def test_brownian():
lambda_0 = 0.05 # reorganization energy (dimensionless)
omega_0 = 1.0 # vibrational frequency (dimensionless)
zeta = 0.5 # damping constant (dimensionless)
max_tier = 5
omega_1 = np.sqrt(omega_0**2 - zeta**2*0.25)
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'
)
s = corr_dict['s'].toarray()
a = corr_dict['a'].toarray()
gamma = corr_dict['gamma'].toarray()
delta = 0
h = np.array([[omega_1, 0],
[0, 0]])
op = np.array([[0, 1],
[1, 0]])
max_terms = 3
corr = Correlation(k_max=max_terms, beta=1)
corr.symm_coeff = np.diag(s)
corr.asymm_coeff = np.diag(a)
corr.exp_coeff = np.diag(gamma)
corr.delta_coeff = delta
corr.print()
heom = Hierachy([max_tier] * max_terms, h, op, corr)
rho_0 = np.zeros((2, 2))
rho_0[0, 0] = 1
init_wfn = heom.gen_extended_rho(rho_0)
solver = MultiLayer(init_wfn, heom.diff())
# Define the obersevable of interest
dat = []
for n, (time, r) in enumerate(solver.propagator(
steps=5000,
ode_inter=0.01,
)):
if n % 100 == 0:
rho = np.reshape(r, (-1, 4))
for n, _rn in enumerate(rho):
if n == 0:
flat_data = [time] + list(rho[0])
dat.append(flat_data)
if n <= 0:
print("Time: {} ; {}: {}".format(time, n, _rn[0] + _rn[-1]))
return np.array(dat)
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 test_drude_train():
eta = 0.05 # reorganization energy (dimensionless)
gamma_c = 0.05 # vibrational frequency (dimensionless)
max_tier = 10
max_terms = 3
J = pyheom.Drudian(eta, gamma_c)
corr_dict = pyheom.noise_decomposition(
J,
T=1, # temperature (dimensionless)
type_LTC='PSD',
n_PSD=max_terms - 1,
type_PSD='N-1/N')
s = corr_dict['s'].toarray()
a = corr_dict['a'].toarray()
gamma = corr_dict['gamma'].toarray()
delta = 0
omega_1 = 0.05
omega_2 = 0.02
H = np.array([[omega_1, omega_2], [omega_2, 0]])
V = np.array([[0, 0], [0, 1]])
corr = Correlation(k_max=max_terms, beta=1)
corr.symm_coeff = np.diag(s)
corr.asymm_coeff = np.diag(a)
corr.exp_coeff = np.diag(gamma)
corr.delta_coeff = delta
corr.print()
heom = Hierachy([max_tier] * max_terms, H, V, corr)
rho_0 = np.zeros((2, 2))
rho_0[0, 0] = 1
# TT HEOM
tensor_train = tensor_train_template(rho_0, [max_tier] * max_terms,
rank=max_tier)
root = tensor_train[0]
leaves_dict = {leaf.name: leaf for leaf in root.leaves()}
all_terms = []
for term in heom.diff():
all_terms.append([(leaves_dict[str(fst)], snd) for fst, snd in term])
solver = MultiLayer(root, all_terms)
solver.ode_method = 'RK45'
solver.snd_order = False
solver.max_ode_steps = 100000
# Define the obersevable of interest
projector = np.zeros((max_tier, 1))
projector[0] = 1.0
dat = []
for n, (time,
r) in enumerate(solver.propagator(
steps=20000,
ode_inter=0.01,
)):
head = root.array
for t in tensor_train[1:]:
spf = Tensor.partial_product(t.array, 1, projector, 0)
head = Tensor.partial_product(head, head.ndim - 1, spf, 0)
rho = np.reshape(head, (4, -1))[:, 0]
flat_data = [time] + list(rho)
dat.append(flat_data)
print("Time {} | Pop_1 {} | Total {}".format(time, rho[0],
rho[0] + rho[-1]))
return np.array(dat)
import pyheom
pyheom.units['energy'] = pyheom.unit.dimensionless
pyheom.units['time'] = pyheom.unit.dimensionless
import tqdm
lambda_0 = 0.01 # reorganization energy (dimensionless)
omega_0 = 1 # vibrational frequency (dimensionless)
zeta = 0.5 # damping constant (dimensionless)
T = 1 # temperature (dimensionless)
max_tier = 5
J = pyheom.brown(lambda_0, zeta, omega_0)
corr_dict = pyheom.noise_decomposition(J,
T=T,
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,
from minitn.lib.units import Quantity
from minitn.lib.logging import Logger
import pyheom
# Bath
lambda_0 = 0.01 # reorganization energy (dimensionless)
omega_0 = 1.0 # vibrational frequency (dimensionless)
zeta = 0.5 # damping constant (dimensionless)
max_tier = 5
max_terms = 2
J = pyheom.Brownian(lambda_0, zeta, omega_0)
corr_dict = pyheom.noise_decomposition(
J,
T=1, # temperature (dimensionless)
type_LTC='PSD',
n_PSD=max_terms - 1,
type_PSD='N-1/N')
# System
n_state = 2
omega_1 = np.sqrt(omega_0**2 - zeta**2 * 0.25)
H = np.array([[omega_1, 0.0], [0.0, 0.0]])
V = np.array([[0.0, 1.0], [1.0, 0.0]])
# init state
rho_0 = np.zeros((n_state, n_state))
rho_0[0, 0] = 1
dt_unit = 0.001
callback_interval = 100
def test_drude_tree():
eta = 0.05 # reorganization energy (dimensionless)
gamma_c = 0.05 # vibrational frequency (dimensionless)
max_tier = 10
max_terms = 3
J = pyheom.Drudian(eta, gamma_c)
corr_dict = pyheom.noise_decomposition(
J,
T=1, # temperature (dimensionless)
type_LTC='PSD',
n_PSD=max_terms - 1,
type_PSD='N-1/N')
s = corr_dict['s'].toarray()
a = corr_dict['a'].toarray()
gamma = corr_dict['gamma'].toarray()
delta = 0
omega_1 = 0.05
omega_2 = 0.02
H = np.array([[omega_1, omega_2], [omega_2, 0]])
V = np.array([[0, 0], [0, 1]])
corr = Correlation(k_max=max_terms, beta=1)
corr.symm_coeff = np.diag(s)
corr.asymm_coeff = np.diag(a)
corr.exp_coeff = np.diag(gamma)
corr.delta_coeff = delta
corr.print()
heom = Hierachy([max_tier] * max_terms, H, V, corr)
rho_0 = np.zeros((2, 2))
rho_0[0, 0] = 1
root = tensor_tree_template(rho_0, [max_tier] * max_terms,
rank=max_tier // 2)
solver = MultiLayer(root, heom.diff(), use_str_name=True)
solver.ode_method = 'RK45'
solver.snd_order = False
solver.max_ode_steps = 100000
dat = []
for n, (time,
r) in enumerate(solver.propagator(
steps=20000,
ode_inter=0.01,
)):
if n % 100 == 0:
head = root.array
print(head.shape)
rho = Tensor.partial_product(r.array, 0, r[0][0].array, 0)
rho = np.reshape(rho, (-1, 4))
flat_data = [time] + list(rho[0])
dat.append(flat_data)
print("Time: {} | Pop 0: {} | Total: {}".format(
flat_data[0], flat_data[1], flat_data[1] + flat_data[-1]))
return np.array(dat)