def generate_state_timeseries(
projection_row, intervention, input_dir
) -> CovidActNowStateTimeseries:
state = US_STATE_ABBREV[projection_row[rc.STATE_FULL_NAME]]
fips = projection_row[rc.FIPS]
raw_dataseries = get_can_projection.get_can_raw_data(
input_dir, state, fips, AggregationLevel.STATE, intervention
)
# join in state testing data onto the timeseries
# left join '%m/%d/%y', so the left join gracefully handles
# missing state testing data (i.e. NE)
testing_df = get_testing_timeseries_by_state(state)
new_df = pd.DataFrame(raw_dataseries).merge(testing_df, on="date", how="left")
can_dataseries = new_df.to_dict(orient="records")
timeseries = []
for data_series in can_dataseries:
timeseries.append(_generate_state_timeseries_row(data_series))
projections = _generate_api_for_projections(projection_row)
if len(timeseries) < 1:
raise Exception(f"State time series empty for {intervention.name}")
state_intervention = get_can_projection.get_intervention_for_state(state)
actuals_ts = combined_datasets.build_us_timeseries_with_all_fields()
actual_latest = combined_datasets.build_us_latest_with_all_fields()
state_latest = actual_latest.get_record_for_state(state)
return CovidActNowStateTimeseries(
population=state_latest[CommonFields.POPULATION],
lat=projection_row[rc.LATITUDE],
long=projection_row[rc.LONGITUDE],
actuals=_generate_actuals(
state_latest, state_intervention
),
stateName=projection_row[rc.STATE_FULL_NAME],
fips=projection_row[rc.FIPS],
lastUpdatedDate=_format_date(projection_row[rc.LAST_UPDATED]),
projections=projections,
timeseries=timeseries,
actuals_timeseries=_generate_actuals_timeseries(
actuals_ts.get_records_for_state(state), state_intervention
),
)
def generate_county_timeseries(projection_row, intervention, input_dir):
state_abbrev = US_STATE_ABBREV[projection_row[rc.STATE_FULL_NAME]]
fips = projection_row[rc.FIPS]
raw_dataseries = get_can_projection.get_can_raw_data(
input_dir, state_abbrev, fips, AggregationLevel.COUNTY, intervention
)
testing_df = get_testing_timeseries_by_fips(fips)
new_df = pd.DataFrame(raw_dataseries).merge(testing_df, on="date", how="left")
can_dataseries = new_df.to_dict(orient="records")
timeseries = []
for data_series in can_dataseries:
timeseries.append(_generate_county_timeseries_row(data_series))
if len(timeseries) < 1:
raise Exception(f"County time series empty for {intervention.name}")
projections = _generate_api_for_projections(projection_row)
state_intervention = get_can_projection.get_intervention_for_state(state_abbrev)
actuals_ts = combined_datasets.build_us_timeseries_with_all_fields()
actual_latest = combined_datasets.build_us_latest_with_all_fields()
fips_latest = actual_latest.get_record_for_fips(fips)
return CovidActNowCountyTimeseries(
population=fips_latest[CommonFields.POPULATION],
lat=projection_row[rc.LATITUDE],
long=projection_row[rc.LONGITUDE],
actuals=_generate_actuals(
fips_latest, state_intervention
),
stateName=projection_row[rc.STATE_FULL_NAME],
countyName=projection_row[rc.COUNTY],
fips=projection_row[rc.FIPS],
lastUpdatedDate=_format_date(projection_row[rc.LAST_UPDATED]),
projections=projections,
timeseries=timeseries,
actuals_timeseries=_generate_actuals_timeseries(
actuals_ts.get_records_for_fips(fips), state_intervention
),
)