From 9437776a4e7614ffd82dc62915a392e9da821b09 Mon Sep 17 00:00:00 2001
From: George Pulickan <>
Date: Wed, 9 Oct 2024 12:56:09 +0100
Subject: [PATCH 01/11] Add George's original recipe for sea level seasonal
 pressure

---
 docs/source/recipes/plot_19_recipe.py | 70 +++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 docs/source/recipes/plot_19_recipe.py

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
new file mode 100644
index 0000000000..7efd238b4d
--- /dev/null
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -0,0 +1,70 @@
+import cfplot as cfp
+import cf
+import numpy as np
+import pandas as pd
+from datetime import timedelta
+import matplotlib.pyplot as plt
+
+# 1. Load the dataset
+file_path = "~/Documents/Datasets/ERA5_monthly_averaged_SST.nc"
+f = cf.read(file_path)
+sst = f[0]  # Select the SST variable
+
+# Collapse data by area mean (average over spatial dimensions)
+am = sst.collapse("area: mean")  # equivalent to "X Y: mean"
+am.squeeze(inplace=True)
+
+# Check available coordinates (already found 'dimensioncoordinate0' as the time coordinate)
+print("Available coordinates:", am.coordinates())
+
+am_data_key = am.coordinate("dimensioncoordinate0", key=True)
+am_copy = am.copy().subspace(**{am_data_key: cf.mam()})
+print(am_copy)
+exit()
+# Retrieve the time coordinate using its internal identifier
+am_data = am.coordinate("dimensioncoordinate0")
+am_data_copy = am_copy.coordinate("dimensioncoordinate0")
+# Convert the time data from 'hours since 1900-01-01' to datetime format
+#time_units = time_coord.units
+#am_time = time_coord.data
+#am_time_datetime = am.subspace(T=cf.dt("2022-12-01 00:00:00"))
+
+# Convert datetime to numeric values for plotting (e.g., using matplotlib date format)
+#am_time_numeric = pd.to_numeric(pd.to_datetime(am_time_datetime))
+
+# Extract data and replace missing values
+#am_data = am.data
+#am_data = np.where(am_data == -32767, np.nan, am_data)
+
+# Create the line plot using the full dataset with numeric x-axis
+
+
+print(am[:10])
+print(am_data[:10].data.datetime_as_string)
+
+cfp.gopen(file="Recipe3.png")  # Start the plot
+cfp.lineplot(
+    x=am_data,
+    y=am,
+    color="blue",
+    title="Global Average Sea Surface Temperature",
+    ylabel="Temperature (K)",
+    xlabel="Time"
+)
+
+cfp.lineplot(
+    x=am_data_copy,
+    y=am_copy,
+    color="red",
+)
+cfp.gclose()  # Finish the plot
+
+
+# Save the plot to a file using matplotlib
+plt.savefig("global_avg_sst_plot.png")
+
+# Show the plot interactively
+plt.show()
+
+print("Plot created and saved successfully.")
+

From 75741e6cd43f7747a1bd0b21dc73f1560344205a Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 14:23:28 +0100
Subject: [PATCH 02/11] Add George recipe info & make fixes for original recipe
 19

---
 docs/source/recipes/plot_19_recipe.py | 60 +++++++--------------------
 1 file changed, 16 insertions(+), 44 deletions(-)

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
index 7efd238b4d..23a1a12c6b 100644
--- a/docs/source/recipes/plot_19_recipe.py
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -1,3 +1,10 @@
+"""
+Recipe 1: Calculating and Plotting Seasonal Mean Pressure at Mean Sea Level
+
+Objective: Calculate and plot the seasonal mean pressure at mean sea level
+from ensemble simulation data for 1941.
+"""
+
 import cfplot as cfp
 import cf
 import numpy as np
@@ -6,65 +13,30 @@
 import matplotlib.pyplot as plt
 
 # 1. Load the dataset
-file_path = "~/Documents/Datasets/ERA5_monthly_averaged_SST.nc"
-f = cf.read(file_path)
+f = cf.read("~/recipes_break/ERA5_monthly_averaged_SST.nc")
 sst = f[0]  # Select the SST variable
 
 # Collapse data by area mean (average over spatial dimensions)
 am = sst.collapse("area: mean")  # equivalent to "X Y: mean"
 am.squeeze(inplace=True)
 
-# Check available coordinates (already found 'dimensioncoordinate0' as the time coordinate)
+# Check available coordinates (already found 'dimensioncoordinate0' as the
+# time coordinate)
 print("Available coordinates:", am.coordinates())
 
-am_data_key = am.coordinate("dimensioncoordinate0", key=True)
-am_copy = am.copy().subspace(**{am_data_key: cf.mam()})
-print(am_copy)
-exit()
-# Retrieve the time coordinate using its internal identifier
-am_data = am.coordinate("dimensioncoordinate0")
-am_data_copy = am_copy.coordinate("dimensioncoordinate0")
-# Convert the time data from 'hours since 1900-01-01' to datetime format
-#time_units = time_coord.units
-#am_time = time_coord.data
-#am_time_datetime = am.subspace(T=cf.dt("2022-12-01 00:00:00"))
-
-# Convert datetime to numeric values for plotting (e.g., using matplotlib date format)
-#am_time_numeric = pd.to_numeric(pd.to_datetime(am_time_datetime))
-
-# Extract data and replace missing values
-#am_data = am.data
-#am_data = np.where(am_data == -32767, np.nan, am_data)
-
-# Create the line plot using the full dataset with numeric x-axis
-
-
-print(am[:10])
-print(am_data[:10].data.datetime_as_string)
+am_dim_key, am_data = am.coordinate("dimensioncoordinate0", item=True)
+am_sub = am.subspace(**{am_dim_key: cf.mam()})
 
-cfp.gopen(file="Recipe3.png")  # Start the plot
+cfp.gopen(file="global_avg_sst_plot.png")
 cfp.lineplot(
-    x=am_data,
-    y=am,
+    am,
     color="blue",
     title="Global Average Sea Surface Temperature",
     ylabel="Temperature (K)",
     xlabel="Time"
 )
-
 cfp.lineplot(
-    x=am_data_copy,
-    y=am_copy,
+    am_sub,
     color="red",
 )
-cfp.gclose()  # Finish the plot
-
-
-# Save the plot to a file using matplotlib
-plt.savefig("global_avg_sst_plot.png")
-
-# Show the plot interactively
-plt.show()
-
-print("Plot created and saved successfully.")
-
+cfp.gclose()

From 53b981b8669328ee439e129b2d35bb555f48ed12 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 15:03:14 +0100
Subject: [PATCH 03/11] Update GP recipe 19 to find per-season max and min

---
 docs/source/recipes/plot_19_recipe.py | 73 +++++++++++++++++++++++----
 1 file changed, 63 insertions(+), 10 deletions(-)

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
index 23a1a12c6b..c96c05dd66 100644
--- a/docs/source/recipes/plot_19_recipe.py
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -17,26 +17,79 @@
 sst = f[0]  # Select the SST variable
 
 # Collapse data by area mean (average over spatial dimensions)
-am = sst.collapse("area: mean")  # equivalent to "X Y: mean"
-am.squeeze(inplace=True)
+am_max = sst.collapse("area: maximum")  # equivalent to "X Y: mean"
+am_min = sst.collapse("area: minimum")  # equivalent to "X Y: mean"
+#am.squeeze(inplace=True)cf.seasons()
+print("AM SEASONAL IS", am_min, am_max)
+# REDUCE TO TEST
+am_min = am_min[-100:]  # final 100 points
+am_max = am_max[-100:]  # final 100 points
 
 # Check available coordinates (already found 'dimensioncoordinate0' as the
 # time coordinate)
-print("Available coordinates:", am.coordinates())
+###print("Available coordinates:", am.coordinates())
+
+###am_dim_key, am_data = am.coordinate("dimensioncoordinate0", item=True)
+am_sub_1 = am_min.collapse("T: mean", group=cf.mam())
+am_sub_2 = am_min.collapse("T: mean", group=cf.jja())
+am_sub_3 = am_min.collapse("T: mean", group=cf.son())
+am_sub_4 = am_min.collapse("T: mean", group=cf.djf())
+am_sub_5 = am_max.collapse("T: mean", group=cf.mam())
+am_sub_6 = am_max.collapse("T: mean", group=cf.jja())
+am_sub_7 = am_max.collapse("T: mean", group=cf.son())
+am_sub_8 = am_max.collapse("T: mean", group=cf.djf())
+
+
+
+"""
+am_sub_1 = am.subspace(**{am_dim_key: cf.mam()})
+am_sub_2 = am.subspace(**{am_dim_key: cf.month(3)})
+am_sub_3 = am.subspace(**{am_dim_key: cf.month(4)})
+am_sub_4 = am.subspace(**{am_dim_key: cf.month(5)})
+am_sub_2 = am_sub_2 - am_sub_1
+am_sub_3 = am_sub_3 - am_sub_1
+am_sub_4 = am_sub_4 - am_sub_1
+"""
 
-am_dim_key, am_data = am.coordinate("dimensioncoordinate0", item=True)
-am_sub = am.subspace(**{am_dim_key: cf.mam()})
 
 cfp.gopen(file="global_avg_sst_plot.png")
+#cfp.lineplot(
+#    am,
+#    color="blue",
+#    title="Global Average Sea Surface Temperature",
+#    ylabel="Temperature (K)",
+#    xlabel="Time"
+#)
+cfp.lineplot(
+    am_sub_1,
+    color="red",
+)
+cfp.lineplot(
+    am_sub_2,
+    color="green",
+)
 cfp.lineplot(
-    am,
+    am_sub_3,
     color="blue",
-    title="Global Average Sea Surface Temperature",
-    ylabel="Temperature (K)",
-    xlabel="Time"
 )
 cfp.lineplot(
-    am_sub,
+    am_sub_4,
+    color="purple",
+)
+cfp.lineplot(
+    am_sub_5,
     color="red",
 )
+cfp.lineplot(
+    am_sub_6,
+    color="green",
+)
+cfp.lineplot(
+    am_sub_7,
+    color="blue",
+)
+cfp.lineplot(
+    am_sub_8,
+    color="purple",
+)
 cfp.gclose()

From 3aaaf40e259abbe75bec2f6e414460db5c7c4e6a Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 16:17:01 +0100
Subject: [PATCH 04/11] Update GP recipe 19 to view interesting window of 1980+

---
 docs/source/recipes/plot_19_recipe.py | 104 +++++++++++++++++---------
 1 file changed, 70 insertions(+), 34 deletions(-)

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
index c96c05dd66..273880fd68 100644
--- a/docs/source/recipes/plot_19_recipe.py
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -19,77 +19,113 @@
 # Collapse data by area mean (average over spatial dimensions)
 am_max = sst.collapse("area: maximum")  # equivalent to "X Y: mean"
 am_min = sst.collapse("area: minimum")  # equivalent to "X Y: mean"
-#am.squeeze(inplace=True)cf.seasons()
 print("AM SEASONAL IS", am_min, am_max)
-# REDUCE TO TEST
-am_min = am_min[-100:]  # final 100 points
-am_max = am_max[-100:]  # final 100 points
 
-# Check available coordinates (already found 'dimensioncoordinate0' as the
-# time coordinate)
-###print("Available coordinates:", am.coordinates())
+# Reduce all timeseries down to just 1980+ since there are some data
+# quality issues before 1970
+am_max = am_max.subspace(T=cf.ge(cf.dt("1980-01-01")))
+am_min = am_min.subspace(T=cf.ge(cf.dt("1980-01-01")))
+print("FINAL FIELDS ARE", am_max, am_min)
 
-###am_dim_key, am_data = am.coordinate("dimensioncoordinate0", item=True)
 am_sub_1 = am_min.collapse("T: mean", group=cf.mam())
 am_sub_2 = am_min.collapse("T: mean", group=cf.jja())
 am_sub_3 = am_min.collapse("T: mean", group=cf.son())
 am_sub_4 = am_min.collapse("T: mean", group=cf.djf())
+
 am_sub_5 = am_max.collapse("T: mean", group=cf.mam())
 am_sub_6 = am_max.collapse("T: mean", group=cf.jja())
 am_sub_7 = am_max.collapse("T: mean", group=cf.son())
 am_sub_8 = am_max.collapse("T: mean", group=cf.djf())
 
 
+cfp.gopen(rows=2, columns=1, bottom=0.2, file="global_avg_sst_plot.png")
 
-"""
-am_sub_1 = am.subspace(**{am_dim_key: cf.mam()})
-am_sub_2 = am.subspace(**{am_dim_key: cf.month(3)})
-am_sub_3 = am.subspace(**{am_dim_key: cf.month(4)})
-am_sub_4 = am.subspace(**{am_dim_key: cf.month(5)})
-am_sub_2 = am_sub_2 - am_sub_1
-am_sub_3 = am_sub_3 - am_sub_1
-am_sub_4 = am_sub_4 - am_sub_1
-"""
+# Put maxima subplot at top since these values are higher, given
+# increasing x axis
+xticks = list(range(1980, 2024))
+xlabels = [None for i in xticks]
 
-
-cfp.gopen(file="global_avg_sst_plot.png")
-#cfp.lineplot(
-#    am,
-#    color="blue",
-#    title="Global Average Sea Surface Temperature",
-#    ylabel="Temperature (K)",
-#    xlabel="Time"
-#)
+cfp.gpos(1)
 cfp.lineplot(
-    am_sub_1,
+    am_max,
+    color="grey",
+    xlabel="",
+    #xticks=xticks,
+    #xticklabels=xlabels,
+)
+cfp.lineplot(
+    am_sub_5,
     color="red",
+    markeredgecolor="red",
+    marker="o",
+    xlabel="",
+    #xticks=xticks,
+    #xticklabels=xlabels,
 )
 cfp.lineplot(
-    am_sub_2,
+    am_sub_6,
     color="green",
+    markeredgecolor="green",
+    marker="o",
+    xlabel="",
+    #xticks=xticks,
+    #xticklabels=xlabels,
 )
 cfp.lineplot(
-    am_sub_3,
+    am_sub_7,
     color="blue",
+    markeredgecolor="blue",
+    marker="o",
+    xlabel="",
+    #xticks=xticks,
+    #xticklabels=xlabels,
 )
 cfp.lineplot(
-    am_sub_4,
+    am_sub_8,
     color="purple",
+    markeredgecolor="purple",
+    marker="o",
+    xlabel="",
+    #xticks=xticks,
+    #xticklabels=xlabels,
 )
+
+# Minima subplot below the maxima one
+cfp.gpos(2)
 cfp.lineplot(
-    am_sub_5,
+    am_min,
+    color="grey",
+)
+#cfp.lineplot(
+#    am,
+#    color="blue",
+#    title="Global Average Sea Surface Temperature",
+#    ylabel="Temperature (K)",
+#    xlabel="Time"
+#)
+cfp.lineplot(
+    am_sub_1,
     color="red",
+    markeredgecolor="red",
+    marker="o"
 )
 cfp.lineplot(
-    am_sub_6,
+    am_sub_2,
     color="green",
+    markeredgecolor="green",
+    marker="o"
 )
 cfp.lineplot(
-    am_sub_7,
+    am_sub_3,
     color="blue",
+    markeredgecolor="blue",
+    marker="o"
 )
 cfp.lineplot(
-    am_sub_8,
+    am_sub_4,
     color="purple",
+    markeredgecolor="purple",
+    marker="o"
 )
+
 cfp.gclose()

From 1543841d366507b620582a86970ff00460bc64c6 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 22:31:59 +0100
Subject: [PATCH 05/11] Update GP recipe 19 to consolidate per-season logic

---
 docs/source/recipes/plot_19_recipe.py | 123 +++++++++-----------------
 1 file changed, 40 insertions(+), 83 deletions(-)

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
index 273880fd68..d8700d6a3c 100644
--- a/docs/source/recipes/plot_19_recipe.py
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -27,105 +27,62 @@
 am_min = am_min.subspace(T=cf.ge(cf.dt("1980-01-01")))
 print("FINAL FIELDS ARE", am_max, am_min)
 
-am_sub_1 = am_min.collapse("T: mean", group=cf.mam())
-am_sub_2 = am_min.collapse("T: mean", group=cf.jja())
-am_sub_3 = am_min.collapse("T: mean", group=cf.son())
-am_sub_4 = am_min.collapse("T: mean", group=cf.djf())
+# TODO
+colours_seasons_map = {
+    "red": (cf.mam(), "Mean across MAM: March, April and May"),
+    "blue": (cf.jja(), "Mean across JJA: June, July and August"),
+    "green": (cf.son(), "Mean across SON: September, October and November"),
+    "purple": (cf.djf(), "Mean across DJF: December, January and February"),
+}
 
-am_sub_5 = am_max.collapse("T: mean", group=cf.mam())
-am_sub_6 = am_max.collapse("T: mean", group=cf.jja())
-am_sub_7 = am_max.collapse("T: mean", group=cf.son())
-am_sub_8 = am_max.collapse("T: mean", group=cf.djf())
-
-
-cfp.gopen(rows=2, columns=1, bottom=0.2, file="global_avg_sst_plot.png")
+cfp.gopen(
+    rows=2, columns=1, bottom=0.1, top=0.75, file="global_avg_sst_plot.png")
 
 # Put maxima subplot at top since these values are higher, given
 # increasing x axis
-xticks = list(range(1980, 2024))
-xlabels = [None for i in xticks]
-
 cfp.gpos(1)
+plt.suptitle(
+    "Global Average Sea Surface Temperature monthly minima\nand maxima "
+    "including seasonal means of these extrema",
+    fontsize=18,
+)
+for colour, season_query in colours_seasons_map.items():
+    query_on_season, season_description = season_query
+    am_sub = am_max.collapse("T: mean", group=query_on_season)
+    cfp.lineplot(
+        am_sub,
+        color=colour,
+        markeredgecolor=colour,
+        marker="o",
+        xlabel="",
+        label=season_description,
+        title="Maxima per month or season",
+        # TODO FONTSIZE HERE
+    )
 cfp.lineplot(
     am_max,
     color="grey",
     xlabel="",
-    #xticks=xticks,
-    #xticklabels=xlabels,
-)
-cfp.lineplot(
-    am_sub_5,
-    color="red",
-    markeredgecolor="red",
-    marker="o",
-    xlabel="",
-    #xticks=xticks,
-    #xticklabels=xlabels,
-)
-cfp.lineplot(
-    am_sub_6,
-    color="green",
-    markeredgecolor="green",
-    marker="o",
-    xlabel="",
-    #xticks=xticks,
-    #xticklabels=xlabels,
-)
-cfp.lineplot(
-    am_sub_7,
-    color="blue",
-    markeredgecolor="blue",
-    marker="o",
-    xlabel="",
-    #xticks=xticks,
-    #xticklabels=xlabels,
-)
-cfp.lineplot(
-    am_sub_8,
-    color="purple",
-    markeredgecolor="purple",
-    marker="o",
-    xlabel="",
-    #xticks=xticks,
-    #xticklabels=xlabels,
+    ylabel="Temperature (K)",
+    label="All months"
 )
 
 # Minima subplot below the maxima one
 cfp.gpos(2)
+for colour, season_query in colours_seasons_map.items():
+    query_on_season, season_description = season_query
+    am_sub = am_min.collapse("T: mean", group=query_on_season)
+    cfp.lineplot(
+        am_sub,
+        color=colour,
+        markeredgecolor=colour,
+        marker="o",
+        xlabel="",
+        title="Minima per month or season"
+    )
 cfp.lineplot(
     am_min,
     color="grey",
 )
-#cfp.lineplot(
-#    am,
-#    color="blue",
-#    title="Global Average Sea Surface Temperature",
-#    ylabel="Temperature (K)",
-#    xlabel="Time"
-#)
-cfp.lineplot(
-    am_sub_1,
-    color="red",
-    markeredgecolor="red",
-    marker="o"
-)
-cfp.lineplot(
-    am_sub_2,
-    color="green",
-    markeredgecolor="green",
-    marker="o"
-)
-cfp.lineplot(
-    am_sub_3,
-    color="blue",
-    markeredgecolor="blue",
-    marker="o"
-)
-cfp.lineplot(
-    am_sub_4,
-    color="purple",
-    markeredgecolor="purple",
-    marker="o"
-)
 
 cfp.gclose()

From 05c8b408e06c44078ea3d197f4b7c1b455678b08 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 23:30:11 +0100
Subject: [PATCH 06/11] Finalise GP recipe 19 plot and functional code

---
 docs/source/recipes/plot_19_recipe.py | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
index d8700d6a3c..25e22eb03b 100644
--- a/docs/source/recipes/plot_19_recipe.py
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -18,16 +18,14 @@
 
 # Collapse data by area mean (average over spatial dimensions)
 am_max = sst.collapse("area: maximum")  # equivalent to "X Y: mean"
-am_min = sst.collapse("area: minimum")  # equivalent to "X Y: mean"
-print("AM SEASONAL IS", am_min, am_max)
+am_min = sst.collapse("area: minimum")
 
 # Reduce all timeseries down to just 1980+ since there are some data
 # quality issues before 1970
 am_max = am_max.subspace(T=cf.ge(cf.dt("1980-01-01")))
 am_min = am_min.subspace(T=cf.ge(cf.dt("1980-01-01")))
-print("FINAL FIELDS ARE", am_max, am_min)
 
-# TODO
+# TODO COMMENT
 colours_seasons_map = {
     "red": (cf.mam(), "Mean across MAM: March, April and May"),
     "blue": (cf.jja(), "Mean across JJA: June, July and August"),
@@ -36,7 +34,7 @@
 }
 
 cfp.gopen(
-    rows=2, columns=1, bottom=0.1, top=0.75, file="global_avg_sst_plot.png")
+    rows=2, columns=1, bottom=0.1, top=0.85, file="global_avg_sst_plot.png")
 
 # Put maxima subplot at top since these values are higher, given
 # increasing x axis
@@ -46,6 +44,9 @@
     "including seasonal means of these extrema",
     fontsize=18,
 )
+# Set limits manually only to allow space so the legend doesn't overlap the
+# data, which isn't possible purely from positioning it anywhere
+cfp.gset(xmin="1980-01-01", xmax="2022-12-01", ymin=304, ymax=312)
 for colour, season_query in colours_seasons_map.items():
     query_on_season, season_description = season_query
     am_sub = am_max.collapse("T: mean", group=query_on_season)
@@ -54,21 +55,19 @@
         color=colour,
         markeredgecolor=colour,
         marker="o",
-        xlabel="",
         label=season_description,
         title="Maxima per month or season",
-        # TODO FONTSIZE HERE
     )
 cfp.lineplot(
     am_max,
     color="grey",
     xlabel="",
-    ylabel="Temperature (K)",
-    label="All months"
+    label="All months",
 )
 
 # Minima subplot below the maxima one
 cfp.gpos(2)
+cfp.gset(xmin="1980-01-01", xmax="2022-12-01", ymin=269, ymax=272)
 for colour, season_query in colours_seasons_map.items():
     query_on_season, season_description = season_query
     am_sub = am_min.collapse("T: mean", group=query_on_season)

From 24e4f92f3da1fd1f0ab96fcb34dadb8e99827d20 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Thu, 10 Oct 2024 12:11:17 +0100
Subject: [PATCH 07/11] Lint recipe 19: black, isort, flake8

---
 docs/source/recipes/plot_19_recipe.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
index 25e22eb03b..161f6792fb 100644
--- a/docs/source/recipes/plot_19_recipe.py
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -6,12 +6,10 @@
 """
 
 import cfplot as cfp
-import cf
-import numpy as np
-import pandas as pd
-from datetime import timedelta
 import matplotlib.pyplot as plt
 
+import cf
+
 # 1. Load the dataset
 f = cf.read("~/recipes_break/ERA5_monthly_averaged_SST.nc")
 sst = f[0]  # Select the SST variable
@@ -34,7 +32,8 @@
 }
 
 cfp.gopen(
-    rows=2, columns=1, bottom=0.1, top=0.85, file="global_avg_sst_plot.png")
+    rows=2, columns=1, bottom=0.1, top=0.85, file="global_avg_sst_plot.png"
+)
 
 # Put maxima subplot at top since these values are higher, given
 # increasing x axis
@@ -77,7 +76,7 @@
         markeredgecolor=colour,
         marker="o",
         xlabel="",
-        title="Minima per month or season"
+        title="Minima per month or season",
     )
 cfp.lineplot(
     am_min,

From 9aeadc362315dc6d9301e59be08c8595cb267059 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Thu, 10 Oct 2024 14:59:33 +0100
Subject: [PATCH 08/11] Flesh out literate prog. comments for recipe 19

---
 docs/source/recipes/plot_19_recipe.py | 67 ++++++++++++++++++---------
 1 file changed, 46 insertions(+), 21 deletions(-)

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
index 161f6792fb..31d61f2599 100644
--- a/docs/source/recipes/plot_19_recipe.py
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -1,29 +1,43 @@
 """
-Recipe 1: Calculating and Plotting Seasonal Mean Pressure at Mean Sea Level
+Calculate and plotting per-season trends in global sea surface extrema
+======================================================================
 
-Objective: Calculate and plot the seasonal mean pressure at mean sea level
-from ensemble simulation data for 1941.
+In this recipe we find the area-based extrema of global sea surface
+temperature per month and, because it is very difficult to
+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.
 """
 
+# %%
+# 1. Import cf-python, cf-plot and other required packages:
 import cfplot as cfp
 import matplotlib.pyplot as plt
 
 import cf
 
-# 1. Load the dataset
+# %%
+# 2. Read the dataset in extract the SST Field from the FieldList:
 f = cf.read("~/recipes_break/ERA5_monthly_averaged_SST.nc")
-sst = f[0]  # Select the SST variable
+sst = f[0]
 
-# Collapse data by area mean (average over spatial dimensions)
-am_max = sst.collapse("area: maximum")  # equivalent to "X Y: mean"
-am_min = sst.collapse("area: minimum")
+# %%
+# 3. Collapse data by area extrema (average over spatial dimensions):
+am_max = sst.collapse("area: maximum")  # equivalent to "X Y: maximum"
+am_min = sst.collapse("area: minimum")  # equivalent to "X Y: minimum"
 
-# Reduce all timeseries down to just 1980+ since there are some data
-# quality issues before 1970
+# %%
+# 4. Reduce all timeseries down to just 1980+ since there are some data
+# quality issues before 1970 and also this window is about perfect size
+# for viewing the trends without the line plot becoming too cluttered:
 am_max = am_max.subspace(T=cf.ge(cf.dt("1980-01-01")))
 am_min = am_min.subspace(T=cf.ge(cf.dt("1980-01-01")))
 
-# TODO COMMENT
+# %%
+# 5. Create a mapping which provides the queries we need to collapse on
+# the four seasons, along with our description of them, as a value, with
+# the key of the string encoding the colour we want to plot these
+# trendlines in. This structure will be iterated over to make our plot:
 colours_seasons_map = {
     "red": (cf.mam(), "Mean across MAM: March, April and May"),
     "blue": (cf.jja(), "Mean across JJA: June, July and August"),
@@ -31,20 +45,21 @@
     "purple": (cf.djf(), "Mean across DJF: December, January and February"),
 }
 
+# %%
+# 6. Create and open the plot file:
 cfp.gopen(
     rows=2, columns=1, bottom=0.1, top=0.85, file="global_avg_sst_plot.png"
 )
 
-# Put maxima subplot at top since these values are higher, given
-# increasing x axis
+# %%
+# 7. Put maxima subplot at top since these values are higher, given
+# increasing x axis. Note we set limits manually with 'gset' only to
+# allow space so the legend doesn't overlap the data, which isn't
+# possible purely from positioning it anywhere within the default plot.
+# Otherwise cf-plot handles this for us. To plot the per-season means
+# of the maxima, we loop through the season query mapping and do a
+# "T: mean" collapse setting the season as the grouping:
 cfp.gpos(1)
-plt.suptitle(
-    "Global Average Sea Surface Temperature monthly minima\nand maxima "
-    "including seasonal means of these extrema",
-    fontsize=18,
-)
-# Set limits manually only to allow space so the legend doesn't overlap the
-# data, which isn't possible purely from positioning it anywhere
 cfp.gset(xmin="1980-01-01", xmax="2022-12-01", ymin=304, ymax=312)
 for colour, season_query in colours_seasons_map.items():
     query_on_season, season_description = season_query
@@ -64,7 +79,10 @@
     label="All months",
 )
 
-# Minima subplot below the maxima one
+# %%
+# 8. Create and add minima subplot below the maxima one. Just like for the
+# maxima case, we plot per-season means by looping through the season query
+# mapping and doing a "T: mean" collapse setting the season as the grouping:
 cfp.gpos(2)
 cfp.gset(xmin="1980-01-01", xmax="2022-12-01", ymin=269, ymax=272)
 for colour, season_query in colours_seasons_map.items():
@@ -83,4 +101,11 @@
     color="grey",
 )
 
+# %%
+# 9. Add an overall title to the plot and close the file to save it:
+plt.suptitle(
+    "Global Average Sea Surface Temperature monthly minima\nand maxima "
+    "including seasonal means of these extrema",
+    fontsize=18,
+)
 cfp.gclose()

From bc2030561e83626c9f3d15574588912beae47860 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Thu, 10 Oct 2024 15:15:30 +0100
Subject: [PATCH 09/11] Final pre-review updates for recipe 19

---
 docs/source/recipes/plot_19_recipe.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
index 31d61f2599..cc8c052493 100644
--- a/docs/source/recipes/plot_19_recipe.py
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -1,6 +1,6 @@
 """
-Calculate and plotting per-season trends in global sea surface extrema
-======================================================================
+Plotting per-season trends in global sea surface tempreature extrema
+====================================================================
 
 In this recipe we find the area-based extrema of global sea surface
 temperature per month and, because it is very difficult to
@@ -19,10 +19,10 @@
 # %%
 # 2. Read the dataset in extract the SST Field from the FieldList:
 f = cf.read("~/recipes_break/ERA5_monthly_averaged_SST.nc")
-sst = f[0]
+sst = f[0]  # this gives the sea surface temperature (SST)
 
 # %%
-# 3. Collapse data by area extrema (average over spatial dimensions):
+# 3. Collapse the SST data by area extrema (extrema over spatial dimensions):
 am_max = sst.collapse("area: maximum")  # equivalent to "X Y: maximum"
 am_min = sst.collapse("area: minimum")  # equivalent to "X Y: minimum"
 
@@ -38,7 +38,7 @@
 # the four seasons, along with our description of them, as a value, with
 # the key of the string encoding the colour we want to plot these
 # trendlines in. This structure will be iterated over to make our plot:
-colours_seasons_map = {
+colours_seasons_mapping = {
     "red": (cf.mam(), "Mean across MAM: March, April and May"),
     "blue": (cf.jja(), "Mean across JJA: June, July and August"),
     "green": (cf.son(), "Mean across SON: September, October and November"),
@@ -61,11 +61,11 @@
 # "T: mean" collapse setting the season as the grouping:
 cfp.gpos(1)
 cfp.gset(xmin="1980-01-01", xmax="2022-12-01", ymin=304, ymax=312)
-for colour, season_query in colours_seasons_map.items():
+for colour, season_query in colours_seasons_mapping.items():
     query_on_season, season_description = season_query
-    am_sub = am_max.collapse("T: mean", group=query_on_season)
+    am_max_collapse = am_max.collapse("T: mean", group=query_on_season)
     cfp.lineplot(
-        am_sub,
+        am_max_collapse,
         color=colour,
         markeredgecolor=colour,
         marker="o",
@@ -85,11 +85,11 @@
 # mapping and doing a "T: mean" collapse setting the season as the grouping:
 cfp.gpos(2)
 cfp.gset(xmin="1980-01-01", xmax="2022-12-01", ymin=269, ymax=272)
-for colour, season_query in colours_seasons_map.items():
+for colour, season_query in colours_seasons_mapping.items():
     query_on_season, season_description = season_query
-    am_sub = am_min.collapse("T: mean", group=query_on_season)
+    am_min_collapse = am_min.collapse("T: mean", group=query_on_season)
     cfp.lineplot(
-        am_sub,
+        am_min_collapse,
         color=colour,
         markeredgecolor=colour,
         marker="o",
@@ -104,8 +104,8 @@
 # %%
 # 9. Add an overall title to the plot and close the file to save it:
 plt.suptitle(
-    "Global Average Sea Surface Temperature monthly minima\nand maxima "
-    "including seasonal means of these extrema",
+    "Global mean sea surface temperature (SST) monthly\nminima and maxima "
+    "showing seasonal means of these extrema",
     fontsize=18,
 )
 cfp.gclose()

From d39a47d9b9533a3673d971b500f069c391b342b2 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Thu, 10 Oct 2024 15:30:58 +0100
Subject: [PATCH 10/11] Add recipe 19 to recipes listing with filter keywords

---
 docs/source/recipes/recipe_list.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/source/recipes/recipe_list.txt b/docs/source/recipes/recipe_list.txt
index f39297f9cb..42ccae6c78 100644
--- a/docs/source/recipes/recipe_list.txt
+++ b/docs/source/recipes/recipe_list.txt
@@ -27,4 +27,6 @@ plot_13_recipe.html#sphx-glr-recipes-plot-13-recipe-py
 plot_14_recipe.html#sphx-glr-recipes-plot-14-recipe-py
 <div class="sphx-glr-thumbcontainer subspace collapse contourmap" tooltip="Subspace, Collapse, Contourmap">
 plot_15_recipe.html#sphx-glr-recipes-plot-15-recipe-py
-<div class="sphx-glr-thumbcontainer histogram subspace" tooltip="Histogram, Subspace">
\ No newline at end of file
+<div class="sphx-glr-thumbcontainer histogram subspace" tooltip="Histogram, Subspace">
+plot_19_recipe.html#sphx-glr-recipes-plot-19-recipe-py
+<div class="sphx-glr-thumbcontainer collapse subspace subplot lineplot" tooltip="Collapse, Subspace, Subplot, Lineplot">

From 87a1932382e8872de9a9ceffa9ae91a44fa9a77f Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Thu, 10 Oct 2024 15:49:33 +0100
Subject: [PATCH 11/11] Update docs/source/recipes/plot_19_recipe.py for
 consistent data path

---
 docs/source/recipes/plot_19_recipe.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
index cc8c052493..bcc8f2823f 100644
--- a/docs/source/recipes/plot_19_recipe.py
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -18,7 +18,7 @@
 
 # %%
 # 2. Read the dataset in extract the SST Field from the FieldList:
-f = cf.read("~/recipes_break/ERA5_monthly_averaged_SST.nc")
+f = cf.read("~/recipes/ERA5_monthly_averaged_SST.nc")
 sst = f[0]  # this gives the sea surface temperature (SST)
 
 # %%