isort:skip_file

Author: davidhassell

cf/test/test_DimensionCoordinate.py

@@ -808,7 +808,6 @@ def test_DimensionCoordinate_anchor(self):
         e = d.anchor(15, cell=True)
         self.assertEqual(e[0].array, d[0].array - 360)
 
-
     def test_DimensionCoordinate_direction(self):
         """Test DimensionCoordinate.direction"""
         d = self.dim.copy()

docs/source/conf.py

@@ -22,7 +22,6 @@
 
 import cf
 
-
 print("\ncf environment:")
 print("-----------------")
 cf.environment()
@@ -392,7 +391,9 @@ def _get_date():
     "inspect_global_variables": True,
     "within_subsection_order": FileNameSortKey,
     "default_thumb_file": "_static/logo.svg",
-    "image_scrapers": ("matplotlib",),  # Ensures Matplotlib images are captured
+    "image_scrapers": (
+        "matplotlib",
+    ),  # Ensures Matplotlib images are captured
     "plot_gallery": "True",  # Enables plot rendering
     "reset_modules": ("matplotlib",),  # Helps with memory management
     "capture_repr": (),

docs/source/recipes/plot_08_recipe.py

@@ -3,6 +3,8 @@
 ====================================================================
 
 In this recipe, we will analyse and plot temperature trends from the HadCRUT.5.0.1.0 dataset for two different time periods. The plotted maps also include stippling, which is used to highlight areas where the temperature trends are statistically significant.
+
+isort:skip_file
 """
 
 # %%

docs/source/recipes/plot_12_recipe.py

@@ -8,6 +8,7 @@
 to plot the `high-quality` retrieval of Aerosol Optical Depth (AOD) from all other
 retrievals.
 
+isort:skip_file
 """
 
 # %%

docs/source/recipes/plot_13_recipe.py

@@ -11,6 +11,7 @@
 Niña events are defined when the Niño 3.4 SSTs exceed +/- 0.4 degrees Celsius for a
 period of six months or more.
 
+isort:skip_file
 """
 
 # %%

docs/source/recipes/plot_17_recipe.py

@@ -6,6 +6,8 @@
 three categories in separate subplots to illustrate the importance of
 choosing a suitable one for given data. To avoid unintended bias and
 misrepresentation, or lack of accessibility, a careful choice must be made.
+
+isort:skip_file
 """
 
 # %%

docs/source/recipes/plot_18_recipe.py

@@ -8,6 +8,7 @@
 calculate the correlation coefficient, to get a measure of the relationship
 between them.
 
+isort:skip_file
 """
 
 # %%

docs/source/recipes/plot_19_recipe.py

@@ -7,6 +7,8 @@
 interpret for trends when in a monthly form, we calculate and plot
 on top of this the mean across each season for both the minima and the
 maxima.
+
+isort:skip_file
 """
 
 # %%
@@ -55,7 +57,10 @@
 # of the maxima, we loop through the season query mapping and do a
 # "T: mean" collapse setting the season as the grouping:
 cfp.gopen(
-    rows=2, columns=1, bottom=0.1, top=0.85,
+    rows=2,
+    columns=1,
+    bottom=0.1,
+    top=0.85,
 )
 cfp.gpos(1)
 cfp.gset(xmin="1980-01-01", xmax="2022-12-01", ymin=304, ymax=312)