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%matplotlib inline

Iris introduction course

2. Loading and Saving

Learning Outcome: by the end of this section, you will be able to use Iris to load datasets from disk as Iris cubes and save Iris cubes back to disk.

Duration: 30 minutes.

Overview:
2.1 Iris Load Functions
2.2 Saving Cubes
2.3 Exercise
2.4 Summary of the Section



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import iris

2.1 Iris Load Functions

There are three main load functions in Iris: load, load_cube and load_cubes.

load is a general purpose loading function. Typically this is where all data analysis will start, before more loading is refined with the more controlled loading from the other two functions.
load_cube returns a single cube from the given source(s) and constraint. There will be exactly one cube, or an exception will be raised.
load_cubes returns a cubelist of cubes from the given sources(s) and constraint(s). There will be exactly one cube per constraint, or an exception will be raised.

Note: load_cube is a special case of load.

Let's compare the result of calling load and load_cube. We start by selecting the air_temp.pp file from Iris' sample data.



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fname = iris.sample_data_path('air_temp.pp')

If we give this filepath to load, we see that Iris returns a cubelist.



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cubes = iris.load(fname)
print(type(cubes))
print(cubes)

A CubeList is a specialised version of a Python list object : If you look at the CubeList reference documentation, you can see that it has the behaviours of an ordinary list, like len(cubes), cubes.remove(x), cubes[index]. However, it prints out in a special way and has additional cube-specific methods, some of which we discuss in later sections (extract, merge and concatenate).

If we pass this filepath instead to iris.load_cube, we see that Iris then returns a cube.



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cube = iris.load_cube(fname)
print(type(cube))
print(cube)

If we compare the first cube in the cubelist returned by calling load and the cube returned by calling load_cube we see that they are equal.



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cubes[0] == cube

Exercise:

Try loading in the `uk_hires.pp` from Iris' sample data, first with iris.load then iris.load_cube. Why does iris.load_cube fail if you only supply the filepath?



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2.2 Saving Cubes

The iris.save function provides a convenient interface to save Cube and CubeList instances.

Below we load the uk_hires.pp file from Iris' provided sample data which returns a cubelist of the cubes that were produced from that file. We then save this cubelist out to netcdf.



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fname = iris.sample_data_path('uk_hires.pp')
cubes = iris.load(fname)
iris.save(cubes, 'saved_cubes.nc')

We can check the ncdump to see what Iris saved:



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!ncdump -h saved_cubes.nc | head -n 20
!rm saved_cubes.nc

Extra keywords can be passed to specific fileformat savers.

Exercise:

Take a look at the above link to the `iris.save` documentation, to see which fileformats iris can save to.

2.3 Section Review Exercise

1. Load the file in iris.sample_data_path('atlantic_profiles.nc'), using iris.load_cube to load in the sea_water_potential_temperature cube only.



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# space for user code ...



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# SAMPLE SOLUTION
# %load solutions/iris_exercise_2.3a

2. Go to the Iris reference documentation for iris.save. What keywords are accepted to iris.save when saving a PP file?



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# SAMPLE SOLUTION
# %load solutions/iris_exercise_2.3b

2.4 Section Summary : Loading and Saving

In this section we learnt:

Iris has different loading functions for different purposes, for example iris.load should be used for exploratory data analysis
Iris supports saving to three fileformats: netCDF, GRIB2 and PP