hpcflow.sdk.core.loop_cache.LoopCache#

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

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]) – 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, Dict]) – Keys are element IDs, values are dicts with keys: input_statuses, input_sources, and task_insert_ID.
zeroth_iters (Dict[int, Tuple]) – 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]) – 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]) – 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.

classmethod build(workflow, loops=None)#

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

Parameters:

data_idx: Dict[int, Dict]#: 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]#: 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, Dict]#

input_statuses, input_sources, and task_insert_ID.

get_iter_IDs(loop)#

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

get_iter_loop_indices(iter_IDs)#

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

iterations: Dict[int, Tuple]#: 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:

zeroth_iters: Dict[int, Tuple]#: 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.