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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0bebc34d-64a9-4961-accd-e3eece6aad5f",
+   "metadata": {},
+   "source": [
+    "# Opening TEMPO Level-2 data usign the new `open_virtual_dataset` functionality in `earthaccess`\n",
+    "\n",
+    "## Summary\n",
+    "\n",
+    "In this tutorial, we will use the `earthaccess.open_virtual_mfdataset()` function to open multiple granules in the data collection for the [TEMPO air quality mission (link)](https://asdc.larc.nasa.gov/project/TEMPO). We will then extract a subset of the data for visualization.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "This tutorial has been designed to run in an AWS cloud compute instance in AWS region us-west-2. However, if you want to run it from your laptop or workstation, everything should work just fine but without the speed benefits of in-cloud access.\n",
+    "\n",
+    "An Earthdata Login account is required to access data from the NASA Earthdata system. Before requesting TEMPO data, we first need to set up our Earthdata Login authentication, as described in the Earthdata Cookbook's [earthaccess tutorial (link)](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/tutorials/earthaccess-demo.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ab9d476-5186-4da8-8329-1270e6e925bd",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "aa4a11af-f182-4021-ad6f-8b198d6bdbde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import earthaccess\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "198318d6-2850-450e-9df0-dd0895ee985d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "auth = earthaccess.login()\n",
+    "\n",
+    "if not auth.authenticated:\n",
+    "    auth.login(\n",
+    "        strategy=\"interactive\", persist=True\n",
+    "    )  # ask for credentials and persist them in a .netrc file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86a3e22a-3af7-4584-9517-7993a8bad9c0",
+   "metadata": {},
+   "source": [
+    "## Get granule links"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b70e220-893c-4451-bfaa-c01e14e0b577",
+   "metadata": {},
+   "source": [
+    "We search for TEMPO Nitrogen Dioxide ($NO_2$) data for a two-hour period (note: times are in UTC) from January, 2024."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7d228d05-4611-4d48-a570-8f44423e9026",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of granules in result: 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = earthaccess.search_data(\n",
+    "    short_name=\"TEMPO_NO2_L2\",\n",
+    "    version=\"V03\",\n",
+    "    # temporal=(\"2024-01-11 12:00\", \"2024-01-11 16:00\"),\n",
+    "    count=20,\n",
+    ")\n",
+    "print(f\"Number of granules in result: {len(results)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c752f56-dfd4-43d0-a786-28446d95ddb0",
+   "metadata": {},
+   "source": [
+    "## Open Virtual Multifile Datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69ca9042-11f1-42da-88eb-39086739555a",
+   "metadata": {},
+   "source": [
+    "Because TEMPO data are processed and archived in a netCDF4 format using a group hierarchy, we want to open all of the groups."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "765022ed-74b8-48be-ba38-45fbf37f9130",
+   "metadata": {},
+   "source": [
+    "### First, we open the root group for all granules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6575f3a9-ae80-4cac-b24f-7601a7e6dd5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_root_group(ds: xr.Dataset) -> xr.Dataset:\n",
+    "    # Add scan number and granule number as new dimensions\n",
+    "    return ds.expand_dims([\"scan_num\"]).assign_coords(scan_num=[ds.attrs[\"scan_num\"]])\n",
+    "\n",
+    "\n",
+    "open_options = {\n",
+    "    \"access\": \"indirect\",\n",
+    "    \"load\": True,\n",
+    "    \"preprocess\": preprocess_root_group,\n",
+    "    \"concat_dim\": \"scan_num\",\n",
+    "    \"coords\": \"all\",\n",
+    "    \"compat\": \"override\",\n",
+    "    \"combine_attrs\": \"drop_conflicts\",\n",
+    "    \"parallel\": False,\n",
+    "}\n",
+    "\n",
+    "result_root = earthaccess.open_virtual_mfdataset(granules=results, **open_options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2501687-24e6-41f6-98c3-97624bc66e95",
+   "metadata": {},
+   "source": [
+    "### Take the new `coords` from the root group, and apply it to the subgroups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f13c11a8-374c-4fe3-95ad-554c84b41e03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_group_scan_num_coords = result_root.coords[\"scan_num\"]\n",
+    "\n",
+    "# Define a modified preprocessing function for subgroups\n",
+    "# When iterating over datasets, a 'global' allows extracting the corresponding coords' value\n",
+    "global my_scan_counter\n",
+    "\n",
+    "\n",
+    "def add_scan_num_coords(ds: xr.Dataset) -> xr.Dataset:\n",
+    "    global my_scan_counter\n",
+    "\n",
+    "    this_ds_scan_num = root_group_scan_num_coords.values[my_scan_counter]\n",
+    "    my_scan_counter += 1\n",
+    "\n",
+    "    # Add scan number and granule number as dimensions\n",
+    "    return ds.expand_dims([\"scan_num\"]).assign_coords(scan_num=[this_ds_scan_num])\n",
+    "\n",
+    "\n",
+    "open_options[\"preprocess\"] = add_scan_num_coords\n",
+    "\n",
+    "# Open the subgroups\n",
+    "my_scan_counter = 0\n",
+    "result_product = earthaccess.open_virtual_mfdataset(\n",
+    "    granules=results, group=\"product\", **open_options\n",
+    ")\n",
+    "\n",
+    "my_scan_counter = 0\n",
+    "result_geolocation = earthaccess.open_virtual_mfdataset(\n",
+    "    granules=results, group=\"geolocation\", **open_options\n",
+    ")\n",
+    "\n",
+    "# --- 3. MERGE ROOT GROUPS WITH SUBGROUPS ---\n",
+    "result_merged = xr.merge([result_root, result_product, result_geolocation])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36220c25-cb60-474f-b7d6-17f1dc659669",
+   "metadata": {},
+   "source": [
+    "### Merge root groups with subgroups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f9a737d2-43f1-4bb8-aac9-24e0396935ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_merged = xr.merge([result_root, result_product, result_geolocation])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f3a70b05-6a4c-4056-ade0-eb06af55babe",
+   "metadata": {},
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+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vertical_column_stratosphere&#x27; (scan_num: 20,\n",
+       "                                                  mirror_step: 123, xtrack: 2048)&gt; Size: 40MB\n",
+       "dask.array&lt;open_dataset-vertical_column_stratosphere, shape=(20, 123, 2048), dtype=float64, chunksize=(1, 123, 2048), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * mirror_step  (mirror_step) float64 984B nan 1.0 2.0 ... 120.0 121.0 122.0\n",
+       "  * scan_num     (scan_num) float64 160B 1.0 1.0 1.0 1.0 1.0 ... 2.0 2.0 2.0 2.0\n",
+       "  * xtrack       (xtrack) float64 16kB nan 1.0 2.0 ... 2.046e+03 2.047e+03\n",
+       "    latitude     (scan_num, mirror_step, xtrack) float32 20MB dask.array&lt;chunksize=(1, 123, 2048), meta=np.ndarray&gt;\n",
+       "    longitude    (scan_num, mirror_step, xtrack) float32 20MB dask.array&lt;chunksize=(1, 123, 2048), meta=np.ndarray&gt;\n",
+       "    time         (scan_num, mirror_step) datetime64[ns] 20kB dask.array&lt;chunksize=(1, 123), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    long_name:  stratosphere nitrogen dioxide vertical column\n",
+       "    units:      molecules/cm^2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vertical_column_stratosphere'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>scan_num</span>: 20</li><li><span class='xr-has-index'>mirror_step</span>: 123</li><li><span class='xr-has-index'>xtrack</span>: 2048</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bc0b036c-e07c-4670-bffc-6c3a1abc5d87' class='xr-array-in' type='checkbox' checked><label for='section-bc0b036c-e07c-4670-bffc-6c3a1abc5d87' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 123, 2048), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 38.44 MiB </td>\n",
+       "                        <td> 1.92 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (20, 123, 2048) </td>\n",
+       "                        <td> (1, 123, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "  <text x=\"7.474299\" y=\"63.092834\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,63.092834)\">20</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-7cea525a-8d3a-4ccb-9fe9-f73e97f2e347' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7cea525a-8d3a-4ccb-9fe9-f73e97f2e347' class='xr-section-summary' >Coordinates: <span>(6)</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'>mirror_step</span></div><div class='xr-var-dims'>(mirror_step)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 1.0 2.0 ... 120.0 121.0 122.0</div><input id='attrs-5bb059ab-4289-4922-9aa8-de0268a187c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bb059ab-4289-4922-9aa8-de0268a187c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c16faf4-f805-4fa8-9a2f-e533314be1c3' class='xr-var-data-in' type='checkbox'><label for='data-6c16faf4-f805-4fa8-9a2f-e533314be1c3' 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>scan mirror position index</dd></dl></div><div class='xr-var-data'><pre>array([ nan,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
+       "        12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,\n",
+       "        24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,\n",
+       "        36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,\n",
+       "        48.,  49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,\n",
+       "        60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,\n",
+       "        72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,\n",
+       "        84.,  85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,\n",
+       "        96.,  97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107.,\n",
+       "       108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119.,\n",
+       "       120., 121., 122.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>scan_num</span></div><div class='xr-var-dims'>(scan_num)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 2.0 2.0 2.0 2.0</div><input id='attrs-5fe9f9de-ce4d-484f-8a8e-70b0ab290095' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5fe9f9de-ce4d-484f-8a8e-70b0ab290095' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c8d1d41-d692-4f84-9e31-6e864f4da442' class='xr-var-data-in' type='checkbox'><label for='data-7c8d1d41-d692-4f84-9e31-6e864f4da442' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2.,\n",
+       "       2., 2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xtrack</span></div><div class='xr-var-dims'>(xtrack)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 1.0 2.0 ... 2.046e+03 2.047e+03</div><input id='attrs-5f19c436-533a-4fb1-be06-29bac8a4dc51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f19c436-533a-4fb1-be06-29bac8a4dc51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e19dd557-7b36-4ab8-83dc-884d5d7c6dfa' class='xr-var-data-in' type='checkbox'><label for='data-e19dd557-7b36-4ab8-83dc-884d5d7c6dfa' 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>pixel index along slit</dd></dl></div><div class='xr-var-data'><pre>array([      nan, 1.000e+00, 2.000e+00, ..., 2.045e+03, 2.046e+03, 2.047e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(scan_num, mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 123, 2048), meta=np.ndarray&gt;</div><input id='attrs-8ae1cac4-8dd6-412e-82af-79c6af9005cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ae1cac4-8dd6-412e-82af-79c6af9005cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe051d67-c905-49d8-a78f-e6cabeff80b0' class='xr-var-data-in' type='checkbox'><label for='data-fe051d67-c905-49d8-a78f-e6cabeff80b0' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>pixel center latitude</dd><dt><span>comment :</span></dt><dd>latitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>bounds :</span></dt><dd>latitude_bounds</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 19.22 MiB </td>\n",
+       "                        <td> 0.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (20, 123, 2048) </td>\n",
+       "                        <td> (1, 123, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"50\" x2=\"144\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,50.56713259987395 24.9485979497544,50.56713259987395\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"70.567133\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"164.948598\" y=\"32.757865\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,32.757865)\">123</text>\n",
+       "  <text x=\"7.474299\" y=\"63.092834\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,63.092834)\">20</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(scan_num, mirror_step, xtrack)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 123, 2048), meta=np.ndarray&gt;</div><input id='attrs-406ae809-5d37-47f3-86b0-1c38d2183664' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-406ae809-5d37-47f3-86b0-1c38d2183664' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-028327f3-f6a8-4fcf-907e-fd04db2541a2' class='xr-var-data-in' type='checkbox'><label for='data-028327f3-f6a8-4fcf-907e-fd04db2541a2' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>pixel center longitude</dd><dt><span>comment :</span></dt><dd>longitude at pixel center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>bounds :</span></dt><dd>longitude_bounds</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 19.22 MiB </td>\n",
+       "                        <td> 0.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (20, 123, 2048) </td>\n",
+       "                        <td> (1, 123, 2048) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"100\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"50\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"70.567133\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"164.948598\" y=\"32.757865\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,32.757865)\">123</text>\n",
+       "  <text x=\"7.474299\" y=\"63.092834\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,63.092834)\">20</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(scan_num, mirror_step)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 123), meta=np.ndarray&gt;</div><input id='attrs-17af0732-7771-4f0a-9e05-7c26268fe624' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17af0732-7771-4f0a-9e05-7c26268fe624' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdaf943b-a2a3-4834-807f-f67cd9a5f161' class='xr-var-data-in' type='checkbox'><label for='data-bdaf943b-a2a3-4834-807f-f67cd9a5f161' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>radiance exposure start time</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10000000000.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 19.22 kiB </td>\n",
+       "                        <td> 0.96 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (20, 123) </td>\n",
+       "                        <td> (1, 123) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"91\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"41\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"61.089280\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >123</text>\n",
+       "  <text x=\"140.000000\" y=\"20.544640\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,20.544640)\">20</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-58eb62a1-d493-460b-9fc3-150e076c77ad' class='xr-section-summary-in' type='checkbox'  ><label for='section-58eb62a1-d493-460b-9fc3-150e076c77ad' 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>mirror_step</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8c6b8bba-d68d-4cca-a7a9-4dd1d423c816' class='xr-index-data-in' type='checkbox'/><label for='index-8c6b8bba-d68d-4cca-a7a9-4dd1d423c816' 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([  nan,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,\n",
+       "       ...\n",
+       "       113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;mirror_step&#x27;, length=123))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>scan_num</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f8ab61d3-a334-49a6-a020-07ebcf8116a9' class='xr-index-data-in' type='checkbox'/><label for='index-f8ab61d3-a334-49a6-a020-07ebcf8116a9' 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([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0,\n",
+       "       2.0, 2.0, 2.0, 2.0, 2.0, 2.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;scan_num&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xtrack</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f36fcf1a-1074-4a0a-8662-1cc1a95805d6' class='xr-index-data-in' type='checkbox'/><label for='index-f36fcf1a-1074-4a0a-8662-1cc1a95805d6' 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([   nan,    1.0,    2.0,    3.0,    4.0,    5.0,    6.0,    7.0,    8.0,\n",
+       "          9.0,\n",
+       "       ...\n",
+       "       2038.0, 2039.0, 2040.0, 2041.0, 2042.0, 2043.0, 2044.0, 2045.0, 2046.0,\n",
+       "       2047.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;xtrack&#x27;, length=2048))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e38efc4-e844-470d-854f-9070a620a7e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e38efc4-e844-470d-854f-9070a620a7e3' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>stratosphere nitrogen dioxide vertical column</dd><dt><span>units :</span></dt><dd>molecules/cm^2</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'vertical_column_stratosphere' (scan_num: 20,\n",
+       "                                                  mirror_step: 123, xtrack: 2048)> Size: 40MB\n",
+       "dask.array<open_dataset-vertical_column_stratosphere, shape=(20, 123, 2048), dtype=float64, chunksize=(1, 123, 2048), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * mirror_step  (mirror_step) float64 984B nan 1.0 2.0 ... 120.0 121.0 122.0\n",
+       "  * scan_num     (scan_num) float64 160B 1.0 1.0 1.0 1.0 1.0 ... 2.0 2.0 2.0 2.0\n",
+       "  * xtrack       (xtrack) float64 16kB nan 1.0 2.0 ... 2.046e+03 2.047e+03\n",
+       "    latitude     (scan_num, mirror_step, xtrack) float32 20MB dask.array<chunksize=(1, 123, 2048), meta=np.ndarray>\n",
+       "    longitude    (scan_num, mirror_step, xtrack) float32 20MB dask.array<chunksize=(1, 123, 2048), meta=np.ndarray>\n",
+       "    time         (scan_num, mirror_step) datetime64[ns] 20kB dask.array<chunksize=(1, 123), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    long_name:  stratosphere nitrogen dioxide vertical column\n",
+       "    units:      molecules/cm^2"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_merged[\"vertical_column_stratosphere\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c512085a-bb5d-47db-9a46-286eed939616",
+   "metadata": {},
+   "source": [
+    "## Visualize part of the opened datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8c259ab8-e493-4b3f-abd8-18b839d4480a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_variable = result_merged[\"vertical_column_stratosphere\"]\n",
+    "\n",
+    "x = result_merged[\"longitude\"].where(result_merged[\"scan_num\"] == 1)\n",
+    "y = result_merged[\"latitude\"].where(result_merged[\"scan_num\"] == 1)\n",
+    "z = z_variable.where(result_merged[\"scan_num\"] == 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "61f2e9e8-8ccd-4360-a430-169122a44143",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "scatter_handle = ax.scatter(x=x, y=y, s=5, c=z)\n",
+    "\n",
+    "clb = fig.colorbar(scatter_handle, orientation=\"horizontal\")\n",
+    "clb.ax.set_label(f\"{z_variable.attrs['long_name']}\\n({z_variable.attrs['units']})\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70ffa40b-6f9b-4632-8358-acdebb471140",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0844c171-6040-4e7a-88be-66d98b489b7f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ce33ebb-b042-4f0e-b71e-f5b5c2347f3f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d39ec1b-c685-4281-8699-44c33c0f5e3e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "682f6de8-f114-47bc-a4eb-87ad8b3f4dc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Opening a single granule\n",
+    "# open_options = {\n",
+    "#     \"access\": \"indirect\",\n",
+    "#     \"load\": True,\n",
+    "# }\n",
+    "\n",
+    "# result_root = earthaccess.open_virtual_dataset(results[0], **open_options)\n",
+    "# result_product = earthaccess.open_virtual_dataset(results[0], group=\"product\", **open_options)\n",
+    "# result_geolocation = earthaccess.open_virtual_dataset(results[0], group=\"geolocation\", **open_options)\n",
+    "\n",
+    "# result_merged = xr.merge([result_root, result_product, result_geolocation])\n",
+    "# result_merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ba3cb0f-4ee6-4980-8488-60a47a851f1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### As is, with mirror_step\n",
+    "# open_options = {\n",
+    "#     \"access\": \"indirect\",\n",
+    "#     \"load\": True,\n",
+    "#     \"concat_dim\": \"mirror_step\",\n",
+    "#     \"coords\": 'minimal',\n",
+    "#     \"compat\": 'override',\n",
+    "#     \"parallel\": False,\n",
+    "#     \"preprocess\": None,\n",
+    "# }\n",
+    "\n",
+    "# result_root = earthaccess.open_virtual_mfdataset(granules=results, **open_options)\n",
+    "# result_product = earthaccess.open_virtual_mfdataset(granules=results, group=\"product\", **open_options)\n",
+    "# result_geolocation = earthaccess.open_virtual_mfdataset(granules=results, group=\"geolocation\", **open_options)\n",
+    "\n",
+    "# result_merged = xr.merge([result_root, result_product, result_geolocation])\n",
+    "# result_merged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfb6dcf9-a0c6-4856-9cb2-7c51b4d9c012",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}