From the webpage:
Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients.
For more information about the goals and direction of the project, please see the Technical Vision.
To get started quickly, follow the Quickstart.
Visit the source repository: https://github.com/ContinuumIO/bokeh
Be sure to follow us on Twitter @bokehplots!
The technical vision makes the case for Bokeh quite well:
Photographers use the Japanese word “bokeh” to describe the blurring of the out-of-focus parts of an image. Its aesthetic quality can greatly enhance a photograph, and photographers artfully use focus to draw attention to subjects of interest. “Good bokeh” contributes visual interest to a photograph and places its subjects in context.
In this vein of focusing on high-impact subjects while always maintaining a relationship to the data background, the Bokeh project attempts to address fundamental challenges of large dataset visualization:
- How do we look at all the data?
- 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?
- Are there automated approaches to accurately reduce large datasets so that outliers and anomalies are still visible, while we meaningfully represent baselines and backgrounds? How can we do this without “washing away” all the interesting bits during a naive downsampling?
- If we treat the pixels and topology of pixels on a screen as a bottleneck in the I/O channel between hard drives and an analyst’s visual cortex, what are the best compression techniques at all levels of the data transformation pipeline?
- How can scientists and data analysts be empowered to use visualization fluidly, not merely as an output facility or one stage of a pipeline, but as an entire mode of engagement with data and models?
- Are language-based approaches for expressing mathematical modeling and data transformations the best way to compose novel interactive graphics?
- What data-oriented interactions (besides mere linked brushing/selection) are useful for fluid, visually-enable analysis?
…
Not likely any time soon but posting data for scientific research in ways that enable interactive analysis by readers (and snapshotting their results) could take debates over data and analysis to a whole new level.
As opposed to debating dots on a graph not of your own making and where alternative analyses are not available.