Running Black

data/data-pipeline/data_pipeline/application.py

@@ -22,9 +22,7 @@ def cli():
     pass
 
 
-@cli.command(
-    help="Clean up all census data folders",
-)
+@cli.command(help="Clean up all census data folders",)
 def census_cleanup():
     """CLI command to clean up the census data folder"""
 
@@ -37,9 +35,7 @@ def census_cleanup():
     logger.info("Cleaned up all census data files")
 
 
-@cli.command(
-    help="Clean up all data folders",
-)
+@cli.command(help="Clean up all data folders",)
 def data_cleanup():
     """CLI command to clean up the all the data folders"""
 
@@ -50,9 +46,7 @@ def data_cleanup():
     logger.info("Cleaned up all data folders")
 
 
-@cli.command(
-    help="Census data download",
-)
+@cli.command(help="Census data download",)
 def census_data_download():
     """CLI command to download all census shape files from the Census FTP and extract the geojson
     to generate national and by state Census Block Group CSVs"""
@@ -64,9 +58,7 @@ def census_data_download():
     logger.info("Completed downloading census data")
 
 
-@cli.command(
-    help="Run all ETL processes or a specific one",
-)
+@cli.command(help="Run all ETL processes or a specific one",)
 @click.option("-d", "--dataset", required=False, type=str)
 def etl_run(dataset: str):
     """Run a specific or all ETL processes
@@ -81,27 +73,21 @@ def etl_run(dataset: str):
     etl_runner(dataset)
 
 
-@cli.command(
-    help="Generate Score",
-)
+@cli.command(help="Generate Score",)
 def score_run():
     """CLI command to generate the score"""
 
     score_generate()
 
 
-@cli.command(
-    help="Generate Geojson files with scores baked in",
-)
+@cli.command(help="Generate Geojson files with scores baked in",)
 def geo_score():
     """CLI command to generate the score"""
 
     score_geo()
 
 
-@cli.command(
-    help="Generate map tiles",
-)
+@cli.command(help="Generate map tiles",)
 def generate_map_tiles():
     """CLI command to generate the map tiles"""
 

data/data-pipeline/data_pipeline/etl/runner.py

@@ -27,11 +27,7 @@ def etl_runner(dataset_to_run: str = None) -> None:
             "module_dir": "census_acs",
             "class_name": "CensusACSETL",
         },
-        {
-            "name": "ejscreen",
-            "module_dir": "ejscreen",
-            "class_name": "EJScreenETL",
-        },
+        {"name": "ejscreen", "module_dir": "ejscreen", "class_name": "EJScreenETL",},
         {
             "name": "housing_and_transportation",
             "module_dir": "housing_and_transportation",
@@ -47,17 +43,12 @@ def etl_runner(dataset_to_run: str = None) -> None:
             "module_dir": "calenviroscreen",
             "class_name": "CalEnviroScreenETL",
         },
-        {
-            "name": "hud_recap",
-            "module_dir": "hud_recap",
-            "class_name": "HudRecapETL",
-        },
+        {"name": "hud_recap", "module_dir": "hud_recap", "class_name": "HudRecapETL",},
     ]
 
     if dataset_to_run:
         dataset_element = next(
-            (item for item in dataset_list if item["name"] == dataset_to_run),
-            None,
+            (item for item in dataset_list if item["name"] == dataset_to_run), None,
         )
         if not dataset_list:
             raise ValueError("Invalid dataset name")

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

@@ -59,9 +59,7 @@ class ScoreETL(ExtractTransformLoad):
         # Load census data
         census_csv = self.DATA_PATH / "dataset" / "census_acs_2019" / "usa.csv"
         self.census_df = pd.read_csv(
-            census_csv,
-            dtype={self.GEOID_FIELD_NAME: "string"},
-            low_memory=False,
+            census_csv, dtype={self.GEOID_FIELD_NAME: "string"}, low_memory=False,
         )
 
         # Load housing and transportation data
@@ -123,8 +121,7 @@ class ScoreETL(ExtractTransformLoad):
 
         # Define a named tuple that will be used for each data set input.
         DataSet = collections.namedtuple(
-            typename="DataSet",
-            field_names=["input_field", "renamed_field", "bucket"],
+            typename="DataSet", field_names=["input_field", "renamed_field", "bucket"],
         )
 
         data_sets = [
@@ -141,9 +138,7 @@ class ScoreETL(ExtractTransformLoad):
                 bucket=None,
             ),
             DataSet(
-                input_field="ACSTOTPOP",
-                renamed_field="Total population",
-                bucket=None,
+                input_field="ACSTOTPOP", renamed_field="Total population", bucket=None,
             ),
             # The following data sets have buckets, because they're used in the score
             DataSet(
@@ -249,9 +244,7 @@ class ScoreETL(ExtractTransformLoad):
         }
 
         self.df.rename(
-            columns=renaming_dict,
-            inplace=True,
-            errors="raise",
+            columns=renaming_dict, inplace=True, errors="raise",
         )
 
         columns_to_keep = [data_set.renamed_field for data_set in data_sets]

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

@@ -46,9 +46,7 @@ class GeoScoreETL(ExtractTransformLoad):
 
         logger.info("Reading score CSV")
         self.score_usa_df = pd.read_csv(
-            self.TILE_SCORE_CSV,
-            dtype={"GEOID10": "string"},
-            low_memory=False,
+            self.TILE_SCORE_CSV, dtype={"GEOID10": "string"}, low_memory=False,
         )
 
     def transform(self) -> None:
@@ -70,8 +68,7 @@ class GeoScoreETL(ExtractTransformLoad):
         ].reset_index(drop=True)
 
         usa_simplified.rename(
-            columns={self.TARGET_SCORE_NAME: self.TARGET_SCORE_RENAME_TO},
-            inplace=True,
+            columns={self.TARGET_SCORE_NAME: self.TARGET_SCORE_RENAME_TO}, inplace=True,
         )
 
         logger.info("Aggregating into tracts (~5 minutes)")
@@ -156,4 +153,4 @@ class GeoScoreETL(ExtractTransformLoad):
 
         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")
+        logger.info("Completed writing usa-low")

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

@@ -43,8 +43,7 @@ class PostScoreETL(ExtractTransformLoad):
 
     def extract(self) -> None:
         super().extract(
-            self.CENSUS_COUNTIES_ZIP_URL,
-            self.TMP_PATH,
+            self.CENSUS_COUNTIES_ZIP_URL, self.TMP_PATH,
         )
 
         logger.info("Reading Counties CSV")
@@ -68,8 +67,7 @@ class PostScoreETL(ExtractTransformLoad):
         # rename some of the columns to prepare for merge
         self.counties_df = self.counties_df[["USPS", "GEOID", "NAME"]]
         self.counties_df.rename(
-            columns={"USPS": "State Abbreviation", "NAME": "County Name"},
-            inplace=True,
+            columns={"USPS": "State Abbreviation", "NAME": "County Name"}, inplace=True,
         )
 
         # remove unnecessary columns

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

@@ -28,8 +28,7 @@ class CalEnviroScreenETL(ExtractTransformLoad):
     def extract(self) -> None:
         logger.info("Downloading CalEnviroScreen Data")
         super().extract(
-            self.CALENVIROSCREEN_FTP_URL,
-            self.TMP_PATH,
+            self.CALENVIROSCREEN_FTP_URL, self.TMP_PATH,
         )
 
     def transform(self) -> None:

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

@@ -39,9 +39,7 @@ def download_census_csvs(data_path: Path) -> None:
             # But using 2010 for now
             cbg_state_url = f"https://www2.census.gov/geo/tiger/TIGER2010/BG/2010/tl_2010_{fips}_bg10.zip"
             unzip_file_from_url(
-                cbg_state_url,
-                data_path / "tmp",
-                data_path / "census" / "shp" / fips,
+                cbg_state_url, data_path / "tmp", data_path / "census" / "shp" / fips,
             )
 
             cmd = (
@@ -80,32 +78,22 @@ def download_census_csvs(data_path: Path) -> None:
             csv_dir_path / f"{state_id}.csv", mode="w", newline=""
         ) as cbg_csv_file:
             cbg_csv_file_writer = csv.writer(
-                cbg_csv_file,
-                delimiter=",",
-                quotechar='"',
-                quoting=csv.QUOTE_MINIMAL,
+                cbg_csv_file, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL,
             )
 
             for geoid10 in geoid10_list:
                 cbg_csv_file_writer.writerow(
-                    [
-                        geoid10,
-                    ]
+                    [geoid10,]
                 )
 
     ## write US csv
     with open(csv_dir_path / "us.csv", mode="w", newline="") as cbg_csv_file:
         cbg_csv_file_writer = csv.writer(
-            cbg_csv_file,
-            delimiter=",",
-            quotechar='"',
-            quoting=csv.QUOTE_MINIMAL,
+            cbg_csv_file, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL,
         )
         for geoid10 in cbg_national:
             cbg_csv_file_writer.writerow(
-                [
-                    geoid10,
-                ]
+                [geoid10,]
             )
 
     ## create national geojson

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

@@ -18,8 +18,7 @@ class EJScreenETL(ExtractTransformLoad):
     def extract(self) -> None:
         logger.info("Downloading EJScreen Data")
         super().extract(
-            self.EJSCREEN_FTP_URL,
-            self.TMP_PATH,
+            self.EJSCREEN_FTP_URL, self.TMP_PATH,
         )
 
     def transform(self) -> None:

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

@@ -34,8 +34,7 @@ class HudHousingETL(ExtractTransformLoad):
     def extract(self) -> None:
         logger.info("Extracting HUD Housing Data")
         super().extract(
-            self.HOUSING_FTP_URL,
-            self.HOUSING_ZIP_FILE_DIR,
+            self.HOUSING_FTP_URL, self.HOUSING_ZIP_FILE_DIR,
         )
 
     def transform(self) -> None:
@@ -49,10 +48,7 @@ class HudHousingETL(ExtractTransformLoad):
             / "140"
             / "Table8.csv"
         )
-        self.df = pd.read_csv(
-            filepath_or_buffer=tmp_csv_file_path,
-            encoding="latin-1",
-        )
+        self.df = pd.read_csv(filepath_or_buffer=tmp_csv_file_path, encoding="latin-1",)
 
         # Rename and reformat block group ID
         self.df.rename(columns={"geoid": self.GEOID_TRACT_FIELD_NAME}, inplace=True)

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

@@ -70,8 +70,7 @@ class TreeEquityScoreETL(ExtractTransformLoad):
         logger.info("Downloading Tree Equity Score Data")
         for state in self.states:
             super().extract(
-                f"{self.TES_URL}{state}.zip.zip",
-                f"{self.TMP_PATH}/{state}",
+                f"{self.TES_URL}{state}.zip.zip", f"{self.TMP_PATH}/{state}",
             )
 
     def transform(self) -> None:

data/data-pipeline/data_pipeline/utils.py

@@ -97,10 +97,7 @@ def remove_all_dirs_from_dir(dir_path: Path) -> None:
 
 
 def unzip_file_from_url(
-    file_url: str,
-    download_path: Path,
-    unzipped_file_path: Path,
-    verify: bool = False,
+    file_url: str, download_path: Path, unzipped_file_path: Path, verify: bool = False,
 ) -> None:
     """Downloads a zip file from a remote URL location and unzips it in a specific directory, removing the temporary file after