* Use a Bokeh Server to respond to range updates with real python code
The downside to "doing it in JS" is that BokehJS is already a large library by JS standards. We can't continually bloat core BokehJS by adding every possible imaginable feature, or it will become unusable. On the other hand, it is now possible to extend Bokeh and BokehJS with custom user models, so something like this might make a prime candidate for that. In fact I believe some folks have already made custom models to support scale-dependent downsampling.
Certainly a Bokeh app is also a reasonable approach, but that's something a user would write because the user knows what kind of downsampling or refined is appropriate to the actual data and use case.
If your data is very large, and you want things like automatic progressive refinement and visual queries across large scales, you might want to look into the related (OSS) DataShader project, that integrates closely with Bokeh:
Last link is a webinar behind a small questionnaire.
On Jun 1, 2016, at 10:30 AM, Jonas Haag <[email protected]> wrote:
On 01 Jun 2016, at 17:23, Aaron Goldenberg <[email protected]> wrote:
We are telling you to separate the tasks of data munging and plotting
Understood, I’m just wondering if this doesn’t actually belong to the plotting component? What if you want to zoom the data further than the aggregation date range? If I understand things correctly I have to decide beforehand what X axis ranges are likely to be viewed? (If my aggregation interval is too small, the plot will be difficult to read; if it is too large, some details will be missing if zoomed in far enough.)
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