散景| Jupyter笔记本| Python | 情节未显示

Question

I've spent the last few weeks learning the Bokeh package (which for visualizations, is excellent in my opinion).

Unfortunately, I have come across a problem that I can't for the life of me, figure out how to solve.

The below two links have been helpful, but I can't seem to replicate for my problem.

Using bokeh to plot interactive pie chart in Jupyter/Python - refer to answer #3

https://github.com/bokeh/bokeh/blob/0.12.9/examples/howto/notebook_comms/Jupyter%20Interactors.ipynb

The below code (in Jupyter) displays the graph correctly and displays the slider correctly, but I'm unsure how to connect the two as when I move the slider, the graph remains static.

I am using Python 3.6 and Bokeh 12.9

N = 300

source = ColumnDataSource(data={'x':random(N), 'y':random(N)}) 

plot = figure(plot_width=950, plot_height=400) 

plot.circle(x='x', y='y', source=source)

callback = CustomJS(code=""" 
if (IPython.notebook.kernel !== undefined) {
    var kernel = IPython.notebook.kernel;
    cmd = "update_plot(" + cb_obj.value + ")";
    kernel.execute(cmd, {}, {})}; 
""")

slider = Slider(start=100, end=1000, value=N, step=10, callback=callback)

def callback(attr, old, new):
    N = slider.value
    source.data={'x':random(N), 'y':random(N)}

slider.on_change('value', callback)

layout = column(slider, plot) 

curdoc().add_root(layout)

show(widgetbox(slider, width = 300)) 

show(plot)

After reading the bokeh documentation and reading a view threads on GitHub, the 'callback' function is a little unclear for me as I'm not entirely sure what to parse to it (if in fact attr, old, new need certain elements parsed too it)

Any help would be greatly appreciated

Hopefully, I haven't missed anything glaringly obvious.

Kind Regards,

Adrian

Answer 1

I suppose your question relates to the server although you have both a CustomJS and a server callback.

I am not familiar with the previous way of doing bokeh server in notebook ( push_notebook ). The new way would be like this: you wrap your code in a function taking one parameter (a document) and your call to add_layout is made on that document. Then you build an app with that function and show it.

This gives:

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

output_notebook()

def modify_doc(doc):
    N = 300

    source = ColumnDataSource(data={'x':random(N), 'y':random(N)}) 

    plot = figure(plot_width=950, plot_height=400) 

    plot.circle(x='x', y='y', source=source)

    slider = Slider(start=100, end=1000, value=N, step=10)

    def callback(attr, old, new):
        N = new  # but slider.value would also work
        source.data={'x': random(N), 'y': random(N)}

    slider.on_change('value', callback)

    layout = column(slider, plot) 

    doc.add_root(layout)

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

Answer 2

You are currently mixing different ways for interactivity but unfortunately you always miss something for each different way.

The slider you use is from bokeh, but unfortunately it looks like slider.on_change only works if you run through the bokeh server. From the documentation :

Use bokeh serve to start the Bokeh server and set up event handlers with .on_change (or for some widgets, .on_click).

I couldn't really find that much on running jupyter notebook and bokeh server, but this issue seems to discuss that possibility. It also mentions bokeh.application but I've never used that, so no idea how that works.

You also use additionally a custom js callback, which calls into the jupyter kernel and tries to execute update_plot(value) , but you never defined such a function, so it does nothing.

Then you need a method to push the data to the output. I guess bokeh server can somehow do that nativly, for jupyter notebooks without the bokeh server push_notebook seems to be the solution. Note that you need show(..., notebook_handle=True) to be able to push.

Solution 1 use the bokeh server

Sliders and others widgets automatically sync their state back to python, so you can use slider.on_change . You don't need the CustomJS. Data flow should look as following:

python script -> bokeh server -> html -> userinput -> bokeh server -> python callbacks -> bokeh server updates plots

Solution 2 use bokeh sliders but sync via CustomJS

If you don't want to run a seperate process you can use the jupyter kernel to execute code in your python notebook. Dataflow:

jupyter notebook -> html -> user input -> customjs -> jupyter kernel -> python callbacks -> push_notebook to update plots

output_notebook()

N = 300

source = ColumnDataSource(data={'x':random(N), 'y':random(N)}) 

plot = figure(plot_width=950, plot_height=400) 

plot.circle(x='x', y='y', source=source)

callback = CustomJS(code=""" 
if (IPython.notebook.kernel !== undefined) {
    var kernel = IPython.notebook.kernel;
    cmd = "update_plot(" + cb_obj.value + ")";
    kernel.execute(cmd, {}, {})}; 
""")

slider = Slider(start=100, end=1000, value=N, step=10, callback=callback)

# must have the same name as the function that the CustomJS tries to call
def update_plot(N):
    source.data={'x':random(N), 'y':random(N)}
    # push notebooks to update plots
    push_notebook()

layout = column(slider, plot) 
# notebook_handle must be true, otherwise push_notebook will not work
h1 = show(layout, notebook_handle=True)

Solution 3 use ipywidgets

If you are not married to the bokeh widgets you can use the ipywidgets which are designed for interactivity in the jupyter notebook. The data flow is as following:

jupyter notebook -> html -> user input -> ipywidgets sync automatically -> python callbacks -> push_notebook

I use here interact but the other widgets should work as expected.

from ipywidgets import interact

output_notebook()

N = 300

source = ColumnDataSource(data={'x':random(N), 'y':random(N)}) 

plot = figure(plot_width=950, plot_height=400) 

plot.circle(x='x', y='y', source=source)

def update_plot(v):
    N = v
    print(N)
    source.data={'x':random(N), 'y':random(N)}
    # push changed plots to the frontend
    push_notebook()

# notebook_handle must be true so that push_notebook works
show(plot, notebook_handle=True)

Note that you need to install ipywidgets properly, which inlcudes calling jupyter nbextension enable --py --sys-prefix widgetsnbextension if you are not using conda. For details see the documentation