Python Matplotlib多网格zorder不使用错误条

Python Matplotlib多网格zorder不使用错误条,python,matplotlib,Python,Matplotlib,我目前正在处理matplotlib在将多个具有不同y轴的错误条绘制到一个子图时的一些奇怪行为 执行此操作时,与第二个errorbar图对应的网格始终与第一个errorbar图重叠。使用zorder选项,我只能将另一个网格移动到错误条下方 我希望两个网格都在两个错误条下面。 有人知道这个问题的解决方案吗,或者这是matplotlib中的一个bug 我正在使用Python 2.7.12和matplotlib 1.5.1 最简单的工作示例: import numpy as np import matp

我目前正在处理matplotlib在将多个具有不同y轴的错误条绘制到一个子图时的一些奇怪行为

执行此操作时,与第二个errorbar图对应的网格始终与第一个errorbar图重叠。使用
zorder
选项,我只能将另一个网格移动到错误条下方

我希望两个网格都在两个错误条下面。 有人知道这个问题的解决方案吗,或者这是matplotlib中的一个bug

我正在使用Python 2.7.12和matplotlib 1.5.1

最简单的工作示例:

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.colors as colors

# numbers of epochs
ttimes_means_list = [251.4, 153.3, 22.0, 202.1, 46.6]
ttimes_stddevs_list = [32.1, 35.1, 12.0, 84.9, 14.7]
# numbers of times
times_means_list = [5231, 3167, 860, 3932, 1244]
times_stddevs_list = [1381, 572, 253, 1445, 215]

labels = ['x1', 'x2', 'x3', 'x4', 'x5']
linewidth=3

xvalues = np.arange(0, len(times_means_list), 1)
xvalues_s = xvalues + 0.1

fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.set_ylabel('Number')
ax1.xaxis.grid(False)
ax1.yaxis.grid(True, color='navy', linewidth=linewidth/2.0)
ax2.xaxis.grid(False)
ax1.set_zorder(0)
ax2.set_zorder(0)
ax2.yaxis.grid(True, color='darkorange', linewidth=linewidth/2.0)
ax2.set_ylabel('Time')
ax1.set_xticks(xvalues)
ax2.set_xticks(xvalues)
ax1.set_xticklabels(labels)
ax2.set_xticklabels(labels)
ax1.set_xlabel('x label')
errplot1 = ax1.errorbar(xvalues, ttimes_means_list, yerr=ttimes_stddevs_list,
        linestyle="None", elinewidth=linewidth, color='navy', label='Number',
        capthick=linewidth, zorder=10)
errplot2 = ax2.errorbar(xvalues_s, times_means_list, yerr=times_stddevs_list, 
        linestyle="None", elinewidth=linewidth, color='darkorange',
        label='Time', capthick=linewidth, zorder=10)
ax1.set_axisbelow(True)
ax2.set_axisbelow(True)
fig.legend((errplot1, errplot2), ('number', 'time'), 
        loc='upper right', numpoints=1)
plt.xlim(-0.5, len(times_means_list)-0.5)
plt.title('Some plot title')
plt.savefig('mwe_plot.pdf')
plt.clf()
输出绘图(橙色网格点与蓝色条重叠):

也许你可以画两次“海军”线

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.colors as colors

# numbers of epochs
ttimes_means_list = [251.4, 153.3, 22.0, 202.1, 46.6]
ttimes_stddevs_list = [32.1, 35.1, 12.0, 84.9, 14.7]
# numbers of times
times_means_list = [5231, 3167, 860, 3932, 1244]
times_stddevs_list = [1381, 572, 253, 1445, 215]

labels = ['x1', 'x2', 'x3', 'x4', 'x5']
linewidth=3

xvalues = np.arange(0, len(times_means_list), 1)
xvalues_s = xvalues + 0.1

fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.set_ylabel('Number')
ax1.xaxis.grid(False)
g1 = ax1.yaxis.grid(True, color='navy', linewidth=linewidth/2.0)
ax2.xaxis.grid(False)
ax1.set_zorder(0)
ax2.set_zorder(0)
g2 = ax2.yaxis.grid(True, color='darkorange', linewidth=linewidth/2.0)
ax2.set_ylabel('Time')
ax1.set_xticks(xvalues)
ax2.set_xticks(xvalues)
ax1.set_xticklabels(labels)
ax2.set_xticklabels(labels)
ax1.set_xlabel('x label')
ax1.errorbar(xvalues, ttimes_means_list, yerr=ttimes_stddevs_list,
        linestyle="None", elinewidth=1, color='navy', label='Number',
        capthick=1, zorder=10,)
errplot2 = ax2.errorbar(xvalues_s, times_means_list, yerr=times_stddevs_list, 
        linestyle="None", elinewidth=linewidth, color='darkorange',
        label='Time', capthick=linewidth, zorder=10)
ax1.set_axisbelow(True)
ax2.set_axisbelow(True)

# draw the 'navy' line again
ax3 = ax1.twinx()
ax3.yaxis.grid(False)
errplot1 = ax3.errorbar(xvalues, ttimes_means_list, yerr=ttimes_stddevs_list,
        linestyle="None", elinewidth=linewidth, color='navy', label='Number',
        capthick=linewidth, zorder=10)

fig.legend((errplot1, errplot2), ('number', 'time'), 
        loc='upper right', numpoints=1)
plt.xlim(-0.5, len(times_means_list)-0.5)
plt.title('Some plot title')
# plt.savefig('mwe_plot.pdf')
# plt.clf()

plt.show()
您将获得:

非常感谢!事实上,这解决了问题。我刚刚添加了
ax3.get_yaxis().set_visible(False)
,以便去掉右侧的附加标签。@ml4294感谢您的改进!