fixes

dcherian · dcherian · commit e7a82a063249 · 2023-06-23T12:38:09.000-06:00
diff --git a/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb b/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb
@@ -14,7 +14,8 @@
    "cell_type": "markdown",
    "id": "afc56d28-6e55-4967-b27d-28e2cc539cc7",
    "metadata": {
-    "tags": []
+    "tags": [],
+    "user_expressions": []
    },
    "source": [
     "Previously we looked at applying functions on numpy arrays, and the concept of core dimensions.\n",
diff --git a/advanced/apply_ufunc/simple_dask_apply_ufunc.ipynb b/advanced/apply_ufunc/simple_dask_apply_ufunc.ipynb
@@ -498,15 +498,16 @@
     "The core dimension for `trapz` is `lon`, and there is only one chunk along `lon`. This means that integrating along `lon` is a \"blockwise\" or \"embarrassingly parallel\" operation and `dask=\"parallelized\"` works quite well. \n",
     "\n",
     "```{tip} Question\n",
-    "Do you understand why `integrate(ds)` when `ds` has a single chunk along `lon` is a \"embarassingly parallel\" operation?\n",
+    "Do you understand why `integrate(ds)` when `ds` has a single chunk along `lon` is a \"embarrassingly parallel\" operation?\n",
     "```"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "4b4707d4-f596-47a8-8a3b-6a4a157f0759",
    "metadata": {
-    "tags": []
+    "tags": [],
+    "user_expressions": []
    },
    "source": [
     "```{tip} Exercise\n",
