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alexgleith/Satellogic_Loading_ODC-STAC.ipynb

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Satellogic_Loading_ODC-STAC.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import stacrs\n",
"from pystac import Item\n",
"from odc.stac import load"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 15 items\n"
]
}
],
"source": [
"url = \"https://s3.us-west-2.amazonaws.com/satellogic-earthview-stac-geoparquet/satellogic-earthview-stac-items.parquet\"\n",
"\n",
"# BBOX over Central Park, New York\n",
"bbox = [-73.9819, 40.7674, -73.9664, 40.7812]\n",
"year = 2022\n",
"\n",
"# Find STAC items\n",
"items_dict = await stacrs.search(url, bbox=bbox, datetime=f\"{year}-01-01T00:00:00.000Z/{year}-12-31T23:59:59.999Z\")\n",
"\n",
"print(f\"Found {len(items_dict)} items\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"items = [Item.from_dict(item) for item in items_dict]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 16MB\n",
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" * y (y) float64 12kB 4.515e+06 4.515e+06 ... 4.513e+06 4.513e+06\n",
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"Data variables:\n",
" analytic (time, y, x) float32 8MB dask.array&lt;chunksize=(1, 1548, 1327), meta=np.ndarray&gt;\n",
" visual (time, y, x) float32 8MB dask.array&lt;chunksize=(1, 1548, 1327), 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-b1e5a633-6213-454d-8d0c-a1e6a22a57e7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b1e5a633-6213-454d-8d0c-a1e6a22a57e7' 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>: 1548</li><li><span class='xr-has-index'>x</span>: 1327</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9854337c-a2a1-4b68-96b0-730a8ab79e53' class='xr-section-summary-in' type='checkbox' checked><label for='section-9854337c-a2a1-4b68-96b0-730a8ab79e53' 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'>4.515e+06 4.515e+06 ... 4.513e+06</div><input id='attrs-13d3dfa2-f74f-441a-bc73-8243813643fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13d3dfa2-f74f-441a-bc73-8243813643fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e7024d3-4380-46a6-9fd1-bee364a6f993' class='xr-var-data-in' type='checkbox'><label for='data-2e7024d3-4380-46a6-9fd1-bee364a6f993' 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>-1.0</dd><dt><span>crs :</span></dt><dd>EPSG:32618</dd></dl></div><div class='xr-var-data'><pre>array([4514982.5, 4514981.5, 4514980.5, ..., 4513437.5, 4513436.5, 4513435.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.859e+05 5.859e+05 ... 5.872e+05</div><input id='attrs-ed258a5e-757f-49c6-b28d-e11e2e833d91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed258a5e-757f-49c6-b28d-e11e2e833d91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4274371-4469-4bfb-9d7b-40190887537e' class='xr-var-data-in' type='checkbox'><label for='data-a4274371-4469-4bfb-9d7b-40190887537e' 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>1.0</dd><dt><span>crs :</span></dt><dd>EPSG:32618</dd></dl></div><div class='xr-var-data'><pre>array([585906.5, 585907.5, 585908.5, ..., 587230.5, 587231.5, 587232.5])</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'>32618</div><input id='attrs-580a9de7-0973-4636-8d67-6f09014d8e8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-580a9de7-0973-4636-8d67-6f09014d8e8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a659665-aef5-482a-8449-e08079e90239' class='xr-var-data-in' type='checkbox'><label for='data-9a659665-aef5-482a-8449-e08079e90239' 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 / UTM zone 18N&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;UTM zone 18N&quot;,METHOD[&quot;Transverse Mercator&quot;,ID[&quot;EPSG&quot;,9807]],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;,-75,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,0.9996,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,500000,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;Navigation and medium accuracy spatial referencing.&quot;],AREA[&quot;Between 78°W and 72°W, northern hemisphere between equator and 84°N, onshore and offshore. Bahamas. Canada - Nunavut; Ontario; Quebec. Colombia. Cuba. Ecuador. Greenland. Haiti. Jamaica. Panama. Turks and Caicos Islands. United States (USA). Venezuela.&quot;],BBOX[0,-78,84,-72]],ID[&quot;EPSG&quot;,32618]]</dd><dt><span>crs_wkt :</span></dt><dd>PROJCRS[&quot;WGS 84 / UTM zone 18N&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;UTM zone 18N&quot;,METHOD[&quot;Transverse Mercator&quot;,ID[&quot;EPSG&quot;,9807]],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;,-75,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433],ID[&quot;EPSG&quot;,8802]],PARAMETER[&quot;Scale factor at natural origin&quot;,0.9996,SCALEUNIT[&quot;unity&quot;,1],ID[&quot;EPSG&quot;,8805]],PARAMETER[&quot;False easting&quot;,500000,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;Navigation and medium accuracy spatial referencing.&quot;],AREA[&quot;Between 78°W and 72°W, northern hemisphere between equator and 84°N, onshore and offshore. Bahamas. Canada - Nunavut; Ontario; Quebec. Colombia. Cuba. Ecuador. Greenland. Haiti. Jamaica. Panama. Turks and Caicos Islands. United States (USA). Venezuela.&quot;],BBOX[0,-78,84,-72]],ID[&quot;EPSG&quot;,32618]]</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 / UTM zone 18N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-75.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>GeoTransform :</span></dt><dd>585906 1 0 4514983 0 -1</dd></dl></div><div class='xr-var-data'><pre>array(32618, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-11-19T06:49:25.622527</div><input id='attrs-c65217d2-59c3-4920-a3d1-4c04115cf97b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c65217d2-59c3-4920-a3d1-4c04115cf97b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee60bf53-5972-4aac-ad85-1207e1d3d1be' class='xr-var-data-in' type='checkbox'><label for='data-ee60bf53-5972-4aac-ad85-1207e1d3d1be' 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;2022-11-19T06:49:25.622527000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b8d840ad-652b-4fc6-8300-697d6ed24b8b' class='xr-section-summary-in' type='checkbox' checked><label for='section-b8d840ad-652b-4fc6-8300-697d6ed24b8b' 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>analytic</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1548, 1327), meta=np.ndarray&gt;</div><input id='attrs-d7220a42-57b9-4a12-9801-bf7fa5247790' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7220a42-57b9-4a12-9801-bf7fa5247790' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f92338ba-8adb-4bb8-808f-c66dc5a91ea1' class='xr-var-data-in' type='checkbox'><label for='data-f92338ba-8adb-4bb8-808f-c66dc5a91ea1' 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> 7.84 MiB </td>\n",
" <td> 7.84 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (1, 1548, 1327) </td>\n",
" <td> (1, 1548, 1327) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1 chunks in 3 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=\"177\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
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"\n",
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"\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>visual</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1548, 1327), meta=np.ndarray&gt;</div><input id='attrs-013a8f00-8c41-446e-a29c-1c8617e76358' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-013a8f00-8c41-446e-a29c-1c8617e76358' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6bae129-021a-444b-a803-01a8c09c9e67' class='xr-var-data-in' type='checkbox'><label for='data-b6bae129-021a-444b-a803-01a8c09c9e67' 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> 7.84 MiB </td>\n",
" <td> 7.84 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (1, 1548, 1327) </td>\n",
" <td> (1, 1548, 1327) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1 chunks in 3 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=\"177\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
" <line x1=\"10\" y1=\"120\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
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"\n",
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"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"24\" y1=\"14\" x2=\"127\" y2=\"14\" style=\"stroke-width:2\" />\n",
" <line x1=\"24\" y1=\"134\" x2=\"127\" y2=\"134\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
" <line x1=\"127\" y1=\"14\" x2=\"127\" y2=\"134\" style=\"stroke-width:2\" />\n",
"\n",
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"\n",
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" <text x=\"76.382706\" y=\"154.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1327</text>\n",
" <text x=\"147.816815\" y=\"74.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,147.816815,74.948598)\">1548</text>\n",
" <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">1</text>\n",
"</svg>\n",
" </td>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc4ff354-f19b-444f-b4a3-71c74265fd25' class='xr-section-summary-in' type='checkbox' ><label for='section-cc4ff354-f19b-444f-b4a3-71c74265fd25' 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><input type='checkbox' disabled/><label></label><input id='index-c3c04fc7-0ed0-4b17-8530-435d04161a8f' class='xr-index-data-in' type='checkbox'/><label for='index-c3c04fc7-0ed0-4b17-8530-435d04161a8f' 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([4514982.5, 4514981.5, 4514980.5, 4514979.5, 4514978.5, 4514977.5,\n",
" 4514976.5, 4514975.5, 4514974.5, 4514973.5,\n",
" ...\n",
" 4513444.5, 4513443.5, 4513442.5, 4513441.5, 4513440.5, 4513439.5,\n",
" 4513438.5, 4513437.5, 4513436.5, 4513435.5],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1548))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1fe4e754-37a9-46f6-a753-ebba9470e224' class='xr-index-data-in' type='checkbox'/><label for='index-1fe4e754-37a9-46f6-a753-ebba9470e224' 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([585906.5, 585907.5, 585908.5, 585909.5, 585910.5, 585911.5, 585912.5,\n",
" 585913.5, 585914.5, 585915.5,\n",
" ...\n",
" 587223.5, 587224.5, 587225.5, 587226.5, 587227.5, 587228.5, 587229.5,\n",
" 587230.5, 587231.5, 587232.5],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1327))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-498273cd-d9a0-4f07-bc3e-fc1598c372b3' class='xr-index-data-in' type='checkbox'/><label for='index-498273cd-d9a0-4f07-bc3e-fc1598c372b3' 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(DatetimeIndex([&#x27;2022-11-19 06:49:25.622527&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee57b182-9e8d-4091-969c-0d943a4111b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ee57b182-9e8d-4091-969c-0d943a4111b1' 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> Size: 16MB\n",
"Dimensions: (y: 1548, x: 1327, time: 1)\n",
"Coordinates:\n",
" * y (y) float64 12kB 4.515e+06 4.515e+06 ... 4.513e+06 4.513e+06\n",
" * x (x) float64 11kB 5.859e+05 5.859e+05 ... 5.872e+05 5.872e+05\n",
" spatial_ref int32 4B 32618\n",
" * time (time) datetime64[ns] 8B 2022-11-19T06:49:25.622527\n",
"Data variables:\n",
" analytic (time, y, x) float32 8MB dask.array<chunksize=(1, 1548, 1327), meta=np.ndarray>\n",
" visual (time, y, x) float32 8MB dask.array<chunksize=(1, 1548, 1327), meta=np.ndarray>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bbox = [-73.9819, 40.7674, -73.9664, 40.7812]\n",
"\n",
"data = load(items, bbox=bbox, chunks={}, groupby=\"solar_day\")\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x1614b1110>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
@bmcandr

bmcandr commented Mar 14, 2025

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TIL I can use stacrs to search STAC GeoParquet a la pystac-client and deserialize the result to pystac objects. Thanks for sharing!

@alexgleith

alexgleith commented Mar 14, 2025

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Author

TIL I can use stacrs to search STAC GeoParquet a la pystac-client and deserialize the result to pystac objects. Thanks for sharing!

And it’s fast too! Well, 30 seconds is pretty good 😌

@bmcandr

bmcandr commented Mar 16, 2025

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I updated my demo notebook to include an example of using stacrs to search for Items and display images on a slippy map via a tile server. See the Visualizing the Imagery on an Interactive Slippy Map section. 🚀

@Kirill888

Kirill888 commented Mar 18, 2025

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@alexgleith

These items are missing raster extension, if you patch them like so:

from pystac.extensions.raster import RasterExtension
from copy import deepcopy

def patch(item_dict):
    item_dict = deepcopy(item_dict)
    item_dict['assets']['visual']['raster:bands'] = [{}]*4    #< or better yet specify
    item_dict['assets']['analytic']['raster:bands'] = [{}]*4  #< correct `data_type` and `nodata` here
    item_dict['stac_extensions'].append(RasterExtension.get_schema_uri())
    return item_dict

you should be able to load bands separately by using bands=['red', 'green', 'blue'] option, it will use analytics band, to load visual bands separately you can use bands=[..., "visual.1", "visual.2", "visual.3"] and then rename I guess.

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