hpcflow.sdk.persistence.json.JsonStoreElementIter

class hpcflow.sdk.persistence.json.JsonStoreElementIter(id_, is_pending, element_ID, EARs_initialised, EAR_IDs, data_idx, schema_parameters, loop_idx=<factory>)

Bases: StoreElementIter[IterMeta, None]

Persisted element iteration that is serialized using JSON.

Methods

append_EAR_IDs	Return a copy, with additional EAR IDs.
decode	Initialise a JsonStoreElementIter from persistent store element iteration data
encode	Prepare store element iteration data for the persistent store.
set_EARs_initialised	Return a copy with EARs_initialised set to True.
to_dict	Prepare data for the user-facing ElementIteration object.
update_loop_idx	Return a copy, with the loop index updated.

Attributes

id_	The ID of this element iteration.
is_pending	Whether the element iteration has changes not yet persisted.
element_ID	Which element is an iteration for.
EARs_initialised	Whether EARs have been initialised for this element iteration.
EAR_IDs	Maps task schema action indices to EARs by ID.
data_idx	Overall data index for the element-iteration, which maps parameter names to parameter data indices.
schema_parameters	List of parameters defined by the associated task schema.
loop_idx	What loops are being handled here and where they're up to.

Parameters:

• id_ (int) –

• is_pending (bool) –

• element_ID (int) –

• EARs_initialised (bool) –

• EAR_IDs (dict[int, list[int]] | None) –

• data_idx (DataIndex) –

• schema_parameters (list[str]) –

• loop_idx (Mapping[str, int]) –

EAR_IDs: dict[int, list[int]] | None

Maps task schema action indices to EARs by ID.

EARs_initialised: bool

Whether EARs have been initialised for this element iteration.

append_EAR_IDs(pend_IDs)

Return a copy, with additional EAR IDs.

Parameters:

pend_IDs (Mapping[int, Sequence[int]]) –

Return type:

Self

data_idx: DataIndex

Overall data index for the element-iteration, which maps parameter names to parameter data indices.

classmethod decode(iter_dat, context)

Initialise a JsonStoreElementIter from persistent store element iteration data

Parameters:

• iter_dat (IterMeta) –

• context (None) –

Return type:

Self

element_ID: int

Which element is an iteration for.

encode(context)

Prepare store element iteration data for the persistent store.

Parameters:

context (None) –

Return type:

IterMeta

id_: int

The ID of this element iteration.

is_pending: bool

Whether the element iteration has changes not yet persisted.

loop_idx: Mapping[str, int]

What loops are being handled here and where they’re up to.

schema_parameters: list[str]

List of parameters defined by the associated task schema.

set_EARs_initialised()

Return a copy with EARs_initialised set to True.

Return type:

Self

to_dict(EARs)

Prepare data for the user-facing ElementIteration object.

Parameters:

EARs (dict[int, dict[str, Any]] | None) –

Return type:

dict[str, Any]

update_loop_idx(loop_idx)

Return a copy, with the loop index updated.

Parameters:

loop_idx (Mapping[str, int]) –

Return type:

Self