Python Result.ntraj方法代码示例

# 需要导入模块: from qutip.solver import Result [as 别名]
# 或者: from qutip.solver.Result import ntraj [as 别名]
def mcsolve_f90(H, psi0, tlist, c_ops, e_ops, ntraj=None,
                options=Options(), sparse_dms=True, serial=False,
                ptrace_sel=[], calc_entropy=False):
    """
    Monte-Carlo wave function solver with fortran 90 backend.
    Usage is identical to qutip.mcsolve, for problems without explicit
    time-dependence, and with some optional input:

    Parameters
    ----------
    H : qobj
        System Hamiltonian.
    psi0 : qobj
        Initial state vector
    tlist : array_like
        Times at which results are recorded.
    ntraj : int
        Number of trajectories to run.
    c_ops : array_like
        ``list`` or ``array`` of collapse operators.
    e_ops : array_like
        ``list`` or ``array`` of operators for calculating expectation values.
    options : Options
        Instance of solver options.
    sparse_dms : boolean
        If averaged density matrices are returned, they will be stored as
        sparse (Compressed Row Format) matrices during computation if
        sparse_dms = True (default), and dense matrices otherwise. Dense
        matrices might be preferable for smaller systems.
    serial : boolean
        If True (default is False) the solver will not make use of the
        multiprocessing module, and simply run in serial.
    ptrace_sel: list
        This optional argument specifies a list of components to keep when
        returning a partially traced density matrix. This can be convenient for
        large systems where memory becomes a problem, but you are only
        interested in parts of the density matrix.
    calc_entropy : boolean
        If ptrace_sel is specified, calc_entropy=True will have the solver
        return the averaged entropy over trajectories in results.entropy. This
        can be interpreted as a measure of entanglement. See Phys. Rev. Lett.
        93, 120408 (2004), Phys. Rev. A 86, 022310 (2012).

    Returns
    -------
    results : Result
        Object storing all results from simulation.

    """
    if ntraj is None:
        ntraj = options.ntraj

    if psi0.type != 'ket':
        raise Exception("Initial state must be a state vector.")
    config.options = options
    # set num_cpus to the value given in qutip.settings
    # if none in Options
    if not config.options.num_cpus:
        config.options.num_cpus = qutip.settings.num_cpus
    # set initial value data
    if options.tidy:
        config.psi0 = psi0.tidyup(options.atol).full()
    else:
        config.psi0 = psi0.full()
    config.psi0_dims = psi0.dims
    config.psi0_shape = psi0.shape
    # set general items
    config.tlist = tlist
    if isinstance(ntraj, (list, np.ndarray)):
        raise Exception("ntraj as list argument is not supported.")
    else:
        config.ntraj = ntraj
        # ntraj_list = [ntraj]
    # set norm finding constants
    config.norm_tol = options.norm_tol
    config.norm_steps = options.norm_steps

    if not options.rhs_reuse:
        config.soft_reset()
        # no time dependence
        config.tflag = 0
        # check for collapse operators
        if len(c_ops) > 0:
            config.cflag = 1
        else:
            config.cflag = 0
        # Configure data
        _mc_data_config(H, psi0, [], c_ops, [], [], e_ops, options, config)

    # 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
#.........这里部分代码省略.........

# 需要导入模块: from qutip.solver import Result [as 别名]
# 或者: from qutip.solver.Result import ntraj [as 别名]
def mcsolve(H, psi0, tlist, c_ops, e_ops, ntraj=None,
            args={}, options=None, progress_bar=True,
            map_func=None, map_kwargs=None):
    """Monte Carlo evolution of a state vector :math:`|\psi \\rangle` for a
    given Hamiltonian and sets of collapse operators, and possibly, operators
    for calculating expectation values. Options for the underlying ODE solver
    are given by the Options class.

    mcsolve supports time-dependent Hamiltonians and collapse operators using
    either Python functions of strings to represent time-dependent
    coefficients. Note that, the system Hamiltonian MUST have at least one
    constant term.

    As an example of a time-dependent problem, consider a Hamiltonian with two
    terms ``H0`` and ``H1``, where ``H1`` is time-dependent with coefficient
    ``sin(w*t)``, and collapse operators ``C0`` and ``C1``, where ``C1`` is
    time-dependent with coeffcient ``exp(-a*t)``.  Here, w and a are constant
    arguments with values ``W`` and ``A``.

    Using the Python function time-dependent format requires two Python
    functions, one for each collapse coefficient. Therefore, this problem could
    be expressed as::

        def H1_coeff(t,args):
            return sin(args['w']*t)

        def C1_coeff(t,args):
            return exp(-args['a']*t)

        H = [H0, [H1, H1_coeff]]

        c_ops = [C0, [C1, C1_coeff]]

        args={'a': A, 'w': W}

    or in String (Cython) format we could write::

        H = [H0, [H1, 'sin(w*t)']]

        c_ops = [C0, [C1, 'exp(-a*t)']]

        args={'a': A, 'w': W}

    Constant terms are preferably placed first in the Hamiltonian and collapse
    operator lists.

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

    psi0 : :class:`qutip.Qobj`
        Initial state vector

    tlist : array_like
        Times at which results are recorded.

    ntraj : int
        Number of trajectories to run.

    c_ops : array_like
        single collapse operator or ``list`` or ``array`` of collapse
        operators.

    e_ops : array_like
        single operator or ``list`` or ``array`` of operators for calculating
        expectation values.

    args : dict
        Arguments for time-dependent Hamiltonian and collapse operator terms.

    options : Options
        Instance of ODE solver options.

    progress_bar: BaseProgressBar
        Optional instance of BaseProgressBar, or a subclass thereof, for
        showing the progress of the simulation. Set to None to disable the
        progress bar.

    map_func: function
        A map function for managing the calls to the single-trajactory solver.

    map_kwargs: dictionary
        Optional keyword arguments to the map_func function.

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

    .. note::

        It is possible to reuse the random number seeds from a previous run
        of the mcsolver by passing the output Result object seeds via the
        Options class, i.e. Options(seeds=prev_result.seeds).
    """

    if debug:
        print(inspect.stack()[0][3])

#.........这里部分代码省略.........

# 需要导入模块: from qutip.solver import Result [as 别名]
# 或者: from qutip.solver.Result import ntraj [as 别名]
    def evolve_serial(self, args):

        if debug:
            print(inspect.stack()[0][3] + ":" + str(os.getpid()))

        # run ntraj trajectories for one process via fortran
        # get args
        queue, ntraj, instanceno, rngseed = args
        # initialize the problem in fortran
        _init_tlist()
        _init_psi0()
        if (self.ptrace_sel != []):
            _init_ptrace_stuff(self.ptrace_sel)
        _init_hamilt()
        if (config.c_num != 0):
            _init_c_ops()
        if (config.e_num != 0):
            _init_e_ops()
        # set options
        qtf90.qutraj_run.n_c_ops = config.c_num
        qtf90.qutraj_run.n_e_ops = config.e_num
        qtf90.qutraj_run.ntraj = ntraj
        qtf90.qutraj_run.unravel_type = self.unravel_type
        qtf90.qutraj_run.average_states = config.options.average_states
        qtf90.qutraj_run.average_expect = config.options.average_expect
        qtf90.qutraj_run.init_result(config.psi0_shape[0],
                                     config.options.atol,
                                     config.options.rtol, mf=self.mf,
                                     norm_steps=config.norm_steps,
                                     norm_tol=config.norm_tol)
        # set optional arguments
        qtf90.qutraj_run.order = config.options.order
        qtf90.qutraj_run.nsteps = config.options.nsteps
        qtf90.qutraj_run.first_step = config.options.first_step
        qtf90.qutraj_run.min_step = config.options.min_step
        qtf90.qutraj_run.max_step = config.options.max_step
        qtf90.qutraj_run.norm_steps = config.options.norm_steps
        qtf90.qutraj_run.norm_tol = config.options.norm_tol
        # use sparse density matrices during computation?
        qtf90.qutraj_run.rho_return_sparse = self.sparse_dms
        # calculate entropy of reduced density matrice?
        qtf90.qutraj_run.calc_entropy = self.calc_entropy
        # run
        show_progress = 1 if debug else 0
        qtf90.qutraj_run.evolve(instanceno, rngseed, show_progress)

        # construct Result instance
        sol = Result()
        sol.ntraj = ntraj
        # sol.col_times = qtf90.qutraj_run.col_times
        # sol.col_which = qtf90.qutraj_run.col_which-1
        sol.col_times, sol.col_which = self.get_collapses(ntraj)
        if (config.e_num == 0):
            sol.states = self.get_states(len(config.tlist), ntraj)
        else:
            sol.expect = self.get_expect(len(config.tlist), ntraj)
        if (self.calc_entropy):
            sol.entropy = self.get_entropy(len(config.tlist))

        if (not self.serial_run):
            # put to queue
            queue.put(sol)
            queue.join()

        # deallocate stuff
        # finalize()
        return sol

# 需要导入模块: from qutip.solver import Result [as 别名]
# 或者: from qutip.solver.Result import ntraj [as 别名]
def mcsolve(H, psi0, tlist, c_ops, e_ops, ntraj=None, args={}, options=Options()):
    """Monte-Carlo evolution of a state vector :math:`|\psi \\rangle` for a
    given Hamiltonian and sets of collapse operators, and possibly, operators
    for calculating expectation values. Options for the underlying ODE solver
    are given by the Options class.

    mcsolve supports time-dependent Hamiltonians and collapse operators using
    either Python functions of strings to represent time-dependent
    coefficients. Note that, the system Hamiltonian MUST have at least one
    constant term.

    As an example of a time-dependent problem, consider a Hamiltonian with two
    terms ``H0`` and ``H1``, where ``H1`` is time-dependent with coefficient
    ``sin(w*t)``, and collapse operators ``C0`` and ``C1``, where ``C1`` is
    time-dependent with coeffcient ``exp(-a*t)``.  Here, w and a are constant
    arguments with values ``W`` and ``A``.

    Using the Python function time-dependent format requires two Python
    functions, one for each collapse coefficient. Therefore, this problem could
    be expressed as::

        def H1_coeff(t,args):
            return sin(args['w']*t)

        def C1_coeff(t,args):
            return exp(-args['a']*t)

        H=[H0,[H1,H1_coeff]]

        c_op_list=[C0,[C1,C1_coeff]]

        args={'a':A,'w':W}

    or in String (Cython) format we could write::

        H=[H0,[H1,'sin(w*t)']]

        c_op_list=[C0,[C1,'exp(-a*t)']]

        args={'a':A,'w':W}

    Constant terms are preferably placed first in the Hamiltonian and collapse
    operator lists.

    Parameters
    ----------
    H : qobj
        System Hamiltonian.
    psi0 : qobj
        Initial state vector
    tlist : array_like
        Times at which results are recorded.
    ntraj : int
        Number of trajectories to run.
    c_ops : array_like
        single collapse operator or ``list`` or ``array`` of collapse
        operators.
    e_ops : array_like
        single operator or ``list`` or ``array`` of operators for calculating
        expectation values.
    args : dict
        Arguments for time-dependent Hamiltonian and collapse operator terms.
    options : Options
        Instance of ODE solver options.

    Returns
    -------
    results : Result
        Object storing all results from simulation.

    """

    if debug:
        print(inspect.stack()[0][3])

    if ntraj is None:
        ntraj = options.ntraj

    if not psi0.isket:
        raise Exception("Initial state must be a state vector.")

    if isinstance(c_ops, Qobj):
        c_ops = [c_ops]

    if isinstance(e_ops, Qobj):
        e_ops = [e_ops]

    if isinstance(e_ops, dict):
        e_ops_dict = e_ops
        e_ops = [e for e in e_ops.values()]
    else:
        e_ops_dict = None

    config.options = options
    if isinstance(ntraj, list):
        config.progress_bar = TextProgressBar(max(ntraj))
    else:
        config.progress_bar = TextProgressBar(ntraj)

    # set num_cpus to the value given in qutip.settings if none in Options
#.........这里部分代码省略.........