A cf.Field object stores a field as defined by the CF data model. It is a container for a data array and metadata comprising properties to describe the physical nature of the data and a coordinate system (called a domain), which describes the positions of each element of the data array.
It is structured in exactly the same way as a CF data model field and, as in the CF data model, all components of a cf.Field object are optional.
The structure may be exposed with three different levels of detail.
The built-in repr function returns a short, one-line description of the field:
>>> type(f)
<class 'cf.field.Field'>
>>> print repr(f)
<CF Field: air_temperature(time(12), latitude(64), longitude(128)) K>
>>> f
<CF Field: air_temperature(time(12), latitude(64), longitude(128)) K>
This gives the identity of the field (air_temperature), the identities and sizes of its data array dimensions (time, latitude and longitude with sizes 12, 64 and 128 respectively) and the units of the field’s data array (K).
The built-in str function returns the same information as the the one-line output, along with short descriptions of the field’s other components:
>>> print f
air_temperature field summary
-----------------------------
Data : air_temperature(time(1200), latitude(64), longitude(128)) K
Cell methods : time: mean (interval: 1.0 month)
Dimensions : time(12) = [ 450-11-01 00:00:00, ..., 451-10-16 12:00:00] noleap calendar
: latitude(64) = [-87.8638000488, ..., 87.8638000488] degrees_north
: longitude(128) = [0.0, ..., 357.1875] degrees_east
: height(1) = [2.0] m
This shows that the field has a cell method and four dimension coordinates, one of which (height) is a coordinate for a size 1 dimension that is not a dimension of the field’s data array. The units and first and last values of the coordinates’ data arrays are given and relative time values are translated into strings.
The field’s dump method returns a string describing each component’s properties, as well as the first and last values of the field’s data array:
>>> print f.dump()
======================
Field: air_temperature
======================
Dimensions
height(1)
latitude(64)
longitude(128)
time(12)
Data(time(12), latitude(64), longitude(128)) = [[[236.512756348, ..., 256.93371582]]] K
cell_methods = time: mean (interval: 1.0 month)
experiment_id = 'pre-industrial control experiment'
long_name = 'Surface Air Temperature'
standard_name = 'air_temperature'
title = 'model output prepared for IPCC AR4'
Dimension coordinate: time
Data(time(12)) = [ 450-11-16 00:00:00, ..., 451-10-16 12:00:00] noleap calendar
Bounds(time(12), 2) = [[ 450-11-01 00:00:00, ..., 451-11-01 00:00:00]] noleap calendar
axis = 'T'
long_name = 'time'
standard_name = 'time'
Dimension coordinate: latitude
Data(latitude(64)) = [-87.8638000488, ..., 87.8638000488] degrees_north
Bounds(latitude(64), 2) = [[-90.0, ..., 90.0]] degrees_north
axis = 'Y'
long_name = 'latitude'
standard_name = 'latitude'
Dimension coordinate: longitude
Data(longitude(128)) = [0.0, ..., 357.1875] degrees_east
Bounds(longitude(128), 2) = [[-1.40625, ..., 358.59375]] degrees_east
axis = 'X'
long_name = 'longitude'
standard_name = 'longitude'
Dimension coordinate: height
Data(height(1)) = [2.0] m
axis = 'Z'
long_name = 'height'
positive = 'up'
standard_name = 'height'
A field’s data array is a cf.Data object and is returned by its data attribute.
>>> f.data
<CF Data: [[[89.0, ..., 66.0]]] K>
The cf.Data object:
A field has reserved attributes which return information on its data. See the list of reserved data attributes for details.
For example, to find the shape of the data and to retrieve the data array as an actual numpy array:
>>> f.shape
(1, 3, 4)
>>> f.array
array([[[ 89., 80., 71.],
[ 85., 76., 67.],
[ 83., 74., 65.],
[ 84., 75., 66.]]])
The data array’s missing value mask may be retrieved with the mask attribute. The mask is returned as a new field with a boolean data array:
>>> m = f.mask
>>> m.data
<CF Data: [[[False, ..., True]]]>
If the field contains no missing data then a mask field with False values is still returned.
Standard CF data variable properties (such as standard_name, units, etc.) all have reserved attribute names. See the list of reserved CF properties for details. These properties may be set, retrieved and deleted like normal python object attributes:
>>> f.standard_name = 'air_temperature'
>>> f.standard_name
'air_temperature'
>>> del f.standard_name
as well as with the dedicated setprop, getprop and delprop field methods:
>>> f.setprop('standard_name', 'air_temperature')
>>> f.getprop('standard_name')
'air_temperature'
>>> f.delprop('standard_name')
Non-standard CF properties must be accessed using these three methods:
>>> f.setprop('foo', 'bar')
>>> f.getprop('foo')
'bar'
>>> f.delprop('foo')
All of the field’s CF properties may be retrieved with the field’s properties attribute:
>>> f.properties
{'cell_methods' : XXXXXXXXXXXXXXXXXXXXXXXXXXX,
'_FillValue': 1e+20,
'foo': 'bar',
'long_name': 'Surface Air Temperature',
'standard_name': 'air_temperature',
'units': 'K'}
A field has other reserved attributes which have a variety of roles. See the list of reserved attributes for details.
Any unreserved attribute may be set on a field object with, in general, no special meaning attached to it. The following unreserved attributes do, however, have particular interpretations:
| Attribute | Description |
|---|---|
| file | The name of the file the field was read from |
| id | An identifier for the field in the absence of a standard name. See the identity method for details. |
| ncvar | A netCDF variable name of the field. |
All of the field’s attributes may be retrieved with the field’s attributes attribute:
>>> f.attributes
{'ncar': 'tas'}
A field has a large range of methods which, in general, either return information about the field or change the field in place. See the list of methods and manipulating fields section for details.
A field’s domain completely describes the field’s coordinate system and is stored in its domain attribute, the value of which is a cf.Domain object.
It contains axes (which describe the field’s dimensionality), dimension coordinate, auxiliary coordinate and cell measure objects (which themselves contain data arrays and properties to describe them) and transform objects (which provide geo-locating metadata for the coordinate objects).
Each item has a unique internal identifier (is a string containing a number), which serves to link related items.
Domain items are stored in the following objects:
| Item | cf object |
|---|---|
| Dimension coordinate object | cf.DimensionCoordinate |
| Auxiliary coordinate object | cf.AuxiliaryCoordinate |
| Cell measure object | cf.CellMeasure |
| Transform object | cf.Transform |
These items may be retrieved with a variety of methods, some specific to each item type (such as cf.Field.dim) and some more generic (such as cf.Field.coords and cf.Field.item):
| Item | Field retrieval methods |
|---|---|
| Dimension coordinate object | dim, dims, coord, coords item, items |
| Auxiliary coordinate object | aux, auxs, coord, coords item, items |
| Cell measure object | cm, cms, item, items |
| Transform object | transform, transforms, item, items |
In each case the singular method form (such as aux) returns an actual domain item whereas the plural method form (such as auxs) returns a dictionary whose keys are the domain item identifiers with corresponding values of the items themselves.
For example, to retrieve a unique dimension coordinate object with a standard name of “time”:
>>> f.dim('time')
<CF DimensionCoordinate: time(12) UNITS>
To retrieve all coordinate objects and their domain identifiers:
>>> f.coords()
{'dim0': <CF DimensionCoordinate: time(12) UNITS>
BLAH
To retrieve all domain items and their domain identifiers:
>>> f.items()
{'dim0': <CF DimensionCoordinate: time(12) UNITS>
BLAH
Common dimensions of variation in the field’s data array and the domain’s items are defined by the domain’s axes.
Each axis has a domain identifier (such as 'dim1') and an integer size and is stored in the domain’s dimension_sizes attribute:
>>> f.domain.dimension_sizes
{'dim1': 19, 'dim0': 12, 'dim2': 73, 'dim3': 96}
Particular axes may be retrieved with the axes method:
>>> f.axes()
set(['dim1', 'dim0' 'dim2' 'dim3'])
>>> f.axes(size=19)
set(['dim1'])
>>> f.axes('time')
set(['dim0'])
The axes spanned by a domain item or the field’s data array may be retrieved with the fields item_axes or data_axes methods respectively:
>>> f.item_axes('time')
['dim0']
>>> f.data_axes()
['dim0', 'dim1' 'dim2' 'dim3']
Note that the field’s data array may contain fewer size 1 dimensions than its domain.
Footnotes
| [1] | Arrays that may have missing or invalid entries |