This document explains the new/changed features of Iris in version 1.12 (View all changes.)
Showcase Feature: New regridding schemes
A new regridding scheme, iris.analysis.UnstructuredNearest, performs nearest-neighbour regridding from “unstructured” onto “structured” grids. Here, “unstructured” means that the data has X and Y coordinate values defined at each horizontal location, instead of the independent X and Y dimensions that constitute a structured grid. For example, data sampled on a trajectory or a tripolar ocean grid would be unstructured.
In addition, added experimental ProjectedUnstructured regridders which use scipy.interpolate.griddata to regrid unstructured data (see iris.experimental.regrid.ProjectedUnstructuredLinear and iris.experimental.regrid.ProjectedUnstructuredNearest). The essential purpose is the same as iris.analysis.UnstructuredNearest. This scheme, by comparison, is generally faster, but less accurate.
Showcase Feature: Fast UM file loading
Support has been added for accelerated loading of UM files (PP and Fieldsfile), when these have a suitable regular “structured” form.
>>> import iris >>> filepath = iris.sample_data_path('uk_hires.pp') >>> from iris.fileformats.um import structured_um_loading >>> with structured_um_loading(): ... cube = iris.load_cube(filepath, 'air_potential_temperature')
This approach can deliver loading which is 10 times faster or more. For example :
You can load data with structured loading and compare the results with those from “normal” loading to check whether they are equivalent.
It is the user’s responsibility to use structured loading only with suitable inputs. Otherwise, odd behaviour and even incorrect loading can result, as input files are not checked as fully as in a normal load.
Although the user loading call for structured loading can be just the same, and the returned results are also often identical, structured loading is not in fact an exact identical replacement for normal loading:
For full details, see : iris.fileformats.um.structured_um_loading().