# Introduction to the cf.Field object¶

A cf.Field object stores a field as defined by the CF-netCDF conventions and 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 filed in the CF data model and, as in the CF data model, all components of a cf.Field object are optional.

## Displaying the contents¶

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:

>>> 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 axes (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)
Axes           : 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 axis that is not a axis 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 describes each component’s properties, as well as the first and last values of the field’s data array:

>>> f.dump()
======================
Field: air_temperature
======================
Axes:
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'


## Data¶

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:

• Contains an N-dimensional array with many similarities to a numpy array.
• Contains the units of the array elements.
• Uses LAMA functionality to store and operate on arrays which are larger then the available memory.
• Supports masked arrays [1], regardless of whether or not it was initialized with a masked array.

## Data attributes¶

Some of a field’s reserved attributes 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.

## CF properties¶

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
{'_FillValue': 1e+20,
'foo': 'bar',
'long_name': 'Surface Air Temperature',
'standard_name': 'air_temperature',
'units': 'K'}


## Other attributes¶

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'}


## Methods¶

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.

## Domain structure¶

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 coordinate reference objects (which relate the field’s coordinate values to locations in a planetary reference frame).

Each item has a unique internal identifier (is a string containing a number), which serves to link related items.

### 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
Coordinate reference object cf.CoordinateReference

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 measure, measures, item, items
Coordinate reference object ref, refs, 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) noleap>


To retrieve all coordinate objects and their domain identifiers:

>>> f.coords()
{'dim0': <CF DimensionCoordinate: time(12) noleap>,
'dim1': <CF DimensionCoordinate: latitude(64) degrees_north>,
'dim2': <CF DimensionCoordinate: longitude(128) degrees_east>,
'dim3': <CF DimensionCoordinate: height(1) m>}


To retrieve all domain items and their domain identifiers:

>>> f.items()
{'dim0': <CF DimensionCoordinate: time(12) noleap>,
'dim1': <CF DimensionCoordinate: latitude(64) degrees_north>,
'dim2': <CF DimensionCoordinate: longitude(128) degrees_east>,
'dim3': <CF DimensionCoordinate: height(1) m>}


In this example, all of the items happen to be coordinates.

### Axes¶

Common axes 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 axes than its domain.

Footnotes

 [1] Arrays that may have missing or invalid entries