How To Get Interactive Bokeh In Jupyter Notebook

I'm gearing up towards using bokeh for an interactive online implementation of some python models I've written. Step 1 is understanding some basic interactive examples, but I can't

Solution 1:

I agree (as a user) that docs could be better on this. I had to search a lot to find the procedure, but when you find it it's not that hard! I modified your code, you can run this inside Jupyter notebook.

The trick is:

from bokeh.application import Application
from bokeh.application.handlers import FunctionHandler
.
.
<your code here>
.
.
#add server-related code inside this modify_doc function
def modify_doc(doc): #use doc as you use curdoc() in bokeh server
    doc.add_root(<your_layout>)
    doc.on_change(...)
    doc.add_periodic_callback(...) 


handler = FunctionHandler(modify_doc)
app = Application(handler)
show(app)

and the modified version of your code:

############ START BOILERPLATE ################ Interactivity -- BOKEHimport bokeh.plotting.figure as bk_figure
from bokeh.io import curdoc, show
from bokeh.layouts import row, widgetbox
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import Slider, TextInput
from bokeh.io import output_notebook # enables plot interface in J notebookimport numpy as np
# init bokehfrom bokeh.application import Application
from bokeh.application.handlers import FunctionHandler


output_notebook()
############ END BOILERPLATE ############# Set up data
N = 200
x = np.linspace(0, 4*np.pi, N)
y = np.sin(x)
source = ColumnDataSource(data=dict(x=x, y=y))

# Set up plot
plot = bk_figure(plot_height=400, plot_width=400, title="my sine wave",
              tools="crosshair,pan,reset,save,wheel_zoom",
              x_range=[0, 4*np.pi], y_range=[-2.5, 2.5])

plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)

# Set up widgets
text = TextInput(title="title", value='my sine wave')
offset = Slider(title="offset", value=0.0, start=-5.0, end=5.0, step=0.1)
amplitude = Slider(title="amplitude", value=1.0, start=-5.0, end=5.0, step=0.1)
phase = Slider(title="phase", value=0.0, start=0.0, end=2*np.pi)
freq = Slider(title="frequency", value=1.0, start=0.1, end=5.1, step=0.1)

# Set up callbacksdefupdate_title(attrname, old, new):
    plot.title.text = text.value



defupdate_data(attrname, old, new):
    # Get the current slider values
    a = amplitude.value
    b = offset.value
    w = phase.value
    k = freq.value

    # Generate the new curve
    x = np.linspace(0, 4*np.pi, N)
    y = a*np.sin(k*x + w) + b

    source.data = dict(x=x, y=y)
    ### I thought I might need a show() here, but it doesn't make a difference if I add one# show(layout)for w in [offset, amplitude, phase, freq]:
    w.on_change('value', update_data)


# Set up layouts and add to document
inputs = widgetbox(text, offset, amplitude, phase, freq)
layout = row(plot,
             widgetbox(text, offset, amplitude, phase, freq))



defmodify_doc(doc):
    doc.add_root(row(layout, width=800))
    doc.title = "Sliders"
    text.on_change('value', update_title)


handler = FunctionHandler(modify_doc)
app = Application(handler)
show(app)