def _from_memoized_dict(cls, mm_dict, serial_memo):
base_matrix = cls._decodemx(mm_dict['base_matrix'])
parameter_array = cls._decodemx(mm_dict['parameter_array'])
left_transform = cls._decodemx(mm_dict['left_transform'])
right_transform = cls._decodemx(mm_dict['right_transform'])
state_space = _StateSpace.from_nice_serialization(mm_dict['state_space'])
return cls(base_matrix, parameter_array, mm_dict['parameter_to_base_indices_map'],
left_transform, right_transform, mm_dict['enforce_real'], mm_dict['evotype'], state_space)
def _from_memoized_dict(cls, mm_dict, serial_memo):
state_space = _StateSpace.from_nice_serialization(
mm_dict['state_space'])
effects = {
lbl: serial_memo[subm_serial_id]
for lbl, subm_serial_id in zip(mm_dict['effect_labels'],
mm_dict['submembers'])
}
return cls(
effects, mm_dict['evotype'],
state_space) # Note: __init__ call signature of derived classes
def _from_nice_serialization(cls, state):
state_space = _StateSpace.from_nice_serialization(state['state_space'])
layer_rules = _LayerRules.from_nice_serialization(state['layer_rules'])
basis = _Basis.from_nice_serialization(state['basis'])
modelmembers = _MMGraph.load_modelmembers_from_serialization_dict(
state['modelmembers'])
simulator = _FSim.from_nice_serialization(state['simulator'])
mdl = cls(state_space, layer_rules, basis, simulator, state['evotype'])
root_dicts = {
'prep_blks': mdl.prep_blks,
'povm_blks': mdl.povm_blks,
'operation_blks': mdl.operation_blks,
'instrument_blks': mdl.instrument_blks,
'factories': mdl.factories,
}
for mm_key, mm_dict in modelmembers.items():
root_key, sub_key = mm_key.split('|')
root_dicts[root_key][sub_key].update(
mm_dict) # Note: sub_keys should already be created
return mdl
def _from_memoized_dict(cls, mm_dict, serial_memo):
state_space = _StateSpace.from_nice_serialization(
mm_dict['state_space'])
# Alternate __init__ because we already have members built:
lbl_member_pairs = [
(lbl, serial_memo[subm_serial_id]) for lbl, subm_serial_id in zip(
mm_dict['member_labels'], mm_dict['submembers'])
]
MT_member = next(
filter(lambda pair: pair[1].index == len(lbl_member_pairs) - 1,
lbl_member_pairs))
param_ops = MT_member.submembers(
) # the final (TP) member has all the param_ops as its submembers
ret = TPInstrument.__new__(TPInstrument)
ret.param_ops = param_ops
ret._readonly = False
_collections.OrderedDict.__init__(ret, lbl_member_pairs)
_mm.ModelMember.__init__(ret, state_space, mm_dict['evotype'])
ret._readonly = True
return ret
def _from_memoized_dict(cls, mm_dict, serial_memo):
matrix = cls._decodemx(mm_dict['dense_matrix'])
state_space = _StateSpace.from_nice_serialization(mm_dict['state_space'])
return cls(matrix, mm_dict['include_off_diags_in_degen_2_blocks'],
mm_dict['tp_constrained_and_unital'], mm_dict['evotype'], state_space)
def _from_memoized_dict(cls, mm_dict, serial_memo):
state_space = _StateSpace.from_nice_serialization(
mm_dict['state_space'])
members = [(lbl, serial_memo[subm_serial_id]) for lbl, subm_serial_id
in zip(mm_dict['member_labels'], mm_dict['submembers'])]
return cls(members, mm_dict['evotype'], state_space)
def _from_memoized_dict(cls, mm_dict, serial_memo):
state_space = _StateSpace.from_nice_serialization(mm_dict['state_space'])
return cls(state_space, mm_dict['target_labels'], serial_memo[mm_dict['submembers'][0]])
def _from_nice_serialization(cls, state):
state_space = _StateSpace.from_nice_serialization(state['state_space'])
return cls(state_space, state['use_cache'])