Secondary Axis

Sometimes we want a secondary axis on a plot, for instance to convert radians to degrees on the same plot. We can do this by making a child axes with only one axis visible via axes.Axes.secondary_xaxis and axes.Axes.secondary_yaxis. This secondary axis can have a different scale than the main axis by providing both a forward and an inverse conversion function in a tuple to the functions kwarg:

import matplotlib.pyplot as plt
import numpy as np
import datetime
import matplotlib.dates as mdates
from matplotlib.ticker import AutoMinorLocator

fig, ax = plt.subplots(constrained_layout=True)
x = np.arange(0, 360, 1)
y = np.sin(2 * x * np.pi / 180)
ax.plot(x, y)
ax.set_xlabel('angle [degrees]')
ax.set_title('Sine wave')

def deg2rad(x):
    return x * np.pi / 180

def rad2deg(x):
    return x * 180 / np.pi

secax = ax.secondary_xaxis('top', functions=(deg2rad, rad2deg))
secax.set_xlabel('angle [rad]')
Sine wave

Here is the case of converting from wavenumber to wavelength in a log-log scale.


In this case, the xscale of the parent is logarithmic, so the child is made logarithmic as well.

fig, ax = plt.subplots(constrained_layout=True)
x = np.arange(0.02, 1, 0.02)
y = np.random.randn(len(x)) ** 2
ax.loglog(x, y)
ax.set_xlabel('f [Hz]')
ax.set_title('Random spectrum')

def forward(x):
    return 1 / x

def inverse(x):
    return 1 / x

secax = ax.secondary_xaxis('top', functions=(forward, inverse))
secax.set_xlabel('period [s]')
Random spectrum

Sometime we want to relate the axes in a transform that is ad-hoc from the data, and is derived empirically. In that case we can set the forward and inverse transforms functions to be linear interpolations from the one data set to the other.

fig, ax = plt.subplots(constrained_layout=True)
xdata = np.arange(1, 11, 0.4)
ydata = np.random.randn(len(xdata))
ax.plot(xdata, ydata, label='Plotted data')

xold = np.arange(0, 11, 0.2)
# fake data set relating x coordinate to another data-derived coordinate.
# xnew must be monotonic, so we sort...
xnew = np.sort(10 * np.exp(-xold / 4) + np.random.randn(len(xold)) / 3)

ax.plot(xold[3:], xnew[3:], label='Transform data')
ax.set_xlabel('X [m]')

def forward(x):
    return np.interp(x, xold, xnew)

def inverse(x):
    return np.interp(x, xnew, xold)

secax = ax.secondary_xaxis('top', functions=(forward, inverse))
secondary axis

A final example translates np.datetime64 to yearday on the x axis and from Celsius to Fahrenheit on the y axis. Note the addition of a third y axis, and that it can be placed using a float for the location argument

dates = [datetime.datetime(2018, 1, 1) + datetime.timedelta(hours=k * 6)
         for k in range(240)]
temperature = np.random.randn(len(dates)) * 4 + 6.7
fig, ax = plt.subplots(constrained_layout=True)

ax.plot(dates, temperature)
ax.set_ylabel(r'$T\ [^oC]$')

def date2yday(x):
    """Convert matplotlib datenum to days since 2018-01-01."""
    y = x - mdates.date2num(datetime.datetime(2018, 1, 1))
    return y

def yday2date(x):
    """Return a matplotlib datenum for *x* days after 2018-01-01."""
    y = x + mdates.date2num(datetime.datetime(2018, 1, 1))
    return y

secax_x = ax.secondary_xaxis('top', functions=(date2yday, yday2date))
secax_x.set_xlabel('yday [2018]')

def celsius_to_fahrenheit(x):
    return x * 1.8 + 32

def fahrenheit_to_celsius(x):
    return (x - 32) / 1.8

secax_y = ax.secondary_yaxis(
    'right', functions=(celsius_to_fahrenheit, fahrenheit_to_celsius))
secax_y.set_ylabel(r'$T\ [^oF]$')

def celsius_to_anomaly(x):
    return (x - np.mean(temperature))

def anomaly_to_celsius(x):
    return (x + np.mean(temperature))

# use of a float for the position:
secax_y2 = ax.secondary_yaxis(
    1.2, functions=(celsius_to_anomaly, anomaly_to_celsius))
secax_y2.set_ylabel(r'$T - \overline{T}\ [^oC]$')
secondary axis


The use of the following functions and methods is shown in this example:


<function Axes.secondary_yaxis at 0x7fba54b42d30>

Total running time of the script: ( 0 minutes 2.892 seconds)

Keywords: matplotlib code example, codex, python plot, pyplot Gallery generated by Sphinx-Gallery