def test_simple(fname=None):
# Type settings
corr.print()
n_dims = [max_tier] * max_terms
heom = Hierachy(n_dims, H, V, corr)
# Adopt MCTDH
root = simple_heom(rho_0, n_dims)
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 = ProjectorSplitting(root, all_terms)
solver = MultiLayer(root, all_terms)
solver.ode_method = 'RK45'
solver.snd_order = False
# Define the obersevable of interest
logger = Logger(filename=fname, level='info').logger
for n, (time, r) in enumerate(solver.propagator(
steps=count,
ode_inter=dt_unit,
)):
try:
if n % callback_interval == 0:
rho = np.reshape(r.array, (-1, 4))[0]
logger.info("{} {} {} {} {}".format(time, rho[0], rho[1], rho[2], rho[3]))
except:
break
return
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 test_drude():
from minitn.heom.noise import Drude
from minitn.lib.units import Quantity
# System
e = Quantity(100, 'cm-1').value_in_au
v = Quantity(50, 'cm-1').value_in_au
# Bath
corr2 = Correlation(k_max=1)
corr2.symm_coeff = [0.0] # [4.66691921e+01 * 9.24899189e+01]
corr2.asymm_coeff = [0.0] # [4.66691921e+01 * -2.35486582e+01]
corr2.exp_coeff = [1.0]
corr2.delta_coeff = 0.0 # delta_coeff()
corr2.print()
h = np.array([[e, v],
[v, 0]])
op = np.array([[1, 0],
[0, -1]])
# Superparameters
max_tier = 5 # (number of possble values for each n_k in the extended rho)
heom = Hierachy([max_tier], h, op, corr2)
phi = np.array([1, 0])
rho_0 = np.tensordot(phi, phi, axes=0)
init_rho = heom.gen_extended_rho(rho_0)
solver = MultiLayer(init_rho, heom.diff())
# Define the obersevable of interest
dat = []
for n, (time, r) in enumerate(solver.propagator(
steps=20000,
ode_inter=0.1,
)):
if n % 100 == 0:
rho = np.reshape(r, (-1, 4))
for n, _ in enumerate(rho):
if n == 0:
flat_data = [time] + list(rho[0])
dat.append(flat_data)
print('Time: {}; rho: {} {} {} {}'.format(*flat_data))
np.savetxt('test.dat', np.array(dat, dtype=np.complex128))
return np.array(dat)
def test_delta(fname=None):
n_dims = [max_tier] * max_terms
heom = Hierachy(n_dims, H, V, corr)
# Adopt MCTDH
root = simple_heom(rho_0, n_dims)
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.atol = 1.e-7
solver.rtol = 1.e-7
# Define the obersevable of interest
logger = Logger(filename=fname, level='info').logger
for n, (time, r) in enumerate(
solver.propagator(
steps=count,
ode_inter=dt_unit,
#split=False,
)):
if n % callback_interval == 0:
rho = np.reshape(r.array, (-1, 4))
logger.info("{} {} {} {} {}".format(time, rho[0, 0], rho[0, 1],
rho[0, 2], rho[0, 3]))
return
def test_heom(fname=None):
n_dims = 2 * dof * [max_tier]
root = simple_heom(rho_0, n_dims)
leaves = root.leaves()
h_list = model.heom_h_list(leaves[0], leaves[1], leaves[2:], beta=None)
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-12
# 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=False,
)):
# 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_simple():
# 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 MCTDH
root = simple_heom(rho_0, n_dims)
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 = ProjectorSplitting(root, all_terms)
solver = MultiLayer(root, all_terms)
solver.ode_method = 'RK45'
solver.snd_order = False
solver.atol = 1.e-7
solver.rtol = 1.e-7
# Define the obersevable of interest
dat = []
for n, (time,
r) in enumerate(solver.propagator(
steps=count,
ode_inter=dt_unit,
)):
try:
if n % callback_interval == 0:
rho = np.reshape(r.array, (-1, 4))
flat_data = [time] + list(rho[0])
dat.append(flat_data)
print("Time: {}; Tr rho_0: {}".format(
time, rho[0, 0] + rho[0, -1]))
except:
break
return np.array(dat)
def test_mctdh(fname=None):
sys_leaf = Leaf(name='sys0')
ph_leaves = []
for n, (omega, g) in enumerate(ph_parameters, 1):
ph_leaf = Leaf(name='ph{}'.format(n))
ph_leaves.append(ph_leaf)
def ph_spf():
t = Tensor(axis=0)
t.name = 'spf' + str(hex(id(t)))[-4:]
return t
graph, root = huffman_tree(ph_leaves, obj_new=ph_spf, n_branch=2)
try:
graph[root].insert(0, sys_leaf)
except KeyError:
ph_leaf = root
root = Tensor()
graph[root] = [sys_leaf, ph_leaf]
finally:
root.name = 'wfn'
root.axis = None
stack = [root]
while stack:
parent = stack.pop()
for child in graph[parent]:
parent.link_to(parent.order, child, 0)
if child in graph:
stack.append(child)
# Define the detailed parameters for the ML-MCTDH tree
h_list = model.wfn_h_list(sys_leaf, ph_leaves)
solver = MultiLayer(root, h_list)
bond_dict = {}
# Leaves
for s, i, t, j in root.linkage_visitor():
if t.name.startswith('sys'):
bond_dict[(s, i, t, j)] = 2
else:
if isinstance(t, Leaf):
bond_dict[(s, i, t, j)] = max_tier
else:
bond_dict[(s, i, t, j)] = rank_wfn
solver.autocomplete(bond_dict)
# set initial root array
init_proj = np.array([[A, 0.0], [B, 0.0]]) / np.sqrt(A**2 + B**2)
root_array = Tensor.partial_product(root.array, 0, init_proj, 1)
root.set_array(root_array)
solver = MultiLayer(root, h_list)
solver.ode_method = 'RK45'
solver.cmf_steps = solver.max_ode_steps # 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,
)):
if n % callback_interval == 0:
t = Quantity(time).convert_to(unit='fs').value
rho = r.partial_env(0, proper=False)
logger.info("{} {} {} {} {}".format(t, rho[0, 0], rho[0, 1],
rho[1, 0], rho[1, 1]))
en = np.trace(rho @ model.h)
logger2.info('{} {}'.format(t, en))