Gallery: simplify quiver example

docs/gallery_code/meteorology/plot_wind_speed.py

@@ -11,71 +11,50 @@
 
 """
 
-import cartopy.crs as ccrs
 import cartopy.feature as cfeat
 import matplotlib.pyplot as plt
-import numpy as np
 
 import iris
-import iris.coord_categorisation
+import iris.plot as iplt
 import iris.quickplot as qplt
 
 
 def main():
-    # Load the u and v components of wind from a pp file
+    # Load the u and v components of wind from a pp file.
     infile = iris.sample_data_path("wind_speed_lake_victoria.pp")
 
     uwind = iris.load_cube(infile, "x_wind")
     vwind = iris.load_cube(infile, "y_wind")
 
-    ulon = uwind.coord("longitude")
-    vlon = vwind.coord("longitude")
-
-    # The longitude points go from 180 to 540, so subtract 360 from them
-    ulon.points = ulon.points - 360.0
-    vlon.points = vlon.points - 360.0
-
-    # Create a cube containing the wind speed
+    # Create a cube containing the wind speed.
     windspeed = (uwind ** 2 + vwind ** 2) ** 0.5
     windspeed.rename("windspeed")
 
-    x = ulon.points
-    y = uwind.coord("latitude").points
-    u = uwind.data
-    v = vwind.data
+    # Plot the wind speed as a contour plot.
+    qplt.contourf(windspeed, 20)
 
-    # Set up axes to show the lake
+    # Show the lake on the current axes.
     lakes = cfeat.NaturalEarthFeature(
         "physical", "lakes", "50m", facecolor="none"
     )
+    plt.gca().add_feature(lakes)
 
-    plt.figure()
-    ax = plt.axes(projection=ccrs.PlateCarree())
-    ax.add_feature(lakes)
-
-    # Get the coordinate reference system used by the data
-    transform = ulon.coord_system.as_cartopy_projection()
-
-    # Plot the wind speed as a contour plot
-    qplt.contourf(windspeed, 20)
-
-    # Add arrows to show the wind vectors
-    plt.quiver(x, y, u, v, pivot="middle", transform=transform)
+    # Add arrows to show the wind vectors.
+    iplt.quiver(uwind, vwind, pivot="middle")
 
     plt.title("Wind speed over Lake Victoria")
     qplt.show()
 
-    # Normalise the data for uniform arrow size
-    u_norm = u / np.sqrt(u ** 2.0 + v ** 2.0)
-    v_norm = v / np.sqrt(u ** 2.0 + v ** 2.0)
+    # Normalise the data for uniform arrow size.
+    u_norm = uwind / windspeed
+    v_norm = vwind / windspeed
 
+    # Make a new figure for the normalised plot.
     plt.figure()
-    ax = plt.axes(projection=ccrs.PlateCarree())
-    ax.add_feature(lakes)
 
     qplt.contourf(windspeed, 20)
-
-    plt.quiver(x, y, u_norm, v_norm, pivot="middle", transform=transform)
+    plt.gca().add_feature(lakes)
+    iplt.quiver(u_norm, v_norm, pivot="middle")
 
     plt.title("Wind speed over Lake Victoria")
     qplt.show()

docs/src/whatsnew/latest.rst

@@ -68,6 +68,9 @@ This document explains the changes made to Iris for this release
 📚 Documentation
 ================
 
+#. `@rcomer`_ updated the "Plotting Wind Direction Using Quiver" Gallery
+   example. (:pull:`4120`)
+
 #. `@trexfeathers`_ included `Iris GitHub Discussions`_ in
    :ref:`get involved <development_where_to_start>`. (:pull:`4307`)