本文整理匯總了Python中qutip.odedata.Odedata.num_expect方法的典型用法代碼示例。如果您正苦於以下問題：Python Odedata.num_expect方法的具體用法？Python Odedata.num_expect怎麽用？Python Odedata.num_expect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.num_expect方法的7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 num_expect [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2: _generic_ode_solve
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import num_expect [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3: floquet_markov_mesolve
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import num_expect [as 別名]
def floquet_markov_mesolve(R, ekets, rho0, tlist, e_ops, f_modes_table=None,
options=None, floquet_basis=True):
"""
Solve the dynamics for the system using the Floquet-Markov master equation.
"""
if options is None:
opt = Odeoptions()
else:
opt = options
if opt.tidy:
R.tidyup()
#
# check initial state
#
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:
#.........這裏部分代碼省略.........
示例4: fsesolve
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import num_expect [as 別名]
def fsesolve(H, psi0, tlist, e_ops=[], T=None, args={}, Tsteps=100):
"""
Solve the Schrodinger equation using the Floquet formalism.
Parameters
----------
H : :class:`qutip.qobj.Qobj`
System Hamiltonian, time-dependent with period `T`.
psi0 : :class:`qutip.qobj`
Initial state vector (ket).
tlist : *list* / *array*
list of times for :math:`t`.
e_ops : list of :class:`qutip.qobj` / callback function
list of operators for which to evaluate expectation values. If this
list is empty, the state vectors for each time in `tlist` will be
returned instead of expectation values.
T : float
The period of the time-dependence of the hamiltonian.
args : dictionary
Dictionary with variables required to evaluate H.
Tsteps : integer
The number of time steps in one driving period for which to
precalculate the Floquet modes. `Tsteps` should be an even number.
Returns
-------
output : :class:`qutip.odedata.Odedata`
An instance of the class :class:`qutip.odedata.Odedata`, which
contains either an *array* of expectation values or an array of
state vectors, for the times specified by `tlist`.
"""
if not T:
# assume that tlist span exactly one period of the driving
T = tlist[-1]
# find the floquet modes for the time-dependent hamiltonian
f_modes_0, f_energies = floquet_modes(H, T, args)
# calculate the wavefunctions using the from the floquet modes
f_modes_table_t = floquet_modes_table(f_modes_0, f_energies,
np.linspace(0, T, Tsteps + 1),
H, T, args)
# setup Odedata for storing the results
output = Odedata()
output.times = tlist
output.solver = "fsesolve"
if isinstance(e_ops, FunctionType):
output.num_expect = 0
expt_callback = True
elif isinstance(e_ops, list):
output.num_expect = len(e_ops)
expt_callback = False
if output.num_expect == 0:
output.states = []
else:
output.expect = []
for op in e_ops:
if op.isherm:
output.expect.append(np.zeros(len(tlist)))
else:
output.expect.append(np.zeros(len(tlist), dtype=complex))
else:
raise TypeError("e_ops must be a list Qobj or a callback function")
psi0_fb = psi0.transform(f_modes_0, True)
for t_idx, t in enumerate(tlist):
f_modes_t = floquet_modes_t_lookup(f_modes_table_t, t, T)
f_states_t = floquet_states(f_modes_t, f_energies, t)
psi_t = psi0_fb.transform(f_states_t, False)
if expt_callback:
# use callback method
e_ops(t, psi_t)
else:
# calculate all the expectation values, or output psi if
# no expectation value operators where defined
if output.num_expect == 0:
output.states.append(Qobj(psi_t))
else:
for e_idx, e in enumerate(e_ops):
output.expect[e_idx][t_idx] = expect(e, psi_t)
return output示例5: mcsolve_f90
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import num_expect [as 別名]
#.........這裏部分代碼省略.........
ntraj = options.ntraj
if psi0.type != 'ket':
raise Exception("Initial state must be a state vector.")
odeconfig.options = options
# set num_cpus to the value given in qutip.settings
# if none in Odeoptions
if not odeconfig.options.num_cpus:
odeconfig.options.num_cpus = qutip.settings.num_cpus
# set initial value data
if options.tidy:
odeconfig.psi0 = psi0.tidyup(options.atol).full()
else:
odeconfig.psi0 = psi0.full()
odeconfig.psi0_dims = psi0.dims
odeconfig.psi0_shape = psi0.shape
# set general items
odeconfig.tlist = tlist
if isinstance(ntraj, (list, np.ndarray)):
raise Exception("ntraj as list argument is not supported.")
else:
odeconfig.ntraj = ntraj
# ntraj_list = [ntraj]
# set norm finding constants
odeconfig.norm_tol = options.norm_tol
odeconfig.norm_steps = options.norm_steps
if not options.rhs_reuse:
odeconfig.soft_reset()
# no time dependence
odeconfig.tflag = 0
# check for collapse operators
if len(c_ops) > 0:
odeconfig.cflag = 1
else:
odeconfig.cflag = 0
# Configure data
_mc_data_config(H, psi0, [], c_ops, [], [], e_ops, options, odeconfig)
# Load Monte Carlo class
mc = _MC_class()
# Set solver type
if (options.method == 'adams'):
mc.mf = 10
elif (options.method == 'bdf'):
mc.mf = 22
else:
if debug:
print('Unrecognized method for ode solver, using "adams".')
mc.mf = 10
# store ket and density matrix dims and shape for convenience
mc.psi0_dims = psi0.dims
mc.psi0_shape = psi0.shape
mc.dm_dims = (psi0 * psi0.dag()).dims
mc.dm_shape = (psi0 * psi0.dag()).shape
# use sparse density matrices during computation?
mc.sparse_dms = sparse_dms
# run in serial?
mc.serial_run = serial or (ntraj == 1)
# are we doing a partial trace for returned states?
mc.ptrace_sel = ptrace_sel
if (ptrace_sel != []):
if debug:
print("ptrace_sel set to " + str(ptrace_sel))
print("We are using dense density matrices during computation " +
"when performing partial trace. Setting sparse_dms = False")
print("This feature is experimental.")
mc.sparse_dms = False
mc.dm_dims = psi0.ptrace(ptrace_sel).dims
mc.dm_shape = psi0.ptrace(ptrace_sel).shape
if (calc_entropy):
if (ptrace_sel == []):
if debug:
print("calc_entropy = True, but ptrace_sel = []. Please set " +
"a list of components to keep when calculating average " +
"entropy of reduced density matrix in ptrace_sel. " +
"Setting calc_entropy = False.")
calc_entropy = False
mc.calc_entropy = calc_entropy
# construct output Odedata object
output = Odedata()
# Run
mc.run()
output.states = mc.sol.states
output.expect = mc.sol.expect
output.col_times = mc.sol.col_times
output.col_which = mc.sol.col_which
if (hasattr(mc.sol, 'entropy')):
output.entropy = mc.sol.entropy
output.solver = 'Fortran 90 Monte Carlo solver'
# simulation parameters
output.times = odeconfig.tlist
output.num_expect = odeconfig.e_num
output.num_collapse = odeconfig.c_num
output.ntraj = odeconfig.ntraj
return output示例6: _generic_ode_solve
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import num_expect [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示例7: mcsolve
# 需要導入模塊: from qutip.odedata import Odedata [as 別名]
# 或者: from qutip.odedata.Odedata import num_expect [as 別名]
#.........這裏部分代碼省略.........
#check if running in iPython and using Cython compiling (then no GUI to work around error)
if odeconfig.options.gui and odeconfig.tflag in array([1,10,11]):
try:
__IPYTHON__
except:
pass
else:
odeconfig.options.gui=False
if qutip.settings.qutip_gui=="NONE":
odeconfig.options.gui=False
#check for collapse operators
if c_terms>0:
odeconfig.cflag=1
else:
odeconfig.cflag=0
#Configure data
_mc_data_config(H,psi0,h_stuff,c_ops,c_stuff,args,e_ops,options)
if odeconfig.tflag in array([1,10,11]): #compile time-depdendent RHS code
os.environ['CFLAGS'] = '-O3 -w'
import pyximport
pyximport.install(setup_args={'include_dirs':[numpy.get_include()]})
if odeconfig.tflag in array([1,11]):
code = compile('from '+odeconfig.tdname+' import cyq_td_ode_rhs,col_spmv,col_expect', '<string>', 'exec')
exec(code, globals())
odeconfig.tdfunc=cyq_td_ode_rhs
odeconfig.colspmv=col_spmv
odeconfig.colexpect=col_expect
else:
code = compile('from '+odeconfig.tdname+' import cyq_td_ode_rhs', '<string>', 'exec')
exec(code, globals())
odeconfig.tdfunc=cyq_td_ode_rhs
try:
os.remove(odeconfig.tdname+".pyx")
except:
print("Error removing pyx file. File not found.")
elif odeconfig.tflag==0:
odeconfig.tdfunc=cyq_ode_rhs
else:#setup args for new parameters when rhs_reuse=True and tdfunc is given
#string based
if odeconfig.tflag in array([1,10,11]):
if any(args):
odeconfig.c_args=[]
arg_items=args.items()
for k in range(len(args)):
odeconfig.c_args.append(arg_items[k][1])
#function based
elif odeconfig.tflag in array([2,3,20,22]):
odeconfig.h_func_args=args
#load monte-carlo class
mc=_MC_class()
#RUN THE SIMULATION
mc.run()
#AFTER MCSOLVER IS DONE --------------------------------------
#-------COLLECT AND RETURN OUTPUT DATA IN ODEDATA OBJECT --------------#
output=Odedata()
output.solver='mcsolve'
#state vectors
if mc.psi_out is not None and odeconfig.options.mc_avg and odeconfig.cflag:
output.states=parfor(_mc_dm_avg,mc.psi_out.T)
elif mc.psi_out is not None:
output.states=mc.psi_out
#expectation values
elif mc.expect_out is not None and odeconfig.cflag and odeconfig.options.mc_avg:#averaging if multiple trajectories
if isinstance(ntraj,int):
output.expect=mean(mc.expect_out,axis=0)
elif isinstance(ntraj,(list,ndarray)):
output.expect=[]
for num in ntraj:
expt_data=mean(mc.expect_out[:num],axis=0)
data_list=[]
if any([op.isherm==False for op in e_ops]):
for k in range(len(e_ops)):
if e_ops[k].isherm:
data_list.append(real(expt_data[k]))
else:
data_list.append(expt_data[k])
else:
data_list=[data for data in expt_data]
output.expect.append(data_list)
else:#no averaging for single trajectory or if mc_avg flag (Odeoptions) is off
if mc.expect_out is not None:
output.expect=mc.expect_out
#simulation parameters
output.times=odeconfig.tlist
output.num_expect=odeconfig.e_num
output.num_collapse=odeconfig.c_num
output.ntraj=odeconfig.ntraj
output.col_times=mc.collapse_times_out
output.col_which=mc.which_op_out
return output