Iris 0.9
In the section Reducing a Cube we saw how to extract a subset of a cube in order to reduce either its dimensionality or its resolution. Instead of downsampling the data, a similar goal can be achieved using statistical operations over all of the data. Suppose we have a cube:
>>> import iris
>>> filename = iris.sample_data_path('uk_hires.pp')
>>> cube = iris.load_strict(filename, 'air_potential_temperature')
>>> print cube
air_potential_temperature (time: 3; model_level_number: 7; grid_latitude: 204; grid_longitude: 187)
Dimension coordinates:
time x - - -
model_level_number - x - -
grid_latitude - - x -
grid_longitude - - - x
Auxiliary coordinates:
forecast_period x - - -
level_height - x - -
sigma - x - -
surface_altitude - - x x
Derived coordinates:
altitude - x x x
Scalar coordinates:
source: Data from Met Office Unified Model 7.03
Attributes:
STASH: m01s00i004
In this case we have a 4 dimensional cube; to mean the vertical (z) dimension down to a single valued extent we can pass the coordinate name and the aggregation definition to the Cube.collapsed() method:
>>> import iris.analysis
>>> vertical_mean = cube.collapsed('model_level_number', iris.analysis.MEAN)
>>> print vertical_mean
air_potential_temperature (time: 3; grid_latitude: 204; grid_longitude: 187)
Dimension coordinates:
time x - -
grid_latitude - x -
grid_longitude - - x
Auxiliary coordinates:
forecast_period x - -
surface_altitude - x x
Derived coordinates:
altitude - x x
Scalar coordinates:
level_height: Cell(point=696.66663, bound=(0.0, 1393.3333)) m
model_level_number: Cell(point=10, bound=(1, 19))
sigma: Cell(point=0.92292976, bound=(0.84585959, 1.0))
source: Data from Met Office Unified Model 7.03
Attributes:
STASH: m01s00i004
history: Mean of air_potential_temperature over model_level_number
Cell methods:
mean: model_level_number
Similarly other analysis operators such as MAX, MIN and STD_DEV can be used instead of MEAN, see iris.analysis for a full list of currently supported operators.
Some operators support additional keywords to the cube.collapsed method. For example, iris.analysis.MEAN supports a weights keyword which can be combined with iris.analysis.cartography.area_weights() to calculate an area average.
Let’s use the same data as was loaded in the previous example. Since grid_latitude and grid_longitude were both point coordinates we must guess bound positions for them in order to calculate the area of the grid boxes:
import iris.analysis.cartography
cube.coord('grid_latitude').guess_bounds()
cube.coord('grid_longitude').guess_bounds()
grid_areas = iris.analysis.cartography.area_weights(cube)
These areas can now be passed to the collapsed method as weights:
>>> new_cube = cube.collapsed(['grid_longitude', 'grid_latitude'], iris.analysis.MEAN, weights=grid_areas)
>>> print new_cube
air_potential_temperature (time: 3; model_level_number: 7)
Dimension coordinates:
time x -
model_level_number - x
Auxiliary coordinates:
forecast_period x -
level_height - x
sigma - x
Derived coordinates:
altitude - x
Scalar coordinates:
grid_latitude: Cell(point=1.5145501, bound=(0.14430022, 2.8848)) degrees
grid_longitude: Cell(point=358.74948, bound=(357.49399, 360.00497)) degrees
source: Data from Met Office Unified Model 7.03
surface_altitude: Cell(point=399.625, bound=(-14.0, 813.25)) m
Attributes:
STASH: m01s00i004
history: Mean of air_potential_temperature over grid_longitude, grid_latitude
Cell methods:
mean: grid_longitude, grid_latitude
Instead of completely collapsing a dimension, other methods can be applied to reduce or filter the number of data points of a particular dimension.
An aggregation on a group of coordinate values can be achieved with Cube.aggregated_by, which can be combined with the iris.coord_categorisation module to group the coordinate in the first place.
First, let’s create two coordinates on a cube which represent the climatological seasons and the season year respectively:
import iris
import iris.coord_categorisation
filename = iris.sample_data_path('ostia_monthly.nc')
cube = iris.load_strict(filename, 'surface_temperature')
iris.coord_categorisation.add_season(cube, 'time', name='clim_season')
iris.coord_categorisation.add_season_year(cube, 'time', name='season_year')
Printing this cube now shows that two extra coordinates exist on the cube:
>>> print cube
surface_temperature (time: 54; latitude: 18; longitude: 432)
Dimension coordinates:
time x - -
latitude - x -
longitude - - x
Auxiliary coordinates:
clim_season x - -
forecast_reference_time x - -
season_year x - -
Scalar coordinates:
forecast_period: 0 hours
Attributes:
Conventions: CF-1.5
STASH: m01s00i024
history: Mean of surface_temperature aggregated over month, year
Cell methods:
mean: month, year
These two coordinates can now be used as groups over which to do an aggregation:
>>> annual_seasonal_mean = cube.aggregated_by(['clim_season', 'season_year'], iris.analysis.MEAN)
>>> print repr(annual_seasonal_mean)
<iris 'Cube' of surface_temperature (*ANONYMOUS*: 19; latitude: 18; longitude: 432)>
The primary change in the cube is that the cube’s data has shrunk on the t axis as a result of the meaning aggregation. We have now collapsed all repeating copies of season (DJF etc.) and year to represent a single position in the t axis. We can see this by printing the first 10 values of the original coordinates:
>>> print cube.coord('clim_season')[:10].points
['mam' 'mam' 'jja' 'jja' 'jja' 'son' 'son' 'son' 'djf' 'djf']
>>> print cube.coord('season_year')[:10].points
[2006 2006 2006 2006 2006 2006 2006 2006 2007 2007]
And then comparing with the first 10 values of the new cube’s coordinates:
>>> print annual_seasonal_mean.coord('clim_season')[:10].points
['mam' 'jja' 'son' 'djf' 'mam' 'jja' 'son' 'djf' 'mam' 'jja']
>>> print annual_seasonal_mean.coord('season_year')[:10].points
[2006 2006 2006 2007 2007 2007 2007 2008 2008 2008]
Because the original data started in April 2006 we have some incomplete seasons (e.g. there were only two months worth of data for mam 2006). In this case we can fix this by removing all of the resultant times which do not cover a three month period (n.b. 3 months = 3 * 30 * 24 = 2160 hours):
>>> spans_three_months = lambda time: (time.bound[1] - time.bound[0]) == 2160
>>> three_months_bound = iris.Constraint(time=spans_three_months)
>>> print annual_seasonal_mean.extract(three_months_bound)
surface_temperature (*ANONYMOUS*: 3; latitude: 18; longitude: 432)
Dimension coordinates:
latitude - x -
longitude - - x
Auxiliary coordinates:
clim_season x - -
forecast_reference_time x - -
season_year x - -
time x - -
Scalar coordinates:
forecast_period: 0 hours
Attributes:
Conventions: CF-1.5
STASH: m01s00i024
history: Mean of surface_temperature aggregated over month, year
Mean of surface_temperature...
Cell methods:
mean: month, year
mean: clim_season, season_year
The final result now represents the seasonal mean temperature for 63 seasons starting from March April May 1990.