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https://github.com/DOI-DO/j40-cejst-2.git
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03a6d3c660
Update logging messages and message consistency This update includes changes to the level of many log messages. Rather than everything being logged at the info level, it differentiates between debug, info, warning, and error messages. It also changes the default log level to info to avoid much of the noise previously in the logs. It also removes many extra log messages, and adds additional decorators at the beginning of each pipeline run.
110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
from glob import glob
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import geopandas as gpd
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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.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 MarylandEJScreenETL(ExtractTransformLoad):
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"""Maryland EJSCREEN class that ingests dataset represented
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here: https://p1.cgis.umd.edu/mdejscreen/help.html
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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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self.MARYLAND_EJSCREEN_URL = (
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settings.AWS_JUSTICE40_DATASOURCES_URL + "/MD_EJScreen.zip"
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)
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self.SHAPE_FILES_PATH = self.get_tmp_path() / "mdejscreen"
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self.OUTPUT_CSV_PATH = self.DATA_PATH / "dataset" / "maryland_ejscreen"
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self.COLUMNS_TO_KEEP = [
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self.GEOID_TRACT_FIELD_NAME,
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field_names.MARYLAND_EJSCREEN_SCORE_FIELD,
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field_names.MARYLAND_EJSCREEN_BURDENED_THRESHOLD_FIELD,
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]
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self.df: pd.DataFrame
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def extract(self) -> None:
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logger.debug("Downloading 207MB Maryland EJSCREEN Data")
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super().extract(
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self.MARYLAND_EJSCREEN_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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list_of_files = list(glob(str(self.SHAPE_FILES_PATH) + "/*.shp"))
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# Ignore counties becauses this is not the level of measurement
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# that is consistent with our current scoring and ranking methodology.
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dfs_list = [
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gpd.read_file(f)
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for f in list_of_files
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if not f.endswith("CountiesEJScore.shp")
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]
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# Set the Census tract as the index and drop the geometry column
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# that produces the census tract boundaries.
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# The latter is because Geopandas raises an exception if there
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# are duplicate geometry columns.
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# Moreover, since the unit of measurement is at the tract level
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# we can consistantly merge this with other datasets
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dfs_list = [
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df.set_index("Census_Tra").drop("geometry", axis=1)
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for df in dfs_list
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]
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# pylint: disable=unsubscriptable-object
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self.df = gpd.GeoDataFrame(pd.concat(dfs_list, axis=1))
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# Reset index so that we no longer have the tract as our index
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self.df = self.df.reset_index()
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# coerce GEODID into integer
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# The only reason why this is done is because Maryland's GEODID's start with
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# "24". This is NOT standard practice and should never be done as rightly pointed
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# out by Lucas: "converting to int would lose the leading 0 and make this geoid invalid".
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# pylint: disable=unsupported-assignment-operation, unsubscriptable-object
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self.df["Census_Tra"] = (self.df["Census_Tra"]).astype(int)
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# Drop the 10 census tracts that are zero: please see here:
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# https://github.com/usds/justice40-tool/issues/239#issuecomment-995821572
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self.df = self.df[self.df["Census_Tra"] != 0]
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# Rename columns
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self.df.rename(
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columns={
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"Census_Tra": self.GEOID_TRACT_FIELD_NAME,
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"EJScore": field_names.MARYLAND_EJSCREEN_SCORE_FIELD,
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},
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inplace=True,
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)
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# This computational step will be used to establish a
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# threshold for burden (line 104)
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self.df[
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field_names.MARYLAND_EJSCREEN_SCORE_FIELD
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+ field_names.PERCENTILE_FIELD_SUFFIX
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] = self.df[field_names.MARYLAND_EJSCREEN_SCORE_FIELD].rank(
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pct=True, ascending=True
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)
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# An arbitrarily chosen threshold is used in the comparison tool output
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self.df[field_names.MARYLAND_EJSCREEN_BURDENED_THRESHOLD_FIELD] = (
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self.df[
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field_names.MARYLAND_EJSCREEN_SCORE_FIELD
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+ field_names.PERCENTILE_FIELD_SUFFIX
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]
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>= 0.75
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)
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def load(self) -> None:
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# write maryland tracts to csv
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self.OUTPUT_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.OUTPUT_CSV_PATH / "maryland.csv", index=False
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)
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