leda 0.4.0

Generate static reports from Jupyter notebooks

leda

Generate static reports with interactive widgets from Jupyter notebooks

Quick Start

Generate a static HTML report from a Jupyter notebook:

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 will automatically include formatting tweaks, including, e.g., hiding all input code.

See the static demos being served by GitHub Pages.

Think of it like:

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

-i (--inject) arg is used to inject user code (and set report params) via a new cell prepended to the notebook during generation. And --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 provides an %%interact magic that makes it easy to create outputs based on widgets, like:

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

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.

In a static mode (using whichever static widget backend is configured), the library will pre-compute all possible combinations of widget states (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).

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.
• 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 expire or change or break.

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 of generation + upload, you could alternatively implement your own leda.gen.base.ReportPublisher and create a generation script of your own).

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():

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.

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.