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I frequently find myself trying to create plots that use a secondary axis to indicate data in a second set of units. For example, I might plot a data set with an x-axis in data number (e.g. the output of an analog to digital converter), and then wish to display the calibrated units on a secondary x-axis (e.g. volts).

There are quite a few examples that do this by creating a secondary axis using twiny(), and then setting the limits of the secondary x-axis to the scaled limits of the primary, and possibly setting the ticks to line up as well.

import matplotlib.pyplot as plt

import numpy as np

from scipy.stats import norm

f, ax = plt.subplots(1)

x_dn = np.arange(4096)

y = norm.pdf(x_dn, 2048, 64)

v = lambda x: x*5.0/4096

ax.plot(x_dn, y)

ax.grid(True)

ax_top = ax.twiny()

ax_top.grid(True)

ax_top.set_xticks([v(x) for x in ax.get_xticks()]) # optional, aligns the grids

ax_top.set_xlim([v(x) for x in ax.get_xlim()])

This isn't too painful if you're only doing it once. However, if you subsequently want to change the limits (from the command line) you have to explicitly set the limits of both axes or they will become out of sync (aside: they also can get out of sync if you set the secondary limits before the ticks, because setting the ticks can change the limits!). If you do set the ticks to line up the grids, then you also have to recompute those for the secondary axis.

ax.set_xlim([1500, 2500]) # now they're out of sync

ax_top.set_xticks([v(x) for x in ax.get_xticks()]) # ticks correct, but in wrong places

ax_top.set_xlim([v(x) for x in ax.get_xlim()]) # all's well again.

It just seems like there ought to be a better way. I apologize if it's out there and I missed it.

Thanks,

--Chad

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