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
89 lines
3.1 KiB
Python
89 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 FileDataSource
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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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logger = get_module_logger(__name__)
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class MichiganEnviroScreenETL(ExtractTransformLoad):
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"""Michigan EJ Screen class that ingests dataset represented
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here: https://www.arcgis.com/apps/webappviewer/index.html?id=dc4f0647dda34959963488d3f519fd24
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This class ingests the data presented in "Assessing the State of Environmental
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Justice in Michigan." Please see the README in this module for further details.
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"""
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def __init__(self):
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# fetch
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self.michigan_ejscreen_url = (
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settings.AWS_JUSTICE40_DATASOURCES_URL
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+ "/michigan_ejscore_12212021.csv"
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)
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# input
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self.michigan_ejscreen_source = (
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self.get_sources_path() / "michigan_ejscore_12212021.csv"
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)
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# output
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self.CSV_PATH = self.DATA_PATH / "dataset" / "michigan_ejscreen"
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self.MICHIGAN_EJSCREEN_PRIORITY_COMMUNITY_THRESHOLD: float = 0.75
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self.COLUMNS_TO_KEEP = [
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self.GEOID_TRACT_FIELD_NAME,
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field_names.MICHIGAN_EJSCREEN_SCORE_FIELD,
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field_names.MICHIGAN_EJSCREEN_PERCENTILE_FIELD,
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field_names.MICHIGAN_EJSCREEN_PRIORITY_COMMUNITY_FIELD,
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]
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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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FileDataSource(
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source=self.michigan_ejscreen_url,
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destination=self.michigan_ejscreen_source,
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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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filepath_or_buffer=self.michigan_ejscreen_source,
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dtype={"GEO_ID": "string"},
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low_memory=False,
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)
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def transform(self) -> None:
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self.df.rename(
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columns={
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"GEO_ID": self.GEOID_TRACT_FIELD_NAME,
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"EJ_Score_Cal_Min": field_names.MICHIGAN_EJSCREEN_SCORE_FIELD,
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"Pct_CalMin": field_names.MICHIGAN_EJSCREEN_PERCENTILE_FIELD,
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},
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inplace=True,
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)
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# Calculate the top quartile of prioritized communities
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# Please see pg. 104 - 109 from source:
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# pg. https://deepblue.lib.umich.edu/bitstream/handle/2027.42/149105/AssessingtheStateofEnvironmentalJusticeinMichigan_344.pdf
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self.df[field_names.MICHIGAN_EJSCREEN_PRIORITY_COMMUNITY_FIELD] = (
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self.df[field_names.MICHIGAN_EJSCREEN_PERCENTILE_FIELD]
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>= self.MICHIGAN_EJSCREEN_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.CSV_PATH.mkdir(parents=True, exist_ok=True)
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self.df[self.COLUMNS_TO_KEEP].to_csv(
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self.CSV_PATH / "michigan_ejscreen.csv", index=False
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)
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