def _wigner_laguerre(rho, xvec, yvec, g, parallel):
"""
Using Laguerre polynomials from scipy to evaluate the Wigner function for
the density matrices :math:`|m><n|`, :math:`W_{mn}`. The total Wigner
function is calculated as :math:`W = \sum_{mn} \\rho_{mn} W_{mn}`.
"""
M = np.prod(rho.shape[0])
X, Y = meshgrid(xvec, yvec)
A = 0.5 * g * (X + 1.0j * Y)
W = zeros(np.shape(A))
# compute wigner functions for density matrices |m><n| and
# weight by all the elements in the density matrix
B = 4 * abs(A) ** 2
if sp.isspmatrix_csr(rho.data):
# for compress sparse row matrices
if parallel:
iterator = (
(m, rho, A, B) for m in range(len(rho.data.indptr) - 1))
W1_out = parfor(_par_wig_eval, iterator)
W += sum(W1_out)
else:
for m in range(len(rho.data.indptr) - 1):
for jj in range(rho.data.indptr[m], rho.data.indptr[m + 1]):
n = rho.data.indices[jj]
if m == n:
W += real(rho[m, m] * (-1) ** m * genlaguerre(m, 0)(B))
elif n > m:
W += 2.0 * real(rho[m, n] * (-1) ** m *
(2 * A) ** (n - m) *
sqrt(factorial(m) / factorial(n)) *
genlaguerre(m, n - m)(B))
else:
# for dense density matrices
B = 4 * abs(A) ** 2
for m in range(M):
if abs(rho[m, m]) > 0.0:
W += real(rho[m, m] * (-1) ** m * genlaguerre(m, 0)(B))
for n in range(m + 1, M):
if abs(rho[m, n]) > 0.0:
W += 2.0 * real(rho[m, n] * (-1) ** m *
(2 * A) ** (n - m) *
sqrt(factorial(m) / factorial(n)) *
genlaguerre(m, n - m)(B))
return 0.5 * W * g ** 2 * np.exp(-B / 2) / pi
def _wigner_laguerre(rho, xvec, yvec, g, parallel):
"""
Using Laguerre polynomials from scipy to evaluate the Wigner function for
the density matrices :math:`|m><n|`, :math:`W_{mn}`. The total Wigner
function is calculated as :math:`W = \sum_{mn} \\rho_{mn} W_{mn}`.
"""
M = prod(rho.shape[0])
X, Y = meshgrid(xvec, yvec)
A = 0.5 * g * (X + 1.0j * Y)
W = zeros(np.shape(A))
# compute wigner functions for density matrices |m><n| and
# weight by all the elements in the density matrix
B = 4 * abs(A) ** 2
if sp.isspmatrix_csr(rho.data):
# for compress sparse row matrices
if parallel:
iterator = (
(m, rho, A, B) for m in range(len(rho.data.indptr) - 1))
W1_out = parfor(_par_wig_eval, iterator)
W += sum(W1_out)
else:
for m in range(len(rho.data.indptr) - 1):
for jj in range(rho.data.indptr[m], rho.data.indptr[m + 1]):
n = rho.data.indices[jj]
if m == n:
W += real(rho[m, m] * (-1) ** m * genlaguerre(m, 0)(B))
elif n > m:
W += 2.0 * real(rho[m, n] * (-1) ** m *
(2 * A) ** (n - m) *
sqrt(factorial(m) / factorial(n)) *
genlaguerre(m, n - m)(B))
else:
# for dense density matrices
B = 4 * abs(A) ** 2
for m in range(M):
if abs(rho[m, m]) > 0.0:
W += real(rho[m, m] * (-1) ** m * genlaguerre(m, 0)(B))
for n in range(m + 1, M):
if abs(rho[m, n]) > 0.0:
W += 2.0 * real(rho[m, n] * (-1) ** m *
(2 * A) ** (n - m) *
sqrt(factorial(m) / factorial(n)) *
genlaguerre(m, n - m)(B))
return 0.5 * W * g ** 2 * np.exp(-B / 2) / pi
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
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().ravel()
else:
config.psi0 = psi0.full().ravel()
config.psi0_dims = psi0.dims
config.psi0_shape = psi0.shape
# set options on ouput states
if config.options.steady_state_average:
config.options.average_states = True
# set general items
config.tlist = tlist
if isinstance(ntraj, (list, ndarray)):
config.ntraj = sort(ntraj)[-1]
else:
config.ntraj = ntraj
# set norm finding constants
config.norm_tol = options.norm_tol
config.norm_steps = options.norm_steps
# convert array based time-dependence to string format
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
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
``list`` or ``array`` of collapse operators.
e_ops : array_like
``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 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
#----
#----------------------------------------------
# SETUP ODE DATA IF NONE EXISTS OR NOT REUSING
#----------------------------------------------
if (not options.rhs_reuse) or (not odeconfig.tdfunc):
#reset odeconfig collapse and time-dependence flags to default values
_reset_odeconfig()
#check for type of time-dependence (if any)
time_type, h_stuff, c_stuff = _ode_checks(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
odeconfig.tflag = time_type
#-Check for PyObjC on Mac platforms
if sys.platform == 'darwin' and odeconfig.options.gui:
try:
import Foundation
except:
odeconfig.options.gui = False
#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 != None and odeconfig.options.mc_avg and odeconfig.cflag:
output.states = parfor(_mc_dm_avg, mc.psi_out.T)
elif mc.psi_out != None:
output.states = mc.psi_out
#expectation values
elif mc.expect_out != 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 != 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
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
#----
#----------------------------------------------
# SETUP ODE DATA IF NONE EXISTS OR NOT REUSING
#----------------------------------------------
if (not options.rhs_reuse) or (not odeconfig.tdfunc):
#reset odeconfig collapse and time-dependence flags to default values
_reset_odeconfig()
#check for type of time-dependence (if any)
time_type,h_stuff,c_stuff=_ode_checks(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
odeconfig.tflag=time_type
#-Check for PyObjC on Mac platforms
if sys.platform=='darwin' and odeconfig.options.gui:
try:
import Foundation
except:
odeconfig.options.gui=False
#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
