def test_filter_table():
ctx = Prosto("My Prosto")
tbl = ctx.populate(
table_name="Base table",
attributes=["A", "B"],
func=
"lambda **m: pd.DataFrame({'A': [1.0, 2.0, 3.0], 'B': ['x', 'yy', 'zzz']})",
tables=[])
# This (boolean) column will be used for filtering
clm = ctx.compute(
name="filter_column",
table=tbl.id,
func=
"lambda x, param: (x['A'] > param) & (x['B'].str.len() < 3)", # Return a boolean Series
columns=["A", "B"],
model={"param": 1.5})
tbl.evaluate()
clm.evaluate()
tbl = ctx.filter(table_name="Filtered table",
attributes=["super"],
func=None,
tables=["Base table"],
columns=["filter_column"])
tbl.evaluate()
assert len(tbl.get_df().columns) == 1 # Only one link-attribute is created
assert len(tbl.get_df()) == 1
assert tbl.get_df()['super'][0] == 1
#
# Test topology
#
topology = Topology(ctx)
topology.translate()
layers = topology.elem_layers
assert len(layers) == 3
assert set([x.id for x in layers[0]]) == {"Base table"}
assert set([x.id for x in layers[1]]) == {"filter_column"}
assert set([x.id for x in layers[2]]) == {"Filtered table"}
def test_filter_inheritance():
"""Test topology augmentation. Use columns from the parent table by automatically adding the merge operation to topology."""
ctx = Prosto("My Prosto")
base_tbl = ctx.populate(
table_name="Base table",
attributes=["A", "B"],
func=
"lambda **m: pd.DataFrame({'A': [1.0, 2.0, 3.0], 'B': ['x', 'yy', 'zzz']})",
tables=[])
# This (boolean) column will be used for filtering
clm = ctx.compute(
name="filter_column",
table=base_tbl.id,
func=
"lambda x, param: (x['A'] > param) & (x['B'].str.len() < 3)", # Return a boolean Series
columns=["A", "B"],
model={"param": 1.5})
f_tbl = ctx.filter(table_name="Filtered table",
attributes=["super"],
func=None,
tables=["Base table"],
columns=["filter_column"])
# In this calculate column, we use a column of the filtered table which actually exists only in the base table
clm = ctx.calculate(name="My column",
table=f_tbl.id,
func="lambda x: x + 1.0",
columns=["A"],
model=None)
ctx.run()
clm_data = f_tbl.get_series('My column')
assert np.isclose(len(clm_data), 1)
assert np.isclose(clm_data[0], 3.0)
# This column had to be added automatically by the augmentation procedure
# It is inherited from the base table and materialized via merge operation
# It stores original values of the inherited base column
clm_data = f_tbl.get_series('A')
assert np.isclose(clm_data[0], 2)
def test_compute():
ctx = Prosto("My Prosto")
tbl = ctx.populate(
table_name="My table", attributes=["A"],
func="lambda **m: pd.DataFrame({'A': [1, 2, 3]})", tables=[]
)
clm = ctx.compute(
name="My column", table=tbl.id,
func="lambda x, **model: x.shift(**model)", columns=["A"], model={"periods": -1}
)
tbl.evaluate()
clm.evaluate()
clm_data = tbl.get_series('My column')
assert np.isclose(clm_data[0], 2.0)
assert np.isclose(clm_data[1], 3.0)
assert pd.isna(clm_data[2])
#
# Test topology
#
topology = Topology(ctx)
topology.translate() # All data will be reset
layers = topology.elem_layers
assert len(layers) == 2
assert set([x.id for x in layers[0]]) == {"My table"}
assert set([x.id for x in layers[1]]) == {"My column"}
ctx.run()
clm_data = tbl.get_series('My column')
assert np.isclose(clm_data[0], 2.0)
assert np.isclose(clm_data[1], 3.0)
assert pd.isna(clm_data[2])
