Matplotlib：将右轴的原点与特定的左轴值对齐

Question

当在Matplotlib中绘制多个y轴时，是否有方法指定如何将右轴的原点（和/或一些ytick标签）与特定值左轴的？

这是我的问题：我想绘制两组数据以及它们的区别（基本上，我试图重现this kind of graph）。

我可以重现它，但我必须手动调整右轴的ylim，以便原点与我想要的左轴对齐。

我推出了一个我使用的代码简化版本的例子。如您所见，我必须手动调整右轴的比例以对齐右轴的原点以及正方形。

import numpy as np 
import scipy as sp 
import matplotlib.pyplot as plt 

grp1 = np.array([1.202, 1.477, 1.223, 1.284, 1.701, 1.724, 1.099, 
       1.242, 1.099, 1.217, 1.291, 1.305, 1.333, 1.246]) 
grp2 = np.array([1.802, 2.399, 2.559, 2.286, 2.460, 2.511, 2.296, 
       1.975]) 

fig = plt.figure(figsize=(6, 6)) 
ax = fig.add_axes([0.17, 0.13, 0.6, 0.7]) 

# remove top and right spines and turn ticks off if no spine 
ax.spines['right'].set_color('none') 
ax.spines['top'].set_color('none') 
ax.spines['bottom'].set_color('none') 
ax.xaxis.set_ticks_position('none') 
ax.yaxis.set_ticks_position('left') 
# postition of tick out 
ax.tick_params(axis='both', direction='out', width=3, length=7, 
     labelsize=24, pad=8) 
ax.spines['left'].set_linewidth(3) 

# plot groups vs random numbers to create dot plot 
ax.plot(np.random.normal(1, 0.05, grp2.size), grp2, 'ok', markersize=10) 
ax.plot(np.random.normal(2, 0.05, grp1.size), grp1, 'ok', markersize=10) 
ax.errorbar(1, np.mean(grp2), fmt='_r', markersize=50, 
     markeredgewidth=3) 
ax.errorbar(2, np.mean(grp1), fmt='_r', markersize=50, 
     markeredgewidth=3) 


ax.set_xlim((0.5, 3.5)) 
ax.set_ylim((0, 2.7)) 

# create right axis 
ax2 = fig.add_axes(ax.get_position(), sharex=ax, frameon=False) 
ax2.spines['left'].set_color('none') 
ax2.spines['top'].set_color('none') 
ax2.spines['bottom'].set_color('none') 
ax2.xaxis.set_ticks_position('none') 
ax2.yaxis.set_ticks_position('right') 
# postition of tick out 
ax2.tick_params(axis='both', direction='out', width=3, length=7, 
     labelsize=24, pad=8) 
ax2.spines['right'].set_linewidth(3) 
ax2.set_xticks([1, 2, 3]) 
ax2.set_xticklabels(('gr2', 'gr1', 'D')) 
ax2.hlines(0, 0.5, 3.5, linestyle='dotted') 
#ax2.hlines((np.mean(adult)-np.mean(nrvm)), 0, 3.5, linestyle='dotted') 

ax2.plot(3, (np.mean(grp1)-np.mean(grp2)), 'sk', markersize=12) 

# manual adjustment so the origin is aligned width left group2 
ax2.set_ylim((-2.3, 0.42)) 
ax2.set_xlim((0.5, 3.5)) 

plt.show() 

Answer 1

你可以做一个小功能，用于计算ax2对齐：使用align_yaxis()

def align_yaxis(ax1, v1, ax2, v2): 
    """adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1""" 
    _, y1 = ax1.transData.transform((0, v1)) 
    _, y2 = ax2.transData.transform((0, v2)) 
    inv = ax2.transData.inverted() 
    _, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2)) 
    miny, maxy = ax2.get_ylim() 
    ax2.set_ylim(miny+dy, maxy+dy) 

，您可以快速对准轴：

#...... your code 

# adjustment so the origin is aligned width left group2 
ax2.set_ylim((0, 2.7)) 
align_yaxis(ax, np.mean(grp2), ax2, 0) 
plt.show() 

Answer 2

以上答案是好的，但有时会删除数据，这里更详细地回答第二个答案，

Matplotlib axis with two scales shared origin

或一个快速黑客

def align_yaxis(ax1, v1, ax2, v2, y2min, y2max): 
    """adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1.""" 

    """where y2max is the maximum value in your secondary plot. I haven't 
    had a problem with minimum values being cut, so haven't set this. This 
    approach doesn't necessarily make for axis limits at nice near units, 
    but does optimist plot space""" 

    _, y1 = ax1.transData.transform((0, v1)) 
    _, y2 = ax2.transData.transform((0, v2)) 
    inv = ax2.transData.inverted() 
    _, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2)) 
    miny, maxy = ax2.get_ylim() 
    scale = 1 
    while scale*(maxy+dy) < y2max: 
     scale += 0.05 
    ax2.set_ylim(scale*(miny+dy), scale*(maxy+dy))