def test_joined_agg_hours_mismatch():
"""
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:
mfl.join_aggregate(RadiusOfGyration("2016-01-01", "2016-01-04"))
assert not w
def test_joined_aggregate(get_dataframe):
"""
Test join aggregate.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-04", level="admin3")
joined = mfl.join_aggregate(RadiusOfGyration("2016-01-01", "2016-01-04"))
assert (
pytest.approx(203.12391560786)
== get_dataframe(joined).set_index("pcod").loc["524 2 05 29"].rog
)
def test_joined_aggregate(get_dataframe):
"""
Test join aggregate.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-04", level="admin3")
joined = mfl.join_aggregate(RadiusOfGyration("2016-01-01", "2016-01-04"))
assert (
pytest.approx(199.956021886114)
== get_dataframe(joined).set_index("name").ix["Rasuwa"].rog
)
def test_most_frequent_locations_column_names(get_dataframe, exemplar_level_param):
"""
MostFrequentLocations().get_dataframe() returns a dataframe.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-02", **exemplar_level_param)
df = get_dataframe(mfl)
assert df.columns.tolist() == mfl.column_names
def test_joined_median_aggregate(get_dataframe):
"""
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 = (
get_dataframe(rog)
.set_index("subscriber")
.join(get_dataframe(mfl).set_index("subscriber"))
.set_index("pcod")
.loc["524 2 05 29"]
.rog.median()
)
assert (
pytest.approx(rawus_avg)
== get_dataframe(joined).set_index("pcod").loc["524 2 05 29"].rog
), rawus_avg
def test_joined_modal_aggregate(get_dataframe):
"""
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 = (
get_dataframe(rog)
.set_index("subscriber")
.join(get_dataframe(mfl).set_index("subscriber"))
.set_index("pcod")
.loc["524 2 05 29"]
.value.mode()[0]
)
assert (
pytest.approx(rawus_mode)
== get_dataframe(joined).set_index("pcod").loc["524 2 05 29"].value
)
def test_equivalent_to_locate_subscribers(get_dataframe):
"""
daily_location() is equivalent to the MostFrequentLocation().
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-02")
mfl_df = get_dataframe(mfl)
dl = daily_location("2016-01-01", method="most-common")
dl_df = get_dataframe(dl)
assert (dl_df == mfl_df).all().all()
def test_lat_lons(get_dataframe):
"""
MostFrequentLocations() has the correct values at the lat-lon level.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-02", level="lat-lon")
df = get_dataframe(mfl)
df.set_index("subscriber", inplace=True)
assert pytest.approx(28.941925079951545) == float(df.ix["1QBlwRo4Kd5v3Ogz"].lat)
assert pytest.approx(82.61895799084449) == float(df.ix["1QBlwRo4Kd5v3Ogz"].lon)
def test_vsites(get_dataframe):
"""
MostFrequentLocation() returns the correct locations.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-02", level="versioned-site")
df = get_dataframe(mfl)
df.set_index("subscriber", inplace=True)
assert "wzrXjw" == df.ix["yqQ6m96rp09dn510"].site_id
assert "qvkp6J" == df.ix["zvaOknzKbEVD2eME"].site_id
def test_equivalent_to_locate_subscribers_with_time(get_dataframe):
"""
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 = get_dataframe(mfl)
dl = daily_location("2016-01-01 18:00:00",
stop="2016-01-02 06:00:00",
method="most-common")
dl_df = get_dataframe(dl)
assert (dl_df == mfl_df).all().all()
def test_lon_lats(get_dataframe):
"""
MostFrequentLocations() has the correct values at the lon-lat spatial unit.
"""
mfl = MostFrequentLocation(
"2016-01-01", "2016-01-02", spatial_unit=make_spatial_unit("lon-lat")
)
df = get_dataframe(mfl)
df.set_index("subscriber", inplace=True)
assert pytest.approx(82.61895799084449) == float(df.loc["1QBlwRo4Kd5v3Ogz"].lon)
assert pytest.approx(28.941925079951545) == float(df.loc["1QBlwRo4Kd5v3Ogz"].lat)
def test_joined_agg_date_mismatch():
"""
Test that join aggregate with mismatched dates raises a warning.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-04", level="admin3")
with pytest.warns(UserWarning):
mfl.join_aggregate(RadiusOfGyration("2016-01-02", "2016-01-04"))
with pytest.warns(UserWarning):
mfl.join_aggregate(RadiusOfGyration("2016-01-01", "2016-01-05"))
