jupyter-ui-poll 0.1.0

Block jupyter cell execution while interacting with widgets

Block Jupyter cell execution while interacting with widgets.

This library is for people familiar with ipywidgets who want to solve the following problem:

1. Display User Interface in Jupyter [1] using ipywidgets [2] or similar

2. Wait for data to be entered (this step is surprisingly non-trivial to implement)

3. Use entered data in cells below

You want to implement a notebook like the one below

# cell 1
ui = make_ui()
display(ui)
data = ui.wait_for_data()

# cell 2
do_things_with(data)

# cell 3.
do_more_tings()

And you want to be able to execute Cells -> Run All menu option and still get correct output.

This library assists in implementing your custom ui.wait_for_data() poll loop. If you have tried implementing such workflow in the past you’ll know that it is not that simple. If you haven’t, see Technical Details section below for an explanation why it’s hard and how jupyter-ui-poll solves it.

Quick, self contained example:

import time
from ipywidgets import Button
from jupyter_ui_poll import ui_events

# Set up simple GUI, button with on_click callback
# that sets ui_done=True and changes button text
ui_done = False
def on_click(btn):
    global ui_done
    ui_done = True
    btn.description = '👍'

btn = Button(description='Click Me')
btn.on_click(on_click)
display(btn)

# Wait for user to press the button
with ui_events() as poll:
    while ui_done is False:
        poll(10)                # React to UI events (upto 10 at a time)
        print('.', end='')
        time.sleep(0.1)
print('done')

For a more detailed tutorial see Example notebook

Installation

This library requires Python 3.5 or greater

pip install jupyter-ui-poll

Technical Details

Jupyter widgets (ipywidgets) provide an excellent foundation to develop interactive data investigation apps directly inside Jupyter notebook or Jupyter lab environment. Jupyter is great at displaying data and ipywidgets provide a mechanism to get input from the user in a more convenient way than entering or changing Python code inside a Jupyter cell. Developer can construct an interactive user interface often used to parameterise information display or other kinds of computation.

Interactivity is handled with callbacks, ipywidget GUI is HTML based, user actions, like clicking a button, trigger JavaScript events that are then translated in to calls to Python code developer registered with the library. It is a significantly different, asynchronous, paradigm then your basic Jupyter notebook which operates in a straightforward blocking, linear fashion. It is not possible to display a Modal UI that would block execution of other Jupyter cells until needed information is supplied by the user.

jupyter-ui-poll allows one to implement a “blocking GUI” inside a Jupyter environment. It is a common requirement to query user for some non-trivial input parameters that are easier to enter via GUI rather than code. User input happens at the top of the notebook, then that data is used in cells below. While this is possible to achieve directly with ipywidgets it requires teaching the user to enter all the needed data before moving on to execute the cells below. This is bound to cause some confusion and also breaks Cells -> Run All functionality.

An obvious solution is to keep running in a loop until all the needed data was entered by the user.

display(app.make_ui())
while not app.have_all_the_data():
    time.sleep(0.1)

A naive version of the code above does not work. This is because no widget events are being processed while executing code inside a Jupyter cell. Callbacks you have registered with the widget library won’t get a chance to run and so state of app.have_all_the_data() won’t ever change. “Execute code inside Jupyter cell” is just another event being processed by the IPython kernel, and only one event is executed at a time. One could ask IPython kernel to process more events by calling kernel.do_one_iteration() in the poll loop. This kinda works, callbacks will be called as input is entered, but IPython will also process “execute cell” events, so Cells -> Run All scenario will still be broken, as code in lower cells will be executed before the data it operates on becomes available.

This library hooks into IPython internal machinery to selectively execute events in a polling fashion, delaying code cell execution events until after interactive part is over.

Basic idea was copied from ipython_blocking [3] project:

1. Overwrite execute_request handler in IPython kernel temporarily

2. Call kernel.do_one_iteration() in a polling fashion until exit conditions are met

3. Reinstate default handler for execute_request

4. Replay code cell execution events cached by custom handler taking care of where output goes, and being careful about exception handling

[1]

https://jupyter.org/

[2]

https://github.com/jupyter-widgets/ipywidgets

[3]

https://github.com/kafonek/ipython_blocking