Python Result.times方法代碼示例

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import 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

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]
 def solve(self, rho0, tlist, options=None):
     """
     Solve the ODE for the evolution of diagonal states and Hamiltonians.
     """
     if options is None:
         options = Options()
     output = Result()
     output.solver = "pisolve"
     output.times = tlist
     output.states = []
     output.states.append(Qobj(rho0))
     rhs_generate = lambda y, tt, M: M.dot(y)
     rho0_flat = np.diag(np.real(rho0.full()))
     L = self.coefficient_matrix()
     rho_t = odeint(rhs_generate, rho0_flat, tlist, args=(L,))
     for r in rho_t[1:]:
         diag = np.diag(r)
         output.states.append(Qobj(diag))
     return output

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [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.solver.Result`

        An instance of the class :class:`qutip.solver.Result`, 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 Result for storing the results
    output = Result()
    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)
    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, True)

        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

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]
def _generic_ode_solve(r, rho0, tlist, e_ops, opt, progress_bar):
    """
    Internal function for solving ME. Solve an ODE which solver parameters
    already setup (r). Calculate the required expectation values or invoke
    callback function at each time step.
    """

    #
    # prepare output array
    #
    n_tsteps = len(tlist)
    e_sops_data = []

    output = Result()
    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)

    dt = np.diff(tlist)
    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, 0)
            else:
                output.expect[m][t_idx] = expect_rho_vec(e_sops_data[m], r.y, 1)

        if t_idx < n_tsteps - 1:
            r.integrate(r.t + dt[t_idx])

    progress_bar.finished()

    if not opt.rhs_reuse and config.tdname is not None:
        try:
            os.remove(config.tdname + ".pyx")
        except:
            pass

    if opt.store_final_state:
        rho.data = vec2mat(r.y)
        output.final_state = Qobj(rho)

    return output

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]
def brmesolve(H, psi0, tlist, a_ops, e_ops=[], spectra_cb=[], c_ops=[],
              args={}, options=Options()):
    """
    Solve the dynamics for a system using the Bloch-Redfield master equation.

    .. note::

        This solver does not currently support time-dependent Hamiltonians.

    Parameters
    ----------

    H : :class:`qutip.Qobj`
        System Hamiltonian.

    rho0 / psi0: :class:`qutip.Qobj`
        Initial density matrix or state vector (ket).

    tlist : *list* / *array*
        List of times for :math:`t`.

    a_ops : list of :class:`qutip.qobj`
        List of system operators that couple to bath degrees of freedom.

    e_ops : list of :class:`qutip.qobj` / callback function
        List of operators for which to evaluate expectation values.

    c_ops : list of :class:`qutip.qobj`
        List of system collapse operators.

    args : *dictionary*
        Placeholder for future implementation, kept for API consistency.

    options : :class:`qutip.solver.Options`
        Options for the solver.

    Returns
    -------

    result: :class:`qutip.solver.Result`

        An instance of the class :class:`qutip.solver.Result`, which contains
        either an array of expectation values, for operators given in e_ops,
        or a list of states for the times specified by `tlist`.
    """

    if not spectra_cb:
        # default to infinite temperature white noise
        spectra_cb = [lambda w: 1.0 for _ in a_ops]

    R, ekets = bloch_redfield_tensor(H, a_ops, spectra_cb, c_ops)

    output = Result()
    output.solver = "brmesolve"
    output.times = tlist

    results = bloch_redfield_solve(R, ekets, psi0, tlist, e_ops, options)

    if e_ops:
        output.expect = results
    else:
        output.states = results

    return output

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]

#.........這裏部分代碼省略.........

        # check for type of time-dependence (if any)
        time_type, h_stuff, c_stuff = _td_format_check(H, c_ops, 'mc')
        c_terms = len(c_stuff[0]) + len(c_stuff[1]) + len(c_stuff[2])
        # set time_type for use in multiprocessing
        config.tflag = time_type

        # check for collapse operators
        if c_terms > 0:
            config.cflag = 1
        else:
            config.cflag = 0

        # Configure data
        _mc_data_config(H, psi0, h_stuff, c_ops, c_stuff, args, e_ops,
                        options, config)

        # compile and load cython functions if necessary
        _mc_func_load(config)

    else:
        # setup args for new parameters when rhs_reuse=True and tdfunc is given
        # string based
        if config.tflag in [1, 10, 11]:
            if any(args):
                config.c_args = []
                arg_items = list(args.items())
                for k in range(len(arg_items)):
                    config.c_args.append(arg_items[k][1])
        # function based
        elif config.tflag in [2, 3, 20, 22]:
            config.h_func_args = args

    # load monte carlo class
    mc = _MC(config)

    # Run the simulation
    mc.run()

    # Remove RHS cython file if necessary
    if not options.rhs_reuse and config.tdname:
        _cython_build_cleanup(config.tdname)

    # AFTER MCSOLVER IS DONE
    # ----------------------

    # Store results in the Result object
    output = Result()
    output.solver = 'mcsolve'
    output.seeds = config.options.seeds
    # state vectors
    if (mc.psi_out is not None and config.options.average_states
            and config.cflag and ntraj != 1):
        output.states = parfor(_mc_dm_avg, mc.psi_out.T)
    elif mc.psi_out is not None:
        output.states = mc.psi_out

    # expectation values
    if (mc.expect_out is not None and config.cflag
            and config.options.average_expect):
        # averaging if multiple trajectories
        if isinstance(ntraj, int):
            output.expect = [np.mean(np.array([mc.expect_out[nt][op]
                                               for nt in range(ntraj)],
                                              dtype=object),
                                     axis=0)
                             for op in range(config.e_num)]
        elif isinstance(ntraj, (list, np.ndarray)):
            output.expect = []
            for num in ntraj:
                expt_data = np.mean(mc.expect_out[:num], axis=0)
                data_list = []
                if any([not op.isherm for op in e_ops]):
                    for k in range(len(e_ops)):
                        if e_ops[k].isherm:
                            data_list.append(np.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 average_expect flag
        # (Options) is off
        if mc.expect_out is not None:
            output.expect = mc.expect_out

    # simulation parameters
    output.times = config.tlist
    output.num_expect = config.e_num
    output.num_collapse = config.c_num
    output.ntraj = config.ntraj
    output.col_times = mc.collapse_times_out
    output.col_which = mc.which_op_out

    if e_ops_dict:
        output.expect = {e: output.expect[n]
                         for n, e in enumerate(e_ops_dict.keys())}

    return output

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]
def _generic_ode_solve(r, rho0, tlist, e_ops, opt, progress_bar):
    """
    Internal function for solving ME. Solve an ODE which solver parameters
    already setup (r). Calculate the required expectation values or invoke
    callback function at each time step.
    """

    #
    # prepare output array
    #
    n_tsteps = len(tlist)
    e_sops_data = []

    output = Result()
    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)

    dt = np.diff(tlist)
    for t_idx, t in enumerate(tlist):
        progress_bar.update(t_idx)

        if not r.successful():
            raise Exception("ODE integration error: Try to increase "
                            "the allowed number of substeps by increasing "
                            "the nsteps parameter in the Options class.")

        if opt.store_states or expt_callback:
            rho.data = dense2D_to_fastcsr_fmode(vec2mat(r.y), rho.shape[0], rho.shape[1])

            if opt.store_states:
                output.states.append(Qobj(rho, isherm=True))

            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, 0)
            else:
                output.expect[m][t_idx] = expect_rho_vec(e_sops_data[m],
                                                         r.y, 1)

        if t_idx < n_tsteps - 1:
            r.integrate(r.t + dt[t_idx])

    progress_bar.finished()

    if (not opt.rhs_reuse) and (config.tdname is not None):
        _cython_build_cleanup(config.tdname)

    if opt.store_final_state:
        rho.data = dense2D_to_fastcsr_fmode(vec2mat(r.y), rho.shape[0], rho.shape[1])
        output.final_state = Qobj(rho, dims=rho0.dims, isherm=True)

    return output

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]
def brmesolve(H, psi0, tlist, a_ops=[], e_ops=[], c_ops=[],
              args={}, use_secular=True, sec_cutoff = 0.1,
              tol=qset.atol,
              spectra_cb=None, options=None,
              progress_bar=None, _safe_mode=True, verbose=False):
    """
    Solves for the dynamics of a system using the Bloch-Redfield master equation,
    given an input Hamiltonian, Hermitian bath-coupling terms and their associated 
    spectrum functions, as well as possible Lindblad collapse operators.
              
    For time-independent systems, the Hamiltonian must be given as a Qobj,
    whereas the bath-coupling terms (a_ops), must be written as a nested list
    of operator - spectrum function pairs, where the frequency is specified by
    the `w` variable.
              
    *Example*

        a_ops = [[a+a.dag(),lambda w: 0.2*(w>=0)]] 
              
    For time-dependent systems, the Hamiltonian, a_ops, and Lindblad collapse
    operators (c_ops), can be specified in the QuTiP string-based time-dependent
    format.  For the a_op spectra, the frequency variable must be `w`, and the 
    string cannot contain any other variables other than the possibility of having
    a time-dependence through the time variable `t`:
                            
    *Example*

        a_ops = [[a+a.dag(), '0.2*exp(-t)*(w>=0)']]
              
    It is also possible to use Cubic_Spline objects for time-dependence.  In
    the case of a_ops, Cubic_Splines must be passed as a tuple:
              
    *Example*
              
        a_ops = [ [a+a.dag(), ( f(w), g(t)] ]
              
    where f(w) and g(t) are strings or Cubic_spline objects for the bath
    spectrum and time-dependence, respectively.
              
    Finally, if one has bath-couplimg terms of the form
    H = f(t)*a + conj[f(t)]*a.dag(), then the correct input format is
              
    *Example*
    
              a_ops = [ [(a,a.dag()), (f(w), g1(t), g2(t))],... ]

    where f(w) is the spectrum of the operators while g1(t) and g2(t)
    are the time-dependence of the operators `a` and `a.dag()`, respectively 
    
    Parameters
    ----------
    H : Qobj / list
        System Hamiltonian given as a Qobj or
        nested list in string-based format.

    psi0: Qobj
        Initial density matrix or state vector (ket).

    tlist : array_like
        List of times for evaluating evolution

    a_ops : list
        Nested list of Hermitian system operators that couple to 
        the bath degrees of freedom, along with their associated
        spectra.

    e_ops : list
        List of operators for which to evaluate expectation values.

    c_ops : list
        List of system collapse operators, or nested list in
        string-based format.

    args : dict 
        Placeholder for future implementation, kept for API consistency.

    use_secular : bool {True}
        Use secular approximation when evaluating bath-coupling terms.
    
    sec_cutoff : float {0.1}
        Cutoff for secular approximation.
    
    tol : float {qutip.setttings.atol}
        Tolerance used for removing small values after 
        basis transformation.
              
    spectra_cb : list
        DEPRECIATED. Do not use.
    
    options : :class:`qutip.solver.Options`
        Options for the solver.
              
    progress_bar : BaseProgressBar
        Optional instance of BaseProgressBar, or a subclass thereof, for
        showing the progress of the simulation.

    Returns
    -------
    result: :class:`qutip.solver.Result`

#.........這裏部分代碼省略.........

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import 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

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]
def essolve(H, rho0, tlist, c_op_list, e_ops):
    """
    Evolution of a state vector or density matrix (`rho0`) for a given
    Hamiltonian (`H`) and set of collapse operators (`c_op_list`), by
    expressing the ODE as an exponential series. The output is either
    the state vector at arbitrary points in time (`tlist`), or the
    expectation values of the supplied operators (`e_ops`).

    Parameters
    ----------
    H : qobj/function_type
        System Hamiltonian.

    rho0 : :class:`qutip.qobj`
        Initial state density matrix.

    tlist : list/array
        ``list`` of times for :math:`t`.

    c_op_list : list of :class:`qutip.qobj`
        ``list`` of :class:`qutip.qobj` collapse operators.

    e_ops : list of :class:`qutip.qobj`
        ``list`` of :class:`qutip.qobj` operators for which to evaluate
        expectation values.


    Returns
    -------
     expt_array : array
        Expectation values of wavefunctions/density matrices for the
        times specified in ``tlist``.


    .. note:: This solver does not support time-dependent Hamiltonians.

    """
    n_expt_op = len(e_ops)
    n_tsteps = len(tlist)

    # Calculate the Liouvillian
    if (c_op_list is None or len(c_op_list) == 0) and isket(rho0):
        L = H
    else:
        L = liouvillian(H, c_op_list)

    es = ode2es(L, rho0)

    # evaluate the expectation values
    if n_expt_op == 0:
        results = [Qobj()] * n_tsteps
    else:
        results = np.zeros([n_expt_op, n_tsteps], dtype=complex)

    for n, e in enumerate(e_ops):
        results[n, :] = expect(e, esval(es, tlist))

    data = Result()
    data.solver = "essolve"
    data.times = tlist
    data.expect = [np.real(results[n, :]) if e.isherm else results[n, :]
                   for n, e in enumerate(e_ops)]

    return data

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]
    def run(self, rho0, tlist):
        """
        Function to solve for an open quantum system using the
        HEOM model.

        Parameters
        ----------
        rho0 : Qobj
            Initial state (density matrix) of the system.

        tlist : list
            Time over which system evolves.

        Returns
        -------
        results : :class:`qutip.solver.Result`
            Object storing all results from the simulation.
        """

        start_run = timeit.default_timer()

        sup_dim = self._sup_dim
        stats = self.stats
        r = self._ode

        if not self._configured:
            raise RuntimeError("Solver must be configured before it is run")
        if stats:
            ss_conf = stats.sections.get('config')
            if ss_conf is None:
                raise RuntimeError("No config section for solver stats")
            ss_run = stats.sections.get('run')
            if ss_run is None:
                ss_run = stats.add_section('run')

        # Set up terms of the matsubara and tanimura boundaries
        output = Result()
        output.solver = "hsolve"
        output.times = tlist
        output.states = []

        if stats: start_init = timeit.default_timer()
        output.states.append(Qobj(rho0))
        rho0_flat = rho0.full().ravel('F') # Using 'F' effectively transposes
        rho0_he = np.zeros([sup_dim*self._N_he], dtype=complex)
        rho0_he[:sup_dim] = rho0_flat
        r.set_initial_value(rho0_he, tlist[0])

        if stats:
            stats.add_timing('initialize',
                             timeit.default_timer() - start_init, ss_run)
            start_integ = timeit.default_timer()

        dt = np.diff(tlist)
        n_tsteps = len(tlist)
        for t_idx, t in enumerate(tlist):
            if t_idx < n_tsteps - 1:
                r.integrate(r.t + dt[t_idx])
                rho = Qobj(r.y[:sup_dim].reshape(rho0.shape), dims=rho0.dims)
                output.states.append(rho)

        if stats:
            time_now = timeit.default_timer()
            stats.add_timing('integrate',
                             time_now - start_integ, ss_run)
            if ss_run.total_time is None:
                ss_run.total_time = time_now - start_run
            else:
                ss_run.total_time += time_now - start_run
            stats.total_time = ss_conf.total_time + ss_run.total_time

        return output

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]
def _generic_ode_solve(r, psi0, tlist, e_ops, opt, progress_bar, dims=None):
    """
    Internal function for solving ODEs.
    """

    #
    # prepare output array
    #
    n_tsteps = len(tlist)
    output = Result()
    output.solver = "sesolve"
    output.times = tlist

    if psi0.isunitary:
        oper_evo = True
        oper_n = dims[0][0]
        norm_dim_factor = np.sqrt(oper_n)
    else:
        oper_evo = False
        norm_dim_factor = 1.0

    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")

    def get_curr_state_data():
        if oper_evo:
            return vec2mat(r.y)
        else:
            return r.y

    #
    # 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():
            raise Exception("ODE integration error: Try to increase "
                            "the allowed number of substeps by increasing "
                            "the nsteps parameter in the Options class.")

        # get the current state / oper data if needed
        cdata = None
        if opt.store_states or opt.normalize_output or n_expt_op > 0:
            cdata = get_curr_state_data()

        if opt.normalize_output:
            # cdata *= _get_norm_factor(cdata, oper_evo)
            cdata *= norm_dim_factor / la_norm(cdata)
            if oper_evo:
                r.set_initial_value(cdata.ravel('F'), r.t)
            else:
                r.set_initial_value(cdata, r.t)

        if opt.store_states:
            output.states.append(Qobj(cdata, dims=dims))

        if expt_callback:
            # use callback method
            e_ops(t, Qobj(cdata, dims=dims))

        for m in range(n_expt_op):
            output.expect[m][t_idx] = cy_expect_psi(e_ops[m].data,
                                                    cdata, 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 config.tdname is not None:
        try:
            os.remove(config.tdname + ".pyx")
        except:
#.........這裏部分代碼省略.........

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [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 = Result()
    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 config.tdname is not None:
        try:
            os.remove(config.tdname + ".pyx")
        except:
            pass

    if opt.store_final_state:
        output.final_state = Qobj(r.y)

    return output

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [as 別名]

#.........這裏部分代碼省略.........
    H, c_ops, args = _td_wrap_array_str(H, c_ops, args, tlist)

    # ----------------------------------------------
    # SETUP ODE DATA IF NONE EXISTS OR NOT REUSING
    # ----------------------------------------------
    if (not options.rhs_reuse) or (not config.tdfunc):
        # reset config collapse and time-dependence flags to default values
        config.soft_reset()

        # check for type of time-dependence (if any)
        time_type, h_stuff, c_stuff = _td_format_check(H, c_ops, "mc")
        h_terms = len(h_stuff[0]) + len(h_stuff[1]) + len(h_stuff[2])
        c_terms = len(c_stuff[0]) + len(c_stuff[1]) + len(c_stuff[2])
        # set time_type for use in multiprocessing
        config.tflag = time_type

        # check for collapse operators
        if c_terms > 0:
            config.cflag = 1
        else:
            config.cflag = 0

        # Configure data
        _mc_data_config(H, psi0, h_stuff, c_ops, c_stuff, args, e_ops, options, config)

        # compile and load cython functions if necessary
        _mc_func_load(config)

    else:
        # setup args for new parameters when rhs_reuse=True and tdfunc is given
        # string based
        if config.tflag in array([1, 10, 11]):
            if any(args):
                config.c_args = []
                arg_items = args.items()
                for k in range(len(args)):
                    config.c_args.append(arg_items[k][1])
        # function based
        elif config.tflag in array([2, 3, 20, 22]):
            config.h_func_args = args

    # load monte-carlo class
    mc = _MC_class(config)

    # RUN THE SIMULATION
    mc.run()

    # remove RHS cython file if necessary
    if not options.rhs_reuse and config.tdname:
        try:
            os.remove(config.tdname + ".pyx")
        except:
            pass

    # AFTER MCSOLVER IS DONE --------------------------------------
    # ------- COLLECT AND RETURN OUTPUT DATA IN ODEDATA OBJECT --------------
    output = Result()
    output.solver = "mcsolve"
    # state vectors
    if mc.psi_out is not None and config.options.average_states and config.cflag and ntraj != 1:
        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 config.cflag and config.options.average_expect:
        # averaging if multiple trajectories
        if isinstance(ntraj, int):
            output.expect = [mean([mc.expect_out[nt][op] for nt in range(ntraj)], axis=0) for op in range(config.e_num)]
        elif isinstance(ntraj, (list, ndarray)):
            output.expect = []
            for num in ntraj:
                expt_data = mean(mc.expect_out[:num], axis=0)
                data_list = []
                if any([not op.isherm for op in e_ops]):
                    for k in range(len(e_ops)):
                        if e_ops[k].isherm:
                            data_list.append(np.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 average_states flag
        # (Options) is off
        if mc.expect_out is not None:
            output.expect = mc.expect_out

    # simulation parameters
    output.times = config.tlist
    output.num_expect = config.e_num
    output.num_collapse = config.c_num
    output.ntraj = config.ntraj
    output.col_times = mc.collapse_times_out
    output.col_which = mc.which_op_out

    if e_ops_dict:
        output.expect = {e: output.expect[n] for n, e in enumerate(e_ops_dict.keys())}

    return output

# 需要導入模塊: from qutip.solver import Result [as 別名]
# 或者: from qutip.solver.Result import times [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 = Options()
    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 = Result()
    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)

    #
    # 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, True))
        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.........