Loading Iris Cubes#
To load a single file into a list of Iris cubes
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.
iris.load() function automatically recognises the format
of the given files and attempts to produce Iris Cubes from their contents.
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:
surface_altitude cube was 2 dimensional with:
the two dimensions have extents of 204 and 187 respectively and are represented by the
air_potential_temperature cubes were 4 dimensional with:
the same length
timedimension of length 3
model_level_numberdimension of length 7
The result of
iris.load() is always a
(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
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.
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
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 >>> 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.
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
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
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)
The cubes returned will not necessarily be in the same order as the order of the filenames.
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.
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
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
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
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'])
iris.Constraint class can be used to restrict coordinate values
on load. For example, to constrain the load to match
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
discussed later in Cube Extraction.
iris.load_cubes() functions are
iris.load() except they can only return
one cube per constraint.
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
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.
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
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
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.
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