Issue 308 python linting (#443)

* Adds flake8, pylint, liccheck, flake8 to dependencies for data-pipeline

* Sets up and runs black autoformatting

* Adds flake8 to tox linting

* Fixes flake8 error F541 f string missing placeholders

* Fixes flake8 E501 line too long

* Fixes flake8 F401 imported but not used

* Adds pylint to tox and disables the following pylint errors:
- C0114: module docstrings
- R0201: method could have been a function
- R0903: too few public methods
- C0103: name case styling
- W0511: fix me
- W1203: f-string interpolation in logging

* Adds utils.py to tox.ini linting, runs black on utils.py

* Fixes import related pylint errors: C0411 and C0412

* Fixes or ignores remaining pylint errors (for discussion later)

* Adds safety and liccheck to tox.ini

data/data-pipeline/.flake8

@@ -0,0 +1,7 @@
+[flake8]
+ignore =
+  E266,  # to many leading '#' for block comment
+  W503  # line break before binary operator
+max-line-length = 150
+max-complexity = 18
+select = B,C,E,F,W,T4,B9

data/data-pipeline/application.py

@@ -1,7 +1,6 @@
 import click
 
 from config import settings
-from etl.sources.census.etl_utils import reset_data_directories as census_reset
 from utils import (
     get_module_logger,
     data_folder_cleanup,
@@ -9,6 +8,7 @@ from utils import (
     temp_folder_cleanup,
 )
 from etl.sources.census.etl import download_census_csvs
+from etl.sources.census.etl_utils import reset_data_directories as census_reset
 from etl.runner import etl_runner, score_generate, score_geo
 
 logger = get_module_logger(__name__)
@@ -30,7 +30,7 @@ def census_cleanup():
     data_path = settings.APP_ROOT / "data"
 
     # census directories
-    logger.info(f"Initializing all census data")
+    logger.info("Initializing all census data")
     census_reset(data_path)
 
     logger.info("Cleaned up all census data files")

data/data-pipeline/config.py

@@ -1,6 +1,7 @@
-from dynaconf import Dynaconf
 from pathlib import Path
 
+from dynaconf import Dynaconf
+
 settings = Dynaconf(
     envvar_prefix="DYNACONF",
     settings_files=["settings.toml", ".secrets.toml"],

data/data-pipeline/etl/base.py

@@ -1,11 +1,10 @@
 from pathlib import Path
-import pathlib
 
 from config import settings
 from utils import unzip_file_from_url, remove_all_from_dir
 
 
-class ExtractTransformLoad(object):
+class ExtractTransformLoad:
     """
     A class used to instantiate an ETL object to retrieve and process data from
     datasets.
@@ -34,9 +33,7 @@ class ExtractTransformLoad(object):
 
         pass
 
-    def extract(
-        self, source_url: str = None, extract_path: Path = None
-    ) -> None:
+    def extract(self, source_url: str = None, extract_path: Path = None) -> None:
         """Extract the data from
         a remote source. By default it provides code to get the file from a source url,
         unzips it and stores it on an extract_path."""

data/data-pipeline/etl/runner.py

@@ -67,9 +67,7 @@ def etl_runner(dataset_to_run: str = None) -> None:
 
     # Run the ETLs for the dataset_list
     for dataset in dataset_list:
-        etl_module = importlib.import_module(
-            f"etl.sources.{dataset['module_dir']}.etl"
-        )
+        etl_module = importlib.import_module(f"etl.sources.{dataset['module_dir']}.etl")
         etl_class = getattr(etl_module, dataset["class_name"])
         etl_instance = etl_class()
 

data/data-pipeline/etl/score/etl_score.py

@@ -4,7 +4,6 @@ import pandas as pd
 
 from etl.base import ExtractTransformLoad
 from utils import get_module_logger
-from etl.sources.census.etl_utils import get_state_fips_codes
 
 logger = get_module_logger(__name__)
 
@@ -28,10 +27,10 @@ class ScoreETL(ExtractTransformLoad):
         self.UNEMPLOYED_FIELD_NAME = "Unemployed civilians (percent)"
         self.LINGUISTIC_ISOLATION_FIELD_NAME = "Linguistic isolation (percent)"
         self.HOUSING_BURDEN_FIELD_NAME = "Housing burden (percent)"
-        self.POVERTY_FIELD_NAME = (
-            "Poverty (Less than 200% of federal poverty line)"
+        self.POVERTY_FIELD_NAME = "Poverty (Less than 200% of federal poverty line)"
+        self.HIGH_SCHOOL_FIELD_NAME = (
+            "Percent individuals age 25 or over with less than high school degree"
         )
-        self.HIGH_SCHOOL_FIELD_NAME = "Percent individuals age 25 or over with less than high school degree"
 
         # There's another aggregation level (a second level of "buckets").
         self.AGGREGATION_POLLUTION = "Pollution Burden"
@@ -55,9 +54,7 @@ class ScoreETL(ExtractTransformLoad):
         self.ejscreen_df = pd.read_csv(
             ejscreen_csv, dtype={"ID": "string"}, low_memory=False
         )
-        self.ejscreen_df.rename(
-            columns={"ID": self.GEOID_FIELD_NAME}, inplace=True
-        )
+        self.ejscreen_df.rename(columns={"ID": self.GEOID_FIELD_NAME}, inplace=True)
 
         # Load census data
         census_csv = self.DATA_PATH / "dataset" / "census_acs_2019" / "usa.csv"
@@ -69,10 +66,7 @@ class ScoreETL(ExtractTransformLoad):
 
         # Load housing and transportation data
         housing_and_transportation_index_csv = (
-            self.DATA_PATH
-            / "dataset"
-            / "housing_and_transportation_index"
-            / "usa.csv"
+            self.DATA_PATH / "dataset" / "housing_and_transportation_index" / "usa.csv"
         )
         self.housing_and_transportation_df = pd.read_csv(
             housing_and_transportation_index_csv,
@@ -89,7 +83,7 @@ class ScoreETL(ExtractTransformLoad):
         )
 
     def transform(self) -> None:
-        logger.info(f"Transforming Score Data")
+        logger.info("Transforming Score Data")
 
         # Join all the data sources that use census block groups
         census_block_group_dfs = [
@@ -106,10 +100,7 @@ class ScoreETL(ExtractTransformLoad):
         )
 
         # Sanity check the join.
-        if (
-            len(census_block_group_df[self.GEOID_FIELD_NAME].str.len().unique())
-            != 1
-        ):
+        if len(census_block_group_df[self.GEOID_FIELD_NAME].str.len().unique()) != 1:
             raise ValueError(
                 f"One of the input CSVs uses {self.GEOID_FIELD_NAME} with a different length."
             )
@@ -119,9 +110,9 @@ class ScoreETL(ExtractTransformLoad):
         census_tract_df = self.hud_housing_df
 
         # Calculate the tract for the CBG data.
-        census_block_group_df[
-            self.GEOID_TRACT_FIELD_NAME
-        ] = census_block_group_df[self.GEOID_FIELD_NAME].str[0:11]
+        census_block_group_df[self.GEOID_TRACT_FIELD_NAME] = census_block_group_df[
+            self.GEOID_FIELD_NAME
+        ].str[0:11]
 
         self.df = census_block_group_df.merge(
             census_tract_df, on=self.GEOID_TRACT_FIELD_NAME
@@ -254,8 +245,7 @@ class ScoreETL(ExtractTransformLoad):
 
         # Rename columns:
         renaming_dict = {
-            data_set.input_field: data_set.renamed_field
-            for data_set in data_sets
+            data_set.input_field: data_set.renamed_field for data_set in data_sets
         }
 
         self.df.rename(
@@ -310,7 +300,7 @@ class ScoreETL(ExtractTransformLoad):
             ) / (max_value - min_value)
 
             # Graph distributions and correlations.
-            min_max_fields = [
+            min_max_fields = [  # noqa: F841
                 f"{data_set.renamed_field}{self.MIN_MAX_FIELD_SUFFIX}"
                 for data_set in data_sets
                 if data_set.renamed_field != self.GEOID_FIELD_NAME
@@ -324,9 +314,7 @@ class ScoreETL(ExtractTransformLoad):
             ]
         ].mean(axis=1)
         self.df["Score B"] = (
-            self.df[
-                "Poverty (Less than 200% of federal poverty line) (percentile)"
-            ]
+            self.df["Poverty (Less than 200% of federal poverty line) (percentile)"]
             * self.df[
                 "Percent individuals age 25 or over with less than high school degree (percentile)"
             ]
@@ -342,21 +330,26 @@ class ScoreETL(ExtractTransformLoad):
             ]
             self.df[f"{bucket}"] = self.df[fields_in_bucket].mean(axis=1)
 
-        # Combine the score from the two Exposures and Environmental Effects buckets into a single score called "Pollution Burden". The math for this score is: (1.0 * Exposures Score + 0.5 * Environment Effects score) / 1.5.
+        # Combine the score from the two Exposures and Environmental Effects buckets
+        # into a single score called "Pollution Burden".
+        # The math for this score is:
+        # (1.0 * Exposures Score + 0.5 * Environment Effects score) / 1.5.
         self.df[self.AGGREGATION_POLLUTION] = (
             1.0 * self.df[f"{self.BUCKET_EXPOSURES}"]
             + 0.5 * self.df[f"{self.BUCKET_ENVIRONMENTAL}"]
         ) / 1.5
 
-        # Average the score from the two Sensitive populations and Socioeconomic factors buckets into a single score called "Population Characteristics".
+        # Average the score from the two Sensitive populations and
+        # Socioeconomic factors buckets into a single score called
+        # "Population Characteristics".
         self.df[self.AGGREGATION_POPULATION] = self.df[
             [f"{self.BUCKET_SENSITIVE}", f"{self.BUCKET_SOCIOECONOMIC}"]
         ].mean(axis=1)
 
-        # Multiply the "Pollution Burden" score and the "Population Characteristics" together to produce the cumulative impact score.
+        # Multiply the "Pollution Burden" score and the "Population Characteristics"
+        # together to produce the cumulative impact score.
         self.df["Score C"] = (
-            self.df[self.AGGREGATION_POLLUTION]
-            * self.df[self.AGGREGATION_POPULATION]
+            self.df[self.AGGREGATION_POLLUTION] * self.df[self.AGGREGATION_POPULATION]
         )
 
         if len(census_block_group_df) > 220333:
@@ -371,12 +364,10 @@ class ScoreETL(ExtractTransformLoad):
         ]
 
         fields_min_max = [
-            f"{field}{self.MIN_MAX_FIELD_SUFFIX}"
-            for field in fields_to_use_in_score
+            f"{field}{self.MIN_MAX_FIELD_SUFFIX}" for field in fields_to_use_in_score
         ]
         fields_percentile = [
-            f"{field}{self.PERCENTILE_FIELD_SUFFIX}"
-            for field in fields_to_use_in_score
+            f"{field}{self.PERCENTILE_FIELD_SUFFIX}" for field in fields_to_use_in_score
         ]
 
         # Calculate "Score D", which uses min-max normalization
@@ -396,17 +387,22 @@ class ScoreETL(ExtractTransformLoad):
             "Score E",
             "Poverty (Less than 200% of federal poverty line)",
         ]:
-            self.df[f"{score_field}{self.PERCENTILE_FIELD_SUFFIX}"] = self.df[score_field].rank(pct=True)
+            self.df[f"{score_field}{self.PERCENTILE_FIELD_SUFFIX}"] = self.df[
+                score_field
+            ].rank(pct=True)
 
             for threshold in [0.25, 0.3, 0.35, 0.4]:
                 fraction_converted_to_percent = int(100 * threshold)
-                self.df[f"{score_field} (top {fraction_converted_to_percent}th percentile)"] = (
-                        self.df[f"{score_field}{self.PERCENTILE_FIELD_SUFFIX}"] >= 1 - threshold
+                self.df[
+                    f"{score_field} (top {fraction_converted_to_percent}th percentile)"
+                ] = (
+                    self.df[f"{score_field}{self.PERCENTILE_FIELD_SUFFIX}"]
+                    >= 1 - threshold
                 )
 
     def load(self) -> None:
-        logger.info(f"Saving Score CSV")
+        logger.info("Saving Score CSV")
 
         # write nationwide csv
         self.SCORE_CSV_PATH.mkdir(parents=True, exist_ok=True)
-        self.df.to_csv(self.SCORE_CSV_PATH / f"usa.csv", index=False)
+        self.df.to_csv(self.SCORE_CSV_PATH / "usa.csv", index=False)

data/data-pipeline/etl/score/etl_score_geo.py

@@ -1,6 +1,7 @@
+import math
+
 import pandas as pd
 import geopandas as gpd
-import math
 
 from etl.base import ExtractTransformLoad
 from utils import get_module_logger
@@ -21,9 +22,7 @@ class GeoScoreETL(ExtractTransformLoad):
         self.SCORE_CSV_PATH = self.DATA_PATH / "score" / "csv"
         self.TILE_SCORE_CSV = self.SCORE_CSV_PATH / "tiles" / "usa.csv"
 
-        self.CENSUS_USA_GEOJSON = (
-            self.DATA_PATH / "census" / "geojson" / "us.json"
-        )
+        self.CENSUS_USA_GEOJSON = self.DATA_PATH / "census" / "geojson" / "us.json"
 
         self.TARGET_SCORE_NAME = "Score E (percentile)"
         self.TARGET_SCORE_RENAME_TO = "E_SCORE"
@@ -36,7 +35,7 @@ class GeoScoreETL(ExtractTransformLoad):
         self.geojson_score_usa_low: gpd.GeoDataFrame
 
     def extract(self) -> None:
-        logger.info(f"Reading US GeoJSON (~6 minutes)")
+        logger.info("Reading US GeoJSON (~6 minutes)")
         self.geojson_usa_df = gpd.read_file(
             self.CENSUS_USA_GEOJSON,
             dtype={"GEOID10": "string"},
@@ -45,7 +44,7 @@ class GeoScoreETL(ExtractTransformLoad):
         )
         self.geojson_usa_df.head()
 
-        logger.info(f"Reading score CSV")
+        logger.info("Reading score CSV")
         self.score_usa_df = pd.read_csv(
             self.TILE_SCORE_CSV,
             dtype={"GEOID10": "string"},
@@ -53,11 +52,11 @@ class GeoScoreETL(ExtractTransformLoad):
         )
 
     def transform(self) -> None:
-        logger.info(f"Pruning Census GeoJSON")
+        logger.info("Pruning Census GeoJSON")
         fields = ["GEOID10", "geometry"]
         self.geojson_usa_df = self.geojson_usa_df[fields]
 
-        logger.info(f"Merging and compressing score CSV with USA GeoJSON")
+        logger.info("Merging and compressing score CSV with USA GeoJSON")
         self.geojson_score_usa_high = self.score_usa_df.merge(
             self.geojson_usa_df, on="GEOID10", how="left"
         )
@@ -75,7 +74,7 @@ class GeoScoreETL(ExtractTransformLoad):
             inplace=True,
         )
 
-        logger.info(f"Aggregating into tracts (~5 minutes)")
+        logger.info("Aggregating into tracts (~5 minutes)")
         usa_tracts = self._aggregate_to_tracts(usa_simplified)
 
         usa_tracts = gpd.GeoDataFrame(
@@ -84,17 +83,15 @@ class GeoScoreETL(ExtractTransformLoad):
             crs="EPSG:4326",
         )
 
-        logger.info(f"Creating buckets from tracts")
+        logger.info("Creating buckets from tracts")
         usa_bucketed = self._create_buckets_from_tracts(
             usa_tracts, self.NUMBER_OF_BUCKETS
         )
 
-        logger.info(f"Aggregating buckets")
+        logger.info("Aggregating buckets")
         usa_aggregated = self._aggregate_buckets(usa_bucketed, agg_func="mean")
 
-        compressed = self._breakup_multipolygons(
-            usa_aggregated, self.NUMBER_OF_BUCKETS
-        )
+        compressed = self._breakup_multipolygons(usa_aggregated, self.NUMBER_OF_BUCKETS)
 
         self.geojson_score_usa_low = gpd.GeoDataFrame(
             compressed,
@@ -118,9 +115,7 @@ class GeoScoreETL(ExtractTransformLoad):
         # assign tracts to buckets by D_SCORE
         state_tracts.sort_values(self.TARGET_SCORE_RENAME_TO, inplace=True)
         SCORE_bucket = []
-        bucket_size = math.ceil(
-            len(state_tracts.index) / self.NUMBER_OF_BUCKETS
-        )
+        bucket_size = math.ceil(len(state_tracts.index) / self.NUMBER_OF_BUCKETS)
         for i in range(len(state_tracts.index)):
             SCORE_bucket.extend([math.floor(i / bucket_size)])
         state_tracts[f"{self.TARGET_SCORE_RENAME_TO}_bucket"] = SCORE_bucket
@@ -155,14 +150,10 @@ class GeoScoreETL(ExtractTransformLoad):
         return compressed
 
     def load(self) -> None:
-        logger.info(f"Writing usa-high (~9 minutes)")
-        self.geojson_score_usa_high.to_file(
-            self.SCORE_HIGH_GEOJSON, driver="GeoJSON"
-        )
-        logger.info(f"Completed writing usa-high")
+        logger.info("Writing usa-high (~9 minutes)")
+        self.geojson_score_usa_high.to_file(self.SCORE_HIGH_GEOJSON, driver="GeoJSON")
+        logger.info("Completed writing usa-high")
 
-        logger.info(f"Writing usa-low (~9 minutes)")
-        self.geojson_score_usa_low.to_file(
-            self.SCORE_LOW_GEOJSON, driver="GeoJSON"
-        )
-        logger.info(f"Completed writing usa-low")
+        logger.info("Writing usa-low (~9 minutes)")
+        self.geojson_score_usa_low.to_file(self.SCORE_LOW_GEOJSON, driver="GeoJSON")
+        logger.info("Completed writing usa-low")

data/data-pipeline/etl/score/etl_score_post.py

@@ -19,9 +19,7 @@ class PostScoreETL(ExtractTransformLoad):
         self.CENSUS_USA_CSV = self.DATA_PATH / "census" / "csv" / "us.csv"
         self.SCORE_CSV_PATH = self.DATA_PATH / "score" / "csv"
 
-        self.STATE_CSV = (
-            self.DATA_PATH / "census" / "csv" / "fips_states_2010.csv"
-        )
+        self.STATE_CSV = self.DATA_PATH / "census" / "csv" / "fips_states_2010.csv"
 
         self.FULL_SCORE_CSV = self.SCORE_CSV_PATH / "full" / "usa.csv"
         self.TILR_SCORE_CSV = self.SCORE_CSV_PATH / "tile" / "usa.csv"
@@ -49,7 +47,7 @@ class PostScoreETL(ExtractTransformLoad):
             self.TMP_PATH,
         )
 
-        logger.info(f"Reading Counties CSV")
+        logger.info("Reading Counties CSV")
         self.counties_df = pd.read_csv(
             self.CENSUS_COUNTIES_TXT,
             sep="\t",
@@ -58,16 +56,14 @@ class PostScoreETL(ExtractTransformLoad):
             encoding="latin-1",
         )
 
-        logger.info(f"Reading States CSV")
+        logger.info("Reading States CSV")
         self.states_df = pd.read_csv(
             self.STATE_CSV, dtype={"fips": "string", "state_code": "string"}
         )
-        self.score_df = pd.read_csv(
-            self.FULL_SCORE_CSV, dtype={"GEOID10": "string"}
-        )
+        self.score_df = pd.read_csv(self.FULL_SCORE_CSV, dtype={"GEOID10": "string"})
 
     def transform(self) -> None:
-        logger.info(f"Transforming data sources for Score + County CSV")
+        logger.info("Transforming data sources for Score + County CSV")
 
         # rename some of the columns to prepare for merge
         self.counties_df = self.counties_df[["USPS", "GEOID", "NAME"]]
@@ -101,7 +97,7 @@ class PostScoreETL(ExtractTransformLoad):
         )
 
         # check if there are census cbgs without score
-        logger.info(f"Removing CBG rows without score")
+        logger.info("Removing CBG rows without score")
 
         ## load cbgs
         cbg_usa_df = pd.read_csv(
@@ -121,19 +117,19 @@ class PostScoreETL(ExtractTransformLoad):
         null_cbg_df = merged_df[merged_df["Score E (percentile)"].isnull()]
 
         # subsctract data sets
-        removed_df = pd.concat(
-            [merged_df, null_cbg_df, null_cbg_df]
-        ).drop_duplicates(keep=False)
+        removed_df = pd.concat([merged_df, null_cbg_df, null_cbg_df]).drop_duplicates(
+            keep=False
+        )
 
         # set the score to the new df
         self.score_county_state_merged = removed_df
 
     def load(self) -> None:
-        logger.info(f"Saving Full Score CSV with County Information")
+        logger.info("Saving Full Score CSV with County Information")
         self.SCORE_CSV_PATH.mkdir(parents=True, exist_ok=True)
         self.score_county_state_merged.to_csv(self.FULL_SCORE_CSV, index=False)
 
-        logger.info(f"Saving Tile Score CSV")
+        logger.info("Saving Tile Score CSV")
         # TODO: check which are the columns we'll use
         # Related to: https://github.com/usds/justice40-tool/issues/302
         score_tiles = self.score_county_state_merged[self.TILES_SCORE_COLUMNS]

data/data-pipeline/etl/sources/calenviroscreen/etl.py

@@ -9,16 +9,12 @@ logger = get_module_logger(__name__)
 class CalEnviroScreenETL(ExtractTransformLoad):
     def __init__(self):
         self.CALENVIROSCREEN_FTP_URL = "https://justice40-data.s3.amazonaws.com/data-sources/CalEnviroScreen_4.0_2021.zip"
-        self.CALENVIROSCREEN_CSV = (
-            self.TMP_PATH / "CalEnviroScreen_4.0_2021.csv"
-        )
+        self.CALENVIROSCREEN_CSV = self.TMP_PATH / "CalEnviroScreen_4.0_2021.csv"
         self.CSV_PATH = self.DATA_PATH / "dataset" / "calenviroscreen4"
 
         # Definining some variable names
         self.CALENVIROSCREEN_SCORE_FIELD_NAME = "calenviroscreen_score"
-        self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME = (
-            "calenviroscreen_percentile"
-        )
+        self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME = "calenviroscreen_percentile"
         self.CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME = (
             "calenviroscreen_priority_community"
         )
@@ -30,14 +26,14 @@ class CalEnviroScreenETL(ExtractTransformLoad):
         self.df: pd.DataFrame
 
     def extract(self) -> None:
-        logger.info(f"Downloading CalEnviroScreen Data")
+        logger.info("Downloading CalEnviroScreen Data")
         super().extract(
             self.CALENVIROSCREEN_FTP_URL,
             self.TMP_PATH,
         )
 
     def transform(self) -> None:
-        logger.info(f"Transforming CalEnviroScreen Data")
+        logger.info("Transforming CalEnviroScreen Data")
 
         # Data from https://calenviroscreen-oehha.hub.arcgis.com/#Data, specifically:
         # https://oehha.ca.gov/media/downloads/calenviroscreen/document/calenviroscreen40resultsdatadictionaryd12021.zip
@@ -67,7 +63,7 @@ class CalEnviroScreenETL(ExtractTransformLoad):
         )
 
     def load(self) -> None:
-        logger.info(f"Saving CalEnviroScreen CSV")
+        logger.info("Saving CalEnviroScreen CSV")
         # write nationwide csv
         self.CSV_PATH.mkdir(parents=True, exist_ok=True)
-        self.df.to_csv(self.CSV_PATH / f"data06.csv", index=False)
+        self.df.to_csv(self.CSV_PATH / "data06.csv", index=False)

data/data-pipeline/etl/sources/census/etl.py

@@ -1,11 +1,12 @@
-import csv
 import os
+import csv
 import json
 from pathlib import Path
+
 import geopandas as gpd
 
-from .etl_utils import get_state_fips_codes
 from utils import unzip_file_from_url, get_module_logger
+from .etl_utils import get_state_fips_codes
 
 logger = get_module_logger(__name__)
 
@@ -29,9 +30,7 @@ def download_census_csvs(data_path: Path) -> None:
 
     for fips in state_fips_codes:
         # check if file exists
-        shp_file_path = (
-            data_path / "census" / "shp" / fips / f"tl_2010_{fips}_bg10.shp"
-        )
+        shp_file_path = data_path / "census" / "shp" / fips / f"tl_2010_{fips}_bg10.shp"
 
         logger.info(f"Checking if {fips} file exists")
         if not os.path.isfile(shp_file_path):
@@ -110,7 +109,7 @@ def download_census_csvs(data_path: Path) -> None:
             )
 
     ## create national geojson
-    logger.info(f"Generating national geojson file")
+    logger.info("Generating national geojson file")
     usa_df = gpd.GeoDataFrame()
 
     for file_name in geojson_dir_path.rglob("*.json"):
@@ -119,7 +118,7 @@ def download_census_csvs(data_path: Path) -> None:
         usa_df = usa_df.append(state_gdf)
 
     usa_df = usa_df.to_crs("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
-    logger.info(f"Writing national geojson file")
+    logger.info("Writing national geojson file")
     usa_df.to_file(geojson_dir_path / "us.json", driver="GeoJSON")
 
     logger.info("Census block groups downloading complete")

data/data-pipeline/etl/sources/census/etl_utils.py

@@ -1,7 +1,8 @@
-from pathlib import Path
-import csv
-import pandas as pd
 import os
+import csv
+
+from pathlib import Path
+import pandas as pd
 from config import settings
 
 from utils import (
@@ -35,7 +36,7 @@ def get_state_fips_codes(data_path: Path) -> list:
 
     # check if file exists
     if not os.path.isfile(fips_csv_path):
-        logger.info(f"Downloading fips from S3 repository")
+        logger.info("Downloading fips from S3 repository")
         unzip_file_from_url(
             settings.AWS_JUSTICE40_DATA_URL + "/Census/fips_states_2010.zip",
             data_path / "tmp",

data/data-pipeline/etl/sources/census_acs/etl.py

@@ -11,14 +11,10 @@ logger = get_module_logger(__name__)
 class CensusACSETL(ExtractTransformLoad):
     def __init__(self):
         self.ACS_YEAR = 2019
-        self.OUTPUT_PATH = (
-            self.DATA_PATH / "dataset" / f"census_acs_{self.ACS_YEAR}"
-        )
+        self.OUTPUT_PATH = self.DATA_PATH / "dataset" / f"census_acs_{self.ACS_YEAR}"
         self.UNEMPLOYED_FIELD_NAME = "Unemployed civilians (percent)"
         self.LINGUISTIC_ISOLATION_FIELD_NAME = "Linguistic isolation (percent)"
-        self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME = (
-            "Linguistic isolation (total)"
-        )
+        self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME = "Linguistic isolation (total)"
         self.LINGUISTIC_ISOLATION_FIELDS = [
             "C16002_001E",
             "C16002_004E",
@@ -28,9 +24,7 @@ class CensusACSETL(ExtractTransformLoad):
         ]
         self.df: pd.DataFrame
 
-    def _fips_from_censusdata_censusgeo(
-        self, censusgeo: censusdata.censusgeo
-    ) -> str:
+    def _fips_from_censusdata_censusgeo(self, censusgeo: censusdata.censusgeo) -> str:
         """Create a FIPS code from the proprietary censusgeo index."""
         fips = "".join([value for (key, value) in censusgeo.params()])
         return fips
@@ -38,9 +32,7 @@ class CensusACSETL(ExtractTransformLoad):
     def extract(self) -> None:
         dfs = []
         for fips in get_state_fips_codes(self.DATA_PATH):
-            logger.info(
-                f"Downloading data for state/territory with FIPS code {fips}"
-            )
+            logger.info(f"Downloading data for state/territory with FIPS code {fips}")
 
             dfs.append(
                 censusdata.download(
@@ -65,13 +57,11 @@ class CensusACSETL(ExtractTransformLoad):
         )
 
     def transform(self) -> None:
-        logger.info(f"Starting Census ACS Transform")
+        logger.info("Starting Census ACS Transform")
 
         # Calculate percent unemployment.
         # TODO: remove small-sample data that should be `None` instead of a high-variance fraction.
-        self.df[self.UNEMPLOYED_FIELD_NAME] = (
-            self.df.B23025_005E / self.df.B23025_003E
-        )
+        self.df[self.UNEMPLOYED_FIELD_NAME] = self.df.B23025_005E / self.df.B23025_003E
 
         # Calculate linguistic isolation.
         individual_limited_english_fields = [
@@ -92,7 +82,7 @@ class CensusACSETL(ExtractTransformLoad):
         self.df[self.LINGUISTIC_ISOLATION_FIELD_NAME].describe()
 
     def load(self) -> None:
-        logger.info(f"Saving Census ACS Data")
+        logger.info("Saving Census ACS Data")
 
         # mkdir census
         self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
@@ -108,6 +98,6 @@ class CensusACSETL(ExtractTransformLoad):
         )
 
     def validate(self) -> None:
-        logger.info(f"Validating Census ACS Data")
+        logger.info("Validating Census ACS Data")
 
         pass

data/data-pipeline/etl/sources/ejscreen/etl.py

@@ -8,20 +8,22 @@ logger = get_module_logger(__name__)
 
 class EJScreenETL(ExtractTransformLoad):
     def __init__(self):
-        self.EJSCREEN_FTP_URL = "https://gaftp.epa.gov/EJSCREEN/2019/EJSCREEN_2019_StatePctile.csv.zip"
+        self.EJSCREEN_FTP_URL = (
+            "https://gaftp.epa.gov/EJSCREEN/2019/EJSCREEN_2019_StatePctile.csv.zip"
+        )
         self.EJSCREEN_CSV = self.TMP_PATH / "EJSCREEN_2019_StatePctiles.csv"
         self.CSV_PATH = self.DATA_PATH / "dataset" / "ejscreen_2019"
         self.df: pd.DataFrame
 
     def extract(self) -> None:
-        logger.info(f"Downloading EJScreen Data")
+        logger.info("Downloading EJScreen Data")
         super().extract(
             self.EJSCREEN_FTP_URL,
             self.TMP_PATH,
         )
 
     def transform(self) -> None:
-        logger.info(f"Transforming EJScreen Data")
+        logger.info("Transforming EJScreen Data")
         self.df = pd.read_csv(
             self.EJSCREEN_CSV,
             dtype={"ID": "string"},
@@ -31,7 +33,7 @@ class EJScreenETL(ExtractTransformLoad):
         )
 
     def load(self) -> None:
-        logger.info(f"Saving EJScreen CSV")
+        logger.info("Saving EJScreen CSV")
         # write nationwide csv
         self.CSV_PATH.mkdir(parents=True, exist_ok=True)
-        self.df.to_csv(self.CSV_PATH / f"usa.csv", index=False)
+        self.df.to_csv(self.CSV_PATH / "usa.csv", index=False)

data/data-pipeline/etl/sources/housing_and_transportation/etl.py

@@ -35,9 +35,7 @@ class HousingTransportationETL(ExtractTransformLoad):
             )
 
             # New file name:
-            tmp_csv_file_path = (
-                zip_file_dir / f"htaindex_data_blkgrps_{fips}.csv"
-            )
+            tmp_csv_file_path = zip_file_dir / f"htaindex_data_blkgrps_{fips}.csv"
             tmp_df = pd.read_csv(filepath_or_buffer=tmp_csv_file_path)
 
             dfs.append(tmp_df)
@@ -45,16 +43,16 @@ class HousingTransportationETL(ExtractTransformLoad):
         self.df = pd.concat(dfs)
 
     def transform(self) -> None:
-        logger.info(f"Transforming Housing and Transportation Data")
+        logger.info("Transforming Housing and Transportation Data")
 
         # Rename and reformat block group ID
         self.df.rename(columns={"blkgrp": self.GEOID_FIELD_NAME}, inplace=True)
-        self.df[self.GEOID_FIELD_NAME] = self.df[
-            self.GEOID_FIELD_NAME
-        ].str.replace('"', "")
+        self.df[self.GEOID_FIELD_NAME] = self.df[self.GEOID_FIELD_NAME].str.replace(
+            '"', ""
+        )
 
     def load(self) -> None:
-        logger.info(f"Saving Housing and Transportation Data")
+        logger.info("Saving Housing and Transportation Data")
 
         self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
         self.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)

data/data-pipeline/etl/sources/hud_housing/etl.py

@@ -1,8 +1,7 @@
 import pandas as pd
 
 from etl.base import ExtractTransformLoad
-from etl.sources.census.etl_utils import get_state_fips_codes
-from utils import get_module_logger, unzip_file_from_url, remove_all_from_dir
+from utils import get_module_logger
 
 logger = get_module_logger(__name__)
 
@@ -11,33 +10,37 @@ class HudHousingETL(ExtractTransformLoad):
     def __init__(self):
         self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "hud_housing"
         self.GEOID_TRACT_FIELD_NAME = "GEOID10_TRACT"
-        self.HOUSING_FTP_URL = "https://www.huduser.gov/portal/datasets/cp/2012thru2016-140-csv.zip"
+        self.HOUSING_FTP_URL = (
+            "https://www.huduser.gov/portal/datasets/cp/2012thru2016-140-csv.zip"
+        )
         self.HOUSING_ZIP_FILE_DIR = self.TMP_PATH / "hud_housing"
 
         # We measure households earning less than 80% of HUD Area Median Family Income by county
         # and paying greater than 30% of their income to housing costs.
         self.HOUSING_BURDEN_FIELD_NAME = "Housing burden (percent)"
         self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME = "HOUSING_BURDEN_NUMERATOR"
-        self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME = (
-            "HOUSING_BURDEN_DENOMINATOR"
-        )
+        self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME = "HOUSING_BURDEN_DENOMINATOR"
 
         # Note: some variable definitions.
         # HUD-adjusted median family income (HAMFI).
-        # The four housing problems are: incomplete kitchen facilities, incomplete plumbing facilities, more than 1 person per room, and cost burden greater than 30%.
+        # The four housing problems are:
+        #   - incomplete kitchen facilities,
+        #   - incomplete plumbing facilities,
+        #   - more than 1 person per room,
+        #   - cost burden greater than 30%.
         # Table 8 is the desired table.
 
         self.df: pd.DataFrame
 
     def extract(self) -> None:
-        logger.info(f"Extracting HUD Housing Data")
+        logger.info("Extracting HUD Housing Data")
         super().extract(
             self.HOUSING_FTP_URL,
             self.HOUSING_ZIP_FILE_DIR,
         )
 
     def transform(self) -> None:
-        logger.info(f"Transforming HUD Housing Data")
+        logger.info("Transforming HUD Housing Data")
 
         # New file name:
         tmp_csv_file_path = (
@@ -53,9 +56,7 @@ class HudHousingETL(ExtractTransformLoad):
         )
 
         # Rename and reformat block group ID
-        self.df.rename(
-            columns={"geoid": self.GEOID_TRACT_FIELD_NAME}, inplace=True
-        )
+        self.df.rename(columns={"geoid": self.GEOID_TRACT_FIELD_NAME}, inplace=True)
 
         # The CHAS data has census tract ids such as `14000US01001020100`
         # Whereas the rest of our data uses, for the same tract, `01001020100`.
@@ -70,69 +71,177 @@ class HudHousingETL(ExtractTransformLoad):
 
         # Owner occupied numerator fields
         OWNER_OCCUPIED_NUMERATOR_FIELDS = [
-            # Key: Column Name	Line_Type	Tenure	Household income	Cost burden	Facilities
-            # T8_est7	Subtotal	Owner occupied	less than or equal to 30% of HAMFI	greater than 30% but less than or equal to 50%	All
+            # Column Name
+            #   Line_Type
+            #   Tenure
+            #   Household income
+            #   Cost burden
+            #   Facilities
             "T8_est7",
-            # T8_est10	Subtotal	Owner occupied	less than or equal to 30% of HAMFI	greater than 50%	All
+            #   Subtotal
+            #   Owner occupied
+            #   less than or equal to 30% of HAMFI
+            #   greater than 30% but less than or equal to 50%
+            #   All
             "T8_est10",
-            # T8_est20	Subtotal	Owner occupied	greater than 30% but less than or equal to 50% of HAMFI	greater than 30% but less than or equal to 50%	All
+            #   Subtotal
+            #   Owner occupied
+            #   less than or equal to 30% of HAMFI
+            #   greater than 50%
+            #   All
             "T8_est20",
-            # T8_est23	Subtotal	Owner occupied	greater than 30% but less than or equal to 50% of HAMFI	greater than 50%	All
+            #   Subtotal
+            #   Owner occupied
+            #   greater than 30% but less than or equal to 50% of HAMFI
+            #   greater than 30% but less than or equal to 50%
+            #   All
             "T8_est23",
-            # T8_est33	Subtotal	Owner occupied	greater than 50% but less than or equal to 80% of HAMFI	greater than 30% but less than or equal to 50%	All
+            #   Subtotal
+            #   Owner occupied
+            #   greater than 30% but less than or equal to 50% of HAMFI
+            #   greater than 50%
+            #   All
             "T8_est33",
-            # T8_est36	Subtotal	Owner occupied	greater than 50% but less than or equal to 80% of HAMFI	greater than 50%	All
+            #   Subtotal
+            #   Owner occupied
+            #   greater than 50% but less than or equal to 80% of HAMFI
+            #   greater than 30% but less than or equal to 50%
+            #   All
             "T8_est36",
+            #   Subtotal
+            #   Owner occupied
+            #   greater than 50% but less than or equal to 80% of HAMFI
+            #   greater than 50%
+            #   All
         ]
 
         # These rows have the values where HAMFI was not computed, b/c of no or negative income.
         OWNER_OCCUPIED_NOT_COMPUTED_FIELDS = [
-            # Key: Column Name	Line_Type	Tenure	Household income	Cost burden	Facilities
-            # T8_est13	Subtotal	Owner occupied	less than or equal to 30% of HAMFI	not computed (no/negative income)	All
+            # Column Name
+            #   Line_Type
+            #   Tenure
+            #   Household income
+            #   Cost burden
+            #   Facilities
             "T8_est13",
-            # T8_est26	Subtotal	Owner occupied	greater than 30% but less than or equal to 50% of HAMFI	not computed (no/negative income)	All
+            #   Subtotal
+            #   Owner occupied
+            #   less than or equal to 30% of HAMFI
+            #   not computed (no/negative income)
+            #   All
             "T8_est26",
-            # T8_est39	Subtotal	Owner occupied	greater than 50% but less than or equal to 80% of HAMFI	not computed (no/negative income)	All
+            #   Subtotal
+            #   Owner occupied
+            #   greater than 30% but less than or equal to 50% of HAMFI
+            #   not computed (no/negative income)
+            #   All
             "T8_est39",
-            # T8_est52	Subtotal	Owner occupied	greater than 80% but less than or equal to 100% of HAMFI	not computed (no/negative income)	All
+            #   Subtotal
+            #   Owner occupied
+            #   greater than 50% but less than or equal to 80% of HAMFI
+            #   not computed (no/negative income)
+            #   All
             "T8_est52",
-            # T8_est65	Subtotal	Owner occupied	greater than 100% of HAMFI	not computed (no/negative income)	All
+            #   Subtotal
+            #   Owner occupied
+            #   greater than 80% but less than or equal to 100% of HAMFI
+            #   not computed (no/negative income)
+            #   All
             "T8_est65",
+            #   Subtotal
+            #   Owner occupied
+            #   greater than 100% of HAMFI
+            #   not computed (no/negative income)
+            #   All
         ]
 
-        # T8_est2	Subtotal	Owner occupied	All	All	All
         OWNER_OCCUPIED_POPULATION_FIELD = "T8_est2"
+        #   Subtotal
+        #   Owner occupied
+        #   All
+        #   All
+        #   All
 
         # Renter occupied numerator fields
         RENTER_OCCUPIED_NUMERATOR_FIELDS = [
-            # Key: Column Name	Line_Type	Tenure	Household income	Cost burden	Facilities
-            # T8_est73	Subtotal	Renter occupied	less than or equal to 30% of HAMFI	greater than 30% but less than or equal to 50%	All
+            # Column Name
+            #   Line_Type
+            #   Tenure
+            #   Household income
+            #   Cost burden
+            #   Facilities
             "T8_est73",
-            # T8_est76	Subtotal	Renter occupied	less than or equal to 30% of HAMFI	greater than 50%	All
+            #   Subtotal
+            #   Renter occupied
+            #   less than or equal to 30% of HAMFI
+            #   greater than 30% but less than or equal to 50%
+            #   All
             "T8_est76",
-            # T8_est86	Subtotal	Renter occupied	greater than 30% but less than or equal to 50% of HAMFI	greater than 30% but less than or equal to 50%	All
+            #   Subtotal
+            #   Renter occupied
+            #   less than or equal to 30% of HAMFI
+            #   greater than 50%
+            #   All
             "T8_est86",
-            # T8_est89	Subtotal	Renter occupied	greater than 30% but less than or equal to 50% of HAMFI	greater than 50%	All
+            #   Subtotal
+            #   Renter occupied
+            #   greater than 30% but less than or equal to 50% of HAMFI
+            #   greater than 30% but less than or equal to 50%
+            #   All
             "T8_est89",
-            # T8_est99	Subtotal	Renter occupied	greater than 50% but less than or equal to 80% of HAMFI	greater than 30% but less than or equal to 50%	All
+            #   Subtotal
+            #   Renter occupied
+            #   greater than 30% but less than or equal to 50% of HAMFI
+            #   greater than 50%
+            #   All
             "T8_est99",
-            # T8_est102	Subtotal	Renter occupied	greater than 50% but less than or equal to 80% of HAMFI	greater than 50%	All
+            #   Subtotal
+            #   Renter occupied	greater than 50% but less than or equal to 80% of HAMFI
+            #   greater than 30% but less than or equal to 50%
+            #   All
             "T8_est102",
+            #   Subtotal
+            #   Renter occupied
+            #   greater than 50% but less than or equal to 80% of HAMFI
+            #   greater than 50%
+            #   All
         ]
 
         # These rows have the values where HAMFI was not computed, b/c of no or negative income.
         RENTER_OCCUPIED_NOT_COMPUTED_FIELDS = [
-            # Key: Column Name	Line_Type	Tenure	Household income	Cost burden	Facilities
-            # T8_est79	Subtotal	Renter occupied	less than or equal to 30% of HAMFI	not computed (no/negative income)	All
+            # Column Name
+            #   Line_Type
+            #   Tenure
+            #   Household income
+            #   Cost burden
+            #   Facilities
             "T8_est79",
-            # T8_est92	Subtotal	Renter occupied	greater than 30% but less than or equal to 50% of HAMFI	not computed (no/negative income)	All
+            #   Subtotal
+            #   Renter occupied	less than or equal to 30% of HAMFI
+            #   not computed (no/negative income)
+            #   All
             "T8_est92",
-            # T8_est105	Subtotal	Renter occupied	greater than 50% but less than or equal to 80% of HAMFI	not computed (no/negative income)	All
+            #   Subtotal
+            #   Renter occupied	greater than 30% but less than or equal to 50% of HAMFI
+            #   not computed (no/negative income)
+            #   All
             "T8_est105",
-            # T8_est118	Subtotal	Renter occupied	greater than 80% but less than or equal to 100% of HAMFI	not computed (no/negative income)	All
+            #   Subtotal
+            #   Renter occupied
+            #   greater than 50% but less than or equal to 80% of HAMFI
+            #   not computed (no/negative income)
+            #   All
             "T8_est118",
-            # T8_est131	Subtotal	Renter occupied	greater than 100% of HAMFI	not computed (no/negative income)	All
+            #   Subtotal
+            #   Renter occupied	greater than 80% but less than or equal to 100% of HAMFI
+            #   not computed (no/negative income)
+            #   All
             "T8_est131",
+            #   Subtotal
+            #   Renter occupied
+            #   greater than 100% of HAMFI
+            #   not computed (no/negative income)
+            #   All
         ]
 
         # T8_est68	Subtotal	Renter occupied	All	All	All
@@ -165,14 +274,12 @@ class HudHousingETL(ExtractTransformLoad):
         # TODO: add small sample size checks
         self.df[self.HOUSING_BURDEN_FIELD_NAME] = self.df[
             self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME
-        ].astype(float) / self.df[
-            self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME
-        ].astype(
+        ].astype(float) / self.df[self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME].astype(
             float
         )
 
     def load(self) -> None:
-        logger.info(f"Saving HUD Housing Data")
+        logger.info("Saving HUD Housing Data")
 
         self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
 

data/data-pipeline/etl/sources/hud_recap/etl.py

@@ -9,7 +9,8 @@ logger = get_module_logger(__name__)
 
 class HudRecapETL(ExtractTransformLoad):
     def __init__(self):
-        self.HUD_RECAP_CSV_URL = "https://opendata.arcgis.com/api/v3/datasets/56de4edea8264fe5a344da9811ef5d6e_0/downloads/data?format=csv&spatialRefId=4326"
+        # pylint: disable=line-too-long
+        self.HUD_RECAP_CSV_URL = "https://opendata.arcgis.com/api/v3/datasets/56de4edea8264fe5a344da9811ef5d6e_0/downloads/data?format=csv&spatialRefId=4326"  # noqa: E501
         self.HUD_RECAP_CSV = (
             self.TMP_PATH
             / "Racially_or_Ethnically_Concentrated_Areas_of_Poverty__R_ECAPs_.csv"
@@ -22,7 +23,7 @@ class HudRecapETL(ExtractTransformLoad):
         self.df: pd.DataFrame
 
     def extract(self) -> None:
-        logger.info(f"Downloading HUD Recap Data")
+        logger.info("Downloading HUD Recap Data")
         download = requests.get(self.HUD_RECAP_CSV_URL, verify=None)
         file_contents = download.content
         csv_file = open(self.HUD_RECAP_CSV, "wb")
@@ -30,7 +31,7 @@ class HudRecapETL(ExtractTransformLoad):
         csv_file.close()
 
     def transform(self) -> None:
-        logger.info(f"Transforming HUD Recap Data")
+        logger.info("Transforming HUD Recap Data")
 
         # Load comparison index (CalEnviroScreen 4)
         self.df = pd.read_csv(self.HUD_RECAP_CSV, dtype={"GEOID": "string"})
@@ -57,7 +58,7 @@ class HudRecapETL(ExtractTransformLoad):
         self.df.sort_values(by=self.GEOID_TRACT_FIELD_NAME, inplace=True)
 
     def load(self) -> None:
-        logger.info(f"Saving HUD Recap CSV")
+        logger.info("Saving HUD Recap CSV")
         # write nationwide csv
         self.CSV_PATH.mkdir(parents=True, exist_ok=True)
-        self.df.to_csv(self.CSV_PATH / f"usa.csv", index=False)
+        self.df.to_csv(self.CSV_PATH / "usa.csv", index=False)

data/data-pipeline/etl/sources/tree_equity_score/etl.py

@@ -3,25 +3,72 @@ import geopandas as gpd
 
 from etl.base import ExtractTransformLoad
 from utils import get_module_logger
-import os
 
 logger = get_module_logger(__name__)
 
 
 class TreeEquityScoreETL(ExtractTransformLoad):
     def __init__(self):
-        self.TES_URL = "https://national-tes-data-share.s3.amazonaws.com/national_tes_share/"
+        self.TES_URL = (
+            "https://national-tes-data-share.s3.amazonaws.com/national_tes_share/"
+        )
         self.TES_CSV = self.TMP_PATH / "tes_2021_data.csv"
         self.CSV_PATH = self.DATA_PATH / "dataset" / "tree_equity_score"
         self.df: gpd.GeoDataFrame
-        self.states =  ["al", "az", "ar", "ca", "co", "ct", "de", "dc", "fl", 
-        "ga", "id", "il", "in", "ia", "ks", "ky", "la", "me", 
-        "md", "ma", "mi", "mn", "ms", "mo", "mt", "ne", "nv", "nh", 
-        "nj", "nm", "ny", "nc", "nd", "oh", "ok", "or", "pa", 
-        "ri", "sc", "sd", "tn", "tx", "ut", "vt", "va", "wa", "wv", "wi", "wy"]
+        self.states = [
+            "al",
+            "az",
+            "ar",
+            "ca",
+            "co",
+            "ct",
+            "de",
+            "dc",
+            "fl",
+            "ga",
+            "id",
+            "il",
+            "in",
+            "ia",
+            "ks",
+            "ky",
+            "la",
+            "me",
+            "md",
+            "ma",
+            "mi",
+            "mn",
+            "ms",
+            "mo",
+            "mt",
+            "ne",
+            "nv",
+            "nh",
+            "nj",
+            "nm",
+            "ny",
+            "nc",
+            "nd",
+            "oh",
+            "ok",
+            "or",
+            "pa",
+            "ri",
+            "sc",
+            "sd",
+            "tn",
+            "tx",
+            "ut",
+            "vt",
+            "va",
+            "wa",
+            "wv",
+            "wi",
+            "wy",
+        ]
 
     def extract(self) -> None:
-        logger.info(f"Downloading Tree Equity Score Data")
+        logger.info("Downloading Tree Equity Score Data")
         for state in self.states:
             super().extract(
                 f"{self.TES_URL}{state}.zip.zip",
@@ -29,14 +76,14 @@ class TreeEquityScoreETL(ExtractTransformLoad):
             )
 
     def transform(self) -> None:
-        logger.info(f"Transforming Tree Equity Score Data")
+        logger.info("Transforming Tree Equity Score Data")
         tes_state_dfs = []
         for state in self.states:
             tes_state_dfs.append(gpd.read_file(f"{self.TMP_PATH}/{state}/{state}.shp"))
         self.df = gpd.GeoDataFrame(pd.concat(tes_state_dfs), crs=tes_state_dfs[0].crs)
 
     def load(self) -> None:
-        logger.info(f"Saving Tree Equity Score GeoJSON")
+        logger.info("Saving Tree Equity Score GeoJSON")
         # write nationwide csv
         self.CSV_PATH.mkdir(parents=True, exist_ok=True)
-        self.df.to_file(self.CSV_PATH / "tes_conus.geojson", driver='GeoJSON')
+        self.df.to_file(self.CSV_PATH / "tes_conus.geojson", driver="GeoJSON")

data/data-pipeline/poetry.lock

@@ -31,6 +31,20 @@ dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "wheel", "p
 docs = ["sphinx"]
 tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
 
+[[package]]
+name = "astroid"
+version = "2.6.5"
+description = "An abstract syntax tree for Python with inference support."
+category = "dev"
+optional = false
+python-versions = "~=3.6"
+
+[package.dependencies]
+lazy-object-proxy = ">=1.4.0"
+typed-ast = {version = ">=1.4.0,<1.5", markers = "implementation_name == \"cpython\" and python_version < \"3.8\""}
+typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.8\""}
+wrapt = ">=1.11,<1.13"
+
 [[package]]
 name = "async-generator"
 version = "1.10"
@@ -203,6 +217,18 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
+[[package]]
+name = "configparser"
+version = "5.0.2"
+description = "Updated configparser from Python 3.8 for Python 2.6+."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
+testing = ["pytest (>=4.6)", "pytest-checkdocs (>=1.2.3)", "pytest-flake8", "pytest-cov", "pytest-enabler", "pytest-black (>=0.3.7)", "pytest-mypy"]
+
 [[package]]
 name = "debugpy"
 version = "1.4.0"
@@ -235,6 +261,22 @@ category = "dev"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "dparse"
+version = "0.5.1"
+description = "A parser for Python dependency files"
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+
+[package.dependencies]
+packaging = "*"
+pyyaml = "*"
+toml = "*"
+
+[package.extras]
+pipenv = ["pipenv"]
+
 [[package]]
 name = "dynaconf"
 version = "3.1.4"
@@ -291,6 +333,20 @@ calc = ["shapely"]
 s3 = ["boto3 (>=1.2.4)"]
 test = ["pytest (>=3)", "pytest-cov", "boto3 (>=1.2.4)", "mock"]
 
+[[package]]
+name = "flake8"
+version = "3.9.2"
+description = "the modular source code checker: pep8 pyflakes and co"
+category = "dev"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+
+[package.dependencies]
+importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
+mccabe = ">=0.6.0,<0.7.0"
+pycodestyle = ">=2.7.0,<2.8.0"
+pyflakes = ">=2.3.0,<2.4.0"
+
 [[package]]
 name = "geopandas"
 version = "0.9.0"
@@ -409,6 +465,20 @@ widgetsnbextension = ">=3.5.0,<3.6.0"
 [package.extras]
 test = ["pytest (>=3.6.0)", "pytest-cov", "mock"]
 
+[[package]]
+name = "isort"
+version = "5.9.3"
+description = "A Python utility / library to sort Python imports."
+category = "dev"
+optional = false
+python-versions = ">=3.6.1,<4.0"
+
+[package.extras]
+pipfile_deprecated_finder = ["pipreqs", "requirementslib"]
+requirements_deprecated_finder = ["pipreqs", "pip-api"]
+colors = ["colorama (>=0.4.3,<0.5.0)"]
+plugins = ["setuptools"]
+
 [[package]]
 name = "jedi"
 version = "0.18.0"
@@ -625,6 +695,27 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "lazy-object-proxy"
+version = "1.6.0"
+description = "A fast and thorough lazy object proxy."
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+
+[[package]]
+name = "liccheck"
+version = "0.6.2"
+description = "Check python packages from requirement.txt and report issues"
+category = "dev"
+optional = false
+python-versions = ">=2.7"
+
+[package.dependencies]
+configparser = {version = "*", markers = "python_version >= \"3.4\""}
+semantic-version = ">=2.7.0"
+toml = "*"
+
 [[package]]
 name = "lxml"
 version = "4.6.3"
@@ -658,6 +749,14 @@ python-versions = ">=3.5"
 [package.dependencies]
 traitlets = "*"
 
+[[package]]
+name = "mccabe"
+version = "0.6.1"
+description = "McCabe checker, plugin for flake8"
+category = "dev"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "mistune"
 version = "0.8.4"
@@ -954,6 +1053,14 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
+[[package]]
+name = "pycodestyle"
+version = "2.7.0"
+description = "Python style guide checker"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
 [[package]]
 name = "pycparser"
 version = "2.20"
@@ -962,6 +1069,14 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
+[[package]]
+name = "pyflakes"
+version = "2.3.1"
+description = "passive checker of Python programs"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
 [[package]]
 name = "pygments"
 version = "2.9.0"
@@ -970,6 +1085,21 @@ category = "main"
 optional = false
 python-versions = ">=3.5"
 
+[[package]]
+name = "pylint"
+version = "2.9.6"
+description = "python code static checker"
+category = "dev"
+optional = false
+python-versions = "~=3.6"
+
+[package.dependencies]
+astroid = ">=2.6.5,<2.7"
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+isort = ">=4.2.5,<6"
+mccabe = ">=0.6,<0.7"
+toml = ">=0.7.1"
+
 [[package]]
 name = "pyparsing"
 version = "2.4.7"
@@ -1108,6 +1238,28 @@ urllib3 = ">=1.21.1,<1.27"
 socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"]
 use_chardet_on_py3 = ["chardet (>=3.0.2,<5)"]
 
+[[package]]
+name = "safety"
+version = "1.10.3"
+description = "Checks installed dependencies for known vulnerabilities."
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+
+[package.dependencies]
+Click = ">=6.0"
+dparse = ">=0.5.1"
+packaging = "*"
+requests = "*"
+
+[[package]]
+name = "semantic-version"
+version = "2.8.5"
+description = "A library implementing the 'SemVer' scheme."
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
 [[package]]
 name = "send2trash"
 version = "1.7.1"
@@ -1312,6 +1464,14 @@ python-versions = "*"
 [package.dependencies]
 notebook = ">=4.4.1"
 
+[[package]]
+name = "wrapt"
+version = "1.12.1"
+description = "Module for decorators, wrappers and monkey patching."
+category = "dev"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "zipp"
 version = "3.5.0"
@@ -1327,7 +1487,7 @@ testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytes
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.7.1"
-content-hash = "e6692af9b40f2508a858739de08cb9c1a2e86b54a219b8196ca736981a61ce4d"
+content-hash = "705b0cf25d9ecd3028ba5b71581b5139608cb3b0b4d13c4817b4f3a49643308c"
 
 [metadata.files]
 appdirs = [
@@ -1362,6 +1522,10 @@ argon2-cffi = [
     {file = "argon2_cffi-20.1.0-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:3aa804c0e52f208973845e8b10c70d8957c9e5a666f702793256242e9167c4e0"},
     {file = "argon2_cffi-20.1.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:36320372133a003374ef4275fbfce78b7ab581440dfca9f9471be3dd9a522428"},
 ]
+astroid = [
+    {file = "astroid-2.6.5-py3-none-any.whl", hash = "sha256:7b963d1c590d490f60d2973e57437115978d3a2529843f160b5003b721e1e925"},
+    {file = "astroid-2.6.5.tar.gz", hash = "sha256:83e494b02d75d07d4e347b27c066fd791c0c74fc96c613d1ea3de0c82c48168f"},
+]
 async-generator = [
     {file = "async_generator-1.10-py3-none-any.whl", hash = "sha256:01c7bf666359b4967d2cda0000cc2e4af16a0ae098cbffcb8472fb9e8ad6585b"},
     {file = "async_generator-1.10.tar.gz", hash = "sha256:6ebb3d106c12920aaae42ccb6f787ef5eefdcdd166ea3d628fa8476abe712144"},
@@ -1460,6 +1624,10 @@ colorama = [
     {file = "colorama-0.4.4-py2.py3-none-any.whl", hash = "sha256:9f47eda37229f68eee03b24b9748937c7dc3868f906e8ba69fbcbdd3bc5dc3e2"},
     {file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
 ]
+configparser = [
+    {file = "configparser-5.0.2-py3-none-any.whl", hash = "sha256:af59f2cdd7efbdd5d111c1976ecd0b82db9066653362f0962d7bf1d3ab89a1fa"},
+    {file = "configparser-5.0.2.tar.gz", hash = "sha256:85d5de102cfe6d14a5172676f09d19c465ce63d6019cf0a4ef13385fc535e828"},
+]
 debugpy = [
     {file = "debugpy-1.4.0-cp27-cp27m-macosx_10_14_x86_64.whl", hash = "sha256:55d12ee03b3b705af5250b8344a87fbd9bb720d00bd9d281d2998dbf9f60c8d3"},
     {file = "debugpy-1.4.0-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:712ef6a4be1ee4b9a954c6f36788ac12686dc1d5eeef501e0b81e1c89c16484d"},
@@ -1530,6 +1698,10 @@ distlib = [
     {file = "distlib-0.3.2-py2.py3-none-any.whl", hash = "sha256:23e223426b28491b1ced97dc3bbe183027419dfc7982b4fa2f05d5f3ff10711c"},
     {file = "distlib-0.3.2.zip", hash = "sha256:106fef6dc37dd8c0e2c0a60d3fca3e77460a48907f335fa28420463a6f799736"},
 ]
+dparse = [
+    {file = "dparse-0.5.1-py3-none-any.whl", hash = "sha256:e953a25e44ebb60a5c6efc2add4420c177f1d8404509da88da9729202f306994"},
+    {file = "dparse-0.5.1.tar.gz", hash = "sha256:a1b5f169102e1c894f9a7d5ccf6f9402a836a5d24be80a986c7ce9eaed78f367"},
+]
 dynaconf = [
     {file = "dynaconf-3.1.4-py2.py3-none-any.whl", hash = "sha256:e6f383b84150b70fc439c8b2757581a38a58d07962aa14517292dcce1a77e160"},
     {file = "dynaconf-3.1.4.tar.gz", hash = "sha256:b2f472d83052f809c5925565b8a2ba76a103d5dc1dbb9748b693ed67212781b9"},
@@ -1553,6 +1725,10 @@ fiona = [
     {file = "Fiona-1.8.20-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:e72e4a5b84ec410be531d4fe4c1a5c87c6c0e92d01116c145c0f1b33f81c8080"},
     {file = "Fiona-1.8.20.tar.gz", hash = "sha256:a70502d2857b82f749c09cb0dea3726787747933a2a1599b5ab787d74e3c143b"},
 ]
+flake8 = [
+    {file = "flake8-3.9.2-py2.py3-none-any.whl", hash = "sha256:bf8fd333346d844f616e8d47905ef3a3384edae6b4e9beb0c5101e25e3110907"},
+    {file = "flake8-3.9.2.tar.gz", hash = "sha256:07528381786f2a6237b061f6e96610a4167b226cb926e2aa2b6b1d78057c576b"},
+]
 geopandas = [
     {file = "geopandas-0.9.0-py2.py3-none-any.whl", hash = "sha256:79f6e557ba0dba76eec44f8351b1c6b42a17c38f5f08fef347e98fe4dae563c7"},
     {file = "geopandas-0.9.0.tar.gz", hash = "sha256:63972ab4dc44c4029f340600dcb83264eb8132dd22b104da0b654bef7f42630a"},
@@ -1581,6 +1757,10 @@ ipywidgets = [
     {file = "ipywidgets-7.6.3-py2.py3-none-any.whl", hash = "sha256:e6513cfdaf5878de30f32d57f6dc2474da395a2a2991b94d487406c0ab7f55ca"},
     {file = "ipywidgets-7.6.3.tar.gz", hash = "sha256:9f1a43e620530f9e570e4a493677d25f08310118d315b00e25a18f12913c41f0"},
 ]
+isort = [
+    {file = "isort-5.9.3-py3-none-any.whl", hash = "sha256:e17d6e2b81095c9db0a03a8025a957f334d6ea30b26f9ec70805411e5c7c81f2"},
+    {file = "isort-5.9.3.tar.gz", hash = "sha256:9c2ea1e62d871267b78307fe511c0838ba0da28698c5732d54e2790bf3ba9899"},
+]
 jedi = [
     {file = "jedi-0.18.0-py2.py3-none-any.whl", hash = "sha256:18456d83f65f400ab0c2d3319e48520420ef43b23a086fdc05dff34132f0fb93"},
     {file = "jedi-0.18.0.tar.gz", hash = "sha256:92550a404bad8afed881a137ec9a461fed49eca661414be45059329614ed0707"},
@@ -1636,6 +1816,34 @@ jupyterlab-widgets = [
     {file = "jupyterlab_widgets-1.0.0-py3-none-any.whl", hash = "sha256:caeaf3e6103180e654e7d8d2b81b7d645e59e432487c1d35a41d6d3ee56b3fef"},
     {file = "jupyterlab_widgets-1.0.0.tar.gz", hash = "sha256:5c1a29a84d3069208cb506b10609175b249b6486d6b1cbae8fcde2a11584fb78"},
 ]
+lazy-object-proxy = [
+    {file = "lazy-object-proxy-1.6.0.tar.gz", hash = "sha256:489000d368377571c6f982fba6497f2aa13c6d1facc40660963da62f5c379726"},
+    {file = "lazy_object_proxy-1.6.0-cp27-cp27m-macosx_10_14_x86_64.whl", hash = "sha256:c6938967f8528b3668622a9ed3b31d145fab161a32f5891ea7b84f6b790be05b"},
+    {file = "lazy_object_proxy-1.6.0-cp27-cp27m-win32.whl", hash = "sha256:ebfd274dcd5133e0afae738e6d9da4323c3eb021b3e13052d8cbd0e457b1256e"},
+    {file = "lazy_object_proxy-1.6.0-cp27-cp27m-win_amd64.whl", hash = "sha256:ed361bb83436f117f9917d282a456f9e5009ea12fd6de8742d1a4752c3017e93"},
+    {file = "lazy_object_proxy-1.6.0-cp27-cp27mu-manylinux1_x86_64.whl", hash = "sha256:d900d949b707778696fdf01036f58c9876a0d8bfe116e8d220cfd4b15f14e741"},
+    {file = "lazy_object_proxy-1.6.0-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:5743a5ab42ae40caa8421b320ebf3a998f89c85cdc8376d6b2e00bd12bd1b587"},
+    {file = "lazy_object_proxy-1.6.0-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:bf34e368e8dd976423396555078def5cfc3039ebc6fc06d1ae2c5a65eebbcde4"},
+    {file = "lazy_object_proxy-1.6.0-cp36-cp36m-win32.whl", hash = "sha256:b579f8acbf2bdd9ea200b1d5dea36abd93cabf56cf626ab9c744a432e15c815f"},
+    {file = "lazy_object_proxy-1.6.0-cp36-cp36m-win_amd64.whl", hash = "sha256:4f60460e9f1eb632584c9685bccea152f4ac2130e299784dbaf9fae9f49891b3"},
+    {file = "lazy_object_proxy-1.6.0-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:d7124f52f3bd259f510651450e18e0fd081ed82f3c08541dffc7b94b883aa981"},
+    {file = "lazy_object_proxy-1.6.0-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:22ddd618cefe54305df49e4c069fa65715be4ad0e78e8d252a33debf00f6ede2"},
+    {file = "lazy_object_proxy-1.6.0-cp37-cp37m-win32.whl", hash = "sha256:9d397bf41caad3f489e10774667310d73cb9c4258e9aed94b9ec734b34b495fd"},
+    {file = "lazy_object_proxy-1.6.0-cp37-cp37m-win_amd64.whl", hash = "sha256:24a5045889cc2729033b3e604d496c2b6f588c754f7a62027ad4437a7ecc4837"},
+    {file = "lazy_object_proxy-1.6.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:17e0967ba374fc24141738c69736da90e94419338fd4c7c7bef01ee26b339653"},
+    {file = "lazy_object_proxy-1.6.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:410283732af311b51b837894fa2f24f2c0039aa7f220135192b38fcc42bd43d3"},
+    {file = "lazy_object_proxy-1.6.0-cp38-cp38-win32.whl", hash = "sha256:85fb7608121fd5621cc4377a8961d0b32ccf84a7285b4f1d21988b2eae2868e8"},
+    {file = "lazy_object_proxy-1.6.0-cp38-cp38-win_amd64.whl", hash = "sha256:d1c2676e3d840852a2de7c7d5d76407c772927addff8d742b9808fe0afccebdf"},
+    {file = "lazy_object_proxy-1.6.0-cp39-cp39-macosx_10_14_x86_64.whl", hash = "sha256:b865b01a2e7f96db0c5d12cfea590f98d8c5ba64ad222300d93ce6ff9138bcad"},
+    {file = "lazy_object_proxy-1.6.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:4732c765372bd78a2d6b2150a6e99d00a78ec963375f236979c0626b97ed8e43"},
+    {file = "lazy_object_proxy-1.6.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:9698110e36e2df951c7c36b6729e96429c9c32b3331989ef19976592c5f3c77a"},
+    {file = "lazy_object_proxy-1.6.0-cp39-cp39-win32.whl", hash = "sha256:1fee665d2638491f4d6e55bd483e15ef21f6c8c2095f235fef72601021e64f61"},
+    {file = "lazy_object_proxy-1.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:f5144c75445ae3ca2057faac03fda5a902eff196702b0a24daf1d6ce0650514b"},
+]
+liccheck = [
+    {file = "liccheck-0.6.2-py2.py3-none-any.whl", hash = "sha256:e6583fc327126695a31a7ed8941e784ecd5c84bb2aecbe2782d925cac5c3fe47"},
+    {file = "liccheck-0.6.2.tar.gz", hash = "sha256:5667be7c9ef6496bd381e709e938e9fe51c31d601afc44965615cdfbce375eab"},
+]
 lxml = [
     {file = "lxml-4.6.3-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:df7c53783a46febb0e70f6b05df2ba104610f2fb0d27023409734a3ecbb78fb2"},
     {file = "lxml-4.6.3-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:1b7584d421d254ab86d4f0b13ec662a9014397678a7c4265a02a6d7c2b18a75f"},
@@ -1724,6 +1932,10 @@ matplotlib-inline = [
     {file = "matplotlib-inline-0.1.2.tar.gz", hash = "sha256:f41d5ff73c9f5385775d5c0bc13b424535c8402fe70ea8210f93e11f3683993e"},
     {file = "matplotlib_inline-0.1.2-py3-none-any.whl", hash = "sha256:5cf1176f554abb4fa98cb362aa2b55c500147e4bdbb07e3fda359143e1da0811"},
 ]
+mccabe = [
+    {file = "mccabe-0.6.1-py2.py3-none-any.whl", hash = "sha256:ab8a6258860da4b6677da4bd2fe5dc2c659cff31b3ee4f7f5d64e79735b80d42"},
+    {file = "mccabe-0.6.1.tar.gz", hash = "sha256:dd8d182285a0fe56bace7f45b5e7d1a6ebcbf524e8f3bd87eb0f125271b8831f"},
+]
 mistune = [
     {file = "mistune-0.8.4-py2.py3-none-any.whl", hash = "sha256:88a1051873018da288eee8538d476dffe1262495144b33ecb586c4ab266bb8d4"},
     {file = "mistune-0.8.4.tar.gz", hash = "sha256:59a3429db53c50b5c6bcc8a07f8848cb00d7dc8bdb431a4ab41920d201d4756e"},
@@ -1879,14 +2091,26 @@ py = [
     {file = "py-1.10.0-py2.py3-none-any.whl", hash = "sha256:3b80836aa6d1feeaa108e046da6423ab8f6ceda6468545ae8d02d9d58d18818a"},
     {file = "py-1.10.0.tar.gz", hash = "sha256:21b81bda15b66ef5e1a777a21c4dcd9c20ad3efd0b3f817e7a809035269e1bd3"},
 ]
+pycodestyle = [
+    {file = "pycodestyle-2.7.0-py2.py3-none-any.whl", hash = "sha256:514f76d918fcc0b55c6680472f0a37970994e07bbb80725808c17089be302068"},
+    {file = "pycodestyle-2.7.0.tar.gz", hash = "sha256:c389c1d06bf7904078ca03399a4816f974a1d590090fecea0c63ec26ebaf1cef"},
+]
 pycparser = [
     {file = "pycparser-2.20-py2.py3-none-any.whl", hash = "sha256:7582ad22678f0fcd81102833f60ef8d0e57288b6b5fb00323d101be910e35705"},
     {file = "pycparser-2.20.tar.gz", hash = "sha256:2d475327684562c3a96cc71adf7dc8c4f0565175cf86b6d7a404ff4c771f15f0"},
 ]
+pyflakes = [
+    {file = "pyflakes-2.3.1-py2.py3-none-any.whl", hash = "sha256:7893783d01b8a89811dd72d7dfd4d84ff098e5eed95cfa8905b22bbffe52efc3"},
+    {file = "pyflakes-2.3.1.tar.gz", hash = "sha256:f5bc8ecabc05bb9d291eb5203d6810b49040f6ff446a756326104746cc00c1db"},
+]
 pygments = [
     {file = "Pygments-2.9.0-py3-none-any.whl", hash = "sha256:d66e804411278594d764fc69ec36ec13d9ae9147193a1740cd34d272ca383b8e"},
     {file = "Pygments-2.9.0.tar.gz", hash = "sha256:a18f47b506a429f6f4b9df81bb02beab9ca21d0a5fee38ed15aef65f0545519f"},
 ]
+pylint = [
+    {file = "pylint-2.9.6-py3-none-any.whl", hash = "sha256:2e1a0eb2e8ab41d6b5dbada87f066492bb1557b12b76c47c2ee8aa8a11186594"},
+    {file = "pylint-2.9.6.tar.gz", hash = "sha256:8b838c8983ee1904b2de66cce9d0b96649a91901350e956d78f289c3bc87b48e"},
+]
 pyparsing = [
     {file = "pyparsing-2.4.7-py2.py3-none-any.whl", hash = "sha256:ef9d7589ef3c200abe66653d3f1ab1033c3c419ae9b9bdb1240a85b024efc88b"},
     {file = "pyparsing-2.4.7.tar.gz", hash = "sha256:c203ec8783bf771a155b207279b9bccb8dea02d8f0c9e5f8ead507bc3246ecc1"},
@@ -2082,6 +2306,14 @@ requests = [
     {file = "requests-2.26.0-py2.py3-none-any.whl", hash = "sha256:6c1246513ecd5ecd4528a0906f910e8f0f9c6b8ec72030dc9fd154dc1a6efd24"},
     {file = "requests-2.26.0.tar.gz", hash = "sha256:b8aa58f8cf793ffd8782d3d8cb19e66ef36f7aba4353eec859e74678b01b07a7"},
 ]
+safety = [
+    {file = "safety-1.10.3-py2.py3-none-any.whl", hash = "sha256:5f802ad5df5614f9622d8d71fedec2757099705c2356f862847c58c6dfe13e84"},
+    {file = "safety-1.10.3.tar.gz", hash = "sha256:30e394d02a20ac49b7f65292d19d38fa927a8f9582cdfd3ad1adbbc66c641ad5"},
+]
+semantic-version = [
+    {file = "semantic_version-2.8.5-py2.py3-none-any.whl", hash = "sha256:45e4b32ee9d6d70ba5f440ec8cc5221074c7f4b0e8918bdab748cc37912440a9"},
+    {file = "semantic_version-2.8.5.tar.gz", hash = "sha256:d2cb2de0558762934679b9a104e82eca7af448c9f4974d1f3eeccff651df8a54"},
+]
 send2trash = [
     {file = "Send2Trash-1.7.1-py3-none-any.whl", hash = "sha256:c20fee8c09378231b3907df9c215ec9766a84ee20053d99fbad854fe8bd42159"},
     {file = "Send2Trash-1.7.1.tar.gz", hash = "sha256:17730aa0a33ab82ed6ca76be3bb25f0433d0014f1ccf63c979bab13a5b9db2b2"},
@@ -2243,6 +2475,9 @@ widgetsnbextension = [
     {file = "widgetsnbextension-3.5.1-py2.py3-none-any.whl", hash = "sha256:bd314f8ceb488571a5ffea6cc5b9fc6cba0adaf88a9d2386b93a489751938bcd"},
     {file = "widgetsnbextension-3.5.1.tar.gz", hash = "sha256:079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7"},
 ]
+wrapt = [
+    {file = "wrapt-1.12.1.tar.gz", hash = "sha256:b62ffa81fb85f4332a4f609cab4ac40709470da05643a082ec1eb88e6d9b97d7"},
+]
 zipp = [
     {file = "zipp-3.5.0-py3-none-any.whl", hash = "sha256:957cfda87797e389580cb8b9e3870841ca991e2125350677b2ca83a0e99390a3"},
     {file = "zipp-3.5.0.tar.gz", hash = "sha256:f5812b1e007e48cff63449a5e9f4e7ebea716b4111f9c4f9a645f91d579bf0c4"},

data/data-pipeline/pyproject.toml

@@ -25,7 +25,79 @@ types-requests = "^2.25.0"
 black = {version = "^21.6b0", allow-prereleases = true}
 mypy = "^0.910"
 tox = "^3.24.0"
+flake8 = "^3.9.2"
+pylint = "^2.9.6"
+liccheck = "^0.6.2"
+safety = "^1.10.3"
 
 [build-system]
 build-backend = "poetry.core.masonry.api"
 requires = ["poetry-core>=1.0.0"]
+
+[tool.pylint]
+
+[tool.pylint."MESSAGE CONTROL"]
+disable = [
+  "C0114",  # Disables module docstrings
+  "R0201",  # Disables method could have been a function
+  "R0903",  # Disables too few public methods
+  "C0103",  # Disables name case styling
+  "W0511",  # Disables FIXME warning
+  "W1203",  # Disables f-string interpolation for logging warning
+  # Errors temporarily ignored for further discussion
+  "W0107",  # Disables unnecessary pass
+  "W0221",  # Disables arguments differ
+  "R0902",  # Disables too many instance attributes
+  "R0914",  # Disables too many local variables
+  "W0621",  # Disables redefined outer name
+  "C0302",  # Disables too many lines in module
+  "R1732",  # Disables consider using "with"
+  "R1720",  # Disables unnecessary "else" after "raise"
+  "C0206",  # Disables consider iteratig with ".items()"
+  "C0200",  # Disables consider using "enumerate" instead of "range" + "len"
+  "W0612",  # Disables unused variable
+  "W0613",  # Disables unused argument
+  "C0116",  # Disables missing function or method docstring
+  "C0115",  # Disables missing class docstring
+]
+
+[tool.pylint.FORMAT]
+max-line-length=150
+
+[tool.pylint.SIMILARITIES]
+# Configures how pylint detects repetitive code
+min-similarity-lines = 4
+ignore-comments = "yes"
+ignore-docstrings = "yes"
+ignore-imports = "yes"
+
+[tool.liccheck]
+# Authorized and unauthorized licenses in LOWER CASE
+authorized_licenses = [
+        "bsd",
+        "new bsd",
+        "bsd license",
+        "bsd 3-clause",
+        "new bsd license",
+        "simplified bsd",
+        "apache",
+        "apache 2.0",
+        "apache license 2.0",
+        "apache software license",
+        "apache software",
+        "gnu lgpl",
+        "gnu lesser general public license v2 (lgplv2)",
+        "gnu general public license v2 (gplv2)",
+        "gnu library or lesser general public license (lgpl)",
+        "lgpl with exceptions or zpl",
+        "isc license",
+        "isc license (iscl)",
+        "mit",
+        "mit license",
+        "mozilla public license 2.0 (mpl 2.0)",
+        "public domain",
+        "python software foundation license",
+        "python software foundation",
+        "zpl 2.1",
+        "gpl v3"
+]

data/data-pipeline/requirements.txt

@@ -1,30 +1,40 @@
-appnope==0.1.2; sys_platform == "darwin" and python_version >= "3.7"
+appdirs==1.4.4; python_full_version >= "3.6.2"
+appnope==0.1.2; sys_platform == "darwin" and python_version >= "3.7" and platform_system == "Darwin"
 argon2-cffi==20.1.0; python_version >= "3.6"
+astroid==2.6.5; python_version >= "3.6" and python_version < "4.0"
 async-generator==1.10; python_full_version >= "3.6.1" and python_version >= "3.7"
 attrs==21.2.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
 backcall==0.2.0; python_version >= "3.7"
-bleach==3.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
+backports.entry-points-selectable==1.1.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "2.7"
+black==21.7b0; python_full_version >= "3.6.2"
+bleach==3.3.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
 censusdata==1.13; python_version >= "2.7"
-certifi==2021.5.30; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
+certifi==2021.5.30; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
 cffi==1.14.6; implementation_name == "pypy" and python_version >= "3.6"
-chardet==4.0.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "2.7"
+charset-normalizer==2.0.3; python_full_version >= "3.6.0" and python_version >= "3"
 click-plugins==1.1.1; python_version >= "3.6"
 click==8.0.1; python_version >= "3.6"
 cligj==0.7.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version < "4" and python_version >= "3.6"
-colorama==0.4.4; python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" and sys_platform == "win32" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0" and sys_platform == "win32"
-debugpy==1.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
+colorama==0.4.4; platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.6.2" and sys_platform == "win32" and python_version < "4.0" and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0")
+configparser==5.0.2; python_version >= "3.6"
+debugpy==1.4.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
 decorator==5.0.9; python_version >= "3.7"
 defusedxml==0.7.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
+distlib==0.3.2; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
+dparse==0.5.1; python_version >= "3.5"
 dynaconf==3.1.4
 entrypoints==0.3; python_version >= "3.7"
+filelock==3.0.12; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
 fiona==1.8.20; python_version >= "3.6"
+flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
 geopandas==0.9.0; python_version >= "3.6"
-idna==2.10; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "2.7"
-importlib-metadata==3.10.1; python_version < "3.8" and python_version >= "3.7"
-ipykernel==6.0.1; python_version >= "3.7"
+idna==3.2; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5"
+importlib-metadata==3.10.1; python_version < "3.8" and python_version >= "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "3.8" or python_full_version >= "3.5.0" and python_version < "3.8" and python_version >= "3.6") and python_full_version >= "3.6.2"
+ipykernel==6.0.3; python_version >= "3.7"
 ipython-genutils==0.2.0; python_version >= "3.7"
 ipython==7.25.0; python_version >= "3.7"
 ipywidgets==7.6.3
+isort==5.9.3; python_full_version >= "3.6.1" and python_version < "4.0" and python_version >= "3.6"
 jedi==0.18.0; python_version >= "3.7"
 jinja2==3.0.1; python_version >= "3.7"
 jsonschema==3.2.0; python_version >= "3.5"
@@ -39,52 +49,72 @@ jupyter-nbextensions-configurator==0.4.1
 jupyter==1.0.0
 jupyterlab-pygments==0.1.2; python_version >= "3.7"
 jupyterlab-widgets==1.0.0; python_version >= "3.6"
+lazy-object-proxy==1.6.0; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.6.0"
+liccheck==0.6.2; python_version >= "2.7"
 lxml==4.6.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
 markupsafe==2.0.1; python_version >= "3.7"
-matplotlib-inline==0.1.2; platform_system == "Darwin" and python_version >= "3.7"
+matplotlib-inline==0.1.2; python_version >= "3.7"
+mccabe==0.6.1; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.5.0"
 mistune==0.8.4; python_version >= "3.7"
 munch==2.5.0; python_version >= "3.6"
+mypy-extensions==0.4.3; python_full_version >= "3.6.2" and python_version >= "3.5"
+mypy==0.910; python_version >= "3.5"
 nbclient==0.5.3; python_full_version >= "3.6.1" and python_version >= "3.7"
 nbconvert==6.1.0; python_version >= "3.7"
 nbformat==5.1.3; python_full_version >= "3.6.1" and python_version >= "3.7"
 nest-asyncio==1.5.1; python_full_version >= "3.6.1" and python_version >= "3.7"
 notebook==6.4.0; python_version >= "3.6"
-numpy==1.21.0; python_version >= "3.7"
+numpy==1.21.1; python_version >= "3.7"
 packaging==21.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
 pandas==1.3.0; python_full_version >= "3.7.1"
 pandocfilters==1.4.3; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
 parso==0.8.2; python_version >= "3.7"
+pathspec==0.9.0; python_full_version >= "3.6.2"
 pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.7"
 pickleshare==0.7.5; python_version >= "3.7"
+platformdirs==2.0.2; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
+pluggy==0.13.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
 prometheus-client==0.11.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
 prompt-toolkit==3.0.19; python_full_version >= "3.6.1" and python_version >= "3.7"
 ptyprocess==0.7.0; sys_platform != "win32" and python_version >= "3.7" and os_name != "nt"
-py==1.10.0; python_version >= "3.6" and python_full_version < "3.0.0" and implementation_name == "pypy" or implementation_name == "pypy" and python_version >= "3.6" and python_full_version >= "3.4.0"
+py==1.10.0; python_version >= "3.6" and python_full_version < "3.0.0" and implementation_name == "pypy" or implementation_name == "pypy" and python_version >= "3.6" and python_full_version >= "3.5.0"
+pycodestyle==2.7.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
 pycparser==2.20; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
+pyflakes==2.3.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
 pygments==2.9.0; python_version >= "3.7"
-pyparsing==2.4.7; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
+pylint==2.9.6; python_version >= "3.6" and python_version < "4.0"
+pyparsing==2.4.7; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
 pyproj==3.1.0; python_version >= "3.7"
 pyrsistent==0.18.0; python_version >= "3.6"
-python-dateutil==2.8.1; python_full_version >= "3.7.1" and python_version >= "3.7"
+python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7"
 pytz==2021.1; python_full_version >= "3.7.1" and python_version >= "2.7"
 pywin32==301; sys_platform == "win32" and python_version >= "3.6"
 pywinpty==1.1.3; os_name == "nt" and python_version >= "3.6"
-pyyaml==5.4.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
+pyyaml==5.4.1; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5"
 pyzmq==22.1.0; python_full_version >= "3.6.1" and python_version >= "3.7"
 qtconsole==5.1.1; python_version >= "3.6"
 qtpy==1.9.0; python_version >= "3.6"
-requests==2.25.1; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
+regex==2021.7.6; python_full_version >= "3.6.2"
+requests==2.26.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0")
+safety==1.10.3; python_version >= "3.5"
+semantic-version==2.8.5; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "2.7"
 send2trash==1.7.1; python_version >= "3.6"
 shapely==1.7.1; python_version >= "3.6"
-six==1.16.0; python_full_version >= "3.7.1" and python_version >= "2.7" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5")
+six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.6" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5")
 terminado==0.10.1; python_version >= "3.6"
 testpath==0.5.0; python_version >= "3.7"
+toml==0.10.2; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.5.0" and python_version >= "3.6" and python_version < "4.0"
+tomli==1.1.0; python_version >= "3.6" and python_full_version >= "3.6.2"
 tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.7"
+tox==3.24.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0")
 traitlets==5.0.5; python_full_version >= "3.6.1" and python_version >= "3.7"
+typed-ast==1.4.3; python_version < "3.8" and python_full_version >= "3.6.2" and python_version >= "3.6" and implementation_name == "cpython"
 types-requests==2.25.0
-typing-extensions==3.10.0.0; python_version < "3.8" and python_version >= "3.6"
-urllib3==1.26.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version < "4" and python_version >= "2.7"
+typing-extensions==3.10.0.0; python_version < "3.8" and python_full_version >= "3.6.2" and python_version >= "3.6"
+urllib3==1.26.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "2.7"
+virtualenv==20.6.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
 wcwidth==0.2.5; python_full_version >= "3.6.1" and python_version >= "3.7"
 webencodings==0.5.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
 widgetsnbextension==3.5.1
+wrapt==1.12.1; python_version >= "3.6" and python_version < "4.0"
 zipp==3.5.0; python_version < "3.8" and python_version >= "3.6"

data/data-pipeline/tox.ini

@@ -1,6 +1,20 @@
 [tox]
 # required because we use pyproject.toml
 isolated_build = true
-envlist = py37, py38, py39
+envlist = py37, py38, py39, lint, checkdeps
 # only checks python versions installed locally
 skip_missing_interpreters = true
+
+[testenv:lint]
+# lints python code in src and tests
+basepython = python3.9
+deps = -rrequirements.txt
+commands = black etl application.py config.py utils.py
+           flake8 etl application.py config.py utils.py
+           # pylint etl application.py config.py utils.py
+
+[testenv:checkdeps]
+# checks the dependencies for security vulnerabilities and open source licenses
+deps = -rrequirements.txt
+commands = safety check
+           liccheck

data/data-pipeline/utils.py

@@ -2,10 +2,11 @@ from pathlib import Path
 import os
 import logging
 import shutil
-import requests
 import zipfile
 import urllib3
 
+import requests
+
 from config import settings
 
 
@@ -133,12 +134,13 @@ def unzip_file_from_url(
     # cleanup temporary file
     os.remove(zip_file_path)
 
+
 def data_folder_cleanup() -> None:
     """Remove all files and directories from the local data/dataset path"""
 
     data_path = settings.APP_ROOT / "data"
 
-    logger.info(f"Initializing all dataset directoriees")
+    logger.info("Initializing all dataset directoriees")
     remove_all_from_dir(data_path / "dataset")
 
 
@@ -147,7 +149,7 @@ def score_folder_cleanup() -> None:
 
     data_path = settings.APP_ROOT / "data"
 
-    logger.info(f"Initializing all score data")
+    logger.info("Initializing all score data")
     remove_all_from_dir(data_path / "score" / "csv")
     remove_all_from_dir(data_path / "score" / "geojson")
 
@@ -157,9 +159,10 @@ def temp_folder_cleanup() -> None:
 
     data_path = settings.APP_ROOT / "data"
 
-    logger.info(f"Initializing all temp directoriees")
+    logger.info("Initializing all temp directoriees")
     remove_all_from_dir(data_path / "tmp")
 
+
 def get_excel_column_name(index: int) -> str:
     """Map a numeric index to the appropriate column in Excel. E.g., column #95 is "CR".
     Only works for the first 1000 columns.