%matplotlib inline

from scipy import *

from qutip import *

H = 0.5 * 2 * pi * sigmax()

psi0 = basis(2, 0)

tlist = linspace(0, 10, 1000)

e_ops = [sigmax(), sigmay(), sigmaz()]

e_ops_dict = {r'$\sigma_x$': sigmax(), r'$\sigma_y$': sigmay(), r'$\sigma_z$': sigmaz()}

c_ops = [sqrt(0.1) * sigmaz()]

result = mesolve(H, psi0, tlist, c_ops, e_ops)

fig, ax = plt.subplots(1,1)

for n, e in enumerate(result.expect):
    ax.plot(tlist, e, label=["sx", "sy", "sz"][n])
    
ax.legend();

result = mesolve(H, psi0, tlist, c_ops, e_ops_dict)

fig, ax = plt.subplots(1,1)

for key, e in result.expect.items():
    ax.plot(tlist, e, label=key)
    
ax.legend();

result = sesolve(H, psi0, tlist, e_ops)

fig, ax = plt.subplots(1,1)

for n, e in enumerate(result.expect):
    ax.plot(tlist, e, label=["sx", "sy", "sz"][n])
    
ax.legend();

result = sesolve(H, psi0, tlist, e_ops_dict)

fig, ax = plt.subplots(1,1)

for key, e in result.expect.items():
    ax.plot(tlist, e, label=key)
    
ax.legend();

result = mcsolve(H, psi0, tlist, c_ops, e_ops, ntraj=15, options=Odeoptions(gui=False))

fig, ax = plt.subplots(1,1)

for n, e in enumerate(result.expect):
    ax.plot(tlist, e, label=["sx", "sy", "sz"][n])
    
ax.legend();

result = mcsolve(H, psi0, tlist, c_ops, e_ops_dict, ntraj=5, options=Odeoptions(gui=False))

fig, ax = plt.subplots(1,1)

for key, e in result.expect.items():
    ax.plot(tlist, e, label=key)
    
ax.legend();