We can probably do a better job of describing all these examples (and I may just lift your great descriptions into our docs, if that's ok!)
Regarding app vs embed. The "apps" are all run *inside* the bokeh-server. So you start them, by doing something like:
bokeh-server --script sliders_app.py
The main reason for this is because, otherwise, to make an "app" outside the server, the only real solutions is to have a long-running process that polls the server for updates. This is not ideal, and apps running directly in the server can utilize much better callbacks. Please note that the "app" concept is still fairly new, and things like how to start, easily spell, and deploy apps is very much open to improvement.
The "embed" examples simply show off how to embed a Bokeh plot in a standard web-app (i.e., you want to serve a plot from Flask that has a plot in it). This can be done with, or without the bokeh-server, but even if you use a bokeh-server, there is no code running *in the bokeh-server* that responds to widgets, or updates plots or data. To update plots you'd have to have a separate python process that connects to the bokeh-server and polls or pushes data to it.
I hope this makes things a bit clearer.
On Dec 15, 2014, at 2:37 PM, Damian Avila <[email protected]> wrote:
On Mon, Dec 15, 2014 at 11:16 AM, Josh Wasserstein <[email protected]> wrote:
I think I got it. It looks like:
- plotting includes the high-level plotting interface (except for charts)
- glyphs: includes the low-level plotting interface
- charts: charts interface (histogram)
- compat includes examples illustrating ggplot and seaborn (compatibility with other packages)
- apps: include widget examples
- embed: Includes the spectrogram example that Peter and Bryan have demoed in their talks (e.g. in Pydata & NYC Nov. 2014).
Yes, you got it... would you care to update the README in a PR? or make an issue so we can properly update it later. Thanks
I would say it is in the embed folder because you are actually embedding the plot object into the Flask app specifically using the bokeh.embed API
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