# 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
    Field: air_temperature (ncvar%tas)
    ----------------------------------
    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 (ncvar%tas)
    ----------------------------------
    experiment_id = 'pre-industrial control experiment'
    long_name = 'Surface Air Temperature'
    standard_name = 'air_temperature'
    title = 'model output prepared for IPCC AR4'

    Domain Axis: height(1)
    Domain Axis: latitude(64)
    Domain Axis: longitude(128)
    Domain Axis: time(12)

    Data(time(12), latitude(64), longitude(128)) = [[[236.512756348, ..., 256.93371582]]] K

    Cell Method: time: mean (interval: 1.0 month)

    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. 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-CF properties (i.e. those which are allowed by the CF conventions but which do not have standardised meanings) must be accessed using these three methods:

    >>> f.setprop('project', 'CMIP7')
    >>> f.getprop('project')
    'CMIP7'
    >>> f.delprop('project')

All of the field’s CF properties may be retrieved with the field’s `properties` method:

    >>> f.properties()
    {'_FillValue': 1e+20,
     'project': 'CMIP7',
     'long_name': 'Surface Air Temperature',
     'standard_name': 'air_temperature',
     'units': 'K'}

## Other attributes

Any other attribute may be set on directly on a field object with, in general, no special meaning attached to it. These attributes are distinct properties (CF and non-CF) since they are not considered as part of the CF conventions and will not be written to files on disk.

The following attributes do, however, have particular interpretations:

Attribute | Description
--- | ---
`id` | An identifier for the field in the absence of a standard name. See the `identity` method for details.
`ncvar` | The netCDF variable name of the field.

All of the field’s attributes may be retrieved with the field’s `attributes()` method:

    >>> f.foo = 'bar'
    >>> f.attributes()
    {'foo': 'bar',
     '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

A field’s domain completely describes the location and nature of the field’s data. It comprises domain 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.

### Domain axes

Common axes of variation in the field’s data array and the domain’s items are defined by the domain’s axes.

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