[python]bokeh学习总结—

本文主要是介绍[python]bokeh学习总结——bokeh.layouts，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

如果希望在同一张图上显示多个图像，可以使用bokeh.layouts类中的方法：

row()
column()
gridplot()
widgetbox()
layout()

row()

row()的作用是将多个图像以行的方式放到同一张图中。

from bokeh.io import output_file, show
from bokeh.layouts import row
from bokeh.plotting import figureoutput_file("layout.html")x = list(range(11))
y0 = x
y1 = [10 - i for i in x]
y2 = [abs(i - 5) for i in x]# create a new plot
s1 = figure(plot_width=250, plot_height=250, title=None)
s1.circle(x, y0, size=10, color="navy", alpha=0.5)# create another one
s2 = figure(plot_width=250, plot_height=250, title=None)
s2.triangle(x, y1, size=10, color="firebrick", alpha=0.5)# create and another
s3 = figure(plot_width=250, plot_height=250, title=None)
s3.square(x, y2, size=10, color="olive", alpha=0.5)# put the results in a row
show(row(s1, s2, s3))

column()

column()的作用是将多个图像以列的方式放到同一张图像中。

from bokeh.io import output_file, show
from bokeh.layouts import column
from bokeh.plotting import figureoutput_file("layout.html")x = list(range(11))
y0 = x
y1 = [10 - i for i in x]
y2 = [abs(i - 5) for i in x]# create a new plot
s1 = figure(plot_width=250, plot_height=250, title=None)
s1.circle(x, y0, size=10, color="navy", alpha=0.5)# create another one
s2 = figure(plot_width=250, plot_height=250, title=None)
s2.triangle(x, y1, size=10, color="firebrick", alpha=0.5)# create and another
s3 = figure(plot_width=250, plot_height=250, title=None)
s3.square(x, y2, size=10, color="olive", alpha=0.5)# put the results in a column and show
show(column(s1, s2, s3))

gridplot()

如果希望以矩阵的方式将多个图像放到同一张图上，可以使用gridplot()函数，如果某个位置的图像为空，可以设定为None。

from bokeh.io import output_file, show
from bokeh.layouts import gridplot
from bokeh.palettes import Viridis3
from bokeh.plotting import figureoutput_file("layout_grid.html")x = list(range(11))
y0 = x
y1 = [10 - i for i in x]
y2 = [abs(i - 5) for i in x]# create three plots
p1 = figure(plot_width=250, plot_height=250, title=None)
p1.circle(x, y0, size=10, color=Viridis3[0])
p2 = figure(plot_width=250, plot_height=250, title=None)
p2.triangle(x, y1, size=10, color=Viridis3[1])
p3 = figure(plot_width=250, plot_height=250, title=None)
p3.square(x, y2, size=10, color=Viridis3[2])# make a grid
grid = gridplot([[p1, p2], [None, p3]])# show the results
show(grid)

要注意的是，gridplot()函数只能接受figure类，不能接受widget类，如果希望一张图像中既有figure类也有widget类，就需要用layout()方法。文档中关于gridplot()的介绍如下，可以看到输入参数只能是Plot类，即figure类：

widgetbox()

如果希望在图像中插入widgets(小部件)，可以使用widgetbox()函数。

from bokeh.io import output_file, show
from bokeh.layouts import widgetbox
from bokeh.models.widgets import Button, RadioButtonGroup, Select, Slideroutput_file("layout_widgets.html")# create some widgets
slider = Slider(start=0, end=10, value=1, step=.1, title="Slider")
button_group = RadioButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
select = Select(title="Option:", value="foo", options=["foo", "bar", "baz", "quux"])
button_1 = Button(label="Button 1")
button_2 = Button(label="Button 2")# put the results in a row
show(widgetbox(button_1, slider, button_group, select, button_2, width=300))

layout()

layout()函数的作用有两个：

将多个图像按照矩阵样式摆放到同一张图中
输入参数可以是Plot, Widget, WidgetBox, Row, Column, ToolbarBox, Spacer，不仅限于Plot类

官网文档中的介绍如下：

来自github中的一个例子如下：

import numpy as npfrom bokeh.layouts import layout
from bokeh.models import CustomJS, Slider, ColumnDataSource, WidgetBox
from bokeh.plotting import figure, output_file, showoutput_file('dashboard.html')tools = 'pan'def bollinger():# Define Bollinger Bands.upperband = np.random.random_integers(100, 150, size=100)lowerband = upperband - 100x_data = np.arange(1, 101)# Bollinger shading glyph:band_x = np.append(x_data, x_data[::-1])band_y = np.append(lowerband, upperband[::-1])p = figure(x_axis_type='datetime', tools=tools)p.patch(band_x, band_y, color='#7570B3', fill_alpha=0.2)p.title.text = 'Bollinger Bands'p.title_location = 'left'p.title.align = 'left'p.plot_height = 600p.plot_width = 800p.grid.grid_line_alpha = 0.4return [p]def slider():x = np.linspace(0, 10, 100)y = np.sin(x)source = ColumnDataSource(data=dict(x=x, y=y))plot = figure(y_range=(-10, 10), tools='', toolbar_location=None,title="Sliders example")plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)callback = CustomJS(args=dict(source=source), code="""var data = source.data;var A = amp.value;var k = freq.value;var phi = phase.value;var B = offset.value;var x = data['x']var y = data['y']for (var i = 0; i < x.length; i++) {y[i] = B + A*Math.sin(k*x[i]+phi);}source.change.emit();""")amp_slider = Slider(start=0.1, end=10, value=1, step=.1, title="Amplitude", callback=callback, callback_policy='mouseup')callback.args["amp"] = amp_sliderfreq_slider = Slider(start=0.1, end=10, value=1, step=.1, title="Frequency", callback=callback)callback.args["freq"] = freq_sliderphase_slider = Slider(start=0, end=6.4, value=0, step=.1, title="Phase", callback=callback)callback.args["phase"] = phase_slideroffset_slider = Slider(start=-5, end=5, value=0, step=.1, title="Offset", callback=callback)callback.args["offset"] = offset_sliderwidgets = WidgetBox(amp_slider, freq_slider, phase_slider, offset_slider)return [widgets, plot]def linked_panning():N = 100x = np.linspace(0, 4 * np.pi, N)y1 = np.sin(x)y2 = np.cos(x)y3 = np.sin(x) + np.cos(x)s1 = figure(tools=tools)s1.circle(x, y1, color="navy", size=8, alpha=0.5)s2 = figure(tools=tools, x_range=s1.x_range, y_range=s1.y_range)s2.circle(x, y2, color="firebrick", size=8, alpha=0.5)s3 = figure(tools='pan, box_select', x_range=s1.x_range)s3.circle(x, y3, color="olive", size=8, alpha=0.5)return [s1, s2, s3]l = layout([bollinger(),slider(),linked_panning(),
], sizing_mode='stretch_both')show(l)