BasisMultiStateSpace

Represents a collection of BasisStateSpace objects. This is commonly generated by application of selection rules to a standard BasisStateSpace. Each of these state spaces is nominally independent of the rest, allowing for combinatorial efficiency later down the line.

 

__init__(self, spaces): 
  • spaces: Iterable

    array of BasisStateSpace objects

  • selection_rules: np.ndarray

    array of rules used to generate the subspace

 

get_space(self, item): 

Just a way to index the space but with type checking

  • item: Any
  • :returns: BasisStateSpace

 

map(self, f): 

 

to_state(self, serializer=None): 

 

@classmethod
from_state(cls, data, serializer=None): 

 

@property
representative_space(self): 

 

@property
basis(self): 

 

@property
full_basis(self): 

 

@property
ndim(self): 

 

__iter__(self): 

 

@property
flat(self): 

 

get_mode(self): 

 

as_indices(self): 

Pulls the full set of indices out of all of the held spaces and returns them as a flat vector

  • :returns: _

 

as_excitations(self): 

Pulls the full set excitations out of all of the held spaces and returns them as a flat vector

  • :returns: _

 

check_indices(self): 

 

to_single(self, track_excitations=True, track_indices=True): 

Condenses the multi state space down to a single BasisStateSpace

  • :returns: _

 

take_states(self, states, track_excitations=True, track_indices=True): 

Takes the intersection of each held space and the specified states

  • states: Any
  • :returns: _

 

take_subspace(self, sel, track_excitations=True, track_indices=True): 

Takes the specified states, making sure each held space only contains states in sel

  • sel: Any
  • :returns: _

 

take_subdimensions(self, inds): 

Takes the subdimensions from each space

  • inds: Any
  • :returns: _

 

drop_states(self, states): 

Take the difference of each held space and the specified states

  • states: Any
  • :returns: _

 

drop_subspace(self, sel): 

Takes the specified states, making sure each held space only contains states in sel

  • sel: Any
  • :returns: _

 

drop_subdimensions(self, inds): 

Takes the subdimensions from each space

  • inds: Any
  • :returns: _

 

__repr__(self): 

 

__getitem__(self, item): 

 

get_representation_indices(self, freqs=None, freq_threshold=None, other=None, selection_rules=None, return_filter=False): 

Generates a set of indices that can be fed into a Representation to provide a sub-representation in this state space. Basically just takes all pairs of indices.

  • :returns: _

 

get_representation_brakets(self, freqs=None, freq_threshold=None, other=None, selection_rules=None, filter=None, return_filter=False): 

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