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
94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
import pandas as pd
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from data_pipeline.config import settings
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from data_pipeline.etl.base import ExtractTransformLoad
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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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from data_pipeline.utils import get_module_logger
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logger = get_module_logger(__name__)
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class CalEnviroScreenETL(ExtractTransformLoad):
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"""California environmental screen
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TODO: Need good description
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"""
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def __init__(self):
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# fetch
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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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# input
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self.calenviroscreen_source = (
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self.get_sources_path() / "CalEnviroScreen_4.0_2021.csv"
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)
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# output
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self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "calenviroscreen4"
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# Defining 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 get_data_sources(self) -> [DataSource]:
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return [
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ZIPDataSource(
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source=self.calenviroscreen_ftp_url,
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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.calenviroscreen_source, dtype={"Census Tract": "string"}
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)
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def transform(self) -> None:
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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.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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# write nationwide csv
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self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
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self.df.to_csv(self.OUTPUT_PATH / "data06.csv", index=False)
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