add minor notes

tutorials/Earthdata_Subset_and_Plot.ipynb

@@ -158,12 +158,12 @@
     "date_range = (date_start, date_end)\n",
     "bbox = (-127.0761, 31.6444, -113.9039, 42.6310)\n",
     "\n",
-    "# For reference, here is a GeoJSON representing the above bounding box:\n",
+    "# For reference (e.g., to visualize in https://geojson.io/), here is a GeoJSON representing the above bounding box:\n",
     "# {\"type\": \"FeatureCollection\", \"features\": [{\"type\": \"Feature\", \"properties\": {}, \"geometry\": {\"type\": \"LineString\", \"bbox\": [-127.0761, 31.6444, -113.9039, 42.631], \"coordinates\": [[-113.9039, 42.631], [-127.0761,42.631], [-127.0761, 31.6444], [-113.9039, 31.6444], [-113.9039, 42.631]]}}]}\n",
     "\n",
     "results = earthaccess.search_data(\n",
     "    concept_id = collection_id,\n",
-    "    #cloud_hosted = True,\n",
+    "    cloud_hosted = True,\n",
     "    temporal = date_range,\n",
     "    bounding_box = bbox,\n",
     ")\n",
@@ -277,6 +277,19 @@
     "We start with a subset that represents the U.S. state of California.  Notice the dimensions of the Dataset and each variable."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29355579-79f2-4ebf-b7d7-5a5c60a59d2e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Display the full dataset's metadata\n",
+    "ds"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -286,6 +299,7 @@
    },
    "outputs": [],
    "source": [
+    "# Prepare a subset and display the new dataset's metadata\n",
     "ds_subset = ds.sel(time='2023-02-24', lat=slice(31, 43), lon=slice(-125, -113)) \n",
     "ds_subset"
    ]
@@ -297,7 +311,9 @@
    "source": [
     "### Plotting\n",
     "\n",
-    "We will first plot using the methods built-in to the `xarray` package."
+    "We will first plot using the methods built-in to the `xarray` package.\n",
+    "\n",
+    "Note that, as opposed to the \"lazy\" loading of metadata previously, this will now perform \"eager\" computation, pulling the required data chunks."
    ]
   },
   {
@@ -883,6 +899,14 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "777d0a2b-9746-42b0-9c65-56076663da1c",
+   "metadata": {},
+   "source": [
+    "A final note about `harmony-py`: As more transformation services are added and configured to work with existing and new NASA data collections, the capabilities you will be able to harness with `harmony-py` will also grow!"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4b0f811e-19fd-4269-b9e9-4b97baef1f70",
@@ -1057,12 +1081,12 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "38a3fd72-b963-4ad3-a4b2-5124fdd60360",
+   "cell_type": "markdown",
+   "id": "a44f1dfd-9bc2-4086-affe-260b3232642b",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "END of Notebook."
+   ]
   }
  ],
  "metadata": {