ダッシュボードのようなやりとりを生成するためにBokehコールバックでデータを切り取ります

Question

bokeh serve機能を使用せずにコールバック関数を使用して棒グラフの対話のようなダッシュボードを作成しようとしています。最終的には、2つのドロップダウンメニューのいずれかが変更された場合、プロットを変更することができます。これまでのところ、これはしきい値がハードコードされている場合にのみ機能します。私はcb_objの値を抽出する方法しか知りませんが、実際には呼び出されないドロップダウンからは抽出しません。私はthisとthis最初の試みを公式に答えて見ました。

from bokeh.io import show, output_notebook, output_file 
from bokeh.models import ColumnDataSource, Whisker 
from bokeh.plotting import figure 
from bokeh.transform import factor_cmap 
from bokeh.models import CustomJS, ColumnDataSource, Slider, Select 
from bokeh.layouts import column 

import numpy as np 
import pandas as pd 


def generate_data(factor=10): 
    rawdata = pd.DataFrame(np.random.rand(10,4)*factor, columns = ["A","B","C","D"]) 
    idx = pd.MultiIndex.from_product([["Exp "+str(i) for i in range(5)], 
           [20,999]],names=["Experiment","Threshold"]) 
    rawdata.index = idx 

    return rawdata.reset_index() 

# Generate data 
output_notebook() 
count_data = generate_data() 
error_data = generate_data(factor=2) 
groups = ["A","B","C","D"] 
initial_counts = count_data[(count_data.Experiment == "Exp 0") 
          & (count_data.Threshold == 20)][["A","B","C","D"]].values[0] 
initial_errors = error_data[(error_data.Experiment == "Exp 0") 
          & (error_data.Threshold == 20)][["A","B","C","D"]].values[0] 

# Create primary sources of data 
count_source = ColumnDataSource(data=count_data) 
error_source = ColumnDataSource(data=error_data) 

# Create plotting source of data 
source = ColumnDataSource(data=dict(groups=groups, counts=initial_counts, 
            upper=initial_counts+initial_errors, 
            lower=initial_counts-initial_errors)) 

# Bar chart and figure 
p = figure(x_range=groups, plot_height=350, toolbar_location=None, title="Values", y_range=(0,20)) 
p.vbar(x='groups', top='counts', width=0.9, source=source, legend="groups", 
     line_color='white', fill_color=factor_cmap('groups', palette=["#962980","#295f96","#29966c","#968529"], 
                factors=groups)) 

# Error bars 
p.add_layout(
    Whisker(source=source, base="groups", upper="upper", lower="lower", level="overlay") 
) 

def callback(source=source, count_source = count_source, error_source=error_source, window=None): 

    def slicer(data_source, experiment, threshold, dummy_col, columns): 
     """ Helper function to enable lookup of data.""" 
     count = 0 
     for row in data_source[dummy_col]: 
      if (data_source["Experiment"][count] == experiment) & (data_source["Threshold"][count] == threshold): 
       result = [data_source[col][count] for col in columns] 

      count+=1 

     return result 

    # Initialise data sources 
    data = source.data 
    count_data = count_source.data 
    error_data = error_source.data 

    # Initialise values 
    experiment = cb_obj.value 
    threshold = 20 
    counts, upper, lower = data["counts"], data["upper"], data["lower"] 

    tempdata = slicer(count_data, experiment, threshold,"Experiment", ["A","B","C","D"]) 
    temperror = slicer(error_data, experiment, threshold,"Experiment", ["A","B","C","D"]) 

    # Select values and emit changes 
    for i in range(len(counts)): 
     counts[i] = tempdata[i] 

    for i in range(len(counts)): 
     upper[i] = counts[i]+temperror[i] 
     lower[i] = counts[i]-temperror[i] 

    source.change.emit() 


exp_dropdown = Select(title="Select:", value="Exp 0", options=list(count_data.Experiment.unique()))  
thr_dropdown = Select(title="Select:", value="12", options=list(count_data.Threshold.astype(str).unique()))  

exp_dropdown.callback = CustomJS.from_py_func(callback) 


p.xgrid.grid_line_color = None 
p.legend.orientation = "horizontal" 
p.legend.location = "top_center" 

layout = column(exp_dropdown,thr_dropdown, p) 

show(layout) 

Answer 1

質問に対する解決策がSelectメニューは、コールバック関数の前に定義する必要があることである：

は、ここに私のコードです。このコードの動作：

from bokeh.io import show, output_notebook, output_file 
from bokeh.models import ColumnDataSource, Whisker 
from bokeh.plotting import figure 
from bokeh.transform import factor_cmap 
from bokeh.models import CustomJS, ColumnDataSource, Slider, Select 
from bokeh.layouts import column 

import numpy as np 
import pandas as pd 


def generate_data(factor=10): 
    rawdata = pd.DataFrame(np.random.rand(10,4)*factor, columns = ["A","B","C","D"]) 
    idx = pd.MultiIndex.from_product([["Exp "+str(i) for i in range(5)], 
           [20,999]],names=["Experiment","Threshold"]) 
    rawdata.index = idx 

    return rawdata.reset_index() 

# Generate data 
output_notebook() 
count_data = generate_data() 
error_data = generate_data(factor=2) 
groups = ["A","B","C","D"] 
initial_counts = count_data[(count_data.Experiment == "Exp 0") 
          & (count_data.Threshold == 20)][["A","B","C","D"]].values[0] 
initial_errors = error_data[(error_data.Experiment == "Exp 0") 
          & (error_data.Threshold == 20)][["A","B","C","D"]].values[0] 

# Create primary sources of data 
count_source = ColumnDataSource(data=count_data) 
error_source = ColumnDataSource(data=error_data) 

# Create plotting source of data 
source = ColumnDataSource(data=dict(groups=groups, counts=initial_counts, 
            upper=initial_counts+initial_errors, 
            lower=initial_counts-initial_errors)) 

# Bar chart and figure 
p = figure(x_range=groups, plot_height=350, toolbar_location=None, title="Values", y_range=(0,20)) 
p.vbar(x='groups', top='counts', width=0.9, source=source, legend="groups", 
     line_color='white', fill_color=factor_cmap('groups', palette=["#962980","#295f96","#29966c","#968529"], 
                factors=groups)) 
# Error bars 
p.add_layout(
    Whisker(source=source, base="groups", upper="upper", lower="lower", level="overlay") 
)  

exp_dropdown = Select(title="Select:", value="Exp 0", options=list(count_data.Experiment.unique()))  
thr_dropdown = Select(title="Select:", value="20", options=list(count_data.Threshold.astype(str).unique())) 

def callback(source=source, count_source = count_source, error_source=error_source, exp_dropdown = exp_dropdown, 
      thr_dropdown=thr_dropdown,window=None): 

    def slicer(data_source, experiment, threshold, dummy_col, columns): 
     """ Helper function to enable lookup of data.""" 
     count = 0 
     for row in data_source[dummy_col]: 
      if (data_source["Experiment"][count] == experiment) & (data_source["Threshold"][count] == threshold): 
       result = [data_source[col][count] for col in columns] 

      count+=1 

     return result 

    # Initialise data sources 
    data = source.data 
    count_data = count_source.data 
    error_data = error_source.data 

    # Initialise values 
    experiment = exp_dropdown.value 
    threshold = thr_dropdown.value 
    counts, upper, lower = data["counts"], data["upper"], data["lower"] 

    tempdata = slicer(count_data, experiment, threshold,"Experiment", ["A","B","C","D"]) 
    temperror = slicer(error_data, experiment, threshold,"Experiment", ["A","B","C","D"]) 

    # Select values and emit changes 
    for i in range(len(counts)): 
     counts[i] = tempdata[i] 

    for i in range(len(counts)): 
     upper[i] = counts[i]+temperror[i] 
     lower[i] = counts[i]-temperror[i] 

    source.change.emit() 


exp_dropdown.callback = CustomJS.from_py_func(callback) 
thr_dropdown.callback = CustomJS.from_py_func(callback) 

p.xgrid.grid_line_color = None 
p.legend.orientation = "horizontal" 
p.legend.location = "top_center" 

layout = column(exp_dropdown,thr_dropdown, p) 

show(layout)