python - Add colorbar to existing axis

Question

I'm making some interactive plots and I would like to add a colorbar legend. I don't want the colorbar to be in its own axes, so I want to add it to the existing axes. I'm having difficulties doing this, as most of the example code I have found creates a new axes for the colorbar.

I have tried the following code using matplotlib.colorbar.ColorbarBase, which adds a colorbar to an existing axes, but it gives me strange results and I can't figure out how to specify attributes of the colorbar (for instance, where on the axes it is placed and what size it is)

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.cm import coolwarm
import numpy as np

x = np.random.uniform(1, 10, 10)
y = np.random.uniform(1, 10, 10)
v = np.random.uniform(1, 10, 10)

fig, ax = plt.subplots()

s = ax.scatter(x, y, c=v, cmap=coolwarm)

matplotlib.colorbar.ColorbarBase(ax=ax, cmap=coolwarm, values=sorted(v),
                                 orientation="horizontal")

Using fig.colorbar instead ofmatplotlib.colorbar.ColorbarBase still doesn't give me quite what I want, and I still don't know how to adjust the attributes of the colorbar.

fig.colorbar(s, ax=ax, cax=ax)

Let's say I want to have the colorbar in the top left corner, stretching about halfway across the top of the plot. How would I go about doing that?

Am I better off writing a custom function for this, maybe using LineCollection?

Answer 1

This technique is usually used for multiple axis in a figure. In this context it is often required to have a colorbar that corresponds in size with the result from imshow. This can be achieved easily with the axes grid tool kit:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

data = np.arange(100, 0, -1).reshape(10, 10)

fig, ax = plt.subplots()
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)

im = ax.imshow(data, cmap='bone')

fig.colorbar(im, cax=cax, orientation='vertical')
plt.show()