本文整理匯總了Python中qutip.solver.Result.jump_times方法的典型用法代碼示例。如果您正苦於以下問題：Python Result.jump_times方法的具體用法？Python Result.jump_times怎麽用？Python Result.jump_times使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類qutip.solver.Result的用法示例。
在下文中一共展示了Result.jump_times方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _smepdpsolve_generic
# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import jump_times [as 別名]
def _smepdpsolve_generic(sso, options, progress_bar):
"""
For internal use. See smepdpsolve.
"""
if debug:
logger.debug(inspect.stack()[0][3])
N_store = len(sso.times)
N_substeps = sso.nsubsteps
dt = (sso.times[1] - sso.times[0]) / N_substeps
nt = sso.ntraj
data = Result()
data.solver = "smepdpsolve"
data.times = sso.times
data.expect = np.zeros((len(sso.e_ops), N_store), dtype=complex)
data.jump_times = []
data.jump_op_idx = []
# Liouvillian for the deterministic part.
# needs to be modified for TD systems
L = liouvillian(sso.H, sso.c_ops)
progress_bar.start(sso.ntraj)
for n in range(sso.ntraj):
progress_bar.update(n)
rho_t = mat2vec(sso.rho0.full()).ravel()
states_list, jump_times, jump_op_idx = \
_smepdpsolve_single_trajectory(data, L, dt, sso.times,
N_store, N_substeps,
rho_t, sso.rho0.dims,
sso.c_ops, sso.e_ops)
data.states.append(states_list)
data.jump_times.append(jump_times)
data.jump_op_idx.append(jump_op_idx)
progress_bar.finished()
# average density matrices
if options.average_states and np.any(data.states):
data.states = [sum([data.states[m][n] for m in range(nt)]).unit()
for n in range(len(data.times))]
# average
data.expect = data.expect / sso.ntraj
# standard error
if nt > 1:
data.se = (data.ss - nt * (data.expect ** 2)) / (nt * (nt - 1))
else:
data.se = None
return data示例2: _ssepdpsolve_generic
# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import jump_times [as 別名]
def _ssepdpsolve_generic(sso, options, progress_bar):
"""
For internal use. See ssepdpsolve.
"""
if debug:
logger.debug(inspect.stack()[0][3])
N_store = len(sso.times)
N_substeps = sso.nsubsteps
dt = (sso.times[1] - sso.times[0]) / N_substeps
nt = sso.ntraj
data = Result()
data.solver = "sepdpsolve"
data.times = sso.tlist
data.expect = np.zeros((len(sso.e_ops), N_store), dtype=complex)
data.ss = np.zeros((len(sso.e_ops), N_store), dtype=complex)
data.jump_times = []
data.jump_op_idx = []
# effective hamiltonian for deterministic part
Heff = sso.H
for c in sso.c_ops:
Heff += -0.5j * c.dag() * c
progress_bar.start(sso.ntraj)
for n in range(sso.ntraj):
progress_bar.update(n)
psi_t = sso.state0.full().ravel()
states_list, jump_times, jump_op_idx = \
_ssepdpsolve_single_trajectory(data, Heff, dt, sso.times,
N_store, N_substeps,
psi_t, sso.state0.dims,
sso.c_ops, sso.e_ops)
data.states.append(states_list)
data.jump_times.append(jump_times)
data.jump_op_idx.append(jump_op_idx)
progress_bar.finished()
# average density matrices
if options.average_states and np.any(data.states):
data.states = [sum([data.states[m][n] for m in range(nt)]).unit()
for n in range(len(data.times))]
# average
data.expect = data.expect / nt
# standard error
if nt > 1:
data.se = (data.ss - nt * (data.expect ** 2)) / (nt * (nt - 1))
else:
data.se = None
# convert complex data to real if hermitian
data.expect = [np.real(data.expect[n, :])
if e.isherm else data.expect[n, :]
for n, e in enumerate(sso.e_ops)]
return data