如何在Jupyter noteb中获得交互式bokeh

Question

我正准备使用bokeh来实现我所编写的一些python模型的交互式在线实现。

步骤1是理解一些基本的交互示例，但我无法在Jupyter笔记本中获得交互运行的介绍性示例。我希望有人能纠正我对什么是bokeh自己的示例代码的复制粘贴的误解。

我知道Bokeh文档并不完美（我修复了对bokeh.plotting.show而不是io.show的过时引用），但我认为我使用的基本结构应该接近正确。

代码基于： https://github.com/bokeh/bokeh/blob/master/examples/app/sliders.py

https://docs.bokeh.org/en/latest/docs/user_guide/notebook.html

############ START BOILERPLATE ############
#### Interactivity -- BOKEH
import 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 notebook
# init bokeh
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 callbacks
def update_title(attrname, old, new):
    plot.title.text = text.value

text.on_change('value', update_title)

def update_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))
curdoc().add_root(row(inputs, layout, width=800))
curdoc().title = "Sliders"

show(layout)

我生成了一个图，如下所示，但当滑块移动时（或当标题文本更新时），图形不会更新

非常感谢你的建议。

注意：我试图使这段代码尽可能接近服务器上的.py文件，从而避免像push_notebook这样的特定于jupyter的解决方法。

Answer 1

我同意（作为用户）docs在这方面可以做得更好。我得找很多方法才能找到手术，但当你发现的时候就没那么难了！我修改了你的代码，你可以在Jupyter笔记本里运行。

诀窍是：

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)

以及代码的修改版本：

############ START BOILERPLATE ############
#### Interactivity -- BOKEH
import 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 notebook
import numpy as np
# init bokeh

from 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 callbacks
def update_title(attrname, old, new):
    plot.title.text = text.value



def update_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))



def modify_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)