HandyPythonScripts.md

@@ -344,6 +344,39 @@ my\_cube.collapsed(\[\'latitude\', \'longitude\'\],MEAN,
 weights=grid\_areas) \#This does the clever maths to work out the grid
 box areas
 
+Example below:
+
+```
+import iris
+import iris.quickplot as qplt
+import matplotlib.pyplot as plt
+from iris import *
+from iris.analysis import *
+from iris.analysis.cartography import *
+
+
+my_cube = iris.load_cube('/data/local_ssd/cj362/S2P3Rv2.0/dhw/MIROC6_ssp245_1970_2100_surfacetemperature_all.nc')
+
+#plot the 1st (0th) day of data as a map
+qplt.pcolormesh(my_cube[0])
+plt.savefig('test.png')
+plt.show()
+
+#note, I've just sepected a subset of the time steps here (1st 1000 days) otherwise it takes for ever.
+my_cube = my_cube[0:1000]
+
+#averaging the data over an area, based on the example script on the wiki
+my_cube.coord('latitude').guess_bounds()
+my_cube.coord('longitude').guess_bounds()
+grid_areas = area_weights(my_cube)
+average_variable = my_cube[0:1000].collapsed(['latitude', 'longitude'],MEAN,weights=grid_areas)
+
+#plot the timeseries:
+qplt.plot(average_variable)
+plt.savefig('test2.png')
+plt.show()
+```
+
 ### Writing a script
 
  see week 1 practical:  [Week 1 An introduction to scientific
@@ -535,36 +568,7 @@ results\_array = seawater.dens(salinity\_data,temperature\_data,1)
 
 e.g.
 
-```
-import iris
-import iris.quickplot as qplt
-import matplotlib.pyplot as plt
-from iris import *
-from iris.analysis import *
-from iris.analysis.cartography import *
-
-
-my_cube = iris.load_cube('/data/local_ssd/cj362/S2P3Rv2.0/dhw/MIROC6_ssp245_1970_2100_surfacetemperature_all.nc')
-
-#plot the 1st (0th) day of data as a map
-qplt.pcolormesh(my_cube[0])
-plt.savefig('test.png')
-plt.show()
-
-#note, I've just sepected a subset of the time steps here (1st 1000 days) otherwise it takes for ever.
-my_cube = my_cube[0:1000]
 
-#averaging the data over an area, based on the example script on the wiki
-my_cube.coord('latitude').guess_bounds()
-my_cube.coord('longitude').guess_bounds()
-grid_areas = area_weights(my_cube)
-average_variable = my_cube[0:1000].collapsed(['latitude', 'longitude'],MEAN,weights=grid_areas)
-
-#plot the timeseries:
-qplt.plot(average_variable)
-plt.savefig('test2.png')
-plt.show()
-       ```
 
 ##### Extracting horizontal slices from a cube (e.g. at a given depth)