# Plotting and Visualization

**Visualization** in scqubits includes energy level diagrams, parameter sweeps, and state representations.

```python
from scqubits import Transmon
import matplotlib.pyplot as plt

transmon = Transmon(EJ=15.0, EC=0.3, ncut=30)

# Energy level diagram
fig, ax = plt.subplots()
evals = transmon.eigenvals(n=6)
ax.hlines(evals, 0, 1, colors='b')
for i, E in enumerate(evals):
    ax.text(1.05, E, f'|{i}⟩', va='center')
ax.set_xlim(0, 1.5)
ax.set_ylabel('Energy (GHz)')
ax.set_title('Transmon Energy Levels')
plt.show()

# Potential and wavefunctions
phi = np.linspace(-np.pi, np.pi, 200)
V = -15.0 * np.cos(phi)  # Josephson potential
ax.plot(phi, V, 'k-', label='V(φ)')

# Overlay wavefunctions (heuristic)
for i in range(3):
    evec = transmon.eigenvecs(n=i+1)[i]
    # (Plotting eigenvectors in phase basis requires basis transformation)
ax.legend()
plt.show()
```

scqubits integrates with matplotlib for standard plots. For complex visualizations, extract matrices and use custom plotting code.

Visualization helps debug designs: identify unwanted level crossings, check convergence, compare parameter effects.