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Add tests for all non-census sources (#1899)

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* Refactor CDC life-expectancy (1554)

* Update to new tract list (#1554)

* Adjust for tests (#1848)

* Add tests for cdc_places (#1848)

* Add EJScreen tests (#1848)

* Add tests for HUD housing (#1848)

* Add tests for GeoCorr (#1848)

* Add persistent poverty tests (#1848)

* Update for sources without zips, for new validation (#1848)

* Update tests for new multi-CSV but (#1848)

Lucas updated the CDC life expectancy data to handle a bug where two
states are missing from the US Overall download. Since virtually none of
our other ETL classes download multiple CSVs directly like this, it
required a pretty invasive new mocking strategy.

* Add basic tests for nature deprived (#1848)

* Add wildfire tests (#1848)

* Add flood risk tests (#1848)

* Add DOT travel tests (#1848)

* Add historic redlining tests (#1848)

* Add tests for ME and WI (#1848)

* Update now that validation exists (#1848)

* Adjust for validation (#1848)

* Add health insurance back to cdc places (#1848)

Ooops

* Update tests with new field (#1848)

* Test for blank tract removal (#1848)

* Add tracts for clipping behavior

* Test clipping and zfill behavior (#1848)

* Fix bad test assumption (#1848)

* Simplify class, add test for tract padding (#1848)

* Fix percentage inversion, update tests (#1848)

Looking through the transformations, I noticed that we were subtracting
a percentage that is usually between 0-100 from 1 instead of 100, and so
were endind up with some surprising results. Confirmed with lucasmbrown-usds

* Add note about first street data (#1848)
This commit is contained in:
Matt Bowen 2022-09-19 15:17:00 -04:00 • committed by GitHub
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88 changed files with 2032 additions and 178 deletions
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20
data/data-pipeline/data_pipeline/etl/sources/persistent_poverty/etl.py
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@ -2,7 +2,7 @@ import functools
import pandas as pd
from data_pipeline.config import settings
from data_pipeline.etl.base import ExtractTransformLoad
from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
from data_pipeline.utils import (
get_module_logger,
unzip_file_from_url,
@ -19,6 +19,10 @@ class PersistentPovertyETL(ExtractTransformLoad):
Codebook: `https://s4.ad.brown.edu/Projects/Diversity/Researcher/LTBDDload/Dfiles/codebooks.pdf`.
"""
NAME = "persistent_poverty"
GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
PUERTO_RICO_EXPECTED_IN_DATA = False
def __init__(self):
self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "persistent_poverty"
@ -75,7 +79,7 @@ class PersistentPovertyETL(ExtractTransformLoad):
def extract(self) -> None:
logger.info("Starting to download 86MB persistent poverty file.")
unzipped_file_path = self.get_tmp_path() / "persistent_poverty"
unzipped_file_path = self.get_tmp_path()
unzip_file_from_url(
file_url=settings.AWS_JUSTICE40_DATASOURCES_URL
@ -155,14 +159,4 @@ class PersistentPovertyETL(ExtractTransformLoad):
)
)
self.df = transformed_df
def load(self) -> None:
logger.info("Saving persistent poverty data.")
# mkdir census
self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
self.df[self.COLUMNS_TO_KEEP].to_csv(
path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
)
self.output_df = transformed_df