diff --git a/docs/_downloads/plot_1_recipe.ipynb b/docs/_downloads/plot_1_recipe.ipynb
deleted file mode 100644
index 8b0ec7545e..0000000000
--- a/docs/_downloads/plot_1_recipe.ipynb
+++ /dev/null
@@ -1,227 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Calculating global mean temperature timeseries\n\nIn this recipe we will calculate and plot monthly and annual global mean temperature timeseries.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "1. Import cf-python and cf-plot:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import cfplot as cfp\n\nimport cf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "2. Read the field constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f = cf.read(\"~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc\")\nprint(f)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "3. Select near surface temperature by index and look at its contents:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp = f[1]\nprint(temp)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "4. Select latitude and longitude dimensions by identities, with two different techniques:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "lon = temp.coordinate(\"long_name=longitude\")\nlat = temp.coordinate(\"Y\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "5. Print the description of near surface temperature using the dump method to show properties of all constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp.dump()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "6. Latitude and longitude dimension coordinate cell bounds are absent, which are created and set:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "a = lat.create_bounds()\nlat.set_bounds(a)\nlat.dump()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "b = lon.create_bounds()\nlon.set_bounds(b)\nlon.dump()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "print(b.array)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "7. Time dimension coordinate cell bounds are similarly created and set for cell sizes of one calendar month:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "time = temp.coordinate(\"long_name=time\")\nc = time.create_bounds(cellsize=cf.M())\ntime.set_bounds(c)\ntime.dump()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "8. Calculate and plot the area weighted mean surface temperature for each time:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "global_avg = temp.collapse(\"area: mean\", weights=True)\ncfp.lineplot(global_avg, color=\"red\", title=\"Global mean surface temperature\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "9. Calculate and plot the annual global mean surface temperature:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "annual_global_avg = global_avg.collapse(\"T: mean\", group=cf.Y())\ncfp.lineplot(\n    annual_global_avg,\n    color=\"red\",\n    title=\"Annual global mean surface temperature\",\n)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/plot_1_recipe.py b/docs/_downloads/plot_1_recipe.py
deleted file mode 100644
index 93b9dde417..0000000000
--- a/docs/_downloads/plot_1_recipe.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""
-Calculating global mean temperature timeseries
-==============================================
-
-In this recipe we will calculate and plot monthly and annual global mean temperature timeseries.
-"""
-
-# %%
-# 1. Import cf-python and cf-plot:
-
-import cfplot as cfp
-
-import cf
-
-# %%
-# 2. Read the field constructs:
-
-f = cf.read("~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc")
-print(f)
-
-# %%
-# 3. Select near surface temperature by index and look at its contents:
-
-temp = f[1]
-print(temp)
-
-# %%
-# 4. Select latitude and longitude dimensions by identities, with two different techniques:
-
-lon = temp.coordinate("long_name=longitude")
-lat = temp.coordinate("Y")
-
-# %%
-# 5. Print the description of near surface temperature using the dump method to show properties of all constructs:
-
-temp.dump()
-
-# %%
-# 6. Latitude and longitude dimension coordinate cell bounds are absent, which are created and set:
-
-a = lat.create_bounds()
-lat.set_bounds(a)
-lat.dump()
-
-# %%
-
-b = lon.create_bounds()
-lon.set_bounds(b)
-lon.dump()
-
-# %%
-
-print(b.array)
-
-# %%
-# 7. Time dimension coordinate cell bounds are similarly created and set for cell sizes of one calendar month:
-
-time = temp.coordinate("long_name=time")
-c = time.create_bounds(cellsize=cf.M())
-time.set_bounds(c)
-time.dump()
-
-# %%
-# 8. Calculate and plot the area weighted mean surface temperature for each time:
-
-global_avg = temp.collapse("area: mean", weights=True)
-cfp.lineplot(global_avg, color="red", title="Global mean surface temperature")
-
-# %%
-# 9. Calculate and plot the annual global mean surface temperature:
-
-annual_global_avg = global_avg.collapse("T: mean", group=cf.Y())
-cfp.lineplot(
-    annual_global_avg,
-    color="red",
-    title="Annual global mean surface temperature",
-)
diff --git a/docs/_downloads/plot_2_recipe.ipynb b/docs/_downloads/plot_2_recipe.ipynb
deleted file mode 100644
index 474afce62e..0000000000
--- a/docs/_downloads/plot_2_recipe.ipynb
+++ /dev/null
@@ -1,274 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Calculating and plotting the global average temperature anomalies\n\nIn this recipe we will calculate and plot the global average temperature anomalies.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "1. Import cf-python and cf-plot:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import cfplot as cfp\n\nimport cf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "2. Read the field constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f = cf.read(\"~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc\")\nprint(f)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "3. Select near surface temperature by index and look at its contents:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp = f[1]\nprint(temp)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "4. Select latitude and longitude dimensions by identities, with two different techniques:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "lon = temp.coordinate(\"long_name=longitude\")\nlat = temp.coordinate(\"Y\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "5. Print the description of near surface temperature to show properties of all constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp.dump()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "6. Latitude and longitude dimension coordinate cell bounds are absent, which are created and set:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "a = lat.create_bounds()\nlat.set_bounds(a)\nlat.dump()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "b = lon.create_bounds()\nlon.set_bounds(b)\nlon.dump()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "print(b.array)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "7. Time dimension coordinate cell bounds are similarly created and set for cell sizes of one calendar month:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "time = temp.coordinate(\"long_name=time\")\nc = time.create_bounds(cellsize=cf.M())\ntime.set_bounds(c)\ntime.dump()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "8. Calculate the area weighted mean surface temperature for each time using the collapse method:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "global_avg = temp.collapse(\"area: mean\", weights=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "9. Calculate the annual global mean surface temperature:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "annual_global_avg = global_avg.collapse(\"T: mean\", group=cf.Y())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "10. The temperature values are averaged for the climatological period of 1961-1990 by defining a subspace within these years using `cf.wi` query instance over subspace and doing a statistical collapse with the collapse method:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "annual_global_avg_61_90 = annual_global_avg.subspace(\n    T=cf.year(cf.wi(1961, 1990))\n)\nprint(annual_global_avg_61_90)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp_clim = annual_global_avg_61_90.collapse(\"T: mean\")\nprint(temp_clim)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "11. The temperature anomaly is then calculated by subtracting these climatological temperature values from the annual global average temperatures and plotted:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp_anomaly = annual_global_avg - temp_clim\ncfp.lineplot(\n    temp_anomaly,\n    color=\"red\",\n    title=\"Global Average Temperature Anomaly (1901-2021)\",\n    ylabel=\"1961-1990 climatology difference \",\n    yunits=\"degree Celcius\",\n)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/plot_2_recipe.py b/docs/_downloads/plot_2_recipe.py
deleted file mode 100644
index 902c796788..0000000000
--- a/docs/_downloads/plot_2_recipe.py
+++ /dev/null
@@ -1,96 +0,0 @@
-"""
-Calculating and plotting the global average temperature anomalies
-=================================================================
-
-In this recipe we will calculate and plot the global average temperature anomalies.
-"""
-
-# %%
-# 1. Import cf-python and cf-plot:
-
-import cfplot as cfp
-
-import cf
-
-# %%
-# 2. Read the field constructs:
-
-f = cf.read("~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc")
-print(f)
-
-# %%
-# 3. Select near surface temperature by index and look at its contents:
-
-temp = f[1]
-print(temp)
-
-# %%
-# 4. Select latitude and longitude dimensions by identities, with two different techniques:
-
-lon = temp.coordinate("long_name=longitude")
-lat = temp.coordinate("Y")
-
-# %%
-# 5. Print the description of near surface temperature to show properties of all constructs:
-
-temp.dump()
-
-# %%
-# 6. Latitude and longitude dimension coordinate cell bounds are absent, which are created and set:
-
-a = lat.create_bounds()
-lat.set_bounds(a)
-lat.dump()
-
-# %%
-
-b = lon.create_bounds()
-lon.set_bounds(b)
-lon.dump()
-
-# %%
-
-print(b.array)
-
-# %%
-# 7. Time dimension coordinate cell bounds are similarly created and set for cell sizes of one calendar month:
-
-time = temp.coordinate("long_name=time")
-c = time.create_bounds(cellsize=cf.M())
-time.set_bounds(c)
-time.dump()
-
-# %%
-# 8. Calculate the area weighted mean surface temperature for each time using the collapse method:
-
-global_avg = temp.collapse("area: mean", weights=True)
-
-# %%
-# 9. Calculate the annual global mean surface temperature:
-
-annual_global_avg = global_avg.collapse("T: mean", group=cf.Y())
-
-# %%
-# 10. The temperature values are averaged for the climatological period of 1961-1990 by defining a subspace within these years using `cf.wi` query instance over subspace and doing a statistical collapse with the collapse method:
-
-annual_global_avg_61_90 = annual_global_avg.subspace(
-    T=cf.year(cf.wi(1961, 1990))
-)
-print(annual_global_avg_61_90)
-
-# %%
-
-temp_clim = annual_global_avg_61_90.collapse("T: mean")
-print(temp_clim)
-
-# %%
-# 11. The temperature anomaly is then calculated by subtracting these climatological temperature values from the annual global average temperatures and plotted:
-
-temp_anomaly = annual_global_avg - temp_clim
-cfp.lineplot(
-    temp_anomaly,
-    color="red",
-    title="Global Average Temperature Anomaly (1901-2021)",
-    ylabel="1961-1990 climatology difference ",
-    yunits="degree Celcius",
-)
diff --git a/docs/_downloads/plot_3_recipe.ipynb b/docs/_downloads/plot_3_recipe.ipynb
deleted file mode 100644
index 946165dad4..0000000000
--- a/docs/_downloads/plot_3_recipe.ipynb
+++ /dev/null
@@ -1,133 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Plotting global mean temperatures spatially\n\nIn this recipe, we will plot the global mean temperature spatially.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "1. Import cf-python and cf-plot:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import cfplot as cfp\n\nimport cf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "2. Read the field constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f = cf.read(\"~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc\")\nprint(f)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "3. Select near surface temperature by index and look at its contents:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp = f[1]\nprint(temp)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "4. Average the monthly mean surface temperature values by the time axis using the collapse method:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "global_avg = temp.collapse(\"mean\", axes=\"long_name=time\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "5. Plot the global mean surface temperatures:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "cfp.con(global_avg, lines=False, title=\"Global mean surface temperature\")"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/plot_3_recipe.py b/docs/_downloads/plot_3_recipe.py
deleted file mode 100644
index 25633b1c54..0000000000
--- a/docs/_downloads/plot_3_recipe.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""
-Plotting global mean temperatures spatially
-===========================================
-
-In this recipe, we will plot the global mean temperature spatially.
-"""
-
-# %%
-# 1. Import cf-python and cf-plot:
-
-import cfplot as cfp
-
-import cf
-
-# %%
-# 2. Read the field constructs:
-
-f = cf.read("~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc")
-print(f)
-
-# %%
-# 3. Select near surface temperature by index and look at its contents:
-
-temp = f[1]
-print(temp)
-
-# %%
-# 4. Average the monthly mean surface temperature values by the time axis using the collapse method:
-
-global_avg = temp.collapse("mean", axes="long_name=time")
-
-# %%
-# 5. Plot the global mean surface temperatures:
-
-cfp.con(global_avg, lines=False, title="Global mean surface temperature")
diff --git a/docs/_downloads/plot_4_recipe.ipynb b/docs/_downloads/plot_4_recipe.ipynb
deleted file mode 100644
index 90e0ecb17e..0000000000
--- a/docs/_downloads/plot_4_recipe.ipynb
+++ /dev/null
@@ -1,137 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Comparing two datasets with different resolutions using regridding\n\nIn this recipe, we will regrid two different datasets with different resolutions. An example use case could be one where the observational dataset with a higher resolution needs to be regridded to that of the model dataset so that they can be compared with each other.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "1. Import cf-python:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import cf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "2. Read the field constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "obs = cf.read(\"~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc\", chunks=None)\nprint(obs)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "model = cf.read(\n    \"~/recipes/tas_Amon_HadGEM3-GC3-1_hist-1p0_r3i1p1f2_gn_185001-201412.nc\"\n)\nprint(model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "3. Select observation and model temperature fields by identity and index respectively, and look at their contents:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "obs_temp = obs.select_field(\"long_name=near-surface temperature\")\nprint(obs_temp)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "model_temp = model[0]\nprint(model_temp)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "4. Regrid observational data to that of the model data and create a new low resolution observational data using bilinear interpolation:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "obs_temp_regrid = obs_temp.regrids(model_temp, method=\"linear\")\nprint(obs_temp_regrid)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/plot_4_recipe.py b/docs/_downloads/plot_4_recipe.py
deleted file mode 100644
index cc3b298412..0000000000
--- a/docs/_downloads/plot_4_recipe.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""
-Comparing two datasets with different resolutions using regridding
-==================================================================
-
-In this recipe, we will regrid two different datasets with different resolutions. An example use case could be one where the observational dataset with a higher resolution needs to be regridded to that of the model dataset so that they can be compared with each other.
-"""
-
-# %%
-# 1. Import cf-python:
-
-import cf
-
-# %%
-# 2. Read the field constructs:
-
-obs = cf.read("~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc", chunks=None)
-print(obs)
-
-# %%
-
-model = cf.read(
-    "~/recipes/tas_Amon_HadGEM3-GC3-1_hist-1p0_r3i1p1f2_gn_185001-201412.nc"
-)
-print(model)
-
-# %%
-# 3. Select observation and model temperature fields by identity and index respectively, and look at their contents:
-
-obs_temp = obs.select_field("long_name=near-surface temperature")
-print(obs_temp)
-
-# %%
-
-model_temp = model[0]
-print(model_temp)
-
-# %%
-# 4. Regrid observational data to that of the model data and create a new low resolution observational data using bilinear interpolation:
-obs_temp_regrid = obs_temp.regrids(model_temp, method="linear")
-print(obs_temp_regrid)
diff --git a/docs/_downloads/plot_5_recipe.ipynb b/docs/_downloads/plot_5_recipe.ipynb
deleted file mode 100644
index 676f954707..0000000000
--- a/docs/_downloads/plot_5_recipe.ipynb
+++ /dev/null
@@ -1,159 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Plotting wind vectors overlaid on precipitation data\n\nIn this recipe we will plot wind vectors, derived from northward and eastward wind components, over precipitation data.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "1. Import cf-python and cf-plot:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import cfplot as cfp\n\nimport cf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "2. Read the field constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f1 = cf.read(\"~/recipes/northward.nc\")\nprint(f1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f2 = cf.read(\"~/recipes/eastward.nc\")\nprint(f2)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f3 = cf.read(\"~/recipes/monthly_precipitation.nc\")\nprint(f3)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "3. Select wind vectors and precipitation data by index and look at their contents:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "v = f1[0]\nprint(v)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "u = f2[0]\nprint(u)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "pre = f3[0]\nprint(pre)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "4. Plot the wind vectors on top of precipitation data for June 1995 by creating a subspace with a date-time object and using [cfplot.con](http://ajheaps.github.io/cf-plot/con.html). Here [cfplot.gopen](http://ajheaps.github.io/cf-plot/gopen.html) is used to define the parts of the plot area, which is closed by [cfplot.gclose](http://ajheaps.github.io/cf-plot/gclose.html); [cfplot.cscale](http://ajheaps.github.io/cf-plot/cscale.html) is used to choose one of the colour maps amongst many available; [cfplot.levs](http://ajheaps.github.io/cf-plot/levs.html) is used to set the contour levels for precipitation data; and [cfplot.vect](http://ajheaps.github.io/cf-plot/vect.html) is used to plot the wind vectors for June 1995:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "june_95 = cf.year(1995) & cf.month(6)\ncfp.gopen()\ncfp.cscale(\"precip4_11lev\")\ncfp.levs(step=100)\ncfp.con(\n    pre.subspace(T=june_95),\n    lines=False,\n    title=\"June 1995 monthly global precipitation\",\n)\ncfp.vect(\n    u=u.subspace(T=june_95),\n    v=v.subspace(T=june_95),\n    key_length=10,\n    scale=35,\n    stride=5,\n)\ncfp.gclose()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/plot_5_recipe.py b/docs/_downloads/plot_5_recipe.py
deleted file mode 100644
index 2f1b4eab0a..0000000000
--- a/docs/_downloads/plot_5_recipe.py
+++ /dev/null
@@ -1,64 +0,0 @@
-"""
-Plotting wind vectors overlaid on precipitation data
-====================================================
-
-In this recipe we will plot wind vectors, derived from northward and eastward wind components, over precipitation data.
-"""
-
-# %%
-# 1. Import cf-python and cf-plot:
-
-import cfplot as cfp
-
-import cf
-
-# %%
-# 2. Read the field constructs:
-
-f1 = cf.read("~/recipes/northward.nc")
-print(f1)
-
-# %%
-
-f2 = cf.read("~/recipes/eastward.nc")
-print(f2)
-
-# %%
-
-f3 = cf.read("~/recipes/monthly_precipitation.nc")
-print(f3)
-
-# %%
-# 3. Select wind vectors and precipitation data by index and look at their contents:
-v = f1[0]
-print(v)
-
-# %%
-
-u = f2[0]
-print(u)
-
-# %%
-
-pre = f3[0]
-print(pre)
-
-# %%
-# 4. Plot the wind vectors on top of precipitation data for June 1995 by creating a subspace with a date-time object and using `cfplot.con <http://ajheaps.github.io/cf-plot/con.html>`_. Here `cfplot.gopen <http://ajheaps.github.io/cf-plot/gopen.html>`_ is used to define the parts of the plot area, which is closed by `cfplot.gclose <http://ajheaps.github.io/cf-plot/gclose.html>`_; `cfplot.cscale <http://ajheaps.github.io/cf-plot/cscale.html>`_ is used to choose one of the colour maps amongst many available; `cfplot.levs <http://ajheaps.github.io/cf-plot/levs.html>`_ is used to set the contour levels for precipitation data; and `cfplot.vect <http://ajheaps.github.io/cf-plot/vect.html>`_ is used to plot the wind vectors for June 1995:
-june_95 = cf.year(1995) & cf.month(6)
-cfp.gopen()
-cfp.cscale("precip4_11lev")
-cfp.levs(step=100)
-cfp.con(
-    pre.subspace(T=june_95),
-    lines=False,
-    title="June 1995 monthly global precipitation",
-)
-cfp.vect(
-    u=u.subspace(T=june_95),
-    v=v.subspace(T=june_95),
-    key_length=10,
-    scale=35,
-    stride=5,
-)
-cfp.gclose()
diff --git a/docs/_downloads/plot_6_recipe.ipynb b/docs/_downloads/plot_6_recipe.ipynb
deleted file mode 100644
index 6ad67ada2d..0000000000
--- a/docs/_downloads/plot_6_recipe.ipynb
+++ /dev/null
@@ -1,205 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Converting from rotated latitude-longitude to regular latitude-longitude\n\nIn this recipe, we will be regridding from a rotated latitude-longitude source domain to a regular latitude-longitude destination domain.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "1. Import cf-python, cf-plot and numpy:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import cfplot as cfp\nimport numpy as np\n\nimport cf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "2. Read the field constructs using read function:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f = cf.read(\"~/recipes/au952a.pd20510414.pp\")\nprint(f)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "3. Select the field by index and print its description to show properties of all constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "gust = f[0]\ngust.dump()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "4. Access the time coordinate of the gust field and retrieve the datetime values of the time coordinate:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "print(gust.coordinate(\"time\").datetime_array)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "5. Create a new instance of the `cf.dt` class with a specified year, month, day, hour, minute, second and microsecond. Then store the result in the variable ``test``:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "test = cf.dt(2051, 4, 14, 1, 30, 0, 0)\nprint(test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "6. Plot the wind gust by creating a subspace for the specified variable ``test`` using [cfplot.con](http://ajheaps.github.io/cf-plot/con.html). Here [cfplot.mapset](http://ajheaps.github.io/cf-plot/mapset.html) is used to set the mapping parameters like setting the map resolution to 50m:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "cfp.mapset(resolution=\"50m\")\ncfp.con(gust.subspace(T=test), lines=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "7. To see the rotated pole data on the native grid, the above steps are repeated and projection is set to rotated in [cfplot.mapset](http://ajheaps.github.io/cf-plot/mapset.html):\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "cfp.mapset(resolution=\"50m\", proj=\"rotated\")\ncfp.con(gust.subspace(T=test), lines=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "8. Create dimension coordinates for the destination grid with the latitude and longitude values for Europe. [np.linspace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html) generates evenly spaced values between the specified latitude and longitude range. Bounds of the target longitude and target latitude are created and spherical regridding is then performed on the gust variable by passing the target latitude and target longitude as arguments. The method also takes an argument ``'linear'`` which specifies the type of regridding method to use. The description of the ``regridded_data`` is finally printed to show properties of all its constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "target_latitude = cf.DimensionCoordinate(\n    data=cf.Data(np.linspace(34, 72, num=10), \"degrees_north\")\n)\ntarget_longitude = cf.DimensionCoordinate(\n    data=cf.Data(np.linspace(-25, 45, num=10), \"degrees_east\")\n)\n\nlon_bounds = target_longitude.create_bounds()\nlat_bounds = target_latitude.create_bounds()\n\ntarget_longitude.set_bounds(lon_bounds)\ntarget_latitude.set_bounds(lat_bounds)\n\nregridded_data = gust.regrids((target_latitude, target_longitude), \"linear\")\nregridded_data.dump()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "9. Step 6 is similarly repeated for the ``regridded_data`` to plot the wind gust on a regular latitude-longitude domain:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "cfp.mapset(resolution=\"50m\")\ncfp.con(regridded_data.subspace(T=test), lines=False)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/plot_6_recipe.py b/docs/_downloads/plot_6_recipe.py
deleted file mode 100644
index 4ca43fad6b..0000000000
--- a/docs/_downloads/plot_6_recipe.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""
-Converting from rotated latitude-longitude to regular latitude-longitude
-========================================================================
-
-In this recipe, we will be regridding from a rotated latitude-longitude source domain to a regular latitude-longitude destination domain.
-"""
-
-# %%
-# 1. Import cf-python, cf-plot and numpy:
-
-import cfplot as cfp
-import numpy as np
-
-import cf
-
-# %%
-# 2. Read the field constructs using read function:
-
-f = cf.read("~/recipes/au952a.pd20510414.pp")
-print(f)
-
-# %%
-# 3. Select the field by index and print its description to show properties of all constructs:
-
-gust = f[0]
-gust.dump()
-
-# %%
-# 4. Access the time coordinate of the gust field and retrieve the datetime values of the time coordinate:
-
-print(gust.coordinate("time").datetime_array)
-
-# %%
-# 5. Create a new instance of the `cf.dt` class with a specified year, month, day, hour, minute, second and microsecond. Then store the result in the variable ``test``:
-test = cf.dt(2051, 4, 14, 1, 30, 0, 0)
-print(test)
-
-# %%
-# 6. Plot the wind gust by creating a subspace for the specified variable ``test`` using `cfplot.con <http://ajheaps.github.io/cf-plot/con.html>`_. Here `cfplot.mapset <http://ajheaps.github.io/cf-plot/mapset.html>`_ is used to set the mapping parameters like setting the map resolution to 50m:
-cfp.mapset(resolution="50m")
-cfp.con(gust.subspace(T=test), lines=False)
-
-# %%
-# 7. To see the rotated pole data on the native grid, the above steps are repeated and projection is set to rotated in `cfplot.mapset <http://ajheaps.github.io/cf-plot/mapset.html>`_:
-cfp.mapset(resolution="50m", proj="rotated")
-cfp.con(gust.subspace(T=test), lines=False)
-
-# %%
-# 8. Create dimension coordinates for the destination grid with the latitude and longitude values for Europe. `np.linspace <https://numpy.org/doc/stable/reference/generated/numpy.linspace.html>`_ generates evenly spaced values between the specified latitude and longitude range. Bounds of the target longitude and target latitude are created and spherical regridding is then performed on the gust variable by passing the target latitude and target longitude as arguments. The method also takes an argument ``'linear'`` which specifies the type of regridding method to use. The description of the ``regridded_data`` is finally printed to show properties of all its constructs:
-
-target_latitude = cf.DimensionCoordinate(
-    data=cf.Data(np.linspace(34, 72, num=10), "degrees_north")
-)
-target_longitude = cf.DimensionCoordinate(
-    data=cf.Data(np.linspace(-25, 45, num=10), "degrees_east")
-)
-
-lon_bounds = target_longitude.create_bounds()
-lat_bounds = target_latitude.create_bounds()
-
-target_longitude.set_bounds(lon_bounds)
-target_latitude.set_bounds(lat_bounds)
-
-regridded_data = gust.regrids((target_latitude, target_longitude), "linear")
-regridded_data.dump()
-
-# %%
-# 9. Step 6 is similarly repeated for the ``regridded_data`` to plot the wind gust on a regular latitude-longitude domain:
-cfp.mapset(resolution="50m")
-cfp.con(regridded_data.subspace(T=test), lines=False)
diff --git a/docs/_downloads/plot_7_recipe.ipynb b/docs/_downloads/plot_7_recipe.ipynb
deleted file mode 100644
index 07d376286c..0000000000
--- a/docs/_downloads/plot_7_recipe.ipynb
+++ /dev/null
@@ -1,151 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Plotting members of a model ensemble\n\nIn this recipe, we will plot the members of a model ensemble.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "1. Import cf-python and cf-plot:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import cfplot as cfp\n\nimport cf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "2. Read the field constructs using read function and store it in the variable ``f``. The * in the filename is a wildcard character which means the function reads all files in the directory that match the specified pattern. [0:5] selects the first five elements of the resulting list:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f = cf.read(\"~/recipes/realization/PRMSL.1941_mem*.nc\")[0:5]\nprint(f)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "3. The description of one of the fields from the list shows ``'realization'`` as a property by which the members of the model ensemble are labelled:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f[1].dump()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "4. An ensemble of the members is then created by aggregating the data in ``f`` along a new ``'realization'`` axis using the cf.aggregate function, and storing the result in the variable ``ensemble``. ``'relaxed_identities=True'`` allows for missing coordinate identities to be inferred. [0] selects the first element of the resulting list. ``id%realization`` now shows as an auxiliary coordinate for the ensemble:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "ensemble = cf.aggregate(\n    f, dimension=(\"realization\",), relaxed_identities=True\n)[0]\nprint(ensemble)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "5. To see the constructs for the ensemble, print the *constructs* attribute:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "print(ensemble.constructs)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "6. Loop over the realizations in the ensemble using the *range* function and the *domain_axis* to determine the size of the realization dimension. For each realization, extract a subspace of the ensemble using the *subspace* method and the ``'id%realization'`` keyword argument along a specific latitude and longitude and plot the realizations from the 4D field using [cfplot.lineplot](http://ajheaps.github.io/cf-plot/lineplot.html).\nA moving average of the ensemble along the time axis, with a window size of 90 (i.e. an approximately 3-month moving average) is calculated using the *moving_window* method. The ``mode='nearest'`` parameter is used to specify how to pad the data outside of the time range. The *squeeze* method removes any dimensions of size 1 from the field to produce a 2D field:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "cfp.gopen()\n\nfor realization in range(1, ensemble.domain_axis(\"id%realization\").size + 1):\n    cfp.lineplot(\n        ensemble.subspace(\n            **{\"id%realization\": realization}, latitude=[0], longitude=[0]\n        ).squeeze(),\n        label=f\"Member {realization}\",\n        linewidth=1.0,\n    )\n\ncfp.lineplot(\n    ensemble.moving_window(\n        method=\"mean\", window_size=90, axis=\"T\", mode=\"nearest\"\n    )[0, :, 0, 0].squeeze(),\n    label=\"Ensemble mean\",\n    linewidth=2.0,\n    color=\"black\",\n    title=\"Model Ensemble Pressure\",\n)\n\ncfp.gclose()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/plot_7_recipe.py b/docs/_downloads/plot_7_recipe.py
deleted file mode 100644
index 5bb5802190..0000000000
--- a/docs/_downloads/plot_7_recipe.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""
-Plotting members of a model ensemble
-====================================
-
-In this recipe, we will plot the members of a model ensemble.
-"""
-
-# %%
-# 1. Import cf-python and cf-plot:
-
-import cfplot as cfp
-
-import cf
-
-# %%
-# 2. Read the field constructs using read function and store it in the variable ``f``. The * in the filename is a wildcard character which means the function reads all files in the directory that match the specified pattern. [0:5] selects the first five elements of the resulting list:
-
-f = cf.read("~/recipes/realization/PRMSL.1941_mem*.nc")[0:5]
-print(f)
-
-# %%
-# 3. The description of one of the fields from the list shows ``'realization'`` as a property by which the members of the model ensemble are labelled:
-
-f[1].dump()
-
-# %%
-# 4. An ensemble of the members is then created by aggregating the data in ``f`` along a new ``'realization'`` axis using the cf.aggregate function, and storing the result in the variable ``ensemble``. ``'relaxed_identities=True'`` allows for missing coordinate identities to be inferred. [0] selects the first element of the resulting list. ``id%realization`` now shows as an auxiliary coordinate for the ensemble:
-
-ensemble = cf.aggregate(
-    f, dimension=("realization",), relaxed_identities=True
-)[0]
-print(ensemble)
-
-
-# %%
-# 5. To see the constructs for the ensemble, print the *constructs* attribute:
-
-print(ensemble.constructs)
-
-# %%
-# 6. Loop over the realizations in the ensemble using the *range* function and the *domain_axis* to determine the size of the realization dimension. For each realization, extract a subspace of the ensemble using the *subspace* method and the ``'id%realization'`` keyword argument along a specific latitude and longitude and plot the realizations from the 4D field using `cfplot.lineplot <http://ajheaps.github.io/cf-plot/lineplot.html>`_.
-# A moving average of the ensemble along the time axis, with a window size of 90 (i.e. an approximately 3-month moving average) is calculated using the *moving_window* method. The ``mode='nearest'`` parameter is used to specify how to pad the data outside of the time range. The *squeeze* method removes any dimensions of size 1 from the field to produce a 2D field:
-
-cfp.gopen()
-
-for realization in range(1, ensemble.domain_axis("id%realization").size + 1):
-    cfp.lineplot(
-        ensemble.subspace(
-            **{"id%realization": realization}, latitude=[0], longitude=[0]
-        ).squeeze(),
-        label=f"Member {realization}",
-        linewidth=1.0,
-    )
-
-cfp.lineplot(
-    ensemble.moving_window(
-        method="mean", window_size=90, axis="T", mode="nearest"
-    )[0, :, 0, 0].squeeze(),
-    label="Ensemble mean",
-    linewidth=2.0,
-    color="black",
-    title="Model Ensemble Pressure",
-)
-
-cfp.gclose()
diff --git a/docs/_downloads/plot_8_recipe.ipynb b/docs/_downloads/plot_8_recipe.ipynb
deleted file mode 100644
index 7e37b71747..0000000000
--- a/docs/_downloads/plot_8_recipe.ipynb
+++ /dev/null
@@ -1,212 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Plotting statistically significant temperature trends with stippling\n\nIn 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.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "1. Import cf-python, cf-plot, numpy and scipy.stats:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import cfplot as cfp\nimport numpy as np\nimport scipy.stats as stats\n\nimport cf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "2. Three functions are defined:\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "* ``linear_trend(data, time_axis)``: This function calculates the linear regression slope and p-value for the input data along the time axis. It takes two arguments: ``'data'``, which represents the temperature anomalies or any other data you want to analyse, and ``'time_axis'``, which represents the corresponding time points for the data. The function uses the [stats.linregress](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html) method from the [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html) library to calculate the slope and p-value of the linear regression. It returns these two values as a tuple:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def linear_trend(data, time_axis):\n    slope, _, _, p_value, _ = stats.linregress(time_axis, data)\n    return slope, p_value"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "* ``create_trend_stipple_obj(temp_data, input_data)``: This function creates a new object with the input data provided and *collapses* the time dimension by taking the mean. It takes two arguments: ``'temp_data'``, which represents the temperature data object, and ``'input_data'``, which is the data to be set in the new object. The function creates a copy of the ``'temp_data'`` object by selecting the first element using index and *squeezes* it to remove the size 1 axis. It then sets the input data with the ``'Y'`` (latitude) and ``'X'`` (longitude) axes, and then *collapses* the time dimension using the ``\"T: mean\"`` operation:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def create_trend_stipple_obj(temp_data, input_data):\n    trend_stipple_obj = temp_data[0].squeeze()\n    trend_stipple_obj.set_data(input_data, axes=[\"Y\", \"X\"])\n    return trend_stipple_obj"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "* ``process_subsets(subset_mask)``: This function processes the subsets of data by applying the ``linear_trend`` function along a specified axis. It takes one argument, ``'subset_mask'``, which is a boolean mask representing the time points to be considered in the analysis. The function first extracts the masked subset of data and then applies the ``linear_trend`` function along the time axis (axis 0) using the [numpy.ma.apply_along_axis](https://numpy.org/doc/stable/reference/generated/numpy.ma.apply_along_axis.html) function. The result is an array containing the slope and p-value for each grid point in the dataset:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def process_subsets(subset_mask):\n    subset_data = masked_data[subset_mask, :, :]\n    return np.ma.apply_along_axis(\n        linear_trend, 0, subset_data, time_axis[subset_mask]\n    )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "3. Read the field constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temperature_data = cf.read(\n    \"~/recipes/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc\"\n)[0]\nprint(temperature_data)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "4. Calculate the annual mean temperature anomalies. The ``'weights=True'`` argument is used take the varying lengths of months into account which ensures that the calculated mean is more accurate. A masked array is created for the annual mean temperature anomalies, masking any invalid values:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "annual_temperature = temperature_data.collapse(\n    \"T: mean\", weights=True, group=cf.Y()\n)\ntime_axis = annual_temperature.coordinate(\"T\").year.array\nmasked_data = np.ma.masked_invalid(annual_temperature.array)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "5. Define two time periods for analysis: 1850-2020 and 1980-2020, along with a significance level (alpha) of 0.05:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "time_periods = [(1850, 2020, \"sub_1850_2020\"), (1980, 2020, \"sub_1980_2020\")]\nalpha = 0.05\nresults = {}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "6. Loop through the time periods, processing the subsets, calculating trend p-values, and creating stipple objects. For each time period, the script calculates the trends and p-values using the ``process_subsets`` function. If the p-value is less than the significance level (alpha = 0.05), a stippling mask is created. The script then creates a new object for the trend and stippling mask using the ``create_trend_stipple_obj`` function:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "for start, end, prefix in time_periods:\n    subset_mask = (time_axis >= start) & (time_axis <= end)\n    subset_trend_pvalue = process_subsets(subset_mask)\n    results[prefix + \"_trend_pvalue\"] = subset_trend_pvalue\n    results[prefix + \"_stipple\"] = subset_trend_pvalue[1] < alpha\n    results[prefix + \"_trend\"] = create_trend_stipple_obj(\n        temperature_data, subset_trend_pvalue[0]\n    )\n    results[prefix + \"_stipple_obj\"] = create_trend_stipple_obj(\n        temperature_data, results[prefix + \"_stipple\"]\n    )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "7. Create two plots - one for the 1850-2020 time period and another for the 1980-2020 time period using [cfplot.con](http://ajheaps.github.io/cf-plot/con.html).\nThe results are multiplied by 10 so that each plot displays the temperature trend in K/decade with stippling to indicate areas where the trend is statistically significant (p-value < 0.05).\nHere [cfplot.gopen](http://ajheaps.github.io/cf-plot/gopen.html) is used to define the parts of the plot area with two rows and one column, and setting the bottom margin to 0.2.\nIt is closed by [cfplot.gclose](http://ajheaps.github.io/cf-plot/gclose.html);\n[cfplot.gpos](http://ajheaps.github.io/cf-plot/gpos.html) is used to set the plotting position of both the plots;\n[cfplot.mapset](http://ajheaps.github.io/cf-plot/mapset.html) is used to set the map projection to Robinson;\n[cfplot.cscale](http://ajheaps.github.io/cf-plot/cscale.html) is used to choose one of the colour maps amongst many available;\n[cfplot.levs](http://ajheaps.github.io/cf-plot/levs.html) is used to set the contour levels;\nand [cfplot.stipple](http://ajheaps.github.io/cf-plot/stipple.html) is used to add stippling to show statistically significant areas:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "cfp.gopen(rows=2, columns=1, bottom=0.2)\n\ncfp.gpos(1)\ncfp.mapset(proj=\"robin\")\ncfp.cscale(\"temp_19lev\")\ncfp.levs(min=-1, max=1, step=0.1)\ncfp.con(\n    results[\"sub_1850_2020_trend\"] * 10,\n    lines=False,\n    colorbar=None,\n    title=\"Temperature Trend 1850-2020\",\n)\ncfp.stipple(\n    results[\"sub_1850_2020_stipple_obj\"],\n    min=1,\n    max=1,\n    size=5,\n    color=\"k\",\n    marker=\".\",\n)\n\ncfp.gpos(2)\ncfp.mapset(proj=\"robin\")\ncfp.cscale(\"temp_19lev\")\ncfp.levs(min=-1, max=1, step=0.1)\ncfp.con(\n    results[\"sub_1980_2020_trend\"] * 10,\n    lines=False,\n    title=\"Temperature Trend 1980-2020\",\n    colorbar_position=[0.1, 0.1, 0.8, 0.02],\n    colorbar_orientation=\"horizontal\",\n    colorbar_title=\"K/decade\",\n)\ncfp.stipple(\n    results[\"sub_1980_2020_stipple_obj\"],\n    min=1,\n    max=1,\n    size=5,\n    color=\"k\",\n    marker=\".\",\n)\n\ncfp.gclose()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.10"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/plot_8_recipe.py b/docs/_downloads/plot_8_recipe.py
deleted file mode 100644
index 010707e14a..0000000000
--- a/docs/_downloads/plot_8_recipe.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""
-Plotting statistically significant temperature trends with stippling
-====================================================================
-
-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.
-"""
-
-# %%
-# 1. Import cf-python, cf-plot, numpy and scipy.stats:
-
-import cfplot as cfp
-import numpy as np
-import scipy.stats as stats
-
-import cf
-
-# %%
-# 2. Three functions are defined:
-
-# %%
-# * ``linear_trend(data, time_axis)``: This function calculates the linear regression slope and p-value for the input data along the time axis. It takes two arguments: ``'data'``, which represents the temperature anomalies or any other data you want to analyse, and ``'time_axis'``, which represents the corresponding time points for the data. The function uses the `stats.linregress <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html>`_ method from the `scipy.stats <https://docs.scipy.org/doc/scipy/reference/stats.html>`_ library to calculate the slope and p-value of the linear regression. It returns these two values as a tuple:
-
-
-def linear_trend(data, time_axis):
-    slope, _, _, p_value, _ = stats.linregress(time_axis, data)
-    return slope, p_value
-
-
-# %%
-# * ``create_trend_stipple_obj(temp_data, input_data)``: This function creates a new object with the input data provided and *collapses* the time dimension by taking the mean. It takes two arguments: ``'temp_data'``, which represents the temperature data object, and ``'input_data'``, which is the data to be set in the new object. The function creates a copy of the ``'temp_data'`` object by selecting the first element using index and *squeezes* it to remove the size 1 axis. It then sets the input data with the ``'Y'`` (latitude) and ``'X'`` (longitude) axes, and then *collapses* the time dimension using the ``"T: mean"`` operation:
-
-
-def create_trend_stipple_obj(temp_data, input_data):
-    trend_stipple_obj = temp_data[0].squeeze()
-    trend_stipple_obj.set_data(input_data, axes=["Y", "X"])
-    return trend_stipple_obj
-
-
-# %%
-# * ``process_subsets(subset_mask)``: This function processes the subsets of data by applying the ``linear_trend`` function along a specified axis. It takes one argument, ``'subset_mask'``, which is a boolean mask representing the time points to be considered in the analysis. The function first extracts the masked subset of data and then applies the ``linear_trend`` function along the time axis (axis 0) using the `numpy.ma.apply_along_axis <https://numpy.org/doc/stable/reference/generated/numpy.ma.apply_along_axis.html>`_ function. The result is an array containing the slope and p-value for each grid point in the dataset:
-
-
-def process_subsets(subset_mask):
-    subset_data = masked_data[subset_mask, :, :]
-    return np.ma.apply_along_axis(
-        linear_trend, 0, subset_data, time_axis[subset_mask]
-    )
-
-
-# %%
-# 3. Read the field constructs:
-
-temperature_data = cf.read(
-    "~/recipes/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc"
-)[0]
-print(temperature_data)
-
-# %%
-# 4. Calculate the annual mean temperature anomalies. The ``'weights=True'`` argument is used take the varying lengths of months into account which ensures that the calculated mean is more accurate. A masked array is created for the annual mean temperature anomalies, masking any invalid values:
-
-annual_temperature = temperature_data.collapse(
-    "T: mean", weights=True, group=cf.Y()
-)
-time_axis = annual_temperature.coordinate("T").year.array
-masked_data = np.ma.masked_invalid(annual_temperature.array)
-
-# %%
-# 5. Define two time periods for analysis: 1850-2020 and 1980-2020, along with a significance level (alpha) of 0.05:
-
-time_periods = [(1850, 2020, "sub_1850_2020"), (1980, 2020, "sub_1980_2020")]
-alpha = 0.05
-results = {}
-
-# %%
-# 6. Loop through the time periods, processing the subsets, calculating trend p-values, and creating stipple objects. For each time period, the script calculates the trends and p-values using the ``process_subsets`` function. If the p-value is less than the significance level (alpha = 0.05), a stippling mask is created. The script then creates a new object for the trend and stippling mask using the ``create_trend_stipple_obj`` function:
-
-for start, end, prefix in time_periods:
-    subset_mask = (time_axis >= start) & (time_axis <= end)
-    subset_trend_pvalue = process_subsets(subset_mask)
-    results[prefix + "_trend_pvalue"] = subset_trend_pvalue
-    results[prefix + "_stipple"] = subset_trend_pvalue[1] < alpha
-    results[prefix + "_trend"] = create_trend_stipple_obj(
-        temperature_data, subset_trend_pvalue[0]
-    )
-    results[prefix + "_stipple_obj"] = create_trend_stipple_obj(
-        temperature_data, results[prefix + "_stipple"]
-    )
-
-# %%
-# 7. Create two plots - one for the 1850-2020 time period and another for the 1980-2020 time period using `cfplot.con <http://ajheaps.github.io/cf-plot/con.html>`_.
-# The results are multiplied by 10 so that each plot displays the temperature trend in K/decade with stippling to indicate areas where the trend is statistically significant (p-value < 0.05).
-# Here `cfplot.gopen <http://ajheaps.github.io/cf-plot/gopen.html>`_ is used to define the parts of the plot area with two rows and one column, and setting the bottom margin to 0.2.
-# It is closed by `cfplot.gclose <http://ajheaps.github.io/cf-plot/gclose.html>`_;
-# `cfplot.gpos <http://ajheaps.github.io/cf-plot/gpos.html>`_ is used to set the plotting position of both the plots;
-# `cfplot.mapset <http://ajheaps.github.io/cf-plot/mapset.html>`_ is used to set the map projection to Robinson;
-# `cfplot.cscale <http://ajheaps.github.io/cf-plot/cscale.html>`_ is used to choose one of the colour maps amongst many available;
-# `cfplot.levs <http://ajheaps.github.io/cf-plot/levs.html>`_ is used to set the contour levels;
-# and `cfplot.stipple <http://ajheaps.github.io/cf-plot/stipple.html>`_ is used to add stippling to show statistically significant areas:
-
-cfp.gopen(rows=2, columns=1, bottom=0.2)
-
-cfp.gpos(1)
-cfp.mapset(proj="robin")
-cfp.cscale("temp_19lev")
-cfp.levs(min=-1, max=1, step=0.1)
-cfp.con(
-    results["sub_1850_2020_trend"] * 10,
-    lines=False,
-    colorbar=None,
-    title="Temperature Trend 1850-2020",
-)
-cfp.stipple(
-    results["sub_1850_2020_stipple_obj"],
-    min=1,
-    max=1,
-    size=5,
-    color="k",
-    marker=".",
-)
-
-cfp.gpos(2)
-cfp.mapset(proj="robin")
-cfp.cscale("temp_19lev")
-cfp.levs(min=-1, max=1, step=0.1)
-cfp.con(
-    results["sub_1980_2020_trend"] * 10,
-    lines=False,
-    title="Temperature Trend 1980-2020",
-    colorbar_position=[0.1, 0.1, 0.8, 0.02],
-    colorbar_orientation="horizontal",
-    colorbar_title="K/decade",
-)
-cfp.stipple(
-    results["sub_1980_2020_stipple_obj"],
-    min=1,
-    max=1,
-    size=5,
-    color="k",
-    marker=".",
-)
-
-cfp.gclose()
diff --git a/docs/_downloads/plot_9_recipe.ipynb b/docs/_downloads/plot_9_recipe.ipynb
deleted file mode 100644
index a2cc7084bc..0000000000
--- a/docs/_downloads/plot_9_recipe.ipynb
+++ /dev/null
@@ -1,234 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Plotting a joint histogram\n\nIn this recipe, we will be creating a joint histogram of PM2.5 mass \nconcentration and 2-metre temperature.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "1. Import cf-python, numpy and matplotlib.pyplot:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport cf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "2. Read the field constructs using read function:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f = cf.read(\"~/recipes/levtype_sfc.nc\")\nprint(f)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "3. Select the PM2.5 mass concentration and 2-metre temperature fields by\nindex and print the description to show properties of all constructs:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "pm25_field = f[2]\npm25_field.dump()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp_field = f[3]\ntemp_field.dump()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "4. Convert the units to degree celsius for the temperature field:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp_field.units = \"degC\"\ntemp_field.get_property(\"units\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "5. Digitize the PM2.5 mass concentration and 2-metre temperature fields.\nThis step counts the number of values in each of the 10 equally sized bins\nspanning the range of the values. The ``'return_bins=True'`` argument makes \nsure that the calculated bins are also returned:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "pm25_indices, pm25_bins = pm25_field.digitize(10, return_bins=True)\ntemp_indices, temp_bins = temp_field.digitize(10, return_bins=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "6. Create a joint histogram of the digitized fields:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "joint_histogram = cf.histogram(pm25_indices, temp_indices)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "7. Get histogram data from the ``joint_histogram``:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "histogram_data = joint_histogram.array"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "8. Calculate bin centers for PM2.5 and temperature bins:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "pm25_bin_centers = [(a + b) / 2 for a, b in pm25_bins]\ntemp_bin_centers = [(a + b) / 2 for a, b in temp_bins]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "9. Create grids for PM2.5 and temperature bins using [numpy.meshgrid](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html):\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "temp_grid, pm25_grid = np.meshgrid(temp_bin_centers, pm25_bin_centers)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "10. Plot the joint histogram using [matplotlib.pyplot.pcolormesh](https://matplotlib.org/stable/api/_as_gen/\nmatplotlib.pyplot.pcolormesh.html). Use [cf.Field.unique](https://ncas-cms.github.io/cf-python/method/cf.Field.unique.html) to get\nthe unique data array values and show the bin boundaries as ticks on the\nplot. ``'style='plain'`` in [matplotlib.axes.Axes.ticklabel_format](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.ticklabel_format.html) \ndisables the scientific notation on the y-axis:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "plt.pcolormesh(temp_grid, pm25_grid, histogram_data, cmap=\"viridis\")\nplt.xlabel(\"2-metre Temperature (degC)\")\nplt.ylabel(\"PM2.5 Mass Concentration (kg m**-3)\")\nplt.xticks(temp_bins.unique().array, rotation=45)\nplt.yticks(pm25_bins.unique().array)\nplt.colorbar(label=\"Frequency\")\nplt.title(\"Joint Histogram of PM2.5 and 2-metre Temperature\", y=1.05)\nplt.ticklabel_format(style=\"plain\")\nplt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.9"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/plot_9_recipe.py b/docs/_downloads/plot_9_recipe.py
deleted file mode 100644
index 2bb7e57b43..0000000000
--- a/docs/_downloads/plot_9_recipe.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""
-Plotting a joint histogram
-==========================
-
-In this recipe, we will be creating a joint histogram of PM2.5 mass 
-concentration and 2-metre temperature.
-"""
-
-# %%
-# 1. Import cf-python, numpy and matplotlib.pyplot:
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-import cf
-
-# %%
-# 2. Read the field constructs using read function:
-f = cf.read("~/recipes/levtype_sfc.nc")
-print(f)
-
-# %%
-# 3. Select the PM2.5 mass concentration and 2-metre temperature fields by
-# index and print the description to show properties of all constructs:
-pm25_field = f[2]
-pm25_field.dump()
-
-# %%
-
-temp_field = f[3]
-temp_field.dump()
-
-# %%
-# 4. Convert the units to degree celsius for the temperature field:
-temp_field.units = "degC"
-temp_field.get_property("units")
-
-# %%
-# 5. Digitize the PM2.5 mass concentration and 2-metre temperature fields.
-# This step counts the number of values in each of the 10 equally sized bins
-# spanning the range of the values. The ``'return_bins=True'`` argument makes 
-# sure that the calculated bins are also returned:
-pm25_indices, pm25_bins = pm25_field.digitize(10, return_bins=True)
-temp_indices, temp_bins = temp_field.digitize(10, return_bins=True)
-
-# %%
-# 6. Create a joint histogram of the digitized fields:
-joint_histogram = cf.histogram(pm25_indices, temp_indices)
-
-# %%
-# 7. Get histogram data from the ``joint_histogram``:
-histogram_data = joint_histogram.array
-
-# %%
-# 8. Calculate bin centers 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]
-
-
-# %%
-# 9. Create grids for PM2.5 and temperature bins using `numpy.meshgrid
-# <https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html>`_:
-temp_grid, pm25_grid = np.meshgrid(temp_bin_centers, pm25_bin_centers)
-
-# %%
-# 10. Plot the joint histogram using `matplotlib.pyplot.pcolormesh
-# <https://matplotlib.org/stable/api/_as_gen/
-# matplotlib.pyplot.pcolormesh.html>`_. Use `cf.Field.unique
-# <https://ncas-cms.github.io/cf-python/method/cf.Field.unique.html>`_ to get
-# the unique data array values and show the bin boundaries as ticks on the
-# plot. ``'style='plain'`` in `matplotlib.axes.Axes.ticklabel_format
-# <https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.ticklabel_format.html>`_ 
-# disables the scientific notation on the y-axis:
-plt.pcolormesh(temp_grid, pm25_grid, histogram_data, cmap="viridis")
-plt.xlabel("2-metre Temperature (degC)")
-plt.ylabel("PM2.5 Mass Concentration (kg m**-3)")
-plt.xticks(temp_bins.unique().array, rotation=45)
-plt.yticks(pm25_bins.unique().array)
-plt.colorbar(label="Frequency")
-plt.title("Joint Histogram of PM2.5 and 2-metre Temperature", y=1.05)
-plt.ticklabel_format(style="plain")
-plt.show()
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-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="calculating-global-mean-temperature-timeseries">
-<span id="sphx-glr-recipes-plot-1-recipe-py"></span><h1>Calculating global mean temperature timeseries<a class="headerlink" href="#calculating-global-mean-temperature-timeseries" title="Permalink to this headline">¶</a></h1>
-<p>In this recipe we will calculate and plot monthly and annual global mean temperature timeseries.</p>
-<ol class="arabic simple">
-<li><p>Import cf-python and cf-plot:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cfplot</span> <span class="k">as</span> <span class="nn">cfp</span>
-
-<span class="kn">import</span> <span class="nn">cf</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Read the field constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span><span class="s2">&quot;~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: ncvar%stn(long_name=time(1452), long_name=latitude(360), long_name=longitude(720))&gt;,
- &lt;CF Field: long_name=near-surface temperature(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) degrees Celsius&gt;]
-</pre></div>
-</div>
-<ol class="arabic simple" start="3">
-<li><p>Select near surface temperature by index and look at its contents:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=near-surface temperature (ncvar%tmp)
------------------------------------------------------
-Data            : long_name=near-surface temperature(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) degrees Celsius
-Dimension coords: long_name=time(1452) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-                : long_name=latitude(360) = [-89.75, ..., 89.75] degrees_north
-                : long_name=longitude(720) = [-179.75, ..., 179.75] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="4">
-<li><p>Select latitude and longitude dimensions by identities, with two different techniques:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lon</span></a> <span class="o">=</span> <a href="../method/cf.Field.coordinate.html#cf.Field.coordinate" title="cf.Field.coordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">coordinate</span></a><span class="p">(</span><span class="s2">&quot;long_name=longitude&quot;</span><span class="p">)</span>
-<a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lat</span></a> <span class="o">=</span> <a href="../method/cf.Field.coordinate.html#cf.Field.coordinate" title="cf.Field.coordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">coordinate</span></a><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="5">
-<li><p>Print the description of near surface temperature using the dump method to show properties of all constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../method/cf.Field.dump.html#cf.Field.dump" title="cf.Field.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-----------------------------------------------------
-Field: long_name=near-surface temperature (ncvar%tmp)
------------------------------------------------------
-Conventions = &#39;CF-1.4&#39;
-_FillValue = 9.96921e+36
-comment = &#39;Access to these data is available to any registered CEDA user.&#39;
-contact = &#39;support@ceda.ac.uk&#39;
-correlation_decay_distance = 1200.0
-history = &#39;Fri 29 Apr 14:35:01 BST 2022 : User f098 : Program makegridsauto.for
-           called by update.for&#39;
-institution = &#39;Data held at British Atmospheric Data Centre, RAL, UK.&#39;
-long_name = &#39;near-surface temperature&#39;
-missing_value = 9.96921e+36
-references = &#39;Information on the data is available at
-              http://badc.nerc.ac.uk/data/cru/&#39;
-source = &#39;Run ID = 2204291347. Data generated from:tmp.2204291209.dtb&#39;
-title = &#39;CRU TS4.06 Mean Temperature&#39;
-units = &#39;degrees Celsius&#39;
-
-Data(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) = [[[--, ..., --]]] degrees Celsius
-
-Domain Axis: long_name=latitude(360)
-Domain Axis: long_name=longitude(720)
-Domain Axis: long_name=time(1452)
-
-Dimension coordinate: long_name=time
-    calendar = &#39;gregorian&#39;
-    long_name = &#39;time&#39;
-    units = &#39;days since 1900-1-1&#39;
-    Data(long_name=time(1452)) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-
-Dimension coordinate: long_name=latitude
-    long_name = &#39;latitude&#39;
-    units = &#39;degrees_north&#39;
-    Data(long_name=latitude(360)) = [-89.75, ..., 89.75] degrees_north
-
-Dimension coordinate: long_name=longitude
-    long_name = &#39;longitude&#39;
-    units = &#39;degrees_east&#39;
-    Data(long_name=longitude(720)) = [-179.75, ..., 179.75] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="6">
-<li><p>Latitude and longitude dimension coordinate cell bounds are absent, which are created and set:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">a</span></a> <span class="o">=</span> <a href="../method/cf.DimensionCoordinate.create_bounds.html#cf.DimensionCoordinate.create_bounds" title="cf.DimensionCoordinate.create_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lat</span><span class="o">.</span><span class="n">create_bounds</span></a><span class="p">()</span>
-<a href="../method/cf.DimensionCoordinate.set_bounds.html#cf.DimensionCoordinate.set_bounds" title="cf.DimensionCoordinate.set_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lat</span><span class="o">.</span><span class="n">set_bounds</span></a><span class="p">(</span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">a</span></a><span class="p">)</span>
-<a href="../method/cf.DimensionCoordinate.dump.html#cf.DimensionCoordinate.dump" title="cf.DimensionCoordinate.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lat</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Dimension coordinate: long_name=latitude
-    long_name = &#39;latitude&#39;
-    units = &#39;degrees_north&#39;
-    Data(360) = [-89.75, ..., 89.75] degrees_north
-    Bounds:units = &#39;degrees_north&#39;
-    Bounds:Data(360, 2) = [[-90.0, ..., 90.0]] degrees_north
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">b</span></a> <span class="o">=</span> <a href="../method/cf.DimensionCoordinate.create_bounds.html#cf.DimensionCoordinate.create_bounds" title="cf.DimensionCoordinate.create_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lon</span><span class="o">.</span><span class="n">create_bounds</span></a><span class="p">()</span>
-<a href="../method/cf.DimensionCoordinate.set_bounds.html#cf.DimensionCoordinate.set_bounds" title="cf.DimensionCoordinate.set_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lon</span><span class="o">.</span><span class="n">set_bounds</span></a><span class="p">(</span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">b</span></a><span class="p">)</span>
-<a href="../method/cf.DimensionCoordinate.dump.html#cf.DimensionCoordinate.dump" title="cf.DimensionCoordinate.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lon</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Dimension coordinate: long_name=longitude
-    long_name = &#39;longitude&#39;
-    units = &#39;degrees_east&#39;
-    Data(720) = [-179.75, ..., 179.75] degrees_east
-    Bounds:units = &#39;degrees_east&#39;
-    Bounds:Data(720, 2) = [[-180.0, ..., 180.0]] degrees_east
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../attribute/cf.Bounds.array.html#cf.Bounds.array" title="cf.Bounds.array" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-attribute"><span class="n">b</span><span class="o">.</span><span class="n">array</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[-180.  -179.5]
- [-179.5 -179. ]
- [-179.  -178.5]
- ...
- [ 178.5  179. ]
- [ 179.   179.5]
- [ 179.5  180. ]]
-</pre></div>
-</div>
-<ol class="arabic simple" start="7">
-<li><p>Time dimension coordinate cell bounds are similarly created and set for cell sizes of one calendar month:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time</span></a> <span class="o">=</span> <a href="../method/cf.Field.coordinate.html#cf.Field.coordinate" title="cf.Field.coordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">coordinate</span></a><span class="p">(</span><span class="s2">&quot;long_name=time&quot;</span><span class="p">)</span>
-<a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">c</span></a> <span class="o">=</span> <a href="../method/cf.DimensionCoordinate.create_bounds.html#cf.DimensionCoordinate.create_bounds" title="cf.DimensionCoordinate.create_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">time</span><span class="o">.</span><span class="n">create_bounds</span></a><span class="p">(</span><span class="n">cellsize</span><span class="o">=</span><a href="../function/cf.M.html#cf.M" title="cf.M" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">M</span></a><span class="p">())</span>
-<a href="../method/cf.DimensionCoordinate.set_bounds.html#cf.DimensionCoordinate.set_bounds" title="cf.DimensionCoordinate.set_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">time</span><span class="o">.</span><span class="n">set_bounds</span></a><span class="p">(</span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">c</span></a><span class="p">)</span>
-<a href="../method/cf.DimensionCoordinate.dump.html#cf.DimensionCoordinate.dump" title="cf.DimensionCoordinate.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">time</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Dimension coordinate: long_name=time
-    calendar = &#39;gregorian&#39;
-    long_name = &#39;time&#39;
-    units = &#39;days since 1900-1-1&#39;
-    Data(1452) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-    Bounds:calendar = &#39;gregorian&#39;
-    Bounds:units = &#39;days since 1900-1-1&#39;
-    Bounds:Data(1452, 2) = [[1901-01-01 00:00:00, ..., 2022-01-01 00:00:00]] gregorian
-</pre></div>
-</div>
-<ol class="arabic simple" start="8">
-<li><p>Calculate and plot the area weighted mean surface temperature for each time:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">global_avg</span></a> <span class="o">=</span> <a href="../method/cf.Field.collapse.html#cf.Field.collapse" title="cf.Field.collapse" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">collapse</span></a><span class="p">(</span><span class="s2">&quot;area: mean&quot;</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/lineplot.html#cfplot.lineplot" title="cfplot.lineplot" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">lineplot</span></a><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">global_avg</span></a><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Global mean surface temperature&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_1_recipe_001.png" srcset="../_images/sphx_glr_plot_1_recipe_001.png" alt="Global mean surface temperature" class = "sphx-glr-single-img"/><ol class="arabic simple" start="9">
-<li><p>Calculate and plot the annual global mean surface temperature:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">annual_global_avg</span></a> <span class="o">=</span> <a href="../method/cf.Field.collapse.html#cf.Field.collapse" title="cf.Field.collapse" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">global_avg</span><span class="o">.</span><span class="n">collapse</span></a><span class="p">(</span><span class="s2">&quot;T: mean&quot;</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><a href="../function/cf.Y.html#cf.Y" title="cf.Y" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">Y</span></a><span class="p">())</span>
-<a href="https://ajheaps.github.io/cf-plot/lineplot.html#cfplot.lineplot" title="cfplot.lineplot" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">lineplot</span></a><span class="p">(</span>
-    <a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">annual_global_avg</span></a><span class="p">,</span>
-    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Annual global mean surface temperature&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_1_recipe_002.png" srcset="../_images/sphx_glr_plot_1_recipe_002.png" alt="Annual global mean surface temperature" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  54.799 seconds)</p>
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-</div>
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-<p><a class="reference download internal" download="" href="../_downloads/plot_1_recipe.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_1_recipe.ipynb</span></code></a></p>
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-<h1 class="logo"><a href="../index.html">cf 3.15.0</a></h1>
-
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-<p class="blurb">A CF-compliant earth science data analysis library</p>
-
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-<p>
-<iframe src="https://ghbtns.com/github-btn.html?user=NCAS-CMS&repo=cf-python&type=star&count=true&size=large&v=2"
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-<li class="toctree-l1"><a class="reference internal" href="../introduction.html"><strong>Introduction</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../cf_data_model.html"><strong>CF data model</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html"><strong>Installation</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../contributing.html"><strong>Contributing</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="index.html"><strong>Recipes using cf</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorial.html"><strong>Tutorial</strong></a></li>
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-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../index.html">Documentation overview</a><ul>
-  <li><a href="index.html"><strong>Recipes using cf</strong></a><ul>
-      <li>Previous: <a href="plot_1_recipe.html" title="previous chapter">Calculating global mean temperature timeseries</a></li>
-      <li>Next: <a href="plot_3_recipe.html" title="next chapter">Plotting global mean temperatures spatially</a></li>
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-<br>
-cf development has been supported by
-the <a href="https://erc.europa.eu/">ERC</a>
-through <a href="https://cordis.europa.eu/project/id/247220">Seachange</a>
-and
-<a href="https://cordis.europa.eu/project/id/786427">Couplet</a>; by
-the <a href="https://ec.europa.eu/programmes/horizon2020/">EC Horizon
-2020 programme</a> through
-<a href="https://cordis.europa.eu/project/id/824084">IS-ENES3</a>;
-by <a href="https://nerc.ukri.org/">NERC</a> through
-<a href="https://gtr.ukri.org/project/0D95A6DB-0B95-48F7-8A8B-7B9A47DEA117">UKFAFMIP</a>;
-and by <a href="https://ncas.ac.uk/">NCAS</a>.
-<br>
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-<p>Click <a class="reference internal" href="#sphx-glr-download-recipes-plot-2-recipe-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="calculating-and-plotting-the-global-average-temperature-anomalies">
-<span id="sphx-glr-recipes-plot-2-recipe-py"></span><h1>Calculating and plotting the global average temperature anomalies<a class="headerlink" href="#calculating-and-plotting-the-global-average-temperature-anomalies" title="Permalink to this headline">¶</a></h1>
-<p>In this recipe we will calculate and plot the global average temperature anomalies.</p>
-<ol class="arabic simple">
-<li><p>Import cf-python and cf-plot:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cfplot</span> <span class="k">as</span> <span class="nn">cfp</span>
-
-<span class="kn">import</span> <span class="nn">cf</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Read the field constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span><span class="s2">&quot;~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: ncvar%stn(long_name=time(1452), long_name=latitude(360), long_name=longitude(720))&gt;,
- &lt;CF Field: long_name=near-surface temperature(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) degrees Celsius&gt;]
-</pre></div>
-</div>
-<ol class="arabic simple" start="3">
-<li><p>Select near surface temperature by index and look at its contents:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=near-surface temperature (ncvar%tmp)
------------------------------------------------------
-Data            : long_name=near-surface temperature(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) degrees Celsius
-Dimension coords: long_name=time(1452) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-                : long_name=latitude(360) = [-89.75, ..., 89.75] degrees_north
-                : long_name=longitude(720) = [-179.75, ..., 179.75] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="4">
-<li><p>Select latitude and longitude dimensions by identities, with two different techniques:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lon</span></a> <span class="o">=</span> <a href="../method/cf.Field.coordinate.html#cf.Field.coordinate" title="cf.Field.coordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">coordinate</span></a><span class="p">(</span><span class="s2">&quot;long_name=longitude&quot;</span><span class="p">)</span>
-<a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lat</span></a> <span class="o">=</span> <a href="../method/cf.Field.coordinate.html#cf.Field.coordinate" title="cf.Field.coordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">coordinate</span></a><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="5">
-<li><p>Print the description of near surface temperature to show properties of all constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../method/cf.Field.dump.html#cf.Field.dump" title="cf.Field.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-----------------------------------------------------
-Field: long_name=near-surface temperature (ncvar%tmp)
------------------------------------------------------
-Conventions = &#39;CF-1.4&#39;
-_FillValue = 9.96921e+36
-comment = &#39;Access to these data is available to any registered CEDA user.&#39;
-contact = &#39;support@ceda.ac.uk&#39;
-correlation_decay_distance = 1200.0
-history = &#39;Fri 29 Apr 14:35:01 BST 2022 : User f098 : Program makegridsauto.for
-           called by update.for&#39;
-institution = &#39;Data held at British Atmospheric Data Centre, RAL, UK.&#39;
-long_name = &#39;near-surface temperature&#39;
-missing_value = 9.96921e+36
-references = &#39;Information on the data is available at
-              http://badc.nerc.ac.uk/data/cru/&#39;
-source = &#39;Run ID = 2204291347. Data generated from:tmp.2204291209.dtb&#39;
-title = &#39;CRU TS4.06 Mean Temperature&#39;
-units = &#39;degrees Celsius&#39;
-
-Data(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) = [[[--, ..., --]]] degrees Celsius
-
-Domain Axis: long_name=latitude(360)
-Domain Axis: long_name=longitude(720)
-Domain Axis: long_name=time(1452)
-
-Dimension coordinate: long_name=time
-    calendar = &#39;gregorian&#39;
-    long_name = &#39;time&#39;
-    units = &#39;days since 1900-1-1&#39;
-    Data(long_name=time(1452)) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-
-Dimension coordinate: long_name=latitude
-    long_name = &#39;latitude&#39;
-    units = &#39;degrees_north&#39;
-    Data(long_name=latitude(360)) = [-89.75, ..., 89.75] degrees_north
-
-Dimension coordinate: long_name=longitude
-    long_name = &#39;longitude&#39;
-    units = &#39;degrees_east&#39;
-    Data(long_name=longitude(720)) = [-179.75, ..., 179.75] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="6">
-<li><p>Latitude and longitude dimension coordinate cell bounds are absent, which are created and set:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">a</span></a> <span class="o">=</span> <a href="../method/cf.DimensionCoordinate.create_bounds.html#cf.DimensionCoordinate.create_bounds" title="cf.DimensionCoordinate.create_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lat</span><span class="o">.</span><span class="n">create_bounds</span></a><span class="p">()</span>
-<a href="../method/cf.DimensionCoordinate.set_bounds.html#cf.DimensionCoordinate.set_bounds" title="cf.DimensionCoordinate.set_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lat</span><span class="o">.</span><span class="n">set_bounds</span></a><span class="p">(</span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">a</span></a><span class="p">)</span>
-<a href="../method/cf.DimensionCoordinate.dump.html#cf.DimensionCoordinate.dump" title="cf.DimensionCoordinate.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lat</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Dimension coordinate: long_name=latitude
-    long_name = &#39;latitude&#39;
-    units = &#39;degrees_north&#39;
-    Data(360) = [-89.75, ..., 89.75] degrees_north
-    Bounds:units = &#39;degrees_north&#39;
-    Bounds:Data(360, 2) = [[-90.0, ..., 90.0]] degrees_north
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">b</span></a> <span class="o">=</span> <a href="../method/cf.DimensionCoordinate.create_bounds.html#cf.DimensionCoordinate.create_bounds" title="cf.DimensionCoordinate.create_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lon</span><span class="o">.</span><span class="n">create_bounds</span></a><span class="p">()</span>
-<a href="../method/cf.DimensionCoordinate.set_bounds.html#cf.DimensionCoordinate.set_bounds" title="cf.DimensionCoordinate.set_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lon</span><span class="o">.</span><span class="n">set_bounds</span></a><span class="p">(</span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">b</span></a><span class="p">)</span>
-<a href="../method/cf.DimensionCoordinate.dump.html#cf.DimensionCoordinate.dump" title="cf.DimensionCoordinate.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">lon</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Dimension coordinate: long_name=longitude
-    long_name = &#39;longitude&#39;
-    units = &#39;degrees_east&#39;
-    Data(720) = [-179.75, ..., 179.75] degrees_east
-    Bounds:units = &#39;degrees_east&#39;
-    Bounds:Data(720, 2) = [[-180.0, ..., 180.0]] degrees_east
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../attribute/cf.Bounds.array.html#cf.Bounds.array" title="cf.Bounds.array" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-attribute"><span class="n">b</span><span class="o">.</span><span class="n">array</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[-180.  -179.5]
- [-179.5 -179. ]
- [-179.  -178.5]
- ...
- [ 178.5  179. ]
- [ 179.   179.5]
- [ 179.5  180. ]]
-</pre></div>
-</div>
-<ol class="arabic simple" start="7">
-<li><p>Time dimension coordinate cell bounds are similarly created and set for cell sizes of one calendar month:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time</span></a> <span class="o">=</span> <a href="../method/cf.Field.coordinate.html#cf.Field.coordinate" title="cf.Field.coordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">coordinate</span></a><span class="p">(</span><span class="s2">&quot;long_name=time&quot;</span><span class="p">)</span>
-<a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">c</span></a> <span class="o">=</span> <a href="../method/cf.DimensionCoordinate.create_bounds.html#cf.DimensionCoordinate.create_bounds" title="cf.DimensionCoordinate.create_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">time</span><span class="o">.</span><span class="n">create_bounds</span></a><span class="p">(</span><span class="n">cellsize</span><span class="o">=</span><a href="../function/cf.M.html#cf.M" title="cf.M" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">M</span></a><span class="p">())</span>
-<a href="../method/cf.DimensionCoordinate.set_bounds.html#cf.DimensionCoordinate.set_bounds" title="cf.DimensionCoordinate.set_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">time</span><span class="o">.</span><span class="n">set_bounds</span></a><span class="p">(</span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">c</span></a><span class="p">)</span>
-<a href="../method/cf.DimensionCoordinate.dump.html#cf.DimensionCoordinate.dump" title="cf.DimensionCoordinate.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">time</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Dimension coordinate: long_name=time
-    calendar = &#39;gregorian&#39;
-    long_name = &#39;time&#39;
-    units = &#39;days since 1900-1-1&#39;
-    Data(1452) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-    Bounds:calendar = &#39;gregorian&#39;
-    Bounds:units = &#39;days since 1900-1-1&#39;
-    Bounds:Data(1452, 2) = [[1901-01-01 00:00:00, ..., 2022-01-01 00:00:00]] gregorian
-</pre></div>
-</div>
-<ol class="arabic simple" start="8">
-<li><p>Calculate the area weighted mean surface temperature for each time using the collapse method:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">global_avg</span></a> <span class="o">=</span> <a href="../method/cf.Field.collapse.html#cf.Field.collapse" title="cf.Field.collapse" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">collapse</span></a><span class="p">(</span><span class="s2">&quot;area: mean&quot;</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="9">
-<li><p>Calculate the annual global mean surface temperature:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">annual_global_avg</span></a> <span class="o">=</span> <a href="../method/cf.Field.collapse.html#cf.Field.collapse" title="cf.Field.collapse" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">global_avg</span><span class="o">.</span><span class="n">collapse</span></a><span class="p">(</span><span class="s2">&quot;T: mean&quot;</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><a href="../function/cf.Y.html#cf.Y" title="cf.Y" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">Y</span></a><span class="p">())</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="10">
-<li><p>The temperature values are averaged for the climatological period of 1961-1990 by defining a subspace within these years using <cite>cf.wi</cite> query instance over subspace and doing a statistical collapse with the collapse method:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">annual_global_avg_61_90</span></a> <span class="o">=</span> <a href="../method/cf.Field.subspace.html#cf.Field.subspace" title="cf.Field.subspace" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">annual_global_avg</span><span class="o">.</span><span class="n">subspace</span></a><span class="p">(</span>
-    <span class="n">T</span><span class="o">=</span><a href="../function/cf.year.html#cf.year" title="cf.year" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">year</span></a><span class="p">(</span><a href="../function/cf.wi.html#cf.wi" title="cf.wi" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">wi</span></a><span class="p">(</span><span class="mi">1961</span><span class="p">,</span> <span class="mi">1990</span><span class="p">))</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">annual_global_avg_61_90</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=near-surface temperature (ncvar%tmp)
------------------------------------------------------
-Data            : long_name=near-surface temperature(long_name=time(30), long_name=latitude(1), long_name=longitude(1)) degrees Celsius
-Cell methods    : area: mean long_name=time(30): mean
-Dimension coords: long_name=time(30) = [1961-07-02 12:00:00, ..., 1990-07-02 12:00:00] gregorian
-                : long_name=latitude(1) = [0.0] degrees_north
-                : long_name=longitude(1) = [0.0] degrees_east
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_clim</span></a> <span class="o">=</span> <a href="../method/cf.Field.collapse.html#cf.Field.collapse" title="cf.Field.collapse" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">annual_global_avg_61_90</span><span class="o">.</span><span class="n">collapse</span></a><span class="p">(</span><span class="s2">&quot;T: mean&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_clim</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=near-surface temperature (ncvar%tmp)
------------------------------------------------------
-Data            : long_name=near-surface temperature(long_name=time(1), long_name=latitude(1), long_name=longitude(1)) degrees Celsius
-Cell methods    : area: mean long_name=time(1): mean
-Dimension coords: long_name=time(1) = [1976-01-01 12:00:00] gregorian
-                : long_name=latitude(1) = [0.0] degrees_north
-                : long_name=longitude(1) = [0.0] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="11">
-<li><p>The temperature anomaly is then calculated by subtracting these climatological temperature values from the annual global average temperatures and plotted:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_anomaly</span></a> <span class="o">=</span> <a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">annual_global_avg</span></a> <span class="o">-</span> <a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_clim</span></a>
-<a href="https://ajheaps.github.io/cf-plot/lineplot.html#cfplot.lineplot" title="cfplot.lineplot" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">lineplot</span></a><span class="p">(</span>
-    <a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_anomaly</span></a><span class="p">,</span>
-    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Global Average Temperature Anomaly (1901-2021)&quot;</span><span class="p">,</span>
-    <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;1961-1990 climatology difference &quot;</span><span class="p">,</span>
-    <span class="n">yunits</span><span class="o">=</span><span class="s2">&quot;degree Celcius&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_2_recipe_001.png" srcset="../_images/sphx_glr_plot_2_recipe_001.png" alt="Global Average Temperature Anomaly (1901-2021)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes  52.252 seconds)</p>
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-<p><a class="reference download internal" download="" href="../_downloads/plot_2_recipe.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_2_recipe.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/plot_2_recipe.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_2_recipe.ipynb</span></code></a></p>
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-<h1 class="logo"><a href="../index.html">cf 3.15.0</a></h1>
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-<p class="blurb">A CF-compliant earth science data analysis library</p>
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-<li class="toctree-l1"><a class="reference internal" href="../aggregation_rules.html"><strong>Aggregation rules</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../performance.html"><strong>Performance</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html"><strong>Releases</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../Changelog.html"><strong>Change log</strong></a></li>
-</ul>
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../index.html">Documentation overview</a><ul>
-  <li><a href="index.html"><strong>Recipes using cf</strong></a><ul>
-      <li>Previous: <a href="plot_2_recipe.html" title="previous chapter">Calculating and plotting the global average temperature anomalies</a></li>
-      <li>Next: <a href="plot_4_recipe.html" title="next chapter">Comparing two datasets with different resolutions using regridding</a></li>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-
-<br>
-cf development has been supported by
-the <a href="https://erc.europa.eu/">ERC</a>
-through <a href="https://cordis.europa.eu/project/id/247220">Seachange</a>
-and
-<a href="https://cordis.europa.eu/project/id/786427">Couplet</a>; by
-the <a href="https://ec.europa.eu/programmes/horizon2020/">EC Horizon
-2020 programme</a> through
-<a href="https://cordis.europa.eu/project/id/824084">IS-ENES3</a>;
-by <a href="https://nerc.ukri.org/">NERC</a> through
-<a href="https://gtr.ukri.org/project/0D95A6DB-0B95-48F7-8A8B-7B9A47DEA117">UKFAFMIP</a>;
-and by <a href="https://ncas.ac.uk/">NCAS</a>.
-<br>
-<br>
-<img src="../_templates/logo_EC.png" height="40">
-<img src="../_templates/logo_ERC.png" height="40">
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-          <div class="body" role="main">
-            
-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-recipes-plot-3-recipe-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="plotting-global-mean-temperatures-spatially">
-<span id="sphx-glr-recipes-plot-3-recipe-py"></span><h1>Plotting global mean temperatures spatially<a class="headerlink" href="#plotting-global-mean-temperatures-spatially" title="Permalink to this headline">¶</a></h1>
-<p>In this recipe, we will plot the global mean temperature spatially.</p>
-<ol class="arabic simple">
-<li><p>Import cf-python and cf-plot:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cfplot</span> <span class="k">as</span> <span class="nn">cfp</span>
-
-<span class="kn">import</span> <span class="nn">cf</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Read the field constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span><span class="s2">&quot;~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: ncvar%stn(long_name=time(1452), long_name=latitude(360), long_name=longitude(720))&gt;,
- &lt;CF Field: long_name=near-surface temperature(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) degrees Celsius&gt;]
-</pre></div>
-</div>
-<ol class="arabic simple" start="3">
-<li><p>Select near surface temperature by index and look at its contents:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=near-surface temperature (ncvar%tmp)
------------------------------------------------------
-Data            : long_name=near-surface temperature(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) degrees Celsius
-Dimension coords: long_name=time(1452) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-                : long_name=latitude(360) = [-89.75, ..., 89.75] degrees_north
-                : long_name=longitude(720) = [-179.75, ..., 179.75] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="4">
-<li><p>Average the monthly mean surface temperature values by the time axis using the collapse method:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">global_avg</span></a> <span class="o">=</span> <a href="../method/cf.Field.collapse.html#cf.Field.collapse" title="cf.Field.collapse" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">collapse</span></a><span class="p">(</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="s2">&quot;long_name=time&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="5">
-<li><p>Plot the global mean surface temperatures:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://ajheaps.github.io/cf-plot/con.html#cfplot.con" title="cfplot.con" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">con</span></a><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">global_avg</span></a><span class="p">,</span> <span class="n">lines</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Global mean surface temperature&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_3_recipe_001.png" srcset="../_images/sphx_glr_plot_3_recipe_001.png" alt="plot 3 recipe" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  36.820 seconds)</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-recipes-plot-3-recipe-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/plot_3_recipe.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_3_recipe.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/plot_3_recipe.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_3_recipe.ipynb</span></code></a></p>
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diff --git a/docs/recipes/plot_4_recipe.html b/docs/recipes/plot_4_recipe.html
deleted file mode 100644
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--- a/docs/recipes/plot_4_recipe.html
+++ /dev/null
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-
-<!DOCTYPE html>
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-
-
-<h1 class="logo"><a href="../index.html">cf 3.15.0</a></h1>
-
-
-
-<p class="blurb">A CF-compliant earth science data analysis library</p>
-
-
-
-
-<p>
-<iframe src="https://ghbtns.com/github-btn.html?user=NCAS-CMS&repo=cf-python&type=star&count=true&size=large&v=2"
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../introduction.html"><strong>Introduction</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../cf_data_model.html"><strong>CF data model</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html"><strong>Installation</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../contributing.html"><strong>Contributing</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="index.html"><strong>Recipes using cf</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorial.html"><strong>Tutorial</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../analysis.html"><strong>Analysis</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../api_reference.html"><strong>API reference</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../aggregation_rules.html"><strong>Aggregation rules</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../performance.html"><strong>Performance</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../releases.html"><strong>Releases</strong></a></li>
-<li class="toctree-l1"><a class="reference internal" href="../Changelog.html"><strong>Change log</strong></a></li>
-</ul>
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../index.html">Documentation overview</a><ul>
-  <li><a href="index.html"><strong>Recipes using cf</strong></a><ul>
-      <li>Previous: <a href="plot_3_recipe.html" title="previous chapter">Plotting global mean temperatures spatially</a></li>
-      <li>Next: <a href="plot_5_recipe.html" title="next chapter">Plotting wind vectors overlaid on precipitation data</a></li>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-
-<br>
-cf development has been supported by
-the <a href="https://erc.europa.eu/">ERC</a>
-through <a href="https://cordis.europa.eu/project/id/247220">Seachange</a>
-and
-<a href="https://cordis.europa.eu/project/id/786427">Couplet</a>; by
-the <a href="https://ec.europa.eu/programmes/horizon2020/">EC Horizon
-2020 programme</a> through
-<a href="https://cordis.europa.eu/project/id/824084">IS-ENES3</a>;
-by <a href="https://nerc.ukri.org/">NERC</a> through
-<a href="https://gtr.ukri.org/project/0D95A6DB-0B95-48F7-8A8B-7B9A47DEA117">UKFAFMIP</a>;
-and by <a href="https://ncas.ac.uk/">NCAS</a>.
-<br>
-<br>
-<img src="../_templates/logo_EC.png" height="40">
-<img src="../_templates/logo_ERC.png" height="40">
-<img src="../_templates/logo_NERC.png" height="40">
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-            
-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-recipes-plot-4-recipe-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="comparing-two-datasets-with-different-resolutions-using-regridding">
-<span id="sphx-glr-recipes-plot-4-recipe-py"></span><h1>Comparing two datasets with different resolutions using regridding<a class="headerlink" href="#comparing-two-datasets-with-different-resolutions-using-regridding" title="Permalink to this headline">¶</a></h1>
-<p>In this recipe, we will regrid two different datasets with different resolutions. An example use case could be one where the observational dataset with a higher resolution needs to be regridded to that of the model dataset so that they can be compared with each other.</p>
-<ol class="arabic simple">
-<li><p>Import cf-python:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cf</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Read the field constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">obs</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span><span class="s2">&quot;~/recipes/cru_ts4.06.1901.2021.tmp.dat.nc&quot;</span><span class="p">,</span> <span class="n">chunks</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">obs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: ncvar%stn(long_name=time(1452), long_name=latitude(360), long_name=longitude(720))&gt;,
- &lt;CF Field: long_name=near-surface temperature(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) degrees Celsius&gt;]
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span>
-    <span class="s2">&quot;~/recipes/tas_Amon_HadGEM3-GC3-1_hist-1p0_r3i1p1f2_gn_185001-201412.nc&quot;</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: air_temperature(time(1980), latitude(144), longitude(192)) K&gt;]
-</pre></div>
-</div>
-<ol class="arabic simple" start="3">
-<li><p>Select observation and model temperature fields by identity and index respectively, and look at their contents:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">obs_temp</span></a> <span class="o">=</span> <a href="../method/cf.FieldList.select_field.html#cf.FieldList.select_field" title="cf.FieldList.select_field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">obs</span><span class="o">.</span><span class="n">select_field</span></a><span class="p">(</span><span class="s2">&quot;long_name=near-surface temperature&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">obs_temp</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=near-surface temperature (ncvar%tmp)
------------------------------------------------------
-Data            : long_name=near-surface temperature(long_name=time(1452), long_name=latitude(360), long_name=longitude(720)) degrees Celsius
-Dimension coords: long_name=time(1452) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-                : long_name=latitude(360) = [-89.75, ..., 89.75] degrees_north
-                : long_name=longitude(720) = [-179.75, ..., 179.75] degrees_east
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_temp</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_temp</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: air_temperature (ncvar%tas)
-----------------------------------
-Data            : air_temperature(time(1980), latitude(144), longitude(192)) K
-Cell methods    : time(1980): mean (interval: 1 hour)
-Dimension coords: time(1980) = [1850-01-16 00:00:00, ..., 2014-12-16 00:00:00] 360_day
-                : latitude(144) = [-89.375, ..., 89.375] degrees_north
-                : longitude(192) = [0.9375, ..., 359.0625] degrees_east
-                : height(1) = [1.5] m
-Coord references: grid_mapping_name:latitude_longitude
-</pre></div>
-</div>
-<ol class="arabic simple" start="4">
-<li><p>Regrid observational data to that of the model data and create a new low resolution observational data using bilinear interpolation:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">obs_temp_regrid</span></a> <span class="o">=</span> <a href="../method/cf.Field.regrids.html#cf.Field.regrids" title="cf.Field.regrids" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">obs_temp</span><span class="o">.</span><span class="n">regrids</span></a><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_temp</span></a><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">obs_temp_regrid</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=near-surface temperature (ncvar%tmp)
------------------------------------------------------
-Data            : long_name=near-surface temperature(long_name=time(1452), latitude(144), longitude(192)) degrees Celsius
-Dimension coords: long_name=time(1452) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-                : latitude(144) = [-89.375, ..., 89.375] degrees_north
-                : longitude(192) = [0.9375, ..., 359.0625] degrees_east
-Coord references: grid_mapping_name:latitude_longitude
-</pre></div>
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-<h1 class="logo"><a href="../index.html">cf 3.15.0</a></h1>
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-<p class="blurb">A CF-compliant earth science data analysis library</p>
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-cf development has been supported by
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-<a href="https://gtr.ukri.org/project/0D95A6DB-0B95-48F7-8A8B-7B9A47DEA117">UKFAFMIP</a>;
-and by <a href="https://ncas.ac.uk/">NCAS</a>.
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-<p>Click <a class="reference internal" href="#sphx-glr-download-recipes-plot-5-recipe-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="plotting-wind-vectors-overlaid-on-precipitation-data">
-<span id="sphx-glr-recipes-plot-5-recipe-py"></span><h1>Plotting wind vectors overlaid on precipitation data<a class="headerlink" href="#plotting-wind-vectors-overlaid-on-precipitation-data" title="Permalink to this headline">¶</a></h1>
-<p>In this recipe we will plot wind vectors, derived from northward and eastward wind components, over precipitation data.</p>
-<ol class="arabic simple">
-<li><p>Import cf-python and cf-plot:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cfplot</span> <span class="k">as</span> <span class="nn">cfp</span>
-
-<span class="kn">import</span> <span class="nn">cf</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Read the field constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f1</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span><span class="s2">&quot;~/recipes/northward.nc&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f1</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: northward_wind(time(1980), latitude(144), longitude(192)) m s-1&gt;]
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f2</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span><span class="s2">&quot;~/recipes/eastward.nc&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f2</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: eastward_wind(time(1980), latitude(144), longitude(192)) m s-1&gt;]
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f3</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span><span class="s2">&quot;~/recipes/monthly_precipitation.nc&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f3</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: long_name=precipitation(long_name=time(1452), latitude(144), longitude(192)) mm/month&gt;]
-</pre></div>
-</div>
-<ol class="arabic simple" start="3">
-<li><p>Select wind vectors and precipitation data by index and look at their contents:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">v</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f1</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">v</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: northward_wind (ncvar%vas)
----------------------------------
-Data            : northward_wind(time(1980), latitude(144), longitude(192)) m s-1
-Cell methods    : area: time(1980): mean
-Dimension coords: time(1980) = [1850-01-16 00:00:00, ..., 2014-12-16 00:00:00] 360_day
-                : latitude(144) = [-89.375, ..., 89.375] degrees_north
-                : longitude(192) = [0.0, ..., 358.125] degrees_east
-                : height(1) = [10.0] m
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">u</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f2</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">u</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: eastward_wind (ncvar%uas)
---------------------------------
-Data            : eastward_wind(time(1980), latitude(144), longitude(192)) m s-1
-Cell methods    : area: time(1980): mean
-Dimension coords: time(1980) = [1850-01-16 00:00:00, ..., 2014-12-16 00:00:00] 360_day
-                : latitude(144) = [-89.375, ..., 89.375] degrees_north
-                : longitude(192) = [0.0, ..., 358.125] degrees_east
-                : height(1) = [10.0] m
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pre</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f3</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pre</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=precipitation (ncvar%pre)
-------------------------------------------
-Data            : long_name=precipitation(long_name=time(1452), latitude(144), longitude(192)) mm/month
-Dimension coords: long_name=time(1452) = [1901-01-16 00:00:00, ..., 2021-12-16 00:00:00] gregorian
-                : latitude(144) = [-89.375, ..., 89.375] degrees_north
-                : longitude(192) = [0.0, ..., 358.125] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="4">
-<li><p>Plot the wind vectors on top of precipitation data for June 1995 by creating a subspace with a date-time object and using <a class="reference external" href="http://ajheaps.github.io/cf-plot/con.html">cfplot.con</a>. Here <a class="reference external" href="http://ajheaps.github.io/cf-plot/gopen.html">cfplot.gopen</a> is used to define the parts of the plot area, which is closed by <a class="reference external" href="http://ajheaps.github.io/cf-plot/gclose.html">cfplot.gclose</a>; <a class="reference external" href="http://ajheaps.github.io/cf-plot/cscale.html">cfplot.cscale</a> is used to choose one of the colour maps amongst many available; <a class="reference external" href="http://ajheaps.github.io/cf-plot/levs.html">cfplot.levs</a> is used to set the contour levels for precipitation data; and <a class="reference external" href="http://ajheaps.github.io/cf-plot/vect.html">cfplot.vect</a> is used to plot the wind vectors for June 1995:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Query.html#cf.Query" title="cf.Query" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">june_95</span></a> <span class="o">=</span> <a href="../function/cf.year.html#cf.year" title="cf.year" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">year</span></a><span class="p">(</span><span class="mi">1995</span><span class="p">)</span> <span class="o">&amp;</span> <a href="../function/cf.month.html#cf.month" title="cf.month" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">month</span></a><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/gopen.html#cfplot.gopen" title="cfplot.gopen" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">gopen</span></a><span class="p">()</span>
-<a href="https://ajheaps.github.io/cf-plot/cscale.html#cfplot.cscale" title="cfplot.cscale" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">cscale</span></a><span class="p">(</span><span class="s2">&quot;precip4_11lev&quot;</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/levs.html#cfplot.levs" title="cfplot.levs" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">levs</span></a><span class="p">(</span><span class="n">step</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/con.html#cfplot.con" title="cfplot.con" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">con</span></a><span class="p">(</span>
-    <a href="../method/cf.Field.subspace.html#cf.Field.subspace" title="cf.Field.subspace" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">pre</span><span class="o">.</span><span class="n">subspace</span></a><span class="p">(</span><span class="n">T</span><span class="o">=</span><a href="../class/cf.Query.html#cf.Query" title="cf.Query" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">june_95</span></a><span class="p">),</span>
-    <span class="n">lines</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;June 1995 monthly global precipitation&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/vect.html#cfplot.vect" title="cfplot.vect" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">vect</span></a><span class="p">(</span>
-    <a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">u</span></a><span class="o">=</span><a href="../method/cf.Field.subspace.html#cf.Field.subspace" title="cf.Field.subspace" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">u</span><span class="o">.</span><span class="n">subspace</span></a><span class="p">(</span><span class="n">T</span><span class="o">=</span><a href="../class/cf.Query.html#cf.Query" title="cf.Query" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">june_95</span></a><span class="p">),</span>
-    <a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">v</span></a><span class="o">=</span><a href="../method/cf.Field.subspace.html#cf.Field.subspace" title="cf.Field.subspace" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">v</span><span class="o">.</span><span class="n">subspace</span></a><span class="p">(</span><span class="n">T</span><span class="o">=</span><a href="../class/cf.Query.html#cf.Query" title="cf.Query" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">june_95</span></a><span class="p">),</span>
-    <span class="n">key_length</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">scale</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
-    <span class="n">stride</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/gclose.html#cfplot.gclose" title="cfplot.gclose" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">gclose</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_5_recipe_001.png" srcset="../_images/sphx_glr_plot_5_recipe_001.png" alt="plot 5 recipe" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  20.444 seconds)</p>
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-<p><a class="reference download internal" download="" href="../_downloads/plot_5_recipe.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_5_recipe.py</span></code></a></p>
-</div>
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-<p><a class="reference download internal" download="" href="../_downloads/plot_5_recipe.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_5_recipe.ipynb</span></code></a></p>
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diff --git a/docs/recipes/plot_6_recipe.html b/docs/recipes/plot_6_recipe.html
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-    <title>Converting from rotated latitude-longitude to regular latitude-longitude &#8212; Documentation</title>
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-<h1 class="logo"><a href="../index.html">cf 3.15.0</a></h1>
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-<p class="blurb">A CF-compliant earth science data analysis library</p>
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-<iframe src="https://ghbtns.com/github-btn.html?user=NCAS-CMS&repo=cf-python&type=star&count=true&size=large&v=2"
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-<li class="toctree-l1"><a class="reference internal" href="../installation.html"><strong>Installation</strong></a></li>
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-      <li>Previous: <a href="plot_5_recipe.html" title="previous chapter">Plotting wind vectors overlaid on precipitation data</a></li>
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-cf development has been supported by
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-and
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-the <a href="https://ec.europa.eu/programmes/horizon2020/">EC Horizon
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-<a href="https://gtr.ukri.org/project/0D95A6DB-0B95-48F7-8A8B-7B9A47DEA117">UKFAFMIP</a>;
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-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="converting-from-rotated-latitude-longitude-to-regular-latitude-longitude">
-<span id="sphx-glr-recipes-plot-6-recipe-py"></span><h1>Converting from rotated latitude-longitude to regular latitude-longitude<a class="headerlink" href="#converting-from-rotated-latitude-longitude-to-regular-latitude-longitude" title="Permalink to this headline">¶</a></h1>
-<p>In this recipe, we will be regridding from a rotated latitude-longitude source domain to a regular latitude-longitude destination domain.</p>
-<ol class="arabic simple">
-<li><p>Import cf-python, cf-plot and numpy:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cfplot</span> <span class="k">as</span> <span class="nn">cfp</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">import</span> <span class="nn">cf</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Read the field constructs using read function:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a> <span class="o">=</span> <span class="n">cf</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="s2">&quot;~/recipes/au952a.pd20510414.pp&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: id%UM_m01s03i463_vn1006(time(8), grid_latitude(432), grid_longitude(444))&gt;]
-</pre></div>
-</div>
-<ol class="arabic simple" start="3">
-<li><p>Select the field by index and print its description to show properties of all constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gust</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<a href="../method/cf.Field.dump.html#cf.Field.dump" title="cf.Field.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">gust</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-----------------------------------------------------------
-Field: id%UM_m01s03i463_vn1006 (ncvar%UM_m01s03i463_vn1006)
------------------------------------------------------------
-Conventions = &#39;CF-1.10&#39;
-_FillValue = -1073741824.0
-history = &#39;Converted from UM/PP by cf-python v3.15.0&#39;
-lbproc = &#39;8192&#39;
-lbtim = &#39;122&#39;
-long_name = &#39;WIND GUST&#39;
-runid = &#39;aaaaa&#39;
-source = &#39;UM vn1006&#39;
-stash_code = &#39;3463&#39;
-submodel = &#39;1&#39;
-um_stash_source = &#39;m01s03i463&#39;
-
-Data(time(8), grid_latitude(432), grid_longitude(444)) = [[[5.587890625, ..., 5.1376953125]]]
-
-Cell Method: time(8): maximum
-
-Domain Axis: grid_latitude(432)
-Domain Axis: grid_longitude(444)
-Domain Axis: height(1)
-Domain Axis: time(8)
-
-Dimension coordinate: time
-    axis = &#39;T&#39;
-    calendar = &#39;360_day&#39;
-    standard_name = &#39;time&#39;
-    units = &#39;days since 2051-1-1&#39;
-    Data(time(8)) = [2051-04-14 01:30:00, ..., 2051-04-14 22:30:00] 360_day
-    Bounds:calendar = &#39;360_day&#39;
-    Bounds:units = &#39;days since 2051-1-1&#39;
-    Bounds:Data(time(8), 2) = [[2051-04-14 00:00:00, ..., 2051-04-15 00:00:00]] 360_day
-
-Dimension coordinate: height
-    axis = &#39;Z&#39;
-    positive = &#39;up&#39;
-    standard_name = &#39;height&#39;
-    units = &#39;m&#39;
-    Data(height(1)) = [-1.0] m
-
-Dimension coordinate: grid_latitude
-    axis = &#39;Y&#39;
-    standard_name = &#39;grid_latitude&#39;
-    units = &#39;degrees&#39;
-    Data(grid_latitude(432)) = [-24.474999085068703, ..., 22.93500065803528] degrees
-    Bounds:units = &#39;degrees&#39;
-    Bounds:Data(grid_latitude(432), 2) = [[-24.52999908477068, ..., 22.990000657737255]] degrees
-
-Dimension coordinate: grid_longitude
-    axis = &#39;X&#39;
-    standard_name = &#39;grid_longitude&#39;
-    units = &#39;degrees&#39;
-    Data(grid_longitude(444)) = [-29.47499145567417, ..., 19.255008280277252] degrees
-    Bounds:units = &#39;degrees&#39;
-    Bounds:Data(grid_longitude(444), 2) = [[-29.52999145537615, ..., 19.31000827997923]] degrees
-
-Auxiliary coordinate: latitude
-    standard_name = &#39;latitude&#39;
-    units = &#39;degrees_north&#39;
-    Data(grid_latitude(432), grid_longitude(444)) = [[20.576467692711244, ..., 66.90225185059428]] degrees_north
-    Bounds:units = &#39;degrees_north&#39;
-    Bounds:Data(grid_latitude(432), grid_longitude(444), 4) = [[[20.50585365074419, ..., 66.82752183591474]]] degrees_north
-
-Auxiliary coordinate: longitude
-    standard_name = &#39;longitude&#39;
-    units = &#39;degrees_east&#39;
-    Data(grid_latitude(432), grid_longitude(444)) = [[-10.577446822867152, ..., 68.72895292160315]] degrees_east
-    Bounds:units = &#39;degrees_east&#39;
-    Bounds:Data(grid_latitude(432), grid_longitude(444), 4) = [[[-10.602339269012642, ..., 68.7357360850507]]] degrees_east
-
-Coordinate reference: grid_mapping_name:rotated_latitude_longitude
-    Coordinate conversion:grid_mapping_name = rotated_latitude_longitude
-    Coordinate conversion:grid_north_pole_latitude = 39.25
-    Coordinate conversion:grid_north_pole_longitude = 198.0
-    Dimension Coordinate: grid_longitude
-    Dimension Coordinate: grid_latitude
-    Auxiliary Coordinate: longitude
-    Auxiliary Coordinate: latitude
-</pre></div>
-</div>
-<ol class="arabic simple" start="4">
-<li><p>Access the time coordinate of the gust field and retrieve the datetime values of the time coordinate:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../method/cf.Field.coordinate.html#cf.Field.coordinate" title="cf.Field.coordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">gust</span><span class="o">.</span><span class="n">coordinate</span></a><span class="p">(</span><span class="s2">&quot;time&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">datetime_array</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[cftime.Datetime360Day(2051, 4, 14, 1, 30, 0, 0, has_year_zero=True)
- cftime.Datetime360Day(2051, 4, 14, 4, 30, 0, 0, has_year_zero=True)
- cftime.Datetime360Day(2051, 4, 14, 7, 30, 0, 0, has_year_zero=True)
- cftime.Datetime360Day(2051, 4, 14, 10, 30, 0, 0, has_year_zero=True)
- cftime.Datetime360Day(2051, 4, 14, 13, 30, 0, 0, has_year_zero=True)
- cftime.Datetime360Day(2051, 4, 14, 16, 30, 0, 0, has_year_zero=True)
- cftime.Datetime360Day(2051, 4, 14, 19, 30, 0, 0, has_year_zero=True)
- cftime.Datetime360Day(2051, 4, 14, 22, 30, 0, 0, has_year_zero=True)]
-</pre></div>
-</div>
-<ol class="arabic simple" start="5">
-<li><p>Create a new instance of the <cite>cf.dt</cite> class with a specified year, month, day, hour, minute, second and microsecond. Then store the result in the variable <code class="docutils literal notranslate"><span class="pre">test</span></code>:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://unidata.github.io/cftime/api.html#cftime.datetime" title="cftime.datetime" class="sphx-glr-backref-module-cftime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test</span></a> <span class="o">=</span> <span class="n">cf</span><span class="o">.</span><span class="n">dt</span><span class="p">(</span><span class="mi">2051</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://unidata.github.io/cftime/api.html#cftime.datetime" title="cftime.datetime" class="sphx-glr-backref-module-cftime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>2051-04-14 01:30:00
-</pre></div>
-</div>
-<ol class="arabic simple" start="6">
-<li><p>Plot the wind gust by creating a subspace for the specified variable <code class="docutils literal notranslate"><span class="pre">test</span></code> using <a class="reference external" href="http://ajheaps.github.io/cf-plot/con.html">cfplot.con</a>. Here <a class="reference external" href="http://ajheaps.github.io/cf-plot/mapset.html">cfplot.mapset</a> is used to set the mapping parameters like setting the map resolution to 50m:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://ajheaps.github.io/cf-plot/mapset.html#cfplot.mapset" title="cfplot.mapset" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">mapset</span></a><span class="p">(</span><span class="n">resolution</span><span class="o">=</span><span class="s2">&quot;50m&quot;</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/con.html#cfplot.con" title="cfplot.con" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">con</span></a><span class="p">(</span><a href="../method/cf.Field.subspace.html#cf.Field.subspace" title="cf.Field.subspace" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">gust</span><span class="o">.</span><span class="n">subspace</span></a><span class="p">(</span><span class="n">T</span><span class="o">=</span><a href="https://unidata.github.io/cftime/api.html#cftime.datetime" title="cftime.datetime" class="sphx-glr-backref-module-cftime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test</span></a><span class="p">),</span> <span class="n">lines</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_6_recipe_001.png" srcset="../_images/sphx_glr_plot_6_recipe_001.png" alt="plot 6 recipe" class = "sphx-glr-single-img"/><ol class="arabic simple" start="7">
-<li><p>To see the rotated pole data on the native grid, the above steps are repeated and projection is set to rotated in <a class="reference external" href="http://ajheaps.github.io/cf-plot/mapset.html">cfplot.mapset</a>:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://ajheaps.github.io/cf-plot/mapset.html#cfplot.mapset" title="cfplot.mapset" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">mapset</span></a><span class="p">(</span><span class="n">resolution</span><span class="o">=</span><span class="s2">&quot;50m&quot;</span><span class="p">,</span> <span class="n">proj</span><span class="o">=</span><span class="s2">&quot;rotated&quot;</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/con.html#cfplot.con" title="cfplot.con" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">con</span></a><span class="p">(</span><a href="../method/cf.Field.subspace.html#cf.Field.subspace" title="cf.Field.subspace" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">gust</span><span class="o">.</span><span class="n">subspace</span></a><span class="p">(</span><span class="n">T</span><span class="o">=</span><a href="https://unidata.github.io/cftime/api.html#cftime.datetime" title="cftime.datetime" class="sphx-glr-backref-module-cftime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test</span></a><span class="p">),</span> <span class="n">lines</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_6_recipe_002.png" srcset="../_images/sphx_glr_plot_6_recipe_002.png" alt="plot 6 recipe" class = "sphx-glr-single-img"/><ol class="arabic simple" start="8">
-<li><p>Create dimension coordinates for the destination grid with the latitude and longitude values for Europe. <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html">np.linspace</a> generates evenly spaced values between the specified latitude and longitude range. Bounds of the target longitude and target latitude are created and spherical regridding is then performed on the gust variable by passing the target latitude and target longitude as arguments. The method also takes an argument <code class="docutils literal notranslate"><span class="pre">'linear'</span></code> which specifies the type of regridding method to use. The description of the <code class="docutils literal notranslate"><span class="pre">regridded_data</span></code> is finally printed to show properties of all its constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_latitude</span></a> <span class="o">=</span> <a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class"><span class="n">cf</span><span class="o">.</span><span class="n">DimensionCoordinate</span></a><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><a href="../class/cf.Data.html#cf.Data" title="cf.Data" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class"><span class="n">cf</span><span class="o">.</span><span class="n">Data</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">34</span><span class="p">,</span> <span class="mi">72</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span> <span class="s2">&quot;degrees_north&quot;</span><span class="p">)</span>
-<span class="p">)</span>
-<a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_longitude</span></a> <span class="o">=</span> <a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class"><span class="n">cf</span><span class="o">.</span><span class="n">DimensionCoordinate</span></a><span class="p">(</span>
-    <span class="n">data</span><span class="o">=</span><a href="../class/cf.Data.html#cf.Data" title="cf.Data" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class"><span class="n">cf</span><span class="o">.</span><span class="n">Data</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="o">-</span><span class="mi">25</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span> <span class="s2">&quot;degrees_east&quot;</span><span class="p">)</span>
-<span class="p">)</span>
-
-<a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lon_bounds</span></a> <span class="o">=</span> <a href="../method/cf.DimensionCoordinate.create_bounds.html#cf.DimensionCoordinate.create_bounds" title="cf.DimensionCoordinate.create_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">target_longitude</span><span class="o">.</span><span class="n">create_bounds</span></a><span class="p">()</span>
-<a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lat_bounds</span></a> <span class="o">=</span> <a href="../method/cf.DimensionCoordinate.create_bounds.html#cf.DimensionCoordinate.create_bounds" title="cf.DimensionCoordinate.create_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">target_latitude</span><span class="o">.</span><span class="n">create_bounds</span></a><span class="p">()</span>
-
-<a href="../method/cf.DimensionCoordinate.set_bounds.html#cf.DimensionCoordinate.set_bounds" title="cf.DimensionCoordinate.set_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">target_longitude</span><span class="o">.</span><span class="n">set_bounds</span></a><span class="p">(</span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lon_bounds</span></a><span class="p">)</span>
-<a href="../method/cf.DimensionCoordinate.set_bounds.html#cf.DimensionCoordinate.set_bounds" title="cf.DimensionCoordinate.set_bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">target_latitude</span><span class="o">.</span><span class="n">set_bounds</span></a><span class="p">(</span><a href="../class/cf.Bounds.html#cf.Bounds" title="cf.Bounds" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lat_bounds</span></a><span class="p">)</span>
-
-<a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">regridded_data</span></a> <span class="o">=</span> <a href="../method/cf.Field.regrids.html#cf.Field.regrids" title="cf.Field.regrids" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">gust</span><span class="o">.</span><span class="n">regrids</span></a><span class="p">((</span><a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_latitude</span></a><span class="p">,</span> <a href="../class/cf.DimensionCoordinate.html#cf.DimensionCoordinate" title="cf.DimensionCoordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_longitude</span></a><span class="p">),</span> <span class="s2">&quot;linear&quot;</span><span class="p">)</span>
-<a href="../method/cf.Field.dump.html#cf.Field.dump" title="cf.Field.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">regridded_data</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>-----------------------------------------------------------
-Field: id%UM_m01s03i463_vn1006 (ncvar%UM_m01s03i463_vn1006)
------------------------------------------------------------
-Conventions = &#39;CF-1.10&#39;
-_FillValue = -1073741824.0
-history = &#39;Converted from UM/PP by cf-python v3.15.0&#39;
-lbproc = &#39;8192&#39;
-lbtim = &#39;122&#39;
-long_name = &#39;WIND GUST&#39;
-runid = &#39;aaaaa&#39;
-source = &#39;UM vn1006&#39;
-stash_code = &#39;3463&#39;
-submodel = &#39;1&#39;
-um_stash_source = &#39;m01s03i463&#39;
-
-Data(time(8), latitude(10), longitude(10)) = [[[--, ..., 6.10885110153462]]]
-
-Cell Method: time(8): maximum
-
-Domain Axis: height(1)
-Domain Axis: latitude(10)
-Domain Axis: longitude(10)
-Domain Axis: time(8)
-
-Dimension coordinate: time
-    axis = &#39;T&#39;
-    calendar = &#39;360_day&#39;
-    standard_name = &#39;time&#39;
-    units = &#39;days since 2051-1-1&#39;
-    Data(time(8)) = [2051-04-14 01:30:00, ..., 2051-04-14 22:30:00] 360_day
-    Bounds:calendar = &#39;360_day&#39;
-    Bounds:units = &#39;days since 2051-1-1&#39;
-    Bounds:Data(time(8), 2) = [[2051-04-14 00:00:00, ..., 2051-04-15 00:00:00]] 360_day
-
-Dimension coordinate: height
-    axis = &#39;Z&#39;
-    positive = &#39;up&#39;
-    standard_name = &#39;height&#39;
-    units = &#39;m&#39;
-    Data(height(1)) = [-1.0] m
-
-Dimension coordinate: latitude
-    standard_name = &#39;latitude&#39;
-    units = &#39;degrees_north&#39;
-    Data(latitude(10)) = [34.0, ..., 72.0] degrees_north
-    Bounds:units = &#39;degrees_north&#39;
-    Bounds:Data(latitude(10), 2) = [[31.88888888888889, ..., 74.11111111111111]] degrees_north
-
-Dimension coordinate: longitude
-    standard_name = &#39;longitude&#39;
-    units = &#39;degrees_east&#39;
-    Data(longitude(10)) = [-25.0, ..., 45.0] degrees_east
-    Bounds:units = &#39;degrees_east&#39;
-    Bounds:Data(longitude(10), 2) = [[-28.88888888888889, ..., 48.888888888888886]] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="9">
-<li><p>Step 6 is similarly repeated for the <code class="docutils literal notranslate"><span class="pre">regridded_data</span></code> to plot the wind gust on a regular latitude-longitude domain:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://ajheaps.github.io/cf-plot/mapset.html#cfplot.mapset" title="cfplot.mapset" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">mapset</span></a><span class="p">(</span><span class="n">resolution</span><span class="o">=</span><span class="s2">&quot;50m&quot;</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/con.html#cfplot.con" title="cfplot.con" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">con</span></a><span class="p">(</span><a href="../method/cf.Field.subspace.html#cf.Field.subspace" title="cf.Field.subspace" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">regridded_data</span><span class="o">.</span><span class="n">subspace</span></a><span class="p">(</span><span class="n">T</span><span class="o">=</span><a href="https://unidata.github.io/cftime/api.html#cftime.datetime" title="cftime.datetime" class="sphx-glr-backref-module-cftime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test</span></a><span class="p">),</span> <span class="n">lines</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_6_recipe_003.png" srcset="../_images/sphx_glr_plot_6_recipe_003.png" alt="plot 6 recipe" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  25.541 seconds)</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-recipes-plot-6-recipe-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/plot_6_recipe.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_6_recipe.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/plot_6_recipe.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_6_recipe.ipynb</span></code></a></p>
-</div>
-</div>
-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
-</section>
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-    <div class="clearer"></div>
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-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
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diff --git a/docs/recipes/plot_7_recipe.html b/docs/recipes/plot_7_recipe.html
deleted file mode 100644
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--- a/docs/recipes/plot_7_recipe.html
+++ /dev/null
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-
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-<h1 class="logo"><a href="../index.html">cf 3.15.0</a></h1>
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-<p class="blurb">A CF-compliant earth science data analysis library</p>
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-<li class="toctree-l1"><a class="reference internal" href="../introduction.html"><strong>Introduction</strong></a></li>
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-<ul>
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-  <li><a href="index.html"><strong>Recipes using cf</strong></a><ul>
-      <li>Previous: <a href="plot_6_recipe.html" title="previous chapter">Converting from rotated latitude-longitude to regular latitude-longitude</a></li>
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-cf development has been supported by
-the <a href="https://erc.europa.eu/">ERC</a>
-through <a href="https://cordis.europa.eu/project/id/247220">Seachange</a>
-and
-<a href="https://cordis.europa.eu/project/id/786427">Couplet</a>; by
-the <a href="https://ec.europa.eu/programmes/horizon2020/">EC Horizon
-2020 programme</a> through
-<a href="https://cordis.europa.eu/project/id/824084">IS-ENES3</a>;
-by <a href="https://nerc.ukri.org/">NERC</a> through
-<a href="https://gtr.ukri.org/project/0D95A6DB-0B95-48F7-8A8B-7B9A47DEA117">UKFAFMIP</a>;
-and by <a href="https://ncas.ac.uk/">NCAS</a>.
-<br>
-<br>
-<img src="../_templates/logo_EC.png" height="40">
-<img src="../_templates/logo_ERC.png" height="40">
-<img src="../_templates/logo_NERC.png" height="40">
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-            
-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-recipes-plot-7-recipe-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="plotting-members-of-a-model-ensemble">
-<span id="sphx-glr-recipes-plot-7-recipe-py"></span><h1>Plotting members of a model ensemble<a class="headerlink" href="#plotting-members-of-a-model-ensemble" title="Permalink to this headline">¶</a></h1>
-<p>In this recipe, we will plot the members of a model ensemble.</p>
-<ol class="arabic simple">
-<li><p>Import cf-python and cf-plot:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cfplot</span> <span class="k">as</span> <span class="nn">cfp</span>
-
-<span class="kn">import</span> <span class="nn">cf</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Read the field constructs using read function and store it in the variable <code class="docutils literal notranslate"><span class="pre">f</span></code>. The * in the filename is a wildcard character which means the function reads all files in the directory that match the specified pattern. [0:5] selects the first five elements of the resulting list:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span><span class="s2">&quot;~/recipes/realization/PRMSL.1941_mem*.nc&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: long_name=Pressure reduced to MSL(time(2920), latitude(256), longitude(512)) Pa&gt;,
- &lt;CF Field: long_name=Pressure reduced to MSL(time(2920), latitude(256), longitude(512)) Pa&gt;,
- &lt;CF Field: long_name=Pressure reduced to MSL(time(2920), latitude(256), longitude(512)) Pa&gt;,
- &lt;CF Field: long_name=Pressure reduced to MSL(time(2920), latitude(256), longitude(512)) Pa&gt;,
- &lt;CF Field: long_name=Pressure reduced to MSL(time(2920), latitude(256), longitude(512)) Pa&gt;]
-</pre></div>
-</div>
-<ol class="arabic simple" start="3">
-<li><p>The description of one of the fields from the list shows <code class="docutils literal notranslate"><span class="pre">'realization'</span></code> as a property by which the members of the model ensemble are labelled:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">dump</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>------------------------------------------------------
-Field: long_name=Pressure reduced to MSL (ncvar%PRMSL)
-------------------------------------------------------
-CDI = &#39;Climate Data Interface version 1.9.3 (http://mpimet.mpg.de/cdi)&#39;
-CDO = &#39;Climate Data Operators version 1.9.3 (http://mpimet.mpg.de/cdo)&#39;
-Conventions = &#39;CF-1.6&#39;
-citation = &#39;&lt;http://onlinelibrary.wiley.com/doi/10.1002/qj.776/abstract&gt;.\n
-            Quarterly J. Roy. Meteorol. Soc./, 137, 1-28. DOI: 10.1002/qj.776.&#39;
-comments = &#39;Data is from \n NOAA-CIRES 20th Century Reanalysis version 3\n,&#39;
-ensemble_members = 10
-experiment = &#39;451 = SODAsi.3 pentad 8 member SSTs climatologically adjusted to
-              HadISST2.3 1981 to 2010 climatology&#39;
-forecast_init_type = 3
-history = &#39;Mon Apr 15 00:59:16 2019: cdo -v -f nc4 -r
-           setpartabn,/global/homes/l/lslivins/convert_grb2_netcdf/cdo_table
-           -copy -setreftime,1800-01-01,00:00:00,3hours PRMSL.1941_004.grb
-           PRMSL.1941_mem004.nc&#39;
-institution = &#39;NOAA ESRL Physical Sciences Division &amp; CU/CIRES \n&#39;
-long_name = &#39;Pressure reduced to MSL&#39;
-observations = &#39;International Surface Pressure Databank version 4.7&#39;
-original_name = &#39;prmsl&#39;
-param = &#39;1.3.0&#39;
-platform = &#39;Model&#39;
-realization = 4
-units = &#39;Pa&#39;
-
-Data(time(2920), latitude(256), longitude(512)) = [[[101351.0, ..., 100562.0]]] Pa
-
-Domain Axis: latitude(256)
-Domain Axis: longitude(512)
-Domain Axis: time(2920)
-
-Dimension coordinate: time
-    axis = &#39;T&#39;
-    calendar = &#39;proleptic_gregorian&#39;
-    standard_name = &#39;time&#39;
-    units = &#39;hours since 1800-1-1 00:00:00&#39;
-    Data(time(2920)) = [1941-01-01 00:00:00, ..., 1941-12-31 21:00:00] proleptic_gregorian
-
-Dimension coordinate: latitude
-    axis = &#39;Y&#39;
-    long_name = &#39;latitude&#39;
-    standard_name = &#39;latitude&#39;
-    units = &#39;degrees_north&#39;
-    Data(latitude(256)) = [89.46282196044922, ..., -89.46282196044922] degrees_north
-
-Dimension coordinate: longitude
-    axis = &#39;X&#39;
-    long_name = &#39;longitude&#39;
-    standard_name = &#39;longitude&#39;
-    units = &#39;degrees_east&#39;
-    Data(longitude(512)) = [0.0, ..., 359.2330017089844] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="4">
-<li><p>An ensemble of the members is then created by aggregating the data in <code class="docutils literal notranslate"><span class="pre">f</span></code> along a new <code class="docutils literal notranslate"><span class="pre">'realization'</span></code> axis using the cf.aggregate function, and storing the result in the variable <code class="docutils literal notranslate"><span class="pre">ensemble</span></code>. <code class="docutils literal notranslate"><span class="pre">'relaxed_identities=True'</span></code> allows for missing coordinate identities to be inferred. [0] selects the first element of the resulting list. <code class="docutils literal notranslate"><span class="pre">id%realization</span></code> now shows as an auxiliary coordinate for the ensemble:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ensemble</span></a> <span class="o">=</span> <a href="../function/cf.aggregate.html#cf.aggregate" title="cf.aggregate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">aggregate</span></a><span class="p">(</span>
-    <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">,</span> <span class="n">dimension</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;realization&quot;</span><span class="p">,),</span> <span class="n">relaxed_identities</span><span class="o">=</span><span class="kc">True</span>
-<span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ensemble</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=Pressure reduced to MSL (ncvar%PRMSL)
-------------------------------------------------------
-Data            : long_name=Pressure reduced to MSL(id%realization(5), time(2920), latitude(256), longitude(512)) Pa
-Dimension coords: time(2920) = [1941-01-01 00:00:00, ..., 1941-12-31 21:00:00] proleptic_gregorian
-                : latitude(256) = [89.46282196044922, ..., -89.46282196044922] degrees_north
-                : longitude(512) = [0.0, ..., 359.2330017089844] degrees_east
-Auxiliary coords: id%realization(id%realization(5)) = [1, ..., 5]
-</pre></div>
-</div>
-<ol class="arabic simple" start="5">
-<li><p>To see the constructs for the ensemble, print the <em>constructs</em> attribute:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../attribute/cf.Field.constructs.html#cf.Field.constructs" title="cf.Field.constructs" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-attribute"><span class="n">ensemble</span><span class="o">.</span><span class="n">constructs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Constructs:
-{&#39;auxiliarycoordinate0&#39;: &lt;CF AuxiliaryCoordinate: id%realization(5) &gt;,
- &#39;dimensioncoordinate0&#39;: &lt;CF DimensionCoordinate: time(2920) hours since 1800-1-1 00:00:00 proleptic_gregorian&gt;,
- &#39;dimensioncoordinate1&#39;: &lt;CF DimensionCoordinate: latitude(256) degrees_north&gt;,
- &#39;dimensioncoordinate2&#39;: &lt;CF DimensionCoordinate: longitude(512) degrees_east&gt;,
- &#39;domainaxis0&#39;: &lt;CF DomainAxis: size(2920)&gt;,
- &#39;domainaxis1&#39;: &lt;CF DomainAxis: size(256)&gt;,
- &#39;domainaxis2&#39;: &lt;CF DomainAxis: size(512)&gt;,
- &#39;domainaxis3&#39;: &lt;CF DomainAxis: size(5)&gt;}
-</pre></div>
-</div>
-<p>6. Loop over the realizations in the ensemble using the <em>range</em> function and the <em>domain_axis</em> to determine the size of the realization dimension. For each realization, extract a subspace of the ensemble using the <em>subspace</em> method and the <code class="docutils literal notranslate"><span class="pre">'id%realization'</span></code> keyword argument along a specific latitude and longitude and plot the realizations from the 4D field using <a class="reference external" href="http://ajheaps.github.io/cf-plot/lineplot.html">cfplot.lineplot</a>.
-A moving average of the ensemble along the time axis, with a window size of 90 (i.e. an approximately 3-month moving average) is calculated using the <em>moving_window</em> method. The <code class="docutils literal notranslate"><span class="pre">mode='nearest'</span></code> parameter is used to specify how to pad the data outside of the time range. The <em>squeeze</em> method removes any dimensions of size 1 from the field to produce a 2D field:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://ajheaps.github.io/cf-plot/gopen.html#cfplot.gopen" title="cfplot.gopen" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">gopen</span></a><span class="p">()</span>
-
-<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">realization</span></a> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <a href="../method/cf.Field.domain_axis.html#cf.Field.domain_axis" title="cf.Field.domain_axis" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">ensemble</span><span class="o">.</span><span class="n">domain_axis</span></a><span class="p">(</span><span class="s2">&quot;id</span><span class="si">%r</span><span class="s2">ealization&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">size</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-    <a href="https://ajheaps.github.io/cf-plot/lineplot.html#cfplot.lineplot" title="cfplot.lineplot" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">lineplot</span></a><span class="p">(</span>
-        <a href="../method/cf.Field.subspace.html#cf.Field.subspace" title="cf.Field.subspace" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">ensemble</span><span class="o">.</span><span class="n">subspace</span></a><span class="p">(</span>
-            <span class="o">**</span><span class="p">{</span><span class="s2">&quot;id</span><span class="si">%r</span><span class="s2">ealization&quot;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">realization</span></a><span class="p">},</span> <span class="n">latitude</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">longitude</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span>
-        <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Member </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">realization</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
-        <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-    <span class="p">)</span>
-
-<a href="https://ajheaps.github.io/cf-plot/lineplot.html#cfplot.lineplot" title="cfplot.lineplot" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">lineplot</span></a><span class="p">(</span>
-    <a href="../method/cf.Field.moving_window.html#cf.Field.moving_window" title="cf.Field.moving_window" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">ensemble</span><span class="o">.</span><span class="n">moving_window</span></a><span class="p">(</span>
-        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="n">window_size</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s2">&quot;T&quot;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span>
-    <span class="p">)[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span>
-    <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Ensemble mean&quot;</span><span class="p">,</span>
-    <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span>
-    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Model Ensemble Pressure&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="https://ajheaps.github.io/cf-plot/gclose.html#cfplot.gclose" title="cfplot.gclose" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">gclose</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_7_recipe_001.png" srcset="../_images/sphx_glr_plot_7_recipe_001.png" alt="Model Ensemble Pressure" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  35.758 seconds)</p>
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-<section class="sphx-glr-example-title" id="plotting-statistically-significant-temperature-trends-with-stippling">
-<span id="sphx-glr-recipes-plot-8-recipe-py"></span><h1>Plotting statistically significant temperature trends with stippling<a class="headerlink" href="#plotting-statistically-significant-temperature-trends-with-stippling" title="Permalink to this headline">¶</a></h1>
-<p>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.</p>
-<ol class="arabic simple">
-<li><p>Import cf-python, cf-plot, numpy and scipy.stats:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cfplot</span> <span class="k">as</span> <span class="nn">cfp</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">stats</span>
-
-<span class="kn">import</span> <span class="nn">cf</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Three functions are defined:</p></li>
-</ol>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">linear_trend(data,</span> <span class="pre">time_axis)</span></code>: This function calculates the linear regression slope and p-value for the input data along the time axis. It takes two arguments: <code class="docutils literal notranslate"><span class="pre">'data'</span></code>, which represents the temperature anomalies or any other data you want to analyse, and <code class="docutils literal notranslate"><span class="pre">'time_axis'</span></code>, which represents the corresponding time points for the data. The function uses the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html">stats.linregress</a> method from the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/stats.html">scipy.stats</a> library to calculate the slope and p-value of the linear regression. It returns these two values as a tuple:</p></li>
-</ul>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">linear_trend</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_axis</span></a><span class="p">):</span>
-    <span class="n">slope</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html#scipy.stats.linregress" title="scipy.stats.linregress" class="sphx-glr-backref-module-scipy-stats sphx-glr-backref-type-py-function"><span class="n">stats</span><span class="o">.</span><span class="n">linregress</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_axis</span></a><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">slope</span><span class="p">,</span> <span class="n">p_value</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">create_trend_stipple_obj(temp_data,</span> <span class="pre">input_data)</span></code>: This function creates a new object with the input data provided and <em>collapses</em> the time dimension by taking the mean. It takes two arguments: <code class="docutils literal notranslate"><span class="pre">'temp_data'</span></code>, which represents the temperature data object, and <code class="docutils literal notranslate"><span class="pre">'input_data'</span></code>, which is the data to be set in the new object. The function creates a copy of the <code class="docutils literal notranslate"><span class="pre">'temp_data'</span></code> object by selecting the first element using index and <em>squeezes</em> it to remove the size 1 axis. It then sets the input data with the <code class="docutils literal notranslate"><span class="pre">'Y'</span></code> (latitude) and <code class="docutils literal notranslate"><span class="pre">'X'</span></code> (longitude) axes, and then <em>collapses</em> the time dimension using the <code class="docutils literal notranslate"><span class="pre">&quot;T:</span> <span class="pre">mean&quot;</span></code> operation:</p></li>
-</ul>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">create_trend_stipple_obj</span><span class="p">(</span><span class="n">temp_data</span><span class="p">,</span> <span class="n">input_data</span><span class="p">):</span>
-    <span class="n">trend_stipple_obj</span> <span class="o">=</span> <span class="n">temp_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-    <span class="n">trend_stipple_obj</span><span class="o">.</span><span class="n">set_data</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Y&quot;</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">trend_stipple_obj</span>
-</pre></div>
-</div>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">process_subsets(subset_mask)</span></code>: This function processes the subsets of data by applying the <code class="docutils literal notranslate"><span class="pre">linear_trend</span></code> function along a specified axis. It takes one argument, <code class="docutils literal notranslate"><span class="pre">'subset_mask'</span></code>, which is a boolean mask representing the time points to be considered in the analysis. The function first extracts the masked subset of data and then applies the <code class="docutils literal notranslate"><span class="pre">linear_trend</span></code> function along the time axis (axis 0) using the <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ma.apply_along_axis.html">numpy.ma.apply_along_axis</a> function. The result is an array containing the slope and p-value for each grid point in the dataset:</p></li>
-</ul>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">process_subsets</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subset_mask</span></a><span class="p">):</span>
-    <span class="n">subset_data</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">masked_data</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subset_mask</span></a><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span>
-    <span class="k">return</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ma.apply_along_axis.html#numpy.ma.apply_along_axis" title="numpy.ma.apply_along_axis" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">apply_along_axis</span></a><span class="p">(</span>
-        <span class="n">linear_trend</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">subset_data</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_axis</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subset_mask</span></a><span class="p">]</span>
-    <span class="p">)</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="3">
-<li><p>Read the field constructs:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temperature_data</span></a> <span class="o">=</span> <a href="../function/cf.read.html#cf.read" title="cf.read" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">read</span></a><span class="p">(</span>
-    <span class="s2">&quot;~/recipes/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc&quot;</span>
-<span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temperature_data</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Field: long_name=blended air_temperature_anomaly over land with sea_water_temperature_anomaly (ncvar%tas_mean)
---------------------------------------------------------------------------------------------------------------
-Data            : long_name=blended air_temperature_anomaly over land with sea_water_temperature_anomaly(time(2074), latitude(36), longitude(72)) K
-Cell methods    : area: mean (interval: 5.0 degrees_north) time(2074): mean (interval: 1 month) realization(1): mean
-Dimension coords: time(2074) = [1850-01-16 12:00:00, ..., 2022-10-16 12:00:00] gregorian
-                : latitude(36) = [-87.5, ..., 87.5] degrees_north
-                : longitude(72) = [-177.5, ..., 177.5] degrees_east
-                : realization(1) = [100] 1
-</pre></div>
-</div>
-<ol class="arabic simple" start="4">
-<li><p>Calculate the annual mean temperature anomalies. The <code class="docutils literal notranslate"><span class="pre">'weights=True'</span></code> argument is used take the varying lengths of months into account which ensures that the calculated mean is more accurate. A masked array is created for the annual mean temperature anomalies, masking any invalid values:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">annual_temperature</span></a> <span class="o">=</span> <a href="../method/cf.Field.collapse.html#cf.Field.collapse" title="cf.Field.collapse" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temperature_data</span><span class="o">.</span><span class="n">collapse</span></a><span class="p">(</span>
-    <span class="s2">&quot;T: mean&quot;</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><a href="../function/cf.Y.html#cf.Y" title="cf.Y" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-function"><span class="n">cf</span><span class="o">.</span><span class="n">Y</span></a><span class="p">()</span>
-<span class="p">)</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_axis</span></a> <span class="o">=</span> <a href="../method/cf.Field.coordinate.html#cf.Field.coordinate" title="cf.Field.coordinate" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">annual_temperature</span><span class="o">.</span><span class="n">coordinate</span></a><span class="p">(</span><span class="s2">&quot;T&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">year</span><span class="o">.</span><span class="n">array</span>
-<a href="https://numpy.org/doc/stable/reference/maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">masked_data</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ma.masked_invalid.html#numpy.ma.masked_invalid" title="numpy.ma.masked_invalid" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">ma</span><span class="o">.</span><span class="n">masked_invalid</span></a><span class="p">(</span><a href="../attribute/cf.Field.array.html#cf.Field.array" title="cf.Field.array" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-attribute"><span class="n">annual_temperature</span><span class="o">.</span><span class="n">array</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="5">
-<li><p>Define two time periods for analysis: 1850-2020 and 1980-2020, along with a significance level (alpha) of 0.05:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_periods</span></a> <span class="o">=</span> <span class="p">[(</span><span class="mi">1850</span><span class="p">,</span> <span class="mi">2020</span><span class="p">,</span> <span class="s2">&quot;sub_1850_2020&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">1980</span><span class="p">,</span> <span class="mi">2020</span><span class="p">,</span> <span class="s2">&quot;sub_1980_2020&quot;</span><span class="p">)]</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">alpha</span></a> <span class="o">=</span> <span class="mf">0.05</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a> <span class="o">=</span> <span class="p">{}</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="6">
-<li><p>Loop through the time periods, processing the subsets, calculating trend p-values, and creating stipple objects. For each time period, the script calculates the trends and p-values using the <code class="docutils literal notranslate"><span class="pre">process_subsets</span></code> function. If the p-value is less than the significance level (alpha = 0.05), a stippling mask is created. The script then creates a new object for the trend and stippling mask using the <code class="docutils literal notranslate"><span class="pre">create_trend_stipple_obj</span></code> function:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">end</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">prefix</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_periods</span></a><span class="p">:</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subset_mask</span></a> <span class="o">=</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_axis</span></a> <span class="o">&gt;=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start</span></a><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_axis</span></a> <span class="o">&lt;=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">end</span></a><span class="p">)</span>
-    <a href="https://numpy.org/doc/stable/reference/maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subset_trend_pvalue</span></a> <span class="o">=</span> <span class="n">process_subsets</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subset_mask</span></a><span class="p">)</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">prefix</span></a> <span class="o">+</span> <span class="s2">&quot;_trend_pvalue&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subset_trend_pvalue</span></a>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">prefix</span></a> <span class="o">+</span> <span class="s2">&quot;_stipple&quot;</span><span class="p">]</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subset_trend_pvalue</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">alpha</span></a>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">prefix</span></a> <span class="o">+</span> <span class="s2">&quot;_trend&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">create_trend_stipple_obj</span><span class="p">(</span>
-        <a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temperature_data</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subset_trend_pvalue</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="p">)</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">prefix</span></a> <span class="o">+</span> <span class="s2">&quot;_stipple_obj&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">create_trend_stipple_obj</span><span class="p">(</span>
-        <a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temperature_data</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">prefix</span></a> <span class="o">+</span> <span class="s2">&quot;_stipple&quot;</span><span class="p">]</span>
-    <span class="p">)</span>
-</pre></div>
-</div>
-<p>7. Create two plots - one for the 1850-2020 time period and another for the 1980-2020 time period using <a class="reference external" href="http://ajheaps.github.io/cf-plot/con.html">cfplot.con</a>.
-The results are multiplied by 10 so that each plot displays the temperature trend in K/decade with stippling to indicate areas where the trend is statistically significant (p-value &lt; 0.05).
-Here <a class="reference external" href="http://ajheaps.github.io/cf-plot/gopen.html">cfplot.gopen</a> is used to define the parts of the plot area with two rows and one column, and setting the bottom margin to 0.2.
-It is closed by <a class="reference external" href="http://ajheaps.github.io/cf-plot/gclose.html">cfplot.gclose</a>;
-<a class="reference external" href="http://ajheaps.github.io/cf-plot/gpos.html">cfplot.gpos</a> is used to set the plotting position of both the plots;
-<a class="reference external" href="http://ajheaps.github.io/cf-plot/mapset.html">cfplot.mapset</a> is used to set the map projection to Robinson;
-<a class="reference external" href="http://ajheaps.github.io/cf-plot/cscale.html">cfplot.cscale</a> is used to choose one of the colour maps amongst many available;
-<a class="reference external" href="http://ajheaps.github.io/cf-plot/levs.html">cfplot.levs</a> is used to set the contour levels;
-and <a class="reference external" href="http://ajheaps.github.io/cf-plot/stipple.html">cfplot.stipple</a> is used to add stippling to show statistically significant areas:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://ajheaps.github.io/cf-plot/gopen.html#cfplot.gopen" title="cfplot.gopen" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">gopen</span></a><span class="p">(</span><span class="n">rows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
-
-<a href="https://ajheaps.github.io/cf-plot/gpos.html#cfplot.gpos" title="cfplot.gpos" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">gpos</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/mapset.html#cfplot.mapset" title="cfplot.mapset" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">mapset</span></a><span class="p">(</span><span class="n">proj</span><span class="o">=</span><span class="s2">&quot;robin&quot;</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/cscale.html#cfplot.cscale" title="cfplot.cscale" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">cscale</span></a><span class="p">(</span><span class="s2">&quot;temp_19lev&quot;</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/levs.html#cfplot.levs" title="cfplot.levs" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">levs</span></a><span class="p">(</span><span class="nb">min</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/con.html#cfplot.con" title="cfplot.con" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">con</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">[</span><span class="s2">&quot;sub_1850_2020_trend&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span><span class="p">,</span>
-    <span class="n">lines</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">colorbar</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Temperature Trend 1850-2020&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/stipple.html#cfplot.stipple" title="cfplot.stipple" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">stipple</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">[</span><span class="s2">&quot;sub_1850_2020_stipple_obj&quot;</span><span class="p">],</span>
-    <span class="nb">min</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
-    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="https://ajheaps.github.io/cf-plot/gpos.html#cfplot.gpos" title="cfplot.gpos" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">gpos</span></a><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/mapset.html#cfplot.mapset" title="cfplot.mapset" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">mapset</span></a><span class="p">(</span><span class="n">proj</span><span class="o">=</span><span class="s2">&quot;robin&quot;</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/cscale.html#cfplot.cscale" title="cfplot.cscale" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">cscale</span></a><span class="p">(</span><span class="s2">&quot;temp_19lev&quot;</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/levs.html#cfplot.levs" title="cfplot.levs" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">levs</span></a><span class="p">(</span><span class="nb">min</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/con.html#cfplot.con" title="cfplot.con" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">con</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">[</span><span class="s2">&quot;sub_1980_2020_trend&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span><span class="p">,</span>
-    <span class="n">lines</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Temperature Trend 1980-2020&quot;</span><span class="p">,</span>
-    <span class="n">colorbar_position</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">],</span>
-    <span class="n">colorbar_orientation</span><span class="o">=</span><span class="s2">&quot;horizontal&quot;</span><span class="p">,</span>
-    <span class="n">colorbar_title</span><span class="o">=</span><span class="s2">&quot;K/decade&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://ajheaps.github.io/cf-plot/stipple.html#cfplot.stipple" title="cfplot.stipple" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">stipple</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">[</span><span class="s2">&quot;sub_1980_2020_stipple_obj&quot;</span><span class="p">],</span>
-    <span class="nb">min</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-    <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
-    <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;.&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="https://ajheaps.github.io/cf-plot/gclose.html#cfplot.gclose" title="cfplot.gclose" class="sphx-glr-backref-module-cfplot sphx-glr-backref-type-py-function"><span class="n">cfp</span><span class="o">.</span><span class="n">gclose</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_8_recipe_001.png" srcset="../_images/sphx_glr_plot_8_recipe_001.png" alt="plot 8 recipe" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  28.188 seconds)</p>
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-<section class="sphx-glr-example-title" id="plotting-a-joint-histogram">
-<span id="sphx-glr-recipes-plot-9-recipe-py"></span><h1>Plotting a joint histogram<a class="headerlink" href="#plotting-a-joint-histogram" title="Permalink to this headline">¶</a></h1>
-<p>In this recipe, we will be creating a joint histogram of PM2.5 mass
-concentration and 2-metre temperature.</p>
-<ol class="arabic simple">
-<li><p>Import cf-python, numpy and matplotlib.pyplot:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">import</span> <span class="nn">cf</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="2">
-<li><p>Read the field constructs using read function:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a> <span class="o">=</span> <span class="n">cf</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="s2">&quot;~/recipes/levtype_sfc.nc&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;CF Field: long_name=Total Aerosol Optical Depth at 550nm(long_name=time(6), long_name=latitude(241), long_name=longitude(480)) ~&gt;,
- &lt;CF Field: mass_concentration_of_pm10_ambient_aerosol_particles_in_air(long_name=time(6), long_name=latitude(241), long_name=longitude(480)) kg m**-3&gt;,
- &lt;CF Field: mass_concentration_of_pm2p5_ambient_aerosol_particles_in_air(long_name=time(6), long_name=latitude(241), long_name=longitude(480)) kg m**-3&gt;,
- &lt;CF Field: long_name=2 metre temperature(long_name=time(6), long_name=latitude(241), long_name=longitude(480)) K&gt;]
-</pre></div>
-</div>
-<p>3. Select the PM2.5 mass concentration and 2-metre temperature fields by
-index and print the description to show properties of all constructs:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pm25_field</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-<a href="../method/cf.Field.dump.html#cf.Field.dump" title="cf.Field.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">pm25_field</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>---------------------------------------------------------------------------------
-Field: mass_concentration_of_pm2p5_ambient_aerosol_particles_in_air (ncvar%pm2p5)
----------------------------------------------------------------------------------
-Conventions = &#39;CF-1.6&#39;
-_FillValue = -32767
-history = &#39;2023-05-02 11:17:49 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-
-           client/bin/grib_to_netcdf.bin -S param -o /cache/tmp/4e68aba0-50a9-
-           4f06-947c-9fc7444b7838-adaptor.mars.internal-1683026269.508034-
-           23606-13-tmp.nc /cache/tmp/4e68aba0-50a9-4f06-947c-9fc7444b7838-
-           adaptor.mars.internal-1683026265.2095394-23606-10-tmp.grib&#39;
-long_name = &#39;Particulate matter d &lt;= 2.5 um&#39;
-missing_value = -32767
-standard_name = &#39;mass_concentration_of_pm2p5_ambient_aerosol_particles_in_air&#39;
-units = &#39;kg m**-3&#39;
-
-Data(long_name=time(6), long_name=latitude(241), long_name=longitude(480)) = [[[1.5032209791448226e-10, ..., 1.1372701346394527e-10]]] kg m**-3
-
-Domain Axis: long_name=latitude(241)
-Domain Axis: long_name=longitude(480)
-Domain Axis: long_name=time(6)
-
-Dimension coordinate: long_name=time
-    calendar = &#39;gregorian&#39;
-    long_name = &#39;time&#39;
-    units = &#39;hours since 1900-01-01 00:00:00.0&#39;
-    Data(long_name=time(6)) = [2022-01-01 00:00:00, ..., 2022-06-01 00:00:00] gregorian
-
-Dimension coordinate: long_name=latitude
-    long_name = &#39;latitude&#39;
-    units = &#39;degrees_north&#39;
-    Data(long_name=latitude(241)) = [90.0, ..., -90.0] degrees_north
-
-Dimension coordinate: long_name=longitude
-    long_name = &#39;longitude&#39;
-    units = &#39;degrees_east&#39;
-    Data(long_name=longitude(480)) = [0.0, ..., 359.25] degrees_east
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_field</span></a> <span class="o">=</span> <a href="../class/cf.FieldList.html#cf.FieldList" title="cf.FieldList" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-<a href="../method/cf.Field.dump.html#cf.Field.dump" title="cf.Field.dump" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp_field</span><span class="o">.</span><span class="n">dump</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>------------------------------------------------
-Field: long_name=2 metre temperature (ncvar%t2m)
-------------------------------------------------
-Conventions = &#39;CF-1.6&#39;
-_FillValue = -32767
-history = &#39;2023-05-02 11:17:49 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-
-           client/bin/grib_to_netcdf.bin -S param -o /cache/tmp/4e68aba0-50a9-
-           4f06-947c-9fc7444b7838-adaptor.mars.internal-1683026269.508034-
-           23606-13-tmp.nc /cache/tmp/4e68aba0-50a9-4f06-947c-9fc7444b7838-
-           adaptor.mars.internal-1683026265.2095394-23606-10-tmp.grib&#39;
-long_name = &#39;2 metre temperature&#39;
-missing_value = -32767
-units = &#39;K&#39;
-
-Data(long_name=time(6), long_name=latitude(241), long_name=longitude(480)) = [[[247.0076398602821, ..., 226.25710500984027]]] K
-
-Domain Axis: long_name=latitude(241)
-Domain Axis: long_name=longitude(480)
-Domain Axis: long_name=time(6)
-
-Dimension coordinate: long_name=time
-    calendar = &#39;gregorian&#39;
-    long_name = &#39;time&#39;
-    units = &#39;hours since 1900-01-01 00:00:00.0&#39;
-    Data(long_name=time(6)) = [2022-01-01 00:00:00, ..., 2022-06-01 00:00:00] gregorian
-
-Dimension coordinate: long_name=latitude
-    long_name = &#39;latitude&#39;
-    units = &#39;degrees_north&#39;
-    Data(long_name=latitude(241)) = [90.0, ..., -90.0] degrees_north
-
-Dimension coordinate: long_name=longitude
-    long_name = &#39;longitude&#39;
-    units = &#39;degrees_east&#39;
-    Data(long_name=longitude(480)) = [0.0, ..., 359.25] degrees_east
-</pre></div>
-</div>
-<ol class="arabic simple" start="4">
-<li><p>Convert the units to degree celsius for the temperature field:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_field</span></a><span class="o">.</span><span class="n">units</span> <span class="o">=</span> <span class="s2">&quot;degC&quot;</span>
-<a href="../method/cf.Field.get_property.html#cf.Field.get_property" title="cf.Field.get_property" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp_field</span><span class="o">.</span><span class="n">get_property</span></a><span class="p">(</span><span class="s2">&quot;units&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&#39;degC&#39;
-</pre></div>
-</div>
-<p>5. Digitize the PM2.5 mass concentration and 2-metre temperature fields.
-This step counts the number of values in each of the 10 equally sized bins
-spanning the range of the values. The <code class="docutils literal notranslate"><span class="pre">'return_bins=True'</span></code> argument makes
-sure that the calculated bins are also returned:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pm25_indices</span></a><span class="p">,</span> <a href="../class/cf.Data.html#cf.Data" title="cf.Data" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pm25_bins</span></a> <span class="o">=</span> <a href="../method/cf.Field.digitize.html#cf.Field.digitize" title="cf.Field.digitize" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">pm25_field</span><span class="o">.</span><span class="n">digitize</span></a><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">return_bins</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_indices</span></a><span class="p">,</span> <a href="../class/cf.Data.html#cf.Data" title="cf.Data" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_bins</span></a> <span class="o">=</span> <a href="../method/cf.Field.digitize.html#cf.Field.digitize" title="cf.Field.digitize" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp_field</span><span class="o">.</span><span class="n">digitize</span></a><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">return_bins</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="6">
-<li><p>Create a joint histogram of the digitized fields:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">joint_histogram</span></a> <span class="o">=</span> <span class="n">cf</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pm25_indices</span></a><span class="p">,</span> <a href="../class/cf.Field.html#cf.Field" title="cf.Field" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_indices</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<ol class="arabic simple" start="7">
-<li><p>Get histogram data from the <code class="docutils literal notranslate"><span class="pre">joint_histogram</span></code>:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">histogram_data</span></a> <span class="o">=</span> <a href="https://ncas-cms.github.io/cfdm/attribute/cfdm.Field.array.html#cfdm.Field.array" title="cfdm.Field.array" class="sphx-glr-backref-module-cfdm sphx-glr-backref-type-py-attribute"><span class="n">joint_histogram</span><span class="o">.</span><span class="n">array</span></a>
-</pre></div>
-</div>
-<ol class="arabic simple" start="8">
-<li><p>Calculate bin centers for PM2.5 and temperature bins:</p></li>
-</ol>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pm25_bin_centers</span></a> <span class="o">=</span> <span class="p">[(</span><span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <a href="../class/cf.Data.html#cf.Data" title="cf.Data" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pm25_bins</span></a><span class="p">]</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_bin_centers</span></a> <span class="o">=</span> <span class="p">[(</span><span class="n">a</span> <span class="o">+</span> <span class="n">b</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <a href="../class/cf.Data.html#cf.Data" title="cf.Data" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_bins</span></a><span class="p">]</span>
-</pre></div>
-</div>
-<p>9. Create grids for PM2.5 and temperature bins using <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html">numpy.meshgrid</a>:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_grid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pm25_grid</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html#numpy.meshgrid" title="numpy.meshgrid" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_bin_centers</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pm25_bin_centers</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<p>10. Plot the joint histogram using <a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pcolormesh.html">matplotlib.pyplot.pcolormesh</a>. Use <a class="reference external" href="https://ncas-cms.github.io/cf-python/method/cf.Field.unique.html">cf.Field.unique</a> to get
-the unique data array values and show the bin boundaries as ticks on the
-plot. <code class="docutils literal notranslate"><span class="pre">'style='plain'</span></code> in <a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.ticklabel_format.html">matplotlib.axes.Axes.ticklabel_format</a>
-disables the scientific notation on the y-axis:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_grid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pm25_grid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/maskedarray.baseclass.html#numpy.ma.MaskedArray" title="numpy.ma.MaskedArray" class="sphx-glr-backref-module-numpy-ma sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">histogram_data</span></a><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;viridis&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;2-metre Temperature (degC)&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;PM2.5 Mass Concentration (kg m**-3)&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><a href="../method/cf.Data.unique.html#cf.Data.unique" title="cf.Data.unique" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">temp_bins</span><span class="o">.</span><span class="n">unique</span></a><span class="p">()</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><a href="../method/cf.Data.unique.html#cf.Data.unique" title="cf.Data.unique" class="sphx-glr-backref-module-cf sphx-glr-backref-type-py-method"><span class="n">pm25_bins</span><span class="o">.</span><span class="n">unique</span></a><span class="p">()</span><span class="o">.</span><span class="n">array</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;Frequency&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Joint Histogram of PM2.5 and 2-metre Temperature&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.05</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ticklabel_format</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s2">&quot;plain&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_9_recipe_001.png" srcset="../_images/sphx_glr_plot_9_recipe_001.png" alt="Joint Histogram of PM2.5 and 2-metre Temperature" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  4.037 seconds)</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-recipes-plot-9-recipe-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/plot_9_recipe.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_9_recipe.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/plot_9_recipe.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_9_recipe.ipynb</span></code></a></p>
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-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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