Creating a colorbar shifts two formerly aligned axis objects relative to each other - Matplotlib

Question

My in my "real world" problem i want to recalculate the x y values written in the tick labeling of my figure after i have zoomed in it in such a way that the origin is always at (0,0) and obviously the relative distances of the values on the x and y axis stay the same. My problem was solved in this thread: Initial solution

The solution includes the creation of one invisible axis that holds the plot and one visible axis that gets different tick lables after zooming.

In my case i want to superimpose multiple countor and coutourf plots. For only one of those plots i want to add a colorbar to the figure! But when i create the colorbar in my script, the two axis objects i have created shift relative to each other. Without the colorbar they are perfectly aligned!

Here is a MWE that roughly recreates the behavior:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import mlab, cm

# Default delta is large because that makes it fast, and it illustrates
# the correct registration between image and contours.
delta = 0.5

extent = (-3, 4, -4, 3)

x = np.arange(-3.0, 4.001, delta)
y = np.arange(-4.0, 3.001, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = (Z1 - Z2) * 10

levels = np.arange(-2.0, 1.601, 0.4)  # Boost the upper limit to avoid truncation errors.

norm = cm.colors.Normalize(vmax=abs(Z).max(), vmin=-abs(Z).max())
cmap = cm.PRGn




# ax is empty
fig, ax = plt.subplots()
ax.set_navigate(False)
# ax2 will hold the plot, but has invisible labels
ax2 = fig.add_subplot(111,zorder=2)

ax2.contourf(X, Y, Z, levels,
                 cmap=cm.get_cmap(cmap, len(levels) - 1),
                 norm=norm,
                 )
ax2.axis("off")

ax.set_xlim(ax2.get_xlim())
ax.set_ylim(ax2.get_ylim())

#
# Declare and register callbacks
def on_lims_change(axes):
    # change limits of ax, when ax2 limits are changed.
    a=ax2.get_xlim()
    ax.set_xlim(0, a[1]-a[0])
    a=ax2.get_ylim()
    ax.set_ylim(0, a[1]-a[0])



sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm ) #Do not show unnecessary parts of the colormap
sm._A = []
cb = plt.colorbar(sm,extend="both", label="units")
cb.ax.tick_params(labelsize=10)


ax2.callbacks.connect('xlim_changed', on_lims_change)
ax2.callbacks.connect('ylim_changed', on_lims_change)
ax.axis('scaled')
plt.axis('scaled')
# Show
plt.show()

Now the contourplot seems to be shifted realtive to the visible axis. I found a few hints online that suggest, that the "colorbar box automatically eats up space from the axes to which it is attached" Link1 Link2

But i do not really know what i need to do to change this behavior nor do i understand if my issue is related.

Please note, that the part:

ax.axis('scaled')
plt.axis('scaled')

is necessary as i need to keep the aspect ratio exactly like it is in the data set!

Thank you in advance!

Answer 1

You can change the position of ax (the empty axes with the labels) to the position of ax2 (the axes showing the data) after adding the colorbar via

ax.set_position(ax2.get_position())

Alternatively, create the colorbar by "steeling" the space from both axes,

cb = fig.colorbar(sm,ax=[ax,ax2], extend="both", label="units")

Both solutions are found in the answers to this linked question.

---

The following are some additional improvements outside the actual scope of the question:

ax.axis('scaled')
ax2.axis('scaled') 

Additionally, put the ax on top if the ax2, such that the contourf plot does not overlap the axes spines.

# put `ax` on top, to let the contours not overlap the shown axes
ax.set_zorder(2)     
ax.patch.set_visible(False)
# ax2 will hold the plot, but has invisible labels
ax2 = fig.add_subplot(111,zorder=1)

Complete code:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import mlab, cm

delta = 0.5
extent = (-3, 4, -4, 3)
x = np.arange(-3.0, 4.001, delta)
y = np.arange(-4.0, 3.001, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = (Z1 - Z2) * 10

levels = np.arange(-2.0, 1.601, 0.4) 

norm = cm.colors.Normalize(vmax=abs(Z).max(), vmin=-abs(Z).max())
cmap = cm.PRGn

# ax is empty
fig, ax = plt.subplots()
ax.set_navigate(False)
 # put `ax` on top, to let the contours not overlap the shown axes
ax.set_zorder(2)     
ax.patch.set_visible(False)
# ax2 will hold the plot, but has invisible labels
ax2 = fig.add_subplot(111,zorder=1)

ax2.contourf(X, Y, Z, levels,
                 cmap=cm.get_cmap(cmap, len(levels) - 1),
                 norm=norm,
                 )
ax2.axis("off")

ax.set_xlim(ax2.get_xlim())
ax.set_ylim(ax2.get_ylim())

#
# Declare and register callbacks
def on_lims_change(axes):
    # change limits of ax, when ax2 limits are changed.
    a=ax2.get_xlim()
    ax.set_xlim(0, a[1]-a[0])
    a=ax2.get_ylim()
    ax.set_ylim(0, a[1]-a[0])


sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm )
sm._A = []
cb = fig.colorbar(sm,ax=[ax,ax2], extend="both", label="units")
cb.ax.tick_params(labelsize=10)

ax2.callbacks.connect('xlim_changed', on_lims_change)
ax2.callbacks.connect('ylim_changed', on_lims_change)
ax.axis('scaled')
ax2.axis('scaled')
#ax.set_position(ax2.get_position())
# Show
plt.show()