diff --git a/docs/source/notebook-components/weaver_example.ipynb b/docs/source/notebook-components/weaver_example.ipynb
index fd69e44..aa7e1f7 100644
--- a/docs/source/notebook-components/weaver_example.ipynb
+++ b/docs/source/notebook-components/weaver_example.ipynb
@@ -1380,7 +1380,7 @@
     "\n",
     "print(\"Fetching output contents...\")\n",
     "resp = requests.get(href)\n",
-    "print(f\"\\nNCDUMP 'output' result content:\\n\\n{resp.text}\")\n"
+    "print(f\"\\nNCDUMP 'output' result content:\\n\\n{resp.text}\")"
    ]
   }
  ],
diff --git a/docs/source/notebooks/subset-user-input.ipynb b/docs/source/notebooks/subset-user-input.ipynb
index 8c251a2..5520146 100644
--- a/docs/source/notebooks/subset-user-input.ipynb
+++ b/docs/source/notebooks/subset-user-input.ipynb
@@ -92,7 +92,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "381879fe6f384658bace24b116549b65",
+       "model_id": "2dc6c1a8453a4120a69de5832ad4a934",
        "version_major": 2,
        "version_minor": 0
       },
@@ -240,7 +240,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3d7f0c9f2cfe41e487c077faa095b6da",
+       "model_id": "d8c47b8a4725475886c1272b3b74e35d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -490,7 +490,9 @@
     "catalog = \"https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/gridded_obs/catalog.xml\"  # TEST_USE_PROD_DATA\n",
     "\n",
     "cat = TDSCatalog(catalog)\n",
-    "data = cat.datasets[0].access_urls[\"OPENDAP\"]\n",
+    "data = [\n",
+    "    cat.datasets[d].access_urls[\"OPENDAP\"] for d in cat.datasets if \"nrcan_v2\" in d\n",
+    "][0]\n",
     "data"
    ]
   },
@@ -888,12 +890,12 @@
        "    license_type:         permissive\n",
        "    license:              https://open.canada.ca/en/open-government-licence-c...\n",
        "    attribution:          The authors provide this data under the Environment...\n",
-       "    citation:             Natural Resources Canada ANUSPLIN interpolated hist...</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-dfeeb6e0-3d9e-468c-9ca6-a2923282a507' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dfeeb6e0-3d9e-468c-9ca6-a2923282a507' 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'>lat</span>: 510</li><li><span class='xr-has-index'>lon</span>: 1068</li><li><span class='xr-has-index'>time</span>: 24837</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-593bd73a-448c-4479-b3db-8c4f5b71f0dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-593bd73a-448c-4479-b3db-8c4f5b71f0dd' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>83.46 83.38 83.29 ... 41.12 41.04</div><input id='attrs-52128276-2e97-4844-aa4b-a9065f1846c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52128276-2e97-4844-aa4b-a9065f1846c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f006962-49a5-46c1-8cb7-d91982fc1647' class='xr-var-data-in' type='checkbox'><label for='data-9f006962-49a5-46c1-8cb7-d91982fc1647' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>_ChunkSizes :</span></dt><dd>510</dd></dl></div><div class='xr-var-data'><pre>array([83.45833 , 83.375   , 83.291664, ..., 41.208332, 41.125   , 41.041668],\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'>-141.0 -140.9 ... -52.13 -52.04</div><input id='attrs-e0f68c79-c617-4c9a-941a-3ca900092568' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0f68c79-c617-4c9a-941a-3ca900092568' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27977705-918c-41cf-8dd7-70db81f478bf' class='xr-var-data-in' type='checkbox'><label for='data-27977705-918c-41cf-8dd7-70db81f478bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>_ChunkSizes :</span></dt><dd>1068</dd></dl></div><div class='xr-var-data'><pre>array([-140.95833 , -140.875   , -140.79167 , ...,  -52.208336,  -52.125004,\n",
-       "        -52.04167 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1950-01-01 ... 2017-12-31</div><input id='attrs-a2ef009c-a872-49c8-8493-37ec31749769' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2ef009c-a872-49c8-8493-37ec31749769' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0fe8f7a-a42f-4edb-bc60-186a6f6b0485' class='xr-var-data-in' type='checkbox'><label for='data-c0fe8f7a-a42f-4edb-bc60-186a6f6b0485' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-01-02T00:00:00.000000000&#x27;,\n",
+       "    citation:             Natural Resources Canada ANUSPLIN interpolated hist...</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-e82dc1d7-3aed-46c4-b13c-469dea27005e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e82dc1d7-3aed-46c4-b13c-469dea27005e' 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'>lat</span>: 510</li><li><span class='xr-has-index'>lon</span>: 1068</li><li><span class='xr-has-index'>time</span>: 24837</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-27b4a246-8fcd-4241-ad0c-2b9b399d0d72' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27b4a246-8fcd-4241-ad0c-2b9b399d0d72' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>83.46 83.38 83.29 ... 41.12 41.04</div><input id='attrs-d67a8987-b768-4a2d-815b-8248b805137a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d67a8987-b768-4a2d-815b-8248b805137a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bde93b2-f65f-45f6-8032-4d46801e81c4' class='xr-var-data-in' type='checkbox'><label for='data-1bde93b2-f65f-45f6-8032-4d46801e81c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>_ChunkSizes :</span></dt><dd>510</dd></dl></div><div class='xr-var-data'><pre>array([83.45833 , 83.375   , 83.291664, ..., 41.208332, 41.125   , 41.041668],\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'>-141.0 -140.9 ... -52.13 -52.04</div><input id='attrs-9faaa17f-a8bb-4e8f-97eb-1a1f5167cc6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9faaa17f-a8bb-4e8f-97eb-1a1f5167cc6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64438927-5f2c-4557-a901-7ca88db69918' class='xr-var-data-in' type='checkbox'><label for='data-64438927-5f2c-4557-a901-7ca88db69918' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>_ChunkSizes :</span></dt><dd>1068</dd></dl></div><div class='xr-var-data'><pre>array([-140.95833 , -140.875   , -140.79167 , ...,  -52.208336,  -52.125004,\n",
+       "        -52.04167 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1950-01-01 ... 2017-12-31</div><input id='attrs-c36390bc-4ba0-497a-9935-aac44b669761' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c36390bc-4ba0-497a-9935-aac44b669761' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e56bbb8-3847-482e-bef0-745ba3b93155' class='xr-var-data-in' type='checkbox'><label for='data-6e56bbb8-3847-482e-bef0-745ba3b93155' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-01-02T00:00:00.000000000&#x27;,\n",
        "       &#x27;1950-01-03T00:00:00.000000000&#x27;, ..., &#x27;2017-12-29T00:00:00.000000000&#x27;,\n",
        "       &#x27;2017-12-30T00:00:00.000000000&#x27;, &#x27;2017-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5343f8b1-b1aa-4c84-961e-7efd5d1809bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5343f8b1-b1aa-4c84-961e-7efd5d1809bf' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmin</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3375, 69, 144), meta=np.ndarray&gt;</div><input id='attrs-8c9c9ea3-cca8-4603-9b4a-040926bcc826' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c9c9ea3-cca8-4603-9b4a-040926bcc826' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33f533ef-ef0c-4f44-876d-a5c16ec79a88' class='xr-var-data-in' type='checkbox'><label for='data-33f533ef-ef0c-4f44-876d-a5c16ec79a88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[366  50  50]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19bf851b-c99f-4552-a14d-e8df25b882d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-19bf851b-c99f-4552-a14d-e8df25b882d1' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmin</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3375, 69, 144), meta=np.ndarray&gt;</div><input id='attrs-c71a66ee-99a8-46d1-b2e4-f7af0bd83ee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c71a66ee-99a8-46d1-b2e4-f7af0bd83ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea421b89-56e8-4191-9d77-f1c382b03811' class='xr-var-data-in' type='checkbox'><label for='data-ea421b89-56e8-4191-9d77-f1c382b03811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[366  50  50]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1013,7 +1015,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3375, 69, 144), meta=np.ndarray&gt;</div><input id='attrs-bb72952e-43a3-4f1c-9bc1-15cdbcbead7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb72952e-43a3-4f1c-9bc1-15cdbcbead7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf515e74-1893-4eda-b24b-ce5449c1f604' class='xr-var-data-in' type='checkbox'><label for='data-cf515e74-1893-4eda-b24b-ce5449c1f604' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[366  50  50]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3375, 69, 144), meta=np.ndarray&gt;</div><input id='attrs-d984ce90-9ed6-4f22-b69b-0f5a975e0c22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d984ce90-9ed6-4f22-b69b-0f5a975e0c22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73db9253-a35c-444c-b878-9bba57478f6b' class='xr-var-data-in' type='checkbox'><label for='data-73db9253-a35c-444c-b878-9bba57478f6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[366  50  50]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1133,7 +1135,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3375, 69, 144), meta=np.ndarray&gt;</div><input id='attrs-608f9cdb-9903-46c4-9065-ec12dac661b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-608f9cdb-9903-46c4-9065-ec12dac661b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54ec7776-f5b0-4aba-8824-15421be5fda8' class='xr-var-data-in' type='checkbox'><label for='data-54ec7776-f5b0-4aba-8824-15421be5fda8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm s-1</dd><dt><span>long_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>standard_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>_ChunkSizes :</span></dt><dd>[366  50  50]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3375, 69, 144), meta=np.ndarray&gt;</div><input id='attrs-792b8d0c-ada3-49d1-8192-3925160ba424' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-792b8d0c-ada3-49d1-8192-3925160ba424' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e6d222-fd0d-47d0-be26-2f501d03a7f1' class='xr-var-data-in' type='checkbox'><label for='data-99e6d222-fd0d-47d0-be26-2f501d03a7f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm s-1</dd><dt><span>long_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>standard_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>_ChunkSizes :</span></dt><dd>[366  50  50]</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1253,32 +1255,32 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-59c4e1e2-17a1-4763-b5ba-a0a403105710' class='xr-section-summary-in' type='checkbox'  ><label for='section-59c4e1e2-17a1-4763-b5ba-a0a403105710' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ecf4ed7a-c902-48df-9c4f-f4efa1e44923' class='xr-index-data-in' type='checkbox'/><label for='index-ecf4ed7a-c902-48df-9c4f-f4efa1e44923' 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(Float64Index([83.45832824707031,            83.375, 83.29166412353516,\n",
-       "              83.20832824707031,            83.125, 83.04166412353516,\n",
-       "              82.95832824707031,            82.875, 82.79166412353516,\n",
-       "              82.70832824707031,\n",
-       "              ...\n",
-       "              41.79166793823242, 41.70833206176758,            41.625,\n",
-       "              41.54166793823242, 41.45833206176758,            41.375,\n",
-       "              41.29166793823242, 41.20833206176758,            41.125,\n",
-       "              41.04166793823242],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=510))</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-967351ec-72d5-4f75-8ccc-0b6d33d448e8' class='xr-index-data-in' type='checkbox'/><label for='index-967351ec-72d5-4f75-8ccc-0b6d33d448e8' 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(Float64Index([ -140.9583282470703,            -140.875,  -140.7916717529297,\n",
-       "               -140.7083282470703,            -140.625,  -140.5416717529297,\n",
-       "               -140.4583282470703,            -140.375,  -140.2916717529297,\n",
-       "               -140.2083282470703,\n",
-       "              ...\n",
-       "               -52.79167175292969, -52.708335876464844, -52.625003814697266,\n",
-       "               -52.54167175292969, -52.458335876464844, -52.375003814697266,\n",
-       "               -52.29167175292969, -52.208335876464844, -52.125003814697266,\n",
-       "               -52.04167175292969],\n",
-       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1068))</pre></div></li><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-dc7ee347-80d5-49ef-8453-62665c50e245' class='xr-index-data-in' type='checkbox'/><label for='index-dc7ee347-80d5-49ef-8453-62665c50e245' 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;1950-01-01&#x27;, &#x27;1950-01-02&#x27;, &#x27;1950-01-03&#x27;, &#x27;1950-01-04&#x27;,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2c923e58-e174-468e-a636-e9d8c6cadf6c' class='xr-section-summary-in' type='checkbox'  ><label for='section-2c923e58-e174-468e-a636-e9d8c6cadf6c' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8ca4e85e-d18a-4903-849d-a63afc133919' class='xr-index-data-in' type='checkbox'/><label for='index-8ca4e85e-d18a-4903-849d-a63afc133919' 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([83.45832824707031,            83.375, 83.29166412353516,\n",
+       "       83.20832824707031,            83.125, 83.04166412353516,\n",
+       "       82.95832824707031,            82.875, 82.79166412353516,\n",
+       "       82.70832824707031,\n",
+       "       ...\n",
+       "       41.79166793823242, 41.70833206176758,            41.625,\n",
+       "       41.54166793823242, 41.45833206176758,            41.375,\n",
+       "       41.29166793823242, 41.20833206176758,            41.125,\n",
+       "       41.04166793823242],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=510))</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-36ec7fab-46a8-4a33-9b7c-86a3adb6cc0f' class='xr-index-data-in' type='checkbox'/><label for='index-36ec7fab-46a8-4a33-9b7c-86a3adb6cc0f' 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([ -140.9583282470703,            -140.875,  -140.7916717529297,\n",
+       "        -140.7083282470703,            -140.625,  -140.5416717529297,\n",
+       "        -140.4583282470703,            -140.375,  -140.2916717529297,\n",
+       "        -140.2083282470703,\n",
+       "       ...\n",
+       "        -52.79167175292969, -52.708335876464844, -52.625003814697266,\n",
+       "        -52.54167175292969, -52.458335876464844, -52.375003814697266,\n",
+       "        -52.29167175292969, -52.208335876464844, -52.125003814697266,\n",
+       "        -52.04167175292969],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1068))</pre></div></li><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-8cbf390c-6d38-4617-a0a9-f35593c71d81' class='xr-index-data-in' type='checkbox'/><label for='index-8cbf390c-6d38-4617-a0a9-f35593c71d81' 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;1950-01-01&#x27;, &#x27;1950-01-02&#x27;, &#x27;1950-01-03&#x27;, &#x27;1950-01-04&#x27;,\n",
        "               &#x27;1950-01-05&#x27;, &#x27;1950-01-06&#x27;, &#x27;1950-01-07&#x27;, &#x27;1950-01-08&#x27;,\n",
        "               &#x27;1950-01-09&#x27;, &#x27;1950-01-10&#x27;,\n",
        "               ...\n",
        "               &#x27;2017-12-22&#x27;, &#x27;2017-12-23&#x27;, &#x27;2017-12-24&#x27;, &#x27;2017-12-25&#x27;,\n",
        "               &#x27;2017-12-26&#x27;, &#x27;2017-12-27&#x27;, &#x27;2017-12-28&#x27;, &#x27;2017-12-29&#x27;,\n",
        "               &#x27;2017-12-30&#x27;, &#x27;2017-12-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=24837, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7da68fcf-a7e0-4e56-a890-d07d212d9ea3' class='xr-section-summary-in' type='checkbox'  ><label for='section-7da68fcf-a7e0-4e56-a890-d07d212d9ea3' class='xr-section-summary' >Attributes: <span>(15)</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-1.5</dd><dt><span>title :</span></dt><dd>NRCAN ANUSPLIN daily gridded dataset : version 2</dd><dt><span>history :</span></dt><dd>Fri Jan 25 14:11:15 2019 : Convert from original format to NetCDF</dd><dt><span>institute_id :</span></dt><dd>NRCAN</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>abstract :</span></dt><dd>Gridded daily observational dataset produced by Natural Resources Canada (NRCan), available at 300 arc second spatial resolution (1/12° grids, ~10 km) over Canada. The version 2 dataset was updated in its entirety compared with version 1 and is not a simple extension of version 1 with additional years of data. Gridding was accomplished with the Australian National University Spline (ANUSPLIN) implementation of the trivariate thin plate splines interpolation method with latitude, longitude and elevation as predictors. </dd><dt><span>dataset_description :</span></dt><dd>https://cfs.nrcan.gc.ca/projects/3/1</dd><dt><span>type :</span></dt><dd>gridded_obs</dd><dt><span>institute :</span></dt><dd>Natural Resources Canada : Canadian Forest Service</dd><dt><span>dataset_id :</span></dt><dd>NRCAN_anusplin_daily_v2</dd><dt><span>version :</span></dt><dd>2.0</dd><dt><span>license_type :</span></dt><dd>permissive</dd><dt><span>license :</span></dt><dd>https://open.canada.ca/en/open-government-licence-canada</dd><dt><span>attribution :</span></dt><dd>The authors provide this data under the Environment and Climate Change Canada Data Server End Use License and demand that any references to or derivative works based on this dataset be cited using the provided citation information</dd><dt><span>citation :</span></dt><dd>Natural Resources Canada ANUSPLIN interpolated historical climate model from McKenney, D.W., M.F. Hutchinson, P. Papadol, K. Lawrence, J. Pedlar, K. Campbell, E. Milewska, R.F. Hopkinson, D. Price, and T. Owen, 2011. Customized Spatial Climate Models for North America. Bull. Amer. Meteor. Soc., 92, 1611-1622, https://doi.org/10.1175/2011BAMS3132.1</dd></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=24837, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e09f8680-e8bf-4ccf-bec2-1f9cc375bf79' class='xr-section-summary-in' type='checkbox'  ><label for='section-e09f8680-e8bf-4ccf-bec2-1f9cc375bf79' class='xr-section-summary' >Attributes: <span>(15)</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-1.5</dd><dt><span>title :</span></dt><dd>NRCAN ANUSPLIN daily gridded dataset : version 2</dd><dt><span>history :</span></dt><dd>Fri Jan 25 14:11:15 2019 : Convert from original format to NetCDF</dd><dt><span>institute_id :</span></dt><dd>NRCAN</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>abstract :</span></dt><dd>Gridded daily observational dataset produced by Natural Resources Canada (NRCan), available at 300 arc second spatial resolution (1/12° grids, ~10 km) over Canada. The version 2 dataset was updated in its entirety compared with version 1 and is not a simple extension of version 1 with additional years of data. Gridding was accomplished with the Australian National University Spline (ANUSPLIN) implementation of the trivariate thin plate splines interpolation method with latitude, longitude and elevation as predictors. </dd><dt><span>dataset_description :</span></dt><dd>https://cfs.nrcan.gc.ca/projects/3/1</dd><dt><span>type :</span></dt><dd>gridded_obs</dd><dt><span>institute :</span></dt><dd>Natural Resources Canada : Canadian Forest Service</dd><dt><span>dataset_id :</span></dt><dd>NRCAN_anusplin_daily_v2</dd><dt><span>version :</span></dt><dd>2.0</dd><dt><span>license_type :</span></dt><dd>permissive</dd><dt><span>license :</span></dt><dd>https://open.canada.ca/en/open-government-licence-canada</dd><dt><span>attribution :</span></dt><dd>The authors provide this data under the Environment and Climate Change Canada Data Server End Use License and demand that any references to or derivative works based on this dataset be cited using the provided citation information</dd><dt><span>citation :</span></dt><dd>Natural Resources Canada ANUSPLIN interpolated historical climate model from McKenney, D.W., M.F. Hutchinson, P. Papadol, K. Lawrence, J. Pedlar, K. Campbell, E. Milewska, R.F. Hopkinson, D. Price, and T. Owen, 2011. Customized Spatial Climate Models for North America. Bull. Amer. Meteor. Soc., 92, 1611-1622, https://doi.org/10.1175/2011BAMS3132.1</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1326,7 +1328,22 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_68544/857013960.py:1: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  lon0 = float(bounds.minx)\n",
+      "/tmp/ipykernel_68544/857013960.py:2: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  lon1 = float(bounds.maxx)\n",
+      "/tmp/ipykernel_68544/857013960.py:3: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  lat0 = float(bounds.miny)\n",
+      "/tmp/ipykernel_68544/857013960.py:4: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  lat1 = float(bounds.maxy)\n"
+     ]
+    }
+   ],
    "source": [
     "lon0 = float(bounds.minx)\n",
     "lon1 = float(bounds.maxx)\n",
@@ -1353,7 +1370,7 @@
     {
      "data": {
       "text/plain": [
-       "'https://pavics.ouranos.ca/wpsoutputs/finch/d00d6fa4-b40a-11ee-acf8-0242ac130007/nrcan_v2_sub.ncml'"
+       "'https://pavics.ouranos.ca/wpsoutputs/finch/a2c299f4-d0f5-11ee-aee0-0242ac13000f/nrcan_v2_sub.ncml'"
       ]
      },
      "execution_count": 16,
@@ -1395,7 +1412,7 @@
     {
      "data": {
       "text/plain": [
-       "'https://pavics.ouranos.ca/wpsoutputs/finch/d1151ece-b40a-11ee-acf8-0242ac130007/nrcan_v2_sub.ncml'"
+       "'https://pavics.ouranos.ca/wpsoutputs/finch/a3aba6f8-d0f5-11ee-aee0-0242ac13000f/nrcan_v2_sub.ncml'"
       ]
      },
      "execution_count": 18,
@@ -1509,7 +1526,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading to /tmp/tmpqet24mhs/out.nc.\n"
+      "Downloading to /tmp/tmpz3n01i2p/out.nc.\n"
      ]
     }
    ],
@@ -1534,7 +1551,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fccd44f0df0>"
+       "<matplotlib.collections.QuadMesh at 0x7fa3111193d0>"
       ]
      },
      "execution_count": 23,
@@ -1543,7 +1560,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1586,9 +1603,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output_url =  https://pavics.ouranos.ca/wpsoutputs/finch/d2117e94-b40a-11ee-acf8-0242ac130007/out.nc\n",
-      "output_path =  /wps_outputs/finch/d2117e94-b40a-11ee-acf8-0242ac130007/out.nc\n",
-      "output_thredds_url =  https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/wps_outputs/finch/d2117e94-b40a-11ee-acf8-0242ac130007/out.nc\n"
+      "output_url =  https://pavics.ouranos.ca/wpsoutputs/finch/a493cd0c-d0f5-11ee-aee0-0242ac13000f/out.nc\n",
+      "output_path =  /wps_outputs/finch/a493cd0c-d0f5-11ee-aee0-0242ac13000f/out.nc\n",
+      "output_thredds_url =  https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/wps_outputs/finch/a493cd0c-d0f5-11ee-aee0-0242ac13000f/out.nc\n"
      ]
     }
    ],
@@ -1617,7 +1634,9 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -3568,12 +3587,12 @@
       "application/vnd.holoviews_exec.v0+json": "",
       "text/html": [
        "<div id='1004'>\n",
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Application\",\"version\":\"2.4.3\"}};\n",
+       "    var render_items = [{\"docid\":\"e5326139-5dfd-49d0-8e6e-4ec827b663d1\",\"root_ids\":[\"1004\"],\"roots\":{\"1004\":\"5c98ff87-1aba-464d-b344-ce2183839141\"}}];\n",
        "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
        "    for (const render_item of render_items) {\n",
        "      for (const root_id of render_item.root_ids) {\n",
diff --git a/docs/source/notebooks/subsetting.ipynb b/docs/source/notebooks/subsetting.ipynb
index 5944c0d..209929d 100644
--- a/docs/source/notebooks/subsetting.ipynb
+++ b/docs/source/notebooks/subsetting.ipynb
@@ -620,7 +620,7 @@
     "res = resp.get()\n",
     "print(\"URL: \", res.output)\n",
     "res = resp.get(asobj=True)\n",
-    "res.output\n"
+    "res.output"
    ]
   },
   {