This page lists software packages that “understand” CF Data. If you have any additions or corrections for this page, please submit an issue on the CF Website GitHub repo (see the Contributing Guide for more details).
The description of each software package should give some indication of the level of support for CF.
The cf-checker is a python tool to check compliance of netCDF files against the CF Conventions. It can be run via a web interface or downloaded for use as a command-line utility on Linux and macOS by installing from PyPI, conda-forge or source code. The cf-checker verifies conformance according to the requirements and recommendations laid out in the CF Conformance Document. It is possible to check conformance against any CF version.
The IOOS Compliance Checker is a python based tool for data providers to check for completeness and community standard compliance of local or remote netCDF files against CF, ACDD, and IOOS Metadata Profile file standards. The Compliance Checker can be used as a command-line tool or as a library that can be integrated into other software.
The Compliance Checker also includes a web-based version that enables a broader audience and improve accessibility for the checker. With the web version, providers can simply provide a link or upload their datasets and get the full suite of capabilities that Compliance Checker offers.
The cfdm Python package implements the CF data model for its internal data structures and so is able to process any CF-compliant dataset. It is not strict about CF-compliance, however, so that partially conformant datasets may be ingested from existing datasets and written to new datasets. This is so that datasets which are partially conformant may nonetheless be modified in memory.
The Python cf package, “cf-python”, is an Earth Science data analysis library. It is built on cfdm and implements the CF data model for its internal data structures so is able to process any CF-compliant dataset. It can read, write and inspect field constructs and manipulate the data and metadata therein by means of statistical operations, collapsing, subspacing, regridding and more. Field constructs from cf can also be visualised with the cf-plot package.
cf-plot is a set of Python functions for making common contour, vector and line plots that climate researchers use. cf-plot generally uses cf-python to present the data and CF attributes for plotting. It can also use numpy arrays of data as the input fields making for flexible plotting of data. cf-plot uses the Python numpy, matplotlib and scipy libraries.
The cf-view Python package is a quick look data exploration tool for netCDF and Met Office PP data. It uses the cf-python and cf-plot packages and reads and writes CF-compliant dataset.
ERDDAP is a scientific data server that gives users a simple, consistent way to download subsets of gridded and tabular scientific datasets in common file formats and make graphs and maps. ERDDAP is a Free and Open Source (Apache and Apache-like) Java Servlet from NOAA NMFS SWFSC Environmental Research Division (ERD).
Ferret is an interactive computer visualization and analysis environment designed to meet the needs of oceanographers and meteorologists analyzing large and complex gridded data sets. It runs on recent Unix and Mac systems, using X windows for display. PyFerret, introduced in 2012, is a Python module wrapping Ferret. PyFerret is an upgrade to Ferret which runs existing Ferret scripts and includes all Ferret functionality with updated graphics capabilities and additional analysis functions. In addition the pyferret module provides Python functions so Python users can easily take advantage of Ferret’s abilities to retrieve, manipulate, visualize, and save data.
Iris implements a data model based on the CF Conventions giving you a powerful, format-agnostic interface for working with your data. It excels when working with multi-dimensional Earth Science data, where tabular representations become unwieldy and inefficient.
CF Standard names, units, and coordinate metadata are built into Iris, giving you a rich and expressive interface for maintaining an accurate representation of your data.
The netCDF Operators toolkit manipulates and analyzes data stored in netCDF-accessible formats, including DAP, HDF4, HDF5, and, most recently, Zarr.
NCO exploits the geophysical expressivity and logic of many CF (Climate & Forecast) metadata conventions including support for UDUNITS, for the attributes Conventions, history, scale_factor, add_offset, coordinates, cell_methods, and cell_measures, for hierarchical datasets, and for auxiliary coordinates.
See the documentation for a full description.
The netCDF Flattener Python package takes netCDF objects that use groups and flattens them while preserving references as described in the Groups section of the CF Conventions. The resulting object is logically equivalent to the original, and can be processed by software that isn’t able to work with files that use netCDF-4 groups.
The netCDF Java library provides an interface for scientific data access. It can be used to read scientific data from a variety of file formats including netCDF, HDF, GRIB, BUFR, and many others, as well as a variety of remote data access protocols. It implements the Unidata Common Data Model which uses CF and other metadata conventions in its Coordinate System and Scientific Feature Type layers. NetCDF-Java can write netCDF-3/4 files that conform to the CF Metadata Conventions.
The THREDDS Data Server (TDS) is a web server that provides metadata and data access for scientific datasets, using OPeNDAP, OGC WMS and WCS, HTTP, and other remote data access protocols. It is also capable of mapping CF metadata to ISO-19115 though the use of ncISO. The TDS can use the NetCDF Markup Language (NcML) to modify datasets in-memory to aid in CF conformance without the need to rewrite the files as stored on disk. The NetCDF Subset Service allows users to subset CF compliant datasets in coordinate space using a REST API. The service returns subsets as CF compliant netCDF-3/4 files, in addition to other formats.
Xarray is a python package designed for working with labelled, multi-dimensional array data, built around the netCDF data model. It uses CF Conventions in several ways, such as encoding / decoding variables and interpreting metadata for visualization.
CF MetaData
gridded - A Python API for accessing / working with gridded model results on multiple grid typesgridded is a python package designed for working with various grids used for (primarily) met/ocean modeling The goal of this package is to present a single way to work with results from ANY model – regardless of what type of grid it was computed on. Currently supported are:
xCDAT (Xarray Climate Data Analysis Tools) is an extension of xarray for climate data analysis on structured grids. It serves as a modern successor to the Community Data Analysis Tools (CDAT) library.
The goal of xCDAT is to provide generalizable features and utilities for simple and robust analysis of climate data. xCDAT’s design philosophy is focused on reducing the overhead required to accomplish certain tasks in xarray. xCDAT aims to be compatible with structured grids that are CF-compliant (e.g., CMIP6). Some key xCDAT features are inspired by or ported from the core CDAT library, while others leverage powerful libraries in the xarray ecosystem (e.g., xESMF, xgcm, cf_xarray) to deliver robust APIs.
cf_xarray - Python package for interpreting CF metadata on xarray objectscf_xarray mainly provides an accessor (DataArray.cf or Dataset.cf) that allows you to interpret Climate and Forecast metadata convention attributes present on xarray objects.
Esri’s ArcGIS Pro supports the ingest, management, data visualization, multidimensional analysis, and authoritative sharing of CF-Conventions compliant netCDF data. NetCDF data can be processed as tables, points, rasters, and, most recently, discrete sampling geometries. In addition, various ArcGIS Pro tools enable multidimensional management (aggregation, subsetting, anomaly detection), spatio-temporal analysis (trend detection, time-series clustering, changepoint detection, hot spot detection), and visualization as 3D features or voxels. Analysis results based on netCDF data can be shared and viewed across the web as interactive maps, 3D features, and voxels.