Matplotlib无偏移符号色条绘制问题：set_useOffset方法失效

I get it, dealing with matplotlib's colorbar formatting can be tricky when you want scientific notation without that annoying offset label. Let's break down why your original approach wasn't working and how to fix it properly.

Why Your Current Code Fails #

In matplotlib 3.3.x, the default colorbar formatter (ScalarFormatter) has built-in logic: when all tick values sit within a narrow range relative to their magnitude (like your 5e15 to 1e16 range), it defaults to using an offset to keep tick labels short. Merely setting set_useOffset(False) doesn't override this behavior entirely because the formatter also checks its powerlimits parameter (default is (0, 4)). Since your values are all in the 1e15-1e16 range, the formatter still thinks an offset is "helpful".

The Solution #

Instead of just toggling useOffset, we need to explicitly configure the formatter to use scientific notation and disable the offset. Here are two reliable ways to do this:

Method 1: Configure the Existing ScalarFormatter

Update your third subplot code to adjust both the offset and force scientific notation:

# Plot -- no offset notation (fixed)
plot_h = axes_h[2].imshow(data, cmap='jet', clim=(CMIN, CMAX))
colorbar_h = plt.colorbar(plot_h, ax=axes_h[2])
# Get the existing formatter and adjust settings
formatter = colorbar_h.formatter
formatter.set_useOffset(False)
formatter.set_scientific(True)
# Force the formatter to recalculate ticks
colorbar_h.update_ticks()

Method 2: Use a Custom ScalarFormatter Instance

You can also create a new formatter with the desired settings directly:

# Plot -- no offset notation (alternative fixed)
plot_h = axes_h[2].imshow(data, cmap='jet', clim=(CMIN, CMAX))
colorbar_h = plt.colorbar(plot_h, ax=axes_h[2])
colorbar_h.formatter = plt.ScalarFormatter(useOffset=False)
colorbar_h.formatter.set_scientific(True)
colorbar_h.update_ticks()

Full Modified Code #

Here's your complete code with the fixed third subplot:

import matplotlib.pyplot as plt
import numpy as np
CMIN, CMAX = 5e15, 1e16
# Create axes
figure_h, axes_h = plt.subplots(3, figsize=(5, 8))
# Create data
data = np.random.uniform(CMIN, CMAX, (10, 10))
# Plot -- default
plot_h = axes_h[0].imshow(data, cmap='jet', clim=(CMIN, CMAX))
c = plt.colorbar(plot_h, ax=axes_h[0])
# Plot -- manual ticks
plot_h = axes_h[1].imshow(data, cmap='jet', clim=(CMIN, CMAX))
colorbar_h = plt.colorbar(plot_h, ax=axes_h[1])
colorbar_h.set_ticks(colorbar_h.get_ticks()) # To prevent 'UserWarning: set_ticks() must have been called.'
colorbar_h.set_ticklabels([f'{f:.0e}' for f in colorbar_h.get_ticks()])
# Plot -- no offset notation (fixed)
plot_h = axes_h[2].imshow(data, cmap='jet', clim=(CMIN, CMAX))
colorbar_h = plt.colorbar(plot_h, ax=axes_h[2])
formatter = colorbar_h.formatter
formatter.set_useOffset(False)
formatter.set_scientific(True)
colorbar_h.update_ticks()
plt.show()

This will give you a colorbar with scientific notation (like 5e15, 7e15, etc.) without any offset label, matching the look of your manual ticklabel approach but without having to generate the labels explicitly.

内容的提问来源于stack exchange，提问作者b1000