def extract_user_stories(
related_identifiers: List[Dict],
user_story_typeid="user-story"
) -> Tuple[Union[None, List], Union[None, List]]:
"""Extract the user stories from a record.
Args:
related_identifiers (List[Dict]): List with Relation Object.
user_story_typeid (str): Resource Type ID of the user-story records.
Returns:
Tuple[Union[None, List], Union[None, List]]: Tuple with the following content:
- First tuple position: Related identifiers that are not user stories
- Second tuple position: User Stories identifiers.
See:
For more information about the Relation Object, please, check the `related-identifiers`
page available on InvenioRDM Documentation:
- https://inveniordm.docs.cern.ch/reference/metadata/#related-identifiersworks-0-n
"""
return py_.partition(
related_identifiers,
lambda x: py_.get(x, "ui.resource_type.id") != user_story_typeid,
)
def report_events_summary(events: List[CalendarEvent]) -> Dict[str, float]:
# Don't count day long events
events = [ev for ev in events if not is_day_long_event(ev)]
total_hours = calculate_time_spent(events) / 60
external_events, internal_events = py_.partition(events, is_event_external)
external_hours = calculate_time_spent(external_events) / 60
personal_events, rest_events = py_.partition(internal_events, is_event_personal)
personal_hours = calculate_time_spent(personal_events) / 60
return {
"total": total_hours,
"external": external_hours,
"personal": personal_hours,
"1:1": calculate_time_spent([ev for ev in rest_events if is_event_one_on_one(ev)]) / 60,
"rest": calculate_time_spent([ev for ev in rest_events if not is_event_one_on_one(ev)]) / 60
}
def update_targets(self, items):
"""
Inserts the new task_types into the task_types collection
Args:
items ([dict]): task_type dicts to insert into task_types collection
"""
with_task_type, without_task_type = py_.partition(items, lambda i: i["task_type"])
if without_task_type:
self.logger.error("No task type found for {}".format(without_task_type))
if len(with_task_type) > 0:
self.task_types.update(with_task_type)
def combine_time_entities_from_slots(
self, slot_tracker: Optional[List[Dict[str, Any]]],
entities: List[BaseEntity]) -> List[BaseEntity]:
previously_filled_time_entity = self.pick_previously_filled_time_entity(
self.get_tracked_slots(slot_tracker))
if not previously_filled_time_entity:
return entities
time_entities, other_entities = py_.partition(
entities,
lambda entity: entity.entity_type in CombineDateTimeOverSlots.
SUPPORTED_ENTITIES,
)
combined_time_entities = [
self.join(entity, previously_filled_time_entity)
for entity in time_entities
]
return combined_time_entities + other_entities
def update_targets(self, items):
target = self.targets[0]
xas_averaged = target.collection
valids, invalids = py_.partition(
mark_lu(py_.flatten(items), target.lu_field, self.dt_fetch),
'valid')
# Remove documents flagging now-valid data as invalid.
xas_averaged.delete_many(
mark_invalid({"mp_id": {
"$in": py_.pluck(valids, 'mp_id')
}}))
for doc in valids:
xas_averaged.update_one(py_.pick(doc, 'mp_id', 'element'),
{'$set': doc},
upsert=True)
for doc in invalids:
xas_averaged.update_one(mark_invalid(py_.pick(doc, 'mp_id')),
{'$set': doc},
upsert=True)
def update_targets(self, items):
xas_averaged = self.targets[0]
xas_averaged.ensure_index([("valid", 1), ("mp_id", 1)])
xas_averaged.ensure_index([("mp_id", 1), ("element", 1)])
xas_averaged.ensure_index([("chemsys", 1), ("element", 1)])
valids, invalids = py_.partition(
mark_lu(py_.flatten(items), xas_averaged.lu_field, self.dt_fetch),
'valid')
# Remove documents flagging now-valid data as invalid.
xas_averaged.collection.delete_many(
mark_invalid({"mp_id": {
"$in": py_.pluck(valids, 'mp_id')
}}))
bulk = xas_averaged.collection.initialize_ordered_bulk_op()
for doc in valids:
(bulk.find(py_.pick(doc, 'mp_id',
'element')).upsert().replace_one(doc))
for doc in invalids:
(bulk.find(mark_invalid(py_.pick(
doc, 'mp_id'))).upsert().replace_one(doc))
bulk.execute()