Hello,
I’ve a question on the secondary
axis feature introduced in matplotlib 3.1 . I’m on version
3.1.1.
My use case is to plot log values but also display the
exponentiated values. I can use a twin axes with the shared scale
and a functional formatter. Here is an example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import LogLocator, FuncFormatter
fig, ax = plt.subplots(1,1)
# twin ax with shared scale # cf. @ImportanceOfBeingErnest atax2 = ax.twiny()
ax.get_shared_x_axes().join(ax, ax2)Plot
ax.plot([-1,2,5], [1,2,3], ‘d-’)
ax.grid()x axis labeling
ax.set_xlabel(‘log2 x-value’)
ax2.xaxis.set_major_formatter(FuncFormatter(lambda x,pos:
f"{2**x:.3g}"))
ax2.set_xlabel(‘x-value’);
This twinx/y approach works, but I wanted originally to use the
new secondary axis feature. I see to options for this:
- use a secondary axes with the log transform
-
use a secondary axes with no transform, and then transform the
display ticks using a FuncFormatter
Option 1 works initially, but breaks when I want to use a
LogLocator to have equally spaced log values:
fig, ax = plt.subplots(1,1)
ax.plot([-1,2,5], [1,2,3], 'd-') ax2 = ax.secondary_xaxis('top', functions=(lambda x: 2**x,np.log2))
# Place ticks at log equally spaced location [doesn’t work]
ax2.xaxis.set_major_locator(LogLocator(2))
Option 2 doesn't work either due. Setting the formatter has no
effect
fig, ax = plt.subplots(1,1)
ax.plot([-1,2,5], [1,2,3], 'd-') ax2 = ax.secondary_xaxis('top') # Format the log values as exponentiated values [doesn't work] ax2.xaxis.set_major_formatter(FuncFormatter(lambda x,pos:f"{2**x:.3g}"))
Is it an expected behavior (or a known bug) that changing the
locator and the formatter of a secondary axis has no effect? Did I
miss something?
In the examples
(),
there is one example (number 3, with interpolated transforms)
which uses secax.xaxis.set_minor_locator(AutoMinorLocator()), but
I don’t know if it is effective or not.
Best,
Pierre