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Backport PR #29073 on branch v3.10.x (Update secondary_axis tutorial) #29168

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Merged
rcomer merged 1 commit into from
Nov 21, 2024
Merged

Backport PR #29073 on branch v3.10.x (Update secondary_axis tutorial) #29168

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52 changes: 25 additions & 27 deletions galleries/examples/subplots_axes_and_figures/secondary_axis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@
import numpy as np

import matplotlib.dates as mdates
from matplotlib.ticker import AutoMinorLocator

fig, ax = plt.subplots(layout='constrained')
x = np.arange(0, 360, 1)
Expand Down Expand Up @@ -96,48 +95,47 @@ def one_over(x):
plt.show()

# %%
# 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.
# Sometime we want to relate the axes in a transform that is ad-hoc from the data, and
# is derived empirically. Or, one axis could be a complicated nonlinear function of the
# other. In these cases we can set the forward and inverse transform functions to be
# linear interpolations from the one set of independent variables to the other.
#
# .. note::
#
# In order to properly handle the data margins, the mapping functions
# (``forward`` and ``inverse`` in this example) need to be defined beyond the
# nominal plot limits.
#
# In the specific case of the numpy linear interpolation, `numpy.interp`,
# this condition can be arbitrarily enforced by providing optional keyword
# arguments *left*, *right* such that values outside the data range are
# mapped well outside the plot limits.
# nominal plot limits. This condition can be enforced by extending the
# interpolation beyond the plotted values, both to the left and the right,
# see ``x1n`` and ``x2n`` below.

fig, ax = plt.subplots(layout='constrained')
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]')
x1_vals = np.arange(2, 11, 0.4)
# second independent variable is a nonlinear function of the other.
x2_vals = x1_vals ** 2
ydata = 50.0 + 20 * np.random.randn(len(x1_vals))
ax.plot(x1_vals, ydata, label='Plotted data')
ax.plot(x1_vals, x2_vals, label=r'$x_2 = x_1^2$')
ax.set_xlabel(r'$x_1$')
ax.legend()

# the forward and inverse functions must be defined on the complete visible axis range
x1n = np.linspace(0, 20, 201)
x2n = x1n**2


def forward(x):
return np.interp(x, xold, xnew)
return np.interp(x, x1n, x2n)


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

return np.interp(x, x2n, x1n)

# use axvline to prove that the derived secondary axis is correctly plotted
ax.axvline(np.sqrt(40), color="grey", ls="--")
ax.axvline(10, color="grey", ls="--")
secax = ax.secondary_xaxis('top', functions=(forward, inverse))
secax.xaxis.set_minor_locator(AutoMinorLocator())
secax.set_xlabel('$X_{other}$')
secax.set_xticks([10, 20, 40, 60, 80, 100])
secax.set_xlabel(r'$x_2$')

plt.show()

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