Python Odedata.ntraj方法代码示例

# 需要导入模块: from qutip.odedata import Odedata [as 别名]
# 或者: from qutip.odedata.Odedata 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 (odeconfig.c_num != 0):
            _init_c_ops()
        if (odeconfig.e_num != 0):
            _init_e_ops()
        # set options
        qtf90.qutraj_run.n_c_ops = odeconfig.c_num
        qtf90.qutraj_run.n_e_ops = odeconfig.e_num
        qtf90.qutraj_run.ntraj = ntraj
        qtf90.qutraj_run.unravel_type = self.unravel_type
        qtf90.qutraj_run.average_states = odeconfig.options.average_states 
        qtf90.qutraj_run.average_expect = odeconfig.options.average_expect
        qtf90.qutraj_run.init_odedata(odeconfig.psi0_shape[0],
                                      odeconfig.options.atol,
                                      odeconfig.options.rtol, mf=self.mf,
                                      norm_steps=odeconfig.norm_steps,
                                      norm_tol=odeconfig.norm_tol)
        # set optional arguments
        qtf90.qutraj_run.order = odeconfig.options.order
        qtf90.qutraj_run.nsteps = odeconfig.options.nsteps
        qtf90.qutraj_run.first_step = odeconfig.options.first_step
        qtf90.qutraj_run.min_step = odeconfig.options.min_step
        qtf90.qutraj_run.max_step = odeconfig.options.max_step
        qtf90.qutraj_run.norm_steps = odeconfig.options.norm_steps
        qtf90.qutraj_run.norm_tol = odeconfig.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 Odedata instance
        sol = Odedata()
        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 (odeconfig.e_num == 0):
            sol.states = self.get_states(len(odeconfig.tlist), ntraj)
        else:
            sol.expect = self.get_expect(len(odeconfig.tlist), ntraj)
        if (self.calc_entropy):
            sol.entropy = self.get_entropy(len(odeconfig.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.odedata import Odedata [as 别名]
# 或者: from qutip.odedata.Odedata import ntraj [as 别名]
def mcsolve_f90(H, psi0, tlist, c_ops, e_ops, ntraj=None,
                options=Odeoptions(), 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 : Odeoptions
        Instance of ODE 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 : Odedata
        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.")
    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
#.........这里部分代码省略.........

# 需要导入模块: from qutip.odedata import Odedata [as 别名]
# 或者: from qutip.odedata.Odedata import ntraj [as 别名]
def mcsolve(H,psi0,tlist,c_ops,e_ops,ntraj=500,args={},options=Odeoptions()):
    """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 Odeoptions 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 : Odeoptions
        Instance of ODE solver options.
    
    Returns
    -------
    results : Odedata    
        Object storing all results from simulation.
        
    """


    # if single operator is passed for c_ops or e_ops, convert it to
    # list containing only that operator
    if isinstance(c_ops, Qobj):
        c_ops = [c_ops]
    if isinstance(e_ops, Qobj):
        e_ops = [e_ops]


    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,ndarray)):
        odeconfig.ntraj=sort(ntraj)[-1]
    else:
        odeconfig.ntraj=ntraj
    #set norm finding constants
    odeconfig.norm_tol=options.norm_tol
    odeconfig.norm_steps=options.norm_steps
    #----
    
#.........这里部分代码省略.........