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.

Hint

There are details at iris.CombineOptions on how Iris works to load fewer and larger cubes : The iris.COMBINE_POLICY object allows the user to control how cubes are combined during the loading process. See the documentation of the iris.CombineOptions class for details.

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 / (K)     (time: 3; model_level_number: 7; grid_latitude: 204; grid_longitude: 187)
1: surface_altitude / (m)              (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 were 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 iris.cube.CubeList (even if it only contains one iris.cube.Cube - see Strict Loading). Anything that can be done with a Python list can be done with an iris.cube.CubeList.

The order of this list should not be relied upon. Ways of loading a specific cube or cubes are covered in Constrained Loading and Strict Loading.

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 can 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 / (K)     (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:
        forecast_reference_time     2009-11-19 04:00:00
    Attributes:
        STASH                       m01s00i004
        source                      'Data from Met Office Unified Model'
        um_version                  '7.3'

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 Iris Data Structures section.

Note

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

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)

Note

The cubes returned will not necessarily be in the same order as the order of the filenames.

Lazy Loading#

In fact when Iris loads data from most file types, it normally only reads the essential descriptive information or metadata : the bulk of the actual data content will only be loaded later, as it is needed. This is referred to as ‘lazy’ data. It allows loading to be much quicker, and to occupy less memory.

For more on the benefits, handling and uses of lazy data, see Real and Lazy Data.

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 matching name will be returned:

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

Note that, the provided name will match against either the standard name, long name, NetCDF variable name or STASH metadata of a cube. Therefore, the previous example using the surface_altitude standard name constraint can also be achieved using the STASH value of m01s00i033:

filename = iris.sample_data_path('uk_hires.pp')
cubes = iris.load(filename, 'm01s00i033')

If further specific name constraint control is required i.e., to constrain against a combination of standard name, long name, NetCDF variable name and/or STASH metadata, consider using the iris.NameConstraint. For example, to constrain against both a standard name of surface_altitude and a STASH of m01s00i033:

filename = iris.sample_data_path('uk_hires.pp')
constraint = iris.NameConstraint(standard_name='surface_altitude', STASH='m01s00i033')
cubes = iris.load(filename, constraint)

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', 'surface_altitude'])

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)

Further details on using iris.Constraint are discussed later in Cube Extraction.

Strict Loading#

The iris.load_cube() and iris.load_cubes() functions are similar to iris.load() except they can only return one cube per constraint. The iris.load_cube() function accepts a single constraint and returns a single cube. The iris.load_cubes() function accepts any number of constraints and returns a list of cubes (as an iris.cube.CubeList). Providing no constraints to iris.load_cube() or iris.load_cubes() 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_cube(filename)
>>> print(cube)
air_temperature / (K)                 (latitude: 73; longitude: 96)
     Dimension coordinates:
          latitude                           x              -
          longitude                          -              x
...
     Cell methods:
          0                           time: mean

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_cube(filename)
Traceback (most recent call last):
...
iris.exceptions.ConstraintMismatchError: Expected exactly one cube, found 2.

Note

All the load functions 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_cube() and iris.load_cubes() 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_cube(filename, 'air_potential_temperature')
print(air_pot_temp)

Should the file not produce 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_cubes(filename, ['surface_altitude', 'air_potential_temperature'])

The result of iris.load_cubes() 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

Load Problems#

The Iris data model - see Iris Data Structures - is highly flexible, but there are many examples of file content that will not be loaded into the data model. These fall into two categories:

  1. Malformations in the file.

    • For example: a variable that is referenced, but is missing.

  2. Content not conformant with the standard for that file type.

    • Most commonly NetCDF file content that is not compliant with the CF conventions - the basis for the Iris data model. But Iris also relies on standards for other file formats such as GRIB Format and Post Processing (PP) Format.

    • Content in a non-NetCDF file that Iris does not know how to map onto CF concepts.

    • CF conventions concepts that Iris does not support yet.

Note

The below approach was introduced in Iris 3.12, and widely used in CF-NetCDF loading by Iris 3.13. We hope to continue spreading it to other file formats, and overlooked corner cases, in future releases.

When Iris encounters problem content in a file, it will not make ‘best efforts’ to parse the content, but will instead redirect it to iris.loading.LOAD_PROBLEMS, as well as issuing a warning to the user. The user is then free to add any operations to their script(s) for incorporating LOAD_PROBLEMS content into the Iris data model, as they see fit.

Find out more about why we chose this approach: Loading Invalid File Content

The below example has ‘booby trapped’ the Iris loading process, to give an impression of the user experience when loading problems are encountered. The example shows typical LOAD_PROBLEMS content, and a deeper inspection of one redirected object. Much more detail is in the API documentation for: iris.loading.LoadProblems.

>>> cube_a1b = iris.load_cube(iris.sample_data_path("A1B_north_america.nc"))
iris/...IrisLoadWarning: Not all file objects were parsed correctly. See iris.loading.LOAD_PROBLEMS for details.
  warnings.warn(message, category=IrisLoadWarning)
>>> print(iris.loading.LOAD_PROBLEMS)
<iris.loading.LoadProblems object at ...>:
  .../A1B_north_america.nc: "'air_temperature' is not a valid standard_name", {'standard_name': 'air_temperature'}
  .../A1B_north_america.nc: "Example coordinate error", unknown / (unknown)                 (-- : 240)
>>> last_problem = iris.loading.LOAD_PROBLEMS.problems[-1]
>>> print(last_problem.loaded)
unknown / (unknown)                 (-- : 240)
    Attributes:...
        IRIS_RAW                    {'axis': 'T', ...}
>>> attributes = last_problem.loaded.attributes[
...     iris.common.LimitedAttributeDict.IRIS_RAW
... ]
>>> pprint(attributes)
{'axis': 'T',
 'bounds': 'time_bnds',
 'calendar': '360_day',
 'standard_name': 'time',
 'units': 'hours since 1970-01-01 00:00:00',
 'var_name': 'time'}