def test_heom(fname=None):
ph_dims = list(np.repeat(model.ph_dims, 2))
n_dims = ph_dims if model.bath_dims is None else ph_dims + model.bath_dims
print(n_dims)
root = tensor_train_template(rho_0, n_dims, rank=rank_heom)
leaves = root.leaves()
h_list = model.heom_h_list(leaves[0], leaves[1], leaves[2:], beta=beta)
solver = MultiLayer(root, h_list)
solver.ode_method = 'RK45'
solver.cmf_steps = solver.max_ode_steps # use constant mean-field
solver.ps_method = 'split'
#solver.svd_err = 1.0e-14
# Define the obersevable of interest
logger = Logger(filename=prefix + fname, level='info').logger
logger2 = Logger(filename=prefix + "en_" + fname, level='info').logger
for n, (time, r) in enumerate(
solver.propagator(
steps=count,
ode_inter=dt_unit,
split=True,
)):
# renormalized by the trace of rho
norm = np.trace(np.reshape(np.reshape(r.array, (4, -1))[:, 0], (2, 2)))
r.set_array(r.array / norm)
if n % callback_interval == 0:
t = Quantity(time).convert_to(unit='fs').value
rho = np.reshape(r.array, (4, -1))[:, 0]
logger.info("{} {} {} {} {}".format(t, rho[0], rho[1], rho[2],
rho[3]))
en = np.trace(np.reshape(rho, (2, 2)) @ model.h)
logger2.info('{} {}'.format(t, en))
return
def test_train(fname=None):
# Type settings
corr = Correlation(k_max=max_terms)
corr.symm_coeff = np.diag(corr_dict['s'].toarray())
corr.asymm_coeff = np.diag(corr_dict['a'].toarray())
corr.exp_coeff = np.diag(corr_dict['gamma'].toarray())
corr.delta_coeff = 0.0 # delta_coeff
corr.print()
n_dims = [max_tier] * max_terms
heom = Hierachy(n_dims, H, V, corr)
# Adopt TT
tensor_train = tensor_train_template(rho_0, n_dims)
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 = ProjectorSplitting(root, all_terms)
solver.ode_method = 'RK45'
solver.snd_order = False
solver.atol = 1.e-7
solver.rtol = 1.e-7
solver.ps_method = 'split-unite'
projector = np.zeros((max_tier, 1))
projector[0] = 1.0
logger = Logger(filename=fname, level='info').logger
for n, (time, _) in enumerate(
solver.propagator(steps=count, ode_inter=dt_unit, split=False)):
if n % callback_interval == 0:
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]
logger.info("{} {} {} {} {}".format(time, rho[0], rho[1], rho[2],
rho[3]))
return
def test_train(fname=None):
# HEOM metas
corr.print()
n_dims = [max_tier] * max_terms
heom = Hierachy(n_dims, H, V, corr)
# 2-site TT
tensor_train = tensor_train_template(rho_0, n_dims, rank=1)
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.svd_err = 1.e-8
solver.svd_rank = max_tier
solver.ps_method = 'unite'
projector = np.zeros((max_tier, 1))
projector[0] = 1.0
logger = Logger(filename=fname, level='info').logger
logger2 = Logger(filename=fname + '_norm', level='info').logger
for n, (time, _) in enumerate(solver.propagator(steps=count, ode_inter=dt_unit, split=True)):
#print('n = {}: '.format(n))
#for t in tensor_train:
# print('{}: {}'.format(t, t.shape))
if n % callback_interval == 0:
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]
logger2.warning("{} {}".format(time, rho[0] + rho[3]))
logger.info("{} {} {} {} {}".format(time, rho[0], rho[1], rho[2], rho[3]))
return
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)