cf.Field.indices¶

Field.
indices
(*mode, **kwargs)[source]¶ Create indices that define a subspace of the field construct.
The subspace is defined by identifying indices based on the metadata constructs.
Metadata constructs are selected by conditions specified on their data. Indices for subspacing are then automatically inferred from where the conditions are met.
The returned tuple of indices may be used to created a subspace by indexing the original field construct with them.
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.
Ancillary masks
When creating an actual subspace with the indices, if the first element of the tuple of indices is
'mask'
then the second element is a tuple of auxiliary masks, and the remaining elements contain the usual indexing information that defines the extent of the subspace. Each auxiliary mask broadcasts to the subspaced data, and when the subspace is actually created, these masks are all automatically applied to the result.See also
subspace
,where
,__getitem__
,__setitem__
,cf.Domain.indices
 Parameters
 mode:
str
, optional There are three modes of operation, each of which provides indices for a different type of subspace:
mode
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.
 kwargs: 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.
 mode:
 Returns
tuple
The indices meeting the conditions.
Examples
>>> q = cf.example_field(0) >>> print(q) Field: specific_humidity (ncvar%q)  Data : specific_humidity(latitude(5), longitude(8)) 1 Cell methods : area: mean Dimension coords: latitude(5) = [75.0, ..., 75.0] degrees_north : longitude(8) = [22.5, ..., 337.5] degrees_east : time(1) = [20190101 00:00:00] >>> indices = q.indices(X=112.5) >>> print(indices) (slice(0, 5, 1), slice(2, 3, 1)) >>> q[indices] <CF Field: specific_humidity(latitude(5), longitude(1)) 1> >>> q.indices(X=112.5, latitude=cf.gt(60)) (slice(1, 5, 1), slice(2, 3, 1)) >>> q.indices(latitude=cf.eq(45)  cf.ge(20)) (array([1, 3, 4]), slice(0, 8, 1)) >>> q.indices(X=[1, 2, 4], Y=slice(None, None, 1)) (slice(4, None, 1), array([1, 2, 4])) >>> q.indices(X=cf.wi(100, 200)) (slice(0, 5, 1), slice(2, 4, 1)) >>> q.indices(X=slice(2, 4)) (slice(0, 5, 1), slice(2, 4, 1)) >>> q.indices('compress', X=[1, 2, 4, 6]) (slice(0, 5, 1), array([1, 2, 4, 6])) >>> q.indices(Y=[True, False, True, True, False]) (array([0, 2, 3]), slice(0, 8, 1)) >>> q.indices('envelope', X=[1, 2, 4, 6]) ('mask', [<CF Data(1, 6): [[False, ..., False]]>], slice(0, 5, 1), slice(1, 7, 1)) >>> indices = q.indices('full', X=[1, 2, 4, 6]) ('mask', [<CF Data(1, 8): [[True, ..., True]]>], slice(0, 5, 1), slice(0, 8, 1)) >>> print(indices) >>> print(q) <CF Field: specific_humidity(latitude(5), longitude(8)) 1>
>>> f = cf.example_field(2) Field: air_potential_temperature (ncvar%air_potential_temperature)  Data : air_potential_temperature(time(120), latitude(5), longitude(8)) K Cell methods : area: mean Dimension coords: time(120) = [19591216 12:00:00, ..., 19691116 00:00:00] : latitude(5) = [75.0, ..., 75.0] degrees_north : longitude(8) = [22.5, ..., 337.5] degrees_east : air_pressure(1) = [850.0] hPa >>> f.indices(T=410.5) (dask.array<isclose, shape=(36,), dtype=bool, chunksize=(36,), chunktype=numpy.ndarray>, slice(None, None, None), slice(None, None, None)) >>> f.indices(T=cf.dt('19611116')) (dask.array<isclose, shape=(36,), dtype=bool, chunksize=(36,), chunktype=numpy.ndarray>, slice(0, 5, 1), slice(0, 8, 1)) >>> indices = f.indices(T=cf.wi(cf.dt('19600301'), ... cf.dt('19611217 07:30'))) >>> indices (dask.array<and_, shape=(36,), dtype=bool, chunksize=(36,), chunktype=numpy.ndarray>, slice(None, None, None), slice(None, None, None)) >>> print(indices[0].compute()) [False False False True True True True True True True True True True True True True True True True True True True True True True False False False False False False False False False False False] >>> print(f[indices]) Field: air_potential_temperature (ncvar%air_potential_temperature)  Data : air_potential_temperature(time(22), latitude(5), longitude(8)) K Cell methods : area: mean Dimension coords: time(22) = [19600316 12:00:00, ..., 19611216 12:00:00] : latitude(5) = [75.0, ..., 75.0] degrees_north : longitude(8) = [22.5, ..., 337.5] degrees_east : air_pressure(1) = [850.0] hPa
>>> f = cf.example_field(1) Field: air_temperature (ncvar%ta)  Data : air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) K Cell methods : grid_latitude(10): grid_longitude(9): mean where land (interval: 0.1 degrees) time(1): maximum Field ancils : air_temperature standard_error(grid_latitude(10), grid_longitude(9)) = [[0.76, ..., 0.32]] K Dimension coords: atmosphere_hybrid_height_coordinate(1) = [1.5] : grid_latitude(10) = [2.2, ..., 1.76] degrees : grid_longitude(9) = [4.7, ..., 1.18] degrees : time(1) = [20190101 00:00:00] Auxiliary coords: latitude(grid_latitude(10), grid_longitude(9)) = [[53.941, ..., 50.225]] degrees_N : longitude(grid_longitude(9), grid_latitude(10)) = [[2.004, ..., 8.156]] degrees_E : long_name=Grid latitude name(grid_latitude(10)) = [, ..., kappa] Cell measures : measure:area(grid_longitude(9), grid_latitude(10)) = [[2391.9657, ..., 2392.6009]] km2 Coord references: grid_mapping_name:rotated_latitude_longitude : standard_name:atmosphere_hybrid_height_coordinate Domain ancils : ncvar%a(atmosphere_hybrid_height_coordinate(1)) = [10.0] m : ncvar%b(atmosphere_hybrid_height_coordinate(1)) = [20.0] : surface_altitude(grid_latitude(10), grid_longitude(9)) = [[0.0, ..., 270.0]] m >>> indices = f.indices(latitude=cf.wi(51.5, 52.4)) >>> indices ('mask', (<CF Data(1, 5, 9): [[[False, ..., False]]]>,), slice(None, None, None), [4, 5, 6], [0, 1, 2, 3, 4, 5, 6, 7, 8]) >>> f[indices] <CF Field: air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(3), grid_longitude(9)) K> >>> print(f[indices].array) [[[264.2 275.9 262.5 264.9 264.7 270.2 270.4  ] [263.9 263.8 272.1 263.7 272.2 264.2 260.0 263.5 270.2] [      270.6 273.0 270.6]]]