def test_aggregate():
df_in = read_csv_and_index_fips_date(
"fips,state,aggregate_level,county,m1,date,foo\n"
"55005,ZZ,county,North County,1,2020-05-01,11\n"
"55005,ZZ,county,North County,2,2020-05-02,22\n"
"55005,ZZ,county,North County,3,2020-05-03,33\n"
"55005,ZZ,county,North County,0,2020-05-04,0\n"
"55006,ZZ,county,South County,0,2020-05-01,0\n"
"55006,ZZ,county,South County,0,2020-05-02,0\n"
"55006,ZZ,county,South County,3,2020-05-03,44\n"
"55006,ZZ,county,South County,4,2020-05-04,55\n"
"55,ZZ,state,Grand State,41,2020-05-01,66\n"
"55,ZZ,state,Grand State,43,2020-05-03,77\n"
).reset_index()
ts_in = MultiRegionDataset.from_fips_timeseries_df(df_in)
agg = statistical_areas.CountyToCBSAAggregator(
county_map={"55005": "10001", "55006": "10001"},
cbsa_title_map={"10001": "Stat Area 1"},
aggregations=[],
)
ts_out = agg.aggregate(ts_in)
assert ts_out.groupby_region().ngroups == 1
ts_cbsa = ts_out.get_one_region(Region.from_cbsa_code("10001"))
assert ts_cbsa.date_indexed["m1"].to_dict() == {
pd.to_datetime("2020-05-01"): 1,
pd.to_datetime("2020-05-02"): 2,
pd.to_datetime("2020-05-03"): 6,
pd.to_datetime("2020-05-04"): 4,
}
def test_aggregate():
ts = TimeseriesDataset.load_csv(
io.StringIO("fips,state,aggregate_level,county,m1,date,foo\n"
"55005,ZZ,county,North County,1,2020-05-01,ab\n"
"55005,ZZ,county,North County,2,2020-05-02,cd\n"
"55005,ZZ,county,North County,3,2020-05-03,ef\n"
"55006,ZZ,county,South County,3,2020-05-03,ef\n"
"55006,ZZ,county,South County,4,2020-05-04,gh\n"
"55,ZZ,state,Grand State,41,2020-05-01,ij\n"
"55,ZZ,state,Grand State,43,2020-05-03,kl\n"))
ts_in = MultiRegionTimeseriesDataset.from_timeseries_and_latest(
ts, ts.latest_values_object())
agg = statistical_areas.CountyToCBSAAggregator(
county_map={
"55005": "10001",
"55006": "10001"
},
cbsa_title_map={"10001": "Stat Area 1"})
ts_out = agg.aggregate(ts_in)
assert ts_out.groupby_region().ngroups == 1
ts_cbsa = ts_out.get_one_region(Region.from_cbsa_code("10001"))
assert ts_cbsa.date_indexed["m1"].to_dict() == {
pd.to_datetime("2020-05-01"): 1,
pd.to_datetime("2020-05-02"): 2,
pd.to_datetime("2020-05-03"): 6,
pd.to_datetime("2020-05-04"): 4,
}
def test_load_from_local_public_data():
agg = statistical_areas.CountyToCBSAAggregator.from_local_public_data()
assert agg.cbsa_title_map["43580"] == "Sioux City, IA-NE-SD"
assert agg.county_map["48187"] == "41700"
ts = TimeseriesDataset.load_csv(
io.StringIO("fips,state,aggregate_level,county,m1,date,foo\n"
"48059,ZZ,county,North County,3,2020-05-03,ef\n"
"48253,ZZ,county,South County,4,2020-05-03,ef\n"))
ts_in = MultiRegionTimeseriesDataset.from_timeseries_and_latest(
ts, ts.latest_values_object())
ts_out = agg.aggregate(ts_in)
ts_cbsa = ts_out.get_one_region(Region.from_cbsa_code("10180"))
assert ts_cbsa.date_indexed["m1"].to_dict() == {
pd.to_datetime("2020-05-03"): 7,
}
def test_load_from_local_public_data():
agg = statistical_areas.CountyToCBSAAggregator.from_local_public_data()
agg = dataclasses.replace(agg, aggregations=[]) # Disable scaled aggregations
assert agg.cbsa_title_map["43580"] == "Sioux City, IA-NE-SD"
assert agg.county_map["48187"] == "41700"
df_in = read_csv_and_index_fips_date(
"fips,state,aggregate_level,county,m1,date,foo\n"
"48059,ZZ,county,North County,3,2020-05-03,33\n"
"48253,ZZ,county,South County,4,2020-05-03,77\n"
"48441,ZZ,county,Other County,2,2020-05-03,41\n"
).reset_index()
ts_in = MultiRegionDataset.from_fips_timeseries_df(df_in)
ts_out = agg.aggregate(ts_in)
ts_cbsa = ts_out.get_one_region(Region.from_cbsa_code("10180"))
assert ts_cbsa.date_indexed["m1"].to_dict() == {
pd.to_datetime("2020-05-03"): 9,
}
