Logo Iris 1.12

Table Of Contents

Previous topic

4. Navigating a cube

Next topic

6. Plotting a cube

This Page

5. Subsetting a Cube

The Loading Iris cubes section of the user guide showed how to load data into multidimensional Iris cubes. However it is often necessary to reduce the dimensionality of a cube down to something more appropriate and/or manageable.

Iris provides several ways of reducing both the amount of data and/or the number of dimensions in your cube depending on the circumstance. In all cases the subset of a valid cube is itself a valid cube.

5.1. Cube extraction

A subset of a cube can be “extracted” from a multi-dimensional cube in order to reduce its dimensionality:

>>> import iris
>>> filename = iris.sample_data_path('space_weather.nc')
>>> cube = iris.load_cube(filename, 'electron density')
>>> equator_slice = cube.extract(iris.Constraint(grid_latitude=0))
>>> print(equator_slice)
electron density / (1E11 e/m^3)     (height: 29; grid_longitude: 31)
     Dimension coordinates:
          height                           x                   -
          grid_longitude                   -                   x
     Auxiliary coordinates:
          latitude                         -                   x
          longitude                        -                   x
     Scalar coordinates:
          grid_latitude: 0.0 degrees
          Conventions: CF-1.5

In this example we start with a 3 dimensional cube, with dimensions of height, grid_latitude and grid_longitude, and extract every point where the latitude is 0, resulting in a 2d cube with axes of height and grid_longitude.


Caution is required when using equality constraints with floating point coordinates such as grid_latitude. Printing the points of a coordinate does not necessarily show the full precision of the underlying number and it is very easy return no matches to a constraint when one was expected. This can be avoided by using a function as the argument to the constraint:

def near_zero(cell):
   """Returns true if the cell is between -0.1 and 0.1."""
   return -0.1 < cell < 0.1

equator_constraint = iris.Constraint(grid_latitude=near_zero)

Often you will see this construct in shorthand using a lambda function definition:

equator_constraint = iris.Constraint(grid_latitude=lambda cell: -0.1 < cell < 0.1)

The extract method could be applied again to the equator_slice cube to get a further subset.

For example to get a height of 9000 metres at the equator the following line extends the previous example:

equator_height_9km_slice = equator_slice.extract(iris.Constraint(height=9000))

The two steps required to get height of 9000 m at the equator can be simplified into a single constraint:

equator_height_9km_slice = cube.extract(iris.Constraint(grid_latitude=0, height=9000))

As we saw in Loading Iris cubes the result of iris.load() is a CubeList. The extract method also exists on a CubeList and behaves in exactly the same way as loading with constraints:

>>> import iris
>>> air_temp_and_fp_6 = iris.Constraint('air_potential_temperature', forecast_period=6)
>>> level_10 = iris.Constraint(model_level_number=10)
>>> filename = iris.sample_data_path('uk_hires.pp')
>>> cubes = iris.load(filename).extract(air_temp_and_fp_6 & level_10)
>>> print(cubes)
0: air_potential_temperature / (K)     (grid_latitude: 204; grid_longitude: 187)
>>> print(cubes[0])
air_potential_temperature / (K)     (grid_latitude: 204; grid_longitude: 187)
     Dimension coordinates:
          grid_latitude                           x                    -
          grid_longitude                          -                    x
     Auxiliary coordinates:
          surface_altitude                        x                    x
     Derived coordinates:
          altitude                                x                    x
     Scalar coordinates:
          forecast_period: 6.0 hours
          forecast_reference_time: 2009-11-19 04:00:00
          level_height: 395.0 m, bound=(360.0, 433.333) m
          model_level_number: 10
          sigma: 0.954993, bound=(0.958939, 0.95068)
          time: 2009-11-19 10:00:00
          STASH: m01s00i004
          source: Data from Met Office Unified Model
          um_version: 7.3

5.2. Cube iteration

A useful way of dealing with a Cube in its entirety is by iterating over its layers or slices. For example, to deal with a 3 dimensional cube (z,y,x) you could iterate over all 2 dimensional slices in y and x which make up the full 3d cube.:

import iris
filename = iris.sample_data_path('hybrid_height.nc')
cube = iris.load_cube(filename)
for yx_slice in cube.slices(['grid_latitude', 'grid_longitude']):

As the original cube had the shape (15, 100, 100) there were 15 latitude longitude slices and hence the line print(repr(yx_slice)) was run 15 times.


The order of latitude and longitude in the list is important; had they been swapped the resultant cube slices would have been transposed.

For further information see Cube.slices.

This method can handle n-dimensional slices by providing more or fewer coordinate names in the list to slices:

import iris
filename = iris.sample_data_path('hybrid_height.nc')
cube = iris.load_cube(filename)
for i, x_slice in enumerate(cube.slices(['grid_longitude'])):
   print(i, repr(x_slice))

The Python function enumerate() is used in this example to provide an incrementing variable i which is printed with the summary of each cube slice. Note that there were 1500 1d longitude cubes as a result of slicing the 3 dimensional cube (15, 100, 100) by longitude (i starts at 0 and 1500 = 15 * 100).


It is often useful to get a single 2d slice from a multidimensional cube in order to develop a 2d plot function, for example. This can be achieved by using the next() function on the result of slices:

first_slice = next(cube.slices(['grid_latitude', 'grid_longitude']))

Once the your code can handle a 2d slice, it is then an easy step to loop over all 2d slices within the bigger cube using the slices method.

5.3. Cube indexing

In the same way that you would expect a numeric multidimensional array to be indexed to take a subset of your original array, you can index a Cube for the same purpose.

Here are some examples of array indexing in numpy:

import numpy as np
# create an array of 12 consecutive integers starting from 0
a = np.arange(12)

print(a[0])     # first element of the array

print(a[-1])    # last element of the array

print(a[0:4])   # first four elements of the array (the same as a[:4])

print(a[-4:])   # last four elements of the array

print(a[::-1])  # gives all of the array, but backwards

# Make a 2d array by reshaping a
b = a.reshape(3, 4)

print(b[0, 0])  # first element of the first and second dimensions

print(b[0])     # first element of the first dimension (+ every other dimension)

# get the second element of the first dimension and all of the second dimension
# in reverse, by steps of two.
print(b[1, ::-2])

Similarly, Iris cubes have indexing capability:

import iris
filename = iris.sample_data_path('hybrid_height.nc')
cube = iris.load_cube(filename)


# get the first element of the first dimension (+ every other dimension)

# get the last element of the first dimension (+ every other dimension)

# get the first 4 elements of the first dimension (+ every other dimension)

# Get the first element of the first and third dimension (+ every other dimension)
print(cube[0, :, 0])

# Get the second element of the first dimension and all of the second dimension
# in reverse, by steps of two.
print(cube[1, ::-2])