惯例，开场标准导入:
from bokeh.io import output_notebook, show
from bokeh.plotting import figure
output_notebook()
有的时候我们需要在图上做一些标记、注释， 来说明其更多的特点或信息。
跨度
import numpy as np
from bokeh.models.annotations import Span
x = np.linspace(0, 20, 200)
y = np.sin(x)
p = figure(y_range=(-2, 2))
p.line(x, y)
upper = Span(location=1, dimension='width', line_color='olive', line_width=4)
p.add_layout(upper)
lower = Span(location=-1, dimension='width', line_color='firebrick', line_width=4)
p.add_layout(lower)
show(p)
bokeh_plot (17).png
通过 Span 定义横向或者纵向的标记线，传入.add_layout后标记在图片上。
一块区域
有的时候， 我们需要标注一块区域， 那么可以使用BoxAnnotation
import numpy as np
from bokeh.models.annotations import BoxAnnotation
x = np.linspace(0, 20, 200)
y = np.sin(x)
p = figure(y_range=(-2, 2))
p.line(x, y)
# region that always fills the top of the plot
upper = BoxAnnotation(bottom=1, fill_alpha=0.1, fill_color='olive')
p.add_layout(upper)
# region that always fills the bottom of the plot
lower = BoxAnnotation(top=-1, fill_alpha=0.1, fill_color='firebrick')
p.add_layout(lower)
# a finite region
center = BoxAnnotation(top=0.6, bottom=-0.3, left=7, right=12, fill_alpha=0.1, fill_color='navy')
p.add_layout(center)
show(p)
bokeh_plot (18).png
标签
from bokeh.models.annotations import Label
from bokeh.plotting import figure
p = figure(x_range=(0,10), y_range=(0,10))
p.circle([2, 5, 8], [4, 7, 6], color="olive", size=10)
label = Label(x=5, y=7, x_offset=12, text="Second Point", text_baseline="middle")
p.add_layout(label)
show(p)
bokeh_plot (20).png
标签批量标注
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, LabelSet
source = ColumnDataSource(data=dict(
temp=[166, 171, 172, 168, 174, 162],
pressure=[165, 189, 220, 141, 260, 174],
names=['A', 'B', 'C', 'D', 'E', 'F']))
p = figure(x_range=(160, 175))
p.scatter(x='temp', y='pressure', size=8, source=source)
p.xaxis.axis_label = 'Temperature (C)'
p.yaxis.axis_label = 'Pressure (lbs)'
labels = LabelSet(x='temp', y='pressure', text='names', level='glyph',
x_offset=5, y_offset=5, source=source, render_mode='canvas')
p.add_layout(labels)
show(p)
bokeh_plot (21).png
箭头
from bokeh.models.annotations import Arrow
from bokeh.models.arrow_heads import OpenHead, NormalHead, VeeHead
p = figure(plot_width=600, plot_height=600)
p.circle(x=[0, 1, 0.5], y=[0, 0, 0.7], radius=0.1,
color=["navy", "yellow", "red"], fill_alpha=0.1)
p.add_layout(Arrow(end=OpenHead(line_color="firebrick", line_width=4),
x_start=0, y_start=0, x_end=1, y_end=0))
p.add_layout(Arrow(end=NormalHead(fill_color="orange"),
x_start=1, y_start=0, x_end=0.5, y_end=0.7))
p.add_layout(Arrow(end=VeeHead(size=35), line_color="red",
x_start=0.5, y_start=0.7, x_end=0, y_end=0))
show(p)
bokeh_plot (22).png
多图例
import numpy as np
x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)
p = figure(height=400)
p.circle(x, y, legend_label="sin(x)")
p.line(x, 2*y, legend_label="2*sin(x)", line_dash=[4, 4], line_color="orange", line_width=2)
show(p)
bokeh_plot (23).png
组合图例
多个图画成相同的图形，叠加在一起
import numpy as np
x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)
p = figure(height=400)
p.circle(x, y, legend_label="sin(x)")
p.line(x, y, legend_label="sin(x)", line_dash=[4, 4], line_color="orange", line_width=2)
show(p)
bokeh_plot (24).png
颜色条
from bokeh.sampledata.autompg import autompg
from bokeh.models import LinearColorMapper, ColorBar
from bokeh.transform import transform
source = ColumnDataSource(autompg)
color_mapper = LinearColorMapper(palette="Viridis256", low=autompg.weight.min(), high=autompg.weight.max())
p = figure(x_axis_label='Horsepower', y_axis_label='MPG', tools='', toolbar_location=None)
p.circle(x='hp', y='mpg', color=transform('weight', color_mapper), size=20, alpha=0.6, source=autompg)
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=12, location=(0,0), title='Weight')
p.add_layout(color_bar, 'right')
show(p)
ColorBar.PNG
这一章节我们接触了各种注解类标记， 范围的限制、关键位置的标记、打标签、画箭头， 多图例的展示， 颜色条等。
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