def J(delta):
return 2. * reorg_energy * freq**2 * ((delta * damping) / ((delta**2 - freq**2)**2 + delta**2 * damping**2))
return J
bias_values = np.linspace(-15., 15., 500)
F2_values = np.zeros((len(beta)+1, bias_values.size))
model = DissipativeDQDModel(Gamma_L, Gamma_R, 0, T_c, underdamped_brownian_oscillator(mode_freq, hr_factor, damping), 1.)
solver = FCSSolver(model.liouvillian(), model.jump_matrix(), np.array([1,1,1,0,0]))
for j,B in enumerate(beta):
model.beta = B
print B
for i,E in enumerate(bias_values):
model.bias = E
solver.L = model.liouvillian()
F2_values[j+1,i] = solver.second_order_fano_factor(0)
model.spectral_density = underdamped_brownian_oscillator(0, hr_factor, damping)
for i,E in enumerate(bias_values):
model.bias = E
solver.L = model.liouvillian()
F2_values[0,i] = solver.second_order_fano_factor(0)
np.savez('../../data/DQD_dissipative_F2_strong_coupling_UBO.npz', bias=bias_values, beta=beta, F2=F2_values)
import matplotlib.pyplot as plt
import matplotlib
font = {'size':18}
matplotlib.rc('font', **font)
