# Tutorial¶

Version 1.8.7.0 for version 1.8 of the CF conventions.

All of the Python code in this tutorial is available in an executable script (download, 12kB).

## Sample datasets¶

This tutorial uses a number of small sample datasets, all of which can be found in the zip file cfdm_tutorial_files.zip (download, 8kB):

Unpack the sample datasets.
$unzip -q cfdm_tutorial_files.zip$ ls -1
cfdm_tutorial_files.zip
contiguous.nc
external.nc
file.nc
gathered.nc
geometry.nc
parent.nc


The tutorial examples assume that the Python session is being run from the directory that contains the zip file and its unpacked contents, and no other files.

## Import¶

The cfdm package is imported as follows:

Import the cfdm package.
>>> import cfdm


Tip

It is possible to change the extent to which cfdm outputs feedback messages and it may be instructive to increase the verbosity whilst working through this tutorial to see and learn more about what cfdm is doing under the hood and about the nature of the dataset being operated on. This can be done for example by running:

Increase the verbosity of cfdm from the default.
>>> cfdm.log_level('INFO')


### CF version¶

The version of the CF conventions and the CF data model being used may be found with the cfdm.CF function:

Retrieve the version of the CF conventions.
>>> cfdm.CF()
'1.8'


This indicates which version of the CF conventions are represented by this release of the cfdm package, and therefore the version can not be changed.

Note, however, that datasets of different versions may be read from, or written to, disk.

## Field construct¶

The central construct (i.e. element) to CF is the field construct. The field construct, that corresponds to a CF-netCDF data variable, includes all of the metadata to describe it:

• descriptive properties that apply to field construct as a whole (e.g. the standard name),

• a data array, and

• “metadata constructs” that describe the locations of each cell of the data array, and the physical nature of each cell’s datum.

A field construct is stored in a cfdm.Field instance, and henceforth the phrase “field construct” will be assumed to mean “cfdm.Field instance”.

## Reading field constructs from datasets¶

The cfdm.read function reads a netCDF file from disk, or from an OPeNDAP URL 1, and returns the contents as a Python list of zero or field constructs. The list contains a field construct to represent each of the CF-netCDF data variables in the file.

Datasets of any version of CF up to and including CF-1.8 can be read.

All formats of netCDF3 and netCDF4 files can be read.

The following file types can be read:

• All formats of netCDF3 and netCDF4 files can be read, containing datasets for versions of CF up to and including CF-1.8.

• Files in CDL format, with or without the data array values.

Note that when reading netCDF4 files that contain hierachical groups, the group structure is saved via the netCDF interface so that it may be re-used, or modified, if the field constructs are written to back to disk.

For example, to read the file file.nc (found in the sample datasets), which contains two field constructs:

Read file.nc and show that the result is a two-element list.
>>> x = cfdm.read('file.nc')
>>> print(type(x))
<type 'list'>
>>> len(x)
2


Descriptive properties are always read into memory, but lazy loading is employed for all data arrays, which means that no data is read into memory until the data is required for inspection or to modify the array contents. This maximises the number of field constructs that may be read within a session, and makes the read operation fast.

The cfdm.read function has optional parameters to

• allow the user to provide files that contain external variables;

• request extra field constructs to be created from “metadata” netCDF variables, i.e. those that are referenced from CF-netCDF data variables, but which are not regarded by default as data variables in their own right;

• request that masking is not applied by convention to data elements (see data masking);

• issue warnings when valid_min, valid_max and valid_range attributes are present (see data masking); and

• display information and issue warnings about the mapping of the netCDF file contents to CF data model constructs.

### CF-compliance¶

If the dataset is partially CF-compliant to the extent that it is not possible to unambiguously map an element of the netCDF dataset to an element of the CF data model, then a field construct is still returned, but may be incomplete. This is so that datasets which are partially conformant may nonetheless be modified in memory and written to new datasets. Such “structural” non-compliance would occur, for example, if the coordinates attribute of a CF-netCDF data variable refers to another variable that does not exist, or refers to a variable that spans a netCDF dimension that does not apply to the data variable. Other types of non-compliance are not checked, such whether or not controlled vocabularies have been adhered to. The structural compliance of the dataset may be checked with the dataset_compliance method of the field construct, as well as optionally displayed when the dataset is read.

## Inspection¶

The contents of a field construct may be inspected at three different levels of detail.

### Minimal detail¶

The built-in repr function returns a short, one-line description:

Inspect the contents of the two field constructs from the dataset and create a Python variable for each of them.
>>> x
[<Field: specific_humidity(latitude(5), longitude(8)) 1>,
<Field: air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) K>]
>>> q = x[0]
>>> t = x[1]
>>> q
<Field: specific_humidity(latitude(5), longitude(8)) 1>


This gives the identity of the field construct (e.g. “specific_humidity”), the identities and sizes of the dimensions spanned by the data array (“latitude” and “longitude” with sizes 5 and 8 respectively) and the units of the data (“1”).

### Medium detail¶

The built-in str function returns similar information as the one-line output, along with short descriptions of the metadata constructs, which include the first and last values of their data arrays:

Inspect the contents of the two field constructs with medium detail.
>>> print(q)
Field: specific_humidity (ncvar%q)
----------------------------------
Data            : specific_humidity(latitude(5), longitude(8)) 1
Cell methods    : area: mean
Dimension coords: time(1) = [2019-01-01 00:00:00]
: latitude(5) = [-75.0, ..., 75.0] degrees_north
: longitude(8) = [22.5, ..., 337.5] degrees_east

>>> print(t)
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)) = [[0.81, ..., 0.78]] K
Dimension coords: time(1) = [2019-01-01 00:00:00]
: atmosphere_hybrid_height_coordinate(1) = [1.5]
: grid_latitude(10) = [2.2, ..., -1.76] degrees
: grid_longitude(9) = [-4.7, ..., -1.18] degrees
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: atmosphere_hybrid_height_coordinate
: rotated_latitude_longitude
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


Note that time values are converted to date-times with the cftime package.

### Full detail¶

The dump method of the field construct gives all properties of all constructs, including metadata constructs and their components, and shows the first and last values of all data arrays:

Inspect the contents of the two field constructs with full detail.
>>> q.dump()
----------------------------------
Field: specific_humidity (ncvar%q)
----------------------------------
Conventions = 'CF-1.7'
project = 'research'
standard_name = 'specific_humidity'
units = '1'

Data(latitude(5), longitude(8)) = [[0.003, ..., 0.032]] 1

Cell Method: area: mean

Domain Axis: latitude(5)
Domain Axis: longitude(8)
Domain Axis: time(1)

Dimension coordinate: latitude
standard_name = 'latitude'
units = 'degrees_north'
Data(latitude(5)) = [-75.0, ..., 75.0] degrees_north
Bounds:Data(latitude(5), 2) = [[-90.0, ..., 90.0]]

Dimension coordinate: longitude
standard_name = 'longitude'
units = 'degrees_east'
Data(longitude(8)) = [22.5, ..., 337.5] degrees_east
Bounds:Data(longitude(8), 2) = [[0.0, ..., 360.0]]

Dimension coordinate: time
standard_name = 'time'
units = 'days since 2018-12-01'
Data(time(1)) = [2019-01-01 00:00:00]

>>> t.dump()
---------------------------------
Field: air_temperature (ncvar%ta)
---------------------------------
Conventions = 'CF-1.7'
project = 'research'
standard_name = 'air_temperature'
units = 'K'

Data(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) = [[[0.0, ..., 89.0]]] K

Cell Method: grid_latitude(10): grid_longitude(9): mean where land (interval: 0.1 degrees)
Cell Method: time(1): maximum

Field Ancillary: air_temperature standard_error
standard_name = 'air_temperature standard_error'
units = 'K'
Data(grid_latitude(10), grid_longitude(9)) = [[0.81, ..., 0.78]] K

Domain Axis: atmosphere_hybrid_height_coordinate(1)
Domain Axis: grid_latitude(10)
Domain Axis: grid_longitude(9)
Domain Axis: time(1)

Dimension coordinate: atmosphere_hybrid_height_coordinate
computed_standard_name = 'altitude'
standard_name = 'atmosphere_hybrid_height_coordinate'
Data(atmosphere_hybrid_height_coordinate(1)) = [1.5]
Bounds:Data(atmosphere_hybrid_height_coordinate(1), 2) = [[1.0, 2.0]]

Dimension coordinate: grid_latitude
standard_name = 'grid_latitude'
units = 'degrees'
Data(grid_latitude(10)) = [2.2, ..., -1.76] degrees
Bounds:Data(grid_latitude(10), 2) = [[2.42, ..., -1.98]]

Dimension coordinate: grid_longitude
standard_name = 'grid_longitude'
units = 'degrees'
Data(grid_longitude(9)) = [-4.7, ..., -1.18] degrees
Bounds:Data(grid_longitude(9), 2) = [[-4.92, ..., -0.96]]

Dimension coordinate: time
standard_name = 'time'
units = 'days since 2018-12-01'
Data(time(1)) = [2019-01-01 00:00:00]

Auxiliary coordinate: latitude
standard_name = 'latitude'
units = 'degrees_N'
Data(grid_latitude(10), grid_longitude(9)) = [[53.941, ..., 50.225]] degrees_N

Auxiliary coordinate: longitude
standard_name = 'longitude'
units = 'degrees_E'
Data(grid_longitude(9), grid_latitude(10)) = [[2.004, ..., 8.156]] degrees_E

Auxiliary coordinate: long_name=Grid latitude name
long_name = 'Grid latitude name'
Data(grid_latitude(10)) = [--, ..., kappa]

Domain ancillary: ncvar%a
units = 'm'
Data(atmosphere_hybrid_height_coordinate(1)) = [10.0] m
Bounds:Data(atmosphere_hybrid_height_coordinate(1), 2) = [[5.0, 15.0]]

Domain ancillary: ncvar%b
Data(atmosphere_hybrid_height_coordinate(1)) = [20.0]
Bounds:Data(atmosphere_hybrid_height_coordinate(1), 2) = [[14.0, 26.0]]

Domain ancillary: surface_altitude
standard_name = 'surface_altitude'
units = 'm'
Data(grid_latitude(10), grid_longitude(9)) = [[0.0, ..., 270.0]] m

Coordinate reference: atmosphere_hybrid_height_coordinate
Coordinate conversion:computed_standard_name = altitude
Coordinate conversion:standard_name = atmosphere_hybrid_height_coordinate
Coordinate conversion:a = Domain Ancillary: ncvar%a
Coordinate conversion:b = Domain Ancillary: ncvar%b
Coordinate conversion:orog = Domain Ancillary: surface_altitude
Dimension Coordinate: atmosphere_hybrid_height_coordinate

Coordinate reference: rotated_latitude_longitude
Coordinate conversion:grid_mapping_name = rotated_latitude_longitude
Coordinate conversion:grid_north_pole_latitude = 38.0
Coordinate conversion:grid_north_pole_longitude = 190.0
Dimension Coordinate: grid_longitude
Dimension Coordinate: grid_latitude
Auxiliary Coordinate: longitude
Auxiliary Coordinate: latitude

Cell measure: measure:area
units = 'km2'
Data(grid_longitude(9), grid_latitude(10)) = [[2391.9657, ..., 2392.6009]] km2


### cfdump¶

The description for every field construct in a file can also be generated from the command line, with minimal, medium or full detail, by using the cfdump tool, for example:

Use cfdump on the command line to inspect the field constructs contained in a dataset. The “-s” option requests short, minimal detail as output.
$cfdump USAGE: cfdump [-s] [-c] [-e file [-e file] ...] [-h] file [-s] Display short, one-line descriptions [-c] Display complete descriptions [-e file] External files [-h] Display the full man page file Name of netCDF file (or URL if DAP access enabled)$ cfdump -s file.nc
Field: specific_humidity(latitude(5), longitude(8)) 1
Field: air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) K


cfdump may also be used with external files.

## Properties¶

Descriptive properties that apply to field construct as a whole may be retrieved with the properties method:

Retrieve all of the descriptive properties
>>> t.properties()
{'Conventions': 'CF-1.7',
'project': 'research',
'standard_name': 'air_temperature',
'units': 'K'}


Individual properties may be accessed and modified with the del_property, get_property, has_property, and set_property methods:

Check is a property exists, retrieve its value, delete it and then set it to a new value.
>>> t.has_property('standard_name')
True
>>> t.get_property('standard_name')
'air_temperature'
>>> t.del_property('standard_name')
'air_temperature'
>>> t.get_property('standard_name', default='not set')
'not set'
>>> t.set_property('standard_name', value='air_temperature')
>>> t.get_property('standard_name', default='not set')
'air_temperature'


A collection of properties may be set at the same time with the set_properties method of the field construct, and all properties may be completely removed with the clear_properties method.

Update the properties with a collection, delete all of the properties, and reinstate the original properties.
>>> original = t.properties()
>>> original
{'Conventions': 'CF-1.7',
'project': 'research',
'standard_name': 'air_temperature',
'units': 'K'}
>>> t.set_properties({'foo': 'bar', 'units': 'K'})
>>> t.properties()
{'Conventions': 'CF-1.7',
'foo': 'bar',
'project': 'research',
'standard_name': 'air_temperature',
'units': 'K'}
>>> t.clear_properties()
{'Conventions': 'CF-1.7',
'foo': 'bar',
'project': 'research',
'standard_name': 'air_temperature',
'units': 'K'}
>>> t.properties()
{}
>>> t.set_properties(original)
>>> t.properties()
{'Conventions': 'CF-1.7',
'project': 'research',
'standard_name': 'air_temperature',
'units': 'K'}


All of the methods related to the properties are listed here.

The metadata constructs describe the field construct that contains them. Each CF data model metadata construct has a corresponding cfdm class:

Class

CF data model construct

Description

DomainAxis

Domain axis

Independent axes of the domain

DimensionCoordinate

Dimension coordinate

Domain cell locations

AuxiliaryCoordinate

Auxiliary coordinate

Domain cell locations

CoordinateReference

Coordinate reference

Domain coordinate systems

DomainAncillary

Domain ancillary

Cell locations in alternative coordinate systems

CellMeasure

Cell measure

Domain cell size or shape

FieldAncillary

Field ancillary

Ancillary metadata which vary within the domain

CellMethod

Cell method

Describes how data represent variation within cells

Metadata constructs of a particular type can be retrieved with the following attributes of the field construct:

Attribute

domain_axes

Domain axes

dimension_coordinates

Dimension coordinates

auxiliary_coordinates

Auxiliary coordinates

coordinate_references

Coordinate references

domain_ancillaries

Domain ancillaries

cell_measures

Cell measures

field_ancillaries

Field ancillaries

cell_methods

Cell methods

Each of these attributes returns a Constructs class instance that maps metadata constructs to unique identifiers called “construct keys”. A Constructs instance has methods for selecting constructs that meet particular criteria (see the section on filtering metadata constructs). It also behaves like a “read-only” Python dictionary, in that it has items, keys and values methods that work exactly like their corresponding dict methods. It also has a get method and indexing like a Python dictionary (see the section on metadata construct access for details).

Retrieve the field construct’s coordinate reference constructs, and access them using dictionary methods.
>>> t.coordinate_references
<Constructs: coordinate_reference(2)>
>>> print(t.coordinate_references)
Constructs:
{'coordinatereference0': <CoordinateReference: atmosphere_hybrid_height_coordinate>,
'coordinatereference1': <CoordinateReference: rotated_latitude_longitude>}
>>> list(t.coordinate_references.keys())
['coordinatereference0', 'coordinatereference1']
>>> for key, value in t.coordinate_references.items():
...     print(key, repr(value))
...
coordinatereference1 <CoordinateReference: rotated_latitude_longitude>
coordinatereference0 <CoordinateReference: atmosphere_hybrid_height_coordinate>

Retrieve the field construct’s dimension coordinate and domain axis constructs.
>>> print(t.dimension_coordinates)
Constructs:
{'dimensioncoordinate0': <DimensionCoordinate: atmosphere_hybrid_height_coordinate(1) >,
'dimensioncoordinate1': <DimensionCoordinate: grid_latitude(10) degrees>,
'dimensioncoordinate2': <DimensionCoordinate: grid_longitude(9) degrees>,
'dimensioncoordinate3': <DimensionCoordinate: time(1) days since 2018-12-01 >}
>>> print(t.domain_axes)
Constructs:
{'domainaxis0': <DomainAxis: size(1)>,
'domainaxis1': <DomainAxis: size(10)>,
'domainaxis2': <DomainAxis: size(9)>,
'domainaxis3': <DomainAxis: size(1)>}


The construct keys (e.g. 'domainaxis1') are usually generated internally and are unique within the field construct. However, construct keys may be different for equivalent metadata constructs from different field constructs, and for different Python sessions.

Metadata constructs of all types may be returned by the constructs attribute of the field construct:

Retrieve all of the field construct’s metadata constructs.
>>> q.constructs
<Constructs: cell_method(1), dimension_coordinate(3), domain_axis(3)>
>>> print(q.constructs)
Constructs:
{'cellmethod0': <CellMethod: area: mean>,
'dimensioncoordinate0': <DimensionCoordinate: latitude(5) degrees_north>,
'dimensioncoordinate1': <DimensionCoordinate: longitude(8) degrees_east>,
'dimensioncoordinate2': <DimensionCoordinate: time(1) days since 2018-12-01 >,
'domainaxis0': <DomainAxis: size(5)>,
'domainaxis1': <DomainAxis: size(8)>,
'domainaxis2': <DomainAxis: size(1)>}
>>> t.constructs
<Constructs: auxiliary_coordinate(3), cell_measure(1), cell_method(2), coordinate_reference(2), dimension_coordinate(4), domain_ancillary(3), domain_axis(4), field_ancillary(1)>
>>> print(t.constructs)
Constructs:
{'auxiliarycoordinate0': <AuxiliaryCoordinate: latitude(10, 9) degrees_N>,
'auxiliarycoordinate1': <AuxiliaryCoordinate: longitude(9, 10) degrees_E>,
'auxiliarycoordinate2': <AuxiliaryCoordinate: long_name=Grid latitude name(10) >,
'cellmeasure0': <CellMeasure: measure:area(9, 10) km2>,
'cellmethod0': <CellMethod: domainaxis1: domainaxis2: mean where land (interval: 0.1 degrees)>,
'cellmethod1': <CellMethod: domainaxis3: maximum>,
'coordinatereference0': <CoordinateReference: atmosphere_hybrid_height_coordinate>,
'coordinatereference1': <CoordinateReference: rotated_latitude_longitude>,
'dimensioncoordinate0': <DimensionCoordinate: atmosphere_hybrid_height_coordinate(1) >,
'dimensioncoordinate1': <DimensionCoordinate: grid_latitude(10) degrees>,
'dimensioncoordinate2': <DimensionCoordinate: grid_longitude(9) degrees>,
'dimensioncoordinate3': <DimensionCoordinate: time(1) days since 2018-12-01 >,
'domainancillary0': <DomainAncillary: ncvar%a(1) m>,
'domainancillary1': <DomainAncillary: ncvar%b(1) >,
'domainancillary2': <DomainAncillary: surface_altitude(10, 9) m>,
'domainaxis0': <DomainAxis: size(1)>,
'domainaxis1': <DomainAxis: size(10)>,
'domainaxis2': <DomainAxis: size(9)>,
'domainaxis3': <DomainAxis: size(1)>,
'fieldancillary0': <FieldAncillary: air_temperature standard_error(10, 9) K>}


## Data¶

The field construct’s data is stored in a Data class instance that is accessed with the data attribute of the field construct.

Retrieve the data and inspect it, showing the shape and some illustrative values.
>>> t.data
<Data(1, 10, 9): [[[262.8, ..., 269.7]]] K>


The Data instance provides access to the full array of values, as well as attributes to describe the array and methods for describing any data compression.

The Data instance provides access to the full array of values, as well as attributes to describe the array and methods for describing any data compression. However, the field construct (and any other construct that contains data) also provides attributes for direct access.

Retrieve a numpy array of the data.
>>> print(t.data.array)
[[[262.8 270.5 279.8 269.5 260.9 265.0 263.5 278.9 269.2]
[272.7 268.4 279.5 278.9 263.8 263.3 274.2 265.7 279.5]
[269.7 279.1 273.4 274.2 279.6 270.2 280.0 272.5 263.7]
[261.7 260.6 270.8 260.3 265.6 279.4 276.9 267.6 260.6]
[264.2 275.9 262.5 264.9 264.7 270.2 270.4 268.6 275.3]
[263.9 263.8 272.1 263.7 272.2 264.2 260.0 263.5 270.2]
[273.8 273.1 268.5 272.3 264.3 278.7 270.6 273.0 270.6]
[267.9 273.5 279.8 260.3 261.2 275.3 271.2 260.8 268.9]
[270.9 278.7 273.2 261.7 271.6 265.8 273.0 278.5 266.4]
[276.4 264.2 276.3 266.1 276.1 268.1 277.0 273.4 269.7]]]

Inspect the data type, number of dimensions, dimension sizes and number of elements of the data.
>>> t.dtype
dtype('float64')
>>> t.ndim
3
>>> t.shape
(1, 10, 9)
>>> t.size
90
>>> t.data.size
90


Note it is preferable to access the data type, number of dimensions, dimension sizes and number of elements of the data via the parent construct, rather than from the Data instance, as there are particular circumstances when there is no Data instance, but the construct nonetheless has data descriptors.

The field construct also has a get_data method as an alternative means of retrieving the data instance, which allows for a default to be returned if no data have been set; as well as a del_data method for removing the data.

All of the methods and attributes related to the data are listed here.

### Data axes¶

The data array of the field construct spans all the domain axis constructs with the possible exception of size one domain axis constructs. The domain axis constructs spanned by the field construct’s data are found with the get_data_axes method of the field construct. For example, the data of the field construct t does not span the size one domain axis construct with key 'domainaxis3'.

Show which data axis constructs are spanned by the field construct’s data.
>>> print(t.domain_axes)
Constructs:
{'domainaxis0': <DomainAxis: size(1)>,
'domainaxis1': <DomainAxis: size(10)>,
'domainaxis2': <DomainAxis: size(9)>,
'domainaxis3': <DomainAxis: size(1)>}
>>> t
<Field: air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) K>
>>> t.shape
(1, 10, 9)
>>> t.get_data_axes()
('domainaxis0', 'domainaxis1', 'domainaxis2')


The data may be set with the set_data method of the field construct. The domain axis constructs spanned by the data may also be set by explicitly providing them via their construct keys. In any case, the data axes may be set at any time with the set_data_axes method of the field construct.

Delete the data and then reinstate it, using the existing data axes.
>>> data = t.del_data()
>>> t.has_data()
False
>>> t.set_data(data, axes=None)
>>> t.data
<Data(1, 10, 9): [[[262.8, ..., 269.7]]] K>


See the section field construct creation for more examples.

### Date-time¶

Data representing date-times is defined as elapsed times since a reference date-time in a particular calendar (Gregorian, by default). The array attribute of the Data instance (and any construct that contains it) returns the elapsed times, and the datetime_array (and any construct that contains it) returns the data as an array of date-time objects.

View date-times aas elapsed time or as date-time objects.
>>> d = cfdm.Data([1, 2, 3], units='days since 2004-2-28')
>>> print(d.array)
[1 2 3]
>>> print(d.datetime_array)
[cftime.DatetimeGregorian(2004-02-29 00:00:00)
cftime.DatetimeGregorian(2004-03-01 00:00:00)
cftime.DatetimeGregorian(2004-03-02 00:00:00)]
>>> e = cfdm.Data([1, 2, 3], units='days since 2004-2-28',
...               calendar='360_day')
>>> print(e.array)
[1 2 3]
>>> print(e.datetime_array)
[cftime.Datetime360Day(2004-02-29 00:00:00)
cftime.Datetime360Day(2004-02-30 00:00:00)
cftime.Datetime360Day(2004-03-01 00:00:00)]


### Manipulating dimensions¶

The dimensions of a field construct’s data may be reordered, have size one dimensions removed and have new new size one dimensions included by using the following field construct methods:

Method

Description

insert_dimension

Insert a new size one data dimension. The new dimension must correspond to an existing size one domain axis construct.

squeeze

Remove size one data dimensions

transpose

Reorder data dimensions

Remove all size one dimensions from the data, noting that metadata constructs which span the corresponding domain axis construct are not affected.
>>> q, t = cfdm.read('file.nc')
>>> t
<CF Field: air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) K>
>>> t2 = t.squeeze()
>>> t2
<CF Field: air_temperature(grid_latitude(10), grid_longitude(9)) K>
>>> print(t2.dimension_coordinates)
Constructs:
{'dimensioncoordinate0': <CF DimensionCoordinate: atmosphere_hybrid_height_coordinate(1) >,
'dimensioncoordinate1': <CF DimensionCoordinate: grid_latitude(10) degrees>,
'dimensioncoordinate2': <CF DimensionCoordinate: grid_longitude(9) degrees>,
'dimensioncoordinate3': <CF DimensionCoordinate: time(1) days since 2018-12-01 >}

Insert a new size one dimension, corresponding to a size one domain axis construct, and then reorder the dimensions.
>>> t3 = t2.insert_dimension(axis='domainaxis3', position=1)
>>> t3
<CF Field: air_temperature(grid_latitude(10), time(1), grid_longitude(9)) K>
>>> t3.transpose([2, 0, 1])
<CF Field: air_temperature(grid_longitude(9), grid_latitude(10), time(1)) K>


When transposing the data dimensions, the dimensions of metadata construct data are, by default, unchanged. It is also possible to permute the data dimensions of the metadata constructs so that they have the same relative order as the field construct:

Also permute the data dimension of metadata constructs using the ‘constructs’ keyword.
>>> t4 = t.transpose([0, 2, 1], constructs=True)


There is always a data mask, which may be thought of as a separate data array of booleans with the same shape as the original data. The data mask is False where the the data has values, and True where the data is missing. The data mask may be inspected with the mask attribute of the data instance, which returns the data mask in a field construct with the same metadata constructs as the original field construct.

Inspect the data mask of a field construct.
>>> 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) = [2019-01-01 00:00:00]
<Data(5, 8): [[False, ..., False]]>
[[False False False False False False False False]
[False False False False False False False False]
[False False False False False False False False]
[False False False False False False False False]
[False False False False False False False False]]

Mask the polar rows (see the “Assignment by index” section) and inspect the new data mask.
>>> q.data[[0, 4], :] = cfdm.masked
[[ True  True  True  True  True  True  True  True]
[False False False False False False False False]
[False False False False False False False False]
[False False False False False False False False]
[ True  True  True  True  True  True  True  True]]


The _FillValue and missing_value attributes of the field construct are not stored as values of the field construct’s data. They are only used when writing the data to a netCDF dataset. Therefore testing for missing data by testing for equality to one of these property values will produce incorrect results; the any method of the Data instance should be used instead.

See if any data points are masked.
>>> q.data.mask.any()
True


The mask of a netCDF dataset array is implied by array values that meet the criteria implied by the missing_value, _FillValue, valid_min, valid_max, and valid_range properties, and is usually applied automatically by cfdm.read. NetCDF data elements that equal the values of the missing_value and _FillValue properties are masked, as are data elements that exceed the value of the valid_max property, subceed the value of the valid_min property, or lie outside of the range defined by the valid_range property.

However, this automatic masking may be bypassed by setting the mask keyword of the cfdm.read function to False. The mask, as defined in the dataset, may subsequently be applied manually with the apply_masking method of the field construct.

>>> cfdm.write(q, 'masked_q.nc')
[9.96920997e+36, 9.96920997e+36, 9.96920997e+36, 9.96920997e+36,
9.96920997e+36, 9.96920997e+36, 9.96920997e+36, 9.96920997e+36],
[0.023 0.036 0.045 0.062 0.046 0.073 0.006 0.066]
[0.11  0.131 0.124 0.146 0.087 0.103 0.057 0.011]
[0.029 0.059 0.039 0.07  0.058 0.072 0.009 0.017]
[9.96920997e+36, 9.96920997e+36, 9.96920997e+36, 9.96920997e+36,
9.96920997e+36, 9.96920997e+36, 9.96920997e+36, 9.96920997e+36]])
[[   --    --    --    --    --    --    --    --]
[0.023 0.036 0.045 0.062 0.046 0.073 0.006 0.066]
[0.11  0.131 0.124 0.146 0.087 0.103 0.057 0.011]
[0.029 0.059 0.039 0.07  0.058 0.072 0.009 0.017]
[   --    --    --    --    --    --    --    --]]


The apply_masking method of the field construct utilises as many of the missing_value, _FillValue, valid_min, valid_max, and valid_range properties as are present and may be used on any construct, not just those that have been read from datasets.

### Indexing¶

When a Data instance is indexed a new instance is created for the part of the data defined by the indices. Indexing follows 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 i-th 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.

Create new data by indexing and show the shape corresponding to the indices.
>>> data = t.data
>>> data.shape
(1, 10, 9)
>>> data[:, :, 1].shape
(1, 10, 1)
>>> data[:, 0].shape
(1, 1, 9)
>>> data[..., 6:3:-1, 3:6].shape
(1, 3, 3)
>>> data[0, [2, 9], [4, 8]].shape
(1, 2, 2)
>>> data[0, :, -2].shape
(1, 10, 1)


### Assignment¶

Values can be changed by assigning to elements selected by indices of the Data instance using rules that are very similar to the numpy indexing rules, the only difference being:

• 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.

A single value may be assigned to any number of elements.

Set a single element to -1, a “column” of elements to -2 and a “square” of elements to -3.
>>> import numpy
>>> t.data[:, 0, 0] = -1
>>> t.data[:, :, 1] = -2
>>> t.data[..., 6:3:-1, 3:6] = -3
>>> print(t.data.array)
[[[ -1.0  -2.0 279.8 269.5 260.9 265.0 263.5 278.9 269.2]
[272.7  -2.0 279.5 278.9 263.8 263.3 274.2 265.7 279.5]
[269.7  -2.0 273.4 274.2 279.6 270.2 280.0 272.5 263.7]
[261.7  -2.0 270.8 260.3 265.6 279.4 276.9 267.6 260.6]
[264.2  -2.0 262.5  -3.0  -3.0  -3.0 270.4 268.6 275.3]
[263.9  -2.0 272.1  -3.0  -3.0  -3.0 260.0 263.5 270.2]
[273.8  -2.0 268.5  -3.0  -3.0  -3.0 270.6 273.0 270.6]
[267.9  -2.0 279.8 260.3 261.2 275.3 271.2 260.8 268.9]
[270.9  -2.0 273.2 261.7 271.6 265.8 273.0 278.5 266.4]
[276.4  -2.0 276.3 266.1 276.1 268.1 277.0 273.4 269.7]]]


An array of values can be assigned, as long as it is broadcastable to the shape defined by the indices, using the numpy broadcasting rules.

Overwrite the square of values of -3 with the numbers 0 to 8, and set the corners of a different square to be either -4 or -5.
>>> t.data[..., 6:3:-1, 3:6] = numpy.arange(9).reshape(3, 3)
>>> t.data[0, [2, 9], [4, 8]] =  cfdm.Data([[-4, -5]])
>>> print(t.data.array)
[[[ -1.0  -2.0 279.8 269.5 260.9 265.0 263.5 278.9 269.2]
[272.7  -2.0 279.5 278.9 263.8 263.3 274.2 265.7 279.5]
[269.7  -2.0 273.4 274.2  -4.0 270.2 280.0 272.5  -5.0]
[261.7  -2.0 270.8 260.3 265.6 279.4 276.9 267.6 260.6]
[264.2  -2.0 262.5   6.0   7.0   8.0 270.4 268.6 275.3]
[263.9  -2.0 272.1   3.0   4.0   5.0 260.0 263.5 270.2]
[273.8  -2.0 268.5   0.0   1.0   2.0 270.6 273.0 270.6]
[267.9  -2.0 279.8 260.3 261.2 275.3 271.2 260.8 268.9]
[270.9  -2.0 273.2 261.7 271.6 265.8 273.0 278.5 266.4]
[276.4  -2.0 276.3 266.1  -4.0 268.1 277.0 273.4  -5.0]]]


Data array elements may be set to missing values by assigning them to the cfdm.masked constant. Missing values may be unmasked by assigning them to any other value.

Set a column of elements to missing values, and then change one of them back to a non-missing value.
>>> t.data[0, :, -2] = cfdm.masked
>>> t.data[0, 5, -2] = -6
>>> print(t.data.array)
[[[ -1.0  -2.0 279.8 269.5 260.9 265.0 263.5    -- 269.2]
[272.7  -2.0 279.5 278.9 263.8 263.3 274.2    -- 279.5]
[269.7  -2.0 273.4 274.2  -4.0 270.2 280.0    --  -5.0]
[261.7  -2.0 270.8 260.3 265.6 279.4 276.9    -- 260.6]
[264.2  -2.0 262.5   6.0   7.0   8.0 270.4    -- 275.3]
[263.9  -2.0 272.1   3.0   4.0   5.0 260.0  -6.0 270.2]
[273.8  -2.0 268.5   0.0   1.0   2.0 270.6    -- 270.6]
[267.9  -2.0 279.8 260.3 261.2 275.3 271.2    -- 268.9]
[270.9  -2.0 273.2 261.7 271.6 265.8 273.0    -- 266.4]
[276.4  -2.0 276.3 266.1  -4.0 268.1 277.0    --  -5.0]]]


### Manipulating dimensions¶

The dimensions of a field construct’s data may be reordered, have size one dimensions removed and have new new size one dimensions included by using the following field construct methods:

Method

Description

transpose

Reorder data dimensions

squeeze

Remove size one data dimensions

insert_dimension

Insert a new size one data dimension. The new dimension must correspond to an existing size one domain axis construct.

Remove all size one dimensions from the data, noting that metadata constructs which span the corresponding domain axis construct are not affected.
>>> t
<Field: air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) K>
>>> t2 = t.squeeze()
>>> t2
<Field: air_temperature(grid_latitude(10), grid_longitude(9)) K>
>>> print(t2.dimension_coordinates)
Constructs:
{'dimensioncoordinate0': <DimensionCoordinate: atmosphere_hybrid_height_coordinate(1) >,
'dimensioncoordinate1': <DimensionCoordinate: grid_latitude(10) degrees>,
'dimensioncoordinate2': <DimensionCoordinate: grid_longitude(9) degrees>,
'dimensioncoordinate3': <DimensionCoordinate: time(1) days since 2018-12-01 >}

Insert a new size one dimension, corresponding to a size one domain axis construct, and then reorder the dimensions.
>>> t3 = t2.insert_dimension(axis='domainaxis3', position=1)
>>> t3
<Field: air_temperature(grid_latitude(10), time(1), grid_longitude(9)) K>
>>> t3.transpose([2, 0, 1])
<Field: air_temperature(grid_longitude(9), grid_latitude(10), time(1)) K>


## Subspacing¶

Creation of a new field construct which spans a subspace of the domain of an existing field construct is achieved by indexing the field itself, rather than its Data instance. This is because the operation must also subspace 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. Subspacing uses the same cfdm indexing rules that apply to the Data class.

Create a new field whose domain spans the first longitude of the original, and with a reversed latitude axis.
>>> print(q)
Field: specific_humidity (ncvar%q)
----------------------------------
Data            : specific_humidity(latitude(5), longitude(8)) 1
Cell methods    : area: mean
Dimension coords: time(1) = [2019-01-01 00:00:00]
: latitude(5) = [-75.0, ..., 75.0] degrees_north
: longitude(8) = [22.5, ..., 337.5] degrees_east

>>> new = q[::-1, 0]
>>> print(new)
Field: specific_humidity (ncvar%q)
----------------------------------
Data            : specific_humidity(latitude(5), longitude(1)) 1
Cell methods    : area: mean
Dimension coords: time(1) = [2019-01-01 00:00:00]
: latitude(5) = [75.0, ..., -75.0] degrees_north
: longitude(1) = [22.5] degrees_east


A Constructs instance has filtering methods for selecting constructs that meet various criteria:

Method

Filter criteria

filter_by_identity

filter_by_type

filter_by_property

Property values

filter_by_axis

The domain axis constructs spanned by the data

filter_by_naxes

The number of domain axis constructs spanned by the data

filter_by_size

The size domain axis constructs

filter_by_measure

Measure value (for cell measure constructs)

filter_by_method

Method value (for cell method constructs)

filter_by_data

Whether or not there could be be data.

filter_by_key

Construct key

filter_by_ncvar

NetCDF variable name (see the netCDF interface)

filter_by_ncdim

NetCDF dimension name (see the netCDF interface)

Each of these methods returns a new Constructs instance that contains the selected constructs.

Get constructs by their type.
>>> print(t.constructs.filter_by_type('dimension_coordinate'))
Constructs:
{'dimensioncoordinate0': <DimensionCoordinate: atmosphere_hybrid_height_coordinate(1) >,
'dimensioncoordinate1': <DimensionCoordinate: grid_latitude(10) degrees>,
'dimensioncoordinate2': <DimensionCoordinate: grid_longitude(9) degrees>,
'dimensioncoordinate3': <DimensionCoordinate: time(1) days since 2018-12-01 >}
>>> print(t.constructs.filter_by_type('cell_method', 'field_ancillary'))
Constructs:
{'cellmethod0': <CellMethod: domainaxis1: domainaxis2: mean where land (interval: 0.1 degrees)>,
'cellmethod1': <CellMethod: domainaxis3: maximum>,
'fieldancillary0': <FieldAncillary: air_temperature standard_error(10, 9) K>}

Get constructs by their properties.
>>> print(t.constructs.filter_by_property(
...             standard_name='air_temperature standard_error'))
Constructs:
{'fieldancillary0': <FieldAncillary: air_temperature standard_error(10, 9) K>}
>>> print(t.constructs.filter_by_property(
...             standard_name='air_temperature standard_error',
...             units='K'))
Constructs:
{'fieldancillary0': <FieldAncillary: air_temperature standard_error(10, 9) K>}
>>> print(t.constructs.filter_by_property(
...             'or',
...             standard_name='air_temperature standard_error',
...             units='m'))
Constructs:
{'domainancillary0': <DomainAncillary: ncvar%a(1) m>,
'domainancillary2': <DomainAncillary: surface_altitude(10, 9) m>,
'fieldancillary0': <FieldAncillary: air_temperature standard_error(10, 9) K>}

Get constructs whose data span the ‘domainaxis1’ domain axis construct; and those which also do not span the ‘domainaxis2’ domain axis construct.
>>> print(t.constructs.filter_by_axis('and', 'domainaxis1'))
Constructs:
{'auxiliarycoordinate0': <AuxiliaryCoordinate: latitude(10, 9) degrees_N>,
'auxiliarycoordinate1': <AuxiliaryCoordinate: longitude(9, 10) degrees_E>,
'auxiliarycoordinate2': <AuxiliaryCoordinate: long_name=Grid latitude name(10) >,
'cellmeasure0': <CellMeasure: measure:area(9, 10) km2>,
'dimensioncoordinate1': <DimensionCoordinate: grid_latitude(10) degrees>,
'domainancillary2': <DomainAncillary: surface_altitude(10, 9) m>,
'fieldancillary0': <FieldAncillary: air_temperature standard_error(10, 9) K>}

Get cell measure constructs by their “measure”.
>>> print(t.constructs.filter_by_measure('area'))
Constructs:
{'cellmeasure0': <CellMeasure: measure:area(9, 10) km2>}

Get cell method constructs by their “method”.
>>> print(t.constructs.filter_by_method('maximum'))
Constructs:
{'cellmethod1': <CellMethod: domainaxis3: maximum>}


As each of these methods returns a Constructs instance, it is easy to perform further filters on their results:

Make selections from previous selections.
>>> print(t.constructs.filter_by_type('auxiliary_coordinate').filter_by_axis('and', 'domainaxis2'))
Constructs:
{'auxiliarycoordinate0': <AuxiliaryCoordinate: latitude(10, 9) degrees_N>,
'auxiliarycoordinate1': <AuxiliaryCoordinate: longitude(9, 10) degrees_E>}
>>> c = t.constructs.filter_by_type('dimension_coordinate')
>>> d = c.filter_by_property(units='degrees')
>>> print(d)
Constructs:
{'dimensioncoordinate1': <DimensionCoordinate: grid_latitude(10) degrees>,
'dimensioncoordinate2': <DimensionCoordinate: grid_longitude(9) degrees>}


Another method of selection is by metadata construct “identity”. Construct identities are used to describe constructs when they are inspected, and so it is often convenient to copy these identities when selecting metadata constructs. For example, the three auxiliary coordinate constructs in the field construct t have identities 'latitude', 'longitude' and 'long_name=Grid latitude name'.

A construct’s identity may be any one of the following

• The value of the standard_name property, e.g. 'air_temperature',

• The value of any property, preceded by the property name and an equals, e.g. 'long_name=Air Temperature', 'axis=X', 'foo=bar', etc.,

• The cell measure, preceded by “measure:”, e.g. 'measure:volume'

• The cell method, preceded by “method:”, e.g. 'method:maximum'

• The netCDF variable name, preceded by “ncvar%”, e.g. 'ncvar%tas' (see the netCDF interface),

• The netCDF dimension name, preceded by “ncdim%” e.g. 'ncdim%z' (see the netCDF interface), and

• The construct key, preceded by “key%” e.g. 'key%auxiliarycoordinate2'.

Get constructs by their identity.
>>> print(t)
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)) = [[0.81, ..., 0.78]] K
Dimension coords: time(1) = [2019-01-01 00:00:00]
: atmosphere_hybrid_height_coordinate(1) = [1.5]
: grid_latitude(10) = [2.2, ..., -1.76] degrees
: grid_longitude(9) = [-4.7, ..., -1.18] degrees
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: atmosphere_hybrid_height_coordinate
: rotated_latitude_longitude
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
>>> print(t.constructs.filter_by_identity('latitude'))
Constructs:
{'auxiliarycoordinate0': <AuxiliaryCoordinate: latitude(10, 9) degrees_N>}
>>> print(t.constructs.filter_by_identity('long_name=Grid latitude name'))
Constructs:
{'auxiliarycoordinate2': <AuxiliaryCoordinate: long_name=Grid latitude name(10) >}
>>> print(t.constructs.filter_by_identity('measure:area'))
Constructs:
{'cellmeasure0': <CellMeasure: measure:area(9, 10) km2>}
>>> print(t.constructs.filter_by_identity('ncvar%b'))
Constructs:
{'domainancillary1': <DomainAncillary: ncvar%b(1) >}


Each construct has an identity method that, by default, returns the least ambiguous identity (defined in the documentation of a construct’s identity method); and an identities method that returns a list of all of the identities that would select the construct.

As a further convenience, selection by construct identity is also possible by providing identities to a call of a Constructs instance itself, and this technique for selecting constructs by identity will be used in the rest of this tutorial:

Construct selection by identity is possible with via the “filter_by_identity” method, or directly from the “Constructs” instance.
>>> print(t.constructs.filter_by_identity('latitude'))
Constructs:
{'auxiliarycoordinate0': <AuxiliaryCoordinate: latitude(10, 9) degrees_N>}
>>> print(t.constructs('latitude'))
Constructs:
{'auxiliarycoordinate0': <AuxiliaryCoordinate: latitude(10, 9) degrees_N>}


Selection by construct key is useful for systematic metadata construct access, or for when a metadata construct is not identifiable by other means:

Get constructs by construct key.
>>> print(t.constructs.filter_by_key('domainancillary2'))
Constructs:
{'domainancillary2': <DomainAncillary: surface_altitude(10, 9) m>}
>>> print(t.constructs.filter_by_key('cellmethod1'))
Constructs:
{'cellmethod1': <CellMethod: domainaxis3: maximum>}
>>> print(t.constructs.filter_by_key('auxiliarycoordinate2', 'cellmeasure0'))
Constructs:
{'auxiliarycoordinate2': <AuxiliaryCoordinate: long_name=Grid latitude name(10) >,
'cellmeasure0': <CellMeasure: measure:area(9, 10) km2>}


If no constructs match the given criteria, then an “empty” Constructs instance is returned:

If no constructs meet the criteria then an empty “Constructs” object is returned.
>>> c = t.constructs('radiation_wavelength')
>>> c
<Constructs: >
>>> print(c)
Constructs:
{}
>>> len(c)
0


The constructs that were not selected by a filter may be returned by the inverse_filter method applied to the results of filters:

Get the constructs that were not selected by a filter.
>>> c = t.constructs.filter_by_type('auxiliary_coordinate')
>>> c
<Constructs: auxiliary_coordinate(3)>
>>> c.inverse_filter()
<Constructs: cell_measure(1), cell_method(2), coordinate_reference(2), dimension_coordinate(4), domain_ancillary(3), domain_axis(4), field_ancillary(1)>


Note that selection by construct type is equivalent to using the particular method of the field construct for retrieving that type of metadata construct:

The bespoke methods for retrieving constructs by type are equivalent to a selection on all of the metadata constructs.
>>> print(t.constructs.filter_by_type('cell_measure'))
Constructs:
{'cellmeasure0': <CellMeasure: measure:area(9, 10) km2>}
>>> print(t.cell_measures)
Constructs:
{'cellmeasure0': <CellMeasure: measure:area(9, 10) km2>}


An individual metadata construct may be returned, without its construct key, by any of the following techniques:

Get the “latitude” metadata construct with its construct identity.
>>> t.construct('latitude')
<AuxiliaryCoordinate: latitude(10, 9) degrees_N>

Get the “latitude” metadata construct key with its construct identity and use the key to get the construct itself
>>> key = t.construct_key('latitude')
>>> t.get_construct(key)
<AuxiliaryCoordinate: latitude(10, 9) degrees_N>

Get the “latitude” metadata construct via its identity and the ‘value’ method.
>>> t.constructs('latitude').value()
<AuxiliaryCoordinate: latitude(10, 9) degrees_N>

Get the “latitude” metadata construct via its construct key and the ‘get’ method.
>>> c = t.constructs.get(key)
<AuxiliaryCoordinate: latitude(10, 9) degrees_N>

Get the “latitude” metadata construct via its construct key and indexing
>>> t.constructs[key]
<AuxiliaryCoordinate: latitude(10, 9) degrees_N>


The construct method of the field construct and the value method of the Constructs instance will raise an exception of there is not a unique metadata construct to return, but this may be replaced with returning a default value or raising a customised exception:

By default an exception is raised if there is not a unique construct that meets the criteria. Alternatively, the value of the “default” parameter is returned.
>>> t.construct('measure:volume')                # Raises Exception
Traceback (most recent call last):
...
ValueError: Can't return zero constructs
>>> t.construct('measure:volume', False)
False
>>> c = t.constructs.filter_by_measure('volume')
>>> len(c)
0
>>> c.value()                                    # Raises Exception
Traceback (most recent call last):
...
ValueError: Can't return zero constructs
>>> c.value(default='No construct')
'No construct'
>>> c.value(default=KeyError('My message'))      # Raises Exception
Traceback (most recent call last):
...
KeyError: 'My message'
>>> d = t.constructs('units=degrees')
>>> len(d)
2
>>> d.value()                                    # Raises Exception
Traceback (most recent call last):
...
ValueError: Can't return 2 constructs
>>> print(d.value(default=None))
None


The get method of a Constructs instance accepts an optional second argument to be returned if the construct key does not exist, exactly like the Python dict.get method.

Metadata constructs share the same API as the field construct for accessing their properties:

Retrieve the “longitude” metadata construct, set a new property, and then inspect all of the properties.
>>> lon = q.construct('longitude')
>>> lon
<DimensionCoordinate: longitude(8) degrees_east>
>>> lon.set_property('long_name', 'Longitude')
>>> lon.properties()
{'units': 'degrees_east',
'long_name': 'Longitude',
'standard_name': 'longitude'}

Get the metadata construct with units of “km2”, find its canonical identity, and all of its valid identities, that may be used for selection by the “filter_by_identity” method
>>> area = t.constructs.filter_by_property(units='km2').value()
>>> area
<CellMeasure: measure:area(9, 10) km2>
>>> area.identity()
'measure:area'
>>> area.identities()
['measure:area', 'units=km2', 'ncvar%cell_measure']


Metadata constructs share the a similar API as the field construct as the field construct for accessing their data:

Retrieve the “longitude” metadata construct, inspect its data, change the third element of the array, and get the data as a numpy array.
>>> lon = q.constructs('longitude').value()
>>> lon
<DimensionCoordinate: longitude(8) degrees_east>
>>> lon.data
<Data(8): [22.5, ..., 337.5] degrees_east>
>>> lon.data[2]
<Data(1): [112.5] degrees_east>
>>> lon.data[2] = 133.33
>>> print(lon.data.array)
[22.5 67.5 133.33 157.5 202.5 247.5 292.5 337.5]


The domain axis constructs spanned by a particular metadata construct’s data are found with the get_data_axes method of the field construct:

Find the construct keys of the domain axis constructs spanned by the data of each metadata construct.
>>> key = t.construct_key('latitude')
>>> key
'auxiliarycoordinate0'
>>> t.get_data_axes(key=key)
('domainaxis1', 'domainaxis2')


The domain axis constructs spanned by all the data of all metadata construct may be found with the data_axes method of the field construct’s Constructs instance:

Find the construct keys of the domain axis constructs spanned by the data of each metadata construct.
>>> t.constructs.data_axes()
{'auxiliarycoordinate0': ('domainaxis1', 'domainaxis2'),
'auxiliarycoordinate1': ('domainaxis2', 'domainaxis1'),
'auxiliarycoordinate2': ('domainaxis1',),
'cellmeasure0': ('domainaxis2', 'domainaxis1'),
'dimensioncoordinate0': ('domainaxis0',),
'dimensioncoordinate1': ('domainaxis1',),
'dimensioncoordinate2': ('domainaxis2',),
'dimensioncoordinate3': ('domainaxis3',),
'domainancillary0': ('domainaxis0',),
'domainancillary1': ('domainaxis0',),
'domainancillary2': ('domainaxis1', 'domainaxis2'),
'fieldancillary0': ('domainaxis1', 'domainaxis2')}


A size one domain axis construct that is not spanned by the field construct’s data may still be spanned by the data of metadata constructs. For example, the data of the field construct t does not span the size one domain axis construct with key 'domainaxis3', but this domain axis construct is spanned by a “time” dimension coordinate construct (with key 'dimensioncoordinate3'). Such a dimension coordinate (i.e. one that applies to a domain axis construct that is not spanned by the field construct’s data) corresponds to a CF-netCDF scalar coordinate variable.

## Time¶

Constructs representing elapsed time (identified by the presence of “reference time” units) have data array values that represent elapsed time since a reference date. These values may be converted into the date-time objects of the cftime package with the datetime_array method of the Data instance.

Inspect the the values of a “time” construct as elapsed times and as date-times.
>>> time = q.construct('time')
>>> time
<DimensionCoordinate: time(1) days since 2018-12-01 >
>>> time.get_property('units')
'days since 2018-12-01'
>>> time.get_property('calendar', default='standard')
'standard'
>>> print(time.data.array)
[ 31.]
>>> print(time.data.datetime_array)
[cftime.DatetimeGregorian(2019, 1, 1, 0, 0, 0, 0, 1, 1)]


## Domain¶

The domain of the CF data model is not a construct, but is defined collectively by various other metadata constructs included in the field construct. It is represented by the Domain class. The domain instance may be accessed with the domain attribute, or get_domain method, of the field construct.

Get the domain, and inspect it.
>>> domain = t.domain
>>> domain
<Domain: {1, 1, 9, 10}>
>>> print(domain)
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) = [2019-01-01 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: atmosphere_hybrid_height_coordinate
: rotated_latitude_longitude
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
>>> description = domain.dump(display=False)


Changes to domain instance are seen by the field construct, and vice versa. This is because the domain instance is merely a “view” of the relevant metadata constructs contained in the field construct.

Change a property of a metadata construct of the domain and show that this change appears in the same metadata data construct of the parent field, and vice versa.
>>> domain_latitude = t.domain.constructs('latitude').value()
>>> field_latitude = t.constructs('latitude').value()
>>> domain_latitude.set_property('test', 'set by domain')
>>> print(field_latitude.get_property('test'))
set by domain
>>> field_latitude.set_property('test', 'set by field')
>>> print(domain_latitude.get_property('test'))
set by field
>>> domain_latitude.del_property('test')
'set by field'
>>> field_latitude.has_property('test')
False


All of the methods and attributes related to the domain are listed here.

## Domain axes¶

A domain axis metadata construct specifies the number of points along an independent axis of the field construct’s domain and is stored in a DomainAxis instance. The size of the axis is retrieved with the get_size method of the domain axis construct.

Get the size of a domain axis construct.
>>> print(q.domain_axes)
Constructs:
{'domainaxis0': <DomainAxis: size(5)>,
'domainaxis1': <DomainAxis: size(8)>,
'domainaxis2': <DomainAxis: size(1)>}
>>> d = q.domain_axes.get('domainaxis1')
>>> d
<DomainAxis: size(8)>
>>> d.get_size()
8


## Coordinates¶

There are two types of coordinate construct, dimension and auxiliary coordinate constructs, which can be retrieved together with the coordinates method of the field construct, as well as individually with the auxiliary_coordinates and dimension_coordinates methods.

Retrieve both types of coordinate constructs.
>>> print(t.coordinates)
Constructs:
{'auxiliarycoordinate0': <AuxiliaryCoordinate: latitude(10, 9) degrees_N>,
'auxiliarycoordinate1': <AuxiliaryCoordinate: longitude(9, 10) degrees_E>,
'auxiliarycoordinate2': <AuxiliaryCoordinate: long_name=Grid latitude name(10) >,
'dimensioncoordinate0': <DimensionCoordinate: atmosphere_hybrid_height_coordinate(1) >,
'dimensioncoordinate1': <DimensionCoordinate: grid_latitude(10) degrees>,
'dimensioncoordinate2': <DimensionCoordinate: grid_longitude(9) degrees>,
'dimensioncoordinate3': <DimensionCoordinate: time(1) days since 2018-12-01 >}


### Bounds¶

A coordinate construct may contain an array of cell bounds that provides the extent of each cell by defining the locations of the cell vertices. This is in addition to the main coordinate data array that contains a representative grid point location for each cell. The cell bounds are stored in a Bounds class instance that is accessed with the bounds attribute, or get_bounds method, of the coordinate construct.

A Bounds instance shares the the same API as the field construct for accessing its data.

Get the Bounds instance of a coordinate construct and inspect its data.
>>> lon = t.constructs('grid_longitude').value()
>>> bounds = lon.bounds
>>> bounds
<Bounds: grid_longitude(9, 2) >
>>> bounds.data
<Data(9, 2): [[-4.92, ..., -0.96]]>
>>> print(bounds.data.array)
[[-4.92 -4.48]
[-4.48 -4.04]
[-4.04 -3.6 ]
[-3.6  -3.16]
[-3.16 -2.72]
[-2.72 -2.28]
[-2.28 -1.84]
[-1.84 -1.4 ]
[-1.4  -0.96]]


The Bounds instance inherits the descriptive properties from its parent coordinate construct, but it may also have its own properties (although setting these is not recommended).

Inspect the inherited and bespoke properties of a Bounds instance.
>>> bounds.inherited_properties()
{'standard_name': 'grid_longitude',
'units': 'degrees'}
>>> bounds.properties()
{}


### Geometry cells¶

For many geospatial applications, cell bounds can not be represented by a simple line or polygon, and different cells may have different numbers of nodes describing their bounds. For example, if each cell describes the areal extent of a watershed, then it is likely that some watersheds will require more nodes than others. Such cells are called geometries.

If a coordinate construct represents geometries then it will have a “geometry” attribute (not a CF property) with one of the values 'point', 'line' or 'polygon'.

This is illustrated with the file geometry.nc (found in the sample datasets):

Read and inspect a dataset containing geometry cell bounds.
>>> f = cfdm.read('geometry.nc')[0]
>>> print(f)
Field: precipitation_amount (ncvar%pr)
--------------------------------------
Data            : precipitation_amount(cf_role=timeseries_id(2), time(4))
Dimension coords: time(4) = [2000-01-02 00:00:00, ..., 2000-01-05 00:00:00]
Auxiliary coords: latitude(cf_role=timeseries_id(2)) = [25.0, 7.0] degrees_north
: longitude(cf_role=timeseries_id(2)) = [10.0, 40.0] degrees_east
: altitude(cf_role=timeseries_id(2)) = [5000.0, 20.0] m
: cf_role=timeseries_id(cf_role=timeseries_id(2)) = [b'x1', b'y2']
Coord references: grid_mapping_name:latitude_longitude
>>> lon = f.construct('longitude')
>>> lon.dump()
Auxiliary coordinate: longitude
standard_name = 'longitude'
units = 'degrees_east'
Data(2) = [10.0, 40.0] degrees_east
Geometry: polygon
Bounds:axis = 'X'
Bounds:standard_name = 'longitude'
Bounds:units = 'degrees_east'
Bounds:Data(2, 3, 4) = [[[20.0, ..., --]]] degrees_east
Interior Ring:Data(2, 3) = [[0, ..., --]]
>>> lon.get_geometry()
'polygon'


Bounds for geometry cells are also stored in a Bounds instance, but one that always has two extra trailing dimensions (rather than one). The fist trailing dimension indexes the distinct parts of a geometry, and the second indexes the nodes of each part. When a part has fewer nodes than another, its nodes dimension is padded with missing data.

Inspect the geometry nodes.
>>> print(lon.bounds.data.array)
[[20.0 10.0  0.0   --]
[ 5.0 10.0 15.0 10.0]
[20.0 10.0  0.0   --]]

[[50.0 40.0 30.0   --]
[  --   --   --   --]
[  --   --   --   --]]]


If a cell is composed of multiple polygon parts, an individual polygon may define an “interior ring”, i.e. a region that is to be omitted from, as opposed to included in, the cell extent. Such cells also have an interior ring array that spans the same domain axes as the coordinate cells, with the addition of one extra dimension that indexes the parts for each cell. This array records whether each polygon is to be included or excluded from the cell, with values of 1 or 0 respectively.

Inspect the interior ring information.
>>> print(lon.get_interior_ring().data.array)
[[0  1  0]
[0 -- --]]


Note that it is preferable to access the data type, number of dimensions, dimension sizes and number of elements of the coordinate construct via the construct’s attributes, rather than the attributes of the Data instance that provides representative values for each cell. This is because the representative cell values for geometries are optional, and if they are missing then the construct attributes are able to infer these attributes from the bounds.

When a field construct containing geometries is written to disk, a CF-netCDF geometry container variable is automatically created, and the cells encoded with the compression techniques defined in the CF conventions.

## Domain ancillaries¶

A domain ancillary construct provides information which is needed for computing the location of cells in an alternative coordinate system. If a domain ancillary construct provides extra coordinates then it may contain cell bounds in addition to its main data array.

Get the data and bounds data of a domain ancillary construct.
>>> a = t.constructs.get('domainancillary0')
>>> print(a.data.array)
[10.]
>>> bounds = a.bounds
>>> bounds
<Bounds: ncvar%a_bounds(1, 2) >
>>> print(bounds.data.array)
[[  5.  15.]]


## Coordinate systems¶

A field construct may contain various coordinate systems. Each coordinate system is either defined by a coordinate reference construct that relates dimension coordinate, auxiliary coordinate and domain ancillary constructs (as is the case for the field construct t), or is inferred from dimension and auxiliary coordinate constructs alone (as is the case for the field construct q).

A coordinate reference construct contains

• references (by construct keys) to the dimension and auxiliary coordinate constructs to which it applies, accessed with the coordinates method of the coordinate reference construct;

Select the vertical coordinate system construct and inspect its coordinate constructs.
>>> crs = t.constructs('standard_name:atmosphere_hybrid_height_coordinate').value()
>>> crs
<CoordinateReference: atmosphere_hybrid_height_coordinate>
>>> crs.dump()
Coordinate Reference: atmosphere_hybrid_height_coordinate
Coordinate conversion:computed_standard_name = altitude
Coordinate conversion:standard_name = atmosphere_hybrid_height_coordinate
Coordinate conversion:a = domainancillary0
Coordinate conversion:b = domainancillary1
Coordinate conversion:orog = domainancillary2
Coordinate: dimensioncoordinate0
>>> crs.coordinates()
{'dimensioncoordinate0'}

Get the datum and inspect its parameters.
>>> crs.datum
>>> crs.datum.parameters()

Get the coordinate conversion and inspect its parameters and referenced domain ancillary constructs.
>>> crs.coordinate_conversion
<CoordinateConversion: Parameters: computed_standard_name, standard_name; Ancillaries: a, b, orog>
>>> crs.coordinate_conversion.parameters()
{'computed_standard_name': 'altitude',
'standard_name': 'atmosphere_hybrid_height_coordinate'}
>>> crs.coordinate_conversion.domain_ancillaries()
{'a': 'domainancillary0',
'b': 'domainancillary1',
'orog': 'domainancillary2'}


## Cell methods¶

A cell method construct describes how the data represent the variation of the physical quantity within the cells of the domain and is stored in a CellMethod instance. A field constructs allows multiple cell method constructs to be recorded.

Inspect the cell methods. The description follows the CF conventions for cell_method attribute strings, apart from the use of construct keys instead of netCDF variable names for cell method axes identification.
>>> print(t.cell_methods)
Constructs:
{'cellmethod0': <CellMethod: domainaxis1: domainaxis2: mean where land (interval: 0.1 degrees)>,
'cellmethod1': <CellMethod: domainaxis3: maximum>}


The application of cell methods is not commutative (e.g. a mean of variances is generally not the same as a variance of means), so a Constructs instance has an ordered method to retrieve the cell method constructs in the same order that they were were added to the field construct during field construct creation.

Retrieve the cell method constructs in the same order that they were applied.
>>> t.cell_methods.ordered()
OrderedDict([('cellmethod0', <CellMethod: domainaxis1: domainaxis2: mean where land (interval: 0.1 degrees)>),
('cellmethod1', <CellMethod: domainaxis3: maximum>)])


The axes to which the method applies, the method itself, and any qualifying properties are accessed with the get_axes, get_method, , get_qualifier and qualifiers methods of the cell method construct.

Get the domain axes constructs to which the cell method construct applies, and the method and other properties.
>>> cm = t.constructs('method:mean').value()
>>> cm
<CellMethod: domainaxis1: domainaxis2: mean where land (interval: 0.1 degrees)>)
>>> cm.get_axes()
('domainaxis1', 'domainaxis2')
>>> cm.get_method()
'mean'
>>> cm.qualifiers()
{'interval': [<Data(): 0.1 degrees>], 'where': 'land'}
>>> cm.get_qualifier('where')
'land'


## Field ancillaries¶

A field ancillary construct provides metadata which are distributed over the same domain as the field construct itself. For example, if a field construct holds a data retrieved from a satellite instrument, a field ancillary construct might provide the uncertainty estimates for those retrievals (varying over the same spatiotemporal domain).

Get the properties and data of a field ancillary construct.
>>> a = t.get_construct('fieldancillary0')
>>> a
<FieldAncillary: air_temperature standard_error(10, 9) K>
>>> a.properties()
{'standard_name': 'air_temperature standard_error',
'units': 'K'}
>>> a.data
<Data(10, 9): [[0.76, ..., 0.32]] K>


## Field creation in memory¶

There are various methods for creating a field construct in memory:

• Ab initio creation: Instantiate instances of field and metadata construct classes and manually provide the connections between them.

• Command modification: Produce the commands that would create an already existing field construct, and then modify them.

• Creation by conversion: Convert a single metadata construct already in memory to an independent field construct

### Ab initio creation¶

Ab initio creation of a field construct has three stages:

Stage 1: The field construct is created without metadata constructs.

Stage 2: Metadata constructs are created independently.

Stage 3: The metadata constructs are inserted into the field construct with cross-references to other, related metadata constructs if required. For example, an auxiliary coordinate construct is related to an ordered list of the domain axis constructs which correspond to its data array dimensions.

There are two equivalent approaches to stages 1 and 2.

Either as much of the content as possible is specified during object instantiation:

Create a field construct with a “standard_name” property. Create dimension coordinate and field ancillary constructs, both with properties and data.
>>> p = cfdm.Field(properties={'standard_name': 'precipitation_flux'})
>>> p
<Field: precipitation_flux>
>>> dc = cfdm.DimensionCoordinate(properties={'long_name': 'Longitude'},
...                               data=cfdm.Data([0, 1, 2.]))
>>> dc
<DimensionCoordinate: long_name=Longitude(3) >
>>> fa = cfdm.FieldAncillary(
...        properties={'standard_name': 'precipitation_flux status_flag'},
...        data=cfdm.Data(numpy.array([0, 0, 2], dtype='int8')))
>>> fa
<FieldAncillary: precipitation_flux status_flag(3) >


or else some or all content is added after instantiation via object methods:

Create empty constructs and provide them with properties and data after instantiation.
>>> p = cfdm.Field()
>>> p
<Field: >
>>> p.set_property('standard_name', 'precipitation_flux')
>>> p
<Field: precipitation_flux>
>>> dc = cfdm.DimensionCoordinate()
>>> dc
<DimensionCoordinate:  >
>>> dc.set_property('long_name', 'Longitude')
>>> dc.set_data(cfdm.Data([1, 2, 3.]))
>>> dc
<DimensionCoordinate: long_name=Longitude(3) >
>>> fa = cfdm.FieldAncillary(
...        data=cfdm.Data(numpy.array([0, 0, 2], dtype='int8')))
>>> fa
<FieldAncillary: (3) >
>>> fa.set_property('standard_name', 'precipitation_flux status_flag')
>>> fa
<FieldAncillary: precipitation_flux status_flag(3) >


For stage 3, the set_construct method of the field construct is used for setting metadata constructs and mapping data array dimensions to domain axis constructs. This method returns the construct key for the metadata construct which can be used when other metadata constructs are added to the field (e.g. to specify which domain axis constructs correspond to a data array), or when other metadata constructs are created (e.g. to identify the domain ancillary constructs forming part of a coordinate reference construct):

Set a domain axis construct and use its construct key when setting the dimension coordinate construct. Also create a cell method construct that applies to the domain axis construct.
>>> longitude_axis = p.set_construct(cfdm.DomainAxis(3))
>>> longitude_axis
'domainaxis0'
>>> key = p.set_construct(dc, axes=longitude_axis)
>>> key
'dimensioncoordinate0'
>>> cm = cfdm.CellMethod(axes=longitude_axis, method='minimum')
>>> p.set_construct(cm)
'cellmethod0'


In general, the order in which metadata constructs are added to the field does not matter, except when one metadata construct is required by another, in which case the former must be added to the field first so that its construct key is available to the latter. Cell method constructs must, however, be set in the relative order in which their methods were applied to the data.

The domain axis constructs spanned by a metadata construct’s data may be changed after insertion with the set_data_axes method of the field construct.

Create a field construct with properties; data; and domain axis, cell method and dimension coordinate metadata constructs (data arrays have been generated with dummy values using numpy.arange).
import numpy
import cfdm

# Initialise the field construct with properties
Q = cfdm.Field(
properties={'project': 'research',
'standard_name': 'specific_humidity',
'units': '1'})

# Create the domain axis constructs
domain_axisT = cfdm.DomainAxis(1)
domain_axisY = cfdm.DomainAxis(5)
domain_axisX = cfdm.DomainAxis(8)

# Insert the domain axis constructs into the field. The
# set_construct method returns the domain axis construct key that
# will be used later to specify which domain axis corresponds to
# which dimension coordinate construct.
axisT = Q.set_construct(domain_axisT)
axisY = Q.set_construct(domain_axisY)
axisX = Q.set_construct(domain_axisX)

# Create and insert the field construct data
data = cfdm.Data(numpy.arange(40.).reshape(5, 8))
Q.set_data(data, axes=[axisY, axisX])

# Create the cell method constructs
cell_method1 = cfdm.CellMethod(axes='area', method='mean')

cell_method2 = cfdm.CellMethod()
cell_method2.set_axes(axisT)
cell_method2.set_method('maximum')

# Insert the cell method constructs into the field in the same
# order that their methods were applied to the data
Q.set_construct(cell_method1)
Q.set_construct(cell_method2)

# Create a "time" dimension coordinate construct, with coordinate
# bounds
dimT = cfdm.DimensionCoordinate(
properties={'standard_name': 'time',
'units': 'days since 2018-12-01'},
data=cfdm.Data([15.5]),
bounds=cfdm.Bounds(data=cfdm.Data([[0,31.]])))

# Create a "longitude" dimension coordinate construct, without
# coordinate bounds
dimX = cfdm.DimensionCoordinate(data=cfdm.Data(numpy.arange(8.)))
dimX.set_properties({'standard_name': 'longitude',
'units': 'degrees_east'})

# Create a "longitude" dimension coordinate construct
dimY = cfdm.DimensionCoordinate(properties={'standard_name': 'latitude',
'units': 'degrees_north'})
array = numpy.arange(5.)
dimY.set_data(cfdm.Data(array))

# Create and insert the latitude coordinate bounds
bounds_array = numpy.empty((5, 2))
bounds_array[:, 0] = array - 0.5
bounds_array[:, 1] = array + 0.5
bounds = cfdm.Bounds(data=cfdm.Data(bounds_array))
dimY.set_bounds(bounds)

# Insert the dimension coordinate constructs into the field,
# specifying to # which domain axis each one corresponds
Q.set_construct(dimT, axes=axisT)
Q.set_construct(dimY, axes=axisY)
Q.set_construct(dimX, axes=axisX)

Inspect the new field construct.
>>> Q.dump()
------------------------
Field: specific_humidity
------------------------
project = 'research'
standard_name = 'specific_humidity'
units = '1'

Data(latitude(5), longitude(8)) = [[0.0, ..., 39.0]] 1

Cell Method: area: mean
Cell Method: time(1): maximum

Domain Axis: latitude(5)
Domain Axis: longitude(8)
Domain Axis: time(1)

Dimension coordinate: time
standard_name = 'time'
units = 'days since 2018-12-01'
Data(time(1)) = [2018-12-16 12:00:00]
Bounds:Data(time(1), 2) = [[2018-12-01 00:00:00, 2019-01-01 00:00:00]]

Dimension coordinate: latitude
standard_name = 'latitude'
units = 'degrees_north'
Data(latitude(5)) = [0.0, ..., 4.0] degrees_north
Bounds:Data(latitude(5), 2) = [[-0.5, ..., 4.5]] degrees_north

Dimension coordinate: longitude
standard_name = 'longitude'
units = 'degrees_east'
Data(longitude(8)) = [0.0, ..., 7.0] degrees_east


The Conventions property does not need to be set because it is automatically included in output files as a netCDF global Conventions attribute, either as the CF version of the cfdm package (as returned by the cfdm.CF function), or else specified via the Conventions keyword of the cfdm.write function. See the section on Writing to disk for details on how to specify additional conventions.

If this field were to be written to a netCDF dataset then, in the absence of predefined names, default netCDF variable and dimension names would be automatically generated (based on standard names where they exist). The setting of bespoke netCDF names is, however, easily done with the netCDF interface.

Set netCDF variable and dimension names for the field and metadata constructs.
Q.nc_set_variable('q')

domain_axisT.nc_set_dimension('time')
domain_axisY.nc_set_dimension('lat')
domain_axisX.nc_set_dimension('lon')

dimT.nc_set_variable('time')
dimY.nc_set_variable('lat')
dimX.nc_set_variable('lon')


Here is a more complete example which creates a field construct that contains every type of metadata construct (again, data arrays have been generated with dummy values using numpy.arange):

Create a field construct that contains at least one instance of each type of metadata construct.
import numpy
import cfdm

# Initialize the field construct
tas = cfdm.Field(
properties={'project': 'research',
'standard_name': 'air_temperature',
'units': 'K'})

# Create and set domain axis constructs
axis_T = tas.set_construct(cfdm.DomainAxis(1))
axis_Z = tas.set_construct(cfdm.DomainAxis(1))
axis_Y = tas.set_construct(cfdm.DomainAxis(10))
axis_X = tas.set_construct(cfdm.DomainAxis(9))

# Set the field construct data
tas.set_data(cfdm.Data(numpy.arange(90.).reshape(10, 9)),
axes=[axis_Y, axis_X])

# Create and set the cell method constructs
cell_method1 = cfdm.CellMethod(
axes=[axis_Y, axis_X],
method='mean',
qualifiers={'where': 'land',
'interval': [cfdm.Data(0.1, units='degrees')]})

cell_method2 = cfdm.CellMethod(axes=axis_T, method='maximum')

tas.set_construct(cell_method1)
tas.set_construct(cell_method2)

# Create and set the field ancillary constructs
field_ancillary = cfdm.FieldAncillary(
properties={'standard_name': 'air_temperature standard_error',
'units': 'K'},
data=cfdm.Data(numpy.arange(90.).reshape(10, 9)))

tas.set_construct(field_ancillary, axes=[axis_Y, axis_X])

# Create and set the dimension coordinate constructs
dimension_coordinate_T = cfdm.DimensionCoordinate(
properties={'standard_name': 'time',
'units': 'days since 2018-12-01'},
data=cfdm.Data([15.5]),
bounds=cfdm.Bounds(data=cfdm.Data([[0., 31]])))

dimension_coordinate_Z = cfdm.DimensionCoordinate(
properties={'computed_standard_name': 'altitude',
'standard_name': 'atmosphere_hybrid_height_coordinate'},
data = cfdm.Data([1.5]),
bounds=cfdm.Bounds(data=cfdm.Data([[1.0, 2.0]])))

dimension_coordinate_Y = cfdm.DimensionCoordinate(
properties={'standard_name': 'grid_latitude',
'units': 'degrees'},
data=cfdm.Data(numpy.arange(10.)),
bounds=cfdm.Bounds(data=cfdm.Data(numpy.arange(20).reshape(10, 2))))

dimension_coordinate_X = cfdm.DimensionCoordinate(
properties={'standard_name': 'grid_longitude',
'units': 'degrees'},
data=cfdm.Data(numpy.arange(9.)),
bounds=cfdm.Bounds(data=cfdm.Data(numpy.arange(18).reshape(9, 2))))

dim_T = tas.set_construct(dimension_coordinate_T, axes=axis_T)
dim_Z = tas.set_construct(dimension_coordinate_Z, axes=axis_Z)
dim_Y = tas.set_construct(dimension_coordinate_Y, axes=axis_Y)
dim_X = tas.set_construct(dimension_coordinate_X, axes=axis_X)

# Create and set the auxiliary coordinate constructs
auxiliary_coordinate_lat = cfdm.AuxiliaryCoordinate(
properties={'standard_name': 'latitude',
'units': 'degrees_north'},
data=cfdm.Data(numpy.arange(90.).reshape(10, 9)))

auxiliary_coordinate_lon = cfdm.AuxiliaryCoordinate(
properties={'standard_name': 'longitude',
'units': 'degrees_east'},
data=cfdm.Data(numpy.arange(90.).reshape(9, 10)))

array = numpy.ma.array(list('abcdefghij'))
auxiliary_coordinate_name = cfdm.AuxiliaryCoordinate(
properties={'long_name': 'Grid latitude name'},
data=cfdm.Data(array))

aux_LAT  = tas.set_construct(auxiliary_coordinate_lat, axes=[axis_Y, axis_X])
aux_LON  = tas.set_construct(auxiliary_coordinate_lon, axes=[axis_X, axis_Y])
aux_NAME = tas.set_construct(auxiliary_coordinate_name, axes=[axis_Y])

# Create and set domain ancillary constructs
domain_ancillary_a = cfdm.DomainAncillary(
properties={'units': 'm'},
data=cfdm.Data([10.]),
bounds=cfdm.Bounds(data=cfdm.Data([[5., 15.]])))

domain_ancillary_b = cfdm.DomainAncillary(
properties={'units': '1'},
data=cfdm.Data([20.]),
bounds=cfdm.Bounds(data=cfdm.Data([[14, 26.]])))

domain_ancillary_orog = cfdm.DomainAncillary(
properties={'standard_name': 'surface_altitude',
'units': 'm'},
data=cfdm.Data(numpy.arange(90.).reshape(10, 9)))

domain_anc_A    = tas.set_construct(domain_ancillary_a, axes=axis_Z)
domain_anc_B    = tas.set_construct(domain_ancillary_b, axes=axis_Z)
domain_anc_OROG = tas.set_construct(domain_ancillary_orog,
axes=[axis_Y, axis_X])

# Create the datum for the coordinate reference constructs

# Create the coordinate conversion for the horizontal coordinate
# reference construct
coordinate_conversion_h = cfdm.CoordinateConversion(
parameters={'grid_mapping_name': 'rotated_latitude_longitude',
'grid_north_pole_latitude': 38.0,
'grid_north_pole_longitude': 190.0})

# Create the coordinate conversion for the vertical coordinate
# reference construct
coordinate_conversion_v = cfdm.CoordinateConversion(
parameters={'standard_name': 'atmosphere_hybrid_height_coordinate',
'computed_standard_name': 'altitude'},
domain_ancillaries={'a': domain_anc_A,
'b': domain_anc_B,
'orog': domain_anc_OROG})

# Create the vertical coordinate reference construct
horizontal_crs = cfdm.CoordinateReference(
datum=datum,
coordinate_conversion=coordinate_conversion_h,
coordinates=[dim_X,
dim_Y,
aux_LAT,
aux_LON])

# Create the vertical coordinate reference construct
vertical_crs = cfdm.CoordinateReference(
datum=datum,
coordinate_conversion=coordinate_conversion_v,
coordinates=[dim_Z])

# Set the coordinate reference constructs
tas.set_construct(horizontal_crs)
tas.set_construct(vertical_crs)

# Create and set the cell measure constructs
cell_measure = cfdm.CellMeasure(measure='area',
properties={'units': 'km2'},
data=cfdm.Data(numpy.arange(90.).reshape(9, 10)))

tas.set_construct(cell_measure, axes=[axis_X, axis_Y])


The new field construct may now be inspected:

Inspect the new field construct.
>>> print(tas)
Field: air_temperature
----------------------
Data            : air_temperature(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)) = [[0.0, ..., 89.0]] K
Dimension coords: time(1) = [2018-12-16 12:00:00]
: atmosphere_hybrid_height_coordinate(1) = [1.5]
: grid_latitude(10) = [0.0, ..., 9.0] degrees
: grid_longitude(9) = [0.0, ..., 8.0] degrees
Auxiliary coords: latitude(grid_latitude(10), grid_longitude(9)) = [[0.0, ..., 89.0]] degrees_north
: longitude(grid_longitude(9), grid_latitude(10)) = [[0.0, ..., 89.0]] degrees_east
: long_name=Grid latitude name(grid_latitude(10)) = [--, ..., j]
Cell measures   : measure:area(grid_longitude(9), grid_latitude(10)) = [[0.0, ..., 89.0]] km2
Coord references: atmosphere_hybrid_height_coordinate
: rotated_latitude_longitude
Domain ancils   : key%domainancillary0(atmosphere_hybrid_height_coordinate(1)) = [10.0] m
: key%domainancillary1(atmosphere_hybrid_height_coordinate(1)) = [20.0] 1
: surface_altitude(grid_latitude(10), grid_longitude(9)) = [[0.0, ..., 89.0]] m


### Command modification¶

It is sometimes convenient to produce the commands that would create an already existing field construct, and then modify them to create the desired field construct. The commands are produced by the creation_commands method of the existing field construct.

Produce the commands that would create an existing field construct.
>>> q, t = cfdm.read('file.nc')
>>> print(q.creation_commands())
#
# field: specific_humidity
field = cfdm.Field()
field.set_properties({'Conventions': 'CF-1.8', 'project': 'research', 'standard_name': 'specific_humidity', 'units': '1'})
field.nc_set_variable('q')
data = cfdm.Data([[0.007, 0.034, 0.003, 0.014, 0.018, 0.037, 0.024, 0.029], [0.023, 0.036, 0.045, 0.062, 0.046, 0.073, 0.006, 0.066], [0.11, 0.131, 0.124, 0.146, 0.087, 0.103, 0.057, 0.011], [0.029, 0.059, 0.039, 0.07, 0.058, 0.072, 0.009, 0.017], [0.006, 0.036, 0.019, 0.035, 0.018, 0.037, 0.034, 0.013]], units='1', dtype='f8')
field.set_data(data)
#
# domain_axis: ncdim%lat
c = cfdm.DomainAxis()
c.set_size(5)
c.nc_set_dimension('lat')
field.set_construct(c, key='domainaxis0', copy=False)
#
# domain_axis: ncdim%lon
c = cfdm.DomainAxis()
c.set_size(8)
c.nc_set_dimension('lon')
field.set_construct(c, key='domainaxis1', copy=False)
#
# domain_axis:
c = cfdm.DomainAxis()
c.set_size(1)
field.set_construct(c, key='domainaxis2', copy=False)
#
# dimension_coordinate: latitude
c = cfdm.DimensionCoordinate()
c.set_properties({'units': 'degrees_north', 'standard_name': 'latitude'})
c.nc_set_variable('lat')
data = cfdm.Data([-75.0, -45.0, 0.0, 45.0, 75.0], units='degrees_north', dtype='f8')
c.set_data(data)
b = cfdm.Bounds()
b.nc_set_variable('lat_bnds')
data = cfdm.Data([[-90.0, -60.0], [-60.0, -30.0], [-30.0, 30.0], [30.0, 60.0], [60.0, 90.0]], units='degrees_north', dtype='f8')
b.set_data(data)
c.set_bounds(b)
field.set_construct(c, axes=('domainaxis0',), key='dimensioncoordinate0', copy=False)
#
# dimension_coordinate: longitude
c = cfdm.DimensionCoordinate()
c.set_properties({'units': 'degrees_east', 'standard_name': 'longitude'})
c.nc_set_variable('lon')
data = cfdm.Data([22.5, 67.5, 112.5, 157.5, 202.5, 247.5, 292.5, 337.5], units='degrees_east', dtype='f8')
c.set_data(data)
b = cfdm.Bounds()
b.nc_set_variable('lon_bnds')
data = cfdm.Data([[0.0, 45.0], [45.0, 90.0], [90.0, 135.0], [135.0, 180.0], [180.0, 225.0], [225.0, 270.0], [270.0, 315.0], [315.0, 360.0]], units='degrees_east', dtype='f8')
b.set_data(data)
c.set_bounds(b)
field.set_construct(c, axes=('domainaxis1',), key='dimensioncoordinate1', copy=False)
#
# dimension_coordinate: time
c = cfdm.DimensionCoordinate()
c.set_properties({'units': 'days since 2018-12-01', 'standard_name': 'time'})
c.nc_set_variable('time')
data = cfdm.Data([31.0], units='days since 2018-12-01', dtype='f8')
c.set_data(data)
field.set_construct(c, axes=('domainaxis2',), key='dimensioncoordinate2', copy=False)
#
# cell_method: mean
c = cfdm.CellMethod()
c.set_method('mean')
c.set_axes(('area',))
field.set_construct(c)
#
# field data axes
field.set_data_axes(('domainaxis0', 'domainaxis1'))


Some example fields are always available from the cfdm.example_field function.

### Creating data from an array on disk¶

All the of above examples use arrays in memory to construct the data instances for the field and metadata constructs. It is, however, possible to create data from arrays that reside on disk. The cfdm.read function creates data in this manner. A pointer to an array in a netCDF file can be stored in a NetCDFArray instance, which is is used to initialize a Data instance.

Define a variable from a dataset with the netCDF package and use it to create a NetCDFArray instance with which to initialize a Data instance.
>>> import netCDF4
>>> nc = netCDF4.Dataset('file.nc', 'r')
>>> v = nc.variables['ta']
>>> netcdf_array = cfdm.NetCDFArray(filename='file.nc', ncvar='ta',
...                                 dtype=v.dtype, ndim=v.ndim,
...                                 shape=v.shape, size=v.size)
>>> data_disk = cfdm.Data(netcdf_array)

Read the netCDF variable’s data into memory and initialise another Data instance with it. Compare the values of the two data instances.
>>> numpy_array = v[...]
>>> data_memory = cfdm.Data(numpy_array)
>>> data_disk.equals(data_memory)
True


Note that data type, number of dimensions, dimension sizes and number of elements of the array on disk that are used to initialize the NetCDFArray instance are those expected by the CF data model, which may be different to those of the netCDF variable in the file (although they are the same in the above example). For example, a netCDF character array of shape (12, 9) is viewed in cfdm as a one-dimensional string array of shape (12,).

### Creation by conversion¶

An independent field construct may be created from an existing metadata construct using convert method of the field construct, which identifies a unique metadata construct and returns a new field construct based on its properties and data. The new field construct always has domain axis constructs corresponding to the data, and (by default) any other metadata constructs that further define its domain.

Create an independent field construct from the “surface altitude” metadata construct.
>>> key = tas.construct_key('surface_altitude')
>>> orog = tas.convert(key)
>>> print(orog)
Field: surface_altitude
-----------------------
Data            : surface_altitude(grid_latitude(10), grid_longitude(9)) m
Dimension coords: grid_latitude(10) = [0.0, ..., 9.0] degrees
: grid_longitude(9) = [0.0, ..., 8.0] degrees
Auxiliary coords: latitude(grid_latitude(10), grid_longitude(9)) = [[0.0, ..., 89.0]] degrees_north
: longitude(grid_longitude(9), grid_latitude(10)) = [[0.0, ..., 89.0]] degrees_east
: long_name=Grid latitude name(grid_latitude(10)) = [--, ..., j]
Cell measures   : measure:area(grid_longitude(9), grid_latitude(10)) = [[0.0, ..., 89.0]] km2
Coord references: rotated_latitude_longitude


The convert method has an option to only include domain axis constructs in the new field construct, with no other metadata constructs.

Create an independent field construct from the “surface altitude” metadata construct, but without a complete domain.
>>> orog1 = tas.convert(key, full_domain=False)
>>> print(orog1)
Field: surface_altitude
-----------------------
Data            : surface_altitude(key%domainaxis2(10), key%domainaxis3(9)) m


The cfdm.read function reads a netCDF dataset and returns the contents as a list of zero or more field constructs, each one corresponding to a unique CF-netCDF data variable in the dataset. For example, the field construct tas that was created manually can be written to a netCDF dataset and then read back into memory:

Write the field construct that was created manually to disk, and then read it back into a new field construct.
>>> cfdm.write(tas, 'tas.nc')
>>> f
[<Field: air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) K>]


The cfdm.read function also allows field constructs to be derived directly from the netCDF variables that correspond to particular types metadata constructs. In this case, the new field constructs will have a domain limited to that which can be inferred from the corresponding netCDF variable, but without the connections that are defined by the parent netCDF data variable. This will often result in a new field construct that has fewer metadata constructs than one created with the convert method.

Read the file, treating formula terms netCDF variables (which map to domain ancillary constructs) as additional CF-netCDF data variables.
>>> fields = cfdm.read('tas.nc', extra='domain_ancillary')
>>> fields
[<Field: ncvar%a(atmosphere_hybrid_height_coordinate(1)) m>,
<Field: air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) K>,
<Field: ncvar%b(atmosphere_hybrid_height_coordinate(1)) 1>,
<Field: surface_altitude(grid_latitude(10), grid_longitude(9)) m>]
>>> orog_from_file = fields[3]
>>> print(orog_from_file)
Field: surface_altitude (ncvar%surface_altitude)
------------------------------------------------
Data            : surface_altitude(grid_latitude(10), grid_longitude(9)) m
Dimension coords: grid_latitude(10) = [0.0, ..., 9.0] degrees
: grid_longitude(9) = [0.0, ..., 8.0] degrees


Comparing the field constructs orog_from_file (created with cfdm.read) and orog (created with the convert method of the tas field construct), the former lacks the auxiliary coordinate, cell measure and coordinate reference constructs of the latter. This is because the surface altitude netCDF variable in tas.nc does not have the coordinates, cell_measures nor grid_mapping netCDF attributes that would link it to auxiliary coordinate, cell measure and grid mapping netCDF variables.

## Copying and equality¶

A field construct may be copied with its copy method. This produces a “deep copy”, i.e. the new field construct is completely independent of the original field.

Copy a field construct and change elements of the copy, showing that the original field construct has not been altered.
>>> u = t.copy()
>>> u.data[0, 0, 0] = -1e30
>>> u.data[0, 0, 0]
<Data(1, 1, 1): [[[-1e+30]]] K>
>>> t.data[0, 0, 0]
<Data(1, 1, 1): [[[-1.0]]] K>
>>> key = u.construct_key('grid_latitude')
>>> u.del_construct(key)
<DimensionCoordinate: grid_latitude(10) degrees>
>>> u.constructs('grid_latitude')
Constructs:
{}
>>> t.constructs('grid_latitude')
Constructs:
{'dimensioncoordinate1': <DimensionCoordinate: grid_latitude(10) degrees>}


Equivalently, the copy.deepcopy function may be used:

Copy a field construct with the built-in copy module.
>>> import copy
>>> u = copy.deepcopy(t)


Metadata constructs may be copied individually in the same manner:

>>> orog = t.constructs('surface_altitude').value().copy()


Arrays within Data instances are copied with a copy-on-write technique. This means that a copy takes up very little memory, even when the original constructs contain very large data arrays, and the copy operation is fast.

### Equality¶

Whether or not two field constructs are the same is tested with either field construct’s equals method.

A field construct is always equal to itself, a copy of itself and a complete subspace of itself. The “verbose” keyword will give some (but not necessarily all) of the reasons why two field constructs are not the same.
>>> t.equals(t)
True
>>> t.equals(t.copy())
True
>>> t.equals(t[...])
True
>>> t.equals(q)
False
>>> t.equals(q, verbose=True)
Field: Different units: 'K', '1'
Field: Different properties
False


Equality is strict by default. This means that for two field constructs to be considered equal they must have corresponding metadata constructs and for each pair of constructs:

• the descriptive properties must be the same (with the exception of the field construct’s Conventions property, which is never checked), and vector-valued properties must have same the size and be element-wise equal, and

• if there are data arrays then they must have same shape, data type and be element-wise equal.

Two real numbers $$x$$ and $$y$$ are considered equal if $$|x - y| \le a_{tol} + r_{tol}|y|$$, where $$a_{tol}$$ (the tolerance on absolute differences) and $$r_{tol}$$ (the tolerance on relative differences) are positive, typically very small numbers. By default both are set to the system epsilon (the difference between 1 and the least value greater than 1 that is representable as a float). Their values may be inspected and changed with the cfdm.atol and cfdm.rtol functions:

The atol and rtol functions allow the numerical equality tolerances to be inspected and changed.
>>> cfdm.atol()
2.220446049250313e-16
>>> cfdm.rtol()
2.220446049250313e-16
>>> original = cfdm.rtol(0.00001)
>>> cfdm.rtol()
1e-05
>>> cfdm.rtol(original)
1e-05
>>> cfdm.rtol()
2.220446049250313e-16


Note that the above equation is not symmetric in $$x$$ and $$y$$, so that for two fields f1 and f2, f1.equals(f2) may be different from f2.equals(f1) in some rare cases.

NetCDF elements, such as netCDF variable and dimension names, do not constitute part of the CF data model and so are not checked on any construct.

The equals method has optional parameters for modifying the criteria for considering two fields to be equal:

• named properties may be omitted from the comparison,

• fill value and missing data value properties may be ignored,

• the data type of data arrays may be ignored, and

• the tolerances on absolute and relative differences for numerical comparisons may be temporarily changed, without changing the default settings.

Metadata constructs may also be tested for equality:

Metadata constructs also have an equals method, that behaves in a similar manner.
>>> orog = t.constructs('surface_altitude').value()
>>> orog.equals(orog.copy())
True


## NetCDF interface¶

The logical CF data model is independent of netCDF, but the CF conventions are designed to enable the processing and sharing of datasets stored in netCDF files. Therefore, the cfdm package includes methods for recording and editing netCDF elements that are not part of the CF model, but are nonetheless often required to interpret and create CF-netCDF datasets. See the section on philosophy for a further discussion.

When a netCDF dataset is read, netCDF elements (such as dimension and variable names, and some attribute values) that do not have a place in the CF data model are, nevertheless, stored within the appropriate cfdm constructs. This allows them to be used when writing field constructs to a new netCDF dataset, and also makes them accessible as filters to a Constructs instance:

Retrieve metadata constructs based on their netCDF names.
>>> print(t.constructs.filter_by_ncvar('b'))
Constructs:
{'domainancillary1': <DomainAncillary: ncvar%b(1) >}
>>> t.constructs('ncvar%x').value()
<DimensionCoordinate: grid_longitude(9) degrees>
>>> t.constructs('ncdim%x')
<Constructs: domain_axis(1)>


Each construct has methods to access the netCDF elements which it requires. For example, the field construct has the following methods:

Method

Description

nc_get_variable

Return the netCDF variable name

nc_set_variable

Set the netCDF variable name

nc_del_variable

Remove the netCDF variable name

nc_has_variable

Whether the netCDF variable name has been set

nc_global_attributes

Return the selection of properties to be written as netCDF global attributes

nc_set_global_attribute

Set a property to be written as a netCDF global attribute

nc_set_global_attributes

Set properties to be written as netCDF global attributes

nc_clear_global_attributes

Clear the selection of properties to be written as netCDF global attributes

nc_group_attributes

Return the selection of properties to be written as netCDF group attributes

nc_set_group_attribute

Set a property to be written as a netCDF group attribute

nc_set_group_attributes

Set properties to be written as netCDF group attributes

nc_clear_group_attributes

Clear the selection of properties to be written as netCDF group attributes

nc_variable_groups

Return the netCDF group structure

nc_set_variable_groups

Set the netCDF group structure

nc_clear_variable_groups

Remove the netCDF group structure

nc_geometry_variable_groups

Return the netCDF geometry variable ggroup structure

nc_set_geometry_variable_groups

Set the netCDF geometry variable group structure

nc_clear_geometry_variable_groups

Remove the netCDF geometry variable group structure

nc_del_component_variable

Remove the netCDF variable name for all components of the given type.

nc_set_component_variable

Set the netCDF variable name for all components of the given type.

nc_set_component_variable_groups

Set the netCDF variable groups hierarchy for all components of the given type.

nc_clear_component_variable_groups

Remove the netCDF variable groups hierarchy for all components of the given type.

nc_del_component_dimension

Remove the netCDF dimension name for all components of the given type.

nc_set_component_dimension

Set the netCDF dimension name for all components of the given type.

nc_set_component_dimension_groups

Set the netCDF dimension groups hierarchy for all components of the given type.

nc_clear_component_dimension_groups

Remove the netCDF dimension groups hierarchy for all components of the given type.

nc_del_component_sample_dimension

Remove the netCDF sample dimension name for all components of the given type.

nc_set_component_sample_dimension

Set the netCDF sample dimension name for all components of the given type.

nc_set_component_sample_dimension_groups

Set the netCDF sample dimension groups hierarchy for all components of the given type.

nc_clear_component_sample_dimension_groups

Remove the netCDF sample dimension groups hierarchy for all components of the given type.

Access netCDF elements associated with the field and metadata constructs.
>>> q.nc_get_variable()
'q'
>>> q.nc_global_attributes()
{'project': None, 'Conventions': None}
>>> q.nc_set_variable('humidity')
>>> q.nc_get_variable()
'humidity'
>>> q.constructs('latitude').value().nc_get_variable()
'lat'


The complete collection of netCDF interface methods is:

Method

Classes

NetCDF element

nc_del_variable

Variable name

nc_get_variable

Variable name

nc_has_variable

Variable name

nc_set_variable

Variable name

nc_variable_groups

Group hierarchy

nc_set_variable_groups

Group hierarchy

nc_clear_variable_groups

Group hierarchy

nc_del_dimension

Dimension name

nc_get_dimension

Dimension name

nc_has_dimension

Dimension name

nc_set_dimension

Dimension name

nc_dimension_groups

Group hierarchy

nc_set_dimension_groups

Group hierarchy

nc_clear_dimension_groups

Group hierarchy

nc_is_unlimited

DomainAxis

Unlimited dimension

nc_set_unlimited

DomainAxis

Unlimited dimension

nc_global_attributes

Field

Global attributes

nc_set_global_attribute

Field

Global attributes

nc_set_global_attributes

Field

Global attributes

nc_clear_global_attributes

Field

Global attributes

nc_variable_groups

Field

Group hierarchy

nc_set_variable_groups

Field

Group hierarchy

nc_clear_variable_groups

Field

Group hierarchy

nc_geometry_variable_groups

Field

Group hierarchy

nc_set_geometry_variable_groups

Field

Group hierarchy

nc_clear_geometry_variable_groups

Field

Group hierarchy

nc_group_attributes

Field

Group attributes

nc_set_group_attribute

Field

Group attributes

nc_set_group_attributes

Field

Group attributes

nc_clear_group_attributes

Field

Group attributes

nc_del_component_variable

Field

Component common netCDF properties

nc_set_component_variable

Field

Component common netCDF properties

nc_set_component_variable_groups

Field

Component common netCDF properties

nc_clear_component_variable_groups

Field

Component common netCDF properties

nc_del_component_dimension

Field

Component common netCDF properties

nc_set_component_dimension

Field

Component common netCDF properties

nc_set_component_dimension_groups

Field

Component common netCDF properties

nc_clear_component_dimension_groups

Field

Component common netCDF properties

nc_del_component_sample_dimension

Field

Component common netCDF properties

nc_set_component_sample_dimension

Field

Component common netCDF properties

nc_set_component_sample_dimension_groups

Field

Component common netCDF properties

nc_clear_component_sample_dimension_groups

Field

Component common netCDF properties

nc_get_external

CellMeasure

External variable status

nc_set_external

CellMeasure

External variable status

nc_del_sample_dimension

Sample dimension name

nc_get_sample_dimension

Sample dimension name

nc_has_sample_dimension

Sample dimension name

nc_set_sample_dimension

Sample dimension name

nc_sample_dimension_groups

Count

Group hierarchy

nc_set_sample_dimension_groups

Count

Group hierarchy

nc_clear_sample_dimension_groups

Count

Group hierarchy

## Writing to disk¶

The cfdm.write function writes a field construct, or a sequence of field constructs, to a new netCDF file on disk:

Write a field construct to a netCDF dataset on disk.
>>> print(q)
Field: specific_humidity (ncvar%humidity)
-----------------------------------------
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) = [2019-01-01 00:00:00]
>>> cfdm.write(q, 'q_file.nc')


The new dataset is structured as follows:

Inspect the new dataset with the ncdump command line tool.
$ncdump -h q_file.nc netcdf q_file { dimensions: lat = 5 ; bounds2 = 2 ; lon = 8 ; variables: double lat_bnds(lat, bounds2) ; double lat(lat) ; lat:units = "degrees_north" ; lat:standard_name = "latitude" ; lat:bounds = "lat_bnds" ; double lon_bnds(lon, bounds2) ; double lon(lon) ; lon:units = "degrees_east" ; lon:standard_name = "longitude" ; lon:bounds = "lon_bnds" ; double time ; time:units = "days since 2018-12-01" ; time:standard_name = "time" ; double humidity(lat, lon) ; humidity:standard_name = "specific_humidity" ; humidity:cell_methods = "area: mean" ; humidity:units = "1" ; humidity:coordinates = "time" ; // global attributes: :Conventions = "CF-1.7" ; :project = "research" ; }  A sequence of field constructs is written in exactly the same way: Write multiple field constructs to a netCDF dataset on disk. >>> x [<Field: specific_humidity(latitude(5), longitude(8)) 1>, <Field: air_temperature(atmosphere_hybrid_height_coordinate(1), grid_latitude(10), grid_longitude(9)) K>] >>> cfdm.write(x, 'new_file.nc')  By default the output file will be for CF-1.8. The cfdm.write function has optional parameters to • set the output netCDF format (all netCDF3 and netCDF4 formats are possible); • specify which field construct properties should become netCDF data variable attributes and which should become netCDF global attributes; • set extra netCDF global attributes; • create external variables in an external file; • specify the version of the CF conventions (from CF-1.6 up to CF-1.8), and of any other conventions that the file adheres to; • change the data type of output data arrays; • apply netCDF compression and packing; • set the endian-ness of the output data; and • specify whether or not netCDF string arrays are to be used. Output netCDF variable and dimension names read from a netCDF dataset are stored in the resulting field constructs, and may also be set manually with the nc_set_variable, nc_set_dimension and nc_set_sample_dimension methods. If a name has not been set then one will be generated internally (usually based on the standard name if it exists). It is possible to create netCDF unlimited dimensions using the nc_set_unlimited method of the domain axis construct. A field construct is not transformed through being written to a file on disk and subsequently read back from that file. Read a file that has been created by writing a field construct, and compare the result with the original field construct in memory. >>> f = cfdm.read('q_file.nc')[0] >>> q.equals(f) True  ### Global attributes¶ The field construct properties that correspond to the standardised description-of-file-contents attributes are automatically written as netCDF global attributes. Other attributes may also be written as netCDF global attributes if they have been identified as such with the global_attributes keyword, or via the nc_set_global_attribute or nc_set_global_attributes method of the field constructs. In either case, the creation of a netCDF global attribute depends on the corresponding property values being identical across all of the field constructs being written to the file. If they are all equal then the property will be written as a netCDF global attribute and not as an attribute of any netCDF data variable; if any differ then the property is written only to each netCDF data variable. Request that the “model” property is written as a netCDF global attribute, using the “global_attributes” keyword. >>> f.set_property('model', 'model_A') >>> cfdm.write(f, 'f_file.nc', global_attributes='model')  Request that the “model” property is written as a netCDF global attribute, using the “nc_set_global_attribute” method. >>> f.nc_global_attributes() {'Conventions': None, 'project': None} >>> f.nc_set_global_attribute('model') >>> f.nc_global_attributes() {'Conventions': None, 'model': None, 'project': None} >>> f.nc_global_attributes(values=True) {'Conventions': 'CF-1.7', 'project': 'research', 'model': 'model_A'} >>> cfdm.write(f, 'f_file.nc')  It is possible to create both a netCDF global attribute and a netCDF data variable attribute with the same name, but with different values. This may be done by assigning the global value to the property name with the nc_set_global_attribute or nc_set_global_attributes method, or by via the file_descriptors keyword. For the former technique, any inconsistencies arising from multiple field constructs being written to the same file will be resolved by omitting the netCDF global attribute from the file. Request that the “information” property is written as netCDF global and data variable attributes, with different values, using the “nc_set_global_attribute” method. >>> f.set_property('information', 'variable information') >>> f.properties() {'Conventions': 'CF-1.7', 'information': 'variable information', 'project': 'research', 'standard_name': 'specific_humidity', 'units': '1'} >>> f.nc_set_global_attribute('information', 'global information') >>> f.nc_global_attributes() {'Conventions': None, 'information': 'global information', 'model': None, 'project': None} >>> cfdm.write(f, 'f_file.nc')  NetCDF global attributes defined with the file_descriptors keyword of the cfdm.write function will always be written as requested, independently of the netCDF data variable attributes, and superseding any global attributes that may have been defined with the global_attributes keyword, or set on the individual field constructs. Insist that the “history” property is written as netCDF a global attribute, with the “file_descriptors” keyword. >>> cfdm.write(f, 'f_file_nc', file_descriptors={'history': 'created in 2020'}) >>> f_file = cfdm.read('f_file.nc')[0] >>> f_file.nc_global_attributes() >>> f_file.properties() {'Conventions': 'CF-1.7', 'history': 'created in 2020', 'information': 'variable information', 'model': 'model_A', 'project': 'research', 'standard_name': 'specific_humidity', 'units': '1'} >>> f_file.nc_global_attributes() {'Conventions': None, 'history': None, 'information': 'global information', 'project': None}  ### Conventions¶ The Conventions netCDF global attribute containing the version of the CF conventions is always automatically created. If the version of the CF conventions has been set as a field property, or with the Conventions keyword of the cfdm.write function, then it is ignored. However, other conventions that may apply can be set with either technique. Two ways to add additional conventions to the “Conventions” netCDF global attribute. >>> f_file.set_property('Conventions', 'UGRID1.0') >>> cfdm.write(f, 'f_file.nc', Conventions='UGRID1.0')  ### Scalar coordinate variables¶ A CF-netCDF scalar (i.e. zero-dimensional) coordinate variable is created from a size one dimension coordinate construct that spans a domain axis construct which is not spanned by the field construct’s data, nor the data of any other metadata construct. This occurs for the field construct q, for which the “time” dimension coordinate construct was to the file q_file.nc as a scalar coordinate variable. To change this so that the “time” dimension coordinate construct is written as a CF-netCDF size one coordinate variable, the field construct’s data must be expanded to span the corresponding size one domain axis construct, by using the insert_dimension method of the field construct. Write the “time” dimension coordinate construct to a (non-scalar) CF-netCDF coordinate variable by inserting the corresponding dimension into the field construct’s data. >>> print(q) Field: specific_humidity (ncvar%humidity) ----------------------------------------- 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) = [2019-01-01 00:00:00] <Field: specific_humidity(latitude(5), longitude(8)) 1> >>> key = q.construct_key('time') >>> axes = q.get_data_axes(key) >>> axes ('domainaxis2',) >>> q2 = q.insert_dimension(axis=axes[0]) >>> q2 <Field: specific_humidity(time(1), latitude(5), longitude(8)) 1> >>> cfdm.write(q2, 'q2_file.nc')  The new dataset is structured as follows (note, relative to file q_file.nc, the existence of the “time” dimension and the lack of a coordinates attribute on the, now three-dimensional, data variable): Inspect the new dataset with the ncdump command line tool. $ ncdump -h q2_file.nc
netcdf q2_file {
dimensions:
lat = 5 ;
bounds2 = 2 ;
lon = 8 ;
time = 1 ;
variables:
double lat_bnds(lat, bounds2) ;
double lat(lat) ;
lat:units = "degrees_north" ;
lat:standard_name = "latitude" ;
lat:bounds = "lat_bnds" ;
double lon_bnds(lon, bounds2) ;
double lon(lon) ;
lon:units = "degrees_east" ;
lon:standard_name = "longitude" ;
lon:bounds = "lon_bnds" ;
double time(time) ;
time:units = "days since 2018-12-01" ;
time:standard_name = "time" ;
double humidity(time, lat, lon) ;
humidity:units = "1" ;
humidity:standard_name = "specific_humidity" ;
humidity:cell_methods = "area: mean" ;

// global attributes:
:Conventions = "CF-1.7" ;
:project = "research" ;
}


### Compressed constructs¶

Constructs that contain compressed data will be automatically written to a dataset with the correct compression encoding. See the section on compression for details.

### Strings¶

String-valued data may be written to netCDF files either as netCDF character arrays with a trailing dimension large enough to contain the longest value, or as netCDF4 string arrays. The former is allowed for all formats of netCDF3 and netCDF4 format files; but string arrays may only be written to netCDF4 format files (note that string arrays can not be written to netCDF4 classic format files).

By default, netCDF string arrays will be created whenever possible, and in all other cases netCDF character arrays will be used. Alternatively, netCDF character arrays can be used in all cases by setting the string keyword of the cfdm.write function.

### Groups¶

NetCDF4 files with hierarchical groups may be created if a group structure is defined by the netCDF variable and dimension names, accessed via the netCDF interface. See the section on hierarchical groups for details.

## Hierarchical groups¶

Hierarchical groups provide a mechanism to structure variables within netCDF4 datasets, with well defined rules for resolving references to out-of-group netCDF variables and dimensions.

A group structure that may be applied when writing to disk can be created ab initio with the netCDF interface. For example, the data variable and a coordinate construct may be moved to a sub-group that has its own group attribute, and a coordinate construct may be moved to a different sub-group:

Create a group structure and write it to disk.
>>> q, t = cfdm.read('file.nc')
>>> print(q)
Field: specific_humidity (ncvar%/forecast/model/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) = [2019-01-01 00:00:00]
>>> q.set_property('comment', 'comment')
>>> q.nc_set_group_attribute('comment', 'group comment')
>>> q.nc_set_variable_groups(['forecast', 'model'])
()
>>> q.construct('time').nc_set_variable_groups(['forecast'])
()
>>> cfdm.write(q, 'grouped.nc')

Inspect the new grouped dataset with the ncdump command line tool.
$ncdump -h grouped.nc netcdf grouped { dimensions: lat = 5 ; bounds2 = 2 ; lon = 8 ; variables: double lat_bnds(lat, bounds2) ; double lat(lat) ; lat:units = "degrees_north" ; lat:standard_name = "latitude" ; lat:bounds = "lat_bnds" ; double lon_bnds(lon, bounds2) ; double lon(lon) ; lon:units = "degrees_east" ; lon:standard_name = "longitude" ; lon:bounds = "lon_bnds" ; // global attributes: :Conventions = "CF-1.8" ; :comment = "comment" ; group: forecast { variables: double time ; time:units = "days since 2018-12-01" ; time:standard_name = "time" ; group: model { variables: double q(lat, lon) ; q:project = "research" ; q:standard_name = "specific_humidity" ; q:units = "1" ; q:coordinates = "time" ; q:cell_methods = "area: mean" ; // group attributes: :comment = "group comment" ; } // group model } // group forecast }  When reading a netCDF dataset, the group structure and groups attributes are recorded and are made accessible via the netCDF interface. Read the grouped file and inspect its group structure. >>> g = cfdm.read('grouped.nc')[0] >>> print(g) Field: specific_humidity (ncvar%/forecast/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) = [2019-01-01 00:00:00] >>> g.nc_get_variable() '/forecast/model/q' >>> g.nc_variable_groups() ('forecast', 'model') >>> g.nc_group_attributes(values=True) {'comment': 'group comment'} >>> g.construct('latitude').nc_get_variable() 'lat'  By default field constructs are written out to a dataset with their groups struct (if any) intact. It is always possible, however, to create a “flat” dataset, i.e. one without any sub-groups. This does not require the removal of the group structure from the field construct and all of its components (although that is possible), as it can be done by simply by overriding the existing group structure by setting the group keyword to cfdm.write to False. Write the field construct to a file with the same group structure, and also to a flat file. >>> cfdm.write(g, 'flat.nc', group=False)  NetCDF variables in the flattened output file will inherit any netCDF group attributes, providing that they are not superceded by variable attributes. The output netCDF variable and dimension names will be taken as the basenames of any that have been pre-defined. This is the case in file flat.nc, for which the netCDF variable q has inherited the comment attribute that was originally set on the /forecast/model group. NetCDF group attributes may be set and accessed via the netCDF interface, for both netCDF variable and netCDF dimensions. Inspect the flat version of the dataset with the ncdump command line tool. $ ncdump -h flat_out.nc
netcdf flat {
dimensions:
lat = 5 ;
bounds2 = 2 ;
lon = 8 ;
variables:
double lat_bnds(lat, bounds2) ;
double lat(lat) ;
lat:units = "degrees_north" ;
lat:standard_name = "latitude" ;
lat:bounds = "lat_bnds" ;
double lon_bnds(lon, bounds2) ;
double lon(lon) ;
lon:units = "degrees_east" ;
lon:standard_name = "longitude" ;
lon:bounds = "lon_bnds" ;
double time ;
time:units = "days since 2018-12-01" ;
time:standard_name = "time" ;
double q(lat, lon) ;
q:comment = "group comment" ;
q:project = "research" ;
q:standard_name = "specific_humidity" ;
q:units = "1" ;
q:coordinates = "time" ;
q:cell_methods = "area: mean" ;

// global attributes:
:Conventions = "CF-1.8" ;
:comment = "comment" ;
}


The fields constructs read from a grouped file are identical to those read from the flat version of the file:

Demonstrate that the field constructs are indpendent of the dataset structure.
>>> f = cfdm.read('flat.nc')[0]
>>> f.equals(g)
True


## External variables¶

External variables are those in a netCDF file that are referred to, but which are not present in it. Instead, such variables are stored in other netCDF files known as “external files”. External variables may, however, be incorporated into the field constructs of the dataset, as if they had actually been stored in the same file, simply by providing the external file names to the cfdm.read function.

This is illustrated with the files parent.nc (found in the sample datasets):

Inspect the parent dataset with the ncdump command line tool.
$ncdump -h parent.nc netcdf parent { dimensions: latitude = 10 ; longitude = 9 ; variables: double latitude(latitude) ; latitude:units = "degrees_north" ; latitude:standard_name = "latitude" ; double longitude(longitude) ; longitude:units = "degrees_east" ; longitude:standard_name = "longitude" ; double eastward_wind(latitude, longitude) ; eastward_wind:units = "m s-1" ; eastward_wind:standard_name = "eastward_wind" ; eastward_wind:cell_measures = "area: areacella" ; // global attributes: :Conventions = "CF-1.7" ; :external_variables = "areacella" ; }  and external.nc (found in the sample datasets): Inspect the external dataset with the ncdump command line tool. $ ncdump -h external.nc
netcdf external {
dimensions:
latitude = 10 ;
longitude = 9 ;
variables:
double areacella(longitude, latitude) ;
areacella:units = "m2" ;
areacella:standard_name = "cell_area" ;

// global attributes:
:Conventions = "CF-1.7" ;
}


The dataset in parent.nc may be read without specifying the external file external.nc. In this case a cell measure construct is still created, but one without any metadata or data:

Read the parent dataset without specifying the location of any external datasets.
>>> u = cfdm.read('parent.nc')[0]
>>> print(u)
Field: eastward_wind (ncvar%eastward_wind)
------------------------------------------
Data            : eastward_wind(latitude(10), longitude(9)) m s-1
Dimension coords: latitude(10) = [0.0, ..., 9.0] degrees
: longitude(9) = [0.0, ..., 8.0] degrees
Cell measures   : measure:area (external variable: ncvar%areacella)

>>> area = u.constructs('measure:area').value()
>>> area
<CellMeasure: measure:area >
>>> area.nc_get_external()
True
>>> area.nc_get_variable()
'areacella'
>>> area.properties()
{}
>>> area.has_data()
False


If this field construct were to be written to disk using cfdm.write, then the output file would be identical to the original parent.nc file, i.e. the netCDF variable name of the cell measure construct (areacella) would be listed by the external_variables global attribute.

However, the dataset may also be read with the external file. In this case a cell measure construct is created with all of the metadata and data from the external file, as if the netCDF cell measure variable had been present in the parent dataset:

Read the parent dataset whilst providing the external dataset containing the external variables.
>>> g = cfdm.read('parent.nc', external='external.nc')[0]
>>> print(g)
Field: eastward_wind (ncvar%eastward_wind)
------------------------------------------
Data            : eastward_wind(latitude(10), longitude(9)) m s-1
Dimension coords: latitude(10) = [0.0, ..., 9.0] degrees
: longitude(9) = [0.0, ..., 8.0] degrees
Cell measures   : cell_area(longitude(9), latitude(10)) = [[100000.5, ..., 100089.5]] m2
>>> area = g.constructs('measure:area').value()
>>> area
<CellMeasure: cell_area(9, 10) m2>
>>> area.nc_get_external()
False
>>> area.nc_get_variable()
'areacella'
>>> area.properties()
{'standard_name': 'cell_area', 'units': 'm2'}
>>> area.data
<Data(9, 10): [[100000.5, ..., 100089.5]] m2>


If this field construct were to be written to disk using cfdm.write then by default the cell measure construct, with all of its metadata and data, would be written to the named output file, along with all of the other constructs. There would be no external_variables global attribute.

To create a reference to an external variable in an output netCDF file, set the status of the cell measure construct to “external” with its nc_set_external method.

Flag the cell measure as external and write the field construct to a new file.
>>> area.nc_set_external(True)
>>> cfdm.write(g, 'new_parent.nc')


To create a reference to an external variable in the an output netCDF file and simultaneously create an external file containing the variable, set the status of the cell measure construct to “external” and provide an external file name to the cfdm.write function:

Write the field construct to a new file and the cell measure construct to an external file.
>>> cfdm.write(g, 'new_parent.nc', external='new_external.nc')


### External files with cfdump¶

One or more external files may also be included with cfdump.

Use cfdump to describe the parent file without resolving the external variable reference.
$cfdump parent.nc Field: eastward_wind (ncvar%eastward_wind) ------------------------------------------ Data : eastward_wind(latitude(10), longitude(9)) m s-1 Dimension coords: latitude(10) = [0.0, ..., 9.0] degrees_north : longitude(9) = [0.0, ..., 8.0] degrees_east Cell measures : measure:area (external variable: ncvar%areacella)  Providing an external file with the “-e” option allows the reference to be resolved. $ cfdump -e external.nc parent.nc
Field: eastward_wind (ncvar%eastward_wind)
------------------------------------------
Data            : eastward_wind(latitude(10), longitude(9)) m s-1
Dimension coords: latitude(10) = [0.0, ..., 9.0] degrees_north
: longitude(9) = [0.0, ..., 8.0] degrees_east
Cell measures   : measure:area(longitude(9), latitude(10)) = [[100000.5, ..., 100089.5]] m2


## Compression¶

The CF conventions have support for saving space by identifying unwanted missing data. Such compression techniques store the data more efficiently and result in no precision loss. The CF data model, however, views compressed arrays in their uncompressed form.

Therefore, the field construct contains domain axis constructs for the compressed dimensions and presents a view of compressed data in its uncompressed form, even though the “underlying array” (i.e. the actual array on disk or in memory that is contained in a Data instance) is compressed. This means that the cfdm package includes algorithms for uncompressing each type of compressed array.

There are two basic types of compression supported by the CF conventions: ragged arrays (as used by discrete sampling geometries) and compression by gathering, each of which has particular implementation details, but the following access patterns and behaviours apply to both:

• A subspace of a field construct is created with indices of the uncompressed form of the data. The new subspace will no longer be compressed, i.e. its underlying arrays will be uncompressed, but the original data will remain compressed. It follows that to uncompress all of the data in a field construct, index the field construct with (indices equivalent to) Ellipsis.

• If data elements are modified by assigning to indices of the uncompressed form of the data, then the compressed underlying array is replaced by its uncompressed form.

• An uncompressed field construct can be compressed, prior to being written to a dataset, with its compress method, which also compresses the metadata constructs as required.

• An compressed field construct can be uncompressed with its uncompress method, which also uncompresses the metadata constructs as required.

• If an underlying array is compressed at the time of writing to disk with the cfdm.write function, then it is written to the file as a compressed array, along with the supplementary netCDF variables and attributes that are required for the encoding. This means that if a dataset using compression is read from disk then it will be written back to disk with the same compression, unless data elements have been modified by assignment.

Examples of all of the above may be found in the sections on discrete sampling geometries and gathering.

### Discrete sampling geometries¶

Discrete sampling geometry (DSG) features may be compressed by combining them using one of three ragged array representations: contiguous, indexed or indexed contiguous.

The count variable that is required to uncompress a contiguous, or indexed contiguous, ragged array is stored in a Count instance and is accessed with the get_count method of the Data instance.

The index variable that is required to uncompress an indexed, or indexed contiguous, ragged array is stored in an Index instance and is accessed with the get_index method of the Data instance.

The contiguous case is is illustrated with the file contiguous.nc (found in the sample datasets):

Inspect the compressed dataset with the ncdump command line tool.
$ncdump -h contiguous.nc dimensions: station = 4 ; obs = 24 ; strlen8 = 8 ; variables: int row_size(station) ; row_size:long_name = "number of observations for this station" ; row_size:sample_dimension = "obs" ; double time(obs) ; time:units = "days since 1970-01-01 00:00:00" ; time:standard_name = "time" ; double lat(station) ; lat:units = "degrees_north" ; lat:standard_name = "latitude" ; double lon(station) ; lon:units = "degrees_east" ; lon:standard_name = "longitude" ; double alt(station) ; alt:units = "m" ; alt:positive = "up" ; alt:standard_name = "height" ; alt:axis = "Z" ; char station_name(station, strlen8) ; station_name:long_name = "station name" ; station_name:cf_role = "timeseries_id" ; double humidity(obs) ; humidity:standard_name = "specific_humidity" ; humidity:coordinates = "time lat lon alt station_name" ; humidity:_FillValue = -999.9 ; // global attributes: :Conventions = "CF-1.7" ; :featureType = "timeSeries" ; }  Reading and inspecting this file shows the data presented in two-dimensional uncompressed form, whilst the underlying array is still in the one-dimension ragged representation described in the file: Read a field construct from a dataset that has been compressed with contiguous ragged arrays, and inspect its data in uncompressed form. >>> h = cfdm.read('contiguous.nc')[0] >>> print(h) Field: specific_humidity (ncvar%humidity) ----------------------------------------- Data : specific_humidity(ncdim%station(4), ncdim%timeseries(9)) Dimension coords: Auxiliary coords: time(ncdim%station(4), ncdim%timeseries(9)) = [[1969-12-29 00:00:00, ..., 1970-01-07 00:00:00]] : latitude(ncdim%station(4)) = [-9.0, ..., 78.0] degrees_north : longitude(ncdim%station(4)) = [-23.0, ..., 178.0] degrees_east : height(ncdim%station(4)) = [0.5, ..., 345.0] m : cf_role:timeseries_id(ncdim%station(4)) = [station1, ..., station4] >>> print(h.data.array) [[0.12 0.05 0.18 -- -- -- -- -- --] [0.05 0.11 0.2 0.15 0.08 0.04 0.06 -- --] [0.15 0.19 0.15 0.17 0.07 -- -- -- --] [0.11 0.03 0.14 0.16 0.02 0.09 0.1 0.04 0.11]]  Inspect the underlying compressed array and the count variable that defines how to uncompress the data. >>> h.data.get_compression_type() 'ragged contiguous' >>> print(h.data.compressed_array) [0.12 0.05 0.18 0.05 0.11 0.2 0.15 0.08 0.04 0.06 0.15 0.19 0.15 0.17 0.07 0.11 0.03 0.14 0.16 0.02 0.09 0.1 0.04 0.11] >>> count_variable = h.data.get_count() >>> count_variable <Count: long_name=number of observations for this station(4) > >>> print(count_variable.data.array) [3 7 5 9]  The timeseries for the second station is easily selected by indexing the “station” axis of the field construct: Get the data for the second station. >>> station2 = h[1] >>> station2 <Field: specific_humidity(ncdim%station(1), ncdim%timeseries(9))> >>> print(station2.data.array) [[0.05 0.11 0.2 0.15 0.08 0.04 0.06 -- --]]  The underlying array of original data remains in compressed form until data array elements are modified: Change an element of the data and show that the underlying array is no longer compressed. >>> h.data.get_compression_type() 'ragged contiguous' >>> h.data[1, 2] = -9 >>> print(h.data.array) [[0.12 0.05 0.18 -- -- -- -- -- --] [0.05 0.11 -9.0 0.15 0.08 0.04 0.06 -- --] [0.15 0.19 0.15 0.17 0.07 -- -- -- --] [0.11 0.03 0.14 0.16 0.02 0.09 0.1 0.04 0.11]] >>> h.data.get_compression_type() ''  The easiest way to create a compressed field construct is to create the equivalent uncompressed field construct and then compress it with its compress method, which also compresses the metadata constructs as required. Create a field construct and then compress it. import numpy import cfdm # Define the array values data = cfdm.Data([[280.0, -99, -99, -99], [281.0, 279.0, 278.0, 279.5]], mask=[[0, 1, 1, 1], [0, 0, 0, 0]]) # Create the field construct T = cfdm.Field() T.set_properties({'standard_name': 'air_temperature', 'units': 'K', 'featureType': 'timeSeries'}) # Create the domain axis constructs X = T.set_construct(cfdm.DomainAxis(4)) Y = T.set_construct(cfdm.DomainAxis(2)) # Set the data for the field T.set_data(data, axes=[Y, X]) # Compress the data T.compress('contiguous', count_properties={'long_name': 'number of obs for this timeseries'}, inplace=True)  The new field construct can now be inspected and written to a netCDF file: Inspect the new field construct and write it to disk. >>> T <Field: air_temperature(key%domainaxis1(2), key%domainaxis0(4)) K> >>> print(T.data.array) [[280.0 -- -- --] [281.0 279.0 278.0 279.5]] >>> T.data.get_compression_type() 'ragged contiguous' >>> print(T.data.compressed_array) [280. 281. 279. 278. 279.5] >>> count_variable = T.data.get_count() >>> count_variable <Count: long_name=number of obs for this timeseries(2) > >>> print(count_variable.data.array) [1 4] >>> cfdm.write(T, 'T_contiguous.nc')  The content of the new file is: Inspect the new compressed dataset with the ncdump command line tool. $ ncdump T_contiguous.nc
netcdf T_contiguous {
dimensions:
dim = 2 ;
element = 5 ;
variables:
int64 count(dim) ;
count:long_name = "number of obs for this timeseries" ;
count:sample_dimension = "element" ;
float air_temperature(element) ;
air_temperature:units = "K" ;
air_temperature:standard_name = "air_temperature" ;

// global attributes:
:Conventions = "CF-1.7" ;
:featureType = "timeSeries" ;
data:

count = 1, 4 ;

air_temperature = 280, 281, 279, 278, 279.5 ;
}


Exactly the same field construct may be also created explicitly with underlying compressed data. A construct with an underlying ragged array is created by initialising a Data instance with a ragged array that is stored in one of three special array objects: RaggedContiguousArray, RaggedIndexedArray or RaggedIndexedContiguousArray.

Create a field construct with compressed data.
import numpy
import cfdm

# Define the ragged array values
ragged_array = cfdm.Data([280, 281, 279, 278, 279.5])

# Define the count array values
count_array = [1, 4]

# Create the count variable
count_variable = cfdm.Count(data=cfdm.Data(count_array))
count_variable.set_property('long_name', 'number of obs for this timeseries')

# Create the contiguous ragged array object, specifying the
# uncompressed shape
array = cfdm.RaggedContiguousArray(
compressed_array=ragged_array,
shape=(2, 4), size=8, ndim=2,
count_variable=count_variable)

# Create the field construct with the domain axes and the ragged
# array
T = cfdm.Field()
T.set_properties({'standard_name': 'air_temperature',
'units': 'K',
'featureType': 'timeSeries'})

# Create the domain axis constructs for the uncompressed array
X = T.set_construct(cfdm.DomainAxis(4))
Y = T.set_construct(cfdm.DomainAxis(2))

# Set the data for the field
T.set_data(cfdm.Data(array), axes=[Y, X])


### Gathering¶

Compression by gathering combines axes of a multidimensional array into a new, discrete axis whilst omitting the missing values and thus reducing the number of values that need to be stored.

The list variable that is required to uncompress a gathered array is stored in a List object and is retrieved with the get_list method of the Data instance.

This is illustrated with the file gathered.nc (found in the sample datasets):

Inspect the compressed dataset with the ncdump command line tool.
$ncdump -h gathered.nc netcdf gathered { dimensions: time = 2 ; lat = 4 ; lon = 5 ; landpoint = 7 ; variables: double time(time) ; time:standard_name = "time" ; time:units = "days since 2000-1-1" ; double lat(lat) ; lat:standard_name = "latitude" ; lat:units = "degrees_north" ; double lon(lon) ; lon:standard_name = "longitude" ; lon:units = "degrees_east" ; int landpoint(landpoint) ; landpoint:compress = "lat lon" ; double pr(time, landpoint) ; pr:standard_name = "precipitation_flux" ; pr:units = "kg m2 s-1" ; // global attributes: :Conventions = "CF-1.7" ; }  Reading and inspecting this file shows the data presented in three-dimensional uncompressed form, whilst the underlying array is still in the two-dimensional gathered representation described in the file: Read a field construct from a dataset that has been compressed by gathering, and inspect its data in uncompressed form. >>> p = cfdm.read('gathered.nc')[0] >>> print(p) Field: precipitation_flux (ncvar%pr) ------------------------------------ Data : precipitation_flux(time(2), latitude(4), longitude(5)) kg m2 s-1 Dimension coords: time(2) = [2000-02-01 00:00:00, 2000-03-01 00:00:00] : latitude(4) = [-90.0, ..., -75.0] degrees_north : longitude(5) = [0.0, ..., 40.0] degrees_east >>> print(p.data.array) [[[-- 0.000122 0.0008 -- -- ] [0.000177 -- 0.000175 0.00058 -- ] [-- -- -- -- -- ] [-- 0.000206 -- 0.0007 -- ]] [[-- 0.000202 0.000174 -- -- ] [0.00084 -- 0.000201 0.0057 -- ] [-- -- -- -- -- ] [-- 0.000223 -- 0.000102 -- ]]]  Inspect the underlying compressed array and the list variable that defines how to uncompress the data. >>> p.data.get_compression_type() 'gathered' >>> print(p.data.compressed_array) [[0.000122 0.0008 0.000177 0.000175 0.00058 0.000206 0.0007 ] [0.000202 0.000174 0.00084 0.000201 0.0057 0.000223 0.000102]] >>> list_variable = p.data.get_list() >>> list_variable <List: ncvar%landpoint(7) > >>> print(list_variable.data.array) [1 2 5 7 8 16 18]  Subspaces based on the uncompressed axes of the field construct are easily created: Get subspaces based on indices of the uncompressed data. >>> p[0] <Field: precipitation_flux(time(1), latitude(4), longitude(5)) kg m2 s-1> >>> p[1, :, 3:5] <Field: precipitation_flux(time(1), latitude(4), longitude(2)) kg m2 s-1>  The underlying array of original data remains in compressed form until data array elements are modified: Change an element of the data and show that the underlying array is no longer compressed. >>> p.data.get_compression_type() 'gathered' >>> p.data[1] = -9 >>> p.data.get_compression_type() ''  A construct with an underlying gathered array is created by initializing a Data instance with a gathered array that is stored in the special GatheredArray array object. The following code creates a simple field construct with an underlying gathered array: Create a field construct with compressed data. import numpy import cfdm # Define the gathered values gathered_array = cfdm.Data([[2, 1, 3], [4, 0, 5]]) # Define the list array values list_array = [1, 4, 5] # Create the list variable list_variable = cfdm.List(data=cfdm.Data(list_array)) # Create the gathered array object, specifying the uncompressed # shape array = cfdm.GatheredArray( compressed_array=gathered_array, compressed_dimension=1, shape=(2, 3, 2), size=12, ndim=3, list_variable=list_variable) # Create the field construct with the domain axes and the gathered # array P = cfdm.Field(properties={'standard_name': 'precipitation_flux', 'units': 'kg m-2 s-1'}) # Create the domain axis constructs for the uncompressed array T = P.set_construct(cfdm.DomainAxis(2)) Y = P.set_construct(cfdm.DomainAxis(3)) X = P.set_construct(cfdm.DomainAxis(2)) # Set the data for the field P.set_data(cfdm.Data(array), axes=[T, Y, X])  Note that, because compression by gathering acts on a subset of the array dimensions, it is necessary to state the position of the compressed dimension in the compressed array (with the compressed_dimension parameter of the GatheredArray initialisation). The new field construct can now be inspected and written a netCDF file: Inspect the new field construct and write it to disk. >>> P <Field: precipitation_flux(key%domainaxis0(2), key%domainaxis1(3), key%domainaxis2(2)) kg m-2 s-1> >>> print(P.data.array) [[[ -- 2.0] [ -- --] [1.0 3.0]] [[ -- 4.0] [ -- --] [0.0 5.0]]] >>> P.data.get_compression_type() 'gathered' >>> print(P.data.compressed_array) [[2. 1. 3.] [4. 0. 5.]] >>> list_variable = P.data.get_list() >>> list_variable <List: (3) > >>> print(list_variable.data.array) [1 4 5] >>> cfdm.write(P, 'P_gathered.nc')  The content of the new file is: Inspect new the compressed dataset with the ncdump command line tool. $ ncdump P_gathered.nc
netcdf P_gathered {
dimensions:
dim = 2 ;
dim_1 = 3 ;
dim_2 = 2 ;
list = 3 ;
variables:
int64 list(list) ;
list:compress = "dim_1 dim_2" ;
float precipitation_flux(dim, list) ;
precipitation_flux:units = "kg m-2 s-1" ;
precipitation_flux:standard_name = "precipitation_flux" ;

// global attributes:
:Conventions = "CF-1.7" ;
data:

list = 1, 4, 5 ;

precipitation_flux =
2, 1, 3,
4, 0, 5 ;
}


## Controlling output messages¶

cfdm will produce messages upon the execution of operations, to provide feedback about:

• the progress of, and under-the-hood steps involved in, the operations it is performing;

• the events that emerge during these operations;

• the nature of the dataset being operated on, including CF compliance issues that may be encountered during the operation.

This feedback may be purely informational, or may convey warning(s) about dataset issues or the potential for future error(s).

It is possible to configure the extent to which messages are output at runtime, i.e. the verbosity of cfdm, so that less serious and/or more detailed messages can be filtered out.

There are two means to do this, which are covered in more detail in the sub-sections below. Namely, you may configure the extent of messaging:

• globally i.e. for all cfdm operations, by setting the cfdm.log_level which controls the project-wide logging;

• for a specific function only (for many functions) by setting that function’s verbose keyword (which overrides the global setting for the duration of the function call).

Both possibilities use a consistent level-based cut-off system, as detailed below.

### Logging¶

All messages from cfdm, excluding exceptions which are always raised in error cases, are incorporated into a logging system which assigns to each a level based on the relative seriousness and/or verbosity. From highest to lowest on this scale, these levels are:

• 'WARNING': conveys a warning;

• 'INFO': provides information concisely, in a few lines or so;

• 'DETAIL': provides information in a more detailed manner than 'INFO';

• 'DEBUG': produces highly-verbose information intended mainly for the purposes of debugging and cfdm library development.

The function cfdm.log_level sets the minimum of these levels for which messages are displayed. Any message marked as being of any lower level will be filtered out. Note it sets the verbosity globally, for all cfdm library operations (unless these are overridden for individual functions, as covered below).

As well as the named log levels above, cfdm.log_level accepts a further identifier, 'DISABLE'. Each of these potential settings has a numerical value that is treated interchangeably and may instead be set (as this may be easier to recall and write, if less explicit). The resulting behaviour in each case is as follows:

Log level

Integer code

Result when set as the log severity level

'DISABLE'

0

Disable all logging messages. Note this does not include exception messages raised by errors.

'WARNING' (default)

1

Only show logging messages that are warnings (those labelled as 'WARNING').

'INFO'

2

Only show logging messages that are warnings or concise informational messages (marked as 'WARNING' or 'INFO' respectively).

'DETAIL'

3

Enable all logging messages except for debugging messages. In other words, show logging messages labelled 'WARNING', 'INFO' and 'DETAIL', but not 'DEBUG'.

'DEBUG'

-1

Enable all logging messages, including debugging messages (labelled as 'DEBUG').

Note 'DEBUG' is intended as a special case for debugging, which should not be required in general usage of cfdm, hence its equivalence to -1 rather than 4 which would follow the increasing integer code pattern. -1 reflects that it is the final value in the sequence, as with Python indexing.

The default value for cfdm.log_level is 'WARNING' (1). However, whilst completing this tutorial, it may be instructive to set the log level to a higher verbosity level such as 'INFO' to gain insight into the internal workings of cfdm calls.

### Function verbosity¶

Functions and methods that involve a particularly high number of steps or especially complex processing, for example the cfdm.read and cfdm.write functions, accept a keyword argument verbose. This be set to change the minimum log level at which messages are displayed for the function/method call only, without being influenced by, or influencing, the global cfdm.log_level value.

A verbose value effectively overrides the value of cfdm.log_level for the function/method along with any functions/methods it calls in turn, until the origin function/method completes.

The verbose argument accepts the same levels as cfdm.log_level (including 0 for 'DISABLE'), as described in the logging section, namely either an integer or a corresponding string for example verbose=2 or equivalently verbose='INFO' (or verbose='info' since case is ignored).

By default, verbose is set to None, in which case the value of the cfdm.log_level` setting is used to determine which messages, if any, are filtered out.

Footnotes

1

Requires the netCDF4 python package to have been built with OPeNDAP support enabled. See http://unidata.github.io/netcdf4-python for details.