Matplotlib：将右轴的原点与特定的左轴值对齐

Question

在 Matplotlib 中绘制多个 y 轴时，有没有办法指定如何将右轴的原点（和/或一些 ytick 标签）与左轴的特定值对齐？

这是我的问题：我想绘制两组数据以及它们的差异（基本上，我试图重现 这种图）。

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值对齐。

我在下面给出了我使用的代码的简化版本的示例。如您所见，我必须手动调整右轴的比例以对齐右轴和正方形的原点。

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

您可以编写一个小函数来计算ax2的对齐方式：

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)

通过使用align_yaxis()，您可以快速对齐轴：

#...... 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

上面的答案是好的，但有时会删掉数据，这里的第二个答案中有更彻底的回答，

Matplotlib 轴具有两个比例共享原点

或快速破解

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))