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Issue 1007: remove some recent additions to Definition L (#1008)

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Lucas Merrill Brown 2021-12-08 10:26:52 -05:00 • committed by GitHub
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1 changed files with 0 additions and 75 deletions
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75
data/data-pipeline/data_pipeline/score/score_l.py
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@ -177,8 +177,6 @@ class ScoreL(Score):
field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD,
field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD,
field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD,
field_names.EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD,
field_names.IMPENETRABLE_SURFACES_LOW_INCOME_FIELD,
]
expected_population_loss_threshold = (
@ -205,28 +203,6 @@ class ScoreL(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
extreme_heat_median_home_value_threshold = (
self.df[
field_names.EXTREME_HEAT_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
) & (
self.df[
field_names.MEDIAN_HOUSE_VALUE_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
<= self.MEDIAN_HOUSE_VALUE_THRESHOLD
)
impenetrable_surfaces_threshold = (
self.df[
field_names.IMPENETRABLE_SURFACES_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
self.df[field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD] = (
expected_population_loss_threshold
& self.df[field_names.FPL_200_SERIES]
@ -242,18 +218,6 @@ class ScoreL(Score):
& self.df[field_names.FPL_200_SERIES]
)
self.df[
field_names.EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD
] = (
extreme_heat_median_home_value_threshold
& self.df[field_names.FPL_200_SERIES]
)
self.df[field_names.IMPENETRABLE_SURFACES_LOW_INCOME_FIELD] = (
impenetrable_surfaces_threshold
& self.df[field_names.FPL_200_SERIES]
)
self._increment_total_eligibility_exceeded(climate_eligibility_columns)
return self.df[climate_eligibility_columns].any(axis="columns")
@ -407,8 +371,6 @@ class ScoreL(Score):
field_names.RMP_LOW_INCOME_FIELD,
field_names.SUPERFUND_LOW_INCOME_FIELD,
field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD,
field_names.AIR_TOXICS_CANCER_RISK_LOW_INCOME_FIELD,
field_names.RESPIRATORY_HAZARD_LOW_INCOME_FIELD,
]
rmp_sites_threshold = (
@ -428,22 +390,6 @@ class ScoreL(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
air_toxics_cancer_risk_threshold = (
self.df[
field_names.AIR_TOXICS_CANCER_RISK_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
respiratory_hazard_risk_threshold = (
self.df[
field_names.RESPIRATORY_HAZARD_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
# individual series-by-series
self.df[field_names.RMP_LOW_INCOME_FIELD] = (
rmp_sites_threshold & self.df[field_names.FPL_200_SERIES]
@ -454,14 +400,6 @@ class ScoreL(Score):
self.df[field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD] = (
tsdf_sites_threshold & self.df[field_names.FPL_200_SERIES]
)
self.df[field_names.AIR_TOXICS_CANCER_RISK_LOW_INCOME_FIELD] = (
air_toxics_cancer_risk_threshold
& self.df[field_names.FPL_200_SERIES]
)
self.df[field_names.RESPIRATORY_HAZARD_LOW_INCOME_FIELD] = (
respiratory_hazard_risk_threshold
& self.df[field_names.FPL_200_SERIES]
)
self._increment_total_eligibility_exceeded(
pollution_eligibility_columns
@ -593,7 +531,6 @@ class ScoreL(Score):
field_names.POVERTY_LOW_HS_EDUCATION_FIELD,
field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD,
field_names.MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD,
field_names.LOW_READING_LOW_HS_EDUCATION_FIELD,
]
high_scool_achievement_rate_threshold = (
@ -635,14 +572,6 @@ class ScoreL(Score):
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
low_reading_threshold = (
self.df[
field_names.LOW_READING_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
>= self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
self.df[field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD] = (
linguistic_isolation_threshold
& high_scool_achievement_rate_threshold
@ -660,10 +589,6 @@ class ScoreL(Score):
unemployment_threshold & high_scool_achievement_rate_threshold
)
self.df[field_names.LOW_READING_LOW_HS_EDUCATION_FIELD] = (
low_reading_threshold & high_scool_achievement_rate_threshold
)
workforce_combined_criteria_for_states = self.df[
workforce_eligibility_columns
].any(axis="columns")