From 3c04fd38d8574aa3473cc36acdab37982097b895 Mon Sep 17 00:00:00 2001 From: Chris Holdgraf Date: Tue, 9 Apr 2024 16:41:53 -0700 Subject: [PATCH] =?UTF-8?q?=F0=9F=93=96=20=20Improve=20accessible=20figure?= =?UTF-8?q?s=20with=20Jupyter=20docs=20(#1077)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Improve accessible figures with Jupyter docs * Clarify figure for a11y --- docs/cross-references.md | 2 +- docs/embed.md | 2 +- docs/figures.md | 2 +- docs/interactive-notebooks.ipynb | 839 ++++++++++++++++--------------- docs/reuse-jupyter-outputs.md | 76 ++- 5 files changed, 505 insertions(+), 416 deletions(-) diff --git a/docs/cross-references.md b/docs/cross-references.md index b81b6fbda..2d5228cb7 100644 --- a/docs/cross-references.md +++ b/docs/cross-references.md @@ -207,7 +207,7 @@ See more about reusing Jupyter outputs in figures, adding placeholders, and othe The following example embeds a figure from [](./interactive-notebooks.ipynb), and can be used in cross references [](#fig-altair-horsepower). -```{figure} #altair-horsepower +```{figure} #img:altair-horsepower :label: fig-altair-horsepower This figure has been included from [](./interactive-notebooks.ipynb) and can be referred to in cross-references through a different label. ``` diff --git a/docs/embed.md b/docs/embed.md index 020c467f5..349d38f84 100644 --- a/docs/embed.md +++ b/docs/embed.md @@ -93,7 +93,7 @@ Here's a nice sunset with a caption! The new label can be referred to in this context, i.e. `[@sunset-figure]`: [@sunset-figure], which refers to the new figure rather than the original image. This allows you to scroll to embedded content on the page, rather than jumping to the original document. Note that this is especially useful with [embedding Jupyter Notebook outputs](./reuse-jupyter-outputs.md). For example: -```{figure} #altair-horsepower +```{figure} #img:altair-horsepower This figure has been included from a Jupyter Notebook and can be referred to in cross-references through a different label. See [](./reuse-jupyter-outputs.md) for more information. ``` diff --git a/docs/figures.md b/docs/figures.md index 9468d90c5..5815b8f78 100644 --- a/docs/figures.md +++ b/docs/figures.md @@ -1,6 +1,6 @@ --- title: Images, Figures and Videos -short_title: Images & Videos +short_title: Images, Figures, & Videos description: MyST Markdown allows you to create images and figures in your documents, including cross-referencing content throughout your pages. thumbnail: ./thumbnails/figures.png --- diff --git a/docs/interactive-notebooks.ipynb b/docs/interactive-notebooks.ipynb index 094c522fe..1f87e650f 100644 --- a/docs/interactive-notebooks.ipynb +++ b/docs/interactive-notebooks.ipynb @@ -1,426 +1,459 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "---\n", - "title: Interactive Notebooks with MyST\n", - "description: MyST allows you to include interactive visualizations directly in your projects using Jupyter Notebooks.\n", - "thumbnail: ./thumbnails/interactive-notebooks.png\n", - "---" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "MyST allows you to directly include Jupyter Notebooks in your books, documents and websites. This page of the documentation is actually a Jupyter Notebook that is rendered directly using MyST.\n", - "\n", - "For example, let us import `altair` and create a demo of an interactive plot!" - ] + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-stderr", - "remove-stdout" - ] - }, - "outputs": [], - "source": [ - "import altair as alt\n", - "from vega_datasets import data\n", - "\n", - "source = data.cars()\n", - "brush = alt.selection_interval(encodings=['x'])\n", - "points = alt.Chart(source).mark_point().encode(\n", - " x='Horsepower:Q',\n", - " y='Miles_per_Gallon:Q',\n", - " size='Acceleration',\n", - " color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))\n", - ").add_params(brush)\n", - "\n", - "bars = alt.Chart(source).mark_bar().encode(\n", - " y='Origin:N',\n", - " color='Origin:N',\n", - " x='count(Origin):Q'\n", - ").transform_filter(brush)" - ] + "tags": [] + }, + "source": [ + "---\n", + "title: Interactive Notebooks with MyST\n", + "description: MyST allows you to include interactive visualizations directly in your projects using Jupyter Notebooks.\n", + "thumbnail: ./thumbnails/interactive-notebooks.png\n", + "---" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "We can now plot the `altair` example, which is fully interactive, try dragging in the plot to select cars by their horsepower." - ] + "tags": [] + }, + "source": [ + "MyST allows you to directly include Jupyter Notebooks in your books, documents and websites. This page of the documentation is actually a Jupyter Notebook that is rendered directly using MyST.\n", + "\n", + "For example, let us import `altair` and create a demo of an interactive plot!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "editable": true, - "scrolled": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-stderr" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.VConcatChart(...)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#| label: altair-horsepower\n", - "points & bars" - ] + "tags": [ + "remove-stderr", + "remove-stdout" + ] + }, + "outputs": [], + "source": [ + "import altair as alt\n", + "from vega_datasets import data\n", + "\n", + "source = data.cars()\n", + "brush = alt.selection_interval(encodings=['x'])\n", + "points = alt.Chart(source).mark_point().encode(\n", + " x='Horsepower:Q',\n", + " y='Miles_per_Gallon:Q',\n", + " size='Acceleration',\n", + " color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))\n", + ").add_params(brush)\n", + "\n", + "bars = alt.Chart(source).mark_bar().encode(\n", + " y='Origin:N',\n", + " color='Origin:N',\n", + " x='count(Origin):Q'\n", + ").transform_filter(brush)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, - { - "cell_type": "markdown", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "This works for non-image outputs as well.\n", - "For example, below we'll **output a Table via a Pandas DataFrame**.\n", - "We'll show the contents of a dataset loaded above, along with syntax to [label the cell in order to be embedded later](reuse-jupyter-outputs.md)." - ] + "tags": [] + }, + "source": [ + "We can now plot the `altair` example, which is fully interactive, try dragging in the plot to select cars by their horsepower." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" }, + "tags": [ + "remove-stderr" + ] + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameMiles_per_GallonCylindersDisplacementHorsepower
0chevrolet chevelle malibu18.08307.0130.0
1buick skylark 32015.08350.0165.0
2plymouth satellite18.08318.0150.0
3amc rebel sst16.08304.0150.0
4ford torino17.08302.0140.0
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" - ], - "text/plain": [ - " Name Miles_per_Gallon Cylinders Displacement \\\n", - "0 chevrolet chevelle malibu 18.0 8 307.0 \n", - "1 buick skylark 320 15.0 8 350.0 \n", - "2 plymouth satellite 18.0 8 318.0 \n", - "3 amc rebel sst 16.0 8 304.0 \n", - "4 ford torino 17.0 8 302.0 \n", - "\n", - " Horsepower \n", - "0 130.0 \n", - "1 165.0 \n", - "2 150.0 \n", - "3 150.0 \n", - "4 140.0 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" ], - "source": [ - "#| label: data-cars\n", - "# Take a subset of cars so it displays nicely\n", - "data.cars().iloc[:5, :5]" + "text/plain": [ + "alt.VConcatChart(...)" ] - }, + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| label: img:altair-horsepower\n", + "points & bars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Non-interactive images are embedded as PNGs:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "And here we demonstrate a text-based output." + "data": { + "image/png": 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" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#| label: img:mpl\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(\"Horsepower\", \"Miles_per_Gallon\",\n", + " c=\"Acceleration\", data=data.cars())\n", + "_ = ax.set(xlabel=\"Horsepower\", ylabel=\"Miles per gallon\",\n", + " title=\"Horsepower and miles per gallon\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "This works for non-image outputs as well.\n", + "For example, below we'll **output a Table via a Pandas DataFrame**.\n", + "We'll show the contents of a dataset loaded above, along with syntax to [label the cell in order to be embedded later](reuse-jupyter-outputs.md)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, + "tags": [] + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The Zen of Python, by Tim Peters\n", - "\n", - "Beautiful is better than ugly.\n", - "Explicit is better than implicit.\n", - "Simple is better than complex.\n", - "Complex is better than complicated.\n", - "Flat is better than nested.\n", - "Sparse is better than dense.\n", - "Readability counts.\n", - "Special cases aren't special enough to break the rules.\n", - "Although practicality beats purity.\n", - "Errors should never pass silently.\n", - "Unless explicitly silenced.\n", - "In the face of ambiguity, refuse the temptation to guess.\n", - "There should be one-- and preferably only one --obvious way to do it.\n", - "Although that way may not be obvious at first unless you're Dutch.\n", - "Now is better than never.\n", - "Although never is often better than *right* now.\n", - "If the implementation is hard to explain, it's a bad idea.\n", - "If the implementation is easy to explain, it may be a good idea.\n", - "Namespaces are one honking great idea -- let's do more of those!\n" - ] - } + "data": { + "text/html": [ + "
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NameMiles_per_GallonCylindersDisplacementHorsepower
0chevrolet chevelle malibu18.08307.0130.0
1buick skylark 32015.08350.0165.0
2plymouth satellite18.08318.0150.0
3amc rebel sst16.08304.0150.0
4ford torino17.08302.0140.0
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" ], - "source": [ - "#| label:zen\n", - "import this" + "text/plain": [ + " Name Miles_per_Gallon Cylinders Displacement \\\n", + "0 chevrolet chevelle malibu 18.0 8 307.0 \n", + "1 buick skylark 320 15.0 8 350.0 \n", + "2 plymouth satellite 18.0 8 318.0 \n", + "3 amc rebel sst 16.0 8 304.0 \n", + "4 ford torino 17.0 8 302.0 \n", + "\n", + " Horsepower \n", + "0 130.0 \n", + "1 165.0 \n", + "2 150.0 \n", + "3 150.0 \n", + "4 140.0 " ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| label: tbl:data-cars\n", + "# Take a subset of cars so it displays nicely\n", + "data.cars().iloc[:5, :5]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, - { - "cell_type": "markdown", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Include notebooks in your MyST site\n", - "\n", - "If you are working with Jupyter `*.ipynb` files, just move your notebooks into the project folder or list them in your table of contents to get them to show up in your website or as a document. `myst` will then include your notebook in parsing, and show the full results as soon as you save your notebook, including any interactive figures.\n", - "\n", - "To customize the title and other frontmatter, ensure the first Jupyter Notebook cell is a markdown cell, and only includes a `YAML` frontmatter block (i.e. surrounded in `---`)." - ] + "tags": [] + }, + "source": [ + "And here we demonstrate a text-based output." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, + "tags": [] + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## MyST in Jupyter User Interfaces\n", - "\n", - "If you'd like to write and read MyST Markdown in Jupyter interfaces, check out the [JupyterLab MyST Extension](./quickstart-jupyter-lab-myst.md).\n", - "It allows for rich rendering of MyST markdown, frontmatter, and cross-references directly in JupyterLab." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + ], + "source": [ + "#| label:zen\n", + "import this" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" + "tags": [] + }, + "source": [ + "## Include notebooks in your MyST site\n", + "\n", + "If you are working with Jupyter `*.ipynb` files, just move your notebooks into the project folder or list them in your table of contents to get them to show up in your website or as a document. `myst` will then include your notebook in parsing, and show the full results as soon as you save your notebook, including any interactive figures.\n", + "\n", + "To customize the title and other frontmatter, ensure the first Jupyter Notebook cell is a markdown cell, and only includes a `YAML` frontmatter block (i.e. surrounded in `---`)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, - "vscode": { - "interpreter": { - "hash": "d7b89e158b719c02a21186c9646700ecf5a8cc5b1b6f738df9b6ffa75e5e74e4" - } - } + "tags": [] + }, + "source": [ + "## MyST in Jupyter User Interfaces\n", + "\n", + "If you'd like to write and read MyST Markdown in Jupyter interfaces, check out the [JupyterLab MyST Extension](./quickstart-jupyter-lab-myst.md).\n", + "It allows for rich rendering of MyST markdown, frontmatter, and cross-references directly in JupyterLab." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" }, - "nbformat": 4, - "nbformat_minor": 4 + "vscode": { + "interpreter": { + "hash": "d7b89e158b719c02a21186c9646700ecf5a8cc5b1b6f738df9b6ffa75e5e74e4" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/docs/reuse-jupyter-outputs.md b/docs/reuse-jupyter-outputs.md index 6646ae96c..d053a002a 100644 --- a/docs/reuse-jupyter-outputs.md +++ b/docs/reuse-jupyter-outputs.md @@ -60,7 +60,7 @@ Any labeled Jupyter cell can be referred to using the standard [cross-reference] ``` The cross-referenced cell will be shown in a hover-preview and link to the notebook cell directly. -For example, [here we cross-reference a cell from the Jupyter Notebooks examples](#data-cars) +For example, [here we cross-reference a cell from the Jupyter Notebooks examples](#tbl:data-cars) ## Embed a cell output @@ -68,12 +68,41 @@ Once a cell is labeled, you can embed its output with the standard [syntax for e For example, the following code embeds a labeled cell defined in [](interactive-notebooks.ipynb): ```md -![](#data-cars) +![](#tbl:data-cars) ``` It results in the following: -![](#data-cars) +![](#tbl:data-cars) + +:::{tip} Define one output per cell +Embedding works best when you generate a single output per cell. +This allows you to attach a label to one object instead of multiple, and generally makes life easier. +If you'd like a single cell to generate multiple outputs for embedding, save each of them to a variable, and then use subsequent cells to display them as outputs. +For example: + +```{code-block} python +:filename: Cell 1 +# Generate two matplotlib figures +fig1, ax1 = plt.subplots() +fig2, ax2 = plt.subplots() +``` + +```{code-block} python +:filename: Cell 2 +#| label: fig:plot1 +# Display the figure so that its output is labeled with `fig:plot1` +fig1 +``` + +```{code-block} python +:filename: Cell 3 +#| label: fig:plot2 +# Display the figure so that its output is labeled with `fig:plot2` +fig2 +``` + +::: ## Embed the entire cell with the `{embed}` directive @@ -84,14 +113,14 @@ For example, to embed **both the cell input and output**, use syntax like: ```` % Embed both the input and output -```{embed} #data-cars +```{embed} #tbl:data-cars :remove-output: false :remove-input: false ``` ```` % Embed both the input and output -```{embed} #data-cars +```{embed} #tbl:data-cars :remove-output: false :remove-input: false ``` @@ -111,7 +140,7 @@ Below we give the figure a new `name` as well, so that we can cross-reference it The following example embeds a figure from [](./interactive-notebooks.ipynb). -```{figure} #altair-horsepower +```{figure} #img:altair-horsepower This figure has been included from [](./interactive-notebooks.ipynb) and can be referred to in cross-references through a different label. ``` @@ -141,6 +170,33 @@ print('hello world') In this case, the placeholder will replace _any_ output from the cell in static exports; outputs will only show up in interactive environments. +### Alternative text for accessibility + +Adding alternative text to images allows you to provide context for the image for readers with assistive technologies, or unreliable internet connections. +By default, Jupyter does not support alternative text for image outputs, but you can use MyST to add alternative text with the `{figure}` directive. +See [](figures.md) for more details. + + +Using the `{figure}` directive allows you to set one or more captions for your figures, which serve accessibility purposes as well. +This works for both static outputs (like Matplotlib) as well as interactive ones (like Altair). +For example, the following `{figure}` directive embeds two cell outputs with captions: + +```` +```{figure} + +![A matplotlib image of the cars data](#img:mpl) + +![An Altair visualization of the cars data!](#img:altair-horsepower) +``` +```` + +```{figure} + +![A matplotlib image of the cars data](#img:mpl) + +![An Altair visualization of the cars data!](#img:altair-horsepower) +``` + ## Outputs as Tables You can wrap tabular outputs (e.g. Pandas DataFrames) with a `{table}` directive in order to assign a caption and include it with your figures. @@ -155,7 +211,7 @@ For example: ```` :::{table} This is my table :label: mytable -![](#data-cars) +![](#tbl:data-cars) ::: ```` @@ -163,7 +219,7 @@ Results in: :::{table} This is my table :label: mytable -![](#data-cars) +![](#tbl:data-cars) ::: ### Use the `{figure}` directive with `kind: table` @@ -172,7 +228,7 @@ This defines a figure but allows the content to be a table. For example, the following syntax: ```` -:::{figure} #data-cars +:::{figure} #tbl:data-cars :label: myothertable :kind: table This is my table caption! @@ -181,7 +237,7 @@ This is my table caption! Results in: -:::{figure} #data-cars +:::{figure} #tbl:data-cars :label: myothertable :kind: table This is my table caption!