Scaled Secondary Axis that is Slaved to Primary

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Scaled Secondary Axis that is Slaved to Primary

Chad Parker-2
All-

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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jni
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Re: Scaled Secondary Axis that is Slaved to Primary

jni
Hi Chad,

Tom Caswell answered a similar question from me last year:

Look towards the very end of the example to see how to do arbitrary things automatically when xlims and ylims change.

HTH!

Juan.


On Fri, Mar 9, 2018, at 2:51 AM, Chad Parker wrote:
All-
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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Re: Scaled Secondary Axis that is Slaved to Primary

Glenn Nelson
In reply to this post by Chad Parker-2
I believe I saw an excellent example of this using Bokeh. There was a map, and you could drag a rectangle in it and have that area show up in a linked box. Also could move the rectangle around. Sorry for lack of information - it's on my work computer and I'm not there. I realize this does not use matplotlib, but it's good to know about alternative ways of doing things.

----
Glenn Nelson in Santa Cruz

On Thu, Mar 8, 2018 at 7:51 AM, Chad Parker <[hidden email]> wrote:
All-

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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Matplotlib-users mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/matplotlib-users



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