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_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_tree_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-10
# 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_uni():
from scipy.integrate import solve_ivp
# System
e = Quantity(100, 'cm-1').value_in_au
v = Quantity(50, 'cm-1').value_in_au
h = np.array([[e, v],
[v, 0]], dtype='double')
phi = np.array([1, 0], dtype='complex')
ode_int = 0.1
diff = lambda t, y: (h @ y) * (-1.0j)
for n in range(20000):
time = ode_int * n
solver = solve_ivp(
diff, (time, time + ode_int), phi, method='RK23', max_step=ode_int,
rtol=1e-8, atol=1e-8
)
phi = solver.y[:, -1]
if n % 100 == 0:
print('Time: {}; rho: {} {}'.format(time, np.abs(phi[0])**2, np.abs(phi[1])**2))
def test_uni_int():
from scipy.integrate import solve_ivp
from scipy.linalg import eigh
# System
e = Quantity(100, 'cm-1').value_in_au
v = Quantity(50, 'cm-1').value_in_au
h = np.array([[e, v],
[v, 0]], dtype='double')
l, u = eigh(h)
uc = np.conj(np.transpose(u))
phi = np.array([1, 0], dtype='complex')
ode_int = 0.1
data = []
for n in range(20000):
time = ode_int * n
vec = np.dot(uc, (np.exp(-1.0j * time * l) * np.dot(u, phi)))
if n % 100 == 0:
data.append([time] + list(np.abs(vec)**2))
np.savetxt('reference.dat', np.array(data, dtype=np.complex128))
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))
from __future__ import absolute_import, division, print_function
from builtins import filter, map, range, zip
from minitn.heom.corr import Drude
from minitn.heom.network import tensor_tree_template
from minitn.heom.propagate import MultiLayer
from minitn.lib.backend import DTYPE, np
from minitn.lib.logging import Logger
from minitn.lib.tools import huffman_tree
from minitn.lib.units import Quantity
from minitn.models.sbm import SBM
from minitn.tensor import Leaf, Tensor
# System: pure dephasing
e = Quantity(5000, 'cm-1').value_in_au
v = Quantity(500, 'cm-1').value_in_au
max_tier = 20
rank_heom = 10
rank_wfn = 8
beta = None
prefix = 'drude_boson_tree_zt_t{}_'.format(max_tier)
ph_parameters = [
#(Quantity(400, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
#(Quantity(800, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
#(Quantity(1200, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
(Quantity(1600, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
]
dof = len(ph_parameters)
# coding: utf-8
from __future__ import absolute_import, division, print_function
from builtins import filter, map, range, zip
from minitn.heom.network import simple_heom, tensor_train_template
from minitn.heom.propagate import MultiLayer
from minitn.lib.backend import DTYPE, np
from minitn.lib.logging import Logger
from minitn.lib.tools import huffman_tree
from minitn.lib.units import Quantity
from minitn.models.sbm import SBM
from minitn.tensor import Leaf, Tensor
# System: pure dephasing
e = Quantity(5000, 'cm-1').value_in_au
v = Quantity(500, 'cm-1').value_in_au
dof = 1
max_tier = 12
rank_heom = 1
rank_wfn = 8
prefix = '{}-DOF_2site_t{}_'.format(dof, max_tier)
ph_parameters = [
#(Quantity(250, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
(Quantity(750, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
]
model = SBM(
sys_ham=np.array([[-0.5 * e, v], [v, 0.5 * e]], dtype=DTYPE),
sys_op=np.array([[-0.5, 0.0], [0.0, 0.5]], dtype=DTYPE),
def sbm_ft(including_bath=False, snd=False):
# Define parameters of the model.
sbm = SpinBosonModel(
including_bath=including_bath,
e1=0.,
e2=Quantity(6500, 'cm-1').value_in_au,
v=Quantity(500, 'cm-1').value_in_au,
omega_list=[Quantity(2100, 'cm-1').value_in_au],
lambda_list=([Quantity(750, 'cm-1').value_in_au]),
dim_list=[10],
stop=Quantity(10000, 'cm-1').value_in_au,
n=32,
dim=30,
lambda_g=Quantity(2250, 'cm-1').value_in_au,
omega_g=Quantity(500, 'cm-1').value_in_au,
lambda_d=Quantity(1250, 'cm-1').value_in_au,
omega_d=Quantity(50, 'cm-1').value_in_au,
mu=Quantity(250, 'cm-1').value_in_au,
tau=Quantity(3, 'fs').value_in_au,
t_d=Quantity(6, 'fs').value_in_au,
omega=Quantity(13000, 'cm-1').value_in_au,
)
# Define the topological structure of the ML-MCTDH tree
graph, root = {
'ROOT': ['ELECs', 'I0s'],
'ELECs': ['ELEC', "ELEC'"],
'I0s': ['I0', "I0'"],
}, 'ROOT'
root = Tensor.generate(graph, root)
# Define the detailed parameters for the MC-MCTDH tree
solver = MultiLayer(root, sbm.h_list, f_list=sbm.f_list, use_str_name=True)
bond_dict = {}
# Leaves
for s, i, t, j in root.linkage_visitor():
if isinstance(t, Leaf):
try:
dim = sbm.dimensions[t.name]
except KeyError:
dim = sbm.dimensions[t.name[:-1]]
bond_dict[(s, i, t, j)] = dim
s_ax = s.axis
p, p_ax = s[s_ax]
bond_dict[(p, p_ax, s, s_ax)] = dim**2 if dim < 9 else 50
solver.autocomplete(bond_dict, max_entangled=True)
# Define the computation details
solver.settings(cmf_steps=10,
ode_method='RK45',
ps_method='split-unite',
snd_order=snd)
logging.info("Size of a wfn: {} complexes".format(len(root.vectorize())))
# Do the imaginary time propogation
inv_tem = 1 / 1000
steps = 100
for time, _ in solver.propagator(
steps=steps,
ode_inter=Quantity(inv_tem / steps / 2, unit='K-1').value_in_au,
split=True,
imaginary=True):
t = 2 * Quantity(time).convert_to(unit='K-1').value
z = solver.relative_partition_function
kelvin = 'inf' if abs(t) < 1.e-14 else 1.0 / t
logging.warning('Temperatue: {} K; ln(Z/Z_0): {}'.format(
kelvin, np.log(z)))
# Define the obersevable of interest
projector = np.array([[0., 0.], [0., 1.]])
op = [[[root[0][0][1][0], projector]]]
# Do the real time propogation
tp_list = []
steps = 100
root.is_normalized = True
for time, _ in solver.propagator(steps=steps,
ode_inter=Quantity(10 / steps,
'fs').value_in_au,
split=True,
imaginary=False):
t = Quantity(time).convert_to(unit='fs').value
p = solver.expection(op=op)
logging.warning('Time: {:.2f} fs; P2: {}'.format(t, p))
tp_list.append((t, p))
return np.array(tp_list)
def sbm_zt(including_bath=False, split=False, snd=False):
sbm_para_dict = {
'including_bath':
including_bath,
'e1':
0.,
'e2':
Quantity(6500, 'cm-1').value_in_au,
'v':
Quantity(500, 'cm-1').value_in_au,
"omega_list": [
Quantity(2100, 'cm-1').value_in_au,
Quantity(650, 'cm-1').value_in_au,
Quantity(400, 'cm-1').value_in_au,
Quantity(150, 'cm-1').value_in_au
],
'lambda_list': ([Quantity(750, 'cm-1').value_in_au] * 4),
'dim_list': [10, 14, 20, 30],
'stop':
Quantity(3 * 2250, 'cm-1').value_in_au,
'n':
32,
'dim':
30,
'lambda_g':
Quantity(2250, 'cm-1').value_in_au,
'omega_g':
Quantity(500, 'cm-1').value_in_au,
'lambda_d':
Quantity(1250, 'cm-1').value_in_au,
'omega_d':
Quantity(50, 'cm-1').value_in_au,
'mu':
Quantity(250, 'cm-1').value_in_au,
'tau':
Quantity(30, 'fs').value_in_au,
't_d':
Quantity(60, 'fs').value_in_au,
'omega':
Quantity(13000, 'cm-1').value_in_au,
}
sbm = SpinBosonModel(**sbm_para_dict)
# Define the topological structure of the ML-MCTDH tree
graph, root = sbm.autograph(n_branch=2)
root = Tensor.generate(graph, root)
# Define the detailed parameters for the MC-MCTDH tree
solver = MultiLayer(root, sbm.h_list, f_list=sbm.f_list, use_str_name=True)
bond_dict = {}
# Leaves
for s, i, t, j in root.linkage_visitor():
if isinstance(t, Leaf):
bond_dict[(s, i, t, j)] = sbm.dimensions[t.name]
# ELEC part
elec_r = root[0][0]
for s, i, t, j in elec_r.linkage_visitor(leaf=False):
raise NotImplementedError()
# INNER part
inner_r = root[1][0] if including_bath else root
if including_bath:
bond_dict[(root, 1, inner_r, 0)] = 60
for s, i, t, j in inner_r.linkage_visitor(leaf=False):
bond_dict[(s, i, t, j)] = 50
# OUTER part
if including_bath:
outer_r = root[2][0]
bond_dict[(root, 2, outer_r, 0)] = 20
for s, i, t, j in root[2][0].linkage_visitor(leaf=False):
bond_dict[(s, i, t, j)] = 10
solver.autocomplete(bond_dict, max_entangled=False)
# Define the computation details
solver.settings(
max_ode_steps=100,
cmf_steps=(1 if split else 10),
ode_method='RK45',
ps_method='s',
snd_order=snd,
)
root.is_normalized = True
# Define the obersevable of interest
projector = np.array([[0., 0.], [0., 1.]])
op = [[[root[0][0], projector]]]
t_p = []
for time, _ in solver.propagator(
steps=20,
ode_inter=Quantity(0.2, 'fs').value_in_au,
split=split,
move_energy=True,
):
t, p = (Quantity(time).convert_to(unit='fs').value,
solver.expection(op=op))
t_p.append((t, p))
logging.warning('Time: {:.2f} fs, P2: {}'.format(t, p))
if np.abs(p) > 0.5:
break
# Save the results
msg = 'split' if split else 'origin'
msg2 = 'snd' if snd else 'fst'
np.savetxt('sbm-zt-{}-{}.dat'.format(msg, msg2), t_p)
Acting on 0-th index.
"""
derivatives = self._diff_h() + self._diff_n()
for k in range(self.k_max):
derivatives.extend(self._diff_k(k))
return derivatives
if __name__ == '__main__':
from minitn.heom.noise import Drude
from minitn.lib.units import Quantity
# System
e = Quantity(6500, 'cm-1').value_in_au
v = Quantity(500, 'cm-1').value_in_au
# Bath
lambda_0 = Quantity(2000, 'cm-1').value_in_au # reorganization energy
omega_0 = Quantity(2000, 'cm-1').value_in_au # vibrational frequency
beta = Quantity(300, 'K').value_in_au # temperature
# Superparameters
max_terms = 5 # (terms used in the expansion of the correlation function)
max_tier = 10 # (number of possble values for each n_k in the extended rho)
h = np.array([[0, v], [v, e]])
op = np.array([[0, 0], [0, 1]])
corr = Drude(lambda_0, omega_0, max_terms, beta)
heom = Hierachy([max_tier] * max_terms, h, op, corr)
def sbm_zt(including_bath=False, split=False, snd=False):
omega = 13000
sbm = SpinBosonModel(
including_bath=including_bath,
e1=0.,
e2=Quantity(6500, 'cm-1').value_in_au,
v=Quantity(500, 'cm-1').value_in_au,
omega_list=[
Quantity(2100, 'cm-1').value_in_au,
Quantity(650, 'cm-1').value_in_au,
Quantity(400, 'cm-1').value_in_au,
Quantity(150, 'cm-1').value_in_au
],
lambda_list=([Quantity(750, 'cm-1').value_in_au] * 4),
dim_list=[10, 14, 20, 30],
stop=Quantity(3 * 2250, 'cm-1').value_in_au,
n=32,
dim=30,
lambda_g=Quantity(2250, 'cm-1').value_in_au,
omega_g=Quantity(500, 'cm-1').value_in_au,
lambda_d=Quantity(1250, 'cm-1').value_in_au,
omega_d=Quantity(50, 'cm-1').value_in_au,
mu=Quantity(250, 'cm-1').value_in_au,
tau=Quantity(30, 'fs').value_in_au,
t_d=Quantity(60, 'fs').value_in_au,
omega=Quantity(omega, 'cm-1').value_in_au,
)
# Define the topological structure of the ML-MCTDH tree
graph, root = sbm.autograph(n_branch=2)
root = Tensor.generate(graph, root)
# Define the detailed parameters for the MC-MCTDH tree
solver = MultiLayer(root, sbm.h_list, f_list=sbm.f_list, use_str_name=True)
bond_dict = {}
# Leaves
for s, i, t, j in root.linkage_visitor():
if isinstance(t, Leaf):
bond_dict[(s, i, t, j)] = sbm.dimensions[t.name]
# ELEC part
elec_r = root[0][0]
for s, i, t, j in elec_r.linkage_visitor(leaf=False):
raise NotImplementedError()
# INNER part
inner_r = root[1][0] if including_bath else root
if including_bath:
bond_dict[(root, 1, inner_r, 0)] = 60
for s, i, t, j in inner_r.linkage_visitor(leaf=False):
bond_dict[(s, i, t, j)] = 50
# OUTER part
if including_bath:
outer_r = root[2][0]
bond_dict[(root, 2, outer_r, 0)] = 20
for s, i, t, j in root[2][0].linkage_visitor(leaf=False):
bond_dict[(s, i, t, j)] = 10
solver.autocomplete(bond_dict, max_entangled=False)
# Define the computation details
solver.settings(
max_ode_steps=100,
cmf_steps=(1 if split else 10),
ode_method='RK45',
ps_method='s',
snd_order=snd,
)
print("Size of a wfn: {} complexes".format(len(root.vectorize())))
root.is_normalized = True
# Define the obersevable of interest
projector = np.array([[0., 0.], [0., 1.]])
op = [[[root[0][0], projector]]]
t_p = []
for time, _ in solver.propagator(
steps=20,
ode_inter=Quantity(0.2, 'fs').value_in_au,
split=split,
move_energy=True,
):
t, p = (Quantity(time).convert_to(unit='fs').value,
solver.expection(op=op))
t_p.append((t, p))
logging.warning('Time: {:.2f} fs, P2: {}'.format(t, p))
if np.abs(p) > 0.5:
break
return np.array(t_p)
from __future__ import absolute_import, division, print_function
from builtins import filter, map, range, zip
from minitn.heom.corr import Drude
from minitn.heom.network import tensor_train_template
from minitn.heom.propagate import MultiLayer
from minitn.lib.backend import DTYPE, np
from minitn.lib.logging import Logger
from minitn.lib.tools import huffman_tree
from minitn.lib.units import Quantity
from minitn.models.sbm import SBM
from minitn.tensor import Leaf, Tensor
# System: pure dephasing
e = Quantity(5000, 'cm-1').value_in_au
v = Quantity(500, 'cm-1').value_in_au
max_tier = 40
rank_heom = max_tier
rank_wfn = max_tier
beta = Quantity(1 / 300, 'K-1').value_in_au
prefix = 'drude_100_300K_t{}_'.format(max_tier)
ph_parameters = [
#(Quantity(400, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
#(Quantity(800, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
#(Quantity(1200, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
#(Quantity(1600, 'cm-1').value_in_au, Quantity(500, 'cm-1').value_in_au),
]
dof = len(ph_parameters)
def ml(dof, e, v, eta, cutoff, scale=5, loc=None, steps=2000, ode_inter=0.1):
f_ = 'dof{}-eta{}.log'.format(dof, eta)
logger = Logger(filename=f_).logger
# define parameters
e = Quantity(e, 'cm-1').value_in_au
v = Quantity(v, 'cm-1').value_in_au
eta = Quantity(eta, 'cm-1').value_in_au
omega0 = Quantity(cutoff, 'cm-1').value_in_au
sys_hamiltonian = np.array([[-e / 2.0, v], [v, e / 2.0]], dtype=DTYPE)
projector = np.array([[0.0, 0.0], [0.0, 1.0]],
dtype=DTYPE) # S in H_SB = S x B
primitive_dim = 100
spf_dim = 20
# Spectrum function
def spec_func(omega):
if 0 < omega < omega0:
return eta
else:
return 0.0
# Define all Leaf tensors and hamiltonian we need
h_list = []
sys_leaf = Leaf(name='sys0')
h_list.append([(sys_leaf, -1.0j * sys_hamiltonian)])
ph_parameters = linear_discretization(spec_func, omega0, dof)
if loc is not None:
adj_pair = (ph_parameters[loc][0], ph_parameters[loc][1] * scale)
ph_parameters[loc] = adj_pair
leaves = []
for n, (omega, g) in enumerate(ph_parameters, 1):
ph = Phonon(primitive_dim, omega)
ph_leaf = Leaf(name='ph{}'.format(n))
leaves.append(ph_leaf)
# hamiltonian ph part
h_list.append([(ph_leaf, -1.0j * ph.hamiltonian)])
# e-ph part
op = ph.annihilation_operator + ph.creation_operator
h_list.append([(ph_leaf, g * op), (sys_leaf, -1.0j * projector)])
def ph_spf(n=0):
n += 1
return Tensor(name='spf{}'.format(n), axis=0)
graph, root = huffman_tree(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
print(graph)
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
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)] = primitive_dim
else:
bond_dict[(s, i, t, j)] = spf_dim
solver.autocomplete(bond_dict)
# set initial root array
a, b = 1.0, 1.0
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)
# Define the computation details
solver.ode_method = 'RK45'
solver.snd_order = True
solver.cmf_steps = 1
root.is_normalized = True
# Define the obersevable of interest
logger.info('''# time/fs rho00 rho01 rho10 rho11''')
for time, _ in solver.propagator(
steps=steps,
ode_inter=Quantity(ode_inter, 'fs').value_in_au,
split=True,
):
t = Quantity(time).convert_to(unit='fs').value
for tensor in root.visitor(axis=None):
tensor.reset()
rho = root.partial_env(0, proper=False)
for tensor in root.visitor(axis=None):
tensor.reset()
flat_data = [t] + list(np.reshape(rho, -1))
logger.info('{} {} {} {} {}'.format(*flat_data))