def _update_aggregate_dataset(self, formula, new_dframe, name, groups,
agg_dataset):
"""Update the aggregated dataset built for `self` with `calculation`.
Proceed with the following steps:
- delete the rows in this dataset from the parent
- recalculate aggregated dataframe from aggregation
- update aggregated dataset with new dataframe and add parent id
- recur on all merged datasets descending from the aggregated
dataset
:param formula: The formula to execute.
:param new_dframe: The DataFrame to aggregate on.
:param name: The name of the aggregation.
:param groups: A column or columns to group on.
:type group: String, list of strings, or None.
:param agg_dataset: The DataSet to store the aggregation in.
"""
# parse aggregation and build column arguments
aggregation, new_columns = self.make_columns(
formula, name, new_dframe)
agg = Aggregator(self.dataset, self.dframe,
groups, aggregation, name)
new_agg_dframe = agg.update(agg_dataset, self, formula, new_columns)
# jsondict from new dframe
new_data = new_agg_dframe.to_jsondict()
for merged_dataset in agg_dataset.merged_datasets:
# remove rows in child from this merged dataset
merged_dataset.remove_parent_observations(
agg_dataset.dataset_id)
# calculate updates on the child
merged_calculator = Calculator(merged_dataset)
call_async(merged_calculator.calculate_updates, merged_calculator,
new_data, parent_dataset_id=agg_dataset.dataset_id)
