使用全局列数据源时替换布局中的图形和表

Question

我正在使用散景 0.12.9。我有一个表格和一个图形，我在回调时将其替换在全局布局中。我通常在构建新图形/表格之前构建ColumnDataSource。现在我想尝试看看我是否可以有一个全局ColumnDataSource以便我可以通过CDSView调整数据（那时不需要替换表格/图形）。

不幸的是，即使为表和绘图保留单独的 CDS 和视图也会失败。单击单选按钮几次时，我收到以下javascript错误： Uncaught TypeError: Cannot read property 'data' of undefined

from datetime import date
from random import randint
from bokeh.models import Line
import numpy as np
import pandas as pd
from bokeh.plotting import figure, output_file, show
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import DataTable, DateFormatter, TableColumn
import bokeh.layouts as layouts
import bokeh.models.widgets as widgets
from bokeh.io import curdoc
from bokeh.models import CustomJS, Slider
from bokeh import palettes
from bokeh.layouts import layout
from bokeh.models import ColumnDataSource, CDSView, IndexFilter
from bokeh.models import widgets

def gen_plot(source=None, view=None):
    p = figure(title='test',
               x_axis_type="datetime",
               plot_width=600, plot_height=400)
    colors = palettes.Category10[10]
    cols = [str(col) for col in source.column_names]
    for ix, col in enumerate(cols):
        if col == 'index':
            continue
        r = p.line(x='index', y=col, source=source, view=view,
                   legend='_' + col,
                   color=colors[ix])
    p.legend.location = "bottom_left"
    return p

def gen_table(source=None, view=None):
    columns = [TableColumn(field=ele, title=ele) for ele
               in source.column_names]
    tab = widgets.DataTable(source=source, view=view, columns=columns,
                            selectable=False,
                            reorderable=False,
                            width=600, height=400)
    return tab

def update(attr, old, new):
    p = gen_plot(source=cdss[0], view=vs[0])
    t = gen_table(source=cdss[1], view=vs[1])
    print l.children
    l.children[1] = p
    l.children[2].children[0] = t

# set up data
cols = ['col1', 'col2', 'col3', 'col4']
df1 = pd.DataFrame(pd.util.testing.getTimeSeriesData())
df1.columns = cols
df2 = pd.DataFrame(pd.util.testing.getTimeSeriesData())
df2.columns = cols
dfs = [df1, df2]
cds1 = ColumnDataSource(df1)
cds2 = ColumnDataSource(df2)
cdss = [cds1, cds2]
filters = [IndexFilter([0, 1, 2, 4])]
filters = []
v1 = CDSView(source=cds1, filters=filters)
v2 = CDSView(source=cds2, filters=filters)
vs = [v1, v2]

# initialize items to replace
p = gen_plot(source=cdss[0], view=vs[0])
t = gen_table(source=cdss[1], view=vs[1])
# initialize controls
radio_wghting = widgets.RadioButtonGroup(labels=["Equal", "Exponential"],
                                         active=0)
radio_wghting.on_change('active', update)
# set up layout
sizing_mode = 'fixed'
l = layout([radio_wghting, p, t], sizing_mode=sizing_mode)
curdoc().add_root(l)
curdoc().title = 'blub'

# call callback initially
update('value', 0, 0)

任何提示都非常感谢！

Answer 1

现在我想尝试看看我是否可以拥有一个全局列数据源 我可以通过CDSView调整数据（无需替换 然后是表/图）。

您显示的代码是您尝试替换图形和表格的代码。

以这种方式替换布局对象的子项时，实际上并没有从curdoc中删除以前的图形，并且文档中的其他元素在其引用中仍具有旧的图形和表。

您可以尝试类似的方式来直接更新源。

for rend in p.renderers:
    try:
        rend.data_source
    except AttributeError:
        pass
    else:
        rend.data_source.data.update(new_data_dictionary)

和

t.source.data.update(new_data_dictionary)

编辑以回答评论

from bokeh.io import curdoc
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, Button
from bokeh.layouts import gridplot, widgetbox
from random import random, choice
import numpy as np
my_data = {1:{'x':[],'y':[],'colo':[],'size':[]}}
kelly_colors = [    '#F3C300','#875692', '#F38400', '#A1CAF1','#BE0032', '#C2B280', '#848482','#008856', '#E68FAC', '#0067A5',
                    '#F99379', '#604E97', '#F6A600','#B3446C', '#DCD300', '#882D17','#8DB600', '#654522', '#E25822','#2B3D26',      ]
x = np.arange(0,50,0.1)
def rand_dict():
    rand_x = [choice(x) for i in range(7)]
    return {'x':rand_x,'y':np.array([random()*100 for i in rand_x]),'colo':np.array([choice(kelly_colors) for i in rand_x]),'size':np.array([(5+int(random()*50)) for i in rand_x])}
def add_stuff():
    global my_data
    my_data[max(my_data.keys())+1] = rand_dict()
    make_doc()
def change_stuff():
    global my_data
    myfig = curdoc().select_one({"name":"myfig"})
    for i,rend in enumerate(myfig.renderers):
        try:
            rend.data_source
        except AttributeError:
            pass
        else:
            my_data[i+1] = rand_dict()
            rend.data_source.data.update(my_data[i+1])
def clear_stuff():
    global my_data
    my_data = {1:{'x':[],'y':[],'colo':[],'size':[]}}
    make_doc()
def make_doc():
    curdoc().clear()
    myfig = figure(plot_width=1000,plot_height=800,outline_line_alpha=0,name='myfig')
    myfig.x_range.start = -5
    myfig.x_range.end = 55
    myfig.y_range.start = -10
    myfig.y_range.end = 110
    myfig.renderers = []
    add_button = Button(label='add stuff',width=100)
    change_button = Button(label='change stuff',width=100)
    clear_button = Button(label='clear stuff',width=100)
    add_button.on_click(add_stuff)
    change_button.on_click(change_stuff)
    clear_button.on_click(clear_stuff)
    grid = gridplot([[myfig,widgetbox(add_button,change_button,clear_button)]],toolbar_location=None)
    curdoc().add_root(grid)
    update_doc()
def update_doc():
    myfig = curdoc().select_one({"name":"myfig"})
    for key in my_data:
        myfig.scatter(x='x',y='y',color='colo',size='size',source=ColumnDataSource(data=my_data[key]))
curdoc().title = 'mytitle'
make_doc()

我喜欢这样做的是，您可以使用 numpy 保存my_data字典，稍后加载它并从那里继续更改您的绘图。

def load_data():
    global my_data
    my_data = np.load(path_to_saved_data).item()
    make_doc()

您可能可以使用熊猫数据帧做类似的事情，我只是更喜欢普通词典。