drafts/stackstac-ncdf.ipynb

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    "import stackstac\n",
    "from pystac_client import Client\n",
    "from stackstac.rio_env import LayeredEnv\n",
    "\n",
    "# ensure we have an earthdata .netrc set, and also need cookie file:\n",
    "import os\n",
    "from pathlib import Path\n",
    "cookies = os.path.expanduser(\"~/.urs_cookies\")\n",
    "Path(cookies).touch()"
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      "/home/jovyan/.virtualenvs/spatial/lib/python3.10/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
      "  times = pd.to_datetime(\n"
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       "</table></div></div></li><li class='xr-section-item'><input id='section-907dca4b-3829-4a00-a3c8-5963f95f6868' class='xr-section-summary-in' type='checkbox'  checked><label for='section-907dca4b-3829-4a00-a3c8-5963f95f6868' class='xr-section-summary' >Coordinates: <span>(9)</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'>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'>2019-12-31T21:00:00</div><input id='attrs-b0208c81-c7e7-4036-abd7-7e49c4621a60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0208c81-c7e7-4036-abd7-7e49c4621a60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49125c2e-82a0-49c3-8495-be56a5ae5a3c' class='xr-var-data-in' type='checkbox'><label for='data-49125c2e-82a0-49c3-8495-be56a5ae5a3c' 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;2019-12-31T21:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U57</div><div class='xr-var-preview xr-preview'>&#x27;20200101090000-JPL-L4_GHRSST-SS...</div><input id='attrs-3f8e5def-d58e-4f73-91fa-d4cd0a38142a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f8e5def-d58e-4f73-91fa-d4cd0a38142a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f55062a-1222-4779-ac97-b23abbeec461' class='xr-var-data-in' type='checkbox'><label for='data-0f55062a-1222-4779-ac97-b23abbeec461' 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;20200101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1&#x27;],\n",
       "      dtype=&#x27;&lt;U57&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;data&#x27;</div><input id='attrs-bb7dcc81-2aae-45ac-87a3-cdd72d44ab6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb7dcc81-2aae-45ac-87a3-cdd72d44ab6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f73088a9-659f-42c5-b562-59d132b68d29' class='xr-var-data-in' type='checkbox'><label for='data-f73088a9-659f-42c5-b562-59d132b68d29' 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;data&#x27;], dtype=&#x27;&lt;U4&#x27;)</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'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-0a0ec853-d5b5-469e-a3e4-00b171713640' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a0ec853-d5b5-469e-a3e4-00b171713640' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48104157-33a2-4b70-a022-4ed947994289' class='xr-var-data-in' type='checkbox'><label for='data-48104157-33a2-4b70-a022-4ed947994289' 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([-180.  , -179.99, -179.98, ...,  179.97,  179.98,  179.99])</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'>90.0 89.99 89.98 ... -89.98 -89.99</div><input id='attrs-92f54e42-d477-4bb5-bedf-234b13084c4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92f54e42-d477-4bb5-bedf-234b13084c4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06e5a246-77b8-4e54-ab17-a5570cfb8744' class='xr-var-data-in' type='checkbox'><label for='data-06e5a246-77b8-4e54-ab17-a5570cfb8744' 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([ 90.  ,  89.99,  89.98, ..., -89.97, -89.98, -89.99])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>start_datetime</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U24</div><div class='xr-var-preview xr-preview'>&#x27;2019-12-31T21:00:00.000Z&#x27;</div><input id='attrs-0b981c98-4ff3-413c-a268-b1c5df5f98cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b981c98-4ff3-413c-a268-b1c5df5f98cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1eed347-96eb-4337-91d9-3f6c7814874e' class='xr-var-data-in' type='checkbox'><label for='data-f1eed347-96eb-4337-91d9-3f6c7814874e' 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;2019-12-31T21:00:00.000Z&#x27;, dtype=&#x27;&lt;U24&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>end_datetime</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U24</div><div class='xr-var-preview xr-preview'>&#x27;2020-01-01T21:00:00.000Z&#x27;</div><input id='attrs-d6945a8e-8e52-4562-a209-5a2f173752cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6945a8e-8e52-4562-a209-5a2f173752cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c73a8474-6d82-45b1-aeef-1d57754d8b38' class='xr-var-data-in' type='checkbox'><label for='data-c73a8474-6d82-45b1-aeef-1d57754d8b38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2020-01-01T21:00:00.000Z&#x27;, dtype=&#x27;&lt;U24&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U69</div><div class='xr-var-preview xr-preview'>&#x27;Download 20200101090000-JPL-L4_...</div><input id='attrs-c5712569-9473-45b8-aee0-b5b263155715' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5712569-9473-45b8-aee0-b5b263155715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ffd4323-fd08-4ac8-b71a-ef32c5f5bd63' class='xr-var-data-in' type='checkbox'><label for='data-0ffd4323-fd08-4ac8-b71a-ef32c5f5bd63' 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;Download 20200101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc&#x27;,\n",
       "      dtype=&#x27;&lt;U69&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-818c1ecb-cfd9-46ee-b0ab-b8f74a1fee31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-818c1ecb-cfd9-46ee-b0ab-b8f74a1fee31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43f82a2a-dd17-4c5f-803a-333acee972aa' class='xr-var-data-in' type='checkbox'><label for='data-43f82a2a-dd17-4c5f-803a-333acee972aa' 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(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b61f7543-a6f2-4134-87d5-0ff3706a718d' class='xr-section-summary-in' type='checkbox'  ><label for='section-b61f7543-a6f2-4134-87d5-0ff3706a718d' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-16b4a5f8-89e7-4e85-a165-50d45cc01c76' class='xr-index-data-in' type='checkbox'/><label for='index-16b4a5f8-89e7-4e85-a165-50d45cc01c76' 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;2019-12-31 21:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0acce583-a819-4ca6-8fe0-cbd5e6bf7b8d' class='xr-index-data-in' type='checkbox'/><label for='index-0acce583-a819-4ca6-8fe0-cbd5e6bf7b8d' 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;data&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</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-4b70df0f-06ee-4672-a037-09e6892ad76b' class='xr-index-data-in' type='checkbox'/><label for='index-4b70df0f-06ee-4672-a037-09e6892ad76b' 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([            -180.0,            -179.99,            -179.98,\n",
       "                  -179.97,            -179.96,            -179.95,\n",
       "                  -179.94,            -179.93,            -179.92,\n",
       "                  -179.91,\n",
       "       ...\n",
       "       179.90000000000003, 179.91000000000003, 179.92000000000002,\n",
       "                   179.93,             179.94,             179.95,\n",
       "       179.95999999999998, 179.97000000000003, 179.98000000000002,\n",
       "                   179.99],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=36000))</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-b91d9060-8446-42fa-95b6-b39e3f2577f4' class='xr-index-data-in' type='checkbox'/><label for='index-b91d9060-8446-42fa-95b6-b39e3f2577f4' 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([              90.0,              89.99,              89.98,\n",
       "                    89.97,              89.96,              89.95,\n",
       "                    89.94,              89.93,              89.92,\n",
       "                    89.91,\n",
       "       ...\n",
       "                    -89.9,             -89.91, -89.92000000000002,\n",
       "                   -89.93,             -89.94, -89.95000000000002,\n",
       "       -89.96000000000001,             -89.97, -89.97999999999999,\n",
       "       -89.99000000000001],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=18000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b35d768f-1535-4b3a-8492-2c87d4c4652d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b35d768f-1535-4b3a-8492-2c87d4c4652d' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), resolutions_xy=(0.01, 0.01))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.01, 0.00,-180.00|\n",
       "| 0.00,-0.01, 90.00|\n",
       "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.01</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'stackstac-3893b89144636d9866bc23c4325591b8' (time: 1,\n",
       "                                                                band: 1,\n",
       "                                                                y: 18000,\n",
       "                                                                x: 36000)>\n",
       "dask.array<fetch_raster_window, shape=(1, 1, 18000, 36000), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time            (time) datetime64[ns] 2019-12-31T21:00:00\n",
       "    id              (time) <U57 '20200101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB...\n",
       "  * band            (band) <U4 'data'\n",
       "  * x               (x) float64 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * y               (y) float64 90.0 89.99 89.98 89.97 ... -89.97 -89.98 -89.99\n",
       "    start_datetime  <U24 '2019-12-31T21:00:00.000Z'\n",
       "    end_datetime    <U24 '2020-01-01T21:00:00.000Z'\n",
       "    title           <U69 'Download 20200101090000-JPL-L4_GHRSST-SSTfnd-MUR-GL...\n",
       "    epsg            int64 4326\n",
       "Attributes:\n",
       "    spec:        RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), r...\n",
       "    crs:         epsg:4326\n",
       "    transform:   | 0.01, 0.00,-180.00|\\n| 0.00,-0.01, 90.00|\\n| 0.00, 0.00, 1...\n",
       "    resolution:  0.01"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A few items from NASA STAC\n",
    "\n",
    "items = (\n",
    "  Client.\n",
    "  open(\"https://cmr.earthdata.nasa.gov/stac/POCLOUD\").\n",
    "  search(\n",
    "    collections = ['MUR-JPL-L4-GLOB-v4.1'],\n",
    "    datetime = \"2020-01-01/2020-01-05\",\n",
    "  ).\n",
    "  item_collection()\n",
    ")\n",
    "\n",
    "\n",
    "# everyone has their own way to set gdal envs, how annoying.\n",
    "# Only necessary for authentication with NASA. how annoying\n",
    "gdal_cloud = dict(\n",
    "    GDAL_INGESTED_BYTES_AT_OPEN=\"32000\",\n",
    "    GDAL_HTTP_MULTIPLEX=\"YES\",\n",
    "    GDAL_HTTP_MERGE_CONSECUTIVE_RANGES=\"YES\",\n",
    "    GDAL_HTTP_VERSION=\"2\",\n",
    "    GDAL_NUM_THREADS=\"ALL_CPUS\",\n",
    "    GDAL_DISABLE_READDIR_ON_OPEN=\"EMPTY_DIR\",\n",
    "    GDAL_HTTP_COOKIEFILE=cookies, \n",
    "    GDAL_HTTP_COOKIEJAR=cookies, \n",
    "    GDAL_HTTP_NETRC=True)\n",
    "\n",
    "gdal_env = LayeredEnv(\n",
    "    always = gdal_cloud,\n",
    "    open = {\"GDAL_HTTP_COOKIEFILE\": cookies, \n",
    "                \"GDAL_HTTP_COOKIEJAR\": cookies, \n",
    "                \"GDAL_HTTP_NETRC\": True\n",
    "           },\n",
    "    read = {'VSI_CACHE': False})\n",
    "\n",
    "\n",
    "# And here we go.  \n",
    "data = stackstac.stack(items[0:1], assets=[\"data\"], epsg = 4326, resolution = 0.01, gdal_env = gdal_env)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feacdb54-b2be-45b1-80bd-2618381ecb9b",
   "metadata": {},
   "source": [
    "But our read appears to fail becuase rasterio refuses to support multiband rasters.  Possibly related to <https://github.com/corteva/rioxarray/issues/174>\n",
    "\n",
    "I'm basing this on the part of the error shown below that goes like:\n",
    "\n",
    "```\n",
    "File ~/.virtualenvs/spatial/lib/python3.10/site-packages/stackstac/rio_reader.py:340, in AutoParallelRioReader._open(self)\n",
    "    337 if ds.count != 1:\n",
    "    338     ds.close()\n",
    "    339     raise RuntimeError(\n",
    "--> 340         f\"Assets must have exactly 1 band, but file {self.url!r} has {ds.count}. \"\n",
    "    341         \"We can't currently handle multi-band rasters (each band has to be \"\n",
    "    342         \"a separate STAC asset), so you'll need to exclude this asset from your analysis.\"\n",
    "    343     )\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f18cdc9-7aaf-4072-9723-b874cee1e7d9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/.virtualenvs/spatial/lib/python3.10/site-packages/stackstac/rio_reader.py:327: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
      "  ds = SelfCleaningDatasetReader(self.url, sharing=False)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Can't read closed raster file",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m()[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/xarray/core/dataarray.py:757\u001b[0m, in \u001b[0;36mDataArray.values\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    748\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    749\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvalues\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m    750\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    751\u001b[0m \u001b[38;5;124;03m    The array's data as a numpy.ndarray.\u001b[39;00m\n\u001b[1;32m    752\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    755\u001b[0m \u001b[38;5;124;03m    type does not support coercion like this (e.g. cupy).\u001b[39;00m\n\u001b[1;32m    756\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 757\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/xarray/core/variable.py:606\u001b[0m, in \u001b[0;36mVariable.values\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    604\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvalues\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    605\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[39;00m\n\u001b[0;32m--> 606\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_as_array_or_item\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/xarray/core/variable.py:306\u001b[0m, in \u001b[0;36m_as_array_or_item\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    292\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_as_array_or_item\u001b[39m(data):\n\u001b[1;32m    293\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the given values as a numpy array, or as an individual item if\u001b[39;00m\n\u001b[1;32m    294\u001b[0m \u001b[38;5;124;03m    it's a 0d datetime64 or timedelta64 array.\u001b[39;00m\n\u001b[1;32m    295\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    304\u001b[0m \u001b[38;5;124;03m    TODO: remove this (replace with np.asarray) once these issues are fixed\u001b[39;00m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 306\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    307\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    308\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/dask/array/core.py:1700\u001b[0m, in \u001b[0;36mArray.__array__\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   1699\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 1700\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1701\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mand\u001b[39;00m x\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m dtype:\n\u001b[1;32m   1702\u001b[0m         x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mastype(dtype)\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/dask/base.py:342\u001b[0m, in \u001b[0;36mDaskMethodsMixin.compute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    319\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[1;32m    320\u001b[0m \n\u001b[1;32m    321\u001b[0m \u001b[38;5;124;03m    This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[38;5;124;03m    dask.compute\u001b[39;00m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 342\u001b[0m     (result,) \u001b[38;5;241m=\u001b[39m \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    343\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/dask/base.py:628\u001b[0m, in \u001b[0;36mcompute\u001b[0;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[1;32m    625\u001b[0m     postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[1;32m    627\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m shorten_traceback():\n\u001b[0;32m--> 628\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    630\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/stackstac/to_dask.py:189\u001b[0m, in \u001b[0;36mfetch_raster_window\u001b[0;34m(reader_table, slices, dtype, fill_value)\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[38;5;66;03m# Only read if the window we're fetching actually overlaps with the asset\u001b[39;00m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m windows\u001b[38;5;241m.\u001b[39mintersect(current_window, asset_window):\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;66;03m# NOTE: when there are multiple assets, we _could_ parallelize these reads with our own threadpool.\u001b[39;00m\n\u001b[1;32m    185\u001b[0m     \u001b[38;5;66;03m# However, that would probably increase memory usage, since the internal, thread-local GDAL datasets\u001b[39;00m\n\u001b[1;32m    186\u001b[0m     \u001b[38;5;66;03m# would end up copied to even more threads.\u001b[39;00m\n\u001b[1;32m    187\u001b[0m \n\u001b[1;32m    188\u001b[0m     \u001b[38;5;66;03m# TODO when the Reader won't be rescaling, support passing `output` to avoid the copy?\u001b[39;00m\n\u001b[0;32m--> 189\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mreader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_window\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    191\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m all_empty:\n\u001b[1;32m    192\u001b[0m         \u001b[38;5;66;03m# Turn `output` from a broadcast-trick array to a real array, so it's writeable\u001b[39;00m\n\u001b[1;32m    193\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    194\u001b[0m             np\u001b[38;5;241m.\u001b[39misnan(data)\n\u001b[1;32m    195\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39misnan(fill_value)\n\u001b[1;32m    196\u001b[0m             \u001b[38;5;28;01melse\u001b[39;00m np\u001b[38;5;241m.\u001b[39mequal(data, fill_value)\n\u001b[1;32m    197\u001b[0m         )\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m    198\u001b[0m             \u001b[38;5;66;03m# Unless the data we just read is all empty anyway\u001b[39;00m\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/stackstac/rio_reader.py:385\u001b[0m, in \u001b[0;36mAutoParallelRioReader.read\u001b[0;34m(self, window, **kwargs)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\u001b[39m(\u001b[38;5;28mself\u001b[39m, window: Window, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m--> 385\u001b[0m     reader \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    387\u001b[0m         result \u001b[38;5;241m=\u001b[39m reader\u001b[38;5;241m.\u001b[39mread(\n\u001b[1;32m    388\u001b[0m             window\u001b[38;5;241m=\u001b[39mwindow,\n\u001b[1;32m    389\u001b[0m             out_dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    393\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    394\u001b[0m         )\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/stackstac/rio_reader.py:381\u001b[0m, in \u001b[0;36mAutoParallelRioReader.dataset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    379\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_lock:\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 381\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    382\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset\n",
      "File \u001b[0;32m~/.virtualenvs/spatial/lib/python3.10/site-packages/stackstac/rio_reader.py:340\u001b[0m, in \u001b[0;36mAutoParallelRioReader._open\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    337\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ds\u001b[38;5;241m.\u001b[39mcount \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    338\u001b[0m     ds\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m    339\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m--> 340\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAssets must have exactly 1 band, but file \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39murl\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcount\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    341\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWe can\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt currently handle multi-band rasters (each band has to be \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    342\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma separate STAC asset), so you\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mll need to exclude this asset from your analysis.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    343\u001b[0m     )\n\u001b[1;32m    345\u001b[0m \u001b[38;5;66;03m# Only make a VRT if the dataset doesn't match the spatial spec we want\u001b[39;00m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec\u001b[38;5;241m.\u001b[39mvrt_params \u001b[38;5;241m!=\u001b[39m {\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcrs\u001b[39m\u001b[38;5;124m\"\u001b[39m: ds\u001b[38;5;241m.\u001b[39mcrs\u001b[38;5;241m.\u001b[39mto_epsg(),\n\u001b[1;32m    348\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m\"\u001b[39m: ds\u001b[38;5;241m.\u001b[39mtransform,\n\u001b[1;32m    349\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheight\u001b[39m\u001b[38;5;124m\"\u001b[39m: ds\u001b[38;5;241m.\u001b[39mheight,\n\u001b[1;32m    350\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m\"\u001b[39m: ds\u001b[38;5;241m.\u001b[39mwidth,\n\u001b[1;32m    351\u001b[0m }:\n",
      "File \u001b[0;32mrasterio/_base.pyx:476\u001b[0m, in \u001b[0;36mrasterio._base.DatasetBase.count.__get__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Can't read closed raster file"
     ]
    }
   ],
   "source": [
    "data.values()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3ec3ba0-2423-4f6b-b4db-002742a0d9c8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rasterio info:\n",
      "  rasterio: 1.3.8\n",
      "      GDAL: 3.6.4\n",
      "      PROJ: 9.0.1\n",
      "      GEOS: 3.11.1\n",
      " PROJ DATA: /srv/conda/envs/notebook/share/proj\n",
      " GDAL DATA: /srv/conda/envs/notebook/share/gdal\n",
      "\n",
      "System:\n",
      "    python: 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:40:32) [GCC 12.3.0]\n",
      "executable: /home/jovyan/.virtualenvs/spatial/bin/python\n",
      "   machine: Linux-6.5.6-76060506-generic-x86_64-with-glibc2.35\n",
      "\n",
      "Python deps:\n",
      "    affine: 2.4.0\n",
      "     attrs: 23.1.0\n",
      "   certifi: 2023.07.22\n",
      "     click: 8.1.7\n",
      "     cligj: 0.7.2\n",
      "    cython: None\n",
      "     numpy: 1.26.0\n",
      "    snuggs: 1.4.7\n",
      "click-plugins: None\n",
      "setuptools: 65.5.0\n"
     ]
    }
   ],
   "source": [
    "import rasterio\n",
    "rasterio.show_versions()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "spatial",
   "language": "python",
   "name": "spatial"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
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 "nbformat": 4,
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