def get_result(self, ntraj=[]):
# Store results in the Result object
if not ntraj:
ntraj = [self.num_traj]
elif not isinstance(ntraj, list):
ntraj = [ntraj]
output = Result()
output.solver = 'mcsolve'
output.seeds = self.seeds
options = self.options
output.options = options
if options.steady_state_average:
output.states = self.steady_state
elif options.average_states and options.store_states:
output.states = self.states
elif options.store_states:
output.states = self.runs_states
if options.store_final_state:
if options.average_states:
output.final_state = self.final_state
else:
output.final_state = self.runs_final_states
if options.average_expect:
output.expect = [self.expect_traj_avg(n) for n in ntraj]
if len(output.expect) == 1:
output.expect = output.expect[0]
else:
output.expect = self.runs_expect
# simulation parameters
output.times = self.tlist
output.num_expect = self.e_ops.e_num
output.num_collapse = len(self.ss.td_c_ops)
output.ntraj = self.num_traj
output.col_times = self.collapse_times
output.col_which = self.collapse_which
return output
def _generic_ode_solve(func,
ode_args,
rho0,
tlist,
e_ops,
opt,
progress_bar,
dims=None):
"""
Internal function for solving ME.
Calculate the required expectation values or invoke
callback function at each time step.
"""
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# This function is made similar to sesolve's one for futur merging in a
# solver class
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# prepare output array
n_tsteps = len(tlist)
output = Result()
output.solver = "mesolve"
output.times = tlist
size = rho0.shape[0]
initial_vector = rho0.full().ravel('F')
r = scipy.integrate.ode(func)
r.set_integrator('zvode',
method=opt.method,
order=opt.order,
atol=opt.atol,
rtol=opt.rtol,
nsteps=opt.nsteps,
first_step=opt.first_step,
min_step=opt.min_step,
max_step=opt.max_step)
if ode_args:
r.set_f_params(*ode_args)
r.set_initial_value(initial_vector, tlist[0])
e_ops_data = []
output.expect = []
if callable(e_ops):
n_expt_op = 0
expt_callback = True
output.num_expect = 1
elif isinstance(e_ops, list):
n_expt_op = len(e_ops)
expt_callback = False
output.num_expect = n_expt_op
if n_expt_op == 0:
# fall back on storing states
opt.store_states = True
else:
for op in e_ops:
if not isinstance(op, Qobj) and callable(op):
output.expect.append(np.zeros(n_tsteps, dtype=complex))
continue
if op.dims != rho0.dims:
raise TypeError(f"e_ops dims ({op.dims}) are not "
f"compatible with the state's "
f"({rho0.dims})")
e_ops_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")
if opt.store_states:
output.states = []
def get_curr_state_data(r):
return vec2mat(r.y)
#
# start evolution
#
dt = np.diff(tlist)
cdata = None
progress_bar.start(n_tsteps)
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:
cdata = get_curr_state_data(r)
fdata = dense2D_to_fastcsr_fmode(cdata, size, size)
# Try to guess if there is a fast path for rho_t
if issuper(rho0) or not rho0.isherm:
rho_t = Qobj(fdata, dims=dims)
else:
rho_t = Qobj(fdata, dims=dims, fast="mc-dm")
if opt.store_states:
output.states.append(rho_t)
if expt_callback:
# use callback method
output.expect.append(e_ops(t, rho_t))
for m in range(n_expt_op):
if not isinstance(e_ops[m], Qobj) and callable(e_ops[m]):
output.expect[m][t_idx] = e_ops[m](t, rho_t)
continue
output.expect[m][t_idx] = expect_rho_vec(
e_ops_data[m], r.y, e_ops[m].isherm and rho0.isherm)
if t_idx < n_tsteps - 1:
r.integrate(r.t + dt[t_idx])
progress_bar.finished()
if opt.store_final_state:
cdata = get_curr_state_data(r)
output.final_state = Qobj(cdata, dims=dims, isherm=rho0.isherm or None)
return output
def _td_brmesolve(H,
psi0,
tlist,
a_ops=[],
e_ops=[],
c_ops=[],
use_secular=True,
tol=qset.atol,
options=None,
progress_bar=None,
_safe_mode=True):
if isket(psi0):
rho0 = ket2dm(psi0)
else:
rho0 = psi0
nrows = rho0.shape[0]
H_terms = []
H_td_terms = []
H_obj = []
A_terms = []
A_td_terms = []
C_terms = []
C_td_terms = []
C_obj = []
spline_count = [0, 0]
if isinstance(H, Qobj):
H_terms.append(H.full('f'))
H_td_terms.append('1')
else:
for kk, h in enumerate(H):
if isinstance(h, Qobj):
H_terms.append(h.full('f'))
H_td_terms.append('1')
elif isinstance(h, list):
H_terms.append(h[0].full('f'))
if isinstance(h[1], Cubic_Spline):
H_obj.append(h[1].coeffs)
spline_count[0] += 1
H_td_terms.append(h[1])
else:
raise Exception('Invalid Hamiltonian specifiction.')
for kk, c in enumerate(c_ops):
if isinstance(c, Qobj):
C_terms.append(c.full('f'))
C_td_terms.append('1')
elif isinstance(c, list):
C_terms.append(c[0].full('f'))
if isinstance(c[1], Cubic_Spline):
C_obj.append(c[1].coeffs)
spline_count[0] += 1
C_td_terms.append(c[1])
else:
raise Exception('Invalid collape operator specifiction.')
for kk, a in enumerate(a_ops):
if isinstance(a, list):
A_terms.append(a[0].full('f'))
A_td_terms.append(a[1])
if isinstance(a[1], tuple):
if not len(a[1]) == 2:
raise Exception('Tuple must be len=2.')
if isinstance(a[1][0], Cubic_Spline):
spline_count[1] += 1
if isinstance(a[1][1], Cubic_Spline):
spline_count[1] += 1
else:
raise Exception('Invalid bath-coupling specifiction.')
string_list = []
for kk, _ in enumerate(H_td_terms):
string_list.append("H_terms[{0}]".format(kk))
for kk, _ in enumerate(H_obj):
string_list.append("H_obj[{0}]".format(kk))
for kk, _ in enumerate(C_td_terms):
string_list.append("C_terms[{0}]".format(kk))
for kk, _ in enumerate(C_obj):
string_list.append("C_obj[{0}]".format(kk))
for kk, _ in enumerate(A_td_terms):
string_list.append("A_terms[{0}]".format(kk))
#Add nrows to parameters
string_list.append('nrows')
parameter_string = ",".join(string_list)
#
# generate and compile new cython code if necessary
#
if not options.rhs_reuse or config.tdfunc is None:
if options.rhs_filename is None:
config.tdname = "rhs" + str(os.getpid()) + str(config.cgen_num)
else:
config.tdname = opt.rhs_filename
cgen = BR_Codegen(
h_terms=len(H_terms),
h_td_terms=H_td_terms,
h_obj=H_obj,
c_terms=len(C_terms),
c_td_terms=C_td_terms,
c_obj=C_obj,
a_terms=len(A_terms),
a_td_terms=A_td_terms,
spline_count=spline_count,
config=config,
sparse=False,
use_secular=use_secular,
use_openmp=options.use_openmp,
omp_thresh=qset.openmp_thresh if qset.has_openmp else None,
omp_threads=options.num_cpus,
atol=tol)
cgen.generate(config.tdname + ".pyx")
code = compile('from ' + config.tdname + ' import cy_td_ode_rhs',
'<string>', 'exec')
exec(code, globals())
config.tdfunc = cy_td_ode_rhs
initial_vector = mat2vec(rho0.full()).ravel()
_ode = scipy.integrate.ode(config.tdfunc)
code = compile('_ode.set_f_params(' + parameter_string + ')', '<string>',
'exec')
_ode.set_integrator('zvode',
method=options.method,
order=options.order,
atol=options.atol,
rtol=options.rtol,
nsteps=options.nsteps,
first_step=options.first_step,
min_step=options.min_step,
max_step=options.max_step)
_ode.set_initial_value(initial_vector, tlist[0])
exec(code, locals())
#
# prepare output array
#
n_tsteps = len(tlist)
e_sops_data = []
output = Result()
output.solver = "brmesolve"
output.times = tlist
if options.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 = []
options.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:
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 _ode.successful():
raise Exception("ODE integration error: Try to increase "
"the allowed number of substeps by increasing "
"the nsteps parameter in the Options class.")
if options.store_states or expt_callback:
rho.data = dense2D_to_fastcsr_fmode(vec2mat(_ode.y), rho.shape[0],
rho.shape[1])
if options.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], _ode.y, 0)
else:
output.expect[m][t_idx] = expect_rho_vec(
e_sops_data[m], _ode.y, 1)
if t_idx < n_tsteps - 1:
_ode.integrate(_ode.t + dt[t_idx])
progress_bar.finished()
if (not options.rhs_reuse) and (config.tdname is not None):
_cython_build_cleanup(config.tdname)
if options.store_final_state:
rho.data = dense2D_to_fastcsr_fmode(vec2mat(_ode.y), rho.shape[0],
rho.shape[1])
output.final_state = Qobj(rho, dims=rho0.dims, isherm=True)
return output
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
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
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
def _td_brmesolve(H, psi0, tlist, a_ops=[], e_ops=[], c_ops=[], args={},
use_secular=True, sec_cutoff=0.1,
tol=qset.atol, options=None,
progress_bar=None,_safe_mode=True,
verbose=False,
_prep_time=0):
if isket(psi0):
rho0 = ket2dm(psi0)
else:
rho0 = psi0
nrows = rho0.shape[0]
H_terms = []
H_td_terms = []
H_obj = []
A_terms = []
A_td_terms = []
C_terms = []
C_td_terms = []
CA_obj = []
spline_count = [0,0]
coupled_ops = []
coupled_lengths = []
coupled_spectra = []
if isinstance(H, Qobj):
H_terms.append(H.full('f'))
H_td_terms.append('1')
else:
for kk, h in enumerate(H):
if isinstance(h, Qobj):
H_terms.append(h.full('f'))
H_td_terms.append('1')
elif isinstance(h, list):
H_terms.append(h[0].full('f'))
if isinstance(h[1], Cubic_Spline):
H_obj.append(h[1].coeffs)
spline_count[0] += 1
H_td_terms.append(h[1])
else:
raise Exception('Invalid Hamiltonian specification.')
for kk, c in enumerate(c_ops):
if isinstance(c, Qobj):
C_terms.append(c.full('f'))
C_td_terms.append('1')
elif isinstance(c, list):
C_terms.append(c[0].full('f'))
if isinstance(c[1], Cubic_Spline):
CA_obj.append(c[1].coeffs)
spline_count[0] += 1
C_td_terms.append(c[1])
else:
raise Exception('Invalid collapse operator specification.')
coupled_offset = 0
for kk, a in enumerate(a_ops):
if isinstance(a, list):
if isinstance(a[0], Qobj):
A_terms.append(a[0].full('f'))
A_td_terms.append(a[1])
if isinstance(a[1], tuple):
if not len(a[1])==2:
raise Exception('Tuple must be len=2.')
if isinstance(a[1][0],Cubic_Spline):
spline_count[1] += 1
if isinstance(a[1][1],Cubic_Spline):
spline_count[1] += 1
elif isinstance(a[0], tuple):
if not isinstance(a[1], tuple):
raise Exception('Invalid bath-coupling specification.')
if (len(a[0])+1) != len(a[1]):
raise Exception('BR a_ops tuple lengths not compatible.')
coupled_ops.append(kk+coupled_offset)
coupled_lengths.append(len(a[0]))
coupled_spectra.append(a[1][0])
coupled_offset += len(a[0])-1
if isinstance(a[1][0],Cubic_Spline):
spline_count[1] += 1
for nn, _a in enumerate(a[0]):
A_terms.append(_a.full('f'))
A_td_terms.append(a[1][nn+1])
if isinstance(a[1][nn+1],Cubic_Spline):
CA_obj.append(a[1][nn+1].coeffs)
spline_count[1] += 1
else:
raise Exception('Invalid bath-coupling specification.')
string_list = []
for kk,_ in enumerate(H_td_terms):
string_list.append("H_terms[{0}]".format(kk))
for kk,_ in enumerate(H_obj):
string_list.append("H_obj[{0}]".format(kk))
for kk,_ in enumerate(C_td_terms):
string_list.append("C_terms[{0}]".format(kk))
for kk,_ in enumerate(CA_obj):
string_list.append("CA_obj[{0}]".format(kk))
for kk,_ in enumerate(A_td_terms):
string_list.append("A_terms[{0}]".format(kk))
#Add nrows to parameters
string_list.append('nrows')
for name, value in args.items():
if isinstance(value, np.ndarray):
raise TypeError('NumPy arrays not valid args for BR solver.')
else:
string_list.append(str(value))
parameter_string = ",".join(string_list)
if verbose:
print('BR prep time:', time.time()-_prep_time)
#
# generate and compile new cython code if necessary
#
if not options.rhs_reuse or config.tdfunc is None:
if options.rhs_filename is None:
config.tdname = "rhs" + str(os.getpid()) + str(config.cgen_num)
else:
config.tdname = opt.rhs_filename
if verbose:
_st = time.time()
cgen = BR_Codegen(h_terms=len(H_terms),
h_td_terms=H_td_terms, h_obj=H_obj,
c_terms=len(C_terms),
c_td_terms=C_td_terms, c_obj=CA_obj,
a_terms=len(A_terms), a_td_terms=A_td_terms,
spline_count=spline_count,
coupled_ops = coupled_ops,
coupled_lengths = coupled_lengths,
coupled_spectra = coupled_spectra,
config=config, sparse=False,
use_secular = use_secular,
sec_cutoff = sec_cutoff,
args=args,
use_openmp=options.use_openmp,
omp_thresh=qset.openmp_thresh if qset.has_openmp else None,
omp_threads=options.num_cpus,
atol=tol)
cgen.generate(config.tdname + ".pyx")
code = compile('from ' + config.tdname + ' import cy_td_ode_rhs',
'<string>', 'exec')
exec(code, globals())
config.tdfunc = cy_td_ode_rhs
if verbose:
print('BR compile time:', time.time()-_st)
initial_vector = mat2vec(rho0.full()).ravel()
_ode = scipy.integrate.ode(config.tdfunc)
code = compile('_ode.set_f_params(' + parameter_string + ')',
'<string>', 'exec')
_ode.set_integrator('zvode', method=options.method,
order=options.order, atol=options.atol,
rtol=options.rtol, nsteps=options.nsteps,
first_step=options.first_step,
min_step=options.min_step,
max_step=options.max_step)
_ode.set_initial_value(initial_vector, tlist[0])
exec(code, locals())
#
# prepare output array
#
n_tsteps = len(tlist)
e_sops_data = []
output = Result()
output.solver = "brmesolve"
output.times = tlist
if options.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 = []
options.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:
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
#
if type(progress_bar)==BaseProgressBar and verbose:
_run_time = time.time()
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 _ode.successful():
raise Exception("ODE integration error: Try to increase "
"the allowed number of substeps by increasing "
"the nsteps parameter in the Options class.")
if options.store_states or expt_callback:
rho.data = dense2D_to_fastcsr_fmode(vec2mat(_ode.y), rho.shape[0], rho.shape[1])
if options.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],
_ode.y, 0)
else:
output.expect[m][t_idx] = expect_rho_vec(e_sops_data[m],
_ode.y, 1)
if t_idx < n_tsteps - 1:
_ode.integrate(_ode.t + dt[t_idx])
progress_bar.finished()
if type(progress_bar)==BaseProgressBar and verbose:
print('BR runtime:', time.time()-_run_time)
if (not options.rhs_reuse) and (config.tdname is not None):
_cython_build_cleanup(config.tdname)
if options.store_final_state:
rho.data = dense2D_to_fastcsr_fmode(vec2mat(_ode.y), rho.shape[0], rho.shape[1])
output.final_state = Qobj(rho, dims=rho0.dims, isherm=True)
return output
def _generic_ode_solve(r, psi0, tlist, e_ops, opt, progress_bar, dims=None):
"""
Internal function for solving ODEs.
"""
if opt.normalize_output:
state_norm_func = norm
else:
state_norm_func = None
#
# 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():
raise Exception("ODE integration error: Try to increase "
"the allowed number of substeps by increasing "
"the nsteps parameter in the Options class.")
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, dims=dims)
return output
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:
pass
if opt.store_final_state:
cdata = get_curr_state_data()
if opt.normalize_output:
cdata *= norm_dim_factor / la_norm(cdata)
output.final_state = Qobj(cdata, dims=dims)
return output
def _generic_ode_solve(func, ode_args, psi0, tlist, e_ops, opt,
progress_bar, dims=None):
"""
Internal function for solving ODEs.
"""
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# This function is made similar to mesolve's one for futur merging in a
# solver class
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# prepare output array
n_tsteps = len(tlist)
output = Result()
output.solver = "sesolve"
output.times = tlist
if psi0.isunitary:
initial_vector = psi0.full().ravel('F')
oper_evo = True
size = psi0.shape[0]
# oper_n = dims[0][0]
# norm_dim_factor = np.sqrt(oper_n)
elif psi0.isket:
initial_vector = psi0.full().ravel()
oper_evo = False
# norm_dim_factor = 1.0
r = scipy.integrate.ode(func)
r.set_integrator('zvode', method=opt.method, order=opt.order,
atol=opt.atol, rtol=opt.rtol, nsteps=opt.nsteps,
first_step=opt.first_step, min_step=opt.min_step,
max_step=opt.max_step)
if ode_args:
r.set_f_params(*ode_args)
r.set_initial_value(initial_vector, tlist[0])
e_ops_data = []
output.expect = []
if callable(e_ops):
n_expt_op = 0
expt_callback = True
output.num_expect = 1
elif isinstance(e_ops, list):
n_expt_op = len(e_ops)
expt_callback = False
output.num_expect = n_expt_op
if n_expt_op == 0:
# fallback on storing states
opt.store_states = True
else:
for op in e_ops:
if not isinstance(op, Qobj) and callable(op):
output.expect.append(np.zeros(n_tsteps, dtype=complex))
continue
if op.isherm:
output.expect.append(np.zeros(n_tsteps))
else:
output.expect.append(np.zeros(n_tsteps, dtype=complex))
if oper_evo:
for e in e_ops:
if isinstance(e, Qobj):
e_ops_data.append(e.dag().data)
continue
e_ops_data.append(e)
else:
for e in e_ops:
if isinstance(e, Qobj):
e_ops_data.append(e.data)
continue
e_ops_data.append(e)
else:
raise TypeError("Expectation parameter must be a list or a function")
if opt.store_states:
output.states = []
if oper_evo:
def get_curr_state_data(r):
return vec2mat(r.y)
else:
def get_curr_state_data(r):
return r.y
#
# start evolution
#
dt = np.diff(tlist)
cdata = None
progress_bar.start(n_tsteps)
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
if opt.store_states or opt.normalize_output \
or n_expt_op > 0 or expt_callback:
cdata = get_curr_state_data(r)
if opt.normalize_output:
# normalize per column
if oper_evo:
cdata /= la_norm(cdata, axis=0)
#cdata *= norm_dim_factor / la_norm(cdata)
r.set_initial_value(cdata.ravel('F'), r.t)
else:
#cdata /= la_norm(cdata)
norm = normalize_inplace(cdata)
if norm > 1e-12:
# only reset the solver if state changed
r.set_initial_value(cdata, r.t)
else:
r._y = cdata
if opt.store_states:
if oper_evo:
fdata = dense2D_to_fastcsr_fmode(cdata, size, size)
output.states.append(Qobj(fdata, dims=dims))
else:
fdata = dense1D_to_fastcsr_ket(cdata)
output.states.append(Qobj(fdata, dims=dims, fast='mc'))
if expt_callback:
# use callback method
output.expect.append(e_ops(t, Qobj(cdata, dims=dims)))
if oper_evo:
for m in range(n_expt_op):
if callable(e_ops_data[m]):
func = e_ops_data[m]
output.expect[m][t_idx] = func(t, Qobj(cdata, dims=dims))
continue
output.expect[m][t_idx] = (e_ops_data[m] * cdata).trace()
else:
for m in range(n_expt_op):
if callable(e_ops_data[m]):
func = e_ops_data[m]
output.expect[m][t_idx] = func(t, Qobj(cdata, dims=dims))
continue
output.expect[m][t_idx] = cy_expect_psi(e_ops_data[m], cdata,
e_ops[m].isherm)
if t_idx < n_tsteps - 1:
r.integrate(r.t + dt[t_idx])
progress_bar.finished()
if opt.store_final_state:
cdata = get_curr_state_data(r)
if opt.normalize_output:
cdata /= la_norm(cdata, axis=0)
# cdata *= norm_dim_factor / la_norm(cdata)
output.final_state = Qobj(cdata, dims=dims)
return output
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
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
