cfbuild 0.0.11

Used for generating netCDF files compliant with CF Conventions or ACDD

CFBUILD

Building CF Compliant netCDF files

This package is designed to help update or build netCDF datasets so that they are compliant with the Attribute Conventions for Data Discovery (ACDD) and the Climate and Forecast (CF) Conventions. It is built to work with multidimensional geo-referenced datasets but may be used on any netCDF file. No guarantee is given as to the accuracy of the updated datasets and user discretion is advised.

Installation

To install cfbuild run pip install cfbuild in your terminal.

Cfbuild requires the udunits2 library which can be installed via conda with: conda install -c conda-forge udunits2

Dependencies

• netCDF4
• pyproj
• cfunits
• isodate
• lxml

Documentation

Read the documentation here: https://cfbuild.readthedocs.io/en/latest/

Classes and Functions

cfbuild.Dataset(dataset_or_filepath, conventions)

dataset_or_filepath: str - An OPeNDAP url or the path to a netCDF file.
conventions: str or list - One or both of ['CF-1.9', 'ACDD-1.3'].

cfbuild.Dataset.to_ncml(output_filepath)

output_filepath: str - A filepath where an ncml file will be created which describes the cfbuild dataset

cfbuild.Dataset.crs(epsg_number)

epsg_number: str or int - An EPSG number that defines the coordinate reference system for the dataset.

cfbuild.Dataset.close()

Close the dataset.

cfbuild.Dataset.group(name)

name: str - The name of the group to be created in the cfbuild dataset.

cfbuild.Dataset.variable(name, data_type, dimensions, variable_type, values)

name: str - The name of the variable to be created in the cfbuild dataset.
data_type: str - The data type of the variable.
dimensions: tuple - The dimensions associated with the variable.
variable_type: str - The Climate and Forecast Convention variable type for the variable. One of Coordinate, Time Coordinate, X Coordinate, Y Coordinate, Z Coordinate', Auxiliary Coordinate', Georeferenced Data Variable, Data Variable, Ancillary Data Variable, Scalar Coordinate, Grid Mapping Variable, Domain Variable, Boundary Variable, Cell Measures Variable.
values: numpy.array - An array of values for the variable.

cfbuild.Dataset.dimension(name, length)

name: str - The name of the dimension to be created in the cfbuild dataset.
length: int or None - The length of the dimension. Use None for an unlimited dimension.

cfbuild.Dataset.attribute(name, value)

name: str - The name of the attribute to be created for dataset.
value: str - The value of the attribute.

cfbuild.NCML(ncml_filepath, dataset)

ncml_filepath: str - A path to an ncml file used to configure the new dataset.
dataset: cfbuild.Dataset - A dataset created using cfbild.Dataset().

cfbuild.NCML.to_nc(nc_filepath, write_mode)

nc_filepath: str - A path to the new netCDF dataset that will be created. writ_mode: str - w or clobber. w will write to a new dataset but will raise an error if a dataset with the same name already exists. clobber uses the netCDF4 clobber function.

Tutorial

Build a dataset

Import the necessary packages

import cfbuild
import numpy as np

Create a new dataset. Add dimensions and variables to the dataset.

ds = cfbuild.Dataset()

time_dimension = ds.dimension('time', None)
lat_dimension = ds.dimension('lat', 181)
lon_dimension = ds.dimension('lon', 361)

time_variable = ds.variable('time', 'int32', ('time',), values=np.arange(0, 20, 1, dtype='int32'))
lat_variable = ds.variable('lat', 'int32', ('lat',), values=np.arange(-90, 90, 1, dtype='int32'))
lon_variable = ds.variable('lon', 'int32', ('lon',), values=np.arange(-180, 180, 1, dtype='int32'))

data_variable = ds.variable('data', 'float64', ('time', 'lat', 'lon',))
data_variable.values = np.random.rand(20,181,361)

Specify the variable type for each variable.

time_variable.variable_type = 'Time Coordinate'
lat_variable.variable_type = 'Y Coordinate'
lon_variable.variable_type = 'X Coordinate'
data_variable.variable_type = 'Georeferenced Data Variable'

Convert the dataset to an ncml file and generate the needed metadata.

ncml = ds.to_ncml('foo/foo/path_to_ncml.nc')

Open the ncml file and edit the metadata. Once the metadata is edited to your satisfaction, convert it to a netCDF file.

ncml.to_nc('foo/foo/path_to_netCDF_file.nc')

Update a dataset

Import the necessary packages

import cfbuild

Create a new dataset. Add dimensions and variables to the dataset.

ds = cfbuild.Dataset('foo/foo/path_to_netCDF_file.nc')
ncml = ds.to_ncml('foo/foo/path_to_ncml.nc')

Open the ncml file and edit the metadata. Once the metadata is edited to your satisfaction, convert it to a netCDF file.

ncml.to_nc('foo/foo/path_to_netCDF_file.nc')

If you have a bunch of datasets with the same data structure that need to be updated: create and ncml file following the same instructions previously listed, create a cfbuild.Dataset object from the dataset that needs to be updated, create a cfbuild.NCML object using the dataset just created and the path to the ncml file, and create a new dataset.

ds = cfbuild.Dataset('foo/foo/path_to_netCDF_file.nc')
ncml = cfbuild.NCML('foo/foo/path_to_ncml.nc', ds)
ncml.to_nc('foo/foo/path_to_new_netCDF_file.nc')

Issues and bugs

If you encounter an issue while using this package, please recorde it in the issues tab of the github repository: https://github.com/jenochjones/cfbuild/issues.

Notes

Modify the ncml file to modify the resulting dataset.

Change a variable name:

<variable name="time" orgName="t" />

Replace values in a variable:

<values start="0" incr="2.25" />