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Continuing my experiments using Sunflow for web data-based visualizations, I recently finished one using data from my delicious bookmark collection. By popular demand, here’s a brief run-down of the process I used to make it.

• Collect a set of delicious bookmark URLs with pydelicious.
• Using PyObjC (should already be installed on OS X), load these URLs into WebViews (one at a time) and save the resulting view content as a jpeg. (This can take ~20s/URL, so you might be waiting a while).
• From the resulting collection of images, generate a header file to be used by ODE that describes the number and sizes of these images.
• Run an ODE gravity simulation using the previously specified sizes for the falling objects.
• When the simulation is complete, dump the positions and rotations of the objects to an external file.
• Using another Python script, read the image files and corresponding position and rotation information generated from ODE to procedurally generate a Sunflow scene file containing the object positions, shaders, and camera information.
• Open the scene file in Sunflow, and render!

None of the above steps are prohibitively difficult in and of themselves, it’s really all about creating a streamlined workflow.