Migrate from nbsphinx/jupyter-sphinx to myst_nb

doc/conf.py

@@ -54,8 +54,6 @@
     "sphinx.ext.mathjax",
     "sphinx.ext.napoleon",
     "jupyter_sphinx",
-    "myst_parser",
-    "nbsphinx",
     "sphinx_autosummary_accessors",
     "sphinx.ext.linkcode",
     "sphinxext.opengraph",
@@ -65,6 +63,7 @@
     "sphinx_inline_tabs",
     "sphinx_remove_toctrees",
     "sphinx_llm.txt",
+    "myst_nb",
 ]
 
 
@@ -78,29 +77,7 @@
 copybutton_prompt_text = r">>> |\.\.\. |\$ |In \[\d*\]: | {2,5}\.{3,}: | {5,8}: "
 copybutton_prompt_is_regexp = True
 
-# NBSphinx configuration
-nbsphinx_timeout = 600
-nbsphinx_execute = "always"
-nbsphinx_allow_errors = False
-nbsphinx_requirejs_path = ""
-#  png2x/retina rendering of figues in docs would also need to modify custom.css:
-# https://github.com/spatialaudio/nbsphinx/issues/464#issuecomment-652729126
-#  .rst-content .image-reference img {
-#   max-width: unset;
-#   width: 100% !important;
-#   height: auto !important;
-#  }
-# nbsphinx_execute_arguments = [
-#     "--InlineBackend.figure_formats=['png2x']",
-# ]
-nbsphinx_prolog = """
-{% set docname = env.doc2path(env.docname, base=None) %}
-
-You can run this notebook in a `live session <https://mybinder.org/v2/gh/pydata/xarray/doc/examples/main?urlpath=lab/tree/doc/{{ docname }}>`_ |Binder| or view it `on Github <https://github.com/pydata/xarray/blob/main/doc/{{ docname }}>`_.
-
-.. |Binder| image:: https://mybinder.org/badge.svg
-   :target: https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/{{ docname }}
-"""
+nb_execution_excludepatterns = ["examples/apply_ufunc_vectorize_1d.ipynb"]
 
 # AutoDoc configuration
 autosummary_generate = True

doc/examples/ROMS_ocean_model.ipynb

@@ -64,7 +64,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Load in ROMS dataset as an xarray object"
+    "## Load in ROMS dataset as an xarray object"
    ]
   },
   {
@@ -85,7 +85,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Add a lazilly calculated vertical coordinates\n",
+    "## Add a lazilly calculated vertical coordinates\n",
     "\n",
     "Write equations to calculate the vertical coordinate. These will be only evaluated when data is requested. Information about the ROMS vertical coordinate can be found [here](https://www.myroms.org/wiki/Vertical_S-coordinate).\n",
     "\n",
@@ -119,17 +119,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### A naive vertical slice\n",
+    "## A naive vertical slice\n",
     "\n",
     "Creating a slice using the s-coordinate as the vertical dimension is typically not very informative."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ds.salt.isel(xi_rho=50, ocean_time=0).plot()"
@@ -157,7 +155,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### A plan view\n",
+    "## A plan view\n",
     "\n",
     "Now make a naive plan view, without any projection information, just using lon/lat as x/y. This looks OK, but will appear compressed because lon and lat do not have an aspect constrained by the projection."
    ]
@@ -206,8 +204,11 @@
   }
  ],
  "metadata": {
+  "execution": {
+   "timeout": 60
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -221,9 +222,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.14.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

doc/examples/apply_ufunc_vectorize_1d.ipynb

@@ -2,9 +2,16 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
-    "## Applying unvectorized functions with `apply_ufunc`"
+    "# Applying unvectorized functions with `apply_ufunc`"
    ]
   },
   {
@@ -26,7 +33,7 @@
     "\n",
     "    xr_interp(xarray_object, dimension_name, new_coordinate_to_interpolate_to)\n",
     "\n",
-    "### Load data\n",
+    "## Load data\n",
     "\n",
     "First let's load an example dataset"
    ]
@@ -110,7 +117,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### No errors are raised so our interpolation is working.\n",
+    "## No errors are raised so our interpolation is working.\n",
     "\n",
     "This function consumes and returns numpy arrays, which means we need to do a lot of work to convert the result back to an xarray object with meaningful metadata. This is where `apply_ufunc` is very useful.\n",
     "\n",
@@ -155,7 +162,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### `exclude_dims`"
+    "## `exclude_dims`"
    ]
   },
   {
@@ -197,7 +204,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Core dimensions\n",
+    "## Core dimensions\n",
     "\n"
    ]
   },
@@ -326,7 +333,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Vectorization with `np.vectorize`"
+    "## Vectorization with `np.vectorize`"
    ]
   },
   {
@@ -484,7 +491,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Parallelization with dask\n",
+    "## Parallelization with dask\n",
     "\n"
    ]
   },
@@ -547,7 +554,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### High performance vectorization: gufuncs, numba & guvectorize\n",
+    "## High performance vectorization: gufuncs, numba & guvectorize\n",
     "\n"
    ]
   },
@@ -714,9 +721,6 @@
    "pygments_lexer": "ipython3",
    "version": "3.8.10"
   },
-  "nbsphinx": {
-   "allow_errors": true
-  },
   "org": null,
   "toc": {
    "base_numbering": 1,

doc/examples/area_weighted_temperature.ipynb

@@ -47,9 +47,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Data\n",
-    "\n",
-    "Load the data, convert to celsius, and resample to daily values"
+    "## Data\n\nLoad the data, convert to celsius, and resample to daily values"
    ]
   },
   {
@@ -104,9 +102,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating weights\n",
-    "\n",
-    "For a rectangular grid the cosine of the latitude is proportional to the grid cell area."
+    "## Creating weights\n\nFor a rectangular grid the cosine of the latitude is proportional to the grid cell area."
    ]
   },
   {
@@ -129,7 +125,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Weighted mean"
+    "## Weighted mean"
    ]
   },
   {
@@ -166,9 +162,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Plot: comparison with unweighted mean\n",
-    "\n",
-    "Note how the weighted mean temperature is higher than the unweighted."
+    "## Plot: comparison with unweighted mean\n\nNote how the weighted mean temperature is higher than the unweighted."
    ]
   },
   {

doc/examples/monthly-means.ipynb

@@ -36,7 +36,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Open the `Dataset`"
+    "## Open the `Dataset`"
    ]
   },
   {
@@ -58,7 +58,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Now for the heavy lifting:\n",
+    "## Now for the heavy lifting:\n",
     "We first have to come up with the weights,\n",
     "- calculate the month length for each monthly data record\n",
     "- calculate weights using `groupby('time.season')`\n",