def test_detailed_land_surface_loading_results(
land_surface_loading_response_dicts, contexts, key, n_rows, n_nodes):
land_surfaces = land_surface_loading_response_dicts[(n_rows, n_nodes)]
land_surfaces_list = land_surfaces["land_surfaces"]
df = pandas.DataFrame(land_surfaces_list)
context = contexts[key]
df["imp_pct"] = 100 * df["imp_area_acres"] / df["area_acres"]
df, msg = parse_configuration_logic(
df=df,
config_section="api_recognize",
config_object="land_surfaces",
context=context,
)
land_surfaces_df = clean_land_surface_dataframe(df)
wet_weather_parameters = init_wq_parameters("land_surface_emc_table",
context)
dry_weather_parameters = init_wq_parameters(
"dry_weather_land_surface_emc_table", context)
seasons = (context.get("project_reference_data",
{}).get("dry_weather_flow_table",
{}).get("seasons", {}))
t = detailed_loading_results(
land_surfaces_df,
wet_weather_parameters,
dry_weather_parameters,
seasons,
)
assert t["area_acres"].sum() == land_surfaces_df["area_acres"].sum()
assert len(t) == len(land_surfaces_list)
if not "no_joins" in key and not "no_params" in key:
assert any(["conc" in c for c in t.columns])
assert any(["load" in c for c in t.columns])
t = detailed_volume_loading_results(land_surfaces_df)
assert t["area_acres"].sum() == land_surfaces_df["area_acres"].sum()
assert len(t) == len(land_surfaces_list)
assert t["runoff_volume_cuft"].sum() > 0
t = detailed_pollutant_loading_results(
land_surfaces_df,
wet_weather_parameters,
dry_weather_parameters,
seasons.keys(),
)
assert t["area_acres"].sum() == land_surfaces_df["area_acres"].sum()
assert len(t) == len(land_surfaces_list)
if not "no_joins" in key and not "no_params" in key:
assert any(["conc" in c for c in t.columns])
assert any(["load" in c for c in t.columns])
def land_surface_loading(
land_surfaces: Dict[str, Any], details: bool, context: Dict[str, Any]
) -> Dict[str, List]:
"""computes loading for volume runoff and pollutants for each land
surface 'sliver' and aggregates values for each node. Returning results
for the slivers is toggled by the `details` kwarg. if 'true' the resonse
includes a 'details' key with the sliver loading. the 'summary' values
aggregate the load to each node_id, and are always returned.
"""
df = pandas.DataFrame(land_surfaces["land_surfaces"])
df["imp_pct"] = 100 * df["imp_area_acres"] / df["area_acres"]
response: Dict[str, Any] = {}
response["errors"] = []
df, messages = parse_configuration_logic(
df=df,
config_section="api_recognize",
config_object="land_surfaces",
context=context,
)
if len(messages) > 0:
response["errors"].extend(messages)
wet_weather_parameters = init_wq_parameters("land_surface_emc_table", context)
dry_weather_parameters = init_wq_parameters(
"dry_weather_land_surface_emc_table", context
)
seasons = (
context.get("project_reference_data", {})
.get("dry_weather_flow_table", {})
.get("seasons", {})
)
detailed_results = detailed_loading_results(
df, wet_weather_parameters, dry_weather_parameters, seasons,
)
summary_results = summary_loading_results(
detailed_results,
wet_weather_parameters,
dry_weather_parameters,
season_names=seasons.keys(),
)
response["summary"] = summary_results.fillna(0).to_dict(orient="records")
if details:
response["details"] = detailed_results.fillna(0).to_dict(orient="records")
return response
def test_build_treatment_facility_nodes_from_long_list(
contexts, valid_treatment_facilities, ctxt_key, has_met_data):
context = contexts[ctxt_key]
tmnt_facilities = pandas.DataFrame(valid_treatment_facilities)
df, messages = parse_configuration_logic(
df=tmnt_facilities,
config_section="api_recognize",
config_object="treatment_facility",
context=context,
)
nodes = build_treatment_facility_nodes(df)
for n in nodes:
if has_met_data:
assert n.get("rain_gauge") is not None
else:
assert n.get("rain_gauge") is None
def test_build_diversion_facility_months_operational(
contexts, valid_treatment_facilities, months_operational, is_zero):
context = contexts["default"]
tmnt_facilities = (pandas.DataFrame(valid_treatment_facilities).query(
"facility_type=='LowFlowFacility'").assign(
months_operational=months_operational))
df, messages = parse_configuration_logic(
df=tmnt_facilities,
config_section="api_recognize",
config_object="treatment_facility",
context=context,
)
nodes = build_treatment_facility_nodes(df)
for n in nodes:
if is_zero:
assert n.get("summer_dry_weather_retention_rate_cfs") == 0.0
else:
assert n.get("summer_dry_weather_retention_rate_cfs") > 0.0
def test_build_treatment_facility_nodes(contexts,
valid_treatment_facility_dicts,
ctxt_key, has_met_data, model,
checkfor):
context = contexts[ctxt_key]
tmnt_facilities = pandas.DataFrame([valid_treatment_facility_dicts[model]])
df, messages = parse_configuration_logic(
df=pandas.DataFrame(tmnt_facilities),
config_section="api_recognize",
config_object="treatment_facility",
context=context,
)
node = build_treatment_facility_nodes(df)[0]
check_val = node.get(checkfor)
assert isinstance(check_val, float)
if has_met_data:
assert node.get("rain_gauge") is not None
else:
assert node.get("rain_gauge") is None