content: import numpy as np
  from matplotlib import pyplot
  import qutip
  
  delta = 0.0  * 2*np.pi
  eps0  = 1.0 * 2*np.pi
  A     = 0.25 * 2*np.pi
  omega = 1.0 * 2*np.pi
  T      = 2*np.pi / omega
  tlist  = np.linspace(0.0, 20 * T, 301)
  psi0   = qutip.basis(2,0)
  
  H0 = - delta/2.0 * qutip.sigmax() - eps0/2.0 * qutip.sigmaz()
  H1 = A/2.0 * qutip.sigmax()
  args = {'w': omega}
  H = [H0, [H1, lambda t, w: np.sin(w * t)]]
  
  # noise power spectrum
  gamma1 = 0.1
  def noise_spectrum(omega):
      return (omega>0) * 0.5 * gamma1 * omega/(2*np.pi)
  
  # solve the floquet-markov master equation
  output = qutip.fmmesolve(
      H, psi0, tlist, [qutip.sigmax()],
      spectra_cb=[noise_spectrum], T=T,
      args=args, options={"store_floquet_states": True}
  )
  
  # calculate expectation values in the computational basis
  p_ex = np.zeros(tlist.shape, dtype=np.complex128)
  for idx, t in enumerate(tlist):
      f_coeff_t = output.floquet_states[idx]
      psi_t = output.floquet_basis.from_floquet_basis(f_coeff_t, t)
      # Alternatively
      psi_t = output.states[idx]
      p_ex[idx] = qutip.expect(qutip.num(2), psi_t)
  
  # For reference: calculate the same thing with mesolve
  output = qutip.mesolve(
      H, psi0, tlist, [np.sqrt(gamma1) * qutip.sigmax()],
      e_ops=[qutip.num(2)], args=args
  )
  p_ex_ref = output.expect[0]
  
  # plot the results
  pyplot.plot(tlist, np.real(p_ex), 'r--', tlist, 1-np.real(p_ex), 'b--')
  pyplot.plot(tlist, np.real(p_ex_ref), 'r', tlist, 1-np.real(p_ex_ref), 'b')
  pyplot.xlabel('Time')
  pyplot.ylabel('Occupation probability')
  pyplot.legend(("Floquet $P_1$", "Floquet $P_0$", "Lindblad $P_1$", "Lindblad $P_0$"))
  pyplot.show()
  