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Stripping thresholds from PR so the UI matches the count

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Add a tuple to skip FIPS 72 when incrementing counter. TODO: clean up so it's a constant.
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Emma Nechamkin 2022-02-17 16:54:33 -05:00 • committed by GitHub
parent 1b76a68838
commit cee13b50cc
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GPG key ID: 4AEE18F83AFDEB23
1 changed files with 35 additions and 12 deletions
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47
data/data-pipeline/data_pipeline/score/score_m.py
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@ -113,15 +113,29 @@ class ScoreM(Score):
) )
def _increment_total_eligibility_exceeded( def _increment_total_eligibility_exceeded(
self, columns_for_subset: list self, columns_for_subset: list, skip_fips: tuple = ()
) -> None: ) -> None:
""" """
Increments the total eligible factors for a given tract Increments the total eligible factors for a given tract
"""
self.df[field_names.THRESHOLD_COUNT] += self.df[columns_for_subset].sum( The new skip_fips argument specifies which (if any) fips codes to
axis=1, skipna=True skip over for incrementing.
) This allows us to essentially skip data we think is of limited veracity,
without overriding any values in the data.
THIS IS A TEMPORARY FIX.
"""
if skip_fips:
self.df[field_names.THRESHOLD_COUNT] += np.where(
self.df[field_names.GEOID_TRACT_FIELD].str.startswith(
skip_fips
),
0,
self.df[columns_for_subset].sum(axis=1, skipna=True),
)
else:
self.df[field_names.THRESHOLD_COUNT] += self.df[
columns_for_subset
].sum(axis=1, skipna=True)
def _climate_factor(self) -> bool: def _climate_factor(self) -> bool:
# In Xth percentile or above for FEMA’s Risk Index (Source: FEMA # In Xth percentile or above for FEMA’s Risk Index (Source: FEMA
@ -182,7 +196,9 @@ class ScoreM(Score):
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
) )
self._increment_total_eligibility_exceeded(climate_eligibility_columns) self._increment_total_eligibility_exceeded(
climate_eligibility_columns, skip_fips=("72")
)
return self.df[climate_eligibility_columns].any(axis="columns") return self.df[climate_eligibility_columns].any(axis="columns")
@ -224,7 +240,9 @@ class ScoreM(Score):
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
) )
self._increment_total_eligibility_exceeded(energy_eligibility_columns) self._increment_total_eligibility_exceeded(
energy_eligibility_columns, skip_fips=("72")
)
return self.df[energy_eligibility_columns].any(axis="columns") return self.df[energy_eligibility_columns].any(axis="columns")
@ -274,7 +292,7 @@ class ScoreM(Score):
) )
self._increment_total_eligibility_exceeded( self._increment_total_eligibility_exceeded(
transportion_eligibility_columns transportion_eligibility_columns, skip_fips=("72")
) )
return self.df[transportion_eligibility_columns].any(axis="columns") return self.df[transportion_eligibility_columns].any(axis="columns")
@ -334,7 +352,9 @@ class ScoreM(Score):
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
) )
self._increment_total_eligibility_exceeded(housing_eligibility_columns) self._increment_total_eligibility_exceeded(
housing_eligibility_columns, skip_fips=("72")
)
return self.df[housing_eligibility_columns].any(axis="columns") return self.df[housing_eligibility_columns].any(axis="columns")
@ -384,7 +404,7 @@ class ScoreM(Score):
) )
self._increment_total_eligibility_exceeded( self._increment_total_eligibility_exceeded(
pollution_eligibility_columns pollution_eligibility_columns, skip_fips=("72")
) )
return self.df[pollution_eligibility_columns].any(axis="columns") return self.df[pollution_eligibility_columns].any(axis="columns")
@ -413,7 +433,8 @@ class ScoreM(Score):
) )
self._increment_total_eligibility_exceeded( self._increment_total_eligibility_exceeded(
[field_names.WASTEWATER_DISCHARGE_LOW_INCOME_LOW_HIGHER_ED_FIELD] [field_names.WASTEWATER_DISCHARGE_LOW_INCOME_LOW_HIGHER_ED_FIELD],
skip_fips=("72"),
) )
return self.df[ return self.df[
@ -490,7 +511,9 @@ class ScoreM(Score):
& self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES] & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
) )
self._increment_total_eligibility_exceeded(health_eligibility_columns) self._increment_total_eligibility_exceeded(
health_eligibility_columns, skip_fips=("72")
)
return self.df[health_eligibility_columns].any(axis="columns") return self.df[health_eligibility_columns].any(axis="columns")