def unpack(self, yt):
rho = utils.from_liouville(yt[:self.ham.nsite**2], self.ham.nsite)
qp = yt[self.ham.nsite**2:].real
q, p = qp[:self.ham.nbath*self.nmode], qp[self.ham.nbath*self.nmode:]
q = q.reshape(self.ham.nbath,self.nmode)
p = p.reshape(self.ham.nbath,self.nmode)
return rho, q, p
def unpack(self, yt):
rho = utils.from_liouville(yt[:self.ham.nsite**2], self.ham.nsite)
qp = yt[self.ham.nsite**2:].real
q, p = qp[:self.ham.nbath * self.nmode], qp[self.ham.nbath *
self.nmode:]
q = q.reshape(self.ham.nbath, self.nmode)
p = p.reshape(self.ham.nbath, self.nmode)
return rho, q, p
def propagate_full(self,
rho_0,
t_init,
t_final,
dt,
markov_tol=1e-3,
markov_time=np.inf,
is_verbose=False):
self.markov_tol = markov_tol
self.markov_time = markov_time
times = np.arange(t_init, t_final, dt)
rho_eig = self.ham.site2eig(rho_0)
integrator = Integrator(self.diffeq_type,
dt,
Omega=self.Omega,
R=self.R,
K=self.K)
if self.method == 'TCL2':
if not hasattr(self, 'redfield_tensor_n'):
self.precompute_redfield_tensor(t_init, t_final, dt)
elif len(self.redfield_tensor_n) < int((t_final - t_init) / dt):
self.precompute_redfield_tensor(t_init, t_final, dt)
if self.method == 'TC2':
self.precompute_redfield_tensor(t_init, t_final, dt)
#self.precompute_redfield_tensor_finegrid(t_init, t_final, dt,
# integrator)
integrator.set_initial_value(utils.to_liouville(rho_eig), t_init)
rhos_site = []
for time in times:
# Retrieve data from integrator
rho_eig = utils.from_liouville(integrator.y)
# Collect results
rho_site = self.ham.eig2site(rho_eig)
rhos_site.append(rho_site)
# Propagate one timestep
integrator.integrate()
if is_verbose:
print("\n--- Finished performing RDM dynamics")
return times, rhos_site
def propagate(self, rho_0, t_init, t_final, dt,
markov_tol = 1e-3, markov_time = np.inf,
is_verbose=True):
"""Propagate the RDM according to Redfield-like dynamics.
Parameters
----------
rho_0 : np.array
The initial RDM.
t_init : float
The initial time.
t_final : float
The final time.
dt : float
The timestep.
markov_tol : float
The relative tolerance at which to decide that the memory tensor
has stopped changing (and henceforth Markovian).
markov_time : float
The hard-coded time to stop re-calculating the memory tensor.
is_verbose : bool
Flag to indicate verbose printing.
Returns
-------
times : list of floats
The times at which the RDM has been calculated.
rhos_site : list of np.arrays
The RDM at each time in the site basis.
rhos_eig : list of np.arrays
The RDM at each time in the system eigen-basis.
"""
self.markov_tol = markov_tol
self.markov_time = markov_time
times = np.arange(t_init, t_final, dt)
rho_site = rho_0.copy()
rho_eig = self.ham.site2eig(rho_site)
integrator = Integrator(self.diffeq_type, dt,
Omega=self.Omega, R=self.R, K=self.K)
if self.method in ['TCL2', 'TC2']:
self.precompute_redfield_tensor(t_init, t_final, dt)
#self.precompute_redfield_tensor_finegrid(t_init, t_final, dt,
# integrator)
integrator.set_initial_value(utils.to_liouville(rho_eig), t_init)
rhos_site = []
rhos_eig = []
for time in times:
# Retrieve data from integrator
rho_eig = utils.from_liouville(integrator.y)
# Collect results
rho_site = self.ham.eig2site(rho_eig)
rhos_site.append(rho_site)
rhos_eig.append(rho_eig)
# Propagate one timestep
integrator.integrate()
if is_verbose:
print "\n--- Finished performing RDM dynamics"
return times, rhos_site, rhos_eig
