font = {'size':20}
matplotlib.rc('font', **font)
#model = DQDModel(remove_elements=True)
def dqd_hamiltonian(bias, T):
return np.array([[0,0,0],[0,bias/2,T],[0,T,-bias/2]])
dqd_lindblad_ops = [np.array([[0,0,0],[1,0,0],[0,0,0]]), np.array([[0,0,1],[0,0,0],[0,0,0]])]
dqd_lindblad_rates = [1., 1.]
solver = FCSSolver(dqd_hamiltonian(0, 3.), dqd_lindblad_ops, dqd_lindblad_rates, np.array([0,1]), reduce_dim=True)
freq_range = np.linspace(0, 10., 100)
bias_values = np.array([0]) #, 1.5, 3., 4.5, 6.])
F2 = np.zeros((bias_values.size, freq_range.size))
plt.axhline(1., ls='--', color='grey')
for i,v in enumerate(bias_values):
#model.bias = v
solver.H = dqd_hamiltonian(v, 3.)
print solver.liouvillian()
#ss = utils.stationary_state_svd(model.liouvillian(), model.density_vector_populations())
#F2[i] = model.second_order_fano_factor(ss, freq_range=freq_range)
F2[i] = solver.second_order_fano_factor(freq_range)
plt.plot(freq_range, F2[i], label=r'$\epsilon$ = ' + str(v), linewidth=3)
plt.legend().draggable()
plt.xlabel('frequency')
plt.ylabel(r'F$^{(2)}$($\omega$)')
plt.show()
