GitLocker: The Coding Marketplace

Description:

leda 0.5.0

leda
Generate static HTML reports with interactive widgets from Jupyter notebooks

Installation
leda is available on PyPI:
pip install leda

Quick Start
Generation
To generate a static HTML report from a Jupyter notebook, run:
python -m leda /path/to/nb.ipynb --output-dir ./outputs/

# Optional args:
python -m leda /path/to/nb.ipynb --output-dir ./outputs/ \
-i "abc = 123" -k "other_kernel" --cell-timeout 100

This generation automatically includes tweaks to the notebook to make
the output look more report-like (e.g., hiding all input code)
See the static demos
being served by GitHub Pages.
leda is like:

voila,
but static, with no need for live kernels
nbconvert/
nbviewer, but with interactive widgets
quarto,
but with interactive widgets
papermill,
but with interactive widgets
pretty-jupyter,
but with interactive widgets

The -i (--inject) arg is used to inject user code (and set report params)
via a new cell prepended to the notebook during generation.
And the --template_name/--theme args allow you to choose between
classic, lab (light/dark), and lab_narrow (light/dark).
Note: leda assumes that all code is run in a trusted environment,
so please be careful.
Interaction/Widgets
leda also provides an %%interact magic
that makes it easy to create outputs based on widgets that work in both dynamic
and static modes, e.g.:
# In[ ]:

import leda
import numpy as np
import pandas as pd

# In[ ]:

leda.init("matplotlib") # Loads `interact` magic when running in Jupyter notebook

# In[ ]:

%%interact column=list("abcdefghij");mult=[1, 2, 3]
df = pd.DataFrame(
np.random.RandomState(42).rand(100, 10), columns=list("abcdefghij")
)
title = f"column={column!r}, mult={mult}"
(df[[column]] * mult).plot(figsize=(15, 8), lw=2, title=title)

There are two types of interact modes: dynamic and static.
Dynamic mode is when you're running the Jupyter notebook
live, in which case you will re-compute the cell output
every time you select a different mult. We always use
ipywidgets as the
dynamic widget backend.
In a static mode (using whichever static widget backend is configured),
the library will pre-compute all possible combinations of widget outputs
(see Cartesian product)
and then render a static HTML report that contains widgets
that look and feel like the dynamic widgets (despite being pre-rendered).
See below for a list of supported static backends.
Report Web UI Server
Unlike voila,
because all report output is static HTML,
you can stand up a report web UI server that suits your needs very easily.
That means:

It's trivial to set up in many cases.
It's as scalable as your web server's ability to distribute static content.
It's more cost-efficient because there are no runtimes whatsoever.
You don't have to worry about old versions no longer working
due to code or data changes, so the historical
archive of old reports never expires or changes or breaks.

For example, you can generate the report to a file,
upload that file to a shared location, and then stand
up a bare-bones nginx server to serve the files.
Instead of having a two-step process of generation + upload,
you could alternatively implement your own leda.ReportPublisher
and create a generation script of your own--or use it as a library
in client script.
Another example is you can simply host a static S3 bucket,
enable website hosting and then either use S3 as a web server
publically or via locked down S3 endpoint.
You could also use GitHub Pages,
much like the static demos page.
Params
Reports can be parametrized so that the user can set
different values for each report run.
In the notebook, just use leda.get_param():
# In[ ]:

import leda

# In[ ]:

data_id = leda.get_param("data_id", dynamic_default=1, static_default=2)

And then change the injected code during each run:
python -m leda /path/to/nb.ipynb --output ./outputs/ -i "data_id = 100"

Modular
leda is built to work with multiple visualization and widget libraries.
It works with these visualization libraries:

matplotlib
plotly

With the default dynamic widget library:

ipywidgets

And with these static widget libraries:

static_ipywidgets
(vendored and modified)
panel

Testing
See the requirements-bundle*.txt for version bundles
that we currently test systematically.
Known Issues

There are multiple issues using matplotlib with panel, including:

The last widget output is not different from the penultimate one: https://github.com/holoviz/panel/issues/1222
All the widget outputs show up sequentially,
instead of being hidden until chosen.
This seems to be a known issue per the panel FAQ;
however, using the example fix provided does not work.