I'm trying to plot a value around the unit sphere using surface plot and facecolors in matplotlib, but my colorbar shows the normalized values instead of the real values. How can I fix this so the colorbar has the right range? import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import cm fig, ax = plt.subplots(subplot_kw={"projection": "3d"}, figsize = (10, 14)) # Make data n_points = 500 r = 1 u = np.linspace(0, 2 * np.pi, n_points) v = np.linspace(0, np.pi, n_points) x = r * np.outer(np.cos(u), np.sin(v)) y = r * np.outer(np.sin(u), np.sin(v)) z = r * np.outer(np.ones(np.size(u)), np.cos(v)) ax.plot_wireframe(x, y, z, color="grey", alpha = 0.1) data = np.random.uniform(0.2, 0.5, n_points) heatmap = np.array(np.meshgrid(data, data))[1] ax.set_aspect("equal") ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') colormap = cm.viridis normaliser = mpl.colors.Normalize(vmin=np.min(heatmap), vmax=np.max(heatmap)) print(np.min(heatmap)) print(np.max(heatmap)) surf = ax.plot_surface( x, y, z, facecolors=colormap(normaliser(heatmap)), shade=False) fig.colorbar(surf, shrink=0.5, aspect=10, label="Singlet yield", pad = 0.05, norm = normaliser) plt.show()  This outputs 0.20009725794516225 and 0.49936395079063567 as min and max in the prints, but you can see the range of the colorbar is 0 to 1 in the following image.  How can I fix this issue and make it so the colorbar has the appropriate colors?
The colorbar function itself doesn't have a norm argument according to the documentation for this function. For minimal alteration, you can pass a matplotlib.cm.ScalarMappable as the first argument of the colorbar call and it works as expected (presuming you also pass the appropriate ax argument). Here is a fully runnable code demonstrating this: import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import cm fig, ax = plt.subplots(subplot_kw={"projection": "3d"}, figsize=(10, 14)) # Make data n_points = 500 r = 1 u = np.linspace(0, 2 * np.pi, n_points) v = np.linspace(0, np.pi, n_points) x = r * np.outer(np.cos(u), np.sin(v)) y = r * np.outer(np.sin(u), np.sin(v)) z = r * np.outer(np.ones(np.size(u)), np.cos(v)) ax.plot_wireframe(x, y, z, color="grey", alpha=0.1) data = np.random.uniform(0.2, 0.5, n_points) heatmap = np.array(np.meshgrid(data, data))[1] ax.set_aspect("equal") ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') colormap = cm.viridis normaliser = mpl.colors.Normalize(vmin=np.min(heatmap), vmax=np.max(heatmap)) print(np.min(heatmap)) print(np.max(heatmap)) surf = ax.plot_surface( x, y, z, facecolors=colormap(normaliser(heatmap)), shade=False) mappable = cm.ScalarMappable(norm=normaliser, cmap=colormap) fig.colorbar(mappable, ax=ax, shrink=0.5, aspect=10, label="Singlet yield", pad=0.05) plt.show()  Here is the output it generates: 