如何定义 Python Bokeh RangeSlider.on_change 回调函数来改变绘图的 IndexFilter?

How to define Python Bokeh RangeSlider.on_change callback function to alter IndexFilter for plots?

我正在尝试为 RangeSlider 实现一个 python 回调函数。滑块值应该告诉 IndexFilter 应该显示哪个索引。

例如:如果 rangeslider.value 是 (3, 25),我的地块应该只有 contain/view 索引从 3 到 25 的数据。

from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource, GMapOptions, CustomJS, CDSView, IndexFilter
from bokeh.plotting import gmap, ColumnDataSource, figure
from bokeh.layouts import column, row
from bokeh.models.widgets import RangeSlider 
import numpy as np

def slider_callback(attr, old, new):
        p.view = CDSView(source=source, filters=[IndexFilter(np.arange(new.value[0], new.value[1]))])
        v.view = CDSView(source=source, filters=[IndexFilter(np.arange(new.value[0], new.value[1]))])


# data set
lon = [[48.7886, 48.7887, 48.7888, 48.7889, 48.789], 
        [48.7876, 48.7877, 48.78878, 48.7879, 48.787], 
        [48.7866, 48.7867, 48.7868, 48.7869, 48.786],
        [48.7856, 48.7857, 48.7858, 48.7859, 48.785],
        [48.7846, 48.7847, 48.7848, 48.7849, 48.784]]
lat = [[8.92, 8.921, 8.922, 8.923, 8.924],
        [8.91, 8.911, 8.912, 8.913, 8.914],
        [8.90, 8.901, 8.902, 8.903, 8.904],
        [8.89, 8.891, 8.892, 8.893, 8.894],
        [8.88, 8.881, 8.882, 8.883, 8.884]]
time = [0, 1, 2, 3, 4, 5]
velocity = [23, 24, 25, 24, 20]
lenght_dataset = len(lon)


# define source and map
source = ColumnDataSource(data = {'x': lon, 'y': lat, 't': time, 'v': velocity})
view = CDSView(source=source, filters=[IndexFilter(np.arange(0, lenght_dataset))])

map_options = GMapOptions(lat=48.7886, lng=8.92, map_type="satellite", zoom=13)

p = gmap("MY_API_KEY", map_options, title="Trajectory Map")
v = figure(plot_width=400, plot_height=400, title="Velocity")


# plot lines on map
p.multi_line('y', 'x', view=view, source=source, line_width=1)
v.line('t', 'v', view=view, source=source, line_width=3)


# slider to limit plotted data
range_slider = RangeSlider(title="Data Range Slider: ", start=0, end=lenght_dataset, value=(0, lenght_dataset), step=1) 

range_slider.on_change('value', slider_callback)


# Layout to plot and output
layout = row(column(p, range_slider),
            column(v)
    )

output_file("diag_plot_bike_data.html")

show(layout)

一些注意事项:

  • time 比其余列长 - 您将收到有关它的警告。在我下面的代码中,我刚刚删除了它的最后一个元素
  • view 与过滤器一般不应用于连续字形,如线条(特别是 v.line - multi_line 很好)。您将收到有关它的警告。但是如果 IndexFilter 中的索引总是连续的,那么你应该没问题。无论哪种方式,您都可以使用段字形来避免警告
  • 在您的回调中,您试图在图形上设置视图 - 视图仅存在于字形渲染器上
  • 一般来说,您不想重新创建视图,而是希望重新创建尽可能少的 Bokeh 模型。理想情况下,您只需更改过滤器的 indices 字段。但是 Bokeh 中缺少一些接线,因此您必须设置视图的 filters 字段,如下所示
  • new Python 回调的参数接收作为第一个参数传递给相应 on_change 调用的属性的新值。在这种情况下,它将是一个元组,所以你应该使用 new[0]
  • 而不是 new.value[0]
  • 由于您决定使用 Python 回调,您不能再使用 show 并拥有静态 HTML 文件 - 您将不得不使用 curdoc().add_rootbokeh serve。 UI 需要 Python 代码到 运行 某处 运行time
  • 当更改滑块值时,您会注意到 multi_line 的单独部分将连接在一起 - 这是一个错误,我刚刚为其创建了 https://github.com/bokeh/bokeh/issues/10589

这是一个工作示例:

from bokeh.io import curdoc
from bokeh.layouts import column, row
from bokeh.models import GMapOptions, CDSView, IndexFilter
from bokeh.models.widgets import RangeSlider
from bokeh.plotting import gmap, ColumnDataSource, figure

lon = [[48.7886, 48.7887, 48.7888, 48.7889, 48.789],
       [48.7876, 48.7877, 48.78878, 48.7879, 48.787],
       [48.7866, 48.7867, 48.7868, 48.7869, 48.786],
       [48.7856, 48.7857, 48.7858, 48.7859, 48.785],
       [48.7846, 48.7847, 48.7848, 48.7849, 48.784]]
lat = [[8.92, 8.921, 8.922, 8.923, 8.924],
       [8.91, 8.911, 8.912, 8.913, 8.914],
       [8.90, 8.901, 8.902, 8.903, 8.904],
       [8.89, 8.891, 8.892, 8.893, 8.894],
       [8.88, 8.881, 8.882, 8.883, 8.884]]
time = [0, 1, 2, 3, 4]
velocity = [23, 24, 25, 24, 20]
lenght_dataset = len(lon)

# define source and map
source = ColumnDataSource(data={'x': lon, 'y': lat, 't': time, 'v': velocity})
view = CDSView(source=source, filters=[IndexFilter(list(range(lenght_dataset)))])

map_options = GMapOptions(lat=48.7886, lng=8.92, map_type="satellite", zoom=13)

p = gmap("API_KEY", map_options, title="Trajectory Map")
v = figure(plot_width=400, plot_height=400, title="Velocity")

p.multi_line('y', 'x', view=view, source=source, line_width=1)
v.line('t', 'v', view=view, source=source, line_width=3)

range_slider = RangeSlider(title="Data Range Slider: ", start=0, end=lenght_dataset, value=(0, lenght_dataset), step=1)


def slider_callback(attr, old, new):
    view.filters = [IndexFilter(list(range(*new)))]


range_slider.on_change('value', slider_callback)

layout = row(column(p, range_slider), column(v))
curdoc().add_root(layout)