What’s new in Iris 1.1
|Date:||7 Dec, 2012
This document explains the new/changed features of Iris in version 1.1.
(View all changes.)
With the release of Iris 1.1, we are introducing support for Mac OS X.
Version 1.1 also sees the first batch of performance enhancements, with
some notable improvements to netCDF/PP import.
Iris 1.1 features
A summary of the main features added with version 1.1:
- Support for Mac OS X.
- GRIB1 import now supports time units of “3 hours”.
- Fieldsfile import now supports unpacked and “CRAY” 32-bit packed data
in 64-bit Fieldsfiles.
- PP file import now supports “CRAY” 32-bit packed data.
- Various performance improvements, particularly for netCDF import,
PP import, and constraints.
- GRIB2 export now supports level types of altitude and height
(codes 102 and 103).
- iris.analysis.cartography.area_weights now supports non-standard
- PP file import now adds the “forecast_reference_time” for fields
where LBTIM is 11, 12, 13, 31, or 32.
- PP file import now supports LBTIM values of 1, 2, and 3.
- Fieldsfile import now has some support for ancillary files.
- Coordinate categorisation functions added for day-of-year and
- GRIB2 import now has partial support for probability data defined
with product template 4.9.
- PP export no longer attempts to set/overwrite the STASH code based on
- Cell comparisons now work consistently, which fixes a bug where
bounded_cell > point_cell compares the point to the bounds but,
point_cell < bounded_cell compares the points.
- Fieldsfile import now correctly recognises pre v3.1 and post v5.2
versions, which fixes a bug where the two were interchanged.
- iris.analysis.trajectory.interpolate now handles hybrid-height.
An add_day_of_year() categorisation
function has been added to the existing suite in
The conventional seasonal categorisation functions have been
complemented by two groups of functions which handle user-defined,
The first group of functions is:
These functions mimic their non-custom versions, but with the addition
of a seasons parameter which is used to define the custom seasons.
These seasons are defined by concatenating the single letter
abbreviations of the relevant, consecutive months.
For example, to categorise a Cube based on “winter” and “summer” months,
one might do:
>>> seasons = ['mamjja', 'sondjf']
>>> iris.coord_categorisation.add_custom_season(cube, 'time', seasons)
['ondjfm' 'ondjfm' 'mamjja' 'mamjja' 'mamjja' 'mamjja' 'mamjja' 'mamjja'
'ondjfm' 'ondjfm' 'ondjfm' 'ondjfm']
The other custom season function is:
This function adds a coordinate containing True/False values determined
by membership of a single custom season.