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I suspect marketing Datashader as an "accumulation buffer" wouldn't have the same effect on its target audience (data visualisation developers) as presenting it

I suspect marketing Datashader as an "accumulation buffer" wouldn't have the same effect on its target audience (data visualisation developers) as presenting it simply as a way to "Turn data into images". I'm also curious (as a fledgling graphics programmer) - what leads you to believe that Datashader uses an accumulation buffer internally? I would think that they use some magic to draw all the points in a single draw call using instanced rendering, but I am very naive :)

mhalle 8y ago
If you watch the video, one of the creators explains this point, which makes datashader different from, say, Bokeh. (Pipeline explained around 7:20 in the video) https://www.youtube.com/watch?v=6m3CFbKmK_c https://www.youtube.com/watch?v=6m3CFbKmK_c D3 and Bokeh and other web-based visualization tools, in general, plot HTML or CSS primitives to the browser. This approach works great for smaller datasets, but doesn't scale to millions/billions. Datashader aggregates (accumulates) graphical representations of data into images, then provides a way to get those to the browser and work well with the other libraries. That high level description leaves out 95% of the critical practical details of visualization, which the creators of datashader handle.
jbednar 8y ago
Datashader's approach is a bit different from an accumulation buffer, though similar in principle. It's not 3D rendering, and has no need for a z ordering; instead it's essentially 2D histogramming. For points, it simply takes each point, calculates which pixel it would land in, and aggregates per pixel, without ever storing all the data points per pixel. The key benefit over something like SciPy's histogram2d functions are in how it is implemented and used -- highly optimized, and highly integrated with viz tools so that it can let you just interact with your data naturally as if you had infinite resolution. Try it and see!
- BubRoss 8y ago
  > For points, it simply takes each point, calculates which pixel it would land in, and aggregates per pixel, without ever storing all the data points per pixel. That is literally what opengl does. If you mean a histogram per pixel in depth, that's literally voxels in perspective space. If there are usability benefits here, that's great, but everything seems to be centered around there being new rendering techniques here, when not only are they not new, they're completely trivial, with solidified names and formalized math.
pwang 8y ago
It's actually not merely an accumulation buffer. It's a shader pipeline that allows for arbitrary Python code to be executed at each stage of data processing. It's actually very much like "renderman for data", but with Python (via Numba, Dask for performance). The pipeline is also built in such a way that it permits front-end JS viewers like Bokeh to drive a very dynamic experience.

mhalle 8y ago

If you watch the video, one of the creators explains this point, which makes datashader different from, say, Bokeh. (Pipeline explained around 7:20 in the video) https://www.youtube.com/watch?v=6m3CFbKmK_c https://www.youtube.com/watch?v=6m3CFbKmK_c D3 and Bokeh and other web-based visualization tools, in general, plot HTML or CSS primitives to the browser. This approach works great for smaller datasets, but doesn't scale to millions/billions. Datashader aggregates (accumulates) graphical representations of data into images, then provides a way to get those to the browser and work well with the other libraries. That high level description leaves out 95% of the critical practical details of visualization, which the creators of datashader handle.

jbednar 8y ago

Datashader's approach is a bit different from an accumulation buffer, though similar in principle. It's not 3D rendering, and has no need for a z ordering; instead it's essentially 2D histogramming. For points, it simply takes each point, calculates which pixel it would land in, and aggregates per pixel, without ever storing all the data points per pixel. The key benefit over something like SciPy's histogram2d functions are in how it is implemented and used -- highly optimized, and highly integrated with viz tools so that it can let you just interact with your data naturally as if you had infinite resolution. Try it and see!

BubRoss 8y ago
> For points, it simply takes each point, calculates which pixel it would land in, and aggregates per pixel, without ever storing all the data points per pixel. That is literally what opengl does. If you mean a histogram per pixel in depth, that's literally voxels in perspective space. If there are usability benefits here, that's great, but everything seems to be centered around there being new rendering techniques here, when not only are they not new, they're completely trivial, with solidified names and formalized math.

pwang 8y ago

It's actually not merely an accumulation buffer. It's a shader pipeline that allows for arbitrary Python code to be executed at each stage of data processing. It's actually very much like "renderman for data", but with Python (via Numba, Dask for performance). The pipeline is also built in such a way that it permits front-end JS viewers like Bokeh to drive a very dynamic experience.