def updated_dframe(self, dataset, formula, dframe):
"""Create a new aggregation and update return updated dframe."""
# build column arguments from original dframe
columns = parse_columns(dataset, formula, self.name, self.dframe)
new_dframe = self.aggregation.eval(columns)
new_columns = [x for x in new_dframe.columns if x not in self.groups]
dframe = dframe.drop(new_columns, axis=1)
dframe = group_join(self.groups, new_dframe, dframe)
return dframe
def update_calculations(record, dataset):
calculations = dataset.calculations(include_aggs=False)
if len(calculations):
dframe = DataFrame(data=record, index=[0])
labels_to_slugs = dataset.schema.labels_to_slugs
for c in calculations:
columns = parse_columns(dataset, c.formula, c.name, dframe=dframe)
record[labels_to_slugs[c.name]] = columns[0][0]
return record
def updated_dframe(self, dataset, formula, dframe):
"""Create a new aggregation and update return updated dframe."""
# build column arguments from original dframe
columns = parse_columns(dataset, formula, self.name, self.dframe)
new_dframe = self.aggregation.eval(columns)
new_columns = [x for x in new_dframe.columns if x not in self.groups]
dframe = dframe.drop(new_columns, axis=1)
dframe = group_join(self.groups, new_dframe, dframe)
return dframe
def update_calculations(record, dataset):
calculations = dataset.calculations(include_aggs=False)
if len(calculations):
dframe = DataFrame(data=record, index=[0])
labels_to_slugs = dataset.schema.labels_to_slugs
for c in calculations:
columns = parse_columns(dataset, c.formula, c.name, dframe=dframe)
record[labels_to_slugs[c.name]] = columns[0][0]
return record
def __create_aggregator(dataset, formula, name, groups, dframe=None):
# TODO this should work with index eventually
columns = parse_columns(dataset, formula, name, dframe, no_index=True)
dependent_columns = Parser.dependent_columns(formula, dataset)
aggregation = Parser.parse_aggregation(formula)
# get dframe with only the necessary columns
select = combine_dicts({group: 1 for group in groups},
{col: 1 for col in dependent_columns})
# ensure at least one column (MONGO_ID) for the count aggregation
query_args = QueryArgs(select=select or {MONGO_ID: 1})
dframe = dataset.dframe(query_args=query_args, keep_mongo_keys=not select)
return Aggregator(dframe, groups, aggregation, name, columns)
def calculate_columns(dataset, calculations):
"""Calculate and store new columns for `calculations`.
The new columns are join t othe Calculation dframe and replace the
dataset's observations.
.. note::
This can result in race-conditions when:
- deleting ``controllers.Datasets.DELETE``
- updating ``controllers.Datasets.POST([dataset_id])``
Therefore, perform these actions asychronously.
:param dataset: The dataset to calculate for.
:param calculations: A list of calculations.
"""
new_cols = None
for c in calculations:
if c.aggregation:
aggregator = __create_aggregator(
dataset, c.formula, c.name, c.groups_as_list)
aggregator.save(dataset)
else:
columns = parse_columns(dataset, c.formula, c.name)
if new_cols is None:
new_cols = DataFrame(columns[0])
else:
new_cols = new_cols.join(columns[0])
if new_cols is not None:
dataset.update_observations(new_cols)
# propagate calculation to any merged child datasets
[__propagate_column(x, dataset) for x in dataset.merged_datasets]
