Using bokeh to plot interactive pie chart in Jupyt

问题:

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 with a Python function' in Bokeh as explained at the bottom of the page here. The pie chart consists of two entries with a slider that can change the shape of one pie 'v2' inside the circle shape of (v1+v2). I have tried to follow the example in bokeh website that shows the interactivity with a sine plot, but I just cannot get it to work with my pie chart. Any help would be greatly appreciated. Below is the code block I am using inside a Jupyter notebook.

import numpy as np
import matplotlib.pyplot as plt
from bokeh.layouts import column
from bokeh.models import CustomJS, ColumnDataSource, Slider
from bokeh.plotting import Figure, output_file, show, output_notebook
from bokeh.charts import Donut, show

#output_file('donut.html')
output_notebook()

v1=1
v2=.2
import pandas as pd
data = pd.Series([v1,v2], index = list('ab')) 
plot = Figure(plot_width=400, plot_height=400)
plot = Donut(data) 
    
    
def pie_chart(source=data,window=None,deltav=None):
    data = source.data
    v2 = deltav.value
    #v2 = data['v2']
    source.trigger('change')
    
slider = Slider(start=.1, end=1., value=.2, step=.1, title="delta-V", callback=CustomJS.from_py_func(pie_chart))
callback.args["deltav"] = slider
    
l = column(slider, plot)
show(l)

回答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, show
from bokeh.layouts import column
from 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.

回答2:

All those suggestions helped! Thanks a bunch.

I am back with a little twist to my original question. Now I would like to perform the same interactive plot using bokeh server. I have tweaked the sine plot example with sliders to generate the code below for the donut chart problem of mine. When I serve it up with bokeh serve, I am getting an error with the definition of 'data' in my code. I think I am close but missing a minor step.

''' 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=["black", "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=.7, 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=.2, step=.1, title="delta-V")

# Set up callbacks
  
def update(attrname, old, new):
    
    # Get the current slider values
    data['end'][0] = slider.value
    source.data = dict(start=start, end=end, color=color)

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"

回答3:

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"

回答4:

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.py

from math import pi
from bokeh.io import curdoc
from bokeh.io import output_file, show
from bokeh.layouts import column
from 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))

回答5:

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 figures

def modify_doc(doc):

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

    # make the plot
    def make_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 plot
    def update(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")