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Working on SVG? What about EPS instead? For scientific publishing EPS is much better unless your visualisation is pretty much an high entropy one.
Please explain why EPS is better.
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There is a full-featured javascript runtime "BokehJS" which is designed from the ground up to be driven by remote (aka server-side) models, which are sent over the wire as JSON. The server-side models are generated programmatically via Python, R, Scala, etc. The cool thing is that a lot of the interactivity is completely native in BokehJS, so you can build interactive Javascript visualizations from Python , and have…
Are the visualizations downloaded at page load only, or can they be readily fashioned with websockets, etc to turn them into an interactive dashboard? I've been considering running out lab experiments off of a webapp, but haven't found an easy enough solution.
It allows for building streaming visualizations or plots with using websockets (implemented using tornado).
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A png isn't a vector format, so the important property of the SVG (or EPS, PDF) is lost. A vectorized bitmap is still a bitmap.
What resolution are you looking for in print? If you export your raster at 300 dpi, and enable lossless PNG compression, shouldn't it suffice for most print purposes?
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There is a full-featured javascript runtime "BokehJS" which is designed from the ground up to be driven by remote (aka server-side) models, which are sent over the wire as JSON. The server-side models are generated programmatically via Python, R, Scala, etc. The cool thing is that a lot of the interactivity is completely native in BokehJS, so you can build interactive Javascript visualizations from Python , and have…
Are the visualizations downloaded at page load only, or can they be readily fashioned with websockets, etc to turn them into an interactive dashboard? I've been considering running out lab experiments off of a webapp, but haven't found an easy enough solution.
Since I have never really liked Matplotlib I would really like to learn Bokeh, but unfortunately its inability to export its visualizations in SVG or similar formats makes it kind of useless for me as a scientist wanting to publish my results.
For example, have a look at this cursory analysis of my Reddit comments that I did late last year. Getting the graphs to look nice felt like pulling teeth at the time.
http://nbviewer.jupyter.org/github/Niksko/redditCommentData/...
That being said, I'm reasonably happy with the results. And that pannable, zoomable line graph is pretty fancy and is easy to set up.
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thx @travisoliphant the requirements are not clear in the docs. This explanation makes more sense.
Thanks for the feedback. We keep a continuous and frequent effort to make documentation more efficient and straightforward .. Building clear is a constant/endless effort and feedback is very important. To clarify, Futures is required for bokeh server as an extra dependency on Python 2.7 since it's not a battery included like for Python > 3.2.. if you conda install Bokeh it should be installed already for you. You can…
This is one of the first questions I ask, "is this code Python3 ready?", so I look at the install requirements first. Maybe I'm an edge case. A short cut might be just to say in big words, Python3 ready.
[0] For example this PIL fork, Pillow (python3) https://pillow.readthedocs.org/en/latest/installation.html
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my question exactly. What does this do that I can't do with highcharts?
I forgot the really important difference between bokeh and highcharts - you can throw 100k points at it and have it be fully interactive without your browser blowing up!
Since I have never really liked Matplotlib I would really like to learn Bokeh, but unfortunately its inability to export its visualizations in SVG or similar formats makes it kind of useless for me as a scientist wanting to publish my results.