python - 关闭散景中的刻度线

Question

我正在研究 Bokeh (0.6.1) 教程并尝试关闭其中一个练习图中的刻度线和标签，散点图：

from __future__ import division

import numpy as np
from six.moves import zip
from bokeh.plotting import *
from bokeh.objects import Range1d

output_file("scatter.html")

figure()

N = 4000

x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = [
    "#%02x%02x%02x" % (r, g, 150) 
    for r, g in zip(np.floor(50+2*x), np.floor(30+2*y))
]

circle(x, y, radius=radii,
       fill_color=colors, fill_alpha=0.6,
       line_color=None, Title="Colorful Scatter")

grid().grid_line_color = None
axis().axis_line_color = None

# QUESTION PART 1: Is this the right way to turn off tick labels?
axis().major_label_text_font_size = '0pt'  
# QUESTION PART 2: ...and how to turn off tick marks also?

show()  # open a browser

我设法关闭了刻度标签，但没有多少搜索文档和谷歌搜索显示关闭刻度线所需的咒语。

此外，我不确定设置axis().major_label_text_font_size为0pt是否是关闭刻度标签的正确方法，或者它是否是一个 kludge。似乎没有其他任何工作。

我错过了一些明显的东西吗？

Answer 1

这个答案是更新的 Bokeh 0.12.4 版本的更新。有关其他信息，这些命令取自Bokeh 文档的样式化视觉属性页面。

要关闭主要和次要刻度线，请将它们的颜色设置为None：

p = bokeh.plotting.figure(plot_width=400, plot_height=400)
p.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p.xaxis.major_tick_line_color = None  # turn off x-axis major ticks
p.xaxis.minor_tick_line_color = None  # turn off x-axis minor ticks

p.yaxis.major_tick_line_color = None  # turn off y-axis major ticks
p.yaxis.minor_tick_line_color = None  # turn off y-axis minor ticks

要关闭刻度标签，请将字体大小设置为'0pt'：

p.xaxis.major_label_text_font_size = '0pt'  # turn off x-axis tick labels
p.yaxis.major_label_text_font_size = '0pt'  # turn off y-axis tick labels

通过将字体颜色设置为“无”可以实现类似的结果，缺点是仍然为刻度标签保留空间。

p.xaxis.major_label_text_color = None  # turn off x-axis tick labels leaving space
p.yaxis.major_label_text_color = None  # turn off y-axis tick labels leaving space 

此代码片段举例说明了删除主要和次要刻度线以及刻度标签。

import bokeh.io
import bokeh.plotting
import bokeh.layouts
bokeh.io.output_file('remove_tick_marks.html')

p0 = bokeh.plotting.figure(plot_width=200, plot_height=200, 
                           x_axis_label='x', y_axis_label='y', 
                           title='original')
p0.circle([1,2,3,4,5], [2,5,8,2,7], size=10)

p1 = bokeh.plotting.figure(plot_width=200, plot_height=200, 
                           x_axis_label='x', y_axis_label='y', 
                           title='remove tick marks')
p1.circle([1,2,3,4,5], [2,5,8,2,7], size=10)
p1.xaxis.major_tick_line_color = None  # turn off x-axis major ticks
p1.xaxis.minor_tick_line_color = None  # turn off x-axis minor ticks
p1.yaxis.major_tick_line_color = None  # turn off y-axis major ticks
p1.yaxis.minor_tick_line_color = None  # turn off y-axis minor ticks

p2 = bokeh.plotting.figure(plot_width=200, plot_height=200, 
                           x_axis_label='x', y_axis_label='y', 
                           title='remove tick labels')
p2.circle([1,2,3,4,5], [2,5,8,2,7], size=10)
p2.xaxis.major_tick_line_color = None  # turn off x-axis major ticks
p2.xaxis.minor_tick_line_color = None  # turn off x-axis minor ticks
p2.yaxis.major_tick_line_color = None  # turn off y-axis major ticks
p2.yaxis.minor_tick_line_color = None  # turn off y-axis minor ticks
p2.xaxis.major_label_text_font_size = '0pt'  # preferred method for removing tick labels
p2.yaxis.major_label_text_font_size = '0pt'  # preferred method for removing tick labels

p3 = bokeh.plotting.figure(plot_width=200, plot_height=200, 
                           x_axis_label='x', y_axis_label='y', 
                           title='notice extra space')
p3.circle([1,2,3,4,5], [2,5,8,2,7], size=10)
p3.xaxis.major_tick_line_color = None  # turn off x-axis major ticks
p3.xaxis.minor_tick_line_color = None  # turn off x-axis minor ticks
p3.yaxis.major_tick_line_color = None  # turn off y-axis major ticks
p3.yaxis.minor_tick_line_color = None  # turn off y-axis minor ticks
p3.xaxis.major_label_text_color = None  #note that this leaves space between the axis and the axis label  
p3.yaxis.major_label_text_color = None  #note that this leaves space between the axis and the axis label  

grid = bokeh.layouts.gridplot([[p0, p1, p2, p3]])
bokeh.io.show(grid)

Answer 2

我不确定一个多星期没有答案是因为人们不知道，还是因为这个问题太明显而被忽略了。

无论如何，希望其他人可能会发现它有用，我发布了这个答案。我找到了一种看起来很像黑客的方法，所以我发布它只是希望有人会改进它......

from __future__ import division

import numpy as np
from six.moves import zip
from bokeh.plotting import *

output_file("scatter.html")

figure()

N = 4000

x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = ["#%02x%02x%02x" % (r, g, 150) 
          for r, g in zip(np.floor(50+2*x), np.floor(30+2*y))]

circle(x, y, radius=radii,
       fill_color=colors, fill_alpha=0.6,
       line_color=None, Title="Colorful Scatter")

grid().grid_line_color = None
axis().axis_line_color = None
curplot().outline_line_color = None

# Turn off tick labels
axis().major_label_text_font_size = '0pt'  
# Turn off tick marks 
axis().major_tick_line_color = None  # turn off major ticks
axis()[0].ticker.num_minor_ticks = 0  # turn off minor ticks
axis()[1].ticker.num_minor_ticks = 0

show()  # open a browser