2d_run_random_forest_model_2020.ipynb

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   "source": [
    "from concurrent.futures import ThreadPoolExecutor\n",
    "\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import odc.geo\n",
    "from odc.stac import load\n",
    "from pystac_client import Client\n",
    "from shapely import geometry\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import datacube\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "import sys, os\n",
    "sys.path.insert(1, os.path.abspath('../Tools'))\n",
    "\n",
    "import rasterio\n",
    "import matplotlib\n",
    "import pickle\n",
    "import joblib\n",
    "from utils import load_data, predict_xr"
   ]
  },
  {
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   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
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   "source": [
    "## Find and load data\n",
    "\n",
    "Load data and set up your array to use for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c1661b94",
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    "# Configure some things up front\n",
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       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (y: 1007, x: 1646)\n",
       "Coordinates:\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "    spatial_ref  int32 3832\n",
       "    time         datetime64[ns] 2020-01-01\n",
       "Data variables: (12/24)\n",
       "    B02          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B03          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B04          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B05          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B06          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    B07          (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    ...           ...\n",
       "    ndmi         (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    ndbi         (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    elevation    (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    mean_vv      (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    mean_vh      (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;\n",
       "    mean_vv_vh   (y, x) float32 dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a220e484-6a7d-4dc5-a79e-774ffed901cc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a220e484-6a7d-4dc5-a79e-774ffed901cc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 1007</li><li><span class='xr-has-index'>x</span>: 1646</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-db97fd48-973d-4666-ab55-82689448fdd6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db97fd48-973d-4666-ab55-82689448fdd6' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>-1.989e+06 ... -1.999e+06</div><input id='attrs-b0c891e3-6e13-402d-a934-aea147a70c31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0c891e3-6e13-402d-a934-aea147a70c31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98b2e7c3-b9a3-43da-bab7-0d51cc25f197' class='xr-var-data-in' type='checkbox'><label for='data-98b2e7c3-b9a3-43da-bab7-0d51cc25f197' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([-1989045., -1989055., -1989065., ..., -1999085., -1999095., -1999105.])</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'>3.055e+06 3.055e+06 ... 3.071e+06</div><input id='attrs-b41f0d09-3a24-473d-8f25-7717b1e746f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b41f0d09-3a24-473d-8f25-7717b1e746f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d9cc58-1e44-4a20-8fe7-062f71c46382' class='xr-var-data-in' type='checkbox'><label for='data-b3d9cc58-1e44-4a20-8fe7-062f71c46382' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([3054855., 3054865., 3054875., ..., 3071285., 3071295., 3071305.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3832</div><input id='attrs-209c306f-e836-4fad-9596-fb6af0ab105f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-209c306f-e836-4fad-9596-fb6af0ab105f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f596eef8-52d0-4131-8b96-142f59b32943' class='xr-var-data-in' type='checkbox'><label for='data-f596eef8-52d0-4131-8b96-142f59b32943' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / PDC Mercator</dd><dt><span>grid_mapping_name :</span></dt><dd>mercator</dd><dt><span>standard_parallel :</span></dt><dd>0.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>150.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_projection_origin :</span></dt><dd>1.0</dd><dt><span>GeoTransform :</span></dt><dd>3054850.0 10.0 0.0 -1989040.0 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(3832, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01</div><input id='attrs-e7cb6a1d-1a40-40c0-86b5-f4b2794b67f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7cb6a1d-1a40-40c0-86b5-f4b2794b67f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22fcd4f2-c5da-4140-88bb-0dcf6be1349a' class='xr-var-data-in' type='checkbox'><label for='data-22fcd4f2-c5da-4140-88bb-0dcf6be1349a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2020-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77b83b7f-3425-470c-8b6b-068f0caf99f3' class='xr-section-summary-in' type='checkbox'  ><label for='section-77b83b7f-3425-470c-8b6b-068f0caf99f3' class='xr-section-summary' >Data variables: <span>(24)</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>B02</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-e60fef88-6869-46c7-899d-fe2388f7817b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e60fef88-6869-46c7-899d-fe2388f7817b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78eef06f-c095-418f-8add-7acf8eabb85a' class='xr-var-data-in' type='checkbox'><label for='data-78eef06f-c095-418f-8add-7acf8eabb85a' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,73.41433778857837 0.0,73.41433778857837\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"93.414338\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1646</text>\n",
       "  <text x=\"140.000000\" y=\"36.707169\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,36.707169)\">1007</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B03</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-5c998815-b094-42a5-ae1f-75fef9449a80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c998815-b094-42a5-ae1f-75fef9449a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cbcbdde-3323-4e12-9891-09e346b59986' class='xr-var-data-in' type='checkbox'><label for='data-7cbcbdde-3323-4e12-9891-09e346b59986' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"73\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,73.41433778857837 0.0,73.41433778857837\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"93.414338\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1646</text>\n",
       "  <text x=\"140.000000\" y=\"36.707169\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,36.707169)\">1007</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B04</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-2f8e3c88-11a4-4c57-a5df-e84bc03e8337' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f8e3c88-11a4-4c57-a5df-e84bc03e8337' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9770daa8-4f92-4242-b77c-7b6007d4911e' class='xr-var-data-in' type='checkbox'><label for='data-9770daa8-4f92-4242-b77c-7b6007d4911e' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"73\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"73\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,73.41433778857837 0.0,73.41433778857837\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"93.414338\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1646</text>\n",
       "  <text x=\"140.000000\" y=\"36.707169\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,36.707169)\">1007</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B05</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-c510d87d-217d-42c5-8b35-9b2f82779cfe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c510d87d-217d-42c5-8b35-9b2f82779cfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22a3fa81-058d-4bb7-916b-60b74687383c' class='xr-var-data-in' type='checkbox'><label for='data-22a3fa81-058d-4bb7-916b-60b74687383c' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B06</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-f1845fe6-42dc-4d2d-8613-e27e1d266927' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1845fe6-42dc-4d2d-8613-e27e1d266927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27f7c81e-7b0a-462b-b9cf-45af051e9952' class='xr-var-data-in' type='checkbox'><label for='data-27f7c81e-7b0a-462b-b9cf-45af051e9952' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B07</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-b00f7035-35c0-4aac-b270-67dbb794a9d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b00f7035-35c0-4aac-b270-67dbb794a9d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85350d1c-3b54-487d-b43c-0a23948d2238' class='xr-var-data-in' type='checkbox'><label for='data-85350d1c-3b54-487d-b43c-0a23948d2238' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B08</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-bb467290-dd5f-4824-abc9-c0e29604d15c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb467290-dd5f-4824-abc9-c0e29604d15c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fc079f6-0443-46c6-aa40-aae40d254718' class='xr-var-data-in' type='checkbox'><label for='data-1fc079f6-0443-46c6-aa40-aae40d254718' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B8A</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-d5511dad-5f73-4f5e-b3b1-0cce13f81962' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5511dad-5f73-4f5e-b3b1-0cce13f81962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e8cc2d9-48fd-4d24-b821-1fc0a7f14b09' class='xr-var-data-in' type='checkbox'><label for='data-8e8cc2d9-48fd-4d24-b821-1fc0a7f14b09' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B11</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-82754d15-b2ff-4f8f-b4e9-fe025dc0efbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82754d15-b2ff-4f8f-b4e9-fe025dc0efbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ba8d60f-016e-4e95-82ea-1bfff7e123c1' class='xr-var-data-in' type='checkbox'><label for='data-4ba8d60f-016e-4e95-82ea-1bfff7e123c1' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"123\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B12</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-2cfb91b6-b37d-43e3-8ce5-9b28e11c2d89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cfb91b6-b37d-43e3-8ce5-9b28e11c2d89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f856609e-5556-4f9f-a5a6-08e8739a0678' class='xr-var-data-in' type='checkbox'><label for='data-f856609e-5556-4f9f-a5a6-08e8739a0678' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "    </tr>\n",
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       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 8 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 13 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndmi</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-d0876fc4-34ba-49f1-b81d-cd2501b20e6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0876fc4-34ba-49f1-b81d-cd2501b20e6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c92dde9-ce9d-4eab-815c-b683e9a64363' class='xr-var-data-in' type='checkbox'><label for='data-2c92dde9-ce9d-4eab-815c-b683e9a64363' 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'><table>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndbi</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-c89c7c5f-f1e6-4f9a-b389-3bbcbcdc45d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c89c7c5f-f1e6-4f9a-b389-3bbcbcdc45d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90a1fec8-587d-47d9-9da6-1d403dd7b7fa' class='xr-var-data-in' type='checkbox'><label for='data-90a1fec8-587d-47d9-9da6-1d403dd7b7fa' 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'><table>\n",
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       "        <td>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-36343531-e22b-45c1-93dc-9d0e8c4a720b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36343531-e22b-45c1-93dc-9d0e8c4a720b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-122b930f-6a4b-47e1-b0bb-9f283b20847a' class='xr-var-data-in' type='checkbox'><label for='data-122b930f-6a4b-47e1-b0bb-9f283b20847a' 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'><table>\n",
       "    <tr>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_vh</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1007, 1646), meta=np.ndarray&gt;</div><input id='attrs-e12dfe38-a9cf-4db6-83a8-0f8afce4f514' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e12dfe38-a9cf-4db6-83a8-0f8afce4f514' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f3f1b40-7a24-46d2-9c08-7cdeecae1386' class='xr-var-data-in' type='checkbox'><label for='data-8f3f1b40-7a24-46d2-9c08-7cdeecae1386' 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'><table>\n",
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       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d3d5d26-a2c9-4135-9a17-984010804328' class='xr-section-summary-in' type='checkbox'  ><label for='section-9d3d5d26-a2c9-4135-9a17-984010804328' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fde8fbcf-fbe8-4f25-82b5-29d1fe0481ef' class='xr-index-data-in' type='checkbox'/><label for='index-fde8fbcf-fbe8-4f25-82b5-29d1fe0481ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1989045.0, -1989055.0, -1989065.0, -1989075.0, -1989085.0, -1989095.0,\n",
       "       -1989105.0, -1989115.0, -1989125.0, -1989135.0,\n",
       "       ...\n",
       "       -1999015.0, -1999025.0, -1999035.0, -1999045.0, -1999055.0, -1999065.0,\n",
       "       -1999075.0, -1999085.0, -1999095.0, -1999105.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1007))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f6a225db-54f3-4870-a0a5-67efc599e866' class='xr-index-data-in' type='checkbox'/><label for='index-f6a225db-54f3-4870-a0a5-67efc599e866' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3054855.0, 3054865.0, 3054875.0, 3054885.0, 3054895.0, 3054905.0,\n",
       "       3054915.0, 3054925.0, 3054935.0, 3054945.0,\n",
       "       ...\n",
       "       3071215.0, 3071225.0, 3071235.0, 3071245.0, 3071255.0, 3071265.0,\n",
       "       3071275.0, 3071285.0, 3071295.0, 3071305.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1646))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5674d355-3310-4e3a-b73d-0230236041f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5674d355-3310-4e3a-b73d-0230236041f5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (y: 1007, x: 1646)\n",
       "Coordinates:\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "    spatial_ref  int32 3832\n",
       "    time         datetime64[ns] 2020-01-01\n",
       "Data variables: (12/24)\n",
       "    B02          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B03          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B04          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B05          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B06          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    B07          (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    ...           ...\n",
       "    ndmi         (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    ndbi         (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    elevation    (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    mean_vv      (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    mean_vh      (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>\n",
       "    mean_vv_vh   (y, x) float32 dask.array<chunksize=(1007, 1646), meta=np.ndarray>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = load_data(bbox, datetime=datetime, chunks=chunks, resolution=10)\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "af9762ab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "loaded_model = joblib.load(\"test_model.dump\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2f4a57c1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "loaded_data = merged.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b942be-dbbe-44c2-8291-d55a5e6d8249",
   "metadata": {},
   "source": [
    "## Run ML classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cb18ecb5-9a5c-4337-b147-c08d59e4fd87",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filled = loaded_data.fillna(-9999.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "07c9cfa5-ff5a-4fc9-b56d-2cd8c8a6e553",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicting...\n",
      "   probabilities...\n"
     ]
    }
   ],
   "source": [
    "# This runs the actual prediction\n",
    "predicted = predict_xr(loaded_model, filled, proba=True)\n",
    "\n",
    "# Convert to int\n",
    "cleaned_predictions = predicted.copy(deep=True)\n",
    "cleaned_predictions.predictions.data = predicted.predictions.data.astype(np.int8)\n",
    "cleaned_predictions.probabilities.data = predicted.probabilities.data.astype(np.float32)\n",
    "\n",
    "cleaned_predictions = cleaned_predictions.rename({\"predictions\": \"lulc\", \"probabilities\": \"prob\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c2c96a26-db0b-421d-ad70-7438d8a4ca81",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (x: 1646, y: 1007)\n",
       "Coordinates:\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "    spatial_ref  int32 3832\n",
       "Data variables:\n",
       "    lulc         (y, x) int8 8 5 5 1 1 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 2 2\n",
       "    prob         (y, x) float32 37.55 35.55 32.43 35.07 ... 72.33 57.48 71.61\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-969802b2-078e-40f8-8238-4896f28a7d3d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-969802b2-078e-40f8-8238-4896f28a7d3d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 1646</li><li><span class='xr-has-index'>y</span>: 1007</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0ba24f6a-7891-4994-bb7a-c92d4ea4d145' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ba24f6a-7891-4994-bb7a-c92d4ea4d145' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.055e+06 3.055e+06 ... 3.071e+06</div><input id='attrs-59db85ba-a217-4638-9360-77128cd3a00a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59db85ba-a217-4638-9360-77128cd3a00a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28bddbaf-d737-49cb-85a1-915db7506f0d' class='xr-var-data-in' type='checkbox'><label for='data-28bddbaf-d737-49cb-85a1-915db7506f0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([3054855., 3054865., 3054875., ..., 3071285., 3071295., 3071305.])</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'>-1.989e+06 ... -1.999e+06</div><input id='attrs-3e15c0f7-5a8c-4b94-98c0-5ff0f0dd5b5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e15c0f7-5a8c-4b94-98c0-5ff0f0dd5b5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cde1a1ea-c930-4d4a-ac84-bf115fc150ed' class='xr-var-data-in' type='checkbox'><label for='data-cde1a1ea-c930-4d4a-ac84-bf115fc150ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([-1989045., -1989055., -1989065., ..., -1999085., -1999095., -1999105.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3832</div><input id='attrs-ca311623-98ea-4e2d-b130-3433028d8b7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca311623-98ea-4e2d-b130-3433028d8b7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbeffef9-f1a6-4624-bf5e-d714cf3b7ab4' class='xr-var-data-in' type='checkbox'><label for='data-dbeffef9-f1a6-4624-bf5e-d714cf3b7ab4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / PDC Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,150],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;3832&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>mercator</dd></dl></div><div class='xr-var-data'><pre>array(3832, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-11005ee3-1b8c-4a61-bc6d-1ad593f89ec2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11005ee3-1b8c-4a61-bc6d-1ad593f89ec2' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lulc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>8 5 5 1 1 3 3 3 ... 4 4 4 4 4 4 2 2</div><input id='attrs-02ee0cb2-e450-4f1e-a83a-c884f892dc28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02ee0cb2-e450-4f1e-a83a-c884f892dc28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd396c46-7ced-45ae-8624-23bcbf48c0ff' class='xr-var-data-in' type='checkbox'><label for='data-dd396c46-7ced-45ae-8624-23bcbf48c0ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[8, 5, 5, ..., 4, 4, 4],\n",
       "       [8, 8, 1, ..., 4, 4, 4],\n",
       "       [8, 1, 1, ..., 2, 4, 4],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 2, 2],\n",
       "       [3, 3, 3, ..., 4, 2, 2]], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prob</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>37.55 35.55 32.43 ... 57.48 71.61</div><input id='attrs-9f13f7f5-3df6-4e19-90ab-bce518ee2413' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f13f7f5-3df6-4e19-90ab-bce518ee2413' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdbae334-a8ae-4501-ab3b-67636ad872d8' class='xr-var-data-in' type='checkbox'><label for='data-cdbae334-a8ae-4501-ab3b-67636ad872d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[37.551964, 35.548325, 32.429474, ..., 61.381554, 66.71418 ,\n",
       "        56.837254],\n",
       "       [52.193157, 37.57168 , 34.280403, ..., 52.457813, 58.59606 ,\n",
       "        58.312363],\n",
       "       [54.08745 , 32.819935, 38.765232, ..., 38.487427, 42.876747,\n",
       "        58.87129 ],\n",
       "       ...,\n",
       "       [42.714016, 39.370872, 36.525646, ..., 76.059204, 42.831608,\n",
       "        41.646057],\n",
       "       [44.622456, 39.141804, 35.343994, ..., 69.17714 , 62.146988,\n",
       "        63.291553],\n",
       "       [42.994522, 37.84066 , 35.05973 , ..., 72.326096, 57.47534 ,\n",
       "        71.6091  ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3e36a1d-0a79-4b51-aec1-61be97a7e807' class='xr-section-summary-in' type='checkbox'  ><label for='section-a3e36a1d-0a79-4b51-aec1-61be97a7e807' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6c93fdc9-66ca-4b0e-970e-48630f32a978' class='xr-index-data-in' type='checkbox'/><label for='index-6c93fdc9-66ca-4b0e-970e-48630f32a978' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3054855.0, 3054865.0, 3054875.0, 3054885.0, 3054895.0, 3054905.0,\n",
       "       3054915.0, 3054925.0, 3054935.0, 3054945.0,\n",
       "       ...\n",
       "       3071215.0, 3071225.0, 3071235.0, 3071245.0, 3071255.0, 3071265.0,\n",
       "       3071275.0, 3071285.0, 3071295.0, 3071305.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1646))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d5d31866-3a23-4380-9ddf-29bb5206a1b7' class='xr-index-data-in' type='checkbox'/><label for='index-d5d31866-3a23-4380-9ddf-29bb5206a1b7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1989045.0, -1989055.0, -1989065.0, -1989075.0, -1989085.0, -1989095.0,\n",
       "       -1989105.0, -1989115.0, -1989125.0, -1989135.0,\n",
       "       ...\n",
       "       -1999015.0, -1999025.0, -1999035.0, -1999045.0, -1999055.0, -1999065.0,\n",
       "       -1999075.0, -1999085.0, -1999095.0, -1999105.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1007))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94bf5c5f-d7ed-4c41-a0e5-08a9ef4519ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-94bf5c5f-d7ed-4c41-a0e5-08a9ef4519ec' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (x: 1646, y: 1007)\n",
       "Coordinates:\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "    spatial_ref  int32 3832\n",
       "Data variables:\n",
       "    lulc         (y, x) int8 8 5 5 1 1 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 2 2\n",
       "    prob         (y, x) float32 37.55 35.55 32.43 35.07 ... 72.33 57.48 71.61\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "949ce84c-6a9a-4918-aaf2-8438b6e87feb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4f8a3f9410>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1333.33x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors\n",
    "\n",
    "classes = [\n",
    "    [1, \"bare_land\", \"#968640\"],\n",
    "    [2, \"forest\", \"#064a00\"],\n",
    "    [3, \"crops\", \"#ffce33\"],\n",
    "    [4, \"grassland\", \"#d7ffa0\"],\n",
    "    [5, \"settlements\", \"#b3b2ae\"],\n",
    "    [6, \"mangroves\", \"#07b28d\"],\n",
    "    [7, \"water\", \"#71a8ff\"],\n",
    "    [8, \"quarry\", \"#b03a2e\"]\n",
    "]\n",
    "\n",
    "values_list = [c[0] for c in classes]\n",
    "color_list = [c[2] for c in classes]\n",
    "\n",
    "# Build a listed colormap.\n",
    "c_map = colors.ListedColormap(color_list)\n",
    "bounds = values_list + [9]\n",
    "norm = colors.BoundaryNorm(bounds, c_map.N)\n",
    "\n",
    "cleaned_predictions.lulc.plot.imshow(cmap=c_map, norm=norm, size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c3d25ca4-dc50-4b2f-8ddc-bfc1274393b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('lulc_2020.tif')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write GeoTIFF\n",
    "cleaned_predictions.lulc.odc.write_cog(\"lulc_2020.tif\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "24a48d19-b363-4e1a-8edd-b39f34146a80",
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (x: 1646, y: 1007)\n",
       "Coordinates:\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "    spatial_ref  int32 3832\n",
       "Data variables:\n",
       "    lulc         (y, x) int8 8 5 5 1 1 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 2 2\n",
       "    prob         (y, x) float32 37.55 35.55 32.43 35.07 ... 72.33 57.48 71.61\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2bfd8105-092f-4143-aeea-8db023caa0a5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2bfd8105-092f-4143-aeea-8db023caa0a5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 1646</li><li><span class='xr-has-index'>y</span>: 1007</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a4b180d5-e73a-4412-8917-758fbd6d9ecb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4b180d5-e73a-4412-8917-758fbd6d9ecb' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.055e+06 3.055e+06 ... 3.071e+06</div><input id='attrs-fd69ccc9-bcc0-4d77-8b9d-b447a8c3b734' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd69ccc9-bcc0-4d77-8b9d-b447a8c3b734' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ff767eb-5abb-4583-ab5d-cb689a3e0fa6' class='xr-var-data-in' type='checkbox'><label for='data-2ff767eb-5abb-4583-ab5d-cb689a3e0fa6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([3054855., 3054865., 3054875., ..., 3071285., 3071295., 3071305.])</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'>-1.989e+06 ... -1.999e+06</div><input id='attrs-c8e1a2a3-6ccd-45a7-a4bb-84d2fa11b389' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8e1a2a3-6ccd-45a7-a4bb-84d2fa11b389' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53bd324c-1182-4599-8864-1220012a649a' class='xr-var-data-in' type='checkbox'><label for='data-53bd324c-1182-4599-8864-1220012a649a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>PROJCRS[&quot;WGS 84 / PDC Mercator&quot;,BASEGEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],ID[&quot;EPSG&quot;,4326]],CONVERSION[&quot;Pacific Disaster Center Mercator&quot;,METHOD[&quot;Mercator (variant A)&quot;,ID[&quot;EPSG&quot;,9804]],PARAMETER[&quot;Latitude of natural origin&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8801]],PARAMETER[&quot;Longitude of natural origin&quot;,150,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,1,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8806]],PARAMETER[&quot;False northing&quot;,0,LENGTHUNIT[&quot;metre&quot;,1],ID[&quot;EPSG&quot;,8807]]],CS[Cartesian,2],AXIS[&quot;(E)&quot;,east,ORDER[1],LENGTHUNIT[&quot;metre&quot;,1]],AXIS[&quot;(N)&quot;,north,ORDER[2],LENGTHUNIT[&quot;metre&quot;,1]],USAGE[SCOPE[&quot;Topographic mapping (small scale).&quot;],AREA[&quot;Pacific Ocean - American Samoa, Antarctica, Australia, Brunei Darussalam, Cambodia, Canada, Chile, China, China - Hong Kong, China - Macao, Cook Islands, Ecuador, Fiji, French Polynesia, Guam, Indonesia, Japan, Kiribati, Democratic People&#x27;s Republic of Korea (North Korea), Republic of Korea (South Korea), Malaysia, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Panama, Papua New Guinea (PNG), Peru, Philippines, Pitcairn, Russian Federation, Samoa, Singapore, Solomon Islands, Taiwan, Thailand, Tokelau, Tonga, Tuvalu, United States (USA), United States Minor Outlying Islands, Vanuatu, Venezuela, Vietnam, Wallis and Futuna.&quot;],BBOX[-60,98.69,66.67,-68]],ID[&quot;EPSG&quot;,3832]]</dd></dl></div><div class='xr-var-data'><pre>array([-1989045., -1989055., -1989065., ..., -1999085., -1999095., -1999105.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3832</div><input id='attrs-112e46b2-71d5-4eed-96f4-177c2bd88437' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-112e46b2-71d5-4eed-96f4-177c2bd88437' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-522cf351-8c5f-4b48-8629-cb18042cd351' class='xr-var-data-in' type='checkbox'><label for='data-522cf351-8c5f-4b48-8629-cb18042cd351' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / PDC Mercator&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Mercator_1SP&quot;],PARAMETER[&quot;central_meridian&quot;,150],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;3832&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>mercator</dd></dl></div><div class='xr-var-data'><pre>array(3832, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9ab3714-0b68-4c44-87c0-95a17ee5b99a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9ab3714-0b68-4c44-87c0-95a17ee5b99a' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lulc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>8 5 5 1 1 3 3 3 ... 4 4 4 4 4 4 2 2</div><input id='attrs-ea025dcd-9c11-41d7-9080-09c9a8e25733' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea025dcd-9c11-41d7-9080-09c9a8e25733' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f800d26a-ca02-4180-9ee0-3de3248cdf2b' class='xr-var-data-in' type='checkbox'><label for='data-f800d26a-ca02-4180-9ee0-3de3248cdf2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[8, 5, 5, ..., 4, 4, 4],\n",
       "       [8, 8, 1, ..., 4, 4, 4],\n",
       "       [8, 1, 1, ..., 2, 4, 4],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 2, 2],\n",
       "       [3, 3, 3, ..., 4, 2, 2]], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prob</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>37.55 35.55 32.43 ... 57.48 71.61</div><input id='attrs-e55e209a-9b12-4b61-bf8d-79447763c424' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e55e209a-9b12-4b61-bf8d-79447763c424' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccab8cf7-3041-4042-95c2-5afda10233e3' class='xr-var-data-in' type='checkbox'><label for='data-ccab8cf7-3041-4042-95c2-5afda10233e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[37.551964, 35.548325, 32.429474, ..., 61.381554, 66.71418 ,\n",
       "        56.837254],\n",
       "       [52.193157, 37.57168 , 34.280403, ..., 52.457813, 58.59606 ,\n",
       "        58.312363],\n",
       "       [54.08745 , 32.819935, 38.765232, ..., 38.487427, 42.876747,\n",
       "        58.87129 ],\n",
       "       ...,\n",
       "       [42.714016, 39.370872, 36.525646, ..., 76.059204, 42.831608,\n",
       "        41.646057],\n",
       "       [44.622456, 39.141804, 35.343994, ..., 69.17714 , 62.146988,\n",
       "        63.291553],\n",
       "       [42.994522, 37.84066 , 35.05973 , ..., 72.326096, 57.47534 ,\n",
       "        71.6091  ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4dbb5ad-4d25-44a5-9096-98ee57552721' class='xr-section-summary-in' type='checkbox'  ><label for='section-f4dbb5ad-4d25-44a5-9096-98ee57552721' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-896b27b0-3702-446b-88e7-db50d6f5340c' class='xr-index-data-in' type='checkbox'/><label for='index-896b27b0-3702-446b-88e7-db50d6f5340c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3054855.0, 3054865.0, 3054875.0, 3054885.0, 3054895.0, 3054905.0,\n",
       "       3054915.0, 3054925.0, 3054935.0, 3054945.0,\n",
       "       ...\n",
       "       3071215.0, 3071225.0, 3071235.0, 3071245.0, 3071255.0, 3071265.0,\n",
       "       3071275.0, 3071285.0, 3071295.0, 3071305.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1646))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4e687afb-9748-4b2d-95ed-2f094a22bd56' class='xr-index-data-in' type='checkbox'/><label for='index-4e687afb-9748-4b2d-95ed-2f094a22bd56' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1989045.0, -1989055.0, -1989065.0, -1989075.0, -1989085.0, -1989095.0,\n",
       "       -1989105.0, -1989115.0, -1989125.0, -1989135.0,\n",
       "       ...\n",
       "       -1999015.0, -1999025.0, -1999035.0, -1999045.0, -1999055.0, -1999065.0,\n",
       "       -1999075.0, -1999085.0, -1999095.0, -1999105.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1007))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6f0b326-051b-4d08-9f47-b0fd8dd8721e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6f0b326-051b-4d08-9f47-b0fd8dd8721e' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (x: 1646, y: 1007)\n",
       "Coordinates:\n",
       "  * x            (x) float64 3.055e+06 3.055e+06 ... 3.071e+06 3.071e+06\n",
       "  * y            (y) float64 -1.989e+06 -1.989e+06 ... -1.999e+06 -1.999e+06\n",
       "    spatial_ref  int32 3832\n",
       "Data variables:\n",
       "    lulc         (y, x) int8 8 5 5 1 1 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 2 2\n",
       "    prob         (y, x) float32 37.55 35.55 32.43 35.07 ... 72.33 57.48 71.61\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4931f399-7510-4436-8873-d834b908e656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('Test_change_detection/bareland_2020.tif')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarry_2020 = cleaned_predictions.lulc.isin([8])\n",
    "quarry_2020 = quarry_2020.astype(np.int32)\n",
    "quarry_2020.odc.write_cog(\"Test_change_detection/quarry_2020.tif\", overwrite=True)\n",
    "bareland_2020 = cleaned_predictions.lulc.isin([1])\n",
    "bareland_2020 = bareland_2020.astype(np.int32)\n",
    "bareland_2020.odc.write_cog(\"Test_change_detection/bareland_2020.tif\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e997d9fc-5ad4-4387-80d0-25623282ae52",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "quarry_2020 = xr.open_dataset(\"Test_change_detection/quarry_2020.tif\")\n",
    "bareland_2020 = xr.open_dataset(\"Test_change_detection/bareland_2020.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e4be63c6-ff24-4ed3-8217-827b91ab2b3e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f4f8a58a690>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 666.667x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quarry_2020.band_data.plot(cmap=\"YlOrBr\", size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a04ee15c-80ce-4d24-8621-6c7493e4a18a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f4f8af5aa10>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 666.667x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bareland_2020.band_data.plot(cmap=\"YlOrBr\", size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aca3a014-16bf-4a63-be71-f16b9f4fea76",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}