From 011978bd423f85c98f56735f65dba70885f08eae Mon Sep 17 00:00:00 2001
From: Natalia Hunt <>
Date: Tue, 8 Oct 2024 16:51:41 +0100
Subject: [PATCH 01/11] Add Natalia's recipe for calculating snow-elevation
 correlation

---
 docs/source/recipes/plot_18_recipe.py | 172 ++++++++++++++++++++++++++
 1 file changed, 172 insertions(+)
 create mode 100644 docs/source/recipes/plot_18_recipe.py

diff --git a/docs/source/recipes/plot_18_recipe.py b/docs/source/recipes/plot_18_recipe.py
new file mode 100644
index 0000000000..e83a6eddb1
--- /dev/null
+++ b/docs/source/recipes/plot_18_recipe.py
@@ -0,0 +1,172 @@
+"""
+Plot to compare two linked datasets : Snow against Elevation
+============================================
+In this recipe, we will plot a dependant variable (in this example snow cover) 
+against an independent variable (elevation). This will be on a contour plot 
+separately and together, then finding the coefficient of correlation and making an 
+elevation line plot.
+"""
+
+# %%
+# 1. Import cf-python, cf-plot and other required packages:
+
+import cfplot as cf
+import cf
+import sys
+import scipy.stats.mstats as mstats
+import matplotlib.pyplot as plt
+
+#%%
+# 2. Read the data in:
+# We are investigating the influence of the land height on the snow cover, 
+#so snow cover is the dependent variable. You can use different variable
+# names for easier understanding.
+# We are selecting the first field in the data with [0]
+
+orog = cf.read(f"~/cfplot_data/1km_elevation.nc")[0]
+snow = cf.read(f"~/cfplot_data/snowcover")[0]
+
+snow_day = snow[0]  # first day, Jan 1st of this year (2024)
+
+#%%
+# 3. Choose the regions:
+# Ensuring both datasets cover it, and also that its not too large as then 
+# regridding could cause the program to crash on small computers (dont 
+# regrid the whole map), as well as being mainly land.
+# Tool for finding rectangular  area for lat and lon points
+# https://www.findlatitudeandlongitude.com/l/Yorkshire%2C+England/5009414/
+
+region_in_mid_uk = [(-3.0, -1.0), (52.0, 55.0)]  # lon then lat
+use_orog = orog.subspace(
+    longitude=cf.wi(*region_in_mid_uk[0]),
+    latitude=cf.wi(*region_in_mid_uk[1])
+)
+
+#%%
+# 4. Subspace snow to same bounding box as orography data (orog)
+
+use_snow = snow_day.subspace(
+    longitude=cf.wi(*region_in_mid_uk[0]),
+    latitude=cf.wi(*region_in_mid_uk[1])
+)
+
+#%%
+# 5. Normalise the data
+# This normalises to between 0 and 1 so multiply by 100 to get a percentage
+
+use_snow_normal = ((use_snow - use_snow.minimum())/ (use_snow.range()))*100 
+print(use_snow_normal.data.stats())
+
+#%%
+# 6. Reassign the units as they are removed by cf-python after calculation
+# Only do this if you are certain the units are convertible to %
+use_snow_normal.override_units("percentage", inplace=True) 
+
+#%%
+# 7. Plot of Snowcover contour
+# First it outputs the newly formatted data, you can change the file names
+# the plots will save as.
+# colour_scale.txt is a colour scale specifically made and saved to show 
+# the data but you could make your own or use existing ones from
+# https://ncas-cms.github.io/cf-plot/build/colour_scales.html
+# You will also need to adjust the labels and axis for your region
+print("SNOW IS", use_snow_normal)
+cfp.gopen(file="snow-1.png")
+cfp.cscale("~/cfplot_data/colour_scale.txt")
+cfp.mapset(resolution="10m")
+cfp.con(use_snow_normal, 
+        lines=False, 
+        title = "Snow Cover Contour Plot", 
+        xticklabels = ("3W", "2W", "1W"), 
+        yticklabels =("52N", "53N", "54N", "55N"), 
+        xticks = (-3, -2, -1), 
+        yticks= (52, 53, 54, 55))
+cfp.gclose()
+print("OROG IS", use_orog)
+
+#%%
+# 8. Plot of 1km Resolution Orography Contour
+# Here the ocean doesnt get coloured out because when it is regridded it gets 
+# masked by the lack of data for the dependant value (snow) over the seas
+
+cfp.gopen(file="orog-1.png")
+cfp.cscale("wiki_2_0_reduced")
+cfp.mapset(resolution="10m")
+cfp.con(use_orog, 
+        lines=False, 
+        title = "1km resolution Orography Contour Plot", 
+        xticklabels = ("3W", "2W", "1W"), 
+        yticklabels =("52N", "53N", "54N", "55N"), 
+        xticks = (-3, -2, -1), 
+        yticks= (52, 53, 54, 55))
+cfp.gclose()
+
+#%%
+# 9. Plot of Vertical Orography Lineplot of 1km Resolution Elevation
+
+lonz = use_orog.construct("longitude").data[0]
+elevation_orog = use_orog.subspace(longitude = lonz)
+xticks_elevation_orog = [52, 52.5, 53, 53.5, 54, 54.5, 55]
+
+cfp.gopen(figsize=(12, 6), file ="orography_vertical.png")
+
+cfp.lineplot(
+    x=elevation_orog.coord('latitude').array,
+    y=elevation_orog.array.squeeze(),
+    color="black",
+    title="Orography Profile over part of UK",
+    ylabel="Elevation (m)",
+    xlabel="Latitude",
+    xticks=xticks_elevation_orog,
+)
+
+cfp.plotvars.plot.fill_between(
+    elevation_orog.coord('latitude').array,
+    0,
+    elevation_orog.array.squeeze(),
+    color='black',
+    alpha=0.7
+)
+cfp.gclose()
+
+#%%
+# 10. Regrid the data to have a comparable array.
+# Here the orography file is regridded to the snow file 
+# as snow is higher resolution (if this doesnt work switch them).
+# The regridded data  is saved as a file, when rerun this could be 
+# commented out and instead read in the regridded file
+# as regridding takes quite awhile
+
+reg = use_orog.regrids(use_snow_normal, method="linear")
+print("REGRIDDED IS", reg, type(reg))
+cf.write(reg, "regridded_data.nc")
+print("DONE WRITING OUT REGRID FILE")
+# This plots the regridded file data
+cfp.gopen(file="regridded-1.png")
+cfp.con(reg, lines=False)
+cfp.gclose()
+
+reg = cf.read("~/cfplot_data/regridded_data.nc")[0]
+
+#%%
+# 11. Compare the elevation and snow cover
+# The "reg" field is the elevation data on the same grid as the snow data.
+# compare this to the snow data itself "use_snow"
+
+reg_data = reg.data
+snow_data = use_snow_normal.data
+print("(REGRIDDED) OROG DATA IS", reg_data, reg_data.shape)
+print("SNOW DATA IS", snow_data, snow_data.shape)
+
+#%%
+# 12. Squeeze snow data to remove the size 1 axes
+
+snow_data = snow_data.squeeze()
+
+#%%
+#  13. Final statistical calculations
+
+sys.setrecursionlimit(1500)
+coeff = mstats.pearsonr(reg_data.array, snow_data.array)
+print(coeff)
+

From 57fac282e6073c9575bb42f6e8221ee0a2254940 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Tue, 8 Oct 2024 16:56:15 +0100
Subject: [PATCH 02/11] Update NH original recipe 18 w/ minor fixes for
 cleanE2E run

---
 docs/source/recipes/plot_18_recipe.py | 51 ++++++---------------------
 1 file changed, 11 insertions(+), 40 deletions(-)

diff --git a/docs/source/recipes/plot_18_recipe.py b/docs/source/recipes/plot_18_recipe.py
index e83a6eddb1..7494259a92 100644
--- a/docs/source/recipes/plot_18_recipe.py
+++ b/docs/source/recipes/plot_18_recipe.py
@@ -10,7 +10,7 @@
 # %%
 # 1. Import cf-python, cf-plot and other required packages:
 
-import cfplot as cf
+import cfplot as cfp
 import cf
 import sys
 import scipy.stats.mstats as mstats
@@ -22,9 +22,9 @@
 #so snow cover is the dependent variable. You can use different variable
 # names for easier understanding.
 # We are selecting the first field in the data with [0]
-
-orog = cf.read(f"~/cfplot_data/1km_elevation.nc")[0]
-snow = cf.read(f"~/cfplot_data/snowcover")[0]
+PATH="~/summerstudents/final-recipes/new-required-datasets"
+orog = cf.read(f"{PATH}/1km_elevation.nc")[0]
+snow = cf.read(f"{PATH}/snowcover")[0]
 
 snow_day = snow[0]  # first day, Jan 1st of this year (2024)
 
@@ -35,7 +35,6 @@
 # regrid the whole map), as well as being mainly land.
 # Tool for finding rectangular  area for lat and lon points
 # https://www.findlatitudeandlongitude.com/l/Yorkshire%2C+England/5009414/
-
 region_in_mid_uk = [(-3.0, -1.0), (52.0, 55.0)]  # lon then lat
 use_orog = orog.subspace(
     longitude=cf.wi(*region_in_mid_uk[0]),
@@ -44,7 +43,6 @@
 
 #%%
 # 4. Subspace snow to same bounding box as orography data (orog)
-
 use_snow = snow_day.subspace(
     longitude=cf.wi(*region_in_mid_uk[0]),
     latitude=cf.wi(*region_in_mid_uk[1])
@@ -53,9 +51,7 @@
 #%%
 # 5. Normalise the data
 # This normalises to between 0 and 1 so multiply by 100 to get a percentage
-
 use_snow_normal = ((use_snow - use_snow.minimum())/ (use_snow.range()))*100 
-print(use_snow_normal.data.stats())
 
 #%%
 # 6. Reassign the units as they are removed by cf-python after calculation
@@ -70,9 +66,8 @@
 # the data but you could make your own or use existing ones from
 # https://ncas-cms.github.io/cf-plot/build/colour_scales.html
 # You will also need to adjust the labels and axis for your region
-print("SNOW IS", use_snow_normal)
 cfp.gopen(file="snow-1.png")
-cfp.cscale("~/cfplot_data/colour_scale.txt")
+###cfp.cscale("~/cfplot_data/colour_scale.txt")
 cfp.mapset(resolution="10m")
 cfp.con(use_snow_normal, 
         lines=False, 
@@ -82,13 +77,11 @@
         xticks = (-3, -2, -1), 
         yticks= (52, 53, 54, 55))
 cfp.gclose()
-print("OROG IS", use_orog)
 
 #%%
 # 8. Plot of 1km Resolution Orography Contour
 # Here the ocean doesnt get coloured out because when it is regridded it gets 
 # masked by the lack of data for the dependant value (snow) over the seas
-
 cfp.gopen(file="orog-1.png")
 cfp.cscale("wiki_2_0_reduced")
 cfp.mapset(resolution="10m")
@@ -103,7 +96,6 @@
 
 #%%
 # 9. Plot of Vertical Orography Lineplot of 1km Resolution Elevation
-
 lonz = use_orog.construct("longitude").data[0]
 elevation_orog = use_orog.subspace(longitude = lonz)
 xticks_elevation_orog = [52, 52.5, 53, 53.5, 54, 54.5, 55]
@@ -133,40 +125,19 @@
 # 10. Regrid the data to have a comparable array.
 # Here the orography file is regridded to the snow file 
 # as snow is higher resolution (if this doesnt work switch them).
-# The regridded data  is saved as a file, when rerun this could be 
-# commented out and instead read in the regridded file
-# as regridding takes quite awhile
-
 reg = use_orog.regrids(use_snow_normal, method="linear")
-print("REGRIDDED IS", reg, type(reg))
-cf.write(reg, "regridded_data.nc")
-print("DONE WRITING OUT REGRID FILE")
+
+
 # This plots the regridded file data
 cfp.gopen(file="regridded-1.png")
 cfp.con(reg, lines=False)
 cfp.gclose()
 
-reg = cf.read("~/cfplot_data/regridded_data.nc")[0]
-
-#%%
-# 11. Compare the elevation and snow cover
-# The "reg" field is the elevation data on the same grid as the snow data.
-# compare this to the snow data itself "use_snow"
-
-reg_data = reg.data
-snow_data = use_snow_normal.data
-print("(REGRIDDED) OROG DATA IS", reg_data, reg_data.shape)
-print("SNOW DATA IS", snow_data, snow_data.shape)
-
 #%%
-# 12. Squeeze snow data to remove the size 1 axes
-
+# 11. Squeeze snow data to remove the size 1 axes
 snow_data = snow_data.squeeze()
 
 #%%
-#  13. Final statistical calculations
-
-sys.setrecursionlimit(1500)
-coeff = mstats.pearsonr(reg_data.array, snow_data.array)
-print(coeff)
-
+#  12. Final statistical calculations
+coefficient = mstats.pearsonr(reg_data.array, snow_data.array)
+print(f"The Pearson correlation coefficient is {coefficient}")

From 727cb451d60529660be69788c8b231a7c604b7ef Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Tue, 8 Oct 2024 17:35:11 +0100
Subject: [PATCH 03/11] Update NH original recipe 18 to produce one plot only
 w/ result

---
 docs/source/recipes/plot_18_recipe.py | 122 +++++++++-----------------
 1 file changed, 40 insertions(+), 82 deletions(-)

diff --git a/docs/source/recipes/plot_18_recipe.py b/docs/source/recipes/plot_18_recipe.py
index 7494259a92..bb2fca5fd7 100644
--- a/docs/source/recipes/plot_18_recipe.py
+++ b/docs/source/recipes/plot_18_recipe.py
@@ -12,6 +12,7 @@
 
 import cfplot as cfp
 import cf
+
 import sys
 import scipy.stats.mstats as mstats
 import matplotlib.pyplot as plt
@@ -25,11 +26,11 @@
 PATH="~/summerstudents/final-recipes/new-required-datasets"
 orog = cf.read(f"{PATH}/1km_elevation.nc")[0]
 snow = cf.read(f"{PATH}/snowcover")[0]
-
+# Could use any of the seven days by indexing differently
 snow_day = snow[0]  # first day, Jan 1st of this year (2024)
 
 #%%
-# 3. Choose the regions:
+# 3. Choose the regions and subspace to same area for both datasets:
 # Ensuring both datasets cover it, and also that its not too large as then 
 # regridding could cause the program to crash on small computers (dont 
 # regrid the whole map), as well as being mainly land.
@@ -40,104 +41,61 @@
     longitude=cf.wi(*region_in_mid_uk[0]),
     latitude=cf.wi(*region_in_mid_uk[1])
 )
-
-#%%
-# 4. Subspace snow to same bounding box as orography data (orog)
 use_snow = snow_day.subspace(
     longitude=cf.wi(*region_in_mid_uk[0]),
     latitude=cf.wi(*region_in_mid_uk[1])
 )
 
 #%%
-# 5. Normalise the data
-# This normalises to between 0 and 1 so multiply by 100 to get a percentage
+# 4. Normalise the data and change the units appropriately.
+# Reassign the units as they are removed by cf-python after calculation
+# Only do this if you are certain the units are convertible to %
+# This normalises to between 0 and 1 so multiply by 100 to get a percentage:
 use_snow_normal = ((use_snow - use_snow.minimum())/ (use_snow.range()))*100 
+# TODO SB not CF Compliant way to handle normalisation, check
+use_snow_normal.override_units("percentage", inplace=True)
 
 #%%
-# 6. Reassign the units as they are removed by cf-python after calculation
-# Only do this if you are certain the units are convertible to %
-use_snow_normal.override_units("percentage", inplace=True) 
+# 5. Regrid the data to have a comparable array.
+# Here the orography file is regridded to the snow file 
+# as snow is higher resolution (if this doesnt work switch them).
+reg = use_orog.regrids(use_snow_normal, method="linear")
+
+#%%
+# 6. Squeeze snow data to remove the size 1 axes
+use_snow_normal = use_snow_normal.squeeze()
+
+#%%
+#  7. Final statistical calculations
+coefficient = mstats.pearsonr(reg.array, use_snow_normal.array)
+print(f"The Pearson correlation coefficient is {coefficient}")
+
 
 #%%
-# 7. Plot of Snowcover contour
+# 8. Plots including title stating the coefficient.
 # First it outputs the newly formatted data, you can change the file names
 # the plots will save as.
 # colour_scale.txt is a colour scale specifically made and saved to show 
 # the data but you could make your own or use existing ones from
 # https://ncas-cms.github.io/cf-plot/build/colour_scales.html
 # You will also need to adjust the labels and axis for your region
-cfp.gopen(file="snow-1.png")
+cfp.gopen(rows=1, columns=2, file="snow_and_orog_on_same_grid.png")
 ###cfp.cscale("~/cfplot_data/colour_scale.txt")
+# Joint config
 cfp.mapset(resolution="10m")
-cfp.con(use_snow_normal, 
-        lines=False, 
-        title = "Snow Cover Contour Plot", 
-        xticklabels = ("3W", "2W", "1W"), 
-        yticklabels =("52N", "53N", "54N", "55N"), 
-        xticks = (-3, -2, -1), 
-        yticks= (52, 53, 54, 55))
-cfp.gclose()
-
-#%%
-# 8. Plot of 1km Resolution Orography Contour
-# Here the ocean doesnt get coloured out because when it is regridded it gets 
-# masked by the lack of data for the dependant value (snow) over the seas
-cfp.gopen(file="orog-1.png")
+label_info = {
+    "xticklabels": ("3W", "2W", "1W"), 
+    "yticklabels": ("52N", "53N", "54N", "55N"), 
+    "xticks": (-3, -2, -1),
+    "yticks": (52, 53, 54, 55),
+}
+
+cfp.gpos(1)
+cfp.con(use_snow_normal,  lines=False, title = "Snow Cover Plot", 
+        **label_info)
+# This plots the regridded file data. The destination grid is the snow grid.
+cfp.gpos(2)
 cfp.cscale("wiki_2_0_reduced")
-cfp.mapset(resolution="10m")
-cfp.con(use_orog, 
-        lines=False, 
-        title = "1km resolution Orography Contour Plot", 
-        xticklabels = ("3W", "2W", "1W"), 
-        yticklabels =("52N", "53N", "54N", "55N"), 
-        xticks = (-3, -2, -1), 
-        yticks= (52, 53, 54, 55))
-cfp.gclose()
-
-#%%
-# 9. Plot of Vertical Orography Lineplot of 1km Resolution Elevation
-lonz = use_orog.construct("longitude").data[0]
-elevation_orog = use_orog.subspace(longitude = lonz)
-xticks_elevation_orog = [52, 52.5, 53, 53.5, 54, 54.5, 55]
-
-cfp.gopen(figsize=(12, 6), file ="orography_vertical.png")
-
-cfp.lineplot(
-    x=elevation_orog.coord('latitude').array,
-    y=elevation_orog.array.squeeze(),
-    color="black",
-    title="Orography Profile over part of UK",
-    ylabel="Elevation (m)",
-    xlabel="Latitude",
-    xticks=xticks_elevation_orog,
-)
-
-cfp.plotvars.plot.fill_between(
-    elevation_orog.coord('latitude').array,
-    0,
-    elevation_orog.array.squeeze(),
-    color='black',
-    alpha=0.7
-)
-cfp.gclose()
-
-#%%
-# 10. Regrid the data to have a comparable array.
-# Here the orography file is regridded to the snow file 
-# as snow is higher resolution (if this doesnt work switch them).
-reg = use_orog.regrids(use_snow_normal, method="linear")
-
-
-# This plots the regridded file data
-cfp.gopen(file="regridded-1.png")
-cfp.con(reg, lines=False)
+cfp.con(reg, lines=False, title="Elevation (1km resolution Orography) Plot",
+        **label_info)
 cfp.gclose()
-
-#%%
-# 11. Squeeze snow data to remove the size 1 axes
-snow_data = snow_data.squeeze()
-
-#%%
-#  12. Final statistical calculations
-coefficient = mstats.pearsonr(reg_data.array, snow_data.array)
-print(f"The Pearson correlation coefficient is {coefficient}")

From 4490550dc5e29c7082e8782d2ce9b5d48bc7b9d3 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 00:51:43 +0100
Subject: [PATCH 04/11] Update NH original recipe 18 to finalise single plot

---
 docs/source/recipes/plot_18_recipe.py | 104 ++++++++++++++++----------
 1 file changed, 64 insertions(+), 40 deletions(-)

diff --git a/docs/source/recipes/plot_18_recipe.py b/docs/source/recipes/plot_18_recipe.py
index bb2fca5fd7..24c9c19721 100644
--- a/docs/source/recipes/plot_18_recipe.py
+++ b/docs/source/recipes/plot_18_recipe.py
@@ -1,101 +1,125 @@
 """
-Plot to compare two linked datasets : Snow against Elevation
-============================================
-In this recipe, we will plot a dependant variable (in this example snow cover) 
-against an independent variable (elevation). This will be on a contour plot 
-separately and together, then finding the coefficient of correlation and making an 
-elevation line plot.
+Plot to compare two linked datasets: Snow against Elevation
+===========================================================
+
+In this recipe, we will plot a dependant variable (in this example snow
+cover) against an independent variable (elevation). This will be on a
+contour plot separately and together, then finding the coefficient of
+correlation and making an elevation line plot.
 """
 
 # %%
 # 1. Import cf-python, cf-plot and other required packages:
-
 import cfplot as cfp
 import cf
 
-import sys
-import scipy.stats.mstats as mstats
 import matplotlib.pyplot as plt
+import scipy.stats.mstats as mstats
 
-#%%
+# %%
 # 2. Read the data in:
-# We are investigating the influence of the land height on the snow cover, 
-#so snow cover is the dependent variable. You can use different variable
+# We are investigating the influence of the land height on the snow cover,
+# so snow cover is the dependent variable. You can use different variable
 # names for easier understanding.
 # We are selecting the first field in the data with [0]
-PATH="~/summerstudents/final-recipes/new-required-datasets"
+PATH = "~/summerstudents/final-recipes/new-required-datasets"
 orog = cf.read(f"{PATH}/1km_elevation.nc")[0]
 snow = cf.read(f"{PATH}/snowcover")[0]
 # Could use any of the seven days by indexing differently
 snow_day = snow[0]  # first day, Jan 1st of this year (2024)
+snow_day.dump()
+orog.dump()
+print("OROG DATA IS", orog.data)
 
-#%%
+# %%
 # 3. Choose the regions and subspace to same area for both datasets:
-# Ensuring both datasets cover it, and also that its not too large as then 
-# regridding could cause the program to crash on small computers (dont 
+# Ensuring both datasets cover it, and also that its not too large as then
+# regridding could cause the program to crash on small computers (dont
 # regrid the whole map), as well as being mainly land.
 # Tool for finding rectangular  area for lat and lon points
 # https://www.findlatitudeandlongitude.com/l/Yorkshire%2C+England/5009414/
 region_in_mid_uk = [(-3.0, -1.0), (52.0, 55.0)]  # lon then lat
 use_orog = orog.subspace(
-    longitude=cf.wi(*region_in_mid_uk[0]),
-    latitude=cf.wi(*region_in_mid_uk[1])
+    longitude=cf.wi(*region_in_mid_uk[0]), latitude=cf.wi(*region_in_mid_uk[1])
 )
 use_snow = snow_day.subspace(
-    longitude=cf.wi(*region_in_mid_uk[0]),
-    latitude=cf.wi(*region_in_mid_uk[1])
+    longitude=cf.wi(*region_in_mid_uk[0]), latitude=cf.wi(*region_in_mid_uk[1])
 )
 
-#%%
+# %%
 # 4. Normalise the data and change the units appropriately.
 # Reassign the units as they are removed by cf-python after calculation
 # Only do this if you are certain the units are convertible to %
 # This normalises to between 0 and 1 so multiply by 100 to get a percentage:
-use_snow_normal = ((use_snow - use_snow.minimum())/ (use_snow.range()))*100 
+use_snow_normal = ((use_snow - use_snow.minimum()) / (use_snow.range())) * 100
 # TODO SB not CF Compliant way to handle normalisation, check
 use_snow_normal.override_units("percentage", inplace=True)
 
-#%%
+# %%
 # 5. Regrid the data to have a comparable array.
-# Here the orography file is regridded to the snow file 
+# Here the orography file is regridded to the snow file
 # as snow is higher resolution (if this doesnt work switch them).
 reg = use_orog.regrids(use_snow_normal, method="linear")
 
-#%%
+# %%
 # 6. Squeeze snow data to remove the size 1 axes
 use_snow_normal = use_snow_normal.squeeze()
 
-#%%
+# %%
 #  7. Final statistical calculations
 coefficient = mstats.pearsonr(reg.array, use_snow_normal.array)
-print(f"The Pearson correlation coefficient is {coefficient}")
+print(f"The Pearson correlation coefficient is: {coefficient}")
 
 
-#%%
+# %%
 # 8. Plots including title stating the coefficient.
 # First it outputs the newly formatted data, you can change the file names
 # the plots will save as.
-# colour_scale.txt is a colour scale specifically made and saved to show 
+# colour_scale.txt is a colour scale specifically made and saved to show
 # the data but you could make your own or use existing ones from
 # https://ncas-cms.github.io/cf-plot/build/colour_scales.html
 # You will also need to adjust the labels and axis for your region
-cfp.gopen(rows=1, columns=2, file="snow_and_orog_on_same_grid.png")
-###cfp.cscale("~/cfplot_data/colour_scale.txt")
-# Joint config
+cfp.gopen(
+    rows=1, columns=2, top=0.85, wspace=0.001,
+    file="snow_and_orog_on_same_grid.png",
+    user_position=True,
+)
+# TODO SB cfp.cscale("~/cfplot_data/colour_scale.txt")
+# Joint config including adding an overall title
+plt.suptitle(
+    (
+        "Snow cover in relation to elevation over the same area of the UK "
+        "at midnight on\n2024-01-01: the correlation coefficient between "
+        f"the two datasets is {coefficient.statistic:.4g} (4 s.f.)"
+    ),
+    fontsize=18,
+)
 cfp.mapset(resolution="10m")
+cfp.setvars(ocean_color="white", lake_color="white")
 label_info = {
-    "xticklabels": ("3W", "2W", "1W"), 
-    "yticklabels": ("52N", "53N", "54N", "55N"), 
+    "xticklabels": ("3W", "2W", "1W"),
+    "yticklabels": ("52N", "53N", "54N", "55N"),
     "xticks": (-3, -2, -1),
     "yticks": (52, 53, 54, 55),
 }
 
+
 cfp.gpos(1)
-cfp.con(use_snow_normal,  lines=False, title = "Snow Cover Plot", 
-        **label_info)
-# This plots the regridded file data. The destination grid is the snow grid.
-cfp.gpos(2)
 cfp.cscale("wiki_2_0_reduced")
-cfp.con(reg, lines=False, title="Elevation (1km resolution Orography) Plot",
-        **label_info)
+cfp.con(
+    reg,
+    lines=False,
+    title="Elevation (from 1km-resolution orography)",
+    colorbar_drawedges=False,
+    **label_info,
+)
+cfp.gpos(2)
+cfp.cscale("precip4_11lev", ncols=22, reverse=1)
+cfp.con(use_snow_normal, lines=False,
+        title="Snow cover extent (from satellite imagery)",
+        colorbar_drawedges=False,
+        yaxis=False,  # since is same as one on first column plot
+        **label_info
+)
+
 cfp.gclose()

From 10354b7e474facb748f4f49e10ede507e54cc6d9 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 01:16:56 +0100
Subject: [PATCH 05/11] Update NH original recipe 18 to improve variable names

---
 docs/source/recipes/plot_18_recipe.py | 39 ++++++++++++---------------
 1 file changed, 17 insertions(+), 22 deletions(-)

diff --git a/docs/source/recipes/plot_18_recipe.py b/docs/source/recipes/plot_18_recipe.py
index 24c9c19721..eff2d252a1 100644
--- a/docs/source/recipes/plot_18_recipe.py
+++ b/docs/source/recipes/plot_18_recipe.py
@@ -27,9 +27,6 @@
 snow = cf.read(f"{PATH}/snowcover")[0]
 # Could use any of the seven days by indexing differently
 snow_day = snow[0]  # first day, Jan 1st of this year (2024)
-snow_day.dump()
-orog.dump()
-print("OROG DATA IS", orog.data)
 
 # %%
 # 3. Choose the regions and subspace to same area for both datasets:
@@ -39,10 +36,10 @@
 # Tool for finding rectangular  area for lat and lon points
 # https://www.findlatitudeandlongitude.com/l/Yorkshire%2C+England/5009414/
 region_in_mid_uk = [(-3.0, -1.0), (52.0, 55.0)]  # lon then lat
-use_orog = orog.subspace(
+sub_orog = orog.subspace(
     longitude=cf.wi(*region_in_mid_uk[0]), latitude=cf.wi(*region_in_mid_uk[1])
 )
-use_snow = snow_day.subspace(
+sub_snow = snow_day.subspace(
     longitude=cf.wi(*region_in_mid_uk[0]), latitude=cf.wi(*region_in_mid_uk[1])
 )
 
@@ -51,23 +48,22 @@
 # Reassign the units as they are removed by cf-python after calculation
 # Only do this if you are certain the units are convertible to %
 # This normalises to between 0 and 1 so multiply by 100 to get a percentage:
-use_snow_normal = ((use_snow - use_snow.minimum()) / (use_snow.range())) * 100
-# TODO SB not CF Compliant way to handle normalisation, check
-use_snow_normal.override_units("percentage", inplace=True)
+sub_snow = ((sub_snow - sub_snow.minimum()) / (sub_snow.range())) * 100
+sub_snow.override_units("1", inplace=True)
 
 # %%
 # 5. Regrid the data to have a comparable array.
 # Here the orography file is regridded to the snow file
 # as snow is higher resolution (if this doesnt work switch them).
-reg = use_orog.regrids(use_snow_normal, method="linear")
+regridded_orog = sub_orog.regrids(sub_snow, method="linear")
 
 # %%
 # 6. Squeeze snow data to remove the size 1 axes
-use_snow_normal = use_snow_normal.squeeze()
+sub_snow = sub_snow.squeeze()
 
 # %%
 #  7. Final statistical calculations
-coefficient = mstats.pearsonr(reg.array, use_snow_normal.array)
+coefficient = mstats.pearsonr(regridded_orog.array, sub_snow.array)
 print(f"The Pearson correlation coefficient is: {coefficient}")
 
 
@@ -78,21 +74,21 @@
 # colour_scale.txt is a colour scale specifically made and saved to show
 # the data but you could make your own or use existing ones from
 # https://ncas-cms.github.io/cf-plot/build/colour_scales.html
-# You will also need to adjust the labels and axis for your region
+# You will also need to adjust the labels and axis for your regridded_orogion
 cfp.gopen(
-    rows=1, columns=2, top=0.85, wspace=0.001,
+    rows=1, columns=2, top=0.85,
     file="snow_and_orog_on_same_grid.png",
     user_position=True,
 )
-# TODO SB cfp.cscale("~/cfplot_data/colour_scale.txt")
+
 # Joint config including adding an overall title
 plt.suptitle(
     (
-        "Snow cover in relation to elevation over the same area of the UK "
-        "at midnight on\n2024-01-01: the correlation coefficient between "
-        f"the two datasets is {coefficient.statistic:.4g} (4 s.f.)"
+        "Snow cover compared to elevation for the same area of the UK "
+        "at midnight\n2024-01-01, with correlation coefficient "
+        f"(on the same grid) of {coefficient.statistic:.4g} (4 s.f.)"
     ),
-    fontsize=18,
+    fontsize=17,
 )
 cfp.mapset(resolution="10m")
 cfp.setvars(ocean_color="white", lake_color="white")
@@ -103,11 +99,11 @@
     "yticks": (52, 53, 54, 55),
 }
 
-
+# PLot the two contour plots as columns
 cfp.gpos(1)
 cfp.cscale("wiki_2_0_reduced")
 cfp.con(
-    reg,
+    regridded_orog,
     lines=False,
     title="Elevation (from 1km-resolution orography)",
     colorbar_drawedges=False,
@@ -115,10 +111,9 @@
 )
 cfp.gpos(2)
 cfp.cscale("precip4_11lev", ncols=22, reverse=1)
-cfp.con(use_snow_normal, lines=False,
+cfp.con(sub_snow, lines=False,
         title="Snow cover extent (from satellite imagery)",
         colorbar_drawedges=False,
-        yaxis=False,  # since is same as one on first column plot
         **label_info
 )
 

From 558c02f42a54b0f7eb2ec8d2e10464903a8fd66d Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 01:37:04 +0100
Subject: [PATCH 06/11] Generalise NH recipe 18 to process any day from week's
 dataset

---
 docs/source/recipes/plot_18_recipe.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/docs/source/recipes/plot_18_recipe.py b/docs/source/recipes/plot_18_recipe.py
index eff2d252a1..7695d884d9 100644
--- a/docs/source/recipes/plot_18_recipe.py
+++ b/docs/source/recipes/plot_18_recipe.py
@@ -27,6 +27,9 @@
 snow = cf.read(f"{PATH}/snowcover")[0]
 # Could use any of the seven days by indexing differently
 snow_day = snow[0]  # first day, Jan 1st of this year (2024)
+# Find day corresponding to aggregated dataset
+snow_day_dt = snow_day.coordinate("time")[0].data
+snow_day_daystring = f"{snow_day_dt.datetime_as_string[0].split(" ")[0]}"
 
 # %%
 # 3. Choose the regions and subspace to same area for both datasets:
@@ -64,6 +67,8 @@
 # %%
 #  7. Final statistical calculations
 coefficient = mstats.pearsonr(regridded_orog.array, sub_snow.array)
+# Need to use scipy.stats.mstats, not just scipy.stats, to account for masked
+# data in the array(s) properly.
 print(f"The Pearson correlation coefficient is: {coefficient}")
 
 
@@ -85,8 +90,9 @@
 plt.suptitle(
     (
         "Snow cover compared to elevation for the same area of the UK "
-        "at midnight\n2024-01-01, with correlation coefficient "
-        f"(on the same grid) of {coefficient.statistic:.4g} (4 s.f.)"
+        f"aggregated across\n day {snow_day_daystring} with correlation "
+        "coefficient (on the same grid) of "
+        f"{coefficient.statistic:.4g} (4 s.f.)"
     ),
     fontsize=17,
 )

From 36824213297e3ef3464098443b50e1a94027efc9 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 11:54:40 +0100
Subject: [PATCH 07/11] Update NH recipe 18 to tidy & add detail to comments

---
 docs/source/recipes/plot_18_recipe.py | 101 ++++++++++++++------------
 1 file changed, 56 insertions(+), 45 deletions(-)

diff --git a/docs/source/recipes/plot_18_recipe.py b/docs/source/recipes/plot_18_recipe.py
index 7695d884d9..ae9e6b434c 100644
--- a/docs/source/recipes/plot_18_recipe.py
+++ b/docs/source/recipes/plot_18_recipe.py
@@ -1,11 +1,13 @@
 """
-Plot to compare two linked datasets: Snow against Elevation
-===========================================================
+Calculating the Pearson correlation coefficient between datasets
+================================================================
+
+In this recipe, we will take two datasets, one for an independent variable
+(in this example elevation) and one for a dependant variable (snow
+cover over a particuar day), regrid them to the same resolution then
+calculate the correlation coefficient, to get a measure of the relationship
+between them.
 
-In this recipe, we will plot a dependant variable (in this example snow
-cover) against an independent variable (elevation). This will be on a
-contour plot separately and together, then finding the coefficient of
-correlation and making an elevation line plot.
 """
 
 # %%
@@ -17,28 +19,34 @@
 import scipy.stats.mstats as mstats
 
 # %%
-# 2. Read the data in:
-# We are investigating the influence of the land height on the snow cover,
-# so snow cover is the dependent variable. You can use different variable
-# names for easier understanding.
-# We are selecting the first field in the data with [0]
+# 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
+# the snow cover extent, so snow cover is the dependent variable. The data
+# was sourced from
+# TODO SOURCES:
 PATH = "~/summerstudents/final-recipes/new-required-datasets"
 orog = cf.read(f"{PATH}/1km_elevation.nc")[0]
 snow = cf.read(f"{PATH}/snowcover")[0]
-# Could use any of the seven days by indexing differently
-snow_day = snow[0]  # first day, Jan 1st of this year (2024)
-# Find day corresponding to aggregated dataset
+
+
+# %%
+# 3. Choose the day of pre-aggregated snow cover to investigate. We will
+# take the first datetime element corresponding to the first day from the
+# datasets, 1st January 2024, but by changing the indexing you can explore
+# other days by changing the index. We also get the string corresponding to
+# the date, to reference later:
+snow_day = snow[0]
 snow_day_dt = snow_day.coordinate("time")[0].data
-snow_day_daystring = f"{snow_day_dt.datetime_as_string[0].split(" ")[0]}"
+snow_day_daystring = f"{snow_day_dt.datetime_as_string[0].split(' ')[0]}"
 
 # %%
-# 3. Choose the regions and subspace to same area for both datasets:
-# Ensuring both datasets cover it, and also that its not too large as then
-# regridding could cause the program to crash on small computers (dont
-# regrid the whole map), as well as being mainly land.
-# Tool for finding rectangular  area for lat and lon points
-# https://www.findlatitudeandlongitude.com/l/Yorkshire%2C+England/5009414/
-region_in_mid_uk = [(-3.0, -1.0), (52.0, 55.0)]  # lon then lat
+# 4. Choose the region to consider to compare the relationship across,
+# which must be defined across both datasets, though not necessarily on the
+# same grid since we regrid to the same grid next and subspace to the same
+# area for both datasets ready for comparison in the next steps. By changing
+# the latitude and longitude points in the tuple below, you can change the
+# area that is used:
+region_in_mid_uk = ((-3.0, -1.0), (52.0, 55.0))
 sub_orog = orog.subspace(
     longitude=cf.wi(*region_in_mid_uk[0]), latitude=cf.wi(*region_in_mid_uk[1])
 )
@@ -47,46 +55,47 @@
 )
 
 # %%
-# 4. Normalise the data and change the units appropriately.
-# Reassign the units as they are removed by cf-python after calculation
-# Only do this if you are certain the units are convertible to %
-# This normalises to between 0 and 1 so multiply by 100 to get a percentage:
-sub_snow = ((sub_snow - sub_snow.minimum()) / (sub_snow.range())) * 100
+# 5. Ensure data quality, since the standard name here corresponds to a
+# unitless fraction, but the values are in the tens, so we need to
+# normalise these to all lie between 0 and 1 and change the units
+# appropriately:
+sub_snow = ((sub_snow - sub_snow.minimum()) / (sub_snow.range()))
 sub_snow.override_units("1", inplace=True)
 
 # %%
-# 5. Regrid the data to have a comparable array.
-# Here the orography file is regridded to the snow file
-# as snow is higher resolution (if this doesnt work switch them).
+# 6. Regrid the data so that they lie on the same grid and therefore each
+# array structure has values with corresponding geospatial points that
+# can be statistically compared. Here the elevation field is regridded to the
+# snow field since the snow is higher-resolution, but the other way round is
+# possible by switching the field order:
 regridded_orog = sub_orog.regrids(sub_snow, method="linear")
 
 # %%
-# 6. Squeeze snow data to remove the size 1 axes
+# 7. Squeeze the snow data to remove the size 1 axes so we have arrays of
+# the same dimensions for each of the two fields to compare:
 sub_snow = sub_snow.squeeze()
 
 # %%
-#  7. Final statistical calculations
+# 8. Finally, perform the statistical calculation by using the SciPy method
+# to find the Pearson correlation coefficient for the two arrays now they are
+# in comparable form. Note we need to use 'scipy.stats.mstats' and not
+# 'scipy.stats' for the 'pearsonr' method, to account for masked
+# data in the array(s) properly:
 coefficient = mstats.pearsonr(regridded_orog.array, sub_snow.array)
-# Need to use scipy.stats.mstats, not just scipy.stats, to account for masked
-# data in the array(s) properly.
 print(f"The Pearson correlation coefficient is: {coefficient}")
 
-
 # %%
-# 8. Plots including title stating the coefficient.
-# First it outputs the newly formatted data, you can change the file names
-# the plots will save as.
-# colour_scale.txt is a colour scale specifically made and saved to show
-# the data but you could make your own or use existing ones from
-# https://ncas-cms.github.io/cf-plot/build/colour_scales.html
-# You will also need to adjust the labels and axis for your regridded_orogion
+# 9. Make a final plot showing the two arrays side-by-side and quoting the
+# determined Pearson correlation coefficient to illustrate the relatoinship
+# 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(
     rows=1, columns=2, top=0.85,
     file="snow_and_orog_on_same_grid.png",
     user_position=True,
 )
 
-# Joint config including adding an overall title
+# Joint configuration of the plots, including adding an overall title
 plt.suptitle(
     (
         "Snow cover compared to elevation for the same area of the UK "
@@ -105,7 +114,7 @@
     "yticks": (52, 53, 54, 55),
 }
 
-# PLot the two contour plots as columns
+# Plot the two contour plots as columns
 cfp.gpos(1)
 cfp.cscale("wiki_2_0_reduced")
 cfp.con(
@@ -116,7 +125,9 @@
     **label_info,
 )
 cfp.gpos(2)
-cfp.cscale("precip4_11lev", ncols=22, reverse=1)
+# Don't add extentions on the colourbar since it can only be 0 to 1 inclusive
+cfp.levs(min=0, max=1, step=0.1, extend="neither")
+cfp.cscale("precip_11lev", ncols=11, reverse=1)
 cfp.con(sub_snow, lines=False,
         title="Snow cover extent (from satellite imagery)",
         colorbar_drawedges=False,

From 73a301810d474d16a38834f893b0114b5ccf080f Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 12:03:54 +0100
Subject: [PATCH 08/11] Add full details of data sources to NH recipe 18

---
 docs/source/recipes/plot_18_recipe.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/docs/source/recipes/plot_18_recipe.py b/docs/source/recipes/plot_18_recipe.py
index ae9e6b434c..2c494cac27 100644
--- a/docs/source/recipes/plot_18_recipe.py
+++ b/docs/source/recipes/plot_18_recipe.py
@@ -21,12 +21,18 @@
 # %%
 # 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
-# the snow cover extent, so snow cover is the dependent variable. The data
-# was sourced from
-# TODO SOURCES:
+# the snow cover extent, so snow cover is the dependent variable. The snow
+# cover data is the
+# 'Snow Cover Extent 2017-present (raster 500 m), Europe, daily – version 1'
+# sourced from the Copernicus Land Monitoring Service which is described at:
+# https://land.copernicus.eu/en/products/snow/snow-cover-extent-europe-v1-0-500m
+# and the elevation data is the 'NOAA NGDC GLOBE topo: elevation data' dataset
+# which can be sourced from the IRI Data Library, or details found, at:
+# http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NGDC/.GLOBE/.topo/index.html.
 PATH = "~/summerstudents/final-recipes/new-required-datasets"
 orog = cf.read(f"{PATH}/1km_elevation.nc")[0]
 snow = cf.read(f"{PATH}/snowcover")[0]
+orog.dump()
 
 
 # %%

From 76daca3fa06061e891fefd61253bf5da1c121fd2 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 12:07:07 +0100
Subject: [PATCH 09/11] Set recipes path to standard in NH recipe 18

---
 docs/source/recipes/plot_18_recipe.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/docs/source/recipes/plot_18_recipe.py b/docs/source/recipes/plot_18_recipe.py
index 2c494cac27..d219bdfe19 100644
--- a/docs/source/recipes/plot_18_recipe.py
+++ b/docs/source/recipes/plot_18_recipe.py
@@ -3,7 +3,7 @@
 ================================================================
 
 In this recipe, we will take two datasets, one for an independent variable
-(in this example elevation) and one for a dependant variable (snow
+(in this example elevation) and one for a dependent variable (snow
 cover over a particuar day), regrid them to the same resolution then
 calculate the correlation coefficient, to get a measure of the relationship
 between them.
@@ -29,11 +29,8 @@
 # and the elevation data is the 'NOAA NGDC GLOBE topo: elevation data' dataset
 # which can be sourced from the IRI Data Library, or details found, at:
 # http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NGDC/.GLOBE/.topo/index.html.
-PATH = "~/summerstudents/final-recipes/new-required-datasets"
-orog = cf.read(f"{PATH}/1km_elevation.nc")[0]
-snow = cf.read(f"{PATH}/snowcover")[0]
-orog.dump()
-
+orog = cf.read("~/recipes/1km_elevation.nc")[0]
+snow = cf.read("~/recipes/snowcover")[0]
 
 # %%
 # 3. Choose the day of pre-aggregated snow cover to investigate. We will

From e14b1b18da2f746e28b3785a9d40946226dd4ab6 Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 12:11:14 +0100
Subject: [PATCH 10/11] Add NH recipe 18 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..42d0b2cd10 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_18_recipe.html#sphx-glr-recipes-plot-18-recipe-py
+<div class="sphx-glr-thumbcontainer regrid stats" tooltip="Regrid, Statistical Operations">

From d841efcf4b5598b4deae052a40f05ffe6b7912ad Mon Sep 17 00:00:00 2001
From: "Sadie L. Bartholomew" <sadie.bartholomew@ncas.ac.uk>
Date: Wed, 9 Oct 2024 12:14:39 +0100
Subject: [PATCH 11/11] Add new keyword 'stats' for filtering to appropriate
 recipes

---
 docs/source/recipes/recipe_list.txt | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/recipes/recipe_list.txt b/docs/source/recipes/recipe_list.txt
index 42d0b2cd10..c571bc4e62 100644
--- a/docs/source/recipes/recipe_list.txt
+++ b/docs/source/recipes/recipe_list.txt
@@ -13,7 +13,7 @@ plot_06_recipe.html#sphx-glr-recipes-plot-06-recipe-py
 plot_07_recipe.html#sphx-glr-recipes-plot-07-recipe-py
 <div class="sphx-glr-thumbcontainer aggregate lineplot subspace" tooltip="Aggregate, Lineplot, Subspace">
 plot_08_recipe.html#sphx-glr-recipes-plot-08-recipe-py
-<div class="sphx-glr-thumbcontainer collapse contourmap" tooltip="Collapse, Contourmap">
+<div class="sphx-glr-thumbcontainer collapse contourmap stats" tooltip="Collapse, Contourmap, Statistical Operations">
 plot_09_recipe.html#sphx-glr-recipes-plot-09-recipe-py
 <div class="sphx-glr-thumbcontainer histogram" tooltip="Histogram">
 plot_10_recipe.html#sphx-glr-recipes-plot-10-recipe-py
@@ -23,9 +23,9 @@ plot_11_recipe.html#sphx-glr-recipes-plot-11-recipe-py
 plot_12_recipe.html#sphx-glr-recipes-plot-12-recipe-py
 <div class="sphx-glr-thumbcontainer contourmap mask" tooltip=" Contourmap, Mask">
 plot_13_recipe.html#sphx-glr-recipes-plot-13-recipe-py
-<div class="sphx-glr-thumbcontainer subspace collapse contourmap lineplot" tooltip="Subspace, Collapse, Contourmap, Lineplot">
+<div class="sphx-glr-thumbcontainer subspace collapse contourmap lineplot stats" tooltip="Subspace, Collapse, Contourmap, Lineplot, Statistical Operations">
 plot_14_recipe.html#sphx-glr-recipes-plot-14-recipe-py
-<div class="sphx-glr-thumbcontainer subspace collapse contourmap" tooltip="Subspace, Collapse, Contourmap">
+<div class="sphx-glr-thumbcontainer subspace collapse contourmap stats" tooltip="Subspace, Collapse, Contourmap, Statistical Operations">
 plot_15_recipe.html#sphx-glr-recipes-plot-15-recipe-py
 <div class="sphx-glr-thumbcontainer histogram subspace" tooltip="Histogram, Subspace">
 plot_18_recipe.html#sphx-glr-recipes-plot-18-recipe-py