def gen_ref():
lambda_0 = 0.05 # reorganization energy (dimensionless)
omega_0 = 1.0 # vibrational frequency (dimensionless)
zeta = 0.5 # damping constant (dimensionless)
max_tier = 5
J = pyheom.Brownian(lambda_0, zeta, omega_0)
corr_dict = pyheom.noise_decomposition(
J,
T = 1, # temperature (dimensionless)
type_LTC = 'PSD',
n_PSD = 1,
type_PSD = 'N-1/N'
)
n_state = 2
omega_1 = np.sqrt(omega_0**2 - zeta**2 * 0.25)
H = np.array([[omega_1, 0],
[0, 0]])
V = np.array([[0, 1],
[1, 0]])
noises = [
dict(V=V, C=corr_dict)
]
h = pyheom.HEOM(
H,
noises,
max_tier=max_tier,
matrix_type='dense',
hierarchy_connection='loop',
)
dt__unit = 5.0e-3
rho_0 = np.zeros((n_state,n_state))
rho_0[0, 0] = 1
h.set_rho(rho_0)
callback_interval = 5
count = 10000
ref = []
def callback(t, rho):
flat_data = [t] + list(np.reshape(rho, -1))
ref.append(flat_data)
h.time_evolution(dt__unit, count, callback, callback_interval)
return np.array(ref)
def gen_ref():
noises = [dict(V=V, C=corr_dict)]
h = pyheom.HEOM(
H,
noises,
max_tier=max_tier,
matrix_type='dense',
hierarchy_connection='loop',
)
h.set_rho(rho_0)
ref = []
def callback(t, rho):
flat_data = [t] + list(np.reshape(rho, -1))
ref.append(flat_data)
h.time_evolution(dt_unit, count, callback, callback_interval)
return np.array(ref)