Define Aspect Ratio When Using Twinx In New Version Of Matplotlib

Current version of matplotlib do not allow box-forced anymore, how should I do the same thing as the answer? I am using matplotlib 3.1.0. After I ploted another set of data on the

Solution 1:

As I just commented on a respective matplotlib issue,

"aspect" in matplotlib always refers to the data, not the axes box. Therefore setting the aspect for twinned or shared axes and letting the box be adjustable actually only makes sense when the scales are the same - or differ by an offset (as opposed to any other linear or nonlinear function). Matplotlib does not perform any check on this, so it disallows for adjustable='box' in such case.

It seems to me that using aspect here is merely a workaround for getting a fixed ratio for the axes box. Matplotlib does not provide any clear codepath for that as of now, but one could e.g. force the axes box into a square space by adjusting the subplot parameters

import numpy as np
import matplotlib.pyplot as plt

def squarify(fig):
    w, h = fig.get_size_inches()
    if w > h:
        t = fig.subplotpars.top
        b = fig.subplotpars.bottom
        axs = h*(t-b)
        l = (1.-axs/w)/2
        fig.subplots_adjust(left=l, right=1-l)
    else:
        t = fig.subplotpars.right
        b = fig.subplotpars.left
        axs = w*(t-b)
        l = (1.-axs/h)/2
        fig.subplots_adjust(bottom=l, top=1-l)


x = np.linspace(0,1.6,50) + 50.0

fig, ax = plt.subplots()
ax2 = ax.twinx()

ax.set(xlim = [50.0, 51.6], ylim = [0.0, 1.1])
ax2.set(ylim = [0.0, 11.0])

ax.plot(x,np.sin(x-50.0),'b')
ax2.plot(x,np.cos(x-50.0)*10.,'r')

ax.grid(True, which='major',linestyle='solid')

squarify(fig)
fig.canvas.mpl_connect("resize_event", lambda evt: squarify(fig))

plt.show()

Also see this answer for more than one subplot.

If you want to use mpl_toolkits and make your hands dirty, this answer would be a good read.

Solution 2:

Thanks to @ImportanceOfBeingErnest, but to make this work in several subplots, I found another way inspired by your answer:

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import Divider, Size
from mpl_toolkits.axes_grid1.axes_divider import AxesDivider

defmake_patch_spines_invisible(ax):
    ax.set_frame_on(True)
    ax.patch.set_visible(False)
    for sp in ax.spines.values():
        sp.set_visible(False)

defdemo_fixed_size_axes():
    fig, axs = plt.subplots(1, 2, figsize=(12, 9))
    axs[0].plot([1, 2, 3])
    axs[1].plot([1, 2, 3.5])
    ax3 = axs[1].twinx()
    ax3.plot([1, 2, 3], [1, 25, 30])

    axs[1].spines['right'].set_visible(False)
    make_patch_spines_invisible(ax4Alt)
    ax4Alt.spines['right'].set_visible(True)

    for ax in fig.get_axes():
        figPos = AxesDivider(ax).get_position()
        h = [Size.Fixed(4)] # has to be fixed
        v = h

        divider = Divider(fig, figPos, h, v, aspect=False)

        ax.set_axes_locator(divider.new_locator(nx=0, ny=0))


if __name__ == "__main__":
    demo_fixed_size_axes()

    plt.show()

The disadvantage is that one has to decide which size to use in inches. I do not fully understand my code though...