def test_median_returns_correct_values(get_dataframe):
"""
features.network.TotalNetworkObjects median aggregate returns correct values.
"""
instance = network.TotalNetworkObjects(
table="calls", period="hour", network_object="versioned-site"
).aggregate(by="day", statistic="median")
#
# This will compare the very first
# value with an independently
# computed value.
#
assert get_dataframe(instance).head(1)["median"][0] == 25
def test_count_returns_correct_values(get_dataframe):
"""
TotalNetworkObjects returns correct values.
"""
instance = network.TotalNetworkObjects(
start="2016-01-01", stop="2016-12-30", table="calls", period="hour"
)
df = get_dataframe(instance)
#
# This will compare the very first
# value with an independently
# computed value.
#
assert df.total[34] == 31
def test_mean_returns_correct_values(get_dataframe):
"""
features.network.TotalNetworkObjects aggregation returns correct values.
"""
instance = network.TotalNetworkObjects(
start="2016-01-01",
stop="2016-12-30",
period="hour",
network_object="versioned-site",
).aggregate(by="day")
#
# This will compare the very first
# value with an independently
# computed value.
#
assert get_dataframe(instance).head(1)["avg"][0] == pytest.approx(28.7916666666)
def test_bad_statistic():
"""Test that invalid stat for aggregate raises value error."""
with pytest.raises(ValueError):
network.TotalNetworkObjects(
start="2016-01-01", stop="2016-12-30", table="calls"
).aggregate(statistic="count")
def test_bad_params(bad_arg, bad_val):
"""Test value errors are raised for bad params"""
with pytest.raises(ValueError):
network.TotalNetworkObjects(
start="2016-01-01", stop="2016-12-30", table="calls", **{bad_arg: bad_val}
)
def test_period_agg_default(period, expected):
"""Correct aggregation period is deduced."""
inst = network.TotalNetworkObjects(
start="2016-01-01", stop="2016-12-30", period=period
).aggregate()
assert inst.by == expected