758e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200811_20200822_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [3.8603000e+04, 1.7649810e+07, 8.7566300e+05, 6.1846740e+06,\n",
       "          3.4397831e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200726_20200807_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [1.0251500e+05, 1.7639010e+07, 1.9205300e+05, 1.5041640e+06,\n",
       "          3.9708839e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200710_20200721_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [6.4019000e+04, 1.7658459e+07, 1.3507300e+05, 7.2930300e+05,\n",
       "          4.0559727e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200624_20200707_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [1.0076900e+05, 1.7651259e+07, 1.3643800e+05, 3.1043300e+05,\n",
       "          4.0947682e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200608_20200626_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [1.0807300e+05, 1.7633159e+07, 9.2012000e+04, 2.0395200e+05,\n",
       "          4.1109385e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200608_20200626_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [1.0807300e+05, 1.7633159e+07, 9.2012000e+04, 2.0395200e+05,\n",
       "          4.1109385e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200523_20200607_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [9.5400000e+04, 1.7762936e+07, 6.6097200e+05, 1.0400430e+06,\n",
       "          3.9587230e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200507_20200509_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [8.6650000e+03, 2.3957863e+07, 1.8780863e+07, 7.9554330e+06,\n",
       "          8.4437570e+06]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200405_20200410_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [6.6404000e+04, 1.7977212e+07, 2.6260000e+06, 2.9784820e+06,\n",
       "          3.5498483e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20200217_20200225_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [3.3369000e+04, 2.0267431e+07, 7.9690030e+06, 7.8116080e+06,\n",
       "          2.3065170e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20191215_20201023_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [3.1528000e+04, 1.8141730e+07, 3.0162450e+06, 4.3372920e+06,\n",
       "          3.3619786e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20191215_20191226_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [3.1528000e+04, 1.8141730e+07, 3.0162450e+06, 4.3372920e+06,\n",
       "          3.3619786e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20191129_20191216_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [4.0520000e+04, 1.9402160e+07, 6.1426440e+06, 6.4811400e+06,\n",
       "          2.7157727e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20191028_20191114_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [3.8700000e+04, 2.0895705e+07, 5.5810930e+06, 4.7408730e+06,\n",
       "          2.7890210e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20191012_20191018_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [5.0731000e+04, 1.7744511e+07, 8.6954400e+05, 2.2820980e+06,\n",
       "          3.8199697e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20190926_20191017_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [3.8904000e+04, 1.9490196e+07, 3.0545360e+06, 4.2616430e+06,\n",
       "          3.2301302e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20190910_20190917_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [6.4459000e+04, 1.7666452e+07, 1.0915010e+06, 4.8517430e+06,\n",
       "          3.5472426e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20190825_20190903_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [7.9130000e+04, 1.8972080e+07, 3.5133030e+06, 4.7883810e+06,\n",
       "          3.1871297e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20190724_20190801_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [1.0355600e+05, 1.7635297e+07, 1.3809600e+05, 1.0421470e+06,\n",
       "          4.0227485e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20190708_20190719_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [2.2865000e+04, 2.0955679e+07, 8.9420730e+06, 5.8891420e+06,\n",
       "          2.3336822e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20190622_20190704_01_T1',\n",
       "  'var': array([[1.0000000e+00, 2.0000000e+00, 3.0000000e+00, 4.0000000e+00,\n",
       "          5.0000000e+00],\n",
       "         [1.0534400e+05, 1.7635371e+07, 6.5012000e+04, 1.6757700e+05,\n",
       "          4.1173277e+07]])},\n",
       " {'tile': 'LC08_L1TP_227065_20190606_20190619_01_T1',\n",
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       " {'tile': 'LC08_L1TP_227065_20130520_20170504_01_T1',\n",
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       "          2.8297969e+07]])}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting earthpy\n",
      "  Downloading earthpy-0.9.2.tar.gz (1.4 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.4 MB 11.1 MB/s eta 0:00:01\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h    Preparing wheel metadata ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: rasterio in /srv/conda/envs/notebook/lib/python3.8/site-packages (from earthpy) (1.2.0)\n",
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      "Building wheels for collected packages: earthpy\n",
      "  Building wheel for earthpy (PEP 517) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for earthpy: filename=earthpy-0.9.2-py3-none-any.whl size=1365089 sha256=7840dacb6214ac6d13d0ce6f3c3ea06a526ef40c6e2fd41f9a58ab9add1eb7dd\n",
      "  Stored in directory: /home/jovyan/.cache/pip/wheels/55/5c/61/98ca676a500734ceda6b410eeab50444e00c5298af736725b7\n",
      "Successfully built earthpy\n",
      "Installing collected packages: earthpy\n",
      "Successfully installed earthpy-0.9.2\n"
     ]
    }
   ],
   "source": [
    "!pip3 install earthpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200928_20201007_01_T1/LC08_L1TP_227065_20200928_20201007_01_T1_MTL.json\n",
      "http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200928_20201007_01_T1/LC08_L1TP_227065_20200928_20201007_01_T1_B5.TIF\n",
      "http://landsat-pds.s3.amazonaws.com/c1/L8/227/065/LC08_L1TP_227065_20200928_20201007_01_T1/LC08_L1TP_227065_20200928_20201007_01_T1_B4.TIF\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import earthpy.plot as ep\n",
    "import earthpy.spatial as es\n",
    "from rasterio.plot import show\n",
    "from matplotlib.colors import ListedColormap\n",
    "item = baselinks[2]\n",
    "# Get MTL \n",
    "#https://landsat-pds.s3.amazonaws.com\n",
    "mtl = item +'_MTL.json'\n",
    "# A hack to get the path to download the MTL file\n",
    "mtl = mtl.replace(\"s3://landsat-pds\", \"https://landsat-pds.s3.amazonaws.com\")\n",
    "if not os.path.exists(mtl):\n",
    "    print(\"Downloading\", mtl)\n",
    "    response = requests.get(mtl)\n",
    "    out_filename = mtl.split('.')[3].split('/')[6]+'.json'\n",
    "    with open(out_filename, 'wb') as f:\n",
    "            f.write(response.content)\n",
    "    # The Landsat Level 1 images are delivered in a quantized format. \n",
    "    # This has to be converted to top-of-atmosphere reflectance using the provided metadata.  \n",
    "    \n",
    "    # One MTL file for a scene - bundled with a list of Band specific TIFF files\n",
    "    with open(out_filename) as f:\n",
    "        metadata = json.load(f)\n",
    "        # M_p for Red\n",
    "        band_number = 4\n",
    "        M_p_red = metadata['L1_METADATA_FILE'] \\\n",
    "                  ['RADIOMETRIC_RESCALING'] \\\n",
    "                  ['REFLECTANCE_MULT_BAND_{}'.format(band_number)]\n",
    "        band_number = 5\n",
    "        M_p_nir = metadata['L1_METADATA_FILE'] \\\n",
    "                  ['RADIOMETRIC_RESCALING'] \\\n",
    "                  ['REFLECTANCE_MULT_BAND_{}'.format(band_number)]\n",
    "        band_number = 4\n",
    "        A_p_red = metadata['L1_METADATA_FILE'] \\\n",
    "                  ['RADIOMETRIC_RESCALING'] \\\n",
    "                  ['REFLECTANCE_ADD_BAND_{}'.format(band_number)]\n",
    "        band_number = 5\n",
    "        A_p_nir = metadata['L1_METADATA_FILE'] \\\n",
    "                  ['RADIOMETRIC_RESCALING'] \\\n",
    "                  ['REFLECTANCE_ADD_BAND_{}'.format(band_number)]\n",
    "    \n",
    "    \n",
    "    # +'_B{}.TIF'.format(4)\n",
    "    redband = item +'_B{}.TIF'.format(4)\n",
    "    nirband = item +'_B{}.TIF'.format(5)\n",
    "    \n",
    "    # split\n",
    "    name = item.split('/')[7]\n",
    "    #\n",
    "    print(nirband)\n",
    "    print(redband)\n",
    "    # read red and nir\n",
    "    red = rioxarray.open_rasterio(redband)\n",
    "    nir = rioxarray.open_rasterio(nirband)\n",
    "    \n",
    "    red_toa = M_p_red * red + A_p_red\n",
    "    nir_toa = M_p_nir * nir + A_p_nir\n",
    "    f, ax = plt.subplots(figsize=(10, 6))\n",
    "    red_toa.plot.hist(ax=ax,\n",
    "       color=\"purple\")\n",
    "    ax.set(title=\"Distribution of red\",\n",
    "       xlabel='reflectance',\n",
    "       ylabel='Frequency')\n",
    "    #plt.show()\n",
    "    \n",
    "    f, ax = plt.subplots(figsize=(10, 6))\n",
    "    nir_toa.plot.hist(ax=ax,\n",
    "       color=\"purple\")\n",
    "    ax.set(title=\"Distribution of nir\",\n",
    "       xlabel='reflectance',\n",
    "       ylabel='Frequency')\n",
    "    #plt.show()\n",
    "    \n",
    "    ndvi = (nir_toa - red_toa) / (nir_toa + red_toa)\n",
    "    lndsat_ndvi = es.normalized_diff(nir_toa, red_toa)\n",
    "    \n",
    "    #save\n",
    "    ndvi.rio.to_raster(\n",
    "        \"./data/vegetation/ndvi/\"+name+\".tiff\",\n",
    "        tiled=True,\n",
    "    )\n",
    "    #show(ndvi, cmap='viridis')\n",
    "    ep.plot_bands(lndsat_ndvi,\n",
    "              cmap='PiYG',\n",
    "              scale=False,\n",
    "              vmin=-1, vmax=1,\n",
    "              title=\"NAIP Derived NDVI\\n 19 September 2015 - Cold Springs Fire, Colorado\")\n",
    "    plt.show()\n",
    "    \n",
    "    # Create classes and apply to NDVI results\n",
    "    ndvi_class_bins = [-np.inf, 0, 0.1, 0.25, 0.4, np.inf]\n",
    "    ndvi_landsat_class = np.digitize(lndsat_ndvi, ndvi_class_bins)\n",
    "\n",
    "    # Apply the nodata mask to the newly classified NDVI data\n",
    "    ndvi_landsat_class = np.ma.masked_where(\n",
    "                np.ma.getmask(lndsat_ndvi), ndvi_landsat_class\n",
    "        )\n",
    "    np.unique(ndvi_landsat_class)\n",
    "    \n",
    "    # Get list of classes\n",
    "    classes = np.unique(ndvi_landsat_class)\n",
    "    classes = classes.tolist()\n",
    "    # The mask returns a value of none in the classes. remove that\n",
    "    classes = classes[0:5]\n",
    "    \n",
    "    print(\"classes\", classes)\n",
    "    \n",
    "    ndvi_landsat_class.plot.hist(ax=ax,\n",
    "       color=\"purple\")\n",
    "    ax.set(title=\"Distribution of reclass\",\n",
    "       xlabel='reflectance',\n",
    "       ylabel='Frequency')\n",
    "    \n",
    "    # Define color map\n",
    "    nbr_colors = [\"gray\", \"y\", \"yellowgreen\", \"g\", \"darkgreen\"]\n",
    "    nbr_cmap = ListedColormap(nbr_colors)\n",
    "\n",
    "    # Define class names\n",
    "    ndvi_cat_names = [\n",
    "    \"No Vegetation\",\n",
    "    \"Bare Area\",\n",
    "    \"Low Vegetation\",\n",
    "    \"Moderate Vegetation\",\n",
    "    \"High Vegetation\",\n",
    "    ]\n",
    "\n",
    "    # Get list of classes\n",
    "    classes = np.unique(ndvi_landsat_class)\n",
    "    classes = classes.tolist()\n",
    "    # The mask returns a value of none in the classes. remove that\n",
    "    classes = classes[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rasterio\n",
    "from matplotlib import pyplot\n",
    "src = rasterio.open(\"./data/vegetation/ndvi/LC08_L1TP_227065_20200928_20201007_01_T1.tiff\")\n",
    "pyplot.imshow(src.read(1),vmin=-1, vmax=1,cmap='PiYG')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of unique values of the said array:\n",
      "[[1.0000000e+00 2.0000000e+00 3.0000000e+00 4.0000000e+00 5.0000000e+00]\n",
      " [8.5806000e+04 1.8093655e+07 1.4772200e+06 4.2532630e+06 3.5314247e+07]]\n"
     ]
    }
   ],
   "source": [
    "import rasterio\n",
    "import numpy as np\n",
    "\n",
    "with rasterio.open('./data/vegetation/ndvi/LC08_L1TP_227065_20200928_20201007_01_T1.tiff') as src:    \n",
    "    array = src.read()\n",
    "    profile = src.profile\n",
    "    bins = np.array([-np.inf, 0, 0.1, 0.25, 0.4, np.inf]) \n",
    "    inds = np.digitize(array, bins)\n",
    "    ind64 = np.float64(inds)\n",
    "    \n",
    "    unique_elements, counts_elements = np.unique(ind64, return_counts=True)\n",
    "    print(\"Frequency of unique values of the said array:\")\n",
    "    print(np.asarray((unique_elements, counts_elements)))\n",
    "\n",
    "    with rasterio.open('./data/vegetation/ndvi/reclass_ndvi_'+ 'LC08_L1TP_227065_20200928_20201007_01_T1.tiff', 'w', **profile) as dst:\n",
    "        dst.write(ind64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Distribution of red'),\n",
       " Text(0.5, 0, 'reflectance'),\n",
       " Text(0, 0.5, 'Frequency')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rasterio\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "import rioxarray\n",
    "src = rasterio.open(\"./data/vegetation/ndvi/reclass_ndvi_LC08_L1TP_227065_20200928_20201007_01_T1.tiff\")\n",
    "pyplot.imshow(src.read(1),cmap='PiYG')\n",
    "pyplot.show()\n",
    "\n",
    "ndvi_reclass = rioxarray.open_rasterio('./data/vegetation/ndvi/reclass_ndvi_LC08_L1TP_227065_20200928_20201007_01_T1.tiff')\n",
    "f, ax = plt.subplots(figsize=(10, 6))\n",
    "ndvi_reclass.plot.hist(ax=ax,\n",
    "       color=\"purple\")\n",
    "ax.set(title=\"Distribution of red\",\n",
    "       xlabel='reflectance',\n",
    "       ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"blockxsize\": 256, \"blockysize\": 256, \"bounds\": [572385.0, -916515.0, 801315.0, -683685.0], \"colorinterp\": [\"gray\"], \"count\": 1, \"crs\": \"EPSG:32621\", \"descriptions\": [null], \"driver\": \"GTiff\", \"dtype\": \"float64\", \"height\": 7761, \"indexes\": [1], \"interleave\": \"band\", \"lnglat\": [-55.3076781983449, -7.235225762164517], \"mask_flags\": [[\"all_valid\"]], \"nodata\": null, \"res\": [30.0, 30.0], \"shape\": [7761, 7631], \"tiled\": true, \"transform\": [30.0, 0.0, 572385.0, 0.0, -30.0, -683685.0, 0.0, 0.0, 1.0], \"units\": [null], \"width\": 7631}\n"
     ]
    }
   ],
   "source": [
    "!rio info ./data/vegetation/ndvi/reclass_ndvi_LC08_L1TP_227065_20200928_20201007_01_T1.tiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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