xarray-contrib
diff --git a/‎_sources/intermediate/indexing/advanced-indexing.ipynb
Lines changed: 13 additions & 30 deletions b/‎_sources/intermediate/indexing/advanced-indexing.ipynb
Lines changed: 13 additions & 30 deletions
diff --git a/‎_sources/workshops/scipy2023/README.md
Lines changed: 9 additions & 1 deletion b/‎_sources/workshops/scipy2023/README.md
Lines changed: 9 additions & 1 deletion
@@ -17,7 +17,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing), Datetime Indexing, and nearest neighbor lookups. In this tutorial, we will lean how Xarray indexing is different from Numpy and how to do vectorized/pointwise indexing using Xarray. \n",
+    "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing), Datetime Indexing, and nearest neighbor lookups. In this tutorial, we will learn how Xarray indexing is different from Numpy and how to do vectorized/pointwise indexing using Xarray. \n",
     "First, let's import packages needed for this repository: "
    ]
   },
@@ -107,6 +107,18 @@
     "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\")  # -- orthogonal indexing"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n",
+    "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n",
+    "\n",
+    "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\")  # -- orthogonal indexing"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -229,35 +241,6 @@
     "```"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Analogously, label-based pointwise-indexing is also possible by the `.sel()` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "da = xr.DataArray(\n",
-    "    np.random.rand(4, 3),\n",
-    "    [\n",
-    "        (\"time\", pd.date_range(\"2000-01-01\", periods=4)),\n",
-    "        (\"space\", [\"IA\", \"IL\", \"IN\"]),\n",
-    "    ],\n",
-    ")\n",
-    "times = xr.DataArray(pd.to_datetime([\"2000-01-03\", \"2000-01-02\", \"2000-01-01\"]), dims=\"new_time\")\n",
-    "\n",
-    "\n",
-    "# -- get data for each state and each time:\n",
-    "da.sel(space=xr.DataArray([\"IA\", \"IL\", \"IN\"], dims=[\"new_time\"]), time=times)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
 
@@ -18,7 +18,11 @@ Organized by:
 
 For the live tutorial, we will be using the SciPy 2023 Nebari JupyterHub at [scipy.quansight.dev](https://scipy.quansight.dev/)
 
-You can follow [this participants' guide to register and sign-in to Nebari](https://docs.google.com/document/d/1vnWhNyUBRpILb2MAHQfTmZQY3pCIaCmroV9ke49nQlE/edit), and:
+You can follow [this participants' guide to register and sign-in to Nebari](https://docs.google.com/document/d/1vnWhNyUBRpILb2MAHQfTmZQY3pCIaCmroV9ke49nQlE/edit).
+
+**NOTE: When you're in `Server Options` page, select the `Medium Instance for xarray` for the machine instance for this tutorial.**
+
+Once you see the Jupyterlab interface spun up, do the following:
 
 
@@ -63,7 +67,11 @@ Once your codespace is launched, the following happens:
 ```
 
 ```{dropdown} Indexing
+{doc}`../../fundamentals/02.1_indexing_Basic`
+
 {doc}`../../intermediate/indexing/advanced-indexing`
+
+{doc}`../../intermediate/indexing/boolean-masking-indexing`
 ```
 
 ```{dropdown} Computational Patterns