matplotlib轴标签的偏移量的因数和偏移

Question

On the axes tick labels in matplotlib, there are two kinds of possible offsets: factors and shifts :

In the lower right corner 1e-8 would be a "factor" and 1.441249698e1 would be a "shift".

There are a lot of answers here showing how to manipulate both of them :

• matplotlib: format axis offset-values to whole numbers or specific number
• How to remove relative shift in matplotlib axis
• How to prevent numbers being changed to exponential form in Python matplotlib figure

I would like to just remove the shifts and can't seem to figure out how to do it. So matplotlib should only be allowed to scale my axis, but not to move the zero point. Is there a simple way to achieve this behaviour?

Answer 1

You can fix the order of magnitude to show on the axis as shown in this question . The idea is to subclass the usual ScalarFormatter and fix the order of magnitude to show. Then setting the useOffset to False will prevent showing some offset, but still shows the factor.

Thwe format "%1.1f" will show only one decimal place. Finally using a MaxNLocator allows to set the maximum number of ticks on the axes.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker

class OOMFormatter(matplotlib.ticker.ScalarFormatter):
    def __init__(self, order=0, fformat="%1.1f", offset=False, mathText=True):
        self.oom = order
        self.fformat = fformat
        matplotlib.ticker.ScalarFormatter.__init__(self,useOffset=offset,useMathText=mathText)
    def _set_orderOfMagnitude(self, nothing):
        self.orderOfMagnitude = self.oom
    def _set_format(self, vmin, vmax):
        self.format = self.fformat
        if self._useMathText:
            self.format = '$%s$' % self.format

x = [0.6e-8+14.41249698, 3.4e-8+14.41249698]
y = [-7.7e-11-1.110934954e-2, -0.8e-11-1.110934954e-2]

fig, ax = plt.subplots()
ax.plot(x,y)

y_formatter = OOMFormatter(-2, "%1.1f")
ax.yaxis.set_major_formatter(y_formatter)
x_formatter = OOMFormatter(1, "%1.1f")
ax.xaxis.set_major_formatter(x_formatter)

ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(2))
ax.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(2))

plt.show()

Keep in mind that this plot is inaccurate and you shouldn't use it in any kind of report you hand to other people as they wouldn't know how to interprete it.