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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#


Deferred Saving#


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))
>>> my_coord.ignore_axis = True
>>> print(guess_coord_axis(my_coord))