2e_run_random_forest_model_2021.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": 19,
   "id": "c1661b94",
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    "# Configure some things up front\n",
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       "}\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:      (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] 2021-01-01\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-14ee9d73-16e2-4812-a406-602dc4ff4361' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-14ee9d73-16e2-4812-a406-602dc4ff4361' 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-ab280d0d-fb9d-4d95-a629-758ad46ebec6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab280d0d-fb9d-4d95-a629-758ad46ebec6' 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-33fa1de3-4553-423e-bfa7-d34109085fb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33fa1de3-4553-423e-bfa7-d34109085fb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d863504-830c-497b-b75a-4a1876b68983' class='xr-var-data-in' type='checkbox'><label for='data-5d863504-830c-497b-b75a-4a1876b68983' 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-d2f659b5-652e-49d6-a8e3-fffac8fb711c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2f659b5-652e-49d6-a8e3-fffac8fb711c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64e74732-f03b-49b7-806b-f9f9b1fff648' class='xr-var-data-in' type='checkbox'><label for='data-64e74732-f03b-49b7-806b-f9f9b1fff648' 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-825a1efc-0e6c-4043-bc6f-03b986bf8978' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-825a1efc-0e6c-4043-bc6f-03b986bf8978' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78d8fb72-e712-4ea7-b629-cb5fb4162d43' class='xr-var-data-in' type='checkbox'><label for='data-78d8fb72-e712-4ea7-b629-cb5fb4162d43' 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'>2021-01-01</div><input id='attrs-e6faf67b-0979-4e98-9f28-53191fec01e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6faf67b-0979-4e98-9f28-53191fec01e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27b286e6-0a9d-42a3-b7d4-617b402ecd64' class='xr-var-data-in' type='checkbox'><label for='data-27b286e6-0a9d-42a3-b7d4-617b402ecd64' 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;2021-01-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce451f51-301a-4c64-88c7-b07580e5c4f2' class='xr-section-summary-in' type='checkbox'  ><label for='section-ce451f51-301a-4c64-88c7-b07580e5c4f2' 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-ebbfe7e9-b504-4fda-a4f2-95e10a8eb57b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebbfe7e9-b504-4fda-a4f2-95e10a8eb57b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08ba9d33-b6cf-417b-a3e3-e887adee42af' class='xr-var-data-in' type='checkbox'><label for='data-08ba9d33-b6cf-417b-a3e3-e887adee42af' 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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       "\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-3873a21d-6bc9-4cbe-942d-b8916c89a232' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3873a21d-6bc9-4cbe-942d-b8916c89a232' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96c7d1e2-6e36-4452-a7a2-6e536fb84b14' class='xr-var-data-in' type='checkbox'><label for='data-96c7d1e2-6e36-4452-a7a2-6e536fb84b14' 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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       "\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-0fafdb86-d982-4489-92ee-2a0d5a55867e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fafdb86-d982-4489-92ee-2a0d5a55867e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d16e0c4-0e72-4fbb-b5d3-9e38f55f87ad' class='xr-var-data-in' type='checkbox'><label for='data-0d16e0c4-0e72-4fbb-b5d3-9e38f55f87ad' 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-6e529b48-a0cf-45e0-b8f8-5e157dccc766' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e529b48-a0cf-45e0-b8f8-5e157dccc766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f91f874f-0b67-48a0-af57-c2d16cc30e19' class='xr-var-data-in' type='checkbox'><label for='data-f91f874f-0b67-48a0-af57-c2d16cc30e19' 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",
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       "        </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-fc169b14-171b-42ce-adab-62b79a897ebe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc169b14-171b-42ce-adab-62b79a897ebe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df70963d-c152-4780-b4d9-6a0510a0e5cb' class='xr-var-data-in' type='checkbox'><label for='data-df70963d-c152-4780-b4d9-6a0510a0e5cb' 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",
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       "                    <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",
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       "        </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-77b4dc94-75e0-4257-857f-a4bba455ee60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77b4dc94-75e0-4257-857f-a4bba455ee60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17f5516f-7d7f-4a78-885d-b48b8769d106' class='xr-var-data-in' type='checkbox'><label for='data-17f5516f-7d7f-4a78-885d-b48b8769d106' 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",
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       "        </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-fa772bef-1fd0-4641-89b4-b99099a6bf41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa772bef-1fd0-4641-89b4-b99099a6bf41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68103138-e6ee-4d3d-ab7e-57c02d101fdd' class='xr-var-data-in' type='checkbox'><label for='data-68103138-e6ee-4d3d-ab7e-57c02d101fdd' 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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       "        </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-7e5904fe-6db1-47e6-9113-f4f82a33c628' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e5904fe-6db1-47e6-9113-f4f82a33c628' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ac751db-60e1-4ee1-a55a-1a62ce08fbfd' class='xr-var-data-in' type='checkbox'><label for='data-4ac751db-60e1-4ee1-a55a-1a62ce08fbfd' 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",
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       "        </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-d0ab97e9-24b1-4a63-bee4-f52b438dff67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0ab97e9-24b1-4a63-bee4-f52b438dff67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-593c92ab-4c4c-4e06-9c8a-0afe9c56449f' class='xr-var-data-in' type='checkbox'><label for='data-593c92ab-4c4c-4e06-9c8a-0afe9c56449f' 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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       "        </td>\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-d8dcffc5-dcce-409e-b1bb-50f1c9879808' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8dcffc5-dcce-409e-b1bb-50f1c9879808' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-605ede8f-4de0-4077-9d5b-c438e8298c65' class='xr-var-data-in' type='checkbox'><label for='data-605ede8f-4de0-4077-9d5b-c438e8298c65' 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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       "                        <td> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "                        <td> (1007, 1646) </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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       "                        <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",
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       "                        <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",
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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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       "                        <td> (1007, 1646) </td>\n",
       "                        <td> (1007, 1646) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 7 graph layers </td>\n",
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       "                        <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 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",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>savi</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-8342b72d-645c-4702-8a38-f5fb9abbdd9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8342b72d-645c-4702-8a38-f5fb9abbdd9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a41ce605-ea82-4ce1-a1f7-91d758c64466' class='xr-var-data-in' type='checkbox'><label for='data-a41ce605-ea82-4ce1-a1f7-91d758c64466' 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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       "                        <th> Array </th>\n",
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       "                <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 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> 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",
       "                    </tr>\n",
       "                    <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-f8669144-f7f7-4271-90fd-b3a566bf655f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8669144-f7f7-4271-90fd-b3a566bf655f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48c770c2-770c-40e6-a86c-bca179467efc' class='xr-var-data-in' type='checkbox'><label for='data-48c770c2-770c-40e6-a86c-bca179467efc' 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",
       "                    \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",
       "                    </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-c0269894-2aef-4e09-8ef7-52f7ac2d6294' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0269894-2aef-4e09-8ef7-52f7ac2d6294' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5c45bcd-7d62-444c-afcc-6e549c9b7746' class='xr-var-data-in' type='checkbox'><label for='data-a5c45bcd-7d62-444c-afcc-6e549c9b7746' 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",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </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",
       "                    </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>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-e2eda418-7ac4-4f88-b233-67fb8df658da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2eda418-7ac4-4f88-b233-67fb8df658da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac868bbb-01f4-498c-a12b-b5b0dac30dfb' class='xr-var-data-in' type='checkbox'><label for='data-ac868bbb-01f4-498c-a12b-b5b0dac30dfb' 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> </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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       "                        <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",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\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> 6.32 MiB </td>\n",
       "                        <td> 6.32 MiB </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5bf7d685-2463-45d2-860d-2aa490228b2a' class='xr-section-summary-in' type='checkbox'  ><label for='section-5bf7d685-2463-45d2-860d-2aa490228b2a' 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-6748c90b-79f5-4f26-a0ec-bc1519b2cf7a' class='xr-index-data-in' type='checkbox'/><label for='index-6748c90b-79f5-4f26-a0ec-bc1519b2cf7a' 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-e51e5484-bb58-4b43-9786-ee78685deabc' class='xr-index-data-in' type='checkbox'/><label for='index-e51e5484-bb58-4b43-9786-ee78685deabc' 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-6e4aef26-a7cd-40e5-9c9b-a41065cb166d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6e4aef26-a7cd-40e5-9c9b-a41065cb166d' 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] 2021-01-01\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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = load_data(bbox, datetime=datetime, chunks=chunks, resolution=10)\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "af9762ab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "loaded_model = joblib.load(\"test_model.dump\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "id": "cb18ecb5-9a5c-4337-b147-c08d59e4fd87",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filled = loaded_data.fillna(-9999.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "id": "c2c96a26-db0b-421d-ad70-7438d8a4ca81",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "}\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 1 1 1 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 4 2\n",
       "    prob         (y, x) float32 42.8 34.08 43.9 39.73 ... 81.37 35.71 73.54\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-3d3e6691-7d1c-4b53-a4ea-af66450303ec' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3d3e6691-7d1c-4b53-a4ea-af66450303ec' 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-02555421-07bd-458c-8108-4f87f9e5c663' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02555421-07bd-458c-8108-4f87f9e5c663' 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-ac9bd7be-2c06-42b5-ad56-d356d20a9475' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac9bd7be-2c06-42b5-ad56-d356d20a9475' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a155b11-1854-4b4c-8416-f44c4bf3ff63' class='xr-var-data-in' type='checkbox'><label for='data-9a155b11-1854-4b4c-8416-f44c4bf3ff63' 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-fa993596-adf7-4509-b960-813bbcb17a4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa993596-adf7-4509-b960-813bbcb17a4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b51af431-8a26-4302-9feb-7d2195349a70' class='xr-var-data-in' type='checkbox'><label for='data-b51af431-8a26-4302-9feb-7d2195349a70' 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-831efdfd-1da2-4a67-9601-0a6184a134b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-831efdfd-1da2-4a67-9601-0a6184a134b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dfa2c29-07cf-4c9d-ae08-b6e7e7e87739' class='xr-var-data-in' type='checkbox'><label for='data-6dfa2c29-07cf-4c9d-ae08-b6e7e7e87739' 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-9ff60c7c-8141-4868-b852-cecc5435a6d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9ff60c7c-8141-4868-b852-cecc5435a6d5' 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 1 1 1 3 3 3 3 ... 4 4 4 4 4 4 4 2</div><input id='attrs-6821ed6e-c43b-4846-a7e9-e18daa971855' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6821ed6e-c43b-4846-a7e9-e18daa971855' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcd5c6f0-8a2b-406a-91fb-4ed58dae6e72' class='xr-var-data-in' type='checkbox'><label for='data-bcd5c6f0-8a2b-406a-91fb-4ed58dae6e72' 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, 1, 1, ..., 4, 4, 4],\n",
       "       [8, 1, 3, ..., 2, 4, 4],\n",
       "       [3, 4, 4, ..., 4, 2, 4],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 4, 2],\n",
       "       [3, 3, 3, ..., 4, 4, 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'>42.8 34.08 43.9 ... 35.71 73.54</div><input id='attrs-024ab9b2-ade0-4a10-99a4-45c8f83cb239' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-024ab9b2-ade0-4a10-99a4-45c8f83cb239' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe46bd79-864d-48fe-86b7-6d3de6c0a242' class='xr-var-data-in' type='checkbox'><label for='data-fe46bd79-864d-48fe-86b7-6d3de6c0a242' 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([[42.79938 , 34.0828  , 43.898956, ..., 62.406044, 55.046543,\n",
       "        60.05221 ],\n",
       "       [44.910896, 41.13642 , 27.424192, ..., 50.82966 , 40.366447,\n",
       "        57.23249 ],\n",
       "       [35.87992 , 35.503273, 33.41176 , ..., 39.503414, 69.919334,\n",
       "        56.159077],\n",
       "       ...,\n",
       "       [41.437294, 63.303123, 73.300865, ..., 81.51772 , 45.113277,\n",
       "        43.781494],\n",
       "       [40.14837 , 60.865486, 66.55205 , ..., 79.90085 , 36.031017,\n",
       "        60.27839 ],\n",
       "       [33.57228 , 77.8199  , 77.87015 , ..., 81.37192 , 35.714165,\n",
       "        73.541115]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b957c9c-83ce-47e4-b92d-bf1a3fc07ffe' class='xr-section-summary-in' type='checkbox'  ><label for='section-4b957c9c-83ce-47e4-b92d-bf1a3fc07ffe' 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-550acc47-3b4b-4b7f-a295-a2690aa2a5b5' class='xr-index-data-in' type='checkbox'/><label for='index-550acc47-3b4b-4b7f-a295-a2690aa2a5b5' 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-17cd73b4-518c-4cf1-b99b-b4a6f615adc5' class='xr-index-data-in' type='checkbox'/><label for='index-17cd73b4-518c-4cf1-b99b-b4a6f615adc5' 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-e4b16cf1-38e3-4af0-bd38-ee28fb65c1c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4b16cf1-38e3-4af0-bd38-ee28fb65c1c4' 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 1 1 1 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 4 2\n",
       "    prob         (y, x) float32 42.8 34.08 43.9 39.73 ... 81.37 35.71 73.54\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "949ce84c-6a9a-4918-aaf2-8438b6e87feb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd062102b90>"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "id": "c3d25ca4-dc50-4b2f-8ddc-bfc1274393b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('lulc_2021.tif')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write GeoTIFF\n",
    "cleaned_predictions.lulc.odc.write_cog(\"lulc_2021.tif\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "24a48d19-b363-4e1a-8edd-b39f34146a80",
   "metadata": {},
   "outputs": [
    {
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       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.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 1 1 1 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 4 2\n",
       "    prob         (y, x) float32 42.8 34.08 43.9 39.73 ... 81.37 35.71 73.54\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-d5b11b76-00cc-4d8a-9807-11db220a9b3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5b11b76-00cc-4d8a-9807-11db220a9b3a' 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-ad1f1db3-d4d1-488f-bf43-7c782eca1ef7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad1f1db3-d4d1-488f-bf43-7c782eca1ef7' 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-7fd79c67-1ecd-48a0-ba14-3bbd92603833' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fd79c67-1ecd-48a0-ba14-3bbd92603833' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b37cdef-5747-4d84-b212-341fad09e1ca' class='xr-var-data-in' type='checkbox'><label for='data-6b37cdef-5747-4d84-b212-341fad09e1ca' 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-688927b3-a8b1-4447-bdca-7f263f2af235' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-688927b3-a8b1-4447-bdca-7f263f2af235' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-068bac5b-78c2-4c46-a4a9-55922b77b33f' class='xr-var-data-in' type='checkbox'><label for='data-068bac5b-78c2-4c46-a4a9-55922b77b33f' 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-b7c34959-423e-44b4-b507-2cf9be8d4236' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7c34959-423e-44b4-b507-2cf9be8d4236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4735c5fb-9b0d-45df-8796-da78b2f29717' class='xr-var-data-in' type='checkbox'><label for='data-4735c5fb-9b0d-45df-8796-da78b2f29717' 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-d563824c-d0dd-4db9-9e37-f55b4c462d19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d563824c-d0dd-4db9-9e37-f55b4c462d19' 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 1 1 1 3 3 3 3 ... 4 4 4 4 4 4 4 2</div><input id='attrs-28e283ff-a907-41a7-8acb-9008eb4e3547' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28e283ff-a907-41a7-8acb-9008eb4e3547' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-573186d6-8580-4889-abd3-c7f9124da33f' class='xr-var-data-in' type='checkbox'><label for='data-573186d6-8580-4889-abd3-c7f9124da33f' 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, 1, 1, ..., 4, 4, 4],\n",
       "       [8, 1, 3, ..., 2, 4, 4],\n",
       "       [3, 4, 4, ..., 4, 2, 4],\n",
       "       ...,\n",
       "       [3, 3, 3, ..., 4, 4, 4],\n",
       "       [3, 3, 3, ..., 4, 4, 2],\n",
       "       [3, 3, 3, ..., 4, 4, 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'>42.8 34.08 43.9 ... 35.71 73.54</div><input id='attrs-fc2866a6-c7df-40b0-ab20-991ac01c8e68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc2866a6-c7df-40b0-ab20-991ac01c8e68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63c74858-2c26-4556-ba07-6d8f5354970e' class='xr-var-data-in' type='checkbox'><label for='data-63c74858-2c26-4556-ba07-6d8f5354970e' 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([[42.79938 , 34.0828  , 43.898956, ..., 62.406044, 55.046543,\n",
       "        60.05221 ],\n",
       "       [44.910896, 41.13642 , 27.424192, ..., 50.82966 , 40.366447,\n",
       "        57.23249 ],\n",
       "       [35.87992 , 35.503273, 33.41176 , ..., 39.503414, 69.919334,\n",
       "        56.159077],\n",
       "       ...,\n",
       "       [41.437294, 63.303123, 73.300865, ..., 81.51772 , 45.113277,\n",
       "        43.781494],\n",
       "       [40.14837 , 60.865486, 66.55205 , ..., 79.90085 , 36.031017,\n",
       "        60.27839 ],\n",
       "       [33.57228 , 77.8199  , 77.87015 , ..., 81.37192 , 35.714165,\n",
       "        73.541115]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-60df9208-f7e0-42f5-847d-ec0b6f17e0b3' class='xr-section-summary-in' type='checkbox'  ><label for='section-60df9208-f7e0-42f5-847d-ec0b6f17e0b3' 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-c0c295fa-e113-44db-a9df-67358f1aa16b' class='xr-index-data-in' type='checkbox'/><label for='index-c0c295fa-e113-44db-a9df-67358f1aa16b' 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-c8c82a27-fe26-4032-a155-5a4ee9949341' class='xr-index-data-in' type='checkbox'/><label for='index-c8c82a27-fe26-4032-a155-5a4ee9949341' 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-92ad1812-6ef4-48c3-8c7d-96c26618f1e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92ad1812-6ef4-48c3-8c7d-96c26618f1e5' 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 1 1 1 3 3 3 3 3 3 3 3 ... 4 4 4 4 4 4 4 4 4 4 4 2\n",
       "    prob         (y, x) float32 42.8 34.08 43.9 39.73 ... 81.37 35.71 73.54\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4931f399-7510-4436-8873-d834b908e656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('Test_change_detection/bareland_2021.tif')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarry_2021 = cleaned_predictions.lulc.isin([8])\n",
    "quarry_2021 = quarry_2021.astype(np.int32)\n",
    "quarry_2021.odc.write_cog(\"Test_change_detection/quarry_2021.tif\", overwrite=True)\n",
    "bareland_2021 = cleaned_predictions.lulc.isin([1])\n",
    "bareland_2021 = bareland_2021.astype(np.int32)\n",
    "bareland_2021.odc.write_cog(\"Test_change_detection/bareland_2021.tif\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e997d9fc-5ad4-4387-80d0-25623282ae52",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "quarry_2021 = xr.open_dataset(\"Test_change_detection/quarry_2021.tif\")\n",
    "bareland_2021 = xr.open_dataset(\"Test_change_detection/bareland_2021.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e4be63c6-ff24-4ed3-8217-827b91ab2b3e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fd040e33fd0>"
      ]
     },
     "execution_count": 31,
     "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_2021.band_data.plot(cmap=\"YlOrBr\", size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a04ee15c-80ce-4d24-8621-6c7493e4a18a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fd043f22e10>"
      ]
     },
     "execution_count": 32,
     "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_2021.band_data.plot(cmap=\"YlOrBr\", size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77925cf0-7219-467a-964a-1d7172427c9a",
   "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
}