Visualising Solver Results

All QuTiP solvers return result objects that include built-in plotting methods. These make it easy to quickly inspect expectation values without writing boilerplate matplotlib code.

Basic Usage

After running any solver, call plot_expect on the result:

import qutip
import numpy as np

H = qutip.num(5)
psi0 = qutip.basis(5, 4)
tlist = np.linspace(0, 10, 100)
a = qutip.destroy(5)

result = qutip.mesolve(H, psi0, tlist, [0.5 * a],
                       e_ops=[a.dag() * a])
result.plot_expect()

Dictionary Labels

When e_ops is passed as a dictionary, the keys are used as legend labels automatically:

result = qutip.mesolve(H, psi0, tlist, [0.5 * a],
                       e_ops={"photons": a.dag() * a,
                              "field": a + a.dag()})
result.plot_expect()

Separate Subplots

To plot each expectation value in its own subplot, use separate_axes=True:

result.plot_expect(separate_axes=True)

Monte Carlo Trajectories

For Monte Carlo results (McResult), individual trajectories can be overlaid on the average:

result = qutip.mcsolve(H, psi0, tlist, [0.5 * a],
                       e_ops=[a.dag() * a],
                       ntraj=50,
                       options={"keep_runs_results": True})

# Show 10 trajectories behind the average
result.plot_expect(show_trajectories=10)

Trajectory styling can be customised:

result.plot_expect(
    show_trajectories=10,
    trajectory_kwargs={"color": "blue", "alpha": 0.1},
)

Photocurrent

Monte Carlo results also support plotting the photocurrent:

result.plot_photocurrent()

Customisation

All methods accept fig and axes keyword arguments for embedding plots in custom layouts:

import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
result.plot_expect(axes=ax1, title="Expectation values")
result.plot_photocurrent(axes=ax2, title="Photocurrent")
plt.tight_layout()

Other options include title, xlabel, ylabel, labels, and show_legend.