def test_returns_df(self):
"""
MostFrequentLocations().get_dataframe() returns a dataframe.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-02", level="admin3")
df = mfl.get_dataframe()
self.assertIs(type(df), pd.DataFrame)
def test_joined_aggregate(self):
"""
Test join aggregate.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-04", level="admin3")
joined = mfl.join_aggregate(
RadiusOfGyration("2016-01-01", "2016-01-04"))
self.assertAlmostEqual(
joined.get_dataframe().set_index("name").ix["Rasuwa"].rog,
199.956021886114)
def test_equivalent_to_locate_subscribers(self):
"""
daily_location() is equivalent to the MostFrequentLocation().
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-02")
mfl_df = mfl.get_dataframe()
dl = daily_location("2016-01-01", method="most-common")
dl_df = dl.get_dataframe()
self.assertTrue((dl_df == mfl_df).all().all())
def test_joined_agg_hours_mismatch(self):
"""
Test that join aggregate with mismatched hours doesn't warn.
"""
mfl = MostFrequentLocation("2016-01-01 10:00",
"2016-01-04",
level="admin3")
with warnings.catch_warnings(record=True) as w:
joined = mfl.join_aggregate(
RadiusOfGyration("2016-01-01", "2016-01-04"))
self.assertFalse(w)
def test_joined_agg_date_mismatch(self):
"""
Test that join aggregate with mismatched dates raises a warning.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-04", level="admin3")
with self.assertWarns(UserWarning):
joined = mfl.join_aggregate(
RadiusOfGyration("2016-01-02", "2016-01-04"))
with self.assertWarns(UserWarning):
joined = mfl.join_aggregate(
RadiusOfGyration("2016-01-01", "2016-01-05"))
def test_joined_median_aggregate(self):
"""
Test join with median aggregate.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-04", level="admin3")
rog = RadiusOfGyration("2016-01-01", "2016-01-04")
joined = mfl.join_aggregate(rog, method="median")
rawus_avg = (rog.get_dataframe().set_index("subscriber").join(
mfl.get_dataframe().set_index("subscriber")).set_index(
"name").ix["Rasuwa"].rog.median())
self.assertAlmostEqual(
joined.get_dataframe().set_index("name").ix["Rasuwa"].rog,
rawus_avg)
def test_joined_modal_aggregate(self):
"""
Test join with modal aggregate.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-04", level="admin3")
rog = SubscriberDegree("2016-01-01", "2016-01-04")
joined = mfl.join_aggregate(rog, method="mode")
rawus_mode = (rog.get_dataframe().set_index("subscriber").join(
mfl.get_dataframe().set_index("subscriber")).set_index(
"name").ix["Rasuwa"].degree.mode()[0])
self.assertAlmostEqual(
joined.get_dataframe().set_index("name").ix["Rasuwa"].degree,
rawus_mode)
def test_lat_lons(self):
"""
MostFrequentLocations() has the correct values at the lat-lon level.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-02", level="lat-lon")
df = mfl.get_dataframe()
df.set_index("subscriber", inplace=True)
self.assertAlmostEqual(float(df.ix["1QBlwRo4Kd5v3Ogz"].lat),
28.941925079951545)
self.assertAlmostEqual(float(df.ix["1QBlwRo4Kd5v3Ogz"].lon),
82.61895799084449)
def test_vsites(self):
"""
MostFrequentLocation() returns the correct locations.
"""
mfl = MostFrequentLocation("2016-01-01",
"2016-01-02",
level="versioned-site")
df = mfl.get_dataframe()
df.set_index("subscriber", inplace=True)
self.assertEqual(df.ix["yqQ6m96rp09dn510"].site_id, "wzrXjw")
self.assertEqual(df.ix["zvaOknzKbEVD2eME"].site_id, "qvkp6J")
def test_equivalent_to_locate_subscribers_with_time(self):
"""
daily_location() is equivalent to the MostFrequentLocation() with timestamps.
"""
mfl = MostFrequentLocation("2016-01-01 18:00:00",
"2016-01-02 06:00:00")
mfl_df = mfl.get_dataframe()
dl = daily_location("2016-01-01 18:00:00",
stop="2016-01-02 06:00:00",
method="most-common")
dl_df = dl.get_dataframe()
self.assertTrue((dl_df == mfl_df).all().all())
def _unsampled_query_obj(self):
"""
Return the underlying flowmachine daily_location object.
Returns
-------
Query
"""
return MostFrequentLocation(
start=self.start,
stop=self.stop,
hours=self.hours,
spatial_unit=self.aggregation_unit,
table=self.event_types,
subscriber_subset=self.subscriber_subset,
)