Bump to Jupyter Book v1.0 (#270)

* jbook1, drop sphinx-exercise * update exercise format * pin numpy<2
xarray-contrib · Jun 24, 2024 · da26b73 · da26b73
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diff --git a/.prettierignore b/.prettierignore
@@ -1 +1 @@
-conda/**
+conda/
diff --git a/_config.yml b/_config.yml
@@ -68,7 +68,6 @@ sphinx:
     # maintain old paths and redirect them (so google results dont go to 404)
     # https://github.com/wpilibsuite/sphinxext-rediraffe
     - sphinxext.rediraffe
-    - sphinx_exercise
   config:
     language: en # accessibility
     # application/vnd.holoviews_load.v0+json, application/vnd.holoviews_exec.v0+json

diff --git a/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb b/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb
@@ -63,18 +63,17 @@
     "    out[index, :] = np.interp(..., array[index, :], ...)\n",
     "```\n",
     "\n",
-    "\n",
-    "```{exercise}\n",
-    ":label: coreloopdims\n",
-    "\n",
+    "::::{admonition} Exercise\n",
+    ":class: tip\n",
     "Consider the example problem of interpolating a 2D array with dimensions `space` and `time` along the `time` dimension.\n",
     "Which dimension is the core dimension, and which is the \"loop dimension\"?\n",
-    "```\n",
-    "```{solution} coreloopdims\n",
+    "\n",
+    ":::{admonition} Solution\n",
     ":class: dropdown\n",
     "\n",
     "`time` is the core dimension, and `space` is the loop dimension.\n",
-    "```\n",
+    ":::\n",
+    "::::\n",
     "\n",
     "## Vectorization\n",
     "\n",

diff --git a/advanced/apply_ufunc/complex-output-numpy.ipynb b/advanced/apply_ufunc/complex-output-numpy.ipynb
@@ -138,19 +138,20 @@
     "tags": []
    },
    "source": [
-    "```{exercise}\n",
-    ":label: newdim\n",
+    "::::{admonition} Exercise\n",
+    ":class: tip\n",
     "\n",
     "Apply the following function using `apply_ufunc`. It adds a new dimension to the input array, let's call it `newdim`. Specify the new dimension using `output_core_dims`. Do you need any `input_core_dims`?\n",
     "\n",
     "```python\n",
     "def add_new_dim(array):\n",
     "    return np.expand_dims(array, axis=-1)\n",
     "```\n",
-    "````{solution} newdim\n",
+    "\n",
+    ":::{admonition} Solution\n",
     ":class: dropdown\n",
     "\n",
-    "``` python\n",
+    "```python\n",
     "def add_new_dim(array):\n",
     "    return np.expand_dims(array, axis=-1)\n",
     "\n",
@@ -161,7 +162,8 @@
     "    output_core_dims=[[\"newdim\"]],\n",
     ")\n",
     "```\n",
-    "````"
+    ":::\n",
+    "::::"
    ]
   },
   {
@@ -327,8 +329,8 @@
     "tags": []
    },
    "source": [
-    "````{exercise}\n",
-    ":label: generalize\n",
+    "::::{admonition} Exercise\n",
+    ":class: tip\n",
     "\n",
     "We presented the concept of \"core dimensions\" as the \"smallest unit of data the function could handle.\" Do you understand how the above use of `apply_ufunc` generalizes to an array with more than one dimension? \n",
     "\n",
@@ -337,9 +339,8 @@
     "air3d = xr.tutorial.load_dataset(\"air_temperature\").air)\n",
     "``` \n",
     "Your goal is to have a minimum and maximum value of temperature across all latitudes for a given time and longitude.\n",
-    "````\n",
     "\n",
-    "````{solution} generalize\n",
+    ":::{admonition} Solution\n",
     ":class: dropdown\n",
     "\n",
     "We want to use `minmax` to compute the minimum and maximum along the \"lat\" dimension always, regardless of how many dimensions are on the input. So we specify `input_core_dims=[[\"lat\"]]`. The output does not contain the \"lat\" dimension, but we expect two returned variables. So we pass an empty list `[]` for each returned array, so `output_core_dims=[[], []]` just as before.\n",
@@ -352,8 +353,8 @@
     "    input_core_dims=[[\"lat\"]],\n",
     "    output_core_dims=[[],[]],\n",
     ")\n",
-    "```\n",
-    "````"
+    ":::\n",
+    "::::"
    ]
   }
  ],

diff --git a/advanced/apply_ufunc/core-dimensions.ipynb b/advanced/apply_ufunc/core-dimensions.ipynb
@@ -335,13 +335,12 @@
     "tags": []
    },
    "source": [
-    "```{exercise}\n",
-    ":label: trapezoid\n",
+    "::::{admonition} Exercise\n",
+    ":class: tip\n",
     "\n",
     "Use `apply_ufunc` to apply `scipy.integrate.trapezoid` along the `time` axis.\n",
-    "```\n",
     "\n",
-    "````{solution} trapezoid\n",
+    ":::{admonition} Solution\n",
     ":class: dropdown\n",
     "\n",
     "```python\n",
@@ -350,7 +349,8 @@
     "\n",
     "xr.apply_ufunc(scipy.integrate.trapezoid, ds, input_core_dims=[[\"time\"]], kwargs={\"axis\": -1})\n",
     "```\n",
-    "````"
+    ":::\n",
+    "::::"
    ]
   }
  ],

diff --git a/advanced/apply_ufunc/dask_apply_ufunc.ipynb b/advanced/apply_ufunc/dask_apply_ufunc.ipynb
@@ -267,15 +267,15 @@
     "Such functions involve the concept of \"core dimensions\". This concept is independent of the underlying array type, and is a property of the applied function. See the [core dimensions with NumPy](core-dimensions) tutorial for more.\n",
     "\n",
     "\n",
-    "```{exercise}\n",
-    ":label: daskmean\n",
+    "::::{admonition} Exercise\n",
+    ":class: tip\n",
     "\n",
     "Use `dask.array.mean` as an example of a function that can handle dask\n",
     "arrays and uses an `axis` kwarg. \n",
-    "```\n",
     "\n",
-    "````{solution} daskmean\n",
+    ":::{admonition} Solution\n",
     ":class: dropdown\n",
+    "\n",
     "```python\n",
     "def time_mean(da):\n",
     "    return xr.apply_ufunc(\n",
@@ -285,10 +285,11 @@
     "        dask=\"allowed\",\n",
     "        kwargs={\"axis\": -1},  # core dimensions are moved to the end\n",
     "    )\n",
-    "\n",
-    "\n",
+    "    \n",
     "time_mean(ds.air)\n",
-    "````\n"
+    "```\n",
+    ":::\n",
+    "::::\n"
    ]
   },
   {
@@ -493,12 +494,11 @@
     "tags": []
    },
    "source": [
-    "```{exercise} \n",
-    ":label: rechunk\n",
+    "::::{admonition} Exercise\n",
+    ":class: tip\n",
     "Apply the integrate function to `ds` after rechunking to have a different chunksize along `lon` using `ds.chunk(lon=4)` (for example). What happens?\n",
-    "```\n",
     "\n",
-    "```{solution} rechunk\n",
+    ":::{admonition} Solution\n",
     ":class: dropdown\n",
     "\n",
     "`apply_ufunc` complains that it cannot automatically parallelize because the dataset `ds` is now chunked along the core dimension `lon`. You should see the following error:\n",
@@ -509,7 +509,8 @@
     "    ``.chunk(dict(lon=-1))``, or pass ``allow_rechunk=True`` in ``dask_gufunc_kwargs`` \n",
     "    but beware that this may significantly increase memory usage.\n",
     "\n",
-    "```"
+    ":::\n",
+    "::::"
    ]
   },
   {
@@ -652,7 +653,7 @@
    "source": [
     "### Adding new dimensions\n",
     "\n",
-    "We use the [expand_dims example](newdim) that changes the size of the input along a single dimension.\n",
+    "We use the `np.expand_dims` to change the size of the input along a single dimension.\n",
     "\n",
     "```python\n",
     "def add_new_dim(array):\n",

diff --git a/advanced/apply_ufunc/numba-vectorization.ipynb b/advanced/apply_ufunc/numba-vectorization.ipynb
@@ -7,9 +7,9 @@
     "tags": []
    },
    "source": [
-    "<img src=\"https://numba.pydata.org/_static/numba-blue-horizontal-rgb.svg\" width=\"40%\" align=\"right\">\n",
+    "# Fast vectorization with Numba\n",
     "\n",
-    "# Fast vectorization with Numba"
+    "<img src=\"https://numba.pydata.org/_static/numba-blue-horizontal-rgb.svg\" width=\"40%\" align=\"right\">"
    ]
   },
   {
@@ -241,12 +241,12 @@
    "id": "18",
    "metadata": {},
    "source": [
-    "```{exercise}\n",
-    ":label: g\n",
+    "::::{admonition} Exercise\n",
+    ":class: tip\n",
     "\n",
     "Apply `g` to `da_dask`\n",
-    "```\n",
-    "````{solution} g\n",
+    "\n",
+    ":::{admonition} Solution\n",
     ":class: dropdown\n",
     "\n",
     "```python\n",
@@ -259,7 +259,8 @@
     "    dask=\"parallelized\",\n",
     ")\n",
     "```\n",
-    "````"
+    ":::\n",
+    "::::"
    ]
   },
   {