Using Bokeh To Plot Interactive Pie Chart In Jupyter/python

I am new to Bokeh and I would really appreciate some help in figuring out how to use Bokeh to plot a simple interactive pie chart in Jupyer/Python. I am planning to use 'CustomJS w

Solution 1:

If you want to interactively update things, then you will be better off using the bokeh.plotting API. For some fairly uninteresting technical reasons, the bokeh.charts API (including Donut) is not well-suited for use cases that require updating things in place.

With bokeh.plotting there is a wedge glyph method that you can use to draw pie charts. Here is a complete example written (using Bokeh 0.12.5) that updates a pie chart with a slider:

from math import pi

from bokeh.io import output_file, showfrom bokeh.layouts import columnfrom bokeh.models import ColumnDataSource, CustomJS, Slider
from bokeh.plotting import figure

output_file("pie.html")

source = ColumnDataSource(data=dict(
    start=[0, 0.2], end=[0.2, 2*pi], color=['firebrick', 'navy']
))

plot = figure()
plot.wedge(x=0, y=0, start_angle='start', end_angle='end', radius=1,
        color='color', alpha=0.6, source=source)

slider = Slider(start=.1, end=1., value=.2, step=.1, title="delta-V")

def update(source=source, slider=slider, window=None):
    data = source.data
    data['end'][0] = slider.value
    source.trigger('change')

slider.js_on_change('value', CustomJS.from_py_func(update))

show(column(slider, plot))

It's slightly more verbose than the Donut version, but the relationship between the data structures on the python side and on the JS side are much more clear and direct.

Solution 2:

I think I found my answer. Here is the code in case it helps

''' Present an interactive function explorer with slider widgets.

Scrub the slider to change the pie shape in the donut plot

Use the ``bokeh serve`` command to run the example by executing:

    bokeh serve donuts.py

at your command prompt. Then navigate to the URL

    http://localhost:5006/donuts

in your browser.

'''
import numpy as np

from bokeh.io import curdoc
from bokeh.layouts import row, widgetbox
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import Slider, TextInput
from bokeh.plotting import figure
from math import pi

# Set up data
source = ColumnDataSource(data=dict(
    start=[0, 0], end=[0., 2*pi], color=["white", "red"]
))

# Set up plot
plot = figure(x_axis_location=None, y_axis_location=None, plot_width=400, plot_height=400,)
plot.annular_wedge(x=0, y=0, start_angle='start', end_angle='end', inner_radius=.4, outer_radius=.8,
        color="color", alpha=1., source=source)
#plot.background_fill_color = None
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None

# Set up widgets
slider = Slider(start=.0, end=round(2*pi,2), value=.0, step=.1, title="delta-V")

# Set up callbacks
  
def update(attrname, old, new):
    
    # Get the current slider values
    z = slider.value
    source.data = dict(start=[pi,pi+z], end=[pi+z, pi], color=["yellow", "red"])

for w in [slider]:
    w.on_change('value', update)


# Set up layouts and add to document
inputs = widgetbox(slider)

curdoc().add_root(row(inputs, plot, width=800))
curdoc().title = "Donut"

Solution 3:

The code from bigreddot didn't work for me. Plot showed but didn't change. Here is my slight modification for anyone that could use it.

#cd file directory
#bokeh serve --show filename.pyfrom math import pi
from bokeh.io import curdoc
from bokeh.io import output_file, showfrom bokeh.layouts import columnfrom bokeh.models import ColumnDataSource, CustomJS, Slider
from bokeh.plotting import figure


source = ColumnDataSource(data=dict(
    start=[0, 0.2], end=[0.2, 2*pi], color=['firebrick', 'navy']
))

plot = figure()
plot.wedge(x=0, y=0, start_angle='start', end_angle='end', radius=1,
        color='color', alpha=0.6, source=source)

slider = Slider(start=.1, end=1., value=.2, step=.1, title="delta-V")

def update(attrname, old, new):
    data = source.data
    data['end'][0] = slider.value
    source.data=data

slider.on_change('value', update)

curdoc().add_root(column(slider, plot))

Solution 4:

I'm using Bokeh 1.0.4 and the answer by bigreddot doesn't work for me. The slider doesn't actually change the plot. I put together a complete working answer that works for me in JupyterLab v0.27.0 using bigreddot's example and this website.

from math import pi

from bokeh.io import show, output_notebook
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, Slider
from bokeh.plotting import figure
from bokeh.application.handlers import FunctionHandler
from bokeh.application import Application

output_notebook() # allow inline output of figuresdefmodify_doc(doc):

    # create the data for the plotdefmake_data(setting):
        return ColumnDataSource(data=dict(start=[0, 0.2], end=[setting, 2*pi], color=['firebrick', 'navy']))

    # make the plotdefmake_plot(source):
        plot = figure()
        plot.wedge(x=0, y=0, start_angle='start', end_angle='end', radius=1,
                color='color', alpha=0.6, source=source)
        return plot

    # update the plotdefupdate(attrname,old,new):
        setting = slider.value
        src = make_data(setting)
        source.data.update(src.data)

    # controls
    slider = Slider(start=.1, end=1., value=.2, step=.1, title="delta-V")
    slider.on_change('value',update)

    source = ColumnDataSource(data=dict(
        start=[0, 0.2], end=[0.2, 2*pi], color=['firebrick', 'navy']
        ))

    p = make_plot(source)
    layout = column(slider, p)
    doc.add_root(layout)

app = Application(FunctionHandler(modify_doc))
doc = app.create_document()
show(app,notebook_url="localhost:8888")