Matplotlib: align the beginning of the right axis with a specific left axis value

Question

When building multiple y axes in Matplotlib, is there a way to specify how to align the origin (and / or some ytick labels) of the right axis with a specific value of the left axis?

Here is my problem: I would like to build two datasets, as well as their difference (basically, I'm trying to reproduce this kind of graph ).

I can play it, but I need to manually adjust the ylim of the right axis so that the origin is aligned with the value I want from the left axis.

I have provided an example of a simplified version of the code used below. As you can see, I need to manually adjust the scale of the right axis to align the beginning of the right axis, as well as the square.

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

You can make a small function that computes ax2 alignment:

 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) 

using align_yaxis() , you can quickly align the axes:

 #...... 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

The above answer is “Good”, but sometimes it cuts out the data, in more detail it answers in the second answer here,

Matplotlib axis with two scales of common origin

or with a quick hack

 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))