NetCDF I/O Handling in Iris

This document provides a basic account of how Iris loads and saves NetCDF files.

Under Construction

This document is still a work in progress, so might include blank or unfinished sections, watch this space!

Chunk Control

Default Chunking

Chunks are, by default, optimised by Iris on load. This will automatically decide the best chunksize for your data without any user input. This is calculated based on a number of factors, including:

• File Variable Chunking

• Full Variable Shape

• Dask Default Chunksize

• Dimension Order: Earlier (outer) dimensions will be prioritised to be split over later (inner) dimensions.

>>> cube = iris.load_cube(tmp_filepath)
>>>
>>> print(cube.shape)
(240, 37, 49)
>>> print(cube.core_data().chunksize)
(60, 37, 49)

For more user control, functionality was updated in PR #5588, with the creation of the iris.fileformats.netcdf.loader.CHUNK_CONTROL class.

Custom Chunking: Set

There are three context manangers within CHUNK_CONTROL. The most basic is set(). This allows you to specify the chunksize for each dimension, and to specify a var_name specifically to change.

Using -1 in place of a chunksize will ensure the chunksize stays the same as the shape, i.e. no optimisation occurs on that dimension.

>>> with CHUNK_CONTROL.set("air_temperature", time=180, latitude=-1, longitude=25):
...     cube = iris.load_cube(tmp_filepath)
>>>
>>> print(cube.core_data().chunksize)
(180, 37, 25)

Note that var_name is optional, and that you don’t need to specify every dimension. If you specify only one dimension, the rest will be optimised using Iris’ default behaviour.

>>> with CHUNK_CONTROL.set(longitude=25):
...     cube = iris.load_cube(tmp_filepath)
>>>
>>> print(cube.core_data().chunksize)
(120, 37, 25)

Custom Chunking: From File

The second context manager is from_file(). This takes chunksizes as defined in the NetCDF file. Any dimensions without specified chunks will default to Iris optimisation.

>>> with CHUNK_CONTROL.from_file():
...     cube = iris.load_cube(tmp_filepath)
>>>
>>> print(cube.core_data().chunksize)
(120, 37, 49)

Custom Chunking: As Dask

The final context manager, as_dask(), bypasses Iris’ optimisation all together, and will take its chunksizes from Dask’s behaviour.

>>> with CHUNK_CONTROL.as_dask():
...    cube = iris.load_cube(tmp_filepath)
>>>
>>> print(cube.core_data().chunksize)
(70, 37, 49)

Split Attributes

TBC

Deferred Saving

TBC

Guessing Coordinate Axes

Iris will attempt to add an axis attribute when saving any coordinate variable in a NetCDF file. E.g:

float longitude(longitude) ;
    longitude:axis = "X" ;

This is achieved by calling iris.util.guess_coord_axis() on each coordinate being saved.

Disabling Axis-Guessing

For some coordinates, guess_coord_axis() will derive an axis that is not appropriate. If you have such a coordinate, you can disable axis-guessing by setting the coordinate’s ignore_axis property to True.

One example (from SciTools/iris#5003) is a coordinate describing pressure thresholds, measured in hecto-pascals. Iris interprets pressure units as indicating a Z-dimension coordinate, since pressure is most commonly used to describe altitude/depth. But a pressure threshold coordinate is instead describing alternate scenarios - not a spatial dimension at all - and it is therefore inappropriate to assign an axis to it.

Worked example:

>>> from iris.coords import DimCoord
>>> from iris.util import guess_coord_axis
>>> my_coord = DimCoord(
...    points=[1000, 1010, 1020],
...    long_name="pressure_threshold",
...    units="hPa",
... )
>>> print(guess_coord_axis(my_coord))
Z
>>> my_coord.ignore_axis = True
>>> print(guess_coord_axis(my_coord))
None