The Loading Iris cubes section of the user guide showed how to load data into multidimensional Iris cubes. However it is often necessary to reduce the dimensionality of a cube down to something more appropriate and/or manageable.
Iris provides several ways of reducing both the amount of data and/or the number of dimensions in your cube depending on the circumstance. In all cases the subset of a valid cube is itself a valid cube.
A subset of a cube can be “extracted” from a multi-dimensional cube in order to reduce its dimensionality:
>>> import iris >>> filename = iris.sample_data_path('space_weather.nc') >>> cube = iris.load_cube(filename, 'electron density') >>> equator_slice = cube.extract(iris.Constraint(grid_latitude=0)) >>> print(equator_slice) electron density / (1E11 e/m^3) (height: 29; grid_longitude: 31) Dimension coordinates: height x - grid_longitude - x Auxiliary coordinates: latitude - x longitude - x Scalar coordinates: grid_latitude: 0.0 degrees Attributes: Conventions: CF-1.5
In this example we start with a 3 dimensional cube, with dimensions of height, grid_latitude and grid_longitude, and extract every point where the latitude is 0, resulting in a 2d cube with axes of height and grid_longitude.
Caution is required when using equality constraints with floating point coordinates such as grid_latitude. Printing the points of a coordinate does not necessarily show the full precision of the underlying number and it is very easy return no matches to a constraint when one was expected. This can be avoided by using a function as the argument to the constraint:
def near_zero(cell): """Returns true if the cell is between -0.1 and 0.1.""" return -0.1 < cell < 0.1 equator_constraint = iris.Constraint(grid_latitude=near_zero)
Often you will see this construct in shorthand using a lambda function definition:
equator_constraint = iris.Constraint(grid_latitude=lambda cell: -0.1 < cell < 0.1)
The extract method could be applied again to the equator_slice cube to get a further subset.
For example to get a height of 9000 metres at the equator the following line extends the previous example:
equator_height_9km_slice = equator_slice.extract(iris.Constraint(height=9000)) print(equator_height_9km_slice)
The two steps required to get height of 9000 m at the equator can be simplified into a single constraint:
equator_height_9km_slice = cube.extract(iris.Constraint(grid_latitude=0, height=9000)) print(equator_height_9km_slice)
>>> import iris >>> air_temp_and_fp_6 = iris.Constraint('air_potential_temperature', forecast_period=6) >>> level_10 = iris.Constraint(model_level_number=10) >>> filename = iris.sample_data_path('uk_hires.pp') >>> cubes = iris.load(filename).extract(air_temp_and_fp_6 & level_10) >>> print(cubes) 0: air_potential_temperature / (K) (grid_latitude: 204; grid_longitude: 187) >>> print(cubes) air_potential_temperature / (K) (grid_latitude: 204; grid_longitude: 187) Dimension coordinates: grid_latitude x - grid_longitude - x Auxiliary coordinates: surface_altitude x x Derived coordinates: altitude x x Scalar coordinates: forecast_period: 6.0 hours forecast_reference_time: 2009-11-19 04:00:00 level_height: 395.0 m, bound=(360.0, 433.333) m model_level_number: 10 sigma: 0.954993, bound=(0.958939, 0.95068) time: 2009-11-19 10:00:00 Attributes: STASH: m01s00i004 source: Data from Met Office Unified Model um_version: 7.3
A useful way of dealing with a Cube in its entirety is by iterating over its layers or slices. For example, to deal with a 3 dimensional cube (z,y,x) you could iterate over all 2 dimensional slices in y and x which make up the full 3d cube.:
import iris filename = iris.sample_data_path('hybrid_height.nc') cube = iris.load_cube(filename) print(cube) for yx_slice in cube.slices(['grid_latitude', 'grid_longitude']): print(repr(yx_slice))
As the original cube had the shape (15, 100, 100) there were 15 latitude longitude slices and hence the line print(repr(yx_slice)) was run 15 times.
The order of latitude and longitude in the list is important; had they been swapped the resultant cube slices would have been transposed.
For further information see Cube.slices.
This method can handle n-dimensional slices by providing more or fewer coordinate names in the list to slices:
import iris filename = iris.sample_data_path('hybrid_height.nc') cube = iris.load_cube(filename) print(cube) for i, x_slice in enumerate(cube.slices(['grid_longitude'])): print(i, repr(x_slice))
The Python function enumerate() is used in this example to provide an incrementing variable i which is printed with the summary of each cube slice. Note that there were 1500 1d longitude cubes as a result of slicing the 3 dimensional cube (15, 100, 100) by longitude (i starts at 0 and 1500 = 15 * 100).
It is often useful to get a single 2d slice from a multidimensional cube in order to develop a 2d plot function, for example. This can be achieved by using the next() function on the result of slices:
first_slice = next(cube.slices(['grid_latitude', 'grid_longitude']))
Once the your code can handle a 2d slice, it is then an easy step to loop over all 2d slices within the bigger cube using the slices method.
In the same way that you would expect a numeric multidimensional array to be indexed to take a subset of your original array, you can index a Cube for the same purpose.
Here are some examples of array indexing in numpy:
import numpy as np # create an array of 12 consecutive integers starting from 0 a = np.arange(12) print(a) print(a) # first element of the array print(a[-1]) # last element of the array print(a[0:4]) # first four elements of the array (the same as a[:4]) print(a[-4:]) # last four elements of the array print(a[::-1]) # gives all of the array, but backwards # Make a 2d array by reshaping a b = a.reshape(3, 4) print(b) print(b[0, 0]) # first element of the first and second dimensions print(b) # first element of the first dimension (+ every other dimension) # get the second element of the first dimension and all of the second dimension # in reverse, by steps of two. print(b[1, ::-2])
Similarly, Iris cubes have indexing capability:
import iris filename = iris.sample_data_path('hybrid_height.nc') cube = iris.load_cube(filename) print(cube) # get the first element of the first dimension (+ every other dimension) print(cube) # get the last element of the first dimension (+ every other dimension) print(cube[-1]) # get the first 4 elements of the first dimension (+ every other dimension) print(cube[0:4]) # Get the first element of the first and third dimension (+ every other dimension) print(cube[0, :, 0]) # Get the second element of the first dimension and all of the second dimension # in reverse, by steps of two. print(cube[1, ::-2])