The sherpa.plot.bokeh_backend module

bokeh is a plotting library that can be used to create interactive plots, which are rendered in a webbrowser. Those plots allow various options to zoom, pan, and scroll out of the box. Many additional interactions can be added by the user later, and we also plan to make Sherpa use more features of this library in the future.

Note

The bokeh backend is new in Sherpa 4.16 and should be considered experimental. We appreciate feedback on how it works for you, or what features are missing; and we anticipate changes to how this backend works in the future.

How to display and save a plot

Bokeh plots are rendered by a webbrowser and, by default, are displayed in a new tab. The plot can be saved to a file by clicking on the save icon in the toolbar at the top of the plot or by using bokeh.io.save.

Once displayed, bokeh has no direct control over the browser tab anymore, so the plot cannot be updated automatically. When doing plots with bokeh, that means that the user needs to call bokeh.plotting.show when all elements have been added.

Jupyter notebooks

Similar to e.g. the %matplotlib inline command in a notebook that specifies how to display matplotlib plots, some setup is required to display bokeh plots inline in a notebook.

Before a plot is created, the following commands need to be run:

>>> from bokeh.io import output_notebook
>>> output_notebook()

See the bokeh documentation for details.

Python scripts and (I)Python terminals

Bokeh plots can either be displayed in a new tab in the default webbrowser or be saved to a file. In both cases, a plot is not continuously updated. Users must explicitly call bokeh.plotting.show to display the plot or bokeh.plotting.save to save the plot to a html file. The reference to the current plot is stored in the current_plot attribute of the backend object:

>>> from bokeh.plotting import show
>>> from sherpa import plot, data
>>> from sherpa.data import Data1D
>>> plot.set_backend('BokehBackend')
>>> x1 = [100, 200, 600, 1200]
>>> y1 = [2000, 2100, 1400, 3050]
>>> d1 = data.Data1D('oned', x1, y1)
>>> plot1 = plot.DataPlot()
>>> plot1.prepare(d1)
>>> plot1.plot()
>>> show(plot.backend.current_fig)

The same is true for the UI interface:

>>> from bokeh.plotting import show, save
>>> from sherpa.astro import ui
>>> from sherpa import plot
>>> from sherpa.astro.data import DataPHA
>>> session = Session()
>>> ui.load_arrays(1, [1, 2, 3], [1, 2, 3], DataPHA)
>>> ui.plot_data()
>>> show(plot.backend.current_fig)
>>> # or
>>> save(plot.backend.current_fig, filename='plot.html')

Suggestions on how to integrate that better into the Sherpa UI are welcome.

Limitations

bokeh itself is quite different from matplotlib in the way plots are displayed in an external program (a webbrowser). Thus, the workflow is different from what we might be used to. See the bokeh documentation on output options for details. In particular:

Bokeh plots cannot easily be exported to png or pdf. They are always rendered in javascript. Bokeh does offer a mechanism to export plots to static images, but it requires additional software and drivers.

For display, bokeh saves temporary files that are then automatically opened in a webbrowser. In some linux systems, browsers are not allowed to open files in the local \temp directory for security reasons, so the user has to manually change the location the bokeh plot is written to with bokeh.io.output_file.

Once displayed, bokeh no longer has control over the webbrowser. For plotting in scripts or when using virtual frame buffers, that might lead to those browser processes staying open and consuming resources. Use bokeh.io.output_file together with bokeh.io.save instead of bokeh.io.show.

Plotting using bokeh.

Classes

BokehBackend(*args, **kwargs)

Sherpa plotting backend for the bokeh package.