def generate_area_summary(
latest_values: dict,
model_output: Optional[CANPyseirLocationOutput],
) -> CovidActNowAreaSummary:
fips = latest_values[CommonFields.FIPS]
state = latest_values[CommonFields.STATE]
state_intervention = get_can_projection.get_intervention_for_state(state)
actuals = _generate_actuals(latest_values, state_intervention)
projections = None
if model_output:
projections = _generate_api_for_projections(model_output)
return CovidActNowAreaSummary(
population=latest_values[CommonFields.POPULATION],
stateName=us_state_abbrev.ABBREV_US_STATE[state],
countyName=latest_values.get(CommonFields.COUNTY),
fips=fips,
lat=latest_values.get(CommonFields.LATITUDE),
long=latest_values.get(CommonFields.LONGITUDE),
actuals=actuals,
# TODO(chris): change this to reflect latest time data updated?
lastUpdatedDate=datetime.utcnow(),
projections=projections,
)
def build_timeseries_for_fips(
intervention,
us_latest,
us_timeseries,
model_output_dir,
fips,
) -> Optional[CovidActNowAreaTimeseries]:
fips_latest = us_latest.get_record_for_fips(fips)
if intervention is Intervention.SELECTED_INTERVENTION:
state = fips_latest[CommonFields.STATE]
intervention = get_can_projection.get_intervention_for_state(state)
model_output = CANPyseirLocationOutput.load_from_model_output_if_exists(
fips, intervention, model_output_dir)
if not model_output and intervention is not Intervention.OBSERVED_INTERVENTION:
# All model output is currently tied to a specific intervention. However,
# we want to generate results for areas that don't have a fit result, but we're not
# duplicating non-model outputs.
return None
try:
area_summary = api.generate_area_summary(fips_latest, model_output)
fips_timeseries = us_timeseries.get_subset(None, fips=fips)
area_timeseries = api.generate_area_timeseries(area_summary,
fips_timeseries,
model_output)
except Exception:
logger.error(f"failed to run output", fips=fips)
return None
return area_timeseries
def generate_api_for_state_projection_row(projection_row) -> CovidActNowStateSummary:
state_abbrev = US_STATE_ABBREV[projection_row[rc.STATE_FULL_NAME]]
projections = _generate_api_for_projections(projection_row)
state_intervention = get_can_projection.get_intervention_for_state(state_abbrev)
state_actuals = combined_datasets.build_us_latest_with_all_fields().get_record_for_state(state_abbrev)
state_result = CovidActNowStateSummary(
population=state_actuals[CommonFields.POPULATION],
lat=projection_row[rc.LATITUDE],
long=projection_row[rc.LONGITUDE],
actuals=_generate_actuals(state_actuals, state_intervention),
stateName=projection_row[rc.STATE_FULL_NAME],
fips=projection_row[rc.FIPS],
lastUpdatedDate=_format_date(projection_row[rc.LAST_UPDATED]),
projections=projections,
)
return state_result
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
),
)
def build_timeseries_for_region(
regional_input: RegionalInput, ) -> Optional[RegionSummaryWithTimeseries]:
intervention = regional_input.intervention
if intervention is Intervention.SELECTED_INTERVENTION:
intervention = get_can_projection.get_intervention_for_state(
regional_input.state)
try:
metrics_timeseries, metrics_latest = generate_metrics_and_latest(
regional_input.timeseries,
regional_input.rt_data,
regional_input.icu_data,
)
region_summary = api.generate_region_summary(regional_input.latest,
metrics_latest, None)
region_timeseries = api.generate_region_timeseries(
region_summary, regional_input.timeseries, metrics_timeseries,
None)
except Exception:
logger.exception(f"Failed to build timeseries for fips.")
return None
return region_timeseries
