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v1.12 (31 Jan 2017)#

This document explains the changes made to Iris for this release (View all changes.)

Features#

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.

A context manager is used to enable fast um loading in all the regular Iris load functions, such as iris.load() and iris.load_cube(), when loading data from UM file types. For example:

>>> 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 :

  • a 78 Gb fieldsfile of 51,840 fields loads in about 13 rather than 190 seconds.

  • a set of 25 800Mb PP files loads in about 21 rather than 220 seconds.

You can load data with structured loading and compare the results with those from “normal” loading to check whether they are equivalent.

  • The results will normally differ, if at all, only in having dimensions in a different order or a different choice of dimension coordinates. In these cases, structured loading can be used with confidence.

  • Ordinary Fieldsfiles (i.e. model outputs) are generally suitable for structured loading. Many PP files also are, especially if produced directly from Fieldsfiles, and retaining the same field ordering.

  • Some inputs however (generally PP) will be unsuitable for structured loading : For instance if a particular combination of vertical levels and time has been omitted, or some fields appear out of order.

  • There are also some known unsupported cases, including data which is produced on pseudo-levels. See the detail documentation on this.

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:

  • results are often somewhat different, especially regarding the order of dimensions and the choice of dimension coordinates.

  • although both constraints and user callbacks are supported, callback routines will generally need to be re-written. This is because a ‘raw’ cube in structured loading generally covers multiple PPfields, which therefore need to be handled as a collection : A grouping object containing them is passed to the callback ‘field’ argument. An example showing callbacks suitable for both normal and structured loading can be seen here.

For full details, see : iris.fileformats.um.structured_um_loading().

  • A skip pattern is introduced to the fields file loader, such that fields which cannot be turned into iris PPField instances are skipped and the remaining fields are loaded. This especially applies to certain types of files that can contain fields with a non-standard LBREL value : Iris can now load such a file, skipping the unreadable field and printing a warning message.

  • Iris can now load PP files containing a PP field whose LBLREC value does not match the field length recorded in the file. A warning message is printed, and all fields up to the offending one are loaded and returned. Previously, this simply resulted in an unrecoverable error.

  • The transpose method of a Cube now results in a lazy transposed view of the original rather than realising the data then transposing it.

  • The iris.analysis.cartography.area_weights() function is now more accurate for single precision input bounds.

  • Iris is now able to read seconds in datetimes provided in NAME trajectory files.

  • Optimisations to trajectory interpolations have resulted in a significant speed improvement.

  • Many new and updated translations between CF spec and STASH codes.

Deprecations#

Documentation#

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