diff --git a/noaa-cdr-sst.ipynb b/noaa-cdr-sst.ipynb
index 84241ac..937969b 100644
--- a/noaa-cdr-sst.ipynb
+++ b/noaa-cdr-sst.ipynb
@@ -8,14 +8,13 @@
    "outputs": [],
    "source": [
     "# !pip install -e \".[icechunk]\"\n",
-    "# NOTE: a change has to be made https://github.com/mpiannucci/kerchunk/blob/v3/kerchunk/utils.py#L55 from mode='w' to read_only=False\n",
     "# !pip install git+https://github.com/mpiannucci/kerchunk@v3\n",
     "# !pip install fsspec s3fs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "3055eff4-9e22-4a95-a7fd-96933f381183",
    "metadata": {},
    "outputs": [
@@ -24,7 +23,7 @@
      "output_type": "stream",
      "text": [
       "Name: icechunk\n",
-      "Version: 0.1.0a5\n",
+      "Version: 0.1.0a7\n",
       "Summary: Transactional storage engine for Zarr designed for use on cloud object storage\n",
       "Home-page: https://github.com/earth-mover/icechunk\n",
       "Author: Earthmover PBC\n",
@@ -42,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "2f69f0bb-316b-452c-b1ba-4d7ef4afcf67",
    "metadata": {},
    "outputs": [],
@@ -60,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "1532c33b-804f-49fa-9fa9-0eb42ea87e26",
    "metadata": {},
    "outputs": [],
@@ -76,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "06bbec92-3974-4859-8bda-353afc7800b9",
    "metadata": {},
    "outputs": [],
@@ -91,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "77fb94c8-870f-4c9e-8421-ac9c17402122",
    "metadata": {},
    "outputs": [],
@@ -107,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "id": "abefd6fa-386a-4e07-a7c8-219d3730eeeb",
    "metadata": {},
    "outputs": [],
@@ -117,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "id": "79a4228a-0e17-4b07-9144-f24fe06db832",
    "metadata": {},
    "outputs": [],
@@ -130,60 +129,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "id": "5fd0c082-8d5e-46a8-a994-fee80baa4ecc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "store = IcechunkStore.create(storage=storage_config, config=virtual_ref_store_config)"
+    "store = await IcechunkStore.create(\n",
+    "    storage=storage_config, config=virtual_ref_store_config, read_only=False\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "id": "55ebbc5f-add2-4de8-81f6-5aaf64d9e2b6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/aimeebarciauskas/github/virtualizarr/virtualizarr/writers/icechunk.py:272: RuntimeWarning: coroutine 'IcechunkStore.set_virtual_ref' was never awaited\n",
+      "  store.set_virtual_ref(\n",
+      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
+      "/Users/aimeebarciauskas/github/virtualizarr/virtualizarr/writers/icechunk.py:272: RuntimeWarning: coroutine 'IcechunkStore.set_virtual_ref' was never awaited\n",
+      "  store.set_virtual_ref(\n",
+      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
+      "/Users/aimeebarciauskas/github/virtualizarr/virtualizarr/writers/icechunk.py:272: RuntimeWarning: coroutine 'IcechunkStore.set_virtual_ref' was never awaited\n",
+      "  store.set_virtual_ref(\n",
+      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
+      "/Users/aimeebarciauskas/github/virtualizarr/virtualizarr/writers/icechunk.py:272: RuntimeWarning: coroutine 'IcechunkStore.set_virtual_ref' was never awaited\n",
+      "  store.set_virtual_ref(\n",
+      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
+      "/Users/aimeebarciauskas/github/virtualizarr/virtualizarr/writers/icechunk.py:272: RuntimeWarning: coroutine 'IcechunkStore.set_virtual_ref' was never awaited\n",
+      "  store.set_virtual_ref(\n",
+      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
+      "/Users/aimeebarciauskas/github/virtualizarr/virtualizarr/writers/icechunk.py:272: RuntimeWarning: coroutine 'IcechunkStore.set_virtual_ref' was never awaited\n",
+      "  store.set_virtual_ref(\n",
+      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
+      "/Users/aimeebarciauskas/github/virtualizarr/virtualizarr/writers/icechunk.py:272: RuntimeWarning: coroutine 'IcechunkStore.set_virtual_ref' was never awaited\n",
+      "  store.set_virtual_ref(\n",
+      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
+      "/Users/aimeebarciauskas/github/virtualizarr/virtualizarr/writers/icechunk.py:272: RuntimeWarning: coroutine 'IcechunkStore.set_virtual_ref' was never awaited\n",
+      "  store.set_virtual_ref(\n",
+      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n"
+     ]
+    }
+   ],
    "source": [
     "virtual_ds.virtualize.to_icechunk(store)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "id": "cae7e34f-d6dd-42aa-9e7a-f0d5420ba0b9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'JXPBY5KT843ZBSVQ2KNG'"
+       "'PZMXKYCPJQRRFXV33Q7G'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "store.commit(\"first 2 days of 202408 data\")"
+    "await store.commit(\"first 2 days of 202408 data\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "id": "9387e1ff-46c1-45fd-9796-0457538209a7",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "b'x^cx\\xd3\\xe2\\x06\\x00\\x04\\x16\\x01\\xb7'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "KeyNotFound",
+     "evalue": "time/c/0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyNotFound\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m store\u001b[38;5;241m.\u001b[39m_store\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime/c/0\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mKeyNotFound\u001b[0m: time/c/0"
+     ]
     }
    ],
    "source": [
@@ -192,456 +225,10 @@
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   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "b6271bd1-bc0b-4901-9901-91aabe508cf7",
    "metadata": {},
-   "outputs": [
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-       ".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; Size: 66MB\n",
-       "Dimensions:  (time: 2, zlev: 1, lat: 720, lon: 1440)\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 16B 2024-08-01T12:00:00 2024-08-02T12:00:00\n",
-       "  * lat      (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
-       "  * lon      (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
-       "  * zlev     (zlev) float32 4B 0.0\n",
-       "Data variables:\n",
-       "    anom     (time, zlev, lat, lon) float64 17MB ...\n",
-       "    err      (time, zlev, lat, lon) float64 17MB ...\n",
-       "    ice      (time, zlev, lat, lon) float64 17MB ...\n",
-       "    sst      (time, zlev, lat, lon) float64 17MB ...\n",
-       "Attributes: (12/37)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    cdm_data_type:              Grid\n",
-       "    comment:                    Data was converted from NetCDF-3 to NetCDF-4 ...\n",
-       "    creator_email:              oisst-help@noaa.gov\n",
-       "    creator_url:                https://www.ncei.noaa.gov/\n",
-       "    date_created:               2024-08-16T09:12:00Z\n",
-       "    ...                         ...\n",
-       "    source:                     ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
-       "    standard_name_vocabulary:   CF Standard Name Table (v40, 25 January 2017)\n",
-       "    summary:                    NOAAs 1/4-degree Daily Optimum Interpolation ...\n",
-       "    time_coverage_end:          2024-08-01T23:59:59Z\n",
-       "    time_coverage_start:        2024-08-01T00:00:00Z\n",
-       "    title:                      NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...</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-7374f7cd-9c34-4f6e-a8a1-0981b0ff4eab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7374f7cd-9c34-4f6e-a8a1-0981b0ff4eab' 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'>time</span>: 2</li><li><span class='xr-has-index'>zlev</span>: 1</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d9bbbc63-e04b-4fbe-b57c-8d526f1664a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9bbbc63-e04b-4fbe-b57c-8d526f1664a8' 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'>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'>2024-08-01T12:00:00 2024-08-02T1...</div><input id='attrs-11d983b8-8c82-4697-a235-88ce3c32c3d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11d983b8-8c82-4697-a235-88ce3c32c3d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32c5464b-0aa4-4e51-8d73-c8508c1d6166' class='xr-var-data-in' type='checkbox'><label for='data-32c5464b-0aa4-4e51-8d73-c8508c1d6166' 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>long_name :</span></dt><dd>Center time of the day</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-08-01T12:00:00.000000000&#x27;, &#x27;2024-08-02T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-784056a2-4ec1-4d7c-a7e4-7bc61cb3aa35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-784056a2-4ec1-4d7c-a7e4-7bc61cb3aa35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-935af25f-e8b0-4919-98c2-89690f30d90f' class='xr-var-data-in' type='checkbox'><label for='data-935af25f-e8b0-4919-98c2-89690f30d90f' 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>grids :</span></dt><dd>Uniform grid from -89.875 to 89.875 by 0.25</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-33a6287b-a2c7-425a-bd07-0295bbceecca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33a6287b-a2c7-425a-bd07-0295bbceecca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09e98a36-4fbd-4d47-a0a1-6b9fbf71fa90' class='xr-var-data-in' type='checkbox'><label for='data-09e98a36-4fbd-4d47-a0a1-6b9fbf71fa90' 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>grids :</span></dt><dd>Uniform grid from 0.125 to 359.875 by 0.25</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
-       "       3.59875e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zlev</span></div><div class='xr-var-dims'>(zlev)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-bfdf01f9-4c8e-4a71-8083-98e2eb837464' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfdf01f9-4c8e-4a71-8083-98e2eb837464' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6d53f27-6629-4820-8b38-67cde215ea49' class='xr-var-data-in' type='checkbox'><label for='data-d6d53f27-6629-4820-8b38-67cde215ea49' 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>actual_range :</span></dt><dd>0, 0</dd><dt><span>long_name :</span></dt><dd>Sea surface height</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([0.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-64f6273d-f94f-4035-8dfe-34eb247248c0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-64f6273d-f94f-4035-8dfe-34eb247248c0' class='xr-section-summary' >Data variables: <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>anom</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c35443a-5427-450f-8aa7-136c9f28568a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c35443a-5427-450f-8aa7-136c9f28568a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b7ff2f1-d1b1-44b4-9480-4cd24c50497a' class='xr-var-data-in' type='checkbox'><label for='data-3b7ff2f1-d1b1-44b4-9480-4cd24c50497a' 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>long_name :</span></dt><dd>Daily sea surface temperature anomalies</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_max :</span></dt><dd>1200</dd><dt><span>valid_min :</span></dt><dd>-1200</dd></dl></div><div class='xr-var-data'><pre>[2073600 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9e63f67-ac5e-4325-946d-a86212f11706' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9e63f67-ac5e-4325-946d-a86212f11706' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8b69703-ff9a-4d01-a6e0-63371c7fbcae' class='xr-var-data-in' type='checkbox'><label for='data-e8b69703-ff9a-4d01-a6e0-63371c7fbcae' 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>long_name :</span></dt><dd>Estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_max :</span></dt><dd>1000</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[2073600 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a52f0894-ee29-4ad3-94d8-be75f3ccb762' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a52f0894-ee29-4ad3-94d8-be75f3ccb762' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f598733e-c897-4ef8-a2dd-8ddba81473a4' class='xr-var-data-in' type='checkbox'><label for='data-f598733e-c897-4ef8-a2dd-8ddba81473a4' 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>long_name :</span></dt><dd>Sea ice concentration</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[2073600 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-13c14d3c-efb6-4bca-a188-c7df9cb7d730' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13c14d3c-efb6-4bca-a188-c7df9cb7d730' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd723340-a010-4fe7-a744-74997f798109' class='xr-var-data-in' type='checkbox'><label for='data-fd723340-a010-4fe7-a744-74997f798109' 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>long_name :</span></dt><dd>Daily sea surface temperature</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_max :</span></dt><dd>4500</dd><dt><span>valid_min :</span></dt><dd>-300</dd></dl></div><div class='xr-var-data'><pre>[2073600 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c690d9c0-46f9-43d8-bb9d-0ba3d15dfc69' class='xr-section-summary-in' type='checkbox'  ><label for='section-c690d9c0-46f9-43d8-bb9d-0ba3d15dfc69' 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-3e9970de-bd89-474e-abdb-b0facf4089f7' class='xr-index-data-in' type='checkbox'/><label for='index-3e9970de-bd89-474e-abdb-b0facf4089f7' 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;2024-08-01 12:00:00&#x27;, &#x27;2024-08-02 12:00:00&#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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-afed16e6-a827-4538-af23-415d7d1769a7' class='xr-index-data-in' type='checkbox'/><label for='index-afed16e6-a827-4538-af23-415d7d1769a7' 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([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,\n",
-       "       -87.875, -87.625,\n",
-       "       ...\n",
-       "        87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,\n",
-       "        89.625,  89.875],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-44821264-062c-46af-8392-bbb7eb19656e' class='xr-index-data-in' type='checkbox'/><label for='index-44821264-062c-46af-8392-bbb7eb19656e' 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([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,   1.875,\n",
-       "         2.125,   2.375,\n",
-       "       ...\n",
-       "       357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,\n",
-       "       359.625, 359.875],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>zlev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67a40002-1bd5-404d-8da1-f117e5067476' class='xr-index-data-in' type='checkbox'/><label for='index-67a40002-1bd5-404d-8da1-f117e5067476' 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([0.0], dtype=&#x27;float32&#x27;, name=&#x27;zlev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e28cac53-58ad-478a-9c9c-b324237ef367' class='xr-section-summary-in' type='checkbox'  ><label for='section-e28cac53-58ad-478a-9c9c-b324237ef367' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.</dd><dt><span>creator_email :</span></dt><dd>oisst-help@noaa.gov</dd><dt><span>creator_url :</span></dt><dd>https://www.ncei.noaa.gov/</dd><dt><span>date_created :</span></dt><dd>2024-08-16T09:12:00Z</dd><dt><span>date_modified :</span></dt><dd>2024-08-16T09:12:00Z</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>360.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.</dd><dt><span>id :</span></dt><dd>oisst-avhrr-v02r01.20240801.nc</dd><dt><span>institution :</span></dt><dd>NOAA/National Centers for Environmental Information</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments &gt; Passive Remote Sensing &gt; Spectrometers/Radiometers &gt; Imaging Spectrometers/Radiometers &gt; AVHRR &gt; Advanced Very High Resolution Radiometer</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Instrument Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Earth Science Keywords</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.25921/RE9P-PT57</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ncei</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>platform :</span></dt><dd>Ships, buoys, Argo floats, MetOp-A, MetOp-B</dd><dt><span>platform_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Platform Keywords</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>product_version :</span></dt><dd>Version v02r01</dd><dt><span>references :</span></dt><dd>Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST, Navy AVHRR SST, and NOAA ACSPO SST. Ice data: NCEP Ice and GSFC Ice.</dd><dt><span>sensor :</span></dt><dd>Thermometer, AVHRR</dd><dt><span>source :</span></dt><dd>ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR, NOAA_ACSP</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>summary :</span></dt><dd>NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs.  A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002</dd><dt><span>time_coverage_end :</span></dt><dd>2024-08-01T23:59:59Z</dd><dt><span>time_coverage_start :</span></dt><dd>2024-08-01T00:00:00Z</dd><dt><span>title :</span></dt><dd>NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1 - Final</dd></dl></div></li></ul></div></div>"
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-       "<xarray.Dataset> Size: 66MB\n",
-       "Dimensions:  (time: 2, zlev: 1, lat: 720, lon: 1440)\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 16B 2024-08-01T12:00:00 2024-08-02T12:00:00\n",
-       "  * lat      (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
-       "  * lon      (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
-       "  * zlev     (zlev) float32 4B 0.0\n",
-       "Data variables:\n",
-       "    anom     (time, zlev, lat, lon) float64 17MB ...\n",
-       "    err      (time, zlev, lat, lon) float64 17MB ...\n",
-       "    ice      (time, zlev, lat, lon) float64 17MB ...\n",
-       "    sst      (time, zlev, lat, lon) float64 17MB ...\n",
-       "Attributes: (12/37)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    cdm_data_type:              Grid\n",
-       "    comment:                    Data was converted from NetCDF-3 to NetCDF-4 ...\n",
-       "    creator_email:              oisst-help@noaa.gov\n",
-       "    creator_url:                https://www.ncei.noaa.gov/\n",
-       "    date_created:               2024-08-16T09:12:00Z\n",
-       "    ...                         ...\n",
-       "    source:                     ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
-       "    standard_name_vocabulary:   CF Standard Name Table (v40, 25 January 2017)\n",
-       "    summary:                    NOAAs 1/4-degree Daily Optimum Interpolation ...\n",
-       "    time_coverage_end:          2024-08-01T23:59:59Z\n",
-       "    time_coverage_start:        2024-08-01T00:00:00Z\n",
-       "    title:                      NOAA/NCEI 1/4 Degree Daily Optimum Interpolat..."
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ds = xr.open_zarr(store, consolidated=False, zarr_format=3)\n",
     "ds"
@@ -657,7 +244,7 @@
   },
   {
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-   "execution_count": 14,
+   "execution_count": null,
    "id": "190c25f9-e000-4b17-83eb-cf551141dfea",
    "metadata": {},
    "outputs": [],
@@ -672,7 +259,7 @@
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-   "execution_count": 15,
+   "execution_count": null,
    "id": "af330082-207a-4f08-aefe-fc15aa8b2eb3",
    "metadata": {},
    "outputs": [],
@@ -688,450 +275,17 @@
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+   "execution_count": null,
    "id": "103b44d2-124a-4de5-8074-e997fd5a1698",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 17MB\n",
-       "Dimensions:  (time: 2, zlev: 1, lat: 720, lon: 1440)\n",
-       "Coordinates:\n",
-       "    lon      (lon) float32 6kB ManifestArray&lt;shape=(1440,), dtype=float32, ch...\n",
-       "    zlev     (zlev) float32 4B ManifestArray&lt;shape=(1,), dtype=float32, chunk...\n",
-       "    lat      (lat) float32 3kB ManifestArray&lt;shape=(720,), dtype=float32, chu...\n",
-       "    time     (time) float32 8B ManifestArray&lt;shape=(2,), dtype=float32, chunk...\n",
-       "Data variables:\n",
-       "    anom     (time, zlev, lat, lon) int16 4MB ManifestArray&lt;shape=(2, 1, 720,...\n",
-       "    err      (time, zlev, lat, lon) int16 4MB ManifestArray&lt;shape=(2, 1, 720,...\n",
-       "    sst      (time, zlev, lat, lon) int16 4MB ManifestArray&lt;shape=(2, 1, 720,...\n",
-       "    ice      (time, zlev, lat, lon) int16 4MB ManifestArray&lt;shape=(2, 1, 720,...\n",
-       "Attributes: (12/37)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    title:                      NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
-       "    references:                 Reynolds, et al.(2007) Daily High-Resolution-...\n",
-       "    source:                     ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
-       "    id:                         oisst-avhrr-v02r01.20240803.nc\n",
-       "    naming_authority:           gov.noaa.ncei\n",
-       "    ...                         ...\n",
-       "    time_coverage_start:        2024-08-03T00:00:00Z\n",
-       "    time_coverage_end:          2024-08-03T23:59:59Z\n",
-       "    metadata_link:              https://doi.org/10.25921/RE9P-PT57\n",
-       "    ncei_template_version:      NCEI_NetCDF_Grid_Template_v2.0\n",
-       "    comment:                    Data was converted from NetCDF-3 to NetCDF-4 ...\n",
-       "    sensor:                     Thermometer, AVHRR</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-b126d1cf-f677-40cb-9dd8-1075a2baaaff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b126d1cf-f677-40cb-9dd8-1075a2baaaff' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 2</li><li><span>zlev</span>: 1</li><li><span>lat</span>: 720</li><li><span>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8fe7e9f5-08fb-461e-909c-dcf1d3a85f5d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8fe7e9f5-08fb-461e-909c-dcf1d3a85f5d' 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>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(1440,), dty...</div><input id='attrs-270db4a3-3846-4a9b-8f47-024786ec4ed7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-270db4a3-3846-4a9b-8f47-024786ec4ed7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81347014-41bc-456d-93a6-ad0f5b049392' class='xr-var-data-in' type='checkbox'><label for='data-81347014-41bc-456d-93a6-ad0f5b049392' 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>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>grids :</span></dt><dd>Uniform grid from 0.125 to 359.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(1440,), dtype=float32, chunks=(1440,)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zlev</span></div><div class='xr-var-dims'>(zlev)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(1,), dtype=...</div><input id='attrs-d7fd23d3-557a-4a67-943a-f19a100b4c0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7fd23d3-557a-4a67-943a-f19a100b4c0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32794398-bf47-49b7-bad6-ed898494b43c' class='xr-var-data-in' type='checkbox'><label for='data-32794398-bf47-49b7-bad6-ed898494b43c' 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>long_name :</span></dt><dd>Sea surface height</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>actual_range :</span></dt><dd>0, 0</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(1,), dtype=float32, chunks=(1,)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(720,), dtyp...</div><input id='attrs-9b598013-597d-4401-805c-6032549d2d49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b598013-597d-4401-805c-6032549d2d49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cad574dd-c947-40a2-a6f8-83e45f81fbbc' class='xr-var-data-in' type='checkbox'><label for='data-cad574dd-c947-40a2-a6f8-83e45f81fbbc' 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>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>grids :</span></dt><dd>Uniform grid from -89.875 to 89.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(720,), dtype=float32, chunks=(720,)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(2,), dtype=...</div><input id='attrs-92954bc6-ff5e-4ce2-8a98-9b0562a67bf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92954bc6-ff5e-4ce2-8a98-9b0562a67bf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2492815b-9ca8-4f29-89c3-caf15aee2640' class='xr-var-data-in' type='checkbox'><label for='data-2492815b-9ca8-4f29-89c3-caf15aee2640' 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>long_name :</span></dt><dd>Center time of the day</dd><dt><span>units :</span></dt><dd>days since 1978-01-01 12:00:00</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(2,), dtype=float32, chunks=(1,)&gt;</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70dd15b0-de61-4d0e-b625-81e589382c5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70dd15b0-de61-4d0e-b625-81e589382c5b' class='xr-section-summary' >Data variables: <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>anom</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(2, 1, 720, ...</div><input id='attrs-27cd530f-0f4b-4750-9a1f-44749652d0da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27cd530f-0f4b-4750-9a1f-44749652d0da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a1e7b46-1673-4e31-9f74-af9221e2a35d' class='xr-var-data-in' type='checkbox'><label for='data-6a1e7b46-1673-4e31-9f74-af9221e2a35d' 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>long_name :</span></dt><dd>Daily sea surface temperature anomalies</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>scale_factor :</span></dt><dd>0.009999999776482582</dd><dt><span>valid_min :</span></dt><dd>-1200</dd><dt><span>valid_max :</span></dt><dd>1200</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(2, 1, 720, 1440), dtype=int16, chunks=(1, 1, 720, 1440)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(2, 1, 720, ...</div><input id='attrs-063568b4-fd97-4718-a011-b4d34beadfbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-063568b4-fd97-4718-a011-b4d34beadfbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c74841e8-6d81-4868-9c35-d0a9e32d72a7' class='xr-var-data-in' type='checkbox'><label for='data-c74841e8-6d81-4868-9c35-d0a9e32d72a7' 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>long_name :</span></dt><dd>Estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>scale_factor :</span></dt><dd>0.009999999776482582</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(2, 1, 720, 1440), dtype=int16, chunks=(1, 1, 720, 1440)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(2, 1, 720, ...</div><input id='attrs-4c995c52-8f87-40b5-b217-d81f6ed29818' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c995c52-8f87-40b5-b217-d81f6ed29818' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fd2a246-5cab-4d02-b90d-c1ead44b7d5e' class='xr-var-data-in' type='checkbox'><label for='data-4fd2a246-5cab-4d02-b90d-c1ead44b7d5e' 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>long_name :</span></dt><dd>Daily sea surface temperature</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>scale_factor :</span></dt><dd>0.009999999776482582</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(2, 1, 720, 1440), dtype=int16, chunks=(1, 1, 720, 1440)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(2, 1, 720, ...</div><input id='attrs-473cb041-e672-433e-bfeb-e04b0dd3885e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-473cb041-e672-433e-bfeb-e04b0dd3885e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1e58263-0647-4569-8491-6793d3028bd2' class='xr-var-data-in' type='checkbox'><label for='data-c1e58263-0647-4569-8491-6793d3028bd2' 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>long_name :</span></dt><dd>Sea ice concentration</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>scale_factor :</span></dt><dd>0.009999999776482582</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(2, 1, 720, 1440), dtype=int16, chunks=(1, 1, 720, 1440)&gt;</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e735a087-7cc4-4844-8c0c-414451b825e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e735a087-7cc4-4844-8c0c-414451b825e2' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-decd7e8c-5710-496c-afc8-b8be40949a5d' class='xr-section-summary-in' type='checkbox'  ><label for='section-decd7e8c-5710-496c-afc8-b8be40949a5d' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>title :</span></dt><dd>NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1 - Final</dd><dt><span>references :</span></dt><dd>Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST, Navy AVHRR SST, and NOAA ACSPO SST. Ice data: NCEP Ice and GSFC Ice.</dd><dt><span>source :</span></dt><dd>ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR, NOAA_ACSP</dd><dt><span>id :</span></dt><dd>oisst-avhrr-v02r01.20240803.nc</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ncei</dd><dt><span>summary :</span></dt><dd>NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs.  A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.</dd><dt><span>date_modified :</span></dt><dd>2024-08-18T09:12:00Z</dd><dt><span>date_created :</span></dt><dd>2024-08-18T09:12:00Z</dd><dt><span>product_version :</span></dt><dd>Version v02r01</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>institution :</span></dt><dd>NOAA/National Centers for Environmental Information</dd><dt><span>creator_url :</span></dt><dd>https://www.ncei.noaa.gov/</dd><dt><span>creator_email :</span></dt><dd>oisst-help@noaa.gov</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Earth Science Keywords</dd><dt><span>platform :</span></dt><dd>Ships, buoys, Argo floats, MetOp-A, MetOp-B</dd><dt><span>platform_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Platform Keywords</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments &gt; Passive Remote Sensing &gt; Spectrometers/Radiometers &gt; Imaging Spectrometers/Radiometers &gt; AVHRR &gt; Advanced Very High Resolution Radiometer</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Instrument Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>360.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>time_coverage_start :</span></dt><dd>2024-08-03T00:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>2024-08-03T23:59:59Z</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.25921/RE9P-PT57</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>comment :</span></dt><dd>Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.</dd><dt><span>sensor :</span></dt><dd>Thermometer, AVHRR</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 17MB\n",
-       "Dimensions:  (time: 2, zlev: 1, lat: 720, lon: 1440)\n",
-       "Coordinates:\n",
-       "    lon      (lon) float32 6kB ManifestArray<shape=(1440,), dtype=float32, ch...\n",
-       "    zlev     (zlev) float32 4B ManifestArray<shape=(1,), dtype=float32, chunk...\n",
-       "    lat      (lat) float32 3kB ManifestArray<shape=(720,), dtype=float32, chu...\n",
-       "    time     (time) float32 8B ManifestArray<shape=(2,), dtype=float32, chunk...\n",
-       "Data variables:\n",
-       "    anom     (time, zlev, lat, lon) int16 4MB ManifestArray<shape=(2, 1, 720,...\n",
-       "    err      (time, zlev, lat, lon) int16 4MB ManifestArray<shape=(2, 1, 720,...\n",
-       "    sst      (time, zlev, lat, lon) int16 4MB ManifestArray<shape=(2, 1, 720,...\n",
-       "    ice      (time, zlev, lat, lon) int16 4MB ManifestArray<shape=(2, 1, 720,...\n",
-       "Attributes: (12/37)\n",
-       "    Conventions:                CF-1.6, ACDD-1.3\n",
-       "    title:                      NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
-       "    references:                 Reynolds, et al.(2007) Daily High-Resolution-...\n",
-       "    source:                     ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
-       "    id:                         oisst-avhrr-v02r01.20240803.nc\n",
-       "    naming_authority:           gov.noaa.ncei\n",
-       "    ...                         ...\n",
-       "    time_coverage_start:        2024-08-03T00:00:00Z\n",
-       "    time_coverage_end:          2024-08-03T23:59:59Z\n",
-       "    metadata_link:              https://doi.org/10.25921/RE9P-PT57\n",
-       "    ncei_template_version:      NCEI_NetCDF_Grid_Template_v2.0\n",
-       "    comment:                    Data was converted from NetCDF-3 to NetCDF-4 ...\n",
-       "    sensor:                     Thermometer, AVHRR"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "virtual_ds_a"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "b6137cc1-b996-4e60-8c12-01eb19930da6",
    "metadata": {},
    "outputs": [],
@@ -1143,28 +297,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "951f9904-d8b7-4d35-9967-dfeb2866151e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "b'x^cx\\xd3\\xe2\\x06\\x00\\x04\\x16\\x01\\xb7'"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "await append_store._store.get(\"time/c/0\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "a465be46-bb81-4e36-b1b6-67c3b8e4b9ec",
    "metadata": {},
    "outputs": [],
@@ -1174,28 +317,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "e9908d2f-664b-4256-b9d4-842df2e512c3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'R1PVFDC7ZS1B2K558K8G'"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "append_store.commit(\"wrote 2 more days of data\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "477094aa-2094-46e2-ae78-516fc2a51690",
    "metadata": {},
    "outputs": [],
@@ -1207,84 +339,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "0ba50f95-b3dd-4893-aa0a-c59985756dad",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "await read_store._store.get(\"time/c/0\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "47a53027-dbae-48aa-85d2-dcbc04e01e61",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "error",
-     "evalue": "Failed to decode variable 'time': Error -3 while decompressing data: unknown compression method",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/conventions.py:451\u001b[0m, in \u001b[0;36mdecode_cf_variables\u001b[0;34m(variables, attributes, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables, use_cftime, decode_timedelta)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 451\u001b[0m     new_vars[k] \u001b[38;5;241m=\u001b[39m \u001b[43mdecode_cf_variable\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    452\u001b[0m \u001b[43m        \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    453\u001b[0m \u001b[43m        \u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    454\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconcat_characters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_item_or_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconcat_characters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    455\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmask_and_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_item_or_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmask_and_scale\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    456\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_item_or_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdecode_times\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    457\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstack_char_dim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstack_char_dim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    458\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_item_or_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    459\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_timedelta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_item_or_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdecode_timedelta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    460\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    461\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/conventions.py:292\u001b[0m, in \u001b[0;36mdecode_cf_variable\u001b[0;34m(name, var, concat_characters, mask_and_scale, decode_times, decode_endianness, stack_char_dim, use_cftime, decode_timedelta)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m decode_times:\n\u001b[0;32m--> 292\u001b[0m     var \u001b[38;5;241m=\u001b[39m \u001b[43mtimes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCFDatetimeCoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cftime\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m decode_endianness \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m var\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39misnative:\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/coding/times.py:1001\u001b[0m, in \u001b[0;36mCFDatetimeCoder.decode\u001b[0;34m(self, variable, name)\u001b[0m\n\u001b[1;32m   1000\u001b[0m calendar \u001b[38;5;241m=\u001b[39m pop_to(attrs, encoding, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcalendar\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1001\u001b[0m dtype \u001b[38;5;241m=\u001b[39m \u001b[43m_decode_cf_datetime_dtype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalendar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muse_cftime\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1002\u001b[0m transform \u001b[38;5;241m=\u001b[39m partial(\n\u001b[1;32m   1003\u001b[0m     decode_cf_datetime,\n\u001b[1;32m   1004\u001b[0m     units\u001b[38;5;241m=\u001b[39munits,\n\u001b[1;32m   1005\u001b[0m     calendar\u001b[38;5;241m=\u001b[39mcalendar,\n\u001b[1;32m   1006\u001b[0m     use_cftime\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_cftime,\n\u001b[1;32m   1007\u001b[0m )\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/coding/times.py:214\u001b[0m, in \u001b[0;36m_decode_cf_datetime_dtype\u001b[0;34m(data, units, calendar, use_cftime)\u001b[0m\n\u001b[1;32m    212\u001b[0m values \u001b[38;5;241m=\u001b[39m indexing\u001b[38;5;241m.\u001b[39mImplicitToExplicitIndexingAdapter(indexing\u001b[38;5;241m.\u001b[39mas_indexable(data))\n\u001b[1;32m    213\u001b[0m example_value \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(\n\u001b[0;32m--> 214\u001b[0m     [\u001b[43mfirst_n_items\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mor\u001b[39;00m [\u001b[38;5;241m0\u001b[39m], last_item(values) \u001b[38;5;129;01mor\u001b[39;00m [\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m    215\u001b[0m )\n\u001b[1;32m    217\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/core/formatting.py:97\u001b[0m, in \u001b[0;36mfirst_n_items\u001b[0;34m(array, n_desired)\u001b[0m\n\u001b[1;32m     96\u001b[0m     array \u001b[38;5;241m=\u001b[39m array\u001b[38;5;241m.\u001b[39m_data\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mravel(\u001b[43mto_duck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m)\u001b[49m)[:n_desired]\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/namedarray/pycompat.py:138\u001b[0m, in \u001b[0;36mto_duck_array\u001b[0;34m(data, **kwargs)\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 138\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \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",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/core/indexing.py:578\u001b[0m, in \u001b[0;36mImplicitToExplicitIndexingAdapter.__array__\u001b[0;34m(self, dtype, copy)\u001b[0m\n\u001b[1;32m    577\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m Version(np\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2.0.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 578\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_duck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m    579\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/core/indexing.py:583\u001b[0m, in \u001b[0;36mImplicitToExplicitIndexingAdapter.get_duck_array\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    582\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_duck_array\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 583\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[43marray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_duck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/core/indexing.py:657\u001b[0m, in \u001b[0;36mLazilyIndexedArray.get_duck_array\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    654\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    655\u001b[0m     \u001b[38;5;66;03m# If the array is not an ExplicitlyIndexedNDArrayMixin,\u001b[39;00m\n\u001b[1;32m    656\u001b[0m     \u001b[38;5;66;03m# it may wrap a BackendArray so use its __getitem__\u001b[39;00m\n\u001b[0;32m--> 657\u001b[0m     array \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    659\u001b[0m \u001b[38;5;66;03m# self.array[self.key] is now a numpy array when\u001b[39;00m\n\u001b[1;32m    660\u001b[0m \u001b[38;5;66;03m# self.array is a BackendArray subclass\u001b[39;00m\n\u001b[1;32m    661\u001b[0m \u001b[38;5;66;03m# and self.key is BasicIndexer((slice(None, None, None),))\u001b[39;00m\n\u001b[1;32m    662\u001b[0m \u001b[38;5;66;03m# so we need the explicit check for ExplicitlyIndexed\u001b[39;00m\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/backends/zarr.py:175\u001b[0m, in \u001b[0;36mZarrArrayWrapper.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    174\u001b[0m     method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_oindex\n\u001b[0;32m--> 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mindexing\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexplicit_indexing_adapter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexing\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mIndexingSupport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mVECTORIZED\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/core/indexing.py:1018\u001b[0m, in \u001b[0;36mexplicit_indexing_adapter\u001b[0;34m(key, shape, indexing_support, raw_indexing_method)\u001b[0m\n\u001b[1;32m   1017\u001b[0m raw_key, numpy_indices \u001b[38;5;241m=\u001b[39m decompose_indexer(key, shape, indexing_support)\n\u001b[0;32m-> 1018\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mraw_indexing_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_key\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtuple\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1019\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m numpy_indices\u001b[38;5;241m.\u001b[39mtuple:\n\u001b[1;32m   1020\u001b[0m     \u001b[38;5;66;03m# index the loaded np.ndarray\u001b[39;00m\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/backends/zarr.py:158\u001b[0m, in \u001b[0;36mZarrArrayWrapper._getitem\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m    156\u001b[0m     \u001b[38;5;66;03m# import pdb; pdb.set_trace()\u001b[39;00m\n\u001b[1;32m    157\u001b[0m     \u001b[38;5;66;03m# try:\u001b[39;00m\n\u001b[0;32m--> 158\u001b[0m     item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_array\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    159\u001b[0m     \u001b[38;5;66;03m# except Exception as e:\u001b[39;00m\n\u001b[1;32m    160\u001b[0m     \u001b[38;5;66;03m#     import traceback\u001b[39;00m\n\u001b[1;32m    161\u001b[0m     \u001b[38;5;66;03m#     print(f\"An error occurred: {e}\")\u001b[39;00m\n\u001b[1;32m    162\u001b[0m     \u001b[38;5;66;03m#     print(\"Stack trace:\")\u001b[39;00m\n\u001b[1;32m    163\u001b[0m     \u001b[38;5;66;03m#     traceback.print_exc()  # Prints the full traceback            \u001b[39;00m\n\u001b[1;32m    164\u001b[0m     \u001b[38;5;66;03m# import pdb; pdb.set_trace()\u001b[39;00m\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/array.py:1953\u001b[0m, in \u001b[0;36mArray.__getitem__\u001b[0;34m(self, selection)\u001b[0m\n\u001b[1;32m   1952\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_pure_orthogonal_indexing(pure_selection, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim):\n\u001b[0;32m-> 1953\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[43mget_orthogonal_selection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpure_selection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1954\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/_compat.py:43\u001b[0m, in \u001b[0;36m_deprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m extra_args \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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     45\u001b[0m \u001b[38;5;66;03m# extra_args > 0\u001b[39;00m\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/array.py:2395\u001b[0m, in \u001b[0;36mArray.get_orthogonal_selection\u001b[0;34m(self, selection, out, fields, prototype)\u001b[0m\n\u001b[1;32m   2394\u001b[0m indexer \u001b[38;5;241m=\u001b[39m OrthogonalIndexer(selection, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetadata\u001b[38;5;241m.\u001b[39mchunk_grid)\n\u001b[0;32m-> 2395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2396\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_async_array\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_selection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2397\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprototype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprototype\u001b[49m\n\u001b[1;32m   2398\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2399\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/sync.py:141\u001b[0m, in \u001b[0;36msync\u001b[0;34m(coro, loop, timeout)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(return_result, \u001b[38;5;167;01mBaseException\u001b[39;00m):\n\u001b[0;32m--> 141\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m return_result\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/sync.py:100\u001b[0m, in \u001b[0;36m_runner\u001b[0;34m(coro)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 100\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m coro\n\u001b[1;32m    101\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ex:\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/array.py:1009\u001b[0m, in \u001b[0;36mAsyncArray._get_selection\u001b[0;34m(self, indexer, prototype, out, fields)\u001b[0m\n\u001b[1;32m   1007\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m product(indexer\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1008\u001b[0m     \u001b[38;5;66;03m# reading chunks and decoding them\u001b[39;00m\n\u001b[0;32m-> 1009\u001b[0m     \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcodec_pipeline\u001b[38;5;241m.\u001b[39mread(\n\u001b[1;32m   1010\u001b[0m         [\n\u001b[1;32m   1011\u001b[0m             (\n\u001b[1;32m   1012\u001b[0m                 \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstore_path \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetadata\u001b[38;5;241m.\u001b[39mencode_chunk_key(chunk_coords),\n\u001b[1;32m   1013\u001b[0m                 \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetadata\u001b[38;5;241m.\u001b[39mget_chunk_spec(chunk_coords, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morder, prototype\u001b[38;5;241m=\u001b[39mprototype),\n\u001b[1;32m   1014\u001b[0m                 chunk_selection,\n\u001b[1;32m   1015\u001b[0m                 out_selection,\n\u001b[1;32m   1016\u001b[0m             )\n\u001b[1;32m   1017\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m chunk_coords, chunk_selection, out_selection \u001b[38;5;129;01min\u001b[39;00m indexer\n\u001b[1;32m   1018\u001b[0m         ],\n\u001b[1;32m   1019\u001b[0m         out_buffer,\n\u001b[1;32m   1020\u001b[0m         drop_axes\u001b[38;5;241m=\u001b[39mindexer\u001b[38;5;241m.\u001b[39mdrop_axes,\n\u001b[1;32m   1021\u001b[0m     )\n\u001b[1;32m   1022\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out_buffer\u001b[38;5;241m.\u001b[39mas_ndarray_like()\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/codec_pipeline.py:440\u001b[0m, in \u001b[0;36mBatchedCodecPipeline.read\u001b[0;34m(self, batch_info, out, drop_axes)\u001b[0m\n\u001b[1;32m    434\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\u001b[39m(\n\u001b[1;32m    435\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    436\u001b[0m     batch_info: Iterable[\u001b[38;5;28mtuple\u001b[39m[ByteGetter, ArraySpec, SelectorTuple, SelectorTuple]],\n\u001b[1;32m    437\u001b[0m     out: NDBuffer,\n\u001b[1;32m    438\u001b[0m     drop_axes: \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m] \u001b[38;5;241m=\u001b[39m (),\n\u001b[1;32m    439\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 440\u001b[0m     \u001b[38;5;28;01mawait\u001b[39;00m concurrent_map(\n\u001b[1;32m    441\u001b[0m         [\n\u001b[1;32m    442\u001b[0m             (single_batch_info, out, drop_axes)\n\u001b[1;32m    443\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m single_batch_info \u001b[38;5;129;01min\u001b[39;00m batched(batch_info, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_size)\n\u001b[1;32m    444\u001b[0m         ],\n\u001b[1;32m    445\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread_batch,\n\u001b[1;32m    446\u001b[0m         config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124masync.concurrency\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    447\u001b[0m     )\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/common.py:67\u001b[0m, in \u001b[0;36mconcurrent_map\u001b[0;34m(items, func, limit)\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m func(\u001b[38;5;241m*\u001b[39mitem)\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mgather(\u001b[38;5;241m*\u001b[39m[asyncio\u001b[38;5;241m.\u001b[39mensure_future(run(item)) \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m items])\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/common.py:65\u001b[0m, in \u001b[0;36mconcurrent_map.<locals>.run\u001b[0;34m(item)\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mwith\u001b[39;00m sem:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m func(\u001b[38;5;241m*\u001b[39mitem)\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/codec_pipeline.py:270\u001b[0m, in \u001b[0;36mBatchedCodecPipeline.read_batch\u001b[0;34m(self, batch_info, out, drop_axes)\u001b[0m\n\u001b[1;32m    262\u001b[0m chunk_bytes_batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m concurrent_map(\n\u001b[1;32m    263\u001b[0m     [\n\u001b[1;32m    264\u001b[0m         (byte_getter, array_spec\u001b[38;5;241m.\u001b[39mprototype)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    268\u001b[0m     config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124masync.concurrency\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    269\u001b[0m )\n\u001b[0;32m--> 270\u001b[0m chunk_array_batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecode_batch(\n\u001b[1;32m    271\u001b[0m     [\n\u001b[1;32m    272\u001b[0m         (chunk_bytes, chunk_spec)\n\u001b[1;32m    273\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m chunk_bytes, (_, chunk_spec, _, _) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[1;32m    274\u001b[0m             chunk_bytes_batch, batch_info, strict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    275\u001b[0m         )\n\u001b[1;32m    276\u001b[0m     ],\n\u001b[1;32m    277\u001b[0m )\n\u001b[1;32m    278\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk_array, (_, chunk_spec, chunk_selection, out_selection) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[1;32m    279\u001b[0m     chunk_array_batch, batch_info, strict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    280\u001b[0m ):\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/codec_pipeline.py:172\u001b[0m, in \u001b[0;36mBatchedCodecPipeline.decode_batch\u001b[0;34m(self, chunk_bytes_and_specs)\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bb_codec, chunk_spec_batch \u001b[38;5;129;01min\u001b[39;00m bb_codecs_with_spec[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]:\n\u001b[0;32m--> 172\u001b[0m     chunk_bytes_batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m bb_codec\u001b[38;5;241m.\u001b[39mdecode(\n\u001b[1;32m    173\u001b[0m         \u001b[38;5;28mzip\u001b[39m(chunk_bytes_batch, chunk_spec_batch, strict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m    174\u001b[0m     )\n\u001b[1;32m    176\u001b[0m ab_codec, chunk_spec_batch \u001b[38;5;241m=\u001b[39m ab_codec_with_spec\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/abc/codec.py:129\u001b[0m, in \u001b[0;36mBaseCodec.decode\u001b[0;34m(self, chunks_and_specs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Decodes a batch of chunks.\u001b[39;00m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;124;03mChunks can be None in which case they are ignored by the codec.\u001b[39;00m\n\u001b[1;32m    119\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;124;03mIterable[CodecInput | None]\u001b[39;00m\n\u001b[1;32m    128\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 129\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m _batching_helper(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decode_single, chunks_and_specs)\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/abc/codec.py:407\u001b[0m, in \u001b[0;36m_batching_helper\u001b[0;34m(func, batch_info)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_batching_helper\u001b[39m(\n\u001b[1;32m    404\u001b[0m     func: Callable[[CodecInput, ArraySpec], Awaitable[CodecOutput \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m]],\n\u001b[1;32m    405\u001b[0m     batch_info: Iterable[\u001b[38;5;28mtuple\u001b[39m[CodecInput \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, ArraySpec]],\n\u001b[1;32m    406\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[CodecOutput \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m]:\n\u001b[0;32m--> 407\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m concurrent_map(\n\u001b[1;32m    408\u001b[0m         \u001b[38;5;28mlist\u001b[39m(batch_info),\n\u001b[1;32m    409\u001b[0m         _noop_for_none(func),\n\u001b[1;32m    410\u001b[0m         config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124masync.concurrency\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    411\u001b[0m     )\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/common.py:67\u001b[0m, in \u001b[0;36mconcurrent_map\u001b[0;34m(items, func, limit)\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m func(\u001b[38;5;241m*\u001b[39mitem)\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mgather(\u001b[38;5;241m*\u001b[39m[asyncio\u001b[38;5;241m.\u001b[39mensure_future(run(item)) \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m items])\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/common.py:65\u001b[0m, in \u001b[0;36mconcurrent_map.<locals>.run\u001b[0;34m(item)\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mwith\u001b[39;00m sem:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m func(\u001b[38;5;241m*\u001b[39mitem)\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/abc/codec.py:420\u001b[0m, in \u001b[0;36m_noop_for_none.<locals>.wrap\u001b[0;34m(chunk, chunk_spec)\u001b[0m\n\u001b[1;32m    419\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m func(chunk, chunk_spec)\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/numcodecs/zarr3.py:125\u001b[0m, in \u001b[0;36m_NumcodecsBytesBytesCodec._decode_single\u001b[0;34m(self, chunk_bytes, chunk_spec)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_decode_single\u001b[39m(\u001b[38;5;28mself\u001b[39m, chunk_bytes: Buffer, chunk_spec: ArraySpec) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Buffer:\n\u001b[0;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39mto_thread(\n\u001b[1;32m    126\u001b[0m         as_numpy_array_wrapper,\n\u001b[1;32m    127\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_codec\u001b[38;5;241m.\u001b[39mdecode,\n\u001b[1;32m    128\u001b[0m         chunk_bytes,\n\u001b[1;32m    129\u001b[0m         chunk_spec\u001b[38;5;241m.\u001b[39mprototype,\n\u001b[1;32m    130\u001b[0m     )\n",
-      "File \u001b[0;32m/usr/local/Cellar/python@3.12/3.12.7_1/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/threads.py:25\u001b[0m, in \u001b[0;36mto_thread\u001b[0;34m(func, *args, **kwargs)\u001b[0m\n\u001b[1;32m     24\u001b[0m func_call \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(ctx\u001b[38;5;241m.\u001b[39mrun, func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m loop\u001b[38;5;241m.\u001b[39mrun_in_executor(\u001b[38;5;28;01mNone\u001b[39;00m, func_call)\n",
-      "File \u001b[0;32m/usr/local/Cellar/python@3.12/3.12.7_1/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\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[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/zarr/core/buffer/cpu.py:213\u001b[0m, in \u001b[0;36mas_numpy_array_wrapper\u001b[0;34m(func, buf, prototype)\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Converts the input of `func` to a numpy array and the output back to `Buffer`.\u001b[39;00m\n\u001b[1;32m    192\u001b[0m \n\u001b[1;32m    193\u001b[0m \u001b[38;5;124;03mThis function is useful when calling a `func` that only support host memory such\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[38;5;124;03m    The result of `func` converted to a `Buffer`\u001b[39;00m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m prototype\u001b[38;5;241m.\u001b[39mbuffer\u001b[38;5;241m.\u001b[39mfrom_bytes(\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_numpy_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/numcodecs/zlib.py:37\u001b[0m, in \u001b[0;36mZlib.decode\u001b[0;34m(self, buf, out)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;66;03m# do decompression\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m dec \u001b[38;5;241m=\u001b[39m \u001b[43m_zlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecompress\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# handle destination - Python standard library zlib module does not\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;66;03m# support direct decompression into buffer, so we have to copy into\u001b[39;00m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# out if given\u001b[39;00m\n",
-      "\u001b[0;31merror\u001b[0m: Error -3 while decompressing data: unknown compression method",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_zarr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mread_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidated\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[43mzarr_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m ds\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/backends/zarr.py:1346\u001b[0m, in \u001b[0;36mopen_zarr\u001b[0;34m(store, group, synchronizer, chunks, decode_cf, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, consolidated, overwrite_encoded_chunks, chunk_store, storage_options, decode_timedelta, use_cftime, zarr_version, zarr_format, use_zarr_fill_value_as_mask, chunked_array_type, from_array_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m   1335\u001b[0m backend_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m   1336\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msynchronizer\u001b[39m\u001b[38;5;124m\"\u001b[39m: synchronizer,\n\u001b[1;32m   1337\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconsolidated\u001b[39m\u001b[38;5;124m\"\u001b[39m: consolidated,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1343\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzarr_format\u001b[39m\u001b[38;5;124m\"\u001b[39m: zarr_format,\n\u001b[1;32m   1344\u001b[0m }\n\u001b[1;32m   1345\u001b[0m \u001b[38;5;66;03m#import pdb; pdb.set_trace()\u001b[39;00m\n\u001b[0;32m-> 1346\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1347\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1348\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1349\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_cf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_cf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1350\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask_and_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask_and_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1351\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1352\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconcat_characters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcat_characters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1353\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_coords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_coords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1354\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzarr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1355\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1356\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1357\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked_array_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked_array_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1358\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfrom_array_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_array_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1359\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbackend_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackend_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1360\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_timedelta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_timedelta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1361\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cftime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1362\u001b[0m \u001b[43m    \u001b[49m\u001b[43mzarr_version\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mzarr_version\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1363\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_zarr_fill_value_as_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_zarr_fill_value_as_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1364\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/backends/api.py:671\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    659\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    660\u001b[0m     decode_cf,\n\u001b[1;32m    661\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    667\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    668\u001b[0m )\n\u001b[1;32m    670\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 671\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \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    676\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    677\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    678\u001b[0m     backend_ds,\n\u001b[1;32m    679\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    689\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    690\u001b[0m )\n\u001b[1;32m    691\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/backends/zarr.py:1436\u001b[0m, in \u001b[0;36mZarrBackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, synchronizer, consolidated, chunk_store, storage_options, stacklevel, zarr_version, zarr_format, store, engine, use_zarr_fill_value_as_mask)\u001b[0m\n\u001b[1;32m   1434\u001b[0m store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m   1435\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n\u001b[0;32m-> 1436\u001b[0m     ds \u001b[38;5;241m=\u001b[39m \u001b[43mstore_entrypoint\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1437\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1438\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmask_and_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask_and_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1439\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1440\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconcat_characters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcat_characters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1441\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_coords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_coords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1442\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1443\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cftime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1444\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_timedelta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_timedelta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1445\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1446\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/backends/store.py:46\u001b[0m, in \u001b[0;36mStoreBackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta)\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28mvars\u001b[39m, attrs \u001b[38;5;241m=\u001b[39m filename_or_obj\u001b[38;5;241m.\u001b[39mload()\n\u001b[1;32m     44\u001b[0m encoding \u001b[38;5;241m=\u001b[39m filename_or_obj\u001b[38;5;241m.\u001b[39mget_encoding()\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28mvars\u001b[39m, attrs, coord_names \u001b[38;5;241m=\u001b[39m \u001b[43mconventions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode_cf_variables\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     47\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mvars\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     48\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     49\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask_and_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask_and_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     50\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     51\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconcat_characters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcat_characters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_coords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_coords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     53\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cftime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cftime\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_timedelta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_timedelta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# import pdb; pdb.set_trace()\u001b[39;00m\n\u001b[1;32m     58\u001b[0m ds \u001b[38;5;241m=\u001b[39m Dataset(\u001b[38;5;28mvars\u001b[39m, attrs\u001b[38;5;241m=\u001b[39mattrs)\n",
-      "File \u001b[0;32m~/github/virtualizarr/venv/lib/python3.12/site-packages/xarray/conventions.py:462\u001b[0m, in \u001b[0;36mdecode_cf_variables\u001b[0;34m(variables, attributes, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables, use_cftime, decode_timedelta)\u001b[0m\n\u001b[1;32m    451\u001b[0m     new_vars[k] \u001b[38;5;241m=\u001b[39m decode_cf_variable(\n\u001b[1;32m    452\u001b[0m         k,\n\u001b[1;32m    453\u001b[0m         v,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    459\u001b[0m         decode_timedelta\u001b[38;5;241m=\u001b[39m_item_or_default(decode_timedelta, k, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m    460\u001b[0m     )\n\u001b[1;32m    461\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 462\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(e)(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to decode variable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m decode_coords \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoordinates\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    464\u001b[0m     var_attrs \u001b[38;5;241m=\u001b[39m new_vars[k]\u001b[38;5;241m.\u001b[39mattrs\n",
-      "\u001b[0;31merror\u001b[0m: Failed to decode variable 'time': Error -3 while decompressing data: unknown compression method"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ds = xr.open_zarr(read_store, consolidated=False, zarr_format=3)\n",
     "ds"
@@ -1293,9 +361,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv",
+   "display_name": "virtualizarr",
    "language": "python",
-   "name": "venv"
+   "name": "virtualizarr"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1307,7 +375,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,