mirror of
https://github.com/DOI-DO/j40-cejst-2.git
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a0d6e55f0a
* WIP on parallelizing * switching to get_tmp_path for nri * switching to get_tmp_path everywhere necessary * fixing linter errors * moving heavy ETLs to front of line * add hold * moving cdc places up * removing unnecessary print * moving h&t up * adding parallel to geo post * better census labels * switching to concurrent futures * fixing output
77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
import pandas as pd
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from data_pipeline.etl.base import ExtractTransformLoad
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from data_pipeline.utils import get_module_logger
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from data_pipeline.config import settings
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logger = get_module_logger(__name__)
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class CalEnviroScreenETL(ExtractTransformLoad):
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def __init__(self):
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self.CALENVIROSCREEN_FTP_URL = (
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settings.AWS_JUSTICE40_DATASOURCES_URL
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+ "/CalEnviroScreen_4.0_2021.zip"
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)
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self.CALENVIROSCREEN_CSV = (
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self.get_tmp_path() / "CalEnviroScreen_4.0_2021.csv"
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)
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self.CSV_PATH = self.DATA_PATH / "dataset" / "calenviroscreen4"
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# Definining some variable names
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self.CALENVIROSCREEN_SCORE_FIELD_NAME = "calenviroscreen_score"
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self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME = (
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"calenviroscreen_percentile"
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)
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self.CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME = (
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"calenviroscreen_priority_community"
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)
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# Choosing constants
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# None of these numbers are final, but just for the purposes of comparison.
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self.CALENVIROSCREEN_PRIORITY_COMMUNITY_THRESHOLD = 75
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self.df: pd.DataFrame
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def extract(self) -> None:
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logger.info("Downloading CalEnviroScreen Data")
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super().extract(
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self.CALENVIROSCREEN_FTP_URL,
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self.get_tmp_path(),
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)
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def transform(self) -> None:
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logger.info("Transforming CalEnviroScreen Data")
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# Data from https://calenviroscreen-oehha.hub.arcgis.com/#Data, specifically:
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# https://oehha.ca.gov/media/downloads/calenviroscreen/document/calenviroscreen40resultsdatadictionaryd12021.zip
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# Load comparison index (CalEnviroScreen 4)
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self.df = pd.read_csv(
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self.CALENVIROSCREEN_CSV, dtype={"Census Tract": "string"}
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)
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self.df.rename(
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columns={
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"Census Tract": self.GEOID_TRACT_FIELD_NAME,
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"DRAFT CES 4.0 Score": self.CALENVIROSCREEN_SCORE_FIELD_NAME,
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"DRAFT CES 4.0 Percentile": self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME,
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},
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inplace=True,
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)
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# Add a leading "0" to the Census Tract to match our format in other data frames.
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self.df[self.GEOID_TRACT_FIELD_NAME] = (
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"0" + self.df[self.GEOID_TRACT_FIELD_NAME]
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)
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# Calculate the top K% of prioritized communities
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self.df[self.CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME] = (
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self.df[self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME]
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>= self.CALENVIROSCREEN_PRIORITY_COMMUNITY_THRESHOLD
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)
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def load(self) -> None:
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logger.info("Saving CalEnviroScreen CSV")
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# write nationwide csv
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self.CSV_PATH.mkdir(parents=True, exist_ok=True)
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self.df.to_csv(self.CSV_PATH / "data06.csv", index=False)
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