GIF of extrapolation

calculations/PMM.py

@@ -111,3 +111,39 @@ axs[1].legend()
 
 plt.tight_layout()  # Adjust spacing between panels
 plt.show()
+
+# %%
+# animation of eigenvalues
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+
+# Prepare figure
+fig, ax = plt.subplots(figsize=(8, 8))
+ax.scatter(np.real(train_data['k']), np.imag(train_data['k']), label='training')
+ax.scatter(np.real(target_data['k']), np.imag(target_data['k']), label='target')
+sc = ax.scatter([], [], marker='x', label='predicted')  # Placeholder for predicted eigenvalues
+ax.set_xlim(-1, 1)  # Adjust limits as needed
+ax.set_ylim(-1, 1)  # Adjust limits as needed
+ax.set_xlabel('Re(k)')
+ax.set_ylabel('Im(k)')
+ax.legend()
+ax.set_title('c = ?')
+
+# Animation function
+def update(c):
+    H = H0 + c * H1
+    evals = torch.linalg.eigvals(H).detach().numpy()
+    sc.set_offsets(np.c_[np.real(evals), np.imag(evals)])
+    ax.set_title(f'c = {c:.2f}')
+    return sc,
+
+# Create animation
+no_frames = 100
+c_steps = np.linspace(min(df['c']), max(df['c']), no_frames)
+ani = FuncAnimation(fig, update, frames=c_steps, interval=100, blit=True)
+
+# Save or display the animation
+ani.save('../temp/PMM.gif', writer='pillow')  # Save as a GIF file
+plt.show()  # Display the animation
+
+# %%