Iris-grib v0.19

The library iris-grib provides functionality for converting between weather and climate datasets that are stored as GRIB files and Iris cubes. GRIB files can be loaded as Iris cubes using iris-grib so that you can use Iris for analysing and visualising the contents of the GRIB files. Iris cubes can be saved to GRIB files using iris-grib.

The contents of iris-grib represent the former grib loading and saving capabilities of Iris itself. These capabilities have been separated into a discrete library so that Iris becomes less monolithic as a library.


To use iris-grib to load existing GRIB files we can make use of the load_cubes() function:

>>> import os
>>> import iris_sample_data
>>> import iris_grib
>>> cubes = iris_grib.load_cubes(os.path.join(iris_sample_data.path,
>>> print cubes
<generator object load_cubes at 0x7f69aba69d70>

As we can see, this returns a generator object. The generator object may be iterated over to access all the Iris cubes loaded from the GRIB file, or converted directly to a list:

>>> cubes = list(cubes)
>>> print cubes
[<iris 'Cube' of air_temperature / (K) (projection_y_coordinate: 200; projection_x_coordinate: 247)>]


There is no functionality in iris-grib that directly replicates iris.load_cube (that is, load a single cube directly rather than returning a length-one CubeList. Instead you could use the following, assuming that the GRIB file you have loaded contains data that can be loaded to a single cube:

>>> cube, = list(cubes)
>>> print cube
air_temperature / (K)               (projection_y_coordinate: 200; projection_x_coordinate: 247)
     Dimension coordinates:
          projection_y_coordinate                           x                             -
          projection_x_coordinate                           -                             x
     Scalar coordinates:
          forecast_period: 6 hours
          forecast_reference_time: 2013-05-20 00:00:00
          pressure: 101500.0 Pa
          time: 2013-05-20 06:00:00

This makes use of an idiom known as variable unpacking.


To use iris-grib to save Iris cubes to a GRIB file we can make use of the save_grib2() function:

>>> iris_grib.save_grib2(my_cube, 'my_file.grib2')


As the function name suggests, only saving to GRIB2 is supported.

Interconnectivity with Iris

You can use the functionality provided by iris-grib directly within Iris without having to explicitly import iris-grib, as long as you have both Iris and iris-grib available to your Python interpreter.

For example:

>>> import iris
>>> import iris_sample_data
>>> cube = iris.load_cube(iris.sample_data_path('polar_stereo.grib2'))

Similarly, you can save your cubes to a GRIB file directly from Iris using iris-grib:

>>>, 'my_file.grib2')

Getting Started

To ensure all iris-grib dependencies, it is sufficient to have installed Iris itself, and ecCodes .

The simplest way to install is with conda , using the conda-forge channel , with the command

$ conda install -c conda-forge iris-grib

Development sources are hosted at .


For recent changes, see Release Notes .

Indices and tables


See also: