2. Loading Iris cubes

To load a single file into a list of Iris cubes the iris.load() function is used:

import iris
filename = '/path/to/file'
cubes = iris.load(filename)

Iris will attempt to return as few cubes as possible by collecting together multiple fields with a shared standard name into a single multidimensional cube.

The iris.load() function automatically recognises the format of the given files and attempts to produce Iris Cubes from their contents.

Note

Currently there is support for CF NetCDF, GRIB 1 & 2, PP and FieldsFiles file formats with a framework for this to be extended to custom formats.

In order to find out what has been loaded, the result can be printed:

>>> import iris
>>> filename = iris.sample_data_path('uk_hires.pp')
>>> cubes = iris.load(filename)
>>> print cubes
0: air_potential_temperature           (time: 3; model_level_number: 7; grid_latitude: 204; grid_longitude: 187)
1: surface_altitude                    (grid_latitude: 204; grid_longitude: 187)

This shows that there were 2 cubes as a result of loading the file, they were: air_potential_temperature and surface_altitude.

The surface_altitude cube was 2 dimensional with:

• the two dimensions have extents of 204 and 187 respectively and are represented by the grid_latitude and grid_longitude coordinates.

The air_potential_temperature cubes was 4 dimensional with:

• the same length grid_latitude and grid_longitude dimensions as surface_altitide
• a time dimension of length 3
• a model_level_number dimension of length 7

Note

The result of iris.load() is always a list of cubes. Anything that can be done with a Python list can be done with the resultant list of cubes.

Hint

Throughout this user guide you will see the function iris.sample_data_path being used to get the filename for the resources used in the examples. The result of this function is just a string.

Using this function allows us to provide examples which will work across platforms and with data installed in different locations, however in practice you will want to use your own strings:

filename = '/path/to/file'
cubes = iris.load(filename)

To get the air potential temperature cube from the list of cubes returned by iris.load() in the previous example, list indexing could be used:

>>> import iris
>>> filename = iris.sample_data_path('uk_hires.pp')
>>> cubes = iris.load(filename)
>>> # get the first cube (list indexing is 0 based)
>>> air_potential_temperature = cubes[0]
>>> print air_potential_temperature
air_potential_temperature           (time: 3; model_level_number: 7; grid_latitude: 204; grid_longitude: 187)
     Dimension coordinates:
          time                           x                      -                 -                    -
          model_level_number             -                      x                 -                    -
          grid_latitude                  -                      -                 x                    -
          grid_longitude                 -                      -                 -                    x
     Auxiliary coordinates:
          forecast_period                x                      -                 -                    -
          level_height                   -                      x                 -                    -
          sigma                          -                      x                 -                    -
          surface_altitude               -                      -                 x                    x
     Derived coordinates:
          altitude                       -                      x                 x                    x
     Scalar coordinates:
          source: Data from Met Office Unified Model 7.03
     Attributes:
          STASH: m01s00i004

Notice that the result of printing a cube is a little more verbose than it was when printing a list of cubes. In addition to the very short summary which is provided when printing a list of cubes, information is provided on the coordinates which constitute the cube in question. This was the output discussed at the end of the Introduction section.

Note

Dimensioned coordinates will have a dimension marker x in the appropriate column for each cube data dimension that they describe.

2.1. Loading multiple files

To load more than one file into a list of cubes, a list of filenames can be provided to iris.load():

filenames = [iris.sample_data_path('uk_hires.pp'),
             iris.sample_data_path('air_temp.pp')]
cubes = iris.load(filenames)

It is also possible to load one or more files with wildcard substitution using the expansion rules defined fnmatch.

For example, to match zero or more characters in the filename, star wildcards can be used:

filename = iris.sample_data_path('GloSea4', '*.pp')
cubes = iris.load(filename)

2.2. Constrained loading

Given a large dataset, it is possible to restrict or constrain the load to match specific Iris cube metadata. Constrained loading provides the ability to generate a cube from a specific subset of data that is of particular interest.

As we have seen, loading the following file creates several Cubes:

filename = iris.sample_data_path('uk_hires.pp')
cubes = iris.load(filename)

Specifying a name as a constraint argument to iris.load() will mean only cubes with a matching name will be returned:

filename = iris.sample_data_path('uk_hires.pp')
cubes = iris.load(filename, 'specific_humidity')

To constrain the load to multiple distinct constraints, a list of constraints can be provided. This is equivalent to running load once for each constraint but is likely to be more efficient:

filename = iris.sample_data_path('uk_hires.pp')
cubes = iris.load(filename, ['air_potential_temperature', 'specific_humidity'])

The iris.Constraint class can be used to restrict coordinate values on load. For example, to constrain the load to match a specific model_level_number:

filename = iris.sample_data_path('uk_hires.pp')
level_10 = iris.Constraint(model_level_number=10)
cubes = iris.load(filename, level_10)

Constraints can be combined using & to represent a more restrictive constraint to load:

filename = iris.sample_data_path('uk_hires.pp')
forecast_6 = iris.Constraint(forecast_period=6)
level_10 = iris.Constraint(model_level_number=10)
cubes = iris.load(filename, forecast_6 & level_10)

As well as being able to combine constraints using &, the iris.Constraint class can accept multiple arguments, and a list of values can be given to constrain a coordinate to one of a collection of values:

filename = iris.sample_data_path('uk_hires.pp')
level_10_or_12_fp_6 = iris.Constraint(model_level_number=[10, 16], forecast_period=6)
cubes = iris.load(filename, level_10_or_16_fp_6)

A common requirement is to limit the value of a coordinate to a specific range, this can be achieved by passing the constraint a function:

def bottom_16_levels(cell):
   # return True or False as to whether the cell in question should be kept
   return cell <= 16

filename = iris.sample_data_path('uk_hires.pp')
level_lt_16 = iris.Constraint(model_level_number=bottom_16_levels)
cubes = iris.load(filename, level_lt_16)

Note

As with many of the examples later in this documentation, the simple function above can be conveniently written as a lambda function on a single line:

bottom_16_levels = lambda cell: cell <= 16

Cube attributes can also be part of the constraint criteria. Supposing a cube attribute of STASH existed, as is the case when loading PP files, then specific STASH codes can be filtered:

filename = iris.sample_data_path('uk_hires.pp')
level_10_with_stash = iris.AttributeConstraint(STASH='m01s00i004') & iris.Constraint(model_level_number=10)
cubes = iris.load(filename, level_10_with_stash)

See also

For advanced usage there are further examples in the iris.Constraint reference documentation.

2.3. Strict loading

The iris.load_strict() function is similar to iris.load() except that it can only return one cube per constraint. Providing no constraints to iris.load_strict() is equivalent to requesting exactly one cube of any type.

A single cube is loaded in the following example:

filename = iris.sample_data_path('air_temp.pp')
cube = iris.load_strict(filename)
print cube

However, when attempting to load data which would result in anything other than one cube, an exception is raised:

filename = iris.sample_data_path('uk_hires.pp')
cube = iris.load_strict(filename)

Note

iris.load_strict() and iris.load() share many of the same features, hence multiple files could be loaded with wildcard filenames or by providing a list of filenames.

The strict nature of iris.load_strict() means that, when combined with constrained loading, it is possible to ensure that precisely what was asked for on load is given - otherwise an exception is raised. This fact can be utilised to make code only run successfully if the data provided has the expected criteria.

For example, suppose that code needed air_potential_temperature in order to run:

import iris
filename = iris.sample_data_path('uk_hires.pp')
air_pot_temp = iris.load_strict(filename, 'air_potential_temperature')
print air_pot_temp

Should the file not contain exactly one cube with a standard name of air potential temperature, an exception will be raised.

Similarly, supposing a routine needed both ‘surface_altitude’ and ‘air_potential_temperature’ to be able to run:

import iris
filename = iris.sample_data_path('uk_hires.pp')
altitude_cube, pot_temp_cube = iris.load_strict(filename, ['surface_altitude', 'air_potential_temperature'])

The result of iris.load_strict() in this case will be a list of 2 cubes ordered by the constraints provided. Multiple assignment has been used to put these two cubes into separate variables.

Note

In Python, lists of a pre-known length and order can be exploited using multiple assignment:

>>> number_one, number_two = [1, 2]
>>> print number_one
1
>>> print number_two
2