symforce.values.index_entry module#

class IndexEntry(offset, storage_dim, stored_datatype, shape=None, item_index=None)[source]#

Bases: object

Contains the structural information needed to reconstruct a single value of a values.Values from storage in method values.Values.from_storage_index()

Meant to be a python parallel to index_entry_t in symforce.lcm

For entry: IndexEntry = v.index()[key] for Values v and string key

Parameters:

  • offset (int) –

  • storage_dim (int) –

  • stored_datatype (dataclasses.InitVar[T.Type]) –

  • shape (T.Optional[T.Tuple[int, ...]]) –

  • item_index (T.Optional[T.Dict[str, T.Any]]) –

offset#

The index of StorageOps.to_storage(v) at which StorageOps.to_storage(v[key]) begins

Type:

int

storage_dim#

The length of StorageOps.to_storage(v[key])

Type:

int

shape#

If datatype() is np.ndarray or sf.Matrix, it’s the shape of v[key]. Otherwise, it’s None.

Type:

T.Optional[T.Tuple[int, …]]

item_index#

v[key].index() if datatype() is Values, if datatype() is list or tuple, is dict d where d[f"{key}_{i}"] equals the IndexEntry of v[key][i], and otherwise is None

Type:

T.Optional[T.Dict[str, T.Any]]

offset: int#
storage_dim: int#
stored_datatype: dataclasses.InitVar[T.Type]#
shape: T.Optional[T.Tuple[int, ...]] = None#
item_index: T.Optional[T.Dict[str, T.Any]] = None#
datatype()[source]#

Returns the type indexed by self

Example:

IndexEntry(offset, storage_dim, stored_datatype).datatype()

returns stored_datatype

Precondition:

The datatype stored must have had its module loaded (i.e., if the stored datatype is sf.Rot3, symforce.geo must have been imported). The datatype must also be accessible from the module (dynamically created types do not do this. For example, the sf.Matrix types with more than 10 rows or columns)

Return type:

Type