cf.Field.subspace¶

Field.
subspace
¶ Create a subspace of the field construct.
Creation of a new field construct which spans a subspace of the domain of an existing field construct is achieved either by identifying indices based on the metadata constructs (subspacing by metadata) or by indexing the field construct directly (subspacing by index).
The subspacing operation, in either case, also subspaces any metadata constructs of the field construct (e.g. coordinate metadata constructs) which span any of the domain axis constructs that are affected. The new field construct is created with the same properties as the original field construct.
Subspacing by metadata
Subspacing by metadata, signified by the use of round brackets, selects metadata constructs and specifies conditions on their data. Indices for subspacing are then automatically inferred from where the conditions are met.
Metadata constructs and the conditions on their data are defined by keyword parameters.
Any domain axes that have not been identified remain unchanged.
Multiple domain axes may be subspaced simultaneously, and it doesn’t matter which order they are specified in.
Subspace criteria may be provided for size 1 domain axes that are not spanned by the field construct’s data.
Explicit indices may also be assigned to a domain axis identified by a metadata construct, with either a Python
slice
object, or a sequence of integers or booleans.For a dimension that is cyclic, a subspace defined by a slice or by a
Query
instance is assumed to “wrap” around the edges of the data.Conditions may also be applied to multidimensional metadata constructs. The “compress” mode is still the default mode (see the positional arguments), but because the indices may not be acting along orthogonal dimensions, some missing data may still need to be inserted into the field construct’s data.
Subspacing by index
Subspacing by indexing, signified by the use of square brackets, uses rules that are very similar to the numpy indexing rules, the only differences being:
An integer index i specified for a dimension reduces the size of this dimension to unity, taking just the ith element, but keeps the dimension itself, so that the rank of the array is not reduced.
When two or more dimensions’ indices are sequences of integers then these indices work independently along each dimension (similar to the way vector subscripts work in Fortran). This is the same indexing behaviour as on a Variable object of the netCDF4 package.
For a dimension that is cyclic, a range of indices specified by a
slice
that spans the edges of the data (such as2:3
or3:2:1
) is assumed to “wrap” around, rather then producing a null result.
See also
 Parameters
 positional arguments: optional
There are three modes of operation, each of which provides a different type of subspace:
argument
Description
'compress'
This is the default mode. Unselected locations are removed to create the returned subspace. Note that if a multidimensional metadata construct is being used to define the indices then some missing data may still be inserted at unselected locations.
'envelope'
The returned subspace is the smallest that contains all of the selected indices. Missing data is inserted at unselected locations within the envelope.
'full'
The returned subspace has the same domain as the original field construct. Missing data is inserted at unselected locations.
'test'
May be used on its own or in addition to one of the other positional arguments. Do not create a subspace, but return
True
orFalse
depending on whether or not it is possible to create specified the subspace. keyword parameters: optional
A keyword name is an identity of a metadata construct, and the keyword value provides a condition for inferring indices that apply to the dimension (or dimensions) spanned by the metadata construct’s data. Indices are created that select every location for which the metadata construct’s data satisfies the condition.
 Returns
Examples
There are further worked examples in the tutorial.
>>> g = f.subspace(X=112.5) >>> g = f.subspace(X=112.5, latitude=cf.gt(60)) >>> g = f.subspace(latitude=cf.eq(45)  cf.ge(20)) >>> g = f.subspace(X=[1, 2, 4], Y=slice(None, None, 1)) >>> g = f.subspace(X=cf.wi(100, 200)) >>> g = f.subspace(X=slice(2, 4)) >>> g = f.subspace(Y=[True, False, True, True, False]) >>> g = f.subspace(T=410.5) >>> g = f.subspace(T=cf.dt('19600416')) >>> g = f.subspace(T=cf.wi(cf.dt('19621101'), ... cf.dt('19670317 07:30'))) >>> g = f.subspace('compress', X=[1, 2, 4, 6]) >>> g = f.subspace('envelope', X=[1, 2, 4, 6]) >>> g = f.subspace('full', X=[1, 2, 4, 6]) >>> g = f.subspace(latitude=cf.wi(51, 53))
>>> g = f.subspace[::1, 0] >>> g = f.subspace[:, :, 1] >>> g = f.subspace[:, 0] >>> g = f.subspace[..., 6:3:1, 3:6] >>> g = f.subspace[0, [2, 3, 9], [4, 8]] >>> g = t.subspace[0, :, 2] >>> g = f.subspace[0, [2, 3, 9], [4, 8]] >>> g = f.subspace[:, 2:3] >>> g = f.subspace[:, 3:2:1] >>> g = f.subspace[..., [True, False, True, True, False]]