def _test_calculator(self):
self.dframe = self.dataset.dframe()
row = self.dframe.irow(0)
columns = self.dframe.columns.tolist()
self.start_num_cols = len(columns)
self.added_num_cols = 0
column_labels_to_slugs = {
column_attrs[Dataset.LABEL]: (column_name) for
(column_name, column_attrs) in self.dataset.schema.items()
}
self.label_list, self.slugified_key_list = [
list(ary) for ary in zip(*column_labels_to_slugs.items())
]
for idx, formula in enumerate(self.calculations):
name = 'test-%s' % idx
self.parser.validate_formula(formula, row)
calculator = Calculator(self.dataset)
groups = self.dataset.split_groups(self.group)
calculator.calculate_column(formula, name, groups)
self.column_labels_to_slugs = self.dataset.schema.labels_to_slugs
self._test_calculation_results(name, formula)
def calculate_task(calculation, dataset):
"""Background task to run a calculation.
Set calculation to failed and raise if an exception occurs.
:param calculation: Calculation to run.
:param dataset: Dataset to run calculation on.
"""
# block until other calculations for this dataset are finished
calculation.restart_if_has_pending(dataset)
dataset.clear_summary_stats()
calculator = Calculator(dataset)
calculator.calculate_column(calculation.formula, calculation.name,
calculation.groups_as_list)
calculation.add_dependencies(dataset, calculator.dependent_columns())
if calculation.aggregation is not None:
dataset.reload()
aggregated_id = dataset.aggregated_datasets_dict[calculation.group]
calculation.set_aggregation_id(aggregated_id)
calculation.ready()