本文整理匯總了Python中qutip.odedata.Odedata.expect[m][t_idx]方法的典型用法代碼示例。如果您正苦於以下問題：Python Odedata.expect[m][t_idx]方法的具體用法？Python Odedata.expect[m][t_idx]怎麽用？Python Odedata.expect[m][t_idx]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.odedata.Odedata的用法示例。
在下文中一共展示了Odedata.expect[m][t_idx]方法的4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: _generic_ode_solve
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import expect[m][t_idx] [as 別名]
def _generic_ode_solve(r, psi0, tlist, e_ops, opt, progress_bar,
state_norm_func=None, dims=None):
"""
Internal function for solving ODEs.
"""
#
# prepare output array
#
n_tsteps = len(tlist)
output = Odedata()
output.solver = "sesolve"
output.times = tlist
if opt.store_states:
output.states = []
if isinstance(e_ops, types.FunctionType):
n_expt_op = 0
expt_callback = True
elif isinstance(e_ops, list):
n_expt_op = len(e_ops)
expt_callback = False
if n_expt_op == 0:
# fallback on storing states
output.states = []
opt.store_states = True
else:
output.expect = []
output.num_expect = n_expt_op
for op in e_ops:
if op.isherm:
output.expect.append(np.zeros(n_tsteps))
else:
output.expect.append(np.zeros(n_tsteps, dtype=complex))
else:
raise TypeError("Expectation parameter must be a list or a function")
#
# start evolution
#
progress_bar.start(n_tsteps)
dt = np.diff(tlist)
for t_idx, t in enumerate(tlist):
progress_bar.update(t_idx)
if not r.successful():
break
if state_norm_func:
data = r.y / state_norm_func(r.y)
r.set_initial_value(data, r.t)
if opt.store_states:
output.states.append(Qobj(r.y,dims=dims))
if expt_callback:
# use callback method
e_ops(t, Qobj(r.y, dims=psi0.dims))
for m in range(n_expt_op):
output.expect[m][t_idx] = cy_expect_psi(e_ops[m].data, r.y, e_ops[m].isherm)
if t_idx < n_tsteps - 1:
r.integrate(r.t + dt[t_idx])
progress_bar.finished()
if not opt.rhs_reuse and odeconfig.tdname is not None:
try:
os.remove(odeconfig.tdname + ".pyx")
except:
pass
if opt.store_final_state:
output.final_state = Qobj(r.y)
return output示例2: floquet_markov_mesolve
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import expect[m][t_idx] [as 別名]
#.........這裏部分代碼省略.........
#
if isket(rho0):
# Got a wave function as initial state: convert to density matrix.
rho0 = ket2dm(rho0)
#
# prepare output array
#
n_tsteps = len(tlist)
dt = tlist[1] - tlist[0]
output = Odedata()
output.solver = "fmmesolve"
output.times = tlist
if isinstance(e_ops, FunctionType):
n_expt_op = 0
expt_callback = True
elif isinstance(e_ops, list):
n_expt_op = len(e_ops)
expt_callback = False
if n_expt_op == 0:
output.states = []
else:
if not f_modes_table:
raise TypeError("The Floquet mode table has to be provided " +
"when requesting expectation values.")
output.expect = []
output.num_expect = n_expt_op
for op in e_ops:
if op.isherm:
output.expect.append(np.zeros(n_tsteps))
else:
output.expect.append(np.zeros(n_tsteps, dtype=complex))
else:
raise TypeError("Expectation parameter must be a list or a function")
#
# transform the initial density matrix to the eigenbasis: from
# computational basis to the floquet basis
#
if ekets is not None:
rho0 = rho0.transform(ekets, True)
#
# setup integrator
#
initial_vector = mat2vec(rho0.full())
r = scipy.integrate.ode(cy_ode_rhs)
r.set_f_params(R.data.data, R.data.indices, R.data.indptr)
r.set_integrator('zvode', method=opt.method, order=opt.order,
atol=opt.atol, rtol=opt.rtol, max_step=opt.max_step)
r.set_initial_value(initial_vector, tlist[0])
#
# start evolution
#
rho = Qobj(rho0)
t_idx = 0
for t in tlist:
if not r.successful():
break
rho.data = vec2mat(r.y)
if expt_callback:
# use callback method
if floquet_basis:
e_ops(t, Qobj(rho))
else:
f_modes_table_t, T = f_modes_table
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
e_ops(t, Qobj(rho).transform(f_modes_t, False))
else:
# calculate all the expectation values, or output rho if
# no operators
if n_expt_op == 0:
if floquet_basis:
output.states.append(Qobj(rho))
else:
f_modes_table_t, T = f_modes_table
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
output.states.append(Qobj(rho).transform(f_modes_t, False))
else:
f_modes_table_t, T = f_modes_table
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
for m in range(0, n_expt_op):
output.expect[m][t_idx] = \
expect(e_ops[m], rho.transform(f_modes_t, False))
r.integrate(r.t + dt)
t_idx += 1
return output示例3: _generic_ode_solve
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import expect[m][t_idx] [as 別名]
def _generic_ode_solve(r, rho0, tlist, e_ops, opt, progress_bar):
"""
Internal function for solving ME.
"""
#
# prepare output array
#
n_tsteps = len(tlist)
dt = tlist[1] - tlist[0]
e_sops_data = []
output = Odedata()
output.solver = "mesolve"
output.times = tlist
if opt.store_states:
output.states = []
if isinstance(e_ops, types.FunctionType):
n_expt_op = 0
expt_callback = True
elif isinstance(e_ops, list):
n_expt_op = len(e_ops)
expt_callback = False
if n_expt_op == 0:
# fall back on storing states
output.states = []
opt.store_states = True
else:
output.expect = []
output.num_expect = n_expt_op
for op in e_ops:
e_sops_data.append(spre(op).data)
if op.isherm and rho0.isherm:
output.expect.append(np.zeros(n_tsteps))
else:
output.expect.append(np.zeros(n_tsteps, dtype=complex))
else:
raise TypeError("Expectation parameter must be a list or a function")
#
# start evolution
#
progress_bar.start(n_tsteps)
rho = Qobj(rho0)
for t_idx, t in enumerate(tlist):
progress_bar.update(t_idx)
if not r.successful():
break
if opt.store_states or expt_callback:
rho.data = vec2mat(r.y)
if opt.store_states:
output.states.append(Qobj(rho))
if expt_callback:
# use callback method
e_ops(t, rho)
for m in range(n_expt_op):
if output.expect[m].dtype == complex:
output.expect[m][t_idx] = expect_rho_vec(e_sops_data[m], r.y)
else:
output.expect[m][t_idx] = np.real(
expect_rho_vec(e_sops_data[m], r.y))
r.integrate(r.t + dt)
progress_bar.finished()
if not opt.rhs_reuse and odeconfig.tdname is not None:
try:
os.remove(odeconfig.tdname + ".pyx")
except:
pass
if opt.store_final_state:
rho.data = vec2mat(r.y)
output.final_state = Qobj(rho)
return output示例4: _generic_ode_solve
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import expect[m][t_idx] [as 別名]
def _generic_ode_solve(r, psi0, tlist, expt_ops, opt,
state_vectorize, state_norm_func=None):
"""
Internal function for solving ODEs.
"""
#
# prepare output array
#
n_tsteps = len(tlist)
dt = tlist[1] - tlist[0]
output = Odedata()
output.solver = "mesolve"
output.times = tlist
if isinstance(expt_ops, types.FunctionType):
n_expt_op = 0
expt_callback = True
elif isinstance(expt_ops, list):
n_expt_op = len(expt_ops)
expt_callback = False
if n_expt_op == 0:
output.states = []
else:
output.expect = []
output.num_expect = n_expt_op
for op in expt_ops:
if op.isherm and psi0.isherm:
output.expect.append(np.zeros(n_tsteps))
else:
output.expect.append(np.zeros(n_tsteps, dtype=complex))
else:
raise TypeError("Expectation parameter must be a list or a function")
#
# start evolution
#
psi = Qobj(psi0)
t_idx = 0
for t in tlist:
if not r.successful():
break
if state_norm_func:
psi.data = state_vectorize(r.y)
state_norm = state_norm_func(psi.data)
psi.data = psi.data / state_norm
r.set_initial_value(r.y / state_norm, r.t)
else:
psi.data = state_vectorize(r.y)
if expt_callback:
# use callback method
expt_ops(t, Qobj(psi))
else:
# calculate all the expectation values,
# or output rho if no operators
if n_expt_op == 0:
output.states.append(Qobj(psi)) # copy psi/rho
else:
for m in range(0, n_expt_op):
output.expect[m][t_idx] = expect(expt_ops[m], psi)
r.integrate(r.t + dt)
t_idx += 1
if not opt.rhs_reuse and odeconfig.tdname is not None:
try:
os.remove(odeconfig.tdname + ".pyx")
except:
pass
return output