notebooks | motivating example | installing | widgets | voila

ubc-jupyterdays-2020-widgets

Here, we will use a motivating example that looks at CO2 data to demonstrate interactive computing with:

Jupyter Widgets and
Voilà dashboards

The example we follow is adapted from the Intro-Jupyter tutorial from ICESat-2Hackweek, which has contributions from: Shane Grigsby (@espg), Lindsey Heagy (@lheagy), Yara Mohajerani (@yaramohajerani), and Fernando Pérez (@fperez).

Notebooks

UBC Students & Faculty can run on Syzygy:
or if you don't have a UBC CWL with full Syzygy access (a "guest" account will not be sufficient) you can use:

There are three notebooks in this repository

1-widgets-an-example.ipynb: A demo notebook that makes use of widgets to explore atmospheric CO2 data and linear models
2-voila.ipynb: A notebook based on the same CO2 data that is meant to be run as a voila dashboard
3-more-on-widgets.ipynb: A notebook with more details on widgets

Motivating Example: Atmospheric Carbon Dioxide

The climate emergency is one of the most pressing issues of our time. At this pivotal moment, the decisions and actions we take today will reverberate beyond our own borders and lifetimes. -- UBC Climate Emergency Engagement

Carbon Dioxide (CO2) is a greenhouse gas, and significant contributor to the warming of the climate. It is an important input into climate models, which we use to make predictions about possible future climate scenarios, and sometimes to make policy decisions, including carbon taxes.

Question: Based on historical CO2 data, can we estimate what CO2 concentrations will be in the future?

Learning Goals

explore the idea of "learning": building a model from data that lets us make predictions
introduce the principles of linear regression, a simple, but powerful method for estimating a linear model of data
explore the impact of outliers in data on the model
discuss the limitations of models

Context

Scripps institute of Oceanography has a research station at Mauna Loa in Hawaii where they have been measuring atmospheric CO2 since 1958. The data we will focus on are the seasonally adjusted data.

Data Source

C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and oceans from 1978 to 2000. I. Global aspects, SIO Reference Series, No. 01-06, Scripps Institution of Oceanography, San Diego, 88 pages, 2001. https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html

Installing

To run the notebooks locally, you will need to have python installed, preferably through anaconda. Please download Python 3.7 or greater.

Once you have downloaded and installed anaconda, you can then clone this repository. From a command line (if you are on windows, please use the anaconda terminal that came with the installation) run

git clone https://github.com/lheagy/ubc-jupyterdays-2020-widgets.git

Then cd into the ubc-jupyterdays-2020-widgets directory:

cd ubc-jupyterdays-2020-widgets

To setup your software environment, we recommend you use the provided conda environment

conda env create -f environment.yml
conda activate ubc-demo

You can then launch Jupyter

jupyter notebook

Jupyter will then launch in your web-browser.

Jupyter widgets

Jupyter widgets allow you to connect interactive controls such as slide bars and toggle buttons to your code.

They can be used to create "notebook apps" that abstract away details of the code and focus conversations on the role of parameters in a computation, or be a quick way to create a "Just-in-time-research-GUI" (GUI: graphical user interface) to explore results.

Getting started

Widgets can be installed with pip

pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension

or conda (which also enables the extension)

conda install -c conda-forge ipywidgets

To enable the Jupyter Lab extension

jupyter labextension install @jupyter-widgets/jupyterlab-manager

For your first interaction with widgets, I recommend trying out interact. The interact function ipywidgets.interact is a quick way to create user interface controls on your functions. At the most basic level, interact autogenerates controls for function arguments, and then calls the function with those arguments when you manipulate the controls interactively. To use interact, you need to define a function that you want to explore.

Voilà Dashboards

Voilà turns Jupyter notebooks into standalone web applications.

Unlike the usual HTML-converted notebooks, each user connecting to the Voilà tornado application gets a dedicated Jupyter kernel which can execute the callbacks to changes in Jupyter interactive widgets.

The intro blog provides an overview and resources for getting up and running with Voilà
The Voilà gallery has a collection of examples built with Voilà and Jupyter widgets.

If you are running on binder, you can use the Voilà button!

Getting Started

Voilà can be installed using conda

conda install -c conda-forge voila

or from PyPI

pip install voila

Once Voilà has been installed,

you can run it as a standalone server
```
voila notebook.ipynb
```
or as a server extention by changing the url to <url-of-my-server>/voila (e.g. if you launched jupyter lab locally, and it was running at http://localhost:8888/lab, then then Voilà would be accessed at http://localhost:8888/voila.
there is also a JupyterLab extention for previewing your dashboard
```
jupyter labextension install @jupyter-voila/jupyterlab-preview
```
by default, Voilà will strip out the source code from view. It can be displayed if the option strip_sources is set to False

Acknowledgements

Many thanks to Abby Azari and Fernando Pérez for taking time to provide feedback on these notebooks!

lheagy / ubc-jupyterdays-2020-widgets