Python Odedata.num_collapse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 num_collapse [as 别名]

#.........这里部分代码省略.........
        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

# 需要导入模块: from qutip.odedata import Odedata [as 别名]
# 或者: from qutip.odedata.Odedata import num_collapse [as 别名]

#.........这里部分代码省略.........
        #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