Test the use of e_ops parameter in dictionary form

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%matplotlib inline


    

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from scipy import *


    

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from qutip import *


    

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H = 0.5 * 2 * pi * sigmax()


    

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psi0 = basis(2, 0)


    

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tlist = linspace(0, 10, 1000)


    

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


    

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


    

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


    

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


    

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

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


    

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


    

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

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


    

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


    

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

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


    

    




/usr/local/lib/python3.3/dist-packages/numpy/core/numeric.py:460: ComplexWarning: Casting complex values to real discards the imaginary part
  return array(a, dtype, copy=False, order=order)






    

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


    

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

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


    

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


    

In [19]:

    

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

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


    

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


    

In [21]:

    

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

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


    

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