Bokeh allows interactive update of their plots in a notebook env. Note that you will have to run this example in your own jupyter notebook, If you want to share a real-time bokeh plot to friends, you will have to work with a web framework (as bottle) and a bokeh server (see https://bokeh.pydata.org/en/latest/docs/user_guide/embed.html#server-data). You can also use only a bokeh server and serve a python file as a bokeh app.

This document will only focus on real time stream in a jupyter notebook environment. The example will be the continuous temporal evolution of a population.

The temporal evolution is described by a simple ordinary differential equation called continuous-time model of logistic growth:

\begin{equation} \frac{\partial N}{\partial t} = -r\,N\left(1 - \frac{N}{K}\right) \end{equation}

We will use the dedicated scipy sub-library for the resolution and Bokeh for real-time plotting.