Update recipe canonical files for spelling fixes & import order

Author: sadielbartholomew

docs/source/recipes/plot_08_recipe.py

@@ -9,10 +9,11 @@
 # 1. Import cf-python, cf-plot, numpy and scipy.stats:
 
 import cfplot as cfp
+import cf
+
 import numpy as np
 import scipy.stats as stats
 
-import cf
 
 # %%
 # 2. Three functions are defined:

docs/source/recipes/plot_09_recipe.py

@@ -52,7 +52,7 @@
 histogram_data = joint_histogram.array
 
 # %%
-# 8. Calculate bin centers for PM2.5 and temperature bins:
+# 8. Calculate bin centres for PM2.5 and temperature bins:
 pm25_bin_centers = [(a + b) / 2 for a, b in pm25_bins]
 temp_bin_centers = [(a + b) / 2 for a, b in temp_bins]
 

docs/source/recipes/plot_10_recipe.py

@@ -42,7 +42,7 @@
 # <https://ncas-cms.github.io/cf-python/function/cf.curl_xy.html>`_ and
 # plotted using `cfplot.con <https://ncas-cms.github.io/cf-plot/build/con.html>`_.
 # The ``with cf.relaxed_identities(True)`` context manager statement prevents
-# the curl opereration broadcasting across the two ``expver`` dimensions because
+# the curl operation broadcasting across the two ``expver`` dimensions because
 # it can't be certain that they are the same as they lack the standardised
 # metadata. Setting
 # ``cf.relaxed_identities(True)`` allows the ``long_name`` to be treated

docs/source/recipes/plot_11_recipe.py

@@ -47,7 +47,7 @@
 global_avg_temp = global_temperature.array.squeeze()
 
 # %%
-# 7. Create a normalization function that maps the interval from the minimum to
+# 7. Create a normalisation function that maps the interval from the minimum to
 # the maximum temperature to the interval [0, 1] for colouring:
 norm_global = plt.Normalize(global_avg_temp.min(), global_avg_temp.max())
 

docs/source/recipes/plot_12_recipe.py

@@ -13,8 +13,8 @@
 # %%
 # 1. Import cf-python, cf-plot and matplotlib.pyplot:
 
-import cfplot as cfp
 import matplotlib.pyplot as plt
+import cfplot as cfp
 
 import cf
 
@@ -44,7 +44,7 @@
 # %%
 # 6. Plot the AOD for all the retrievals using
 # `cfplot.con <https://ncas-cms.github.io/cf-plot/build/con.html>`_. Here the argument
-# ``'ptype'`` specifies the type of plot to use (latituide-longitude here) and
+# ``'ptype'`` specifies the type of plot to use (latitude-longitude here) and
 # the argument ``'lines=False'`` does not draw contour lines:
 cfp.con(f=aod.array, x=lon.array, y=lat.array, ptype=1, lines=False)
 

docs/source/recipes/plot_13_recipe.py

@@ -14,12 +14,17 @@
 """
 
 # %%
-# 1. Import cf-python and cf-plot:
+# 1. Import cf-python and cf-plot, as well as some other libraries for use
+# in next steps.
+
+import cartopy.crs as ccrs
+import matplotlib.patches as mpatches
 
 import cfplot as cfp
 
 import cf
 
+
 # %%
 # 2. Read and select the SST by index and look at its contents:
 sst = cf.read("~/recipes/ERA5_monthly_averaged_SST.nc")[0]
@@ -55,8 +60,6 @@
 #   and
 #   `Text <https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html>`_
 #   function with cf-plot plot object (``cfp.plotvars.plot``):
-import cartopy.crs as ccrs
-import matplotlib.patches as mpatches
 
 cfp.gopen()
 cfp.mapset(proj="cyl", lonmin=0, lonmax=360, latmin=-90, latmax=90)
@@ -160,7 +163,7 @@
 cfp.gclose()
 
 # %%
-# 6. Calculate the Niño 3.4 index and standardize it to create an anomaly index.
+# 6. Calculate the Niño 3.4 index and standardise it to create an anomaly index.
 # The `collapse <https://ncas-cms.github.io/cf-python/method/cf.Field.collapse.html>`_
 # method is used to calculate the mean over the longitude (X) and latitude (Y)
 # dimensions:
@@ -179,7 +182,7 @@
 std_dev = base_period.collapse("T: sd")
 
 # %%
-# 8. The line for variable ``nino34_anomaly`` calculates the standardized
+# 8. The line for variable ``nino34_anomaly`` calculates the standardised
 # anomaly for each time step in the ``nino34_index`` data. It subtracts the
 # ``climatology`` from the ``nino34_index`` and then divides by the ``std_dev``.
 # The resulting ``nino34_anomaly`` data represents how much the SST in the Niño

docs/source/recipes/plot_14_recipe.py

@@ -16,7 +16,7 @@
 import cf
 
 # %%
-# 2. Read and select the 200 hpa geopotential by index and look at its contents:
+# 2. Read and select the 200 hPa geopotential by index and look at its contents:
 gp = cf.read("~/recipes/ERA5_monthly_averaged_z200.nc")[0]
 print(gp)
 
@@ -42,7 +42,7 @@
 #   set various attributes of the plot, like setting the thickness of the lines
 #   that represent continents;
 # - `cfplot.con <https://ncas-cms.github.io/cf-plot/build/con.html>`_ plots the contour
-#   lines representing the 200 hpa geopotential height values without filling
+#   lines representing the 200 hPa geopotential height values without filling
 #   between the contour lines (``fill=False``) and no colour bar
 #   (``colorbar=False``);
 # - `cfplot.levs <https://ncas-cms.github.io/cf-plot/build/levs.html>`_ is used to
@@ -136,7 +136,7 @@
 #   of temperature anomalies without contour lines (``lines=False``);
 # - `cfplot.levs() <https://ncas-cms.github.io/cf-plot/build/levs.html>`_ is used to
 #   reset contour levels to default after which the steps to plot the contour
-#   lines representing the 200 hpa geopotential height values, the approximate
+#   lines representing the 200 hPa geopotential height values, the approximate
 #   polar-front jet and subtropical jet from Step 5 are repeated:
 cfp.gopen()
 cfp.mapset(proj="robin")

docs/source/recipes/plot_15_recipe.py

@@ -17,7 +17,7 @@
 flags, as determined by the original 100 m resolution data. It retains the
 original spatial extent of the data while reducing its spatial complexity.
 Regridding, on the other hand, involves interpolating the data onto a new grid,
-which can introduce artifacts and distortions in the data.
+which can introduce artefacts and distortions in the data.
 
 """
 
@@ -31,7 +31,8 @@
 import cf
 
 # %%
-# 2. Read and select land use data by index and see properties of all construcs:
+# 2. Read and select land use data by index and see properties of
+# all constructs:
 f = cf.read("~/recipes/output.tif.nc")[0]
 f.dump()
 
@@ -102,7 +103,7 @@ def extract_data(country_name):
 # - The ``percentages`` variable calculates the percentage of each bin by
 #   dividing the ``bin_counts`` by the ``total_valid_sub_cells`` and multiplying
 #   by 100.
-# - The ``bin_indices`` variable calculates the center of each bin by averaging
+# - The ``bin_indices`` variable calculates the centre of each bin by averaging
 #   the bin edges. This is done by adding the ``bins.array[:-1, 0]`` and
 #   ``bins.array[1:, 0]`` arrays and dividing by 2.
 # - The ``ax.bar`` function is called to plot the histogram as a bar chart on

docs/source/recipes/plot_17_recipe.py

@@ -11,8 +11,8 @@
 # %%
 # 1. Import cf-python and cf-plot:
 
-import cfplot as cfp
 import matplotlib.pyplot as plt
+import cfplot as cfp
 
 import cf
 

docs/source/recipes/plot_18_recipe.py

@@ -4,20 +4,21 @@
 
 In this recipe, we will take two datasets, one for an independent variable
 (in this example elevation) and one for a dependent variable (snow
-cover over a particuar day), regrid them to the same resolution then
+cover over a particular day), regrid them to the same resolution then
 calculate the correlation coefficient, to get a measure of the relationship
 between them.
 
 """
 
 # %%
 # 1. Import cf-python, cf-plot and other required packages:
-import cfplot as cfp
 import matplotlib.pyplot as plt
 import scipy.stats.mstats as mstats
+import cfplot as cfp
 
 import cf
 
+
 # %%
 # 2. Read the data in and unpack the Fields from FieldLists using indexing.
 # In our example We are investigating the influence of the land height on
@@ -89,7 +90,7 @@
 
 # %%
 # 9. Make a final plot showing the two arrays side-by-side and quoting the
-# determined Pearson correlation coefficient to illustrate the relatoinship
+# determined Pearson correlation coefficient to illustrate the relationship
 # and its strength visually. We use 'gpos' to position the plots in two
 # columns and apply some specific axes ticks and labels for clarity.
 cfp.gopen(