import numpy as np
import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 3, layout="constrained")

eta = np.linspace(0, 1)
cap_lambda_vals = np.array([-24, -12, 0, 12, 24])
for cap_lambda in cap_lambda_vals:
  # Some data
  f = 2 * eta - 2 * eta**3 + eta**4 + cap_lambda * eta / 6 * (1 - eta)**3
  df_deta = 2 - 6 * eta**2 + 4 * eta**3 + cap_lambda / \
      6 * (1 - 6 * eta + 9 * eta**2 - 4 * eta**3)
  d2f_deta2 = -12 * eta + 12 * eta**2 + \
      cap_lambda / 6 * (-6 + 18 * eta - 12 * eta**2)

  # Plot data
  axes[0].plot(f, eta, label=f"$\Lambda = {cap_lambda}$")
  axes[1].plot(df_deta, eta, label=f"$\Lambda = {cap_lambda}$")
  axes[2].plot(d2f_deta2, eta, label=f"$\Lambda = {cap_lambda}$")

# Add a legend to the figure, using the handles from the plot you want to
# create the legend for.
fig.legend(loc="outside upper center", ncol=len(cap_lambda_vals),
           handles=axes[0].get_legend_handles_labels()[0],
           mode="expand")

plt.show()