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Comment by pwang | Hacker News Reader

Bokeh (http://bokeh.pydata.org) has excellent IPython notebook support, and provides interactive, live plots inside the notebook.

It also has matplotlib support, so you can trivially turn Matplotlib figures into interactive web plots (e.g. this interactive plot built via Seaborn, which is a statistical plotting package that uses matplotlib): http://bokeh.pydata.org/docs/gallery/violin.html

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The "technical vision" document is really off-putting. It starts with some BS about photography and then asks me

What are the best perceptual approaches to honestly and accurately represent the data to domain experts and SMEs so they can apply their intuition to the data?

I dunno mate, what's an SME for fucking starters?

How do the interactive bokeh plots work? Is it necessary to start up a separate server, or can it work through the widget communication channels?

Actually, Bokeh plots are not (yet) integrated with the widget infrastructure at all, and work in IPython 1.x. There is a fully embeddable javascript library (BokehJS) which handles all the interactivity entirely in the browser. Data can be directly and fully embedded in the DOM.

If you want to view larger data, access the downsampling capabilities of the Bokeh plot server, coordinate views between multiple instances of the notebook, or do streaming and animated plots, then you will need to run the bokeh server.

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