old_contents: 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, 101)
  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,args: np.sin(args['w'] * t)]]
  
  # noise power spectrum
  gamma1 = 0.1
  def noise_spectrum(omega):
      return 0.5 * gamma1 * omega/(2*np.pi)
  
  # find the floquet modes for the time-dependent hamiltonian        
  f_modes_0, f_energies = qutip.floquet_modes(H, T, args)
  
  # precalculate mode table
  f_modes_table_t = qutip.floquet_modes_table(
      f_modes_0, f_energies, np.linspace(0, T, 500 + 1), H, T, args,
  )
  
  # solve the floquet-markov master equation
  output = qutip.fmmesolve(H, psi0, tlist, [qutip.sigmax()], [], [noise_spectrum], T, args)
  
  # calculate expectation values in the computational basis
  p_ex = np.zeros(tlist.shape, dtype=np.complex128)
  for idx, t in enumerate(tlist):
      f_modes_t = qutip.floquet_modes_t_lookup(f_modes_table_t, t, T)
      p_ex[idx] = qutip.expect(qutip.num(2), output.states[idx].transform(f_modes_t, True))
  
  # For reference: calculate the same thing with mesolve
  output = qutip.mesolve(H, psi0, tlist,
                         [np.sqrt(gamma1) * qutip.sigmax()], [qutip.num(2)],
                         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()
  
new_contents: 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, 101)
  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,args: np.sin(args['w'] * t)]]
  
  # noise power spectrum
  gamma1 = 0.1
  def noise_spectrum(omega):
      return 0.5 * gamma1 * omega/(2*np.pi)
  
  # find the floquet modes for the time-dependent hamiltonian        
  f_modes_0, f_energies = qutip.floquet_modes(H, T, args)
  
  # precalculate mode table
  f_modes_table_t = qutip.floquet_modes_table(
      f_modes_0, f_energies, np.linspace(0, T, 500 + 1), H, T, args,
  )
  
  # solve the floquet-markov master equation
  output = qutip.fmmesolve(H, psi0, tlist, [qutip.sigmax()], [], [noise_spectrum], T, args)
  
  # calculate expectation values in the computational basis
  p_ex = np.zeros(tlist.shape, dtype=np.complex128)
  for idx, t in enumerate(tlist):
      f_modes_t = qutip.floquet_modes_t_lookup(f_modes_table_t, t, T)
      f_states_t = qutip.floquet_states(f_modes_t, f_energies, t)
      p_ex[idx] = qutip.expect(qutip.num(2), output.states[idx].transform(f_states_t, True))
  
  # For reference: calculate the same thing with mesolve
  output = qutip.mesolve(H, psi0, tlist,
                         [np.sqrt(gamma1) * qutip.sigmax()], [qutip.num(2)],
                         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()
  
subject: insert missing conversion from modes to states