Minor automatic reformatting

esmvaltool/diag_scripts/kcs/global_matching.py

@@ -1,15 +1,19 @@
 """Align the target model with the CMIP ensemble."""
 import logging
-from pathlib import Path
 from itertools import product
+from pathlib import Path
 
 import matplotlib.pyplot as plt
 import pandas as pd
 import xarray as xr
 
-from esmvaltool.diag_scripts.shared import (get_diagnostic_filename,
-                                            get_plot_filename, run_diagnostic,
-                                            select_metadata, ProvenanceLogger)
+from esmvaltool.diag_scripts.shared import (
+    ProvenanceLogger,
+    get_diagnostic_filename,
+    get_plot_filename,
+    run_diagnostic,
+    select_metadata,
+)
 
 logger = logging.getLogger(Path(__file__).name)
 
@@ -53,9 +57,9 @@ def get_cmip_dt(metadata, year, percentile):
 def get_resampling_period(target_dts, cmip_dt):
     """Return 30-year time bounds of the resampling period.
 
-    This is the period for which the target model delta T
-    matches the cmip delta T for a specific year.
-    Uses a 30-year rolling window to get the best match.
+    This is the period for which the target model delta T matches the
+    cmip delta T for a specific year. Uses a 30-year rolling window to
+    get the best match.
     """
     target_dts = target_dts.rolling(time=30, center=True,
                                     min_periods=30).mean()
@@ -85,8 +89,8 @@ def _timeline(axes, yloc, interval):
 def make_plot(metadata, scenarios, cfg, provenance):
     """Make figure 3, left graph.
 
-    Multimodel values as line, reference value in black square,
-    steering variables in dark dots.
+    Multimodel values as line, reference value in black square, steering
+    variables in dark dots.
     """
     fig, axes = plt.subplots()
     for member in select_metadata(metadata, variable_group='tas_cmip'):