问题描述
在某些情况下，当使用对数刻度时，matplotlib 会错误地显示带有误差条的绘图.假设这些数据(例如在pylab中):
In some cases matplotlib shows plot with errorbars errorneously when using logarithmic scale. Suppose these data (within pylab for example):
s=[19.0, 20.0, 21.0, 22.0, 24.0]
v=[36.5, 66.814250000000001, 130.17750000000001, 498.57466666666664, 19.41]
verr=[0.28999999999999998, 80.075044597909169, 71.322124839818571, 650.11015891565125, 0.02]
errorbar(s,v,yerr=verr)
我得到了一个正常的结果，但是当我切换到对数刻度时:
and I get a normal result but when I switch to logarithmic scale:
yscale('log')
我得到一个图，其中一些误差条不可见，但您仍然可以看到一些误差条的上限.(见下文.)为什么会发生这种情况，我该如何解决?
I get a plot in which some errorbars are not visible, although you can still see some of the error bar caps. (See below.) Why is this happening, and how can I fix it?
推荐答案
问题是对于某些点， v-verr 变为负数，对数轴上无法显示< = 0的值(log(x), x 未定义)要解决此问题，您可以使用不对称错误并强制违规点的结果值高于零.
The problem is that for some points v-verr is becoming negative, values <=0 cannot be shown on a logarithmic axis (log(x), x<=0 is undefined) To get around this you can use asymmetric errors and force the resulting values to be above zero for the offending points.
在误差大于值 verr>=v 的任何点，我们分配 verr=.999v 在这种情况下，误差条将接近于零.
At any point for which errors are bigger than value verr>=v we assign verr=.999v in this case the error bar will go close to zero.
这是脚本
import matplotlib.pyplot as plt
import numpy as np
s=[19.0, 20.0, 21.0, 22.0, 24.0]
v=np.array([36.5, 66.814250000000001, 130.17750000000001, 498.57466666666664, 19.41])
verr=np.array([0.28999999999999998, 80.075044597909169, 71.322124839818571, 650.11015891565125, 0.02])
verr2 = np.array(verr)
verr2[verr>=v] = v[verr>=v]*.999999
plt.errorbar(s,v,yerr=[verr2,verr])
plt.ylim(1E1,1E4)
plt.yscale('log')
plt.show()
这是结果
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