def test_get_data_no_columns(scalar_dataset):
ds = scalar_dataset
with pytest.warns(None) as record:
ref = mut_queries.get_data(ds.conn, ds.table_name, [])
assert ref == [[]]
assert len(record) == 2
assert str(record[0].message).startswith("The function <get_data>")
assert str(record[1].message).startswith("get_data")
def get_data(self,
*params: Union[str, ParamSpec, _BaseParameter],
start: Optional[int] = None,
end: Optional[int] = None) -> List[List[Any]]:
"""
Returns the values stored in the DataSet for the specified parameters.
The values are returned as a list of lists, SQL rows by SQL columns,
e.g. datapoints by parameters. The data type of each element is based
on the datatype provided when the DataSet was created. The parameter
list may contain a mix of string parameter names, QCoDeS Parameter
objects, and ParamSpec objects (as long as they have a `name` field).
If provided, the start and end arguments select a range of results
by result count (index). If the range is empty - that is, if the end is
less than or equal to the start, or if start is after the current end
of the DataSet – then a list of empty arrays is returned.
For a more type independent and easier to work with view of the data
you may want to consider using
:py:meth:`.get_parameter_data`
Args:
*params: string parameter names, QCoDeS Parameter objects, and
ParamSpec objects
start: start value of selection range (by result count); ignored
if None
end: end value of selection range (by results count); ignored if
None
Returns:
list of lists SQL rows of data by SQL columns. Each SQL row is a
datapoint and each SQL column is a parameter. Each element will
be of the datatypes stored in the database (numeric, array or
string)
"""
valid_param_names = self._validate_parameters(*params)
return get_data(self.conn, self.table_name, valid_param_names, start,
end)
def test_get_data_no_columns(scalar_dataset):
ds = scalar_dataset
ref = mut_queries.get_data(ds.conn, ds.table_name, [])
assert ref == [[]]
