matplotlib:每个x轴刻度线的分组误差线 [英] matplotlib: grouping error bars for each x-axes tick

问题描述

我正在尝试使用matplotlib绘制误差线，但要求略有不同.所以，设置如下:

I am trying to use matplotlib to plot error bars but have a slightly different requirements. So, the setup is as follows:

我有 3 种不同的方法，我正在比较 10 种不同的参数设置.因此，在 y 轴上，我有 3 种方法给出的模型拟合误差，在 x 轴上，我有不同的参数设置.

I have 3 different methods that I am comparing across 10 different parameter setting. So, on the y-axes I have the model fitting errors as given by the 3 methods and on the x-axes, I have the different parameter settings.

因此，对于每个参数设置，我想获得与这三种方法相对应的3个误差线图.理想情况下，我想为每个参数设置绘制 95% 置信区间以及每种方法的最小值和最大值.

So, for each parameter setting, I would like to get 3 error bar plots corresponding to the three methods. Ideally, I would like to plot the 95% confidence interval and also the minimum and maximum for each method for each parameter setting.

一些示例数据可以模拟为:

Some example data can be simulated as:

parameters = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
mean_1 = [10.1, 12.1, 13.6, 14.5, 18.8, 11.8, 28.5]
std_1 = [2.6, 5.7, 4.3, 8.5, 11.8, 5.3, 2.5]

mean_2 = [10.1, 12.1, 13.6, 14.5, 18.8, 11.8, 28.5]
std_1 = [2.6, 5.7, 4.3, 8.5, 11.8, 5.3, 2.5]

mean_3 = [10.1, 12.1, 13.6, 14.5, 18.8, 11.8, 28.5]
std_3 = [2.6, 5.7, 4.3, 8.5, 11.8, 5.3, 2.5]

我将这些值保持不变，因为从绘图的角度来看，它没有任何改变.我看到了 matplotlib.errorbar 方法，但我不知道如何像在我的情况下那样在一个 x 轴值上为多种方法扩展它.另外，我不确定如何为每种方法添加 [min，max] 标记.

I have kept the values same as it does not change anything from the plotting point of view. I see matplotlib.errorbar method but I do not know how to extend it for multiple methods over one single x-axes value as I have in my case. Additionally, I am not sure how to add the [min, max] markers for each of the methods.

推荐答案

将您的参数列表作为x轴，将 mean_1 作为y值并将 std_1 作为错误，您可以使用

Taking your parameters list as x axis, mean_1 as y value and std_1 as errors you can plot an errorbar chart with

pylab.errorbar(parameters, mean_1, yerr=std_1, fmt='bo')

如果误差条不对称，即您有 lower_err 和 upper_err，则语句读取

In case the error bars are not symmetric, i.e. you have lower_err and upper_err, the statement reads

pylab.errorbar(parameters, mean_1, yerr=[lower_err, upper_err], fmt='bo')

对于x方向错误，关键字 xerr 也可以使用，现在希望可以不言自明.要显示几个(在您的情况下为3个)不同的数据集，您可以采用以下方式:

The same works with keyword xerr for errors in x direction, which is now hopefully self-explanatory. To show several (in your case 3) different datasets, you can go the following way:

# import pylab and numpy
import numpy as np
import pylab as pl

# define datasets
parameters = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
mean_1 = [10.1, 12.1, 13.6, 14.5, 18.8, 11.8, 28.5]
std_1 = [2.6, 5.7, 4.3, 8.5, 11.8, 5.3, 2.5]

mean_2 = [10.1, 12.1, 13.6, 14.5, 18.8, 11.8, 28.5]
std_2 = [2.6, 5.7, 4.3, 8.5, 11.8, 5.3, 2.5]

mean_3 = [10.1, 12.1, 13.6, 14.5, 18.8, 11.8, 28.5]
std_3 = [2.6, 5.7, 4.3, 8.5, 11.8, 5.3, 2.5]


# here comes the plotting;
# to achieve a grouping, two things are extra here: 
# 1. Don't use line plot but circular markers and different marker color
# 2. slightly displace the datasets in x direction to avoid overlap 
#    and create visual grouping
pl.errorbar(np.array(parameters)-0.01, mean_1, yerr=std_1, fmt='bo')
pl.errorbar(parameters, mean_2, yerr=std_2, fmt='go')
pl.errorbar(np.array(parameters)+0.01, mean_3, yerr=std_3, fmt='ro')
pl.show()

这是关于 pylab.errorbar，您必须在其中明确给出错误.另一种方法是使用 pylab.boxplot 并为每个模型制作一个箱形图，但是因此，我想我需要每个模型每个参数的完整分布，而不仅仅是均值和标准差.

This is about pylab.errorbar, where you have to give the errors explicitly. An alternative approach is to use pylab.boxplot and to prodice a boxplot for each model, but therefore I guess I'll need the full distribution per model per parameter instead of just mean and std.

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