lint

_delphi_utils_python/delphi_utils/__init__.py

@@ -4,15 +4,14 @@
 from __future__ import absolute_import
 
 from .archive import ArchiveDiffer, GitArchiveDiffer, S3ArchiveDiffer
-from .export import create_export_csv, create_backup_csv
-from .utils import read_params
-
-from .slack_notifier import SlackNotifier
-from .logger import get_structured_logger
+from .export import create_backup_csv, create_export_csv
 from .geomap import GeoMapper
-from .smooth import Smoother
-from .signal import add_prefix
+from .logger import get_structured_logger
 from .nancodes import Nans
+from .signal import add_prefix
+from .slack_notifier import SlackNotifier
+from .smooth import Smoother
+from .utils import read_params
 from .weekday import Weekday
 
 __version__ = "0.3.25"

_delphi_utils_python/delphi_utils/export.py

@@ -1,17 +1,18 @@
 """Export data in the format expected by the Delphi API."""
 # -*- coding: utf-8 -*-
-from datetime import datetime
 import gzip
-from os.path import join, getsize
-from typing import Optional
 import logging
+from datetime import datetime
+from os.path import getsize, join
+from typing import Optional
 
-from epiweeks import Week
 import numpy as np
 import pandas as pd
+from epiweeks import Week
 
 from .nancodes import Nans
 
+
 def filter_contradicting_missing_codes(df, sensor, metric, date, logger=None):
     """Find values with contradictory missingness codes, filter them, and log."""
     columns = ["val", "se", "sample_size"]
@@ -142,7 +143,7 @@ def create_backup_csv(
     geo_res: Optional[str] = None,
     sensor: Optional[str] = None,
     metric: Optional[str] = None,
-    logger: Optional[logging.Logger] = None
+    logger: Optional[logging.Logger] = None,
 ):
     """Save data for use as a backup.
 
@@ -186,18 +187,18 @@ def create_backup_csv(
 
         # Label the file with today's date (the date the data was fetched).
         if not issue:
-            issue = datetime.today().strftime('%Y%m%d')
+            issue = datetime.today().strftime("%Y%m%d")
 
         backup_filename = [issue, geo_res, metric, sensor]
         backup_filename = "_".join(filter(None, backup_filename)) + ".csv.gz"
         backup_file = join(backup_dir, backup_filename)
 
-        with gzip.open(backup_file, 'wt', newline='') as f:
+        with gzip.open(backup_file, "wt", newline="") as f:
             df.to_csv(f, index=False, na_rep="NA")
 
         if logger:
             logger.info(
                 "Backup file created",
                 backup_file=backup_file,
-                backup_size=getsize(backup_file)
+                backup_size=getsize(backup_file),
             )