我正在使用Bokeh尝试创建一个图形,当用户“悬停”时数据点将在悬停工具中显示另一个图形,显示有关该数据点的其他信息(即,在主图中数据点是平均值在设定的时间间隔内的时间序列,我希望悬停工具显示该时间间隔内的所有数据)。
user guide(下面复制的完整代码)提供了一种解决方案:使用自定义HTML工具提示来引用文件中的图形。但是,这需要我创建文件中的所有数字(可能超过10,000)以供参考。这是一个太大的时间开销,所以我希望有一个更好的解决方案。即:悬浮工具是否可以动态运行python代码,以便它们可以交互显示数据图?
(示例图片,取自用户指南,代码如下)
以下代码于2019年3月19日从bokeh user guide复制而来。
from bokeh.plotting import figure, output_file, show, ColumnDataSource
output_file("toolbar.html")
source = ColumnDataSource(data=dict(
x=[1, 2, 3, 4, 5],
y=[2, 5, 8, 2, 7],
desc=['A', 'b', 'C', 'd', 'E'],
imgs=[
'https://bokeh.pydata.org/static/snake.jpg',
'https://bokeh.pydata.org/static/snake2.png',
'https://bokeh.pydata.org/static/snake3D.png',
'https://bokeh.pydata.org/static/snake4_TheRevenge.png',
'https://bokeh.pydata.org/static/snakebite.jpg'
],
fonts=[
'<i>italics</i>',
'<pre>pre</pre>',
'<b>bold</b>',
'<small>small</small>',
'<del>del</del>'
]
))
TOOLTIPS = """
<div>
<div>
<img
src="@imgs" height="42" alt="@imgs" width="42"
style="float: left; margin: 0px 15px 15px 0px;"
border="2"
></img>
</div>
<div>
<span style="font-size: 17px; font-weight: bold;">@desc</span>
<span style="font-size: 15px; color: #966;">[$index]</span>
</div>
<div>
<span>@fonts{safe}</span>
</div>
<div>
<span style="font-size: 15px;">Location</span>
<span style="font-size: 10px; color: #696;">($x, $y)</span>
</div>
</div>
"""
p = figure(plot_width=400, plot_height=400, tooltips=TOOLTIPS,
title="Mouse over the dots")
p.circle('x', 'y', size=20, source=source)
show(p)
您只能在Bokeh服务器应用程序中使用Python回调。似乎不可能对HoverTool使用Python回调(它必须始终是JS回调,否则会出现此错误:ValueError: expected an instance of type Callback, got <function callback at 0x114fdbb90> of type function
)。
以下解决方案使用JS回调,当在主图上悬停圆圈时显示一个小的“工具提示图”(适用于Bokeh v1.0.4,并且仅当Bokeh文档中有2个图时):
from bokeh.plotting import figure, show
from bokeh.layouts import gridplot, Row
from bokeh.models import ColumnDataSource, CDSView, BooleanFilter, CustomJS, BoxSelectTool, HoverTool
import pandas as pd
data = {'x': [1, 2, 3],
'y':[1, 2, 3],
'xs':[[9, 8, 7], [6, 5, 4], [3, 2, 1]],
'ys':[[29, 28, 29], [27, 28, 27], [25, 25, 20]]}
source = ColumnDataSource(data)
plot = figure(title = 'PLOT IN HOVER TOOLTIP', tools = '')
circles = plot.circle('x', 'y', size = 20, source = source)
plot_tooltip = figure(name = 'plot_tooltip', plot_width = 200, plot_height = 200, x_axis_location = None, y_axis_location = None, title = None, tools = 'hover', tooltips = [("x", "@x"), ("y", "@y")], toolbar_location = None)
lines = plot_tooltip.line('x', 'y', source = ColumnDataSource({'x': [], 'y': []}))
circles2 = plot_tooltip.circle('x', 'y', source = ColumnDataSource({'x': [], 'y': []}))
code = """
var indices = cb_data.index['1d'].indices;
if (indices.length > 0){
if(plot_tooltip.x_range.bounds == null)
{
Bokeh.documents[0].add_root(plot_tooltip)
}
const idx = indices[0]
lines.data_source.data['x'] = source.data['xs'][idx]
lines.data_source.data['y'] = source.data['ys'][idx]
lines.data_source.change.emit();
circles.data_source.data['x'] = source.data['xs'][idx]
circles.data_source.data['y'] = source.data['ys'][idx]
circles.data_source.change.emit();
div = document.getElementsByClassName('bk-root')[1];
div.style = "position:absolute; left:" + cb_data.geometry['sx'] + "px; top:" + cb_data.geometry['sy'] + "px;";
} """
callback = CustomJS(args = dict(source = source, lines = lines, circles = circles2, plot_tooltip = plot_tooltip), code = code)
hover = HoverTool()
hover.callback = callback
hover.tooltips = None
hover.renderers = [circles]
plot.add_tools(hover)
show(plot)
结果: