我有一行数据要在不使用twiny（）的情况下进行热映射，因为它将存在对齐问题。
我在这个网站上搜索了很多东西，这就是我到目前为止所获得的：
import numpy as np
import matplotlib.pyplot as plt
x = "0.2, 0.3, 0.4, 0.5, 0.6".split(",")
y = "180, 175, 170, 169, 150".split(",")
z = [[5000, 4800, 4500, 4450, 4300]]
fig, ax1 = plt.subplots()
image = z
im = ax1.imshow(image, cmap=plt.cm.Blues, interpolation='nearest')
plt.colorbar(im)
ax1.set_xticks(np.arange(len(x)), minor=False)
ax1.set_xticklabels(x, minor=False)
#ax1.set_yticklabels(y, minor=False)
ax1.tick_params(labelbottom='on',labeltop='on', labelleft="off")
plt.show()
如您所见，上轴与下轴的文字完全相同。
我想要的是将
y放在上述轴上。提前致谢 ：）
最佳答案
似乎twiny和纵横比设置为equal的轴不想在相同的轴上运行。在我看来，这似乎是一个错误，但可能有一个解释。
因此，让我们通过在彼此顶部绘制两个轴来解决此问题。这听起来并不简单，因为如果两个子图位于同一位置，matplotlib会将它们解释为相同的图。但是，使用add_plot不会出现此类问题。
import numpy as np
import matplotlib.pyplot as plt
x = "0.2, 0.3, 0.4, 0.5, 0.6".split(",")
y = "180, 175, 170, 169, 150".split(",")
z = [[5000, 4800, 4500, 4450, 4300]]
fig = plt.figure()
ax1 = fig.add_subplot(111)
image = z
im = ax1.imshow(image, cmap=plt.cm.Blues, interpolation='nearest')
plt.colorbar(im)
ax1.set_xticks(np.arange(len(x)), minor=False)
ax1.set_xticklabels(x, minor=False)
ax1.tick_params(labelbottom='on',labeltop='off', labelleft="off",
top='off', left='off', right='off')
# create another axes on the same position:
# - create second axes on top of the first one without background
# - make the background invisible
# - set the x scale according to that of `ax1`
# - set the top ticks on and everything else off
# - set the size according to the size of `ax1`
ax2 = fig.add_axes(ax1.get_position(), frameon=False)
ax2.tick_params(labelbottom='off',labeltop='on', labelleft="off", labelright='off',
bottom='off', left='off', right='off')
ax2.set_xlim(ax1.get_xlim())
ax2.set_xticks(np.arange(len(y)))
ax2.set_xticklabels(y, minor=False)
plt.draw()
ax2.set_position(ax1.get_position())
plt.draw()
plt.show()
在
plt.draw()之前需要set_position，否则由于使用get_position方面，ax1将为equal返回错误的位置。 （这可能是twiny失败的原因。）如果需要多行，则解决方案没有什么不同：
import numpy as np
import matplotlib.pyplot as plt
x = "0.2, 0.3, 0.4, 0.5, 0.6".split(",")
y = "180, 175, 170, 169, 150".split(",")
z = [[5000, 4800, 4500, 4450, 4300]]
numRows = 8
fig, subaxes = plt.subplots(nrows=numRows, ncols=1)
axeslist = subaxes.flatten()
for ax in axeslist:
im = ax.imshow(z, cmap=plt.cm.Blues, interpolation='nearest')
ax.tick_params(labelbottom='off',labeltop='off', labelleft="off", labelright='off',
bottom='off', top='off', left='off', right='off')
if ax == axeslist[0]:
ax.set_title('Avg. (s)\n', size=13)
elif ax == axeslist[-1]:
ax.tick_params(bottom='on', labelbottom='on')
ax.set_xticks(range(len(x)))
ax.set_xticklabels(x)
# reserve some space between the subplots
fig.subplots_adjust(hspace=0.07*(numRows-1))
# create the overlay images, add them as extra properties of the original images
for ax in axeslist:
axnew = fig.add_axes(ax.get_position(), frameon=False)
axnew.tick_params(labelbottom='off',labeltop='on', labelleft="off", labelright='off',
bottom='off', top='on', left='off', right='off')
axnew.set_xlim(ax.get_xlim())
axnew.set_xticks(range(len(y)))
axnew.set_xticklabels(y)
ax.extra_axes = axnew
# update the secondary axes positions
# draw() only if there was something changed (important!)
def update_secondary(event=None):
position_changed = False
for ax in axeslist:
if ax.extra_axes.get_position().bounds == ax.get_position().bounds:
continue
position_changed = True
ax.extra_axes.set_position(ax.get_position())
if position_changed:
plt.draw()
# register the secondary axes updater as a callback
fig.canvas.mpl_connect('draw_event', update_secondary)
# make sure everything is drawn
plt.draw()
由于必须在绘制完所有其他内容之后执行覆盖更新，因此此处由后端的
draw_event完成。结果是，由于某种原因重新绘制了图像后，将重新调整叠加层，并且如果更改了任何位置，则将重新绘制场景。这有效，但并不美观。