如何连接汇总统计和原始数据与情景的阴谋

Question

我有一个数据集，我正在绘制汇总统计数据，我希望能够选择一个有趣的点并在底下绘制原始数据子图。下面的代码可以从一个jupyter笔记本运行，看看输出应该是什么样子。理想情况下，我将能够点击第一个图中靠近（1,10）的点，并在第二个图中查看聚集在10左右的原始数据。

底部附近注释掉的代码是我定义回调的尝试，但我认为我对于如何使用taptools和回调有个基本的误解。我如何告诉散景，它应该调用具有特定参数的特定python例程（基于点击的点），然后重新加载第二个数字？

from string import ascii_lowercase 

import numpy as np 
import pandas as pd 

from bokeh.plotting import figure, output_notebook, show, gridplot 
from bokeh.models import ColumnDataSource, widgets, HoverTool, TapTool 

output_notebook() 

class RawPlot(): 
    def __init__(self, fig, col, raw): 
     self.fig = fig 
     self.raw = raw 
     self.circ = self.fig.circle(x='index', y='raw', size=1, 
            source=ColumnDataSource(raw[col].reset_index())) 
    # ideally I would have a callback to RawPlot.update to change the underlying data 
    def update(self, col): 
     self.circ.data_source = ColumnDataSource(self.raw[col].reset_index()) 

# generate the example data 
rawdat = pd.DataFrame(np.random.random((1024, 4)) + np.arange(4)*10, 
         columns=pd.MultiIndex.from_tuples([(s, 'raw') for s in ascii_lowercase[:4]])) 
# compute summary statistics and show in a bokeh figure 
stats = rawdat.describe().T.reset_index() 
pstat = figure() 
pstat.circle(x='index', y='mean', source=ColumnDataSource(stats), size=12) 

# show the raw data of the first column 
praw = figure() 
rawplot = RawPlot(praw, 'a', rawdat) 
# this was my attempt at being able to change which column's raw data was plotted. It failed 
# taptool = pstat.select(type=TapTool) 
# taptool.callback = rawplot.update("@level_0") 
show(gridplot([[pstat, praw]])) 

Answer 1

下面的代码以我想要的方式处理交互。它与测试案例足够好地运行在散景服务器上。但是，从笔记本运行代码的交互性并不相同（选择pstat中的数据不会更新praw中的数据）。因此output_notebook和push_notebook被注释掉。

from string import ascii_lowercase 

import numpy as np 
import pandas as pd 

from bokeh.plotting import figure, show, gridplot 
from bokeh.models import ColumnDataSource, widgets, HoverTool, TapTool, BoxSelectTool 
from bokeh.io import output_notebook, push_notebook, output_server 
from bokeh.resources import INLINE 

# generate the example data 
rawdat = pd.DataFrame(np.random.random((1024, 4)) + np.arange(4)*10, 
         columns=pd.MultiIndex.from_tuples([(s, 'raw') for s in ascii_lowercase[:4]])) 
# compute summary statistics and show in a bokeh figure 
stats = rawdat.describe().T.reset_index() 
statsource = ColumnDataSource(stats[['mean']]) 

TOOLS = [TapTool(), BoxSelectTool()] 
pstat = figure(tools=TOOLS) 
pstat.circle(x='index', y='mean', source=statsource, size=12) 

# show the raw data of the first column 
praw = figure() 
col = rawdat.columns.levels[0][0] 
rawsource = ColumnDataSource(rawdat[col].reset_index()) 
circ = praw.circle(x='index', y='raw', size=1, 
        source=rawsource) 

# update the raw data_source when a new summary is chosen 
def update(attr, old, new): 
    ind = new['1d']['indices'][0] 
    col = rawdat.columns.levels[0][ind] 
    rawsource.data['raw'] = rawdat[col].values.ravel() 
    # push_notebook() 
statsource.on_change('selected', update) 

# serve the figures 
output_server("foo") 
# output_notebook() 
show(gridplot([[pstat, praw]]))