hpcflow.sdk.core.loop_cache.LoopCache

class hpcflow.sdk.core.loop_cache.LoopCache(element_dependents, elements, zeroth_iters, data_idx, iterations, task_iterations)

Bases: object

Class to store a cache for use in Workflow.add_empty_loop() and WorkflowLoop.add_iterations(). Use build() to get a new instance.

Parameters:

• element_dependents (dict[int, dict[int, DependentDescriptor]]) – Keys are element IDs, values are dicts whose keys are element IDs that depend on the key element ID (via Element.get_dependent_elements_recursively), and whose values are dicts with keys: group_names, which is a tuple of the string group names associated with the dependent element’s element set.

• elements (dict[int, ElementDescriptor]) – Keys are element IDs, values are dicts with keys: input_statuses, input_sources, and task_insert_ID.

• zeroth_iters (dict[int, tuple[int, DataIndex]]) – Keys are element IDs, values are data associated with the zeroth iteration of that element, namely a tuple of iteration ID and ElementIteration.data_idx.

• data_idx (dict[int, dict[LoopIndex[str, int], DataIndex]]) – Keys are element IDs, values are data associated with all iterations of that element, namely a dict whose keys are the iteration loop index as a tuple, and whose values are data indices via ElementIteration.get_data_idx().

• iterations (dict[int, tuple[int, int]]) – Keys are iteration IDs, values are tuples of element ID and iteration index within that element.

• task_iterations (dict[int, list[int]]) – Keys are task insert IDs, values are list of all iteration IDs associated with that task.

Methods

add_iteration	Update the cache to include a newly added iteration.
build	Build a cache of data for use in adding loops and iterations.
get_iter_IDs	Retrieve a list of iteration IDs belonging to a given loop.
get_iter_loop_indices	Retrieve the mapping from element to loop index for each given iteration.
update_loop_indices	Set the loop indices for a named loop to the given list of iteration IDs.

Attributes

element_dependents	Keys are element IDs, values are dicts whose keys are element IDs that depend on the key element ID (via Element.get_dependent_elements_recursively), and whose values are dicts with keys: group_names, which is a tuple of the string group names associated with the dependent element's element set.
elements	input_statuses, input_sources, and task_insert_ID.
zeroth_iters	Keys are element IDs, values are data associated with the zeroth iteration of that element, namely a tuple of iteration ID and ElementIteration.data_idx.
data_idx	Keys are element IDs, values are data associated with all iterations of that element, namely a dict whose keys are the iteration loop index as a tuple, and whose values are data indices via ElementIteration.get_data_idx().
iterations	Keys are iteration IDs, values are tuples of element ID and iteration index within that element.
task_iterations	Keys are task insert IDs, values are list of all iteration IDs associated with that task.

add_iteration(iter_ID, task_insert_ID, element_ID, loop_idx, data_idx)

Update the cache to include a newly added iteration.

Parameters:

• iter_ID (int) –

• task_insert_ID (int) –

• element_ID (int) –

• loop_idx (LoopIndex[str, int]) –

• data_idx (DataIndex) –

classmethod build(workflow, loops=None)

Build a cache of data for use in adding loops and iterations.

Parameters:

• workflow (Workflow) –

• loops (list[Loop] | None) –

Return type:

Self

data_idx: dict[int, dict[LoopIndex[str, int], DataIndex]]

Keys are element IDs, values are data associated with all iterations of that element, namely a dict whose keys are the iteration loop index as a tuple, and whose values are data indices via ElementIteration.get_data_idx().

element_dependents: dict[int, dict[int, DependentDescriptor]]

Keys are element IDs, values are dicts whose keys are element IDs that depend on the key element ID (via Element.get_dependent_elements_recursively), and whose values are dicts with keys: group_names, which is a tuple of the string group names associated with the dependent element’s element set.

elements: dict[int, ElementDescriptor]

input_statuses, input_sources, and task_insert_ID.

Type:

Keys are element IDs, values are dicts with keys

get_iter_IDs(loop)

Retrieve a list of iteration IDs belonging to a given loop.

Parameters:

loop (Loop) –

Return type:

list[int]

get_iter_loop_indices(iter_IDs)

Retrieve the mapping from element to loop index for each given iteration.

Parameters:

iter_IDs (list[int]) –

Return type:

Sequence[Mapping[str, int]]

iterations: dict[int, tuple[int, int]]

Keys are iteration IDs, values are tuples of element ID and iteration index within that element.

task_iterations: dict[int, list[int]]

Keys are task insert IDs, values are list of all iteration IDs associated with that task.

update_loop_indices(new_loop_name, iter_IDs)

Set the loop indices for a named loop to the given list of iteration IDs.

Parameters:

• new_loop_name (str) –

• iter_IDs (list[int]) –

Return type:

None

zeroth_iters: dict[int, tuple[int, DataIndex]]

Keys are element IDs, values are data associated with the zeroth iteration of that element, namely a tuple of iteration ID and ElementIteration.data_idx.