signals=["xlim_changed", "ylim_changed", "zlim_changed",
 "xscale_changed", "yscale_changed", "zscale_changed"])

self.axes.callbacks.process(f"{name}scale_changed", value)

from mpl_toolkits.axisartist.parasite_axes import HostAxes
import matplotlib.pyplot as plt
def xscale_onchange_host(axes):
 print(f"Host: {axes.get_xscale()}")
def xscale_onchange_par2(axes):
 print(f"Parasite 2: {axes.get_xscale()}")
def xscale_onchange_par1(axes):
 print(f"Parasite 1: {axes.get_xscale()}")
fig = plt.figure()
ax = fig.add_subplot(111)
ax.callbacks.connect('xscale_changed', xscale_onchange_host)
ax.set_xscale('log')
host = fig.add_axes([0.15, 0.1, 0.65, 0.8], axes_class=HostAxes)
par1 = host.get_aux_axes(viewlim_mode=None, sharex=host)
par2 = host.get_aux_axes(viewlim_mode=None, sharex=host)
host.axis["right"].set_visible(False)
par1.axis["right"].set_visible(True)
par1.axis["right"].major_ticklabels.set_visible(True)
par1.axis["right"].label.set_visible(True)
par1.axes.callbacks.connect('xscale_changed', xscale_onchange_par1)
par1.axes.set_xscale("linear")
par2.axis["right2"] = par2.new_fixed_axis(loc="right", offset=(60, 0))
par2.axes.callbacks.connect('xscale_changed', xscale_onchange_par2)
par2.axes.set_xscale("linear")
p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature")
p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity")
host.set(xlim=(0, 2), ylim=(0, 2), xlabel="Distance", ylabel="Density")
par1.set(ylim=(0, 4), ylabel="Temperature")
par2.set(ylim=(1, 65), ylabel="Velocity")
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
par2.axis["right2"].label.set_color(p3.get_color())
plt.show()

import numpy as np
import matplotlib.pyplot as plt
# setup some generic data
N = 37
x, y = np.mgrid[:N, :N]
Z = (np.cos(x*0.2) + np.sin(y*0.3))
# mask out the negative and positive values, respectively
Zpos = np.ma.masked_less(Z, 0)
Zneg = np.ma.masked_greater(Z, 0)
fig, (ax1, ax2, ax3) = plt.subplots(figsize=(13, 3), ncols=3)
def ax3scale_change(axes):
 print(f"Color bar: {axes.get_xscale()}")
ax3.callbacks.connect('xscale_changed', ax3scale_change)
ax3.set_xscale('linear')
# plot just the positive data and save the
# color "mappable" object returned by ax1.imshow
pos = ax1.imshow(Zpos, cmap='Blues', interpolation='none')
# add the colorbar using the figure's method,
# telling which mappable we're talking about and
# which axes object it should be near
fig.colorbar(pos, ax=ax1)
# repeat everything above for the negative data
# you can specify location, anchor and shrink the colorbar
neg = ax2.imshow(Zneg, cmap='Reds_r', interpolation='none')
fig.colorbar(neg, ax=ax2, location='right', anchor=(0, 0.3), shrink=0.7)
# Plot both positive and negative values between +/- 1.2
pos_neg_clipped = ax3.imshow(Z, cmap='RdBu', vmin=-1.2, vmax=1.2,
 interpolation='none')
# Add minorticks on the colorbar to make it easy to read the
# values off the colorbar.
cbar = fig.colorbar(pos_neg_clipped, ax=ax3, extend='both')
cbar.minorticks_on()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
def xscale_onchange(axes):
 print(f"X scale: {axes}")
def yscale_onchange(axes):
 print(f"Y scale: {axes}")
def zscale_onchange(axes):
 print(f"Z scale: {axes}")
ax = plt.figure().add_subplot(projection='3d')
# Plot a sin curve using the x and y axes.
x = np.linspace(0, 1, 100)
y = np.sin(x * 2 * np.pi) / 2 + 0.5
ax.plot(x, y, zs=0, zdir='z', label='curve in (x, y)')
# Plot scatterplot data (20 2D points per colour) on the x and z axes.
colors = ('r', 'g', 'b', 'k')
# Fixing random state for reproducibility
np.random.seed(19680801)
x = np.random.sample(20 * len(colors))
y = np.random.sample(20 * len(colors))
c_list = []
for c in colors:
 c_list.extend([c] * 20)
# By using zdir='y', the y value of these points is fixed to the zs value 0
# and the (x, y) points are plotted on the x and z axes.
ax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x, z)')
ax.callbacks.connect('xscale_changed', xscale_onchange)
ax.callbacks.connect('yscale_changed', yscale_onchange)
ax.callbacks.connect('zscale_changed', zscale_onchange)
# Make legend, set axes limits and labels
ax.legend()
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.set_zlim(0, 1)
ax.set_xscale('linear')
ax.set_yscale('linear')
ax.set_zscale('linear')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
# Customize the view angle so it's easier to see that the scatter points lie
# on the plane y=0
ax.view_init(elev=20., azim=-35, roll=0)
plt.show()