mirror of
https://github.com/DOI-DO/j40-cejst-2.git
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6f39033dde
* Add a rough prototype allowing a developer to pre-download data sources for all ETLs * Update code to be more production-ish * Move fetch to Extract part of ETL * Create a downloader to house all downloading operations * Remove unnecessary "name" in data source * Format source files with black * Fix issues from pylint and get the tests working with the new folder structure * Clean up files with black * Fix unzip test * Add caching notes to README * Fix tests (linting and case sensitivity bug) * Address PR comments and add API keys for census where missing * Merging comparator changes from main into this branch for the sake of the PR * Add note on using cache (-u) during pipeline
99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
import pandas as pd
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from data_pipeline.etl.base import ExtractTransformLoad
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from data_pipeline.etl.base import ValidGeoLevel
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from data_pipeline.score import field_names
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from data_pipeline.utils import get_module_logger
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from data_pipeline.etl.datasource import DataSource
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from data_pipeline.etl.datasource import ZIPDataSource
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logger = get_module_logger(__name__)
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class EJSCREENETL(ExtractTransformLoad):
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"""Load updated EJSCREEN data."""
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NAME = "ejscreen"
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GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
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INPUT_GEOID_TRACT_FIELD_NAME: str = "ID"
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def __init__(self):
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# fetch
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self.ejscreen_url = "https://gaftp.epa.gov/EJSCREEN/2021/EJSCREEN_2021_USPR_Tracts.csv.zip"
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# input
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self.ejscreen_source = (
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self.get_sources_path() / "EJSCREEN_2021_USPR_Tracts.csv"
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)
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# output
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self.CSV_PATH = self.DATA_PATH / "dataset" / "ejscreen"
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self.df: pd.DataFrame
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self.COLUMNS_TO_KEEP = [
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self.GEOID_TRACT_FIELD_NAME,
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# pylint: disable=duplicate-code
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field_names.AIR_TOXICS_CANCER_RISK_FIELD,
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field_names.RESPIRATORY_HAZARD_FIELD,
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field_names.DIESEL_FIELD,
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field_names.PM25_FIELD,
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field_names.OZONE_FIELD,
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field_names.TRAFFIC_FIELD,
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field_names.RMP_FIELD,
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field_names.TSDF_FIELD,
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field_names.NPL_FIELD,
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field_names.WASTEWATER_FIELD,
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field_names.HOUSEHOLDS_LINGUISTIC_ISO_FIELD,
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field_names.POVERTY_FIELD,
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field_names.OVER_64_FIELD,
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field_names.UNDER_5_FIELD,
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field_names.LEAD_PAINT_FIELD,
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field_names.UST_FIELD,
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]
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def get_data_sources(self) -> [DataSource]:
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return [
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ZIPDataSource(
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source=self.ejscreen_url, destination=self.get_sources_path()
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)
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]
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def extract(self, use_cached_data_sources: bool = False) -> None:
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super().extract(
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use_cached_data_sources
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) # download and extract data sources
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self.df = pd.read_csv(
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self.ejscreen_source,
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dtype={self.INPUT_GEOID_TRACT_FIELD_NAME: str},
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# EJSCREEN writes the word "None" for NA data.
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na_values=["None"],
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low_memory=False,
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)
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def transform(self) -> None:
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# rename ID to Tract ID
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self.output_df = self.df.rename(
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columns={
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self.INPUT_GEOID_TRACT_FIELD_NAME: self.GEOID_TRACT_FIELD_NAME,
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"CANCER": field_names.AIR_TOXICS_CANCER_RISK_FIELD,
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"RESP": field_names.RESPIRATORY_HAZARD_FIELD,
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"DSLPM": field_names.DIESEL_FIELD,
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"PM25": field_names.PM25_FIELD,
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"OZONE": field_names.OZONE_FIELD,
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"PTRAF": field_names.TRAFFIC_FIELD,
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"PRMP": field_names.RMP_FIELD,
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"PTSDF": field_names.TSDF_FIELD,
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"PNPL": field_names.NPL_FIELD,
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"PWDIS": field_names.WASTEWATER_FIELD,
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"LINGISOPCT": field_names.HOUSEHOLDS_LINGUISTIC_ISO_FIELD,
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"LOWINCPCT": field_names.POVERTY_FIELD,
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"OVER64PCT": field_names.OVER_64_FIELD,
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"UNDER5PCT": field_names.UNDER_5_FIELD,
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"PRE1960PCT": field_names.LEAD_PAINT_FIELD,
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"UST": field_names.UST_FIELD, # added for 2021 update
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},
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)
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