scottyhq/8615.ipynb

## 8615.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic example of Xarray Mosaic (https://github.com/pydata/xarray/discussions/8615)\n",
    "# import matplotlib.patches as patches\n",
    "import pystac_client\n",
    "import planetary_computer\n",
    "import geopandas\n",
    "import numpy as np\n",
    "from geocube.api.core import make_geocube\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 297,
       "width": 556
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Start with 3 Open-access Overlapping Landsat-9 from MSPC\n",
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
    "     modifier=planetary_computer.sign_inplace\n",
    "    )\n",
    "# Found here https://planetarycomputer.microsoft.com/explore\n",
    "stacids = ['LC09_L2SP_047027_20230916_02_T1',\n",
    "           'LC09_L2SP_046027_20230909_02_T1',\n",
    "           'LC09_L2SP_045027_20230902_02_T1']\n",
    "search = catalog.search(collections=[\"landsat-c2-l2\"], \n",
    "                        ids=stacids)\n",
    "items = search.item_collection()\n",
    "gf = geopandas.GeoDataFrame.from_features(items.to_dict(), crs=\"epsg:4326\")\n",
    "gf.plot(color=['c','m','y'], alpha=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (landsat:scene_id: 3, y: 214, x: 618)\n",
       "Coordinates:\n",
       "  * y                 (y) float64 48.5 48.49 48.48 48.47 ... 46.39 46.38 46.37\n",
       "  * x                 (x) float64 -124.9 -124.9 -124.9 ... -118.8 -118.7 -118.7\n",
       "  * landsat:scene_id  (landsat:scene_id) &lt;U21 &#x27;LC90450272023245LGN00&#x27; ... &#x27;LC...\n",
       "    spatial_ref       int64 0\n",
       "Data variables:\n",
       "    value             (landsat:scene_id, y, x) float64 nan nan nan ... nan nan</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-0b4893b0-3116-4f8c-87c4-8775a83930e3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0b4893b0-3116-4f8c-87c4-8775a83930e3' 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'>landsat:scene_id</span>: 3</li><li><span class='xr-has-index'>y</span>: 214</li><li><span class='xr-has-index'>x</span>: 618</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7ace15e0-6ed1-4b84-8925-d9518c764839' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ace15e0-6ed1-4b84-8925-d9518c764839' 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'>48.5 48.49 48.48 ... 46.38 46.37</div><input id='attrs-474cfc4e-9be5-420b-a562-8e2c686ef437' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-474cfc4e-9be5-420b-a562-8e2c686ef437' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-188dccd4-d3c5-4fdc-8ec7-fb5d716df00f' class='xr-var-data-in' type='checkbox'><label for='data-188dccd4-d3c5-4fdc-8ec7-fb5d716df00f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([48.505, 48.495, 48.485, ..., 46.395, 46.385, 46.375])</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'>-124.9 -124.9 ... -118.7 -118.7</div><input id='attrs-aac9e83c-14b2-4c6d-b2ad-d2ce896571e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aac9e83c-14b2-4c6d-b2ad-d2ce896571e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6a4c2a2-c9ec-4d7a-9e5d-613899270115' class='xr-var-data-in' type='checkbox'><label for='data-e6a4c2a2-c9ec-4d7a-9e5d-613899270115' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-124.905, -124.895, -124.885, ..., -118.755, -118.745, -118.735])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>landsat:scene_id</span></div><div class='xr-var-dims'>(landsat:scene_id)</div><div class='xr-var-dtype'>&lt;U21</div><div class='xr-var-preview xr-preview'>&#x27;LC90450272023245LGN00&#x27; ... &#x27;LC9...</div><input id='attrs-d54a95c0-d41f-4fba-9db5-52799ceed4b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d54a95c0-d41f-4fba-9db5-52799ceed4b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f2ad96a-710d-417c-ae42-a99755c0908a' class='xr-var-data-in' type='checkbox'><label for='data-7f2ad96a-710d-417c-ae42-a99755c0908a' 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;LC90450272023245LGN00&#x27;, &#x27;LC90460272023252LGN00&#x27;,\n",
       "       &#x27;LC90470272023259LGN00&#x27;], dtype=&#x27;&lt;U21&#x27;)</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'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-48383320-d092-4d70-b129-cf4e19e6428c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48383320-d092-4d70-b129-cf4e19e6428c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6c1bc5e-5d17-4845-a06e-4e013d979024' class='xr-var-data-in' type='checkbox'><label for='data-c6c1bc5e-5d17-4845-a06e-4e013d979024' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</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</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-124.91 0.01 0.0 48.51 0.0 -0.01</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9d556c2-f7c8-4d34-9798-cb2963fe25f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9d556c2-f7c8-4d34-9798-cb2963fe25f0' class='xr-section-summary' >Data variables: <span>(1)</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>value</span></div><div class='xr-var-dims'>(landsat:scene_id, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-691977c1-e310-4a54-b954-e971a6ce4d8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-691977c1-e310-4a54-b954-e971a6ce4d8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6319502a-b1ff-46d1-9f96-6890012782d9' class='xr-var-data-in' type='checkbox'><label for='data-6319502a-b1ff-46d1-9f96-6890012782d9' 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>name :</span></dt><dd>value</dd><dt><span>long_name :</span></dt><dd>value</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f2df585e-039d-4769-8f91-c6993af5ca4b' class='xr-section-summary-in' type='checkbox'  ><label for='section-f2df585e-039d-4769-8f91-c6993af5ca4b' class='xr-section-summary' >Indexes: <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-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6c097df-f9c8-492f-acea-6c18fe3580c3' class='xr-index-data-in' type='checkbox'/><label for='index-e6c097df-f9c8-492f-acea-6c18fe3580c3' 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([48.504999999999995,             48.495,  48.48499999999999,\n",
       "       48.474999999999994, 48.464999999999996,             48.455,\n",
       "        48.44499999999999, 48.434999999999995,             48.425,\n",
       "        48.41499999999999,\n",
       "       ...\n",
       "       46.464999999999996,             46.455,  46.44499999999999,\n",
       "       46.434999999999995,             46.425,  46.41499999999999,\n",
       "       46.404999999999994, 46.394999999999996,             46.385,\n",
       "        46.37499999999999],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=214))</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-212592a3-0b3a-4e8b-a6be-b52b495dfb48' class='xr-index-data-in' type='checkbox'/><label for='index-212592a3-0b3a-4e8b-a6be-b52b495dfb48' 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([           -124.905,            -124.895,            -124.885,\n",
       "                  -124.875,            -124.865,            -124.855,\n",
       "                  -124.845, -124.83500000000001,            -124.825,\n",
       "                  -124.815,\n",
       "       ...\n",
       "                  -118.825,            -118.815,            -118.805,\n",
       "                  -118.795,            -118.785,            -118.775,\n",
       "                  -118.765,            -118.755,            -118.745,\n",
       "                  -118.735],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=618))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>landsat:scene_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ddb0c7c7-7492-4943-bf0a-160739e01d9b' class='xr-index-data-in' type='checkbox'/><label for='index-ddb0c7c7-7492-4943-bf0a-160739e01d9b' 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([&#x27;LC90450272023245LGN00&#x27;, &#x27;LC90460272023252LGN00&#x27;,\n",
       "       &#x27;LC90470272023259LGN00&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;landsat:scene_id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7399d934-7f84-4743-9048-5b7607be7774' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7399d934-7f84-4743-9048-5b7607be7774' 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:           (landsat:scene_id: 3, y: 214, x: 618)\n",
       "Coordinates:\n",
       "  * y                 (y) float64 48.5 48.49 48.48 48.47 ... 46.39 46.38 46.37\n",
       "  * x                 (x) float64 -124.9 -124.9 -124.9 ... -118.8 -118.7 -118.7\n",
       "  * landsat:scene_id  (landsat:scene_id) <U21 'LC90450272023245LGN00' ... 'LC...\n",
       "    spatial_ref       int64 0\n",
       "Data variables:\n",
       "    value             (landsat:scene_id, y, x) float64 nan nan nan ... nan nan"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn satellite footprints into Xarray example data (each footprint area has value of 1,2,3)\n",
    "gf['value'] = gf.index+1\n",
    "res = 0.01 # be sure to match output projection units (here degrees)\n",
    "ds = make_geocube(\n",
    "    vector_data=gf,\n",
    "    group_by = 'landsat:scene_id',\n",
    "    measurements=['value'],\n",
    "    resolution=res,\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 186,
       "width": 886
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = ds['value'].plot.imshow(col='landsat:scene_id', levels=4, cbar_kwargs=dict(shrink=0.8, ticks=[1,2,3]))\n",
    "aspect = 1/np.cos(np.deg2rad(47))\n",
    "[axis.set_aspect('equal') for axis in grid.axs.ravel()];\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 286,
       "width": 551
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Custom Xarray Reduce function to select from set of pixels\n",
    "def first_valid_value(arr, axis=None):\n",
    "    idx = np.nanargmax(np.isfinite(arr), axis=axis, keepdims=True)\n",
    "    vals = np.take_along_axis(arr, idx, axis=axis).squeeze() \n",
    "    return vals\n",
    "\n",
    "# Mosaic & subset\n",
    "mosaic = ds.value.reduce(first_valid_value, dim='landsat:scene_id')\n",
    "subset = mosaic.sel(y=slice(48,47), x=slice(-124,-119))\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "mosaic.plot.imshow(ax=ax, levels=4, cbar_kwargs=dict(ticks=[1,2,3], shrink=0.6))\n",
    "aoi = patches.Rectangle((-124, 47), 5, 1, linewidth=1, edgecolor='r', facecolor='none')\n",
    "ax.add_patch(aoi)\n",
    "ax.set_aspect(1/np.cos(np.deg2rad(47)))\n",
    "ax.set_title('mosaic');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "xarray",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}