bokeh是python中一款基于网页的画图工具库，画出的图像以html格式保存。

一个简单的例子：

from bokeh.plotting import figure, output_file, show



 



output_file("patch.html")



 



p = figure(plot_width=400, plot_height=400)



 



# add a patch renderer with an alpha an line width



p.patch([1, 2, 3, 4, 5], [6, 7, 8, 7, 3], alpha=0.5, line_width=2)



 



show(p)

画出图像后：

代码中有一行为

from bokeh.plotting import figure

figure是一个什么类型的数据？通过查看源代码，发现原来figure是一个函数，返回值为Figure类，Figure类以来自bokeh.models中的Plot类为父类，Figure类继承了Plot类中的各种属性。

from ..models import ColumnDataSource, Plot, Title, Tool, GraphRenderer



 



class Figure(Plot):



省略...



 



def figure(**kwargs):



    ''' Create a new :class:`~bokeh.plotting.figure.Figure` for plotting.



 



    Figure objects have many glyph methods that can be used to draw



    vectorized graphical glyphs:



 



    .. hlist::



        :columns: 3



 



{glyph_methods}



 



    There are also two specialized methods for stacking bars:



 



    * :func:`~bokeh.plotting.figure.Figure.hbar_stack`



    * :func:`~bokeh.plotting.figure.Figure.vbar_stack`



 



    And one specialized method for making simple hexbin plots:



 



    * :func:`~bokeh.plotting.figure.Figure.hexbin`



 



    In addition to the standard :class:`~bokeh.plotting.figure.Figure`



    property values (e.g. ``plot_width`` or ``sizing_mode``) the following



    additional options can be passed as well:



 



    .. bokeh-options:: FigureOptions



        :module: bokeh.plotting.figure



 



    Returns:



       Figure



 



    '''



 



    return Figure(**kwargs)

Plotting with Basic Glyphs

Creating Figures

Scatter Markers

画出圆形可以使用circle()方法：

from bokeh.plotting import figure, output_file, show



 



# output to static HTML file



output_file("line.html")



 



p = figure(plot_width=400, plot_height=400)



 



# add a circle renderer with a size, color, and alpha



p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color="navy", alpha=0.5)



 



# show the results



show(p)

得到的图形为：

同样的，如果要画出方形，可以使用square()方法，参数都是一样，将代码中的circle替换为square即可：

from bokeh.plotting import figure, output_file, show



 



# output to static HTML file



output_file("square.html")



 



p = figure(plot_width=400, plot_height=400)



 



# add a square renderer with a size, color, and alpha



p.square([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color="olive", alpha=0.5)



 



# show the results



show(p)

画出的图形为：

还有许多其它图形函数，其参数也都是一样，x表示x轴的数据，y表示y轴的数据，size表示图形的大小。还有一些参数包含angle——表示角度的大小，radius——表示图形的半径。

详细使用方法见：

https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.x

以annular_wedge()函数为例：

from bokeh.plotting import figure, output_file, show



 



# output to static HTML file



output_file("square.html")



 



p = figure()



x = [61,62,63,64,65]



y = [66,67,68,69,70]



 



# add a square renderer with a size, color, and alpha



p.annular_wedge(x=x, y=y, inner_radius=0.1, outer_radius=0.3, start_angle=0, end_angle=5, direction='anticlock')



 



# show the results



show(p)

Line Glyphs

Single Lines

from bokeh.plotting import figure, output_file, show



 



output_file("line.html")



 



p = figure(plot_width=400, plot_height=400)



 



# add a line renderer



p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], line_width=2)



 



show(p)

Step Lines

from bokeh.plotting import figure, output_file, show



 



output_file("line.html")



 



p = figure(plot_width=400, plot_height=400)



 



# add a steps renderer



p.step([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], line_width=2, mode="center")



 



show(p)

Multiple Lines

from bokeh.plotting import figure, output_file, show



 



output_file("patch.html")



 



p = figure(plot_width=400, plot_height=400)



 



p.multi_line([[1, 3, 2], [3, 4, 6, 6]], [[2, 1, 4], [4, 7, 8, 5]],



             color=["firebrick", "navy"], alpha=[0.8, 0.3], line_width=4)



 



show(p)

需要注意的是，第一个list表示x轴的数据，[[1,3,2],[3,4,6,6]]中的两个list代表lines是分离的；第二个list表示y轴的数据。

Missing Points

NaN可以作为line()和multi_line()函数参数的一部分，用该值可以表示不连续点。若x=NaN，则对应的y值将被忽略。

from bokeh.plotting import figure, output_file, show



 



output_file("line.html")



 



p = figure(plot_width=400, plot_height=400)



 



# add a line renderer with a NaN



nan = float('nan')



p.line([1, 2, 3, nan, 3, 5], [6, 7, 2, 4, 4, 5], line_width=2)



 



show(p)

Bars and Rectangles

Rectangles

from bokeh.plotting import figure, show, output_file



 



output_file('rectangles.html')



 



p = figure(plot_width=400, plot_height=400)



p.quad(top=[2, 3, 4], bottom=[1, 2, 3], left=[1, 2, 3],



       right=[1.2, 2.5, 3.7], color="#B3DE69")



 



show(p)

还有一个例子：

from math import pi




from bokeh.plotting import figure, show, output_file



 



output_file('rectangles_rotated.html')



 



p = figure(plot_width=400, plot_height=400)



p.rect(x=[1, 2, 3], y=[1, 2, 3], width=0.2, height=40, color="#CAB2D6",



       angle=pi/3, height_units="screen")



 



show(p)

Bars

vertical bars：

from bokeh.plotting import figure, show, output_file



 



output_file('vbar.html')



 



p = figure(plot_width=400, plot_height=400)



p.vbar(x=[1, 2, 3], width=0.5, bottom=0,



       top=[1.2, 2.5, 3.7], color="firebrick")



 



show(p)

horizon bars：

from bokeh.plotting import figure, show, output_file



 



output_file('hbar.html')



 



p = figure(plot_width=400, plot_height=400)



p.hbar(y=[1, 2, 3], height=0.5, left=0,



       right=[1.2, 2.5, 3.7], color="navy")



 



show(p)

Hex Tiles

import numpy as np



 




from bokeh.io import output_file, show




from bokeh.plotting import figure




from bokeh.util.hex import axial_to_cartesian



 



output_file("hex_coords.py")



 



q = np.array([0,  0, 0, -1, -1,  1, 1])



r = np.array([0, -1, 1,  0,  1, -1, 0])



 



p = figure(plot_width=400, plot_height=400, toolbar_location=None)



p.grid.visible = False




 



p.hex_tile(q, r, size=1, fill_color=["firebrick"]*3 + ["navy"]*4,



           line_color="white", alpha=0.5)



 



x, y = axial_to_cartesian(q, r, 1, "pointytop")



 



p.text(x, y, text=["(%d, %d)" % (q,r) for (q, r) in zip(q, r)],



       text_baseline="middle", text_align="center")



 



show(p)

import numpy as np



 




from bokeh.io import output_file, show




from bokeh.plotting import figure




from bokeh.transform import linear_cmap




from bokeh.util.hex import hexbin



 



n = 50000




x = np.random.standard_normal(n)



y = np.random.standard_normal(n)



 



bins = hexbin(x, y, 0.1)



 



p = figure(tools="wheel_zoom,reset", match_aspect=True, background_fill_color='#440154')



p.grid.visible = False




 



p.hex_tile(q="q", r="r", size=0.1, line_color=None, source=bins,



           fill_color=linear_cmap('counts', 'Viridis256', 0, max(bins.counts)))



 



output_file("hex_tile.html")



 



show(p)

Patch Glyphs

Single Patches

from bokeh.plotting import figure, output_file, show



 



output_file("patch.html")



 



p = figure(plot_width=400, plot_height=400)



 



# add a patch renderer with an alpha an line width



p.patch([1, 2, 3, 4, 5], [6, 7, 8, 7, 3], alpha=0.5, line_width=2)



 



show(p)

Multiple Patches

from bokeh.plotting import figure, output_file, show



 



output_file("patch.html")



 



p = figure(plot_width=400, plot_height=400)



 



p.patches([[1, 3, 2], [3, 4, 6, 6]], [[2, 1, 4], [4, 7, 8, 5]],



          color=["firebrick", "navy"], alpha=[0.8, 0.3], line_width=2)



 



show(p)

Missing Points

from bokeh.plotting import figure, output_file, show



 



output_file("patch.html")



 



p = figure(plot_width=400, plot_height=400)



 



# add a patch renderer with a NaN value



nan = float('nan')



p.patch([1, 2, 3, nan, 4, 5, 6], [6, 7, 5, nan, 7, 3, 6], alpha=0.5, line_width=2)



 



show(p)

Ovals and Ellipses

from math import pi




from bokeh.plotting import figure, show, output_file



 



output_file('ovals.html')



 



p = figure(plot_width=400, plot_height=400)



p.oval(x=[1, 2, 3], y=[1, 2, 3], width=0.2, height=40, color="#CAB2D6",



       angle=pi/3, height_units="screen")



 



show(p)

from math import pi




from bokeh.plotting import figure, show, output_file



 



output_file('ellipses.html')



 



p = figure(plot_width=400, plot_height=400)



p.ellipse(x=[1, 2, 3], y=[1, 2, 3], width=[0.2, 0.3, 0.1], height=0.3,



          angle=pi/3, color="#CAB2D6")



 



show(p)

Segments and Rays

Sometimes it is useful to be able to draw many individual line segments at once. Bokeh provides the segment() and ray() glyph methods to render these.

from bokeh.plotting import figure, show



 



p = figure(plot_width=400, plot_height=400)



p.segment(x0=[1, 2, 3], y0=[1, 2, 3], x1=[1.2, 2.4, 3.1],



          y1=[1.2, 2.5, 3.7], color="#F4A582", line_width=3)



 



show(p)

The ray() function accepts start points x, y with a length (in screen units) and an angle. The default angle_units are "rad" but can also be changed to "deg". To have an “infinite” ray, that always extends to the edge of the plot, specify 0 for the length:

from bokeh.plotting import figure, show



 



p = figure(plot_width=400, plot_height=400)



p.ray(x=[1, 2, 3], y=[1, 2, 3], length=45, angle=[30, 45, 60],



      angle_units="deg", color="#FB8072", line_width=2)



 



show(p)

Wedges and Arcs

from bokeh.plotting import figure, show



 



p = figure(plot_width=400, plot_height=400)



p.arc(x=[1, 2, 3], y=[1, 2, 3], radius=0.1, start_angle=0.4, end_angle=4.8, color="navy")



 



show(p)

from bokeh.plotting import figure, show



 



p = figure(plot_width=400, plot_height=400)



p.wedge(x=[1, 2, 3], y=[1, 2, 3], radius=0.2, start_angle=0.4, end_angle=4.8,



        color="firebrick", alpha=0.6, direction="clock")



 



show(p)

from bokeh.plotting import figure, show



 



p = figure(plot_width=400, plot_height=400)



p.annular_wedge(x=[1, 2, 3], y=[1, 2, 3], inner_radius=0.1, outer_radius=0.25,



                start_angle=0.4, end_angle=4.8, color="green", alpha=0.6)



 



show(p)

from bokeh.plotting import figure, show



 



p = figure(plot_width=400, plot_height=400)



p.annulus(x=[1, 2, 3], y=[1, 2, 3], inner_radius=0.1, outer_radius=0.25,



          color="orange", alpha=0.6)



 



show(p)

Combining Multiple Glyphs

from bokeh.plotting import figure, output_file, show



 



x = [1, 2, 3, 4, 5]



y = [6, 7, 8, 7, 3]



 



output_file("multiple.html")



 



p = figure(plot_width=400, plot_height=400)



 



# add both a line and circles on the same plot



p.line(x, y, line_width=2)



p.circle(x, y, fill_color="white", size=8)



 



show(p)

Setting Ranges

两种方法设置range：

1.可以通过从bokeh.models中导入Range1d(x,y)对象来实现：

By default, Bokeh will attempt to automatically set the data bounds of plots to fit snugly around the data. Sometimes you may need to set a plot’s range explicitly. This can be accomplished by setting the x_range or y_range properties using a Range1dobject that gives the start and end points of the range you want:

p.x_range = Range1d(0, 100)

2.在figure()里直接调用x_range()和y_range():

As a convenience, the figure() function can also accept tuples of (start, end) as values for the x_range or y_range parameters.

看一个例子：

from bokeh.plotting import figure, output_file, show




from bokeh.models import Range1d



 



output_file("title.html")



 



# create a new plot with a range set with a tuple



p = figure(plot_width=400, plot_height=400, x_range=(0, 20))



 



# set a range using a Range1d



p.y_range = Range1d(0, 15)



 



p.circle([1, 2, 3, 4, 5], [2, 5, 8, 2, 7], size=10)



 



show(p)

Specifying Axis Types

Categorical Axes

上面的所有例子中，x和y轴都是数字，有些时候希望坐标轴显示的是字符，可以使用如下方法：

from bokeh.plotting import figure, output_file, show



 



factors = ["a", "b", "c", "d", "e", "f", "g", "h"]



x = [50, 40, 65, 10, 25, 37, 80, 60]



 



output_file("categorical.html")



 



p = figure(y_range=factors)



 



p.circle(x, factors, size=15, fill_color="orange", line_color="green", line_width=3)



 



show(p)

此时，y轴是factors:

Log Scale Axes

from bokeh.plotting import figure, output_file, show



 



x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]



y = [10**xx for xx in x]



 



output_file("log.html")



 



# create a new plot with a log axis type



p = figure(plot_width=400, plot_height=400, y_axis_type="log")



 



p.line(x, y, line_width=2)



p.circle(x, y, fill_color="white", size=8)



 



show(p)

Twin Axes

from numpy import pi, arange, sin, linspace



 




from bokeh.plotting import output_file, figure, show




from bokeh.models import LinearAxis, Range1d



 



x = arange(-2*pi, 2*pi, 0.1)



y = sin(x)



y2 = linspace(0, 100, len(y))



 



output_file("twin_axis.html")



 



p = figure(x_range=(-6.5, 6.5), y_range=(-1.1, 1.1))



 



p.circle(x, y, color="red")



 



p.extra_y_ranges = {"foo": Range1d(start=0, end=100)}



p.circle(x, y2, color="blue", y_range_name="foo")



p.add_layout(LinearAxis(y_range_name="foo"), 'left')



 



show(p)