update notebooks based on review feedback

Author: mo-jeff

datasets/met-office/met-office-global-deterministic-height.ipynb

@@ -13,7 +13,9 @@
    "id": "941120d0",
    "metadata": {},
    "source": [
-    "Set-up the pystac client to access the Microsoft Planetary Computer catalog"
+    "This example notebook provides a walkthrough accessing the [Met Office UK Near Surface collection](https://planetarycomputer.microsoft.com/dataset/met-office-uk-deterministic-near-surface) on Microsoft Planetary Computer. This notebook outputs an image of UK surface temperatures across a forecast period.\n",
+    "\n",
+    "First, import required libraries and set-up the pystac client to access the Planetary Computer STAC API."
    ]
   },
   {
@@ -23,8 +25,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import fsspec\n",
+    "import matplotlib.pyplot as plt\n",
     "from pystac_client import Client\n",
     "import planetary_computer\n",
+    "import xarray as xr\n",
     "\n",
     "catalog = Client.open(\n",
     "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
@@ -42,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "8f95ecac",
    "metadata": {},
    "outputs": [],
@@ -56,10 +61,11 @@
     "            \"op\": \"=\",\n",
     "            \"args\": [\n",
     "                {\"property\": \"forecast:reference_datetime\"},\n",
-    "                \"2026-01-21T09:00:00Z\",\n",
+    "                \"2026-01-30T09:00:00Z\",\n",
     "            ],\n",
     "        },\n",
     "        {\"op\": \"=\", \"args\": [{\"property\": \"forecast:horizon\"}, \"PT0054H00M\"]},\n",
+    "        {\"op\": \"=\", \"args\": [{\"property\": \"forecast:variable\"}, \"surface_temperature\"]},\n",
     "    ],\n",
     "}"
    ]
@@ -77,35 +83,25 @@
    "execution_count": null,
    "id": "edb71afa",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "StopIteration",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mStopIteration\u001b[39m                             Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      1\u001b[39m search = catalog.search(\n\u001b[32m      2\u001b[39m     collections=collections,\n\u001b[32m      3\u001b[39m     datetime=\u001b[33m\"\u001b[39m\u001b[33m2026-01-21T09:00:00Z\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      4\u001b[39m )\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m asset_url = \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msearch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mitem\u001b[49m\u001b[43m.\u001b[49m\u001b[43mproperties\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mforecast:horizon\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mPT0054H00M\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m.assets[asset_id].href\n",
-      "\u001b[31mStopIteration\u001b[39m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "search = catalog.search(\n",
-    "    collections=collections,\n",
-    "    filter_lang=\"cql2-json\",\n",
-    "    filter=forecast_extension_filters,\n",
+    "    collections=collections, filter_lang=\"cql2-json\", filter=forecast_extension_filters\n",
     ")\n",
     "\n",
-    "asset_url = next(search.items()).assets[asset_id].href"
+    "items = search.item_collection()\n",
+    "print (f\"Item Dictionary - {items.items[0].assets}\")\n",
+    "\n",
+    "asset_url = items.items[0].assets[asset_id].href\n",
+    "print(f\"URL for specific NetCDF - {asset_url}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "56d27e19",
    "metadata": {},
    "source": [
-    "Example usage: Plot NetCDF data on a map"
+    "Example usage: Open and inspect NetCDF data"
    ]
   },
   {
@@ -115,19 +111,33 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import fsspec\n",
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open())\n",
+    "example_netcdf = xr.open_dataset(fsspec.open(asset_url, expand=True).open(), decode_timedelta=True)\n",
+    "example_netcdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffa6be66",
+   "metadata": {},
+   "source": [
+    "Plot surface temperatures on a map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "820f3c35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "plt.figure(figsize=(10, 5))\n",
     "example_netcdf[\"surface_temperature\"].plot()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "PlanetaryComputerExamples",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -141,7 +151,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.2"
+   "version": "3.13.11"
   }
  },
  "nbformat": 4,