# Saving variables to embed (glue)#

The glue submodule allows you to store variables in the notebooks outputs, by keys, then reference those keys to embed the outputs inline of your text content.1

Changed in version 0.14.0: The glue roles and directives now only identify keys in the same notebook, by default. To glue keys from other notebooks, see Embedding outputs from other pages.

## Save variables in code cells#

You can use myst_nb.glue() to assign the output of a variable to a key of your choice. glue will store all of the information that is normally used to display that variable (ie, whatever happens when you display the variable by putting it at the end of a cell). Choose a key that you will remember, as you will use it later.

The following code glues a variable inside the notebook:

from myst_nb import glue
a = "my variable!"
glue("my_variable", a)

'my variable!'


You can then insert it into your text like so: ‘my variable!’.

That was accomplished with the following code: {glue}my_variable.

### Saving different variable types#

You can glue anything in your notebook and display it later with {glue}. Here we’ll show how to glue and paste numbers and images. We’ll simulate some data and run a simple bootstrap on it. We’ll hide most of this process below, to focus on the glueing part.

Hide code cell content
# Simulate some data and bootstrap the mean of the data
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

n_points = 10000
n_boots = 1000
mean, sd = (3, .2)
data = sd*np.random.randn(n_points) + mean
bootstrap_indices = np.random.randint(0, n_points, n_points*n_boots).reshape((n_boots, n_points))


In the cell below, data contains our data, and bootstrap_indices is a collection of sample indices in each bootstrap. Below we’ll calculate a few statistics of interest, and glue() them into the notebook.

# Calculate the mean of a bunch of random samples
means = data[bootstrap_indices].mean(0)
# Calcualte the 95% confidence interval for the mean
clo, chi = np.percentile(means, [2.5, 97.5])

# Store the values in our notebook
glue("boot_mean", means.mean())
glue("boot_clo", clo)
glue("boot_chi", chi)

2.9988595801383164

2.986883988262369

3.011129098089692


By default, glue will display the value of the variable you are gluing. This is useful for sanity-checking its value at glue-time. If you’d like to prevent display, use the display=False option. Note that below, we also overwrite the value of boot_chi (but using the same value):

glue("boot_chi_notdisplayed", chi, display=False)


You can also glue visualizations, such as matplotlib figures (here we use display=False to ensure that the figure isn’t plotted twice):

# Visualize the historgram with the intervals
fig, ax = plt.subplots()
ax.hist(means)
for ln in [clo, chi]:
ax.axvline(ln, ls='--', c='r')
ax.set_title("Bootstrap distribution and 95% CI")

# And a wider figure to show a timeseries
fig2, ax = plt.subplots(figsize=(6, 2))
ax.plot(np.sort(means), lw=3, c='r')
ax.set_axis_off()

glue("boot_fig", fig, display=False)
glue("sorted_means_fig", fig2, display=False)


The same can be done for DataFrames (or other table-like objects) as well.

bootstrap_subsets = data[bootstrap_indices][:3, :5].T
df = pd.DataFrame(bootstrap_subsets, columns=["first", "second", "third"])
glue("df_tbl", df)

first second third
0 3.007859 2.654367 3.070595
1 2.731413 2.820367 3.138878
2 3.094030 2.767409 2.728241
3 2.793381 2.904026 2.945650
4 2.868091 2.944348 3.453647

Tip

Since we are going to paste this figure into our document at a later point, you may wish to remove the output here, using the remove-output tag (see Remove parts of cells).

## Embedding variables in the page#

Once you have glued variables into a notebook, you can then paste those variables into your text in your book anywhere you like (even on other pages). These variables can be pasted using one of the roles or directives in the glue: family.

### The glue role/directive#

The simplest role and directive are glue (a.k.a. glue:any), which paste the glued output inline or as a block respectively, with no additional formatting. Simply add:

{glue} your-key



For example, we’ll paste the plot we generated above with the following text:

{glue} boot_fig



Here’s how it looks:

Or we can paste inline objects like so:

Inline text; {glue}boot_mean, and figure; {glue}boot_fig.


Inline text; 2.9988595801383164, and figure; .

Tip

We recommend using wider, shorter figures when plotting in-line, with a ratio around 6x2. For example, here’s is an in-line figure of sorted means from our bootstrap: . It can be used to make a visual point that isn’t too complex! For more ideas, check out how sparklines are used.

Next we’ll cover some more specific pasting functionality, which gives you more control over how the outputs look in your pages.

## Controlling the output format#

You can control the pasted outputs by using a sub-command of {glue}. These are called like so: {glue:subcommand}key. These subcommands allow you to control more of the look, feel, and content of the pasted output.

Tip

When you use {glue} you are actually using a short-hand for {glue:any}. This is a generic command that doesn’t make many assumptions about what you are gluing.

### The glue:text role#

The glue:text role, is specific to text/plain outputs. For example, the following text:

The mean of the bootstrapped distribution was {glue:text}boot_mean (95% confidence interval {glue:text}boot_clo/{glue:text}boot_chi).


Is rendered as:

The mean of the bootstrapped distribution was 2.9988595801383164 (95% confidence interval 2.986883988262369/3.011129098089692)

Note

glue:text only works with glued variables that contain a text/plain output.

With glue:text we can add formatting to the output, by specifying a format spec string after a :: {glue:text}mykey:<format_spec>

The <format_spec> should be a valid Python format specifier.

This is particularly useful if you are displaying numbers and want to round the results. For example, the following: My rounded mean: {glue:text}boot_mean:.2f  will be rendered like this:

My rounded mean: 3.00 (95% CI: 2.99/3.01).

### The glue:figure directive#

With glue:figure you can apply more formatting to figure like objects, such as giving them a caption and referenceable label:

glue:figure directive options#

Option

Type

Description

alt

text

Alternate text of an image

height

length

The desired height of an image

width

length or percentage

The width of an image

scale

percentage

The uniform scaling factor of an image

class

text

A space-separated list of class names for the image

figwidth

length or percentage

The width of the figure

figclass

text

A space-separated list of class names for the figure

align

text

left, center, or right

name

text

referenceable label for the figure

{glue:figure} boot_fig
:alt: "Alternative title"
:figwidth: 300px
:name: "fig-boot"

This is a **caption**, with an embedded {glue:text} element: {glue:text}boot_mean:.2f!


Here is a {ref}reference to the figure <fig-boot>


Here is a reference to the figure

Here’s a table:

{glue:figure} df_tbl
:figwidth: 300px
:name: "tbl:df"

A caption for a pandas table.



### The glue:math directive#

The glue:math directive, is specific to latex math outputs (glued variables that contain a text/latex mimetype), and works similarly to the sphinx math directive.

glue:math directive options#

Option

Type

Description

nowrap

flag

Prevent any wrapping of the given math in a math environment

class

text

A space-separated list of class names

label or name

text

referenceable label for the figure

import sympy as sym
f = sym.Function('f')
y = sym.Function('y')
n = sym.symbols(r'\alpha')
f = y(n)-2*y(n-1/sym.pi)-5*y(n-2)
glue("sym_eq", sym.rsolve(f,y(n),[1,4]))

$\displaystyle \left(\sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} - \frac{2 \sqrt{5} i}{5}\right) + \left(- \sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} + \frac{2 \sqrt{5} i}{5}\right)$
Insert the equation here:

{glue:math} sym_eq
:label: eq-sym


Which we reference as Equation {eq}eq-sym


Insert the equation here:

(1)#$\displaystyle \left(\sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} - \frac{2 \sqrt{5} i}{5}\right) + \left(- \sqrt{5} i\right)^{\alpha} \left(\frac{1}{2} + \frac{2 \sqrt{5} i}{5}\right)$

Which we reference as Equation (1).

Note

glue:math only works with glued variables that contain a text/latex output.

### The glue:md role/directive#

With glue:md, you can output text/markdown, that will be integrated into your page.

from IPython.display import Markdown
glue("inline_md", Markdown(
"inline **markdown** with a [link](glue/main), "
"and a nested glue value: {glue}boot_mean"
), display=False)
glue("block_md", Markdown("""

Then some text, and anything nested.

python
print("Hello world!")

"""
), display=False)


The format of the markdown can be specified as:

For example, the following role/directive will glue inline/block MyST Markdown, as if it was part of the original document.

Here is some {glue:md}inline_md:myst!

{glue:md} block_md
:format: myst



Here is some inline markdown with a link, and a nested glue value: 2.9988595801383164!

Then some text, and anything nested.

print("Hello world!")


## Embedding outputs from other pages#

Certain glue roles and directives can be used to paste content from other notebooks, by specifying the (relative) path to them.

Tip

Sometimes you’d like to use variables from notebooks that are not meant to be shown to users. In this case, you should bundle the notebook with the rest of your content pages, but include orphan: true in the metadata of the notebook.

For example, the following example pastes glue variables from An orphaned notebook:

- A cross-pasted any role: {glue}orphaned_nb.ipynb::var_text
- A cross-pasted text role: {glue:text}orphaned_nb.ipynb::var_float:.2E

A cross-pasted any directive:

{glue} var_text
:doc: orphaned_nb.ipynb


• A cross-pasted any role: 'My orphaned variable!'

• A cross-pasted text role: 3.33E-01

A cross-pasted any directive:

'My orphaned variable!'


Here are a few more specific and advanced uses of the glue submodule.

### Embedding into tables#

In addition to pasting blocks of outputs, or in-line with text, you can also paste directly into tables. This allows you to compose complex collections of structured data using outputs that were generated in other notebooks. For example the following table:

| name                            |       plot                  | mean                      | ci                                                 |
|:-------------------------------:|:---------------------------:|---------------------------|----------------------------------------------------|
| histogram and raw text          | {glue}boot_fig           | {glue}boot_mean        | {glue}boot_clo-{glue}boot_chi                |
| sorted means and formatted text | {glue}sorted_means_fig   | {glue:text}boot_mean:.3f| {glue:text}boot_clo:.3f-{glue:text}boot_chi:.3f|


Results in:

name

plot

mean

ci

histogram and raw text

2.9988595801383164

2.986883988262369-3.011129098089692

sorted means and formatted text

2.999

2.987-3.011

1

This notebook can be downloaded as glue.ipynb and glue.md