From 3bae826525003393732f39507183d37860708699 Mon Sep 17 00:00:00 2001
From: Julia Kent <46687291+jukent@users.noreply.github.com>
Date: Wed, 15 May 2024 08:22:06 -0600
Subject: [PATCH] change admonitions
---
core/xarray/computation-masking.ipynb | 15 ++++++---------
core/xarray/dask-arrays-xarray.ipynb | 8 +++-----
2 files changed, 9 insertions(+), 14 deletions(-)
diff --git a/core/xarray/computation-masking.ipynb b/core/xarray/computation-masking.ipynb
index 3e3c29f5b..c4cc28751 100644
--- a/core/xarray/computation-masking.ipynb
+++ b/core/xarray/computation-masking.ipynb
@@ -335,11 +335,10 @@
"id": "5d176ad8-15f1-4ecc-ab3e-898cef3b4e18",
"metadata": {},
"source": [
- "\n",
+ "```{admonition} Info\n",
+ ":class: tip\n",
"\n",
- "In the above code example, we are extracting components of date/time data by way of the time coordinate's `.dt` attribute. This attribute is a `DatetimeAccessor` object that contains additional attributes for units of time, such as hour, day, and year. Since we are splitting the data into monthly data, we use the `month` attribute of .dt in this example. (In addition, there exists similar functionality in Pandas; see the [official documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.month.html) for details.)\n",
- " \n",
- "
"
+ "In the above code example, we are extracting components of date/time data by way of the time coordinate's `.dt` attribute. This attribute is a `DatetimeAccessor` object that contains additional attributes for units of time, such as hour, day, and year. Since we are splitting the data into monthly data, we use the `month` attribute of .dt in this example. (In addition, there exists similar functionality in Pandas; see the [official documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.month.html) for details.)"
]
},
{
@@ -505,12 +504,10 @@
"id": "d9f768be-a960-4417-bb1e-9785ca9ca4ea",
"metadata": {},
"source": [
- "\n",
- " \n",
+ "```{admonition} Info\n",
+ ":class: warning\n",
"\n",
- "Many geoscientific algorithms perform operations over data contained in many different grid cells. However, if the grid cells are not equivalent in size, the operation is not scientifically valid by default. Fortunately, this can be fixed by weighting the data in each grid cell by the size of the cell. Weighting data in Xarray is simple, as Xarray has a built-in weighting method, known as [`.weighted()`](https://xarray.pydata.org/en/stable/user-guide/computation.html#weighted-array-reductions).\n",
- "\n",
- "
\n"
+ "Many geoscientific algorithms perform operations over data contained in many different grid cells. However, if the grid cells are not equivalent in size, the operation is not scientifically valid by default. Fortunately, this can be fixed by weighting the data in each grid cell by the size of the cell. Weighting data in Xarray is simple, as Xarray has a built-in weighting method, known as [`.weighted()`](https://xarray.pydata.org/en/stable/user-guide/computation.html#weighted-array-reductions).\n"
]
},
{
diff --git a/core/xarray/dask-arrays-xarray.ipynb b/core/xarray/dask-arrays-xarray.ipynb
index 4f8e4996f..02ee0dc4b 100644
--- a/core/xarray/dask-arrays-xarray.ipynb
+++ b/core/xarray/dask-arrays-xarray.ipynb
@@ -167,12 +167,10 @@
"id": "ebbafe88-bb79-436c-aa3b-a9c5f31ff1ec",
"metadata": {},
"source": [
- "\n",
- "
Specifying Chunks
\n",
- " In this tutorial, we specify Dask Array chunks in a block shape. However, there are many additional ways to specify chunks; see
this documentation for more details.\n",
+ "```{admonition} Specifying Chunks\n",
+ ":class: tip\n",
"\n",
- "