add a zarr sample + vsizip

workshop/jupyter/content/data/README.md

@@ -17,6 +17,8 @@ and sometimes also GeoJSON.
 NB "Antarctica" has been removed from the "countries" datasets as it gave
 problems with reprojections...
 
-The poi-timis.geojson dataset has been downloaded from geofabrik.de, then filtered
-on certain types and bounds. Data provided by Open Street Map Editors under the
+`sample_geozarr.zarr.zip` has been produced in the scope of this training using numpy with random numbers. 
+
+`poi-timis.geojson` has been downloaded from geofabrik.de, then filtered
+on certain types and bounds. Data provided by OpenStreetMap editors under the
 Open Database License (ODbL) 1.0.

workshop/jupyter/content/notebooks/05-raster-data.ipynb

@@ -92,6 +92,27 @@
     "r = plt.imshow(ds.GetRasterBand(1).ReadAsArray())\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GDAL supports a wide variety of input format, and can also combine it with virtual filesystems, such as within a zipfile"
+    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from osgeo import gdal\n",
+    "import matplotlib.pyplot as plt\n",
+    "# Notice how the zarr file is referenced within the zip file\n",
+    "raster_path = '/vsizip/../data/sample_geozarr.zarr.zip/sample_geozarr.zarr'\n",
+    "dataset = gdal.Open(raster_path)\n",
+    "r = plt.imshow(ds.GetRasterBand(1).ReadAsArray())\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},