为什么matplotlib需要在plt.scatter（）之前设置日志比例而不是plt.plot（）？

Question

I found out in this helpful answer that plt.scatter() and plt.plot() behave differently when a logrithmic scale is used on the y axis.

With plot , I can change to log any time before I use plt.show() , but log has to be set up-front, before the scatter method is used.

Is this just a historical and irreversible artifact in matplotlib, or is this in the 'unexpected behavior' category?

import matplotlib.pyplot as plt

X = [0.997, 2.643, 0.354, 0.075, 1.0, 0.03, 2.39, 0.364, 0.221, 0.437]
Y = [15.487507, 2.320735, 0.085742, 0.303032, 1.0, 0.025435, 4.436435,
     0.025435, 0.000503, 2.320735]

plt.figure()

plt.subplot(2,2,1)
plt.scatter(X, Y)
plt.xscale('log')
plt.yscale('log')
plt.title('scatter - scale last')   

plt.subplot(2,2,2)
plt.plot(X, Y)
plt.xscale('log')
plt.yscale('log')
plt.title('plot - scale last')   

plt.subplot(2,2,3)
plt.xscale('log')
plt.yscale('log')
plt.scatter(X, Y)
plt.title('scatter - scale first')   


plt.subplot(2,2,4)
plt.xscale('log')
plt.yscale('log')
plt.plot(X, Y)
plt.title('plot - scale first')   


plt.show()

Answer 1

This somehow has to do with the the display area (axes limits) calculated by matplotlib .

This behaviour is fixed by manually editing the axes range by using set_xlim and set_ylim methods.

plt.figure()
plt.scatter(X, Y)
plt.yscale('log')
plt.xscale('log')
axes = plt.gca()
axes.set_xlim([min(X),max(X)])
axes.set_ylim([min(Y),max(Y)])
plt.show()

The exact reason of this behavior is however not yet figured out by me. Suggestions are welcomed.

EDIT

As mentioned in comments section, apparently Matplotlib has identified Autoscaling has fundamental problems as a Release Critical Issue on their official Github repo, which would be fixed in upcoming versions. Thanks.