我有一行数据，我想在不使用 twiny() 的情况下对其进行热映射，因为它会出现对齐问题。 我在这个网站上搜索了很多，这就是我到目前为止所达到的:
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，上轴的文本与下轴的文本完全相同。 我想要的是将 y 放在上面的轴上。
提前致谢:)
请您参考如下方法:
似乎twiny和纵横比设置为 equal 的轴不想生活在同一个轴上。对我来说，这似乎是一个错误，但可能会有一个解释。
因此，让我们通过在彼此之上绘制两个轴来解决这个问题。这并不像听起来那么微不足道，因为如果两个子图位于同一位置，matplotlib将它们解释为同一个。但是，使用 add_plot不存在这样的问题。
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()
plt.draw()在 set_position 之前需要否则 get_position将为 ax1 返回错误的位置由于使用 equal方面。 (这可能就是 twiny 失败的原因。)
如果你需要有多行，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()
由于覆盖更新必须在绘制完所有其他内容后执行，因此此处由 draw_event 完成从后端。结果是由于某种原因重新绘制图像后，重新调整叠加层，如果任何位置发生变化，都会重新绘制场景。
这可行但不漂亮。
