BokehBackend

class spb.backends.bokeh.BokehBackend(*args, **kwargs)[source]

A backend for plotting SymPy’s symbolic expressions using Bokeh. This implementation only supports 2D plots.

Parameters:
aspectstr

Set the aspect ratio of a 2D plot. Default to None. Set it to "equal" to sets equal spacing on the axis.

rendering_kwdict, optional

A dictionary of keywords/values which is passed to Matplotlib’s plot functions to customize the appearance of lines, surfaces, images, contours, quivers, streamlines… To learn more about customization:

  • Refer to:

    • [1] to customize lines plots. Default to: dict(line_width = 2).

    • [3] to customize scatter plots. Default to: dict(marker = "circle").

  • Default options for quiver plots:

    dict(
    scale = 1,
    pivot = "mid",      # "mid", "tip" or "tail"
    arrow_heads = True,  # show/hide arrow
    line_width = 1
)

  • Default options for streamline plots: dict(line_width=2, line_alpha=0.8)

axisboolean, optional

Turns on/off the axis visibility (and associated tick labels). Default to True (axis are visible).

themestr, optional

Set the theme. Find more Bokeh themes at [2] .

update_eventbool, optional

If True, it binds pan/zoom events in order to automatically compute new data as the user interact with the plot. Default to False.

annotationslist, optional

A list of dictionaries specifying the type of annotation required. The keys in the dictionary should be equivalent to the arguments of the bokeh.models.LabelSet class. This feature is experimental. It might get removed in the future.

markerslist, optional

A list of dictionaries specifying the type the markers required. The keys in the dictionary should be equivalent to the arguments of the bokeh.models.Scatter class. This feature is experimental. It might get removed in the future.

rectangleslist, optional

A list of dictionaries specifying the dimensions of the rectangles to be plotted. The "args" key must contain the bokeh.models.ColumnDataSource object containing the data. All other keyword arguments will be passed to the bokeh.models.Rect class. This feature is experimental. It might get removed in the future.

filldict, optional

A dictionary specifying the type of color filling required in the plot. The keys in the dictionary should be equivalent to the arguments of the bokeh.models.VArea class. This feature is experimental. It might get removed in the future.

See also

Plot, MatplotlibBackend, PlotlyBackend, K3DBackend

Notes

By providing update_event=True to any plot function, this backend binds pan/zoom events in order to automatically compute new data as the user interact with the plot.

When executing this mode of operation inside:

  • Jupyter Notebook/Lab: no problem has been encountered (with Firefox/Chrome).

  • A standard Python interpreter:

    • No problem has been encountered with Chrome.

    • Memory leaks has been observed with Firefox. Watch out your system monitor!

References

BokehBackend.fig

Returns the figure.

spb.backends.bokeh.BokehBackend.append(self, arg)

Adds an element from a plot’s series to an existing plot.

Parameters:
argBaseSeries

An instance of BaseSeries which will be used to generate the numerical data.

See also

extend

Examples

Consider two Plot objects, p1 and p2. To add the second plot’s first series object to the first, use the append method, like so:

>>> from sympy import symbols
>>> from spb import plot
>>> x = symbols('x')
>>> p1 = plot(x*x, show=False)
>>> p2 = plot(x, show=False)
>>> p1.append(p2[0])
>>> p1
Plot object containing:
[0]: cartesian line: x**2 for x over (-10.0, 10.0)
[1]: cartesian line: x for x over (-10.0, 10.0)
>>> p1.show()

(Source code, png)

../../_images/bokeh-1.png
spb.backends.bokeh.BokehBackend.extend(self, arg)

Adds all series from another plot.

Parameters:
argPlot or sequence of BaseSeries

See also

append

Examples

Consider two Plot objects, p1 and p2. To add the second plot to the first, use the extend method, like so:

>>> from sympy import symbols
>>> from spb import plot
>>> x = symbols('x')
>>> p1 = plot(x**2, show=False)
>>> p2 = plot(x, -x, show=False)
>>> p1.extend(p2)
>>> p1
Plot object containing:
[0]: cartesian line: x**2 for x over (-10.0, 10.0)
[1]: cartesian line: x for x over (-10.0, 10.0)
[2]: cartesian line: -x for x over (-10.0, 10.0)
>>> p1.show()

(Source code, png)

../../_images/bokeh-2.png
spb.backends.bokeh.BokehBackend.save(self, path, **kwargs)

Export the plot to a static picture or to an interactive html file.

Refer to [4] and [5] to visualize all the available keyword arguments.

Notes

  1. In order to export static pictures, the user also need to install the packages listed in [6].

  2. When exporting a fully portable html file, by default the necessary Javascript libraries will be loaded with a CDN. This creates the smallest file size possible, but it requires an internet connection in order to view/load the file and its dependencies.

References

spb.backends.bokeh.BokehBackend.show(self)

Visualize the plot on the screen.

spb.backends.bokeh.BokehBackend.update_interactive(self, params)

Implement the logic to update the data generated by interactive-widget plots.

Parameters:
paramsdict

Map parameter-symbols to numeric values.