def save(self, dataset, formula, name, group_str=None):
"""Parse, save, and calculate a formula.
Validate `formula` and `group_str` for the given `dataset`. If the
formula and group are valid for the dataset, then save a new
calculation for them under `name`. Finally, create a background task
to compute the calculation.
Calculations are initially saved in a **pending** state, after the
calculation has finished processing it will be in a **ready** state.
:param dataset: The DataSet to save.
:param formula: The formula to save.
:param name: The name of the formula.
:param group_str: Columns to group on.
:type group_str: String, list or strings, or None.
:raises: `ParseError` if an invalid formula was supplied.
"""
# ensure that the formula is parsable
groups = self.split_groups(group_str) if group_str else []
Parser.validate(dataset, formula, groups)
aggregation = Parser.parse_aggregation(formula)
if aggregation:
# set group if aggregation and group unset
group_str = group_str or ''
# check that name is unique for aggregation
aggregated_dataset = dataset.aggregated_dataset(groups)
if aggregated_dataset:
name = _check_name_and_make_unique(name, aggregated_dataset)
else:
# set group if aggregation and group unset
name = _check_name_and_make_unique(name, dataset)
record = {
DATASET_ID: dataset.dataset_id,
self.AGGREGATION: aggregation,
self.FORMULA: formula,
self.GROUP: group_str,
self.NAME: name,
self.STATE: self.STATE_PENDING,
}
super(self.__class__, self).save(record)
return self
def save(self, dataset, formula, name, group_str=None):
"""Parse, save, and calculate a formula.
Validate `formula` and `group_str` for the given `dataset`. If the
formula and group are valid for the dataset, then save a new
calculation for them under `name`. Finally, create a background task
to compute the calculation.
Calculations are initially saved in a **pending** state, after the
calculation has finished processing it will be in a **ready** state.
:param dataset: The DataSet to save.
:param formula: The formula to save.
:param name: The name of the formula.
:param group_str: Columns to group on.
:type group_str: String, list or strings, or None.
:raises: `ParseError` if an invalid formula was supplied.
"""
# ensure that the formula is parsable
groups = self.split_groups(group_str) if group_str else []
Parser.validate(dataset, formula, groups)
aggregation = Parser.parse_aggregation(formula)
if aggregation:
# set group if aggregation and group unset
group_str = group_str or ''
# check that name is unique for aggregation
aggregated_dataset = dataset.aggregated_dataset(groups)
if aggregated_dataset:
name = _check_name_and_make_unique(name, aggregated_dataset)
else:
# set group if aggregation and group unset
name = _check_name_and_make_unique(name, dataset)
record = {
DATASET_ID: dataset.dataset_id,
self.AGGREGATION: aggregation,
self.FORMULA: formula,
self.GROUP: group_str,
self.NAME: name,
self.STATE: self.STATE_PENDING,
}
super(self.__class__, self).save(record)
return self
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)