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Merge branch 'main' into create-bounds
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cf/test/test_DimensionCoordinate.py

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@@ -817,7 +817,6 @@ def test_DimensionCoordinate_anchor(self):
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e = d.anchor(15, cell=True)
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self.assertEqual(e[0].array, d[0].array - 360)
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def test_DimensionCoordinate_direction(self):
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"""Test DimensionCoordinate.direction"""
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d = self.dim.copy()

docs/source/conf.py

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

docs/source/recipes/plot_08_recipe.py

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====================================================================
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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.
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isort:skip_file
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"""
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# %%

docs/source/recipes/plot_12_recipe.py

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

docs/source/recipes/plot_13_recipe.py

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

docs/source/recipes/plot_17_recipe.py

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

docs/source/recipes/plot_18_recipe.py

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calculate the correlation coefficient, to get a measure of the relationship
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between them.
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isort:skip_file
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"""
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# %%

docs/source/recipes/plot_19_recipe.py

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

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