\input texinfo @c -*-texinfo-*-
@comment $Id: netcdf.texi,v 1.82 2010/05/15 20:43:13 dmh Exp $
@c %**start of header
@setfilename netcdf.info
@settitle The NetCDF Users' Guide
@setcontentsaftertitlepage
@c Combine the variable, concept, and function indices.
@synindex vr cp
@synindex fn cp
@c %**end of header

@c version.texi is automatically generated by automake and contains
@c defined variables VERSION, UPDATED, UPDATED-MONTH.
@include version.texi
@include defines.texi

@ifinfo
@dircategory netCDF scientific data format
@direntry
* netcdf: (netcdf).         @value{n-man}
@end direntry
@end ifinfo

@titlepage
@title @value{n-man}
@subtitle Data Model, Programming Interfaces, and Format for Self-Describing, Portable Data
@subtitle NetCDF Version @value{VERSION}
@subtitle @value{UPDATED-MONTH}
@author Russ Rew, Glenn Davis, Steve Emmerson, Harvey Davies, Ed
Hartnett, and Dennis Heimbigner
@author Unidata Program Center
@page
@vskip 0pt plus 1filll
@insertcopying
@end titlepage

@ifnottex
@node Top, Foreword, (dir), (dir)
@top NetCDF Users Guide
@cindex C++ API
@cindex Fortran 77 API
@cindex Fortran 90 API
@cindex C API
@cindex API, C++
@cindex API, Fortran 77 
@cindex API, Fortran 90 
@cindex API, C
@cindex Interface Guide, C
@cindex Interface Guide, C++
@cindex Interface Guide, Fortran 77
@cindex Interface Guide, Fortran 90

This guide describes the netCDF object model. This document applies to
netCDF version @value{VERSION}, and was last updated on
@value{UPDATED}.

Interface guides are available for C (@pxref{Top, @value{c-man},,
netcdf-c, @value{c-man}}), C++ (@pxref{Top, @value{cxx-man},,
netcdf-cxx, @value{cxx-man}}), Fortran 77 (@pxref{Top,
@value{f77-man},, netcdf-f77, @value{f77-man}}), and Fortran 90
(@pxref{Top, @value{f90-man},, netcdf-f90, @value{f90-man}}).

Separate documentation for the netCDF Java library can be found at the
netCDF-Java website, @uref{@value{netcdf-java-url}}.

For installation and porting information, see @ref{Top,
@value{i-man},, netcdf-install, @value{i-man}}.

@end ifnottex

@menu
* Foreword::                    Foreword from 1996 Manual
* Summary::                     Orientation
* Introduction::                What is NetCDF?
* Dataset Components::          What's in a NetCDF File?
* Data::                        How to Store Data
* Structure::                   Behind the Scenes
* NetCDF Utilities::            Ncdump, ncgen, and nccopy
* Units::                       Using UDUNITS
* Attribute Conventions::       Creating Human-Readable Files
* File Format::                 Description of NetCDF Binary Formats
* Internal Dispatch Table::     Description of experimental dispatch mechanism
* Combined Index::              Index of Concepts and Functions

@detailmenu
 --- The Detailed Node Listing ---

Introduction

* Interface::                   The NetCDF Interface
* Not DBMS::                    NetCDF is not a Database
* Format::                      The NetCDF File Format
* Which Format::                Selecting the Underlying NetCDF Format
* Performance::                 What about Performance?
* Archival::                    Is NetCDF a Good Archive Format?
* Conventions::                 Creating Self-Describing Data Conforming to Conventions
* Background::                  The Evolution of the NetCDF Interface
* Whats New::                   Latest Developments in NetCDF
* Limitations::                 Limitations of NetCDF
* Future ::                     Plans for Future Development
* References::                  Papers Relating to Scientific Data

Components of a NetCDF Dataset

* Data Model::                  How NetCDF Sees Data
* Dimensions::                  Specifying Data Shape
* Variables::                   Storing Data
* Attributes::                  Storing Metadata
* Attributes and Variables::    Attributes vs. Variables

Data

* External Types::              Integers, Floats, and so on
* Classic Data Structures::     Complex Data in Classic Format
* User Defined Types::          Complex Data in NetCDF-4/HDF5 Format
* Data Access::                 Reading and Writing Data
* Type Conversion::             Changing Type of Numeric Data

Forms of Data Access

* C Section Access::            A C Example
* Fortran Section Access::      A Fortran Example

File Structure and Performance

* Classic File Parts::          The Classic and 64-bit Offset File
* NetCDF-4 File Parts::         The NetCDF-4/HDF5 File
* XDR Layer::                   Classic Machine Interoperability
* Large File Support::          Files that Exceed 2 GiBytes
* 64 bit Offset Limitations::   Limitations on File and Data Size
* Classic Limitations::         Limitations on File and Data Size
* The NetCDF-3 IO Layer::       Classic I/O Described
* UNICOS Optimization::         Some Cray Optimizations
* Chunking::                    NetCDF-4/HDF5 Files Read/Write Chunks
* Parallel Access::             Parallel I/O with NetCDF-4
* Interoperability with HDF5::  Using HDF5 with NetCDF-4
* DAP Support::                 

Improving Performance With Chunking

* Chunk Cache::                 
* Default Chunking::            
* Default Chunking 4_0_1::      
* Parallel Chunking::           
* bm_file::                     

NetCDF Utilities

* CDL Syntax::                  Creating a File without Code
* CDL Data Types::              Describing Types in CDL
* CDL Constants::               Constant Values in CDL
* ncgen::                       Turning CDL into Classic or Enhanced Data Files
* ncdump::                      Turning Data Files into CDL (or XML)
* nccopy::                      Copying, Converting, Compressing, and Chunking Data Files 
* ncgen3::                      Turning CDL into Classic Data Files

File Format Specification

* NetCDF Classic Format::       The Original Binary Format
* 64-bit Offset Format::        Supporting Larger Variables
* NetCDF-4 Format::             Uses HDF5 
* NetCDF-4 Classic Model Format::  HDF5 with NetCDF Limitations
* HDF4 SD Format::              

The NetCDF Classic Format Specification

* Classic Format Spec::         Detailed Format Information
* Computing Offsets::           How to Get the Data You Want
* Examples::                    The Binary Layout of some Simple Files

@end detailmenu
@end menu

@node Foreword, Summary, Top, Top
@unnumbered Foreword

Unidata (@uref{@value{unidata-url}}) is a National Science
Foundation-sponsored program empowering U.S. universities, through
innovative applications of computers and networks, to make the best
use of atmospheric and related data for enhancing education and
research. For analyzing and displaying such data, the Unidata Program
Center offers universities several supported software packages
developed by other organizations. Underlying these is a
Unidata-developed system for acquiring and managing data in real time,
making practical the Unidata principle that each university should
acquire and manage its own data holdings as local requirements
dictate. It is significant that the Unidata program has no data
center--the management of data is a "distributed" function.

The Network Common Data Form (netCDF) software described in this guide
was originally intended to provide a common data access method for the
various Unidata applications. These deal with a variety of data types
that encompass single-point observations, time series,
regularly-spaced grids, and satellite or radar images.

The netCDF software functions as an I/O library, callable from C,
FORTRAN, C++, Perl, or other language for which a netCDF library is
available. The library stores and retrieves data in self-describing,
machine-independent datasets. Each netCDF dataset can contain
multidimensional, named variables (with differing types that include
integers, reals, characters, bytes, etc.), and each variable may be
accompanied by ancillary data, such as units of measure or descriptive
text. The interface includes a method for appending data to existing
netCDF datasets in prescribed ways, functionality that is not unlike a
(fixed length) record structure. However, the netCDF library also
allows direct-access storage and retrieval of data by variable name
and index and therefore is useful only for disk-resident (or
memory-resident) datasets.

NetCDF access has been implemented in about half of Unidata's
software, so far, and it is planned that such commonality will extend
across all Unidata applications in order to:

@itemize

@item
Facilitate the use of common datasets by distinct applications.

@item
Permit datasets to be transported between or shared by dissimilar
computers transparently, i.e., without translation.  

@item
Reduce the programming effort usually spent interpreting formats.  

@item
Reduce errors arising from misinterpreting data and ancillary data.

@item
Facilitate using output from one application as input to another.

@item
Establish an interface standard which simplifies the inclusion of new
software into the Unidata system.

@end itemize

A measure of success has been achieved. NetCDF is now in use on
computing platforms that range from personal computers to
supercomputers and include most UNIX-based workstations. It can be
used to create a complex dataset on one computer (say in FORTRAN) and
retrieve that same self-describing dataset on another computer (say in
C) without intermediate translations--netCDF datasets can be
transferred across a network, or they can be accessed remotely using a
suitable network file system or remote access protocols.

Because we believe that the use of netCDF access in non-Unidata
software will benefit Unidata's primary constituency--such use may
result in more options for analyzing and displaying Unidata
information--the netCDF library is distributed without licensing or
other significant restrictions, and current versions can be obtained
via anonymous FTP. Apparently the software has been well received by a
wide range of institutions beyond the atmospheric science community,
and a substantial number of public domain and commercial data analysis
systems can now accept netCDF datasets as input.

Several organizations have adopted netCDF as a data access standard,
and there is an effort underway at the National Center for
Supercomputer Applications (NCSA, which is associated with the
University of Illinois at Urbana-Champaign) to support the netCDF
programming interfaces as a means to store and retrieve data in "HDF
files," i.e., in the format used by the popular NCSA tools. We have
encouraged and cooperated with these efforts.

Questions occasionally arise about the level of support provided for
the netCDF software. Unidata's formal position, stated in the
copyright notice which accompanies the netCDF library, is that the
software is provided "as is". In practice, the software is updated
from time to time, and Unidata intends to continue making improvements
for the foreseeable future. Because Unidata's mission is to serve
geoscientists at U.S. universities, problems reported by that
community necessarily receive the greatest attention.

We hope the reader will find the software useful and will give us
feedback on its application as well as suggestions for its
improvement.

David Fulker, 1996

Unidata Program Center Director,
University Corporation for Atmospheric Research

@node Summary, Introduction, Foreword, Top
@unnumbered Summary
@cindex supported programming languages
@cindex API, C
@cindex API, Fortran
@cindex API, F90
@cindex API, C++
@cindex API, Java
@cindex C API
@cindex Fortran API
@cindex F90 API
@cindex C++ API
@cindex Java API

The purpose of the Network Common Data Form (netCDF) interface is to
allow you to create, access, and share array-oriented data in a form
that is self-describing and portable. "Self-describing" means that a
dataset includes information defining the data it contains. "Portable"
means that the data in a dataset is represented in a form that can be
accessed by computers with different ways of storing integers,
characters, and floating-point numbers. Using the netCDF interface for
creating new datasets makes the data portable. Using the netCDF
interface in software for data access, management, analysis, and
display can make the software more generally useful.

The netCDF software includes C, Fortran 77, Fortran 90, and C++
interfaces for accessing netCDF data. These libraries are available
for many common computing platforms.

The community of netCDF users has contributed ports of the software to
additional platforms and interfaces for other programming languages as
well. Source code for netCDF software libraries is freely available to
encourage the sharing of both array-oriented data and the software
that makes the data useful.

This User's Guide presents the netCDF data model. It explains how the
netCDF data model uses dimensions, variables, and attributes to store
data. Language specific programming guides are available for C
(@pxref{Top, @value{c-man},, netcdf-c, @value{c-man}}), C++
(@pxref{Top, @value{cxx-man},, netcdf-cxx, @value{cxx-man}}), Fortran
77 (@pxref{Top, @value{f77-man},, netcdf-f77, @value{f77-man}}), and
Fortran 90 (@pxref{Top, @value{f90-man},, netcdf-f90,
@value{f90-man}}).

Reference documentation for UNIX systems, in the form of UNIX 'man'
pages for the C and FORTRAN interfaces is also available at the netCDF
web site (@uref{@value{netcdf-url}}), and with the netCDF
distribution.

The latest version of this document, and the language specific guides,
can be found at the netCDF web site, @uref{@value{docs-url}}, along
with extensive additional information about netCDF, including pointers
to other software that works with netCDF data.

Separate documentation of the Java netCDF library can be found at
@uref{@value{netcdf-java-url}}.

For installation and porting information @xref{Top, @value{i-man},,
netcdf-install, @value{i-man}}.

@node Introduction, Dataset Components, Summary, Top
@chapter Introduction

@menu
* Interface::                   The NetCDF Interface
* Not DBMS::                    NetCDF is not a Database
* Format::                      The NetCDF File Format
* Which Format::                Selecting the Underlying NetCDF Format
* Performance::                 What about Performance?
* Archival::                    Is NetCDF a Good Archive Format?
* Conventions::                 Creating Self-Describing Data Conforming to Conventions
* Background::                  The Evolution of the NetCDF Interface
* Whats New::                   Latest Developments in NetCDF
* Limitations::                 Limitations of NetCDF
* Future ::                     Plans for Future Development
* References::                  Papers Relating to Scientific Data
@end menu

@node Interface, Not DBMS, Introduction, Introduction
@section The NetCDF Interface

The Network Common Data Form, or netCDF, is an interface to a library
of data access functions for storing and retrieving data in the form
of arrays. An array is an n-dimensional (where n is 0, 1, 2, @dots{})
rectangular structure containing items which all have the same data
type (e.g., 8-bit character, 32-bit integer). A @dfn{scalar} (simple
single value) is a 0-dimensional array.

NetCDF is an abstraction that supports a view of data as a collection
of self-describing, portable objects that can be accessed through a
simple interface. Array values may be accessed directly, without
knowing details of how the data are stored. Auxiliary information
about the data, such as what units are used, may be stored with the
data. Generic utilities and application programs can access netCDF
datasets and transform, combine, analyze, or display specified fields
of the data. The development of such applications has led to improved
accessibility of data and improved re-usability of software for
array-oriented data management, analysis, and display.

The netCDF software implements an abstract data type, which means that
all operations to access and manipulate data in a netCDF dataset must
use only the set of functions provided by the interface. The
representation of the data is hidden from applications that use the
interface, so that how the data are stored could be changed without
affecting existing programs. The physical representation of netCDF
data is designed to be independent of the computer on which the data
were written.

Unidata supports the netCDF interfaces for C, (@pxref{Top,,, netcdf-c,
@value{c-man}}), FORTRAN 77 (@pxref{Top,,, netcdf-f77,
@value{f77-man}}), FORTRAN 90 (@pxref{Top,,, netcdf-f90,
@value{f90-man}}), and C++ (@pxref{Top, ,, netcdf-cxx,
@value{cxx-man}}).

The netCDF library is supported for various UNIX operating systems. A
MS Windows port is also available. The software is also ported and
tested on a few other operating systems, with assistance from users
with access to these systems, before each major release. Unidata's
netCDF software is freely available via FTP to encourage its
widespread use. (@uref{@value{ftp-site}}).

For detailed installation instructions, see the Porting and
Installation Guide. @xref{Top,, , netcdf-install, @value{i-man}}.

@node Not DBMS, Format, Interface, Introduction
@section NetCDF Is Not a Database Management System
@cindex data base
@cindex DBMS

Why not use an existing database management system for storing
array-oriented data? Relational database software is not suitable for
the kinds of data access supported by the netCDF interface.

First, existing database systems that support the relational model do
not support multidimensional objects (arrays) as a basic unit of data
access. Representing arrays as relations makes some useful kinds of
data access awkward and provides little support for the abstractions
of multidimensional data and coordinate systems. A quite different
data model is needed for array-oriented data to facilitate its
retrieval, modification, mathematical manipulation and visualization.

Related to this is a second problem with general-purpose database
systems: their poor performance on large arrays. Collections of
satellite images, scientific model outputs and long-term global
weather observations are beyond the capabilities of most database
systems to organize and index for efficient retrieval.

Finally, general-purpose database systems provide, at significant cost
in terms of both resources and access performance, many facilities
that are not needed in the analysis, management, and display of
array-oriented data. For example, elaborate update facilities, audit
trails, report formatting, and mechanisms designed for
transaction-processing are unnecessary for most scientific
applications.

@node Format, Which Format, Not DBMS, Introduction
@section The netCDF File Format
@cindex XDR format

Until version 3.6.0, all versions of netCDF employed only one binary
data format, now referred to as netCDF classic format. NetCDF classic
is the default format for all versions of netCDF.

In version 3.6.0 a new binary format was introduced, 64-bit offset
format. Nearly identical to netCDF classic format, it uses 64-bit
offsets (hence the name), and allows users to create far larger
datasets. 

In version 4.0.0 a third binary format was introduced: the HDF5
format. Starting with this version, the netCDF library can use HDF5
files as its base format. (Only HDF5 files created with netCDF-4 can
be understood by netCDF-4). 

By default, netCDF uses the classic format. To use the 64-bit offset
or netCDF-4/HDF5 format, set the appropriate constant when creating
the file.

To achieve network-transparency (machine-independence), netCDF classic
and 64-bit offset formats are implemented in terms of an external
representation much like XDR (eXternal Data Representation, see
@uref{http://www.ietf.org/rfc/rfc1832.txt}), a standard for describing
and encoding data. This representation provides encoding of data into
machine-independent sequences of bits. It has been implemented on a
wide variety of computers, by assuming only that eight-bit bytes can
be encoded and decoded in a consistent way. The IEEE 754
floating-point standard is used for floating-point data
representation.

Descriptions of the overall structure of netCDF classic and 64-bit
offset files are provided later in this manual. @xref{Structure}.

The details of the classic and 64-bit offset formats are described in
an appendix.  @xref{File Format}. However, users are discouraged from
using the format specification to develop independent low-level
software for reading and writing netCDF files, because this could lead
to compatibility problems if the format is ever modified.

@node Which Format, Performance, Format, Introduction
@section How to Select the Format
@cindex format selection advice

With three different base formats, care must be taken in creating data
files to choose the correct base format.

The format of a netCDF file is determined at create time. 

When opening an existing netCDF file the netCDF library will
transparently detect its format and adjust accordingly. However,
netCDF library versions earlier than 3.6.0 cannot read 64-bit offset
format files, and library versions before 4.0 can't read netCDF-4/HDF5
files. NetCDF classic format files (even if created by version
3.6.0 or later) remain compatible with older versions of the netCDF
library. 

Users are encouraged to use netCDF classic format to distribute data,
for maximum portability.

To select 64-bit offset or netCDF-4 format files, C programmers should
use flag NC_64BIT_OFFSET or NC_NETCDF4 in function
nc_create. @xref{nc_create,,, netcdf-c, @value{c-man}}.

In Fortran, use flag nf_64bit_offset or nf_format_netcdf4 in function
NF_CREATE. @xref{NF_CREATE,,, netcdf-f77, @value{f77-man}}.

It is also possible to change the default creation format, to convert
a large body of code without changing every create call. C programmers
see @ref{nc_set_default_format,,, netcdf-c, @value{c-man}}. Fortran
programs see @ref{NF_SET_DEFAULT_FORMAT,,, netcdf-f77,
@value{f77-man}}.

@subsection NetCDF Classic Format

The original netCDF format is identified using four bytes in the file
header. All files in this format have ``CDF\001'' at the beginning of the
file. In this documentation this format is referred to as ``netCDF
classic format.''

NetCDF classic format is identical to the format used by every
previous version of netCDF. It has maximum portability, and is still
the default netCDF format.

For some users, the various 2 GiB format limitations of the classic
format become a problem. (@pxref{Classic Limitations}). 

@subsection NetCDF 64-bit Offset Format

For these users, 64-bit offset format is a natural choice. It greatly
eases the size restrictions of netCDF classic files (@pxref{64 bit
Offset Limitations}).

Files with the 64-bit offsets are identified with a ``CDF\002'' at the
beginning of the file. In this documentation this format is called
``64-bit offset format.''

Since 64-bit offset format was introduced in version 3.6.0, earlier
versions of the netCDF library can't read 64-bit offset files.

@subsection NetCDF-4 Format

In version 4.0, netCDF included another new underlying format:
HDF5.

NetCDF-4 format files offer new features such as groups, compound
types, variable length arrays, new unsigned integer types, parallel
I/O access, etc. None of these new features can be used with classic
or 64-bit offset files. 

NetCDF-4 files can't be created at all, unless the netCDF configure
script is run with --enable-netcdf-4. This also requires version 1.8.0
of HDF5.

For the netCDF-4.0 release, netCDF-4 features are only available from
the C and Fortran interfaces. We plan to bring netCDF-4 features to the
CXX API in a future release of netCDF.

NetCDF-4 files can't be read by any version of the netCDF library
previous to 4.0. (But they can be read by HDF5, version 1.8.0 or
better).

For more discussion of format issues see @ref{Versions, @value{tut-man},,
netcdf-tutorial, @value{tut-man}}.

@node Performance, Archival, Which Format, Introduction
@section What about Performance?
@cindex performance, introduction

One of the goals of netCDF is to support efficient access to small
subsets of large datasets. To support this goal, netCDF uses direct
access rather than sequential access. This can be much more efficient
when the order in which data is read is different from the order in
which it was written, or when it must be read in different orders for
different applications.

The amount of overhead for a portable external representation depends
on many factors, including the data type, the type of computer, the
granularity of data access, and how well the implementation has been
tuned to the computer on which it is run. This overhead is typically
small in comparison to the overall resources used by an
application. In any case, the overhead of the external representation
layer is usually a reasonable price to pay for portable data access.

Although efficiency of data access has been an important concern in
designing and implementing netCDF, it is still possible to use the
netCDF interface to access data in inefficient ways: for example, by
requesting a slice of data that requires a single value from each
record. Advice on how to use the interface efficiently is provided in
@ref{Structure}.

The use of HDF5 as a data format adds significant overhead in metadata
operations, less so in data access operations. We continue to study
the challenge of implementing netCDF-4/HDF5 format without
compromising performance.

@node Archival, Conventions, Performance, Introduction
@section Is NetCDF a Good Archive Format?
@cindex archive format
@cindex compression

NetCDF classic or 64-bit offset formats can be used as a
general-purpose archive format for storing arrays. Compression of data
is possible with netCDF (e.g., using arrays of eight-bit or 16-bit
integers to encode low-resolution floating-point numbers instead of
arrays of 32-bit numbers), or the resulting data file may be
compressed before storage (but must be uncompressed before it is
read). Hence, using these netCDF formats may require more space than
special-purpose archive formats that exploit knowledge of particular
characteristics of specific datasets.

With netCDF-4/HDF5 format, the zlib library can provide compression on
a per-variable basis. That is, some variables may be compressed,
others not. In this case the compression and decompression of data
happen transparently to the user, and the data may be stored, read,
and written compressed. 

@node Conventions, Background, Archival, Introduction
@section Creating Self-Describing Data conforming to Conventions
@cindex applications, generic, conventions
@cindex conventions, introduction

The mere use of netCDF is not sufficient to make data
"self-describing" and meaningful to both humans and machines. The
names of variables and dimensions should be meaningful and conform to
any relevant conventions. Dimensions should have corresponding
coordinate variables where sensible.

Attributes play a vital role in providing ancillary information. It is
important to use all the relevant standard attributes using the
relevant conventions. For a description of reserved attributes (used by
the netCDF library) and attribute conventions for generic application
software, see @ref{Attribute Conventions}.

A number of groups have defined their own additional conventions and
styles for netCDF data. Descriptions of these conventions, as well as
examples incorporating them can be accessed from the netCDF
Conventions site, @uref{@value{netcdf-url}/conventions.html}.

These conventions should be used where suitable. Additional
conventions are often needed for local use. These should be
contributed to the above netCDF conventions site if likely to interest
other users in similar areas.

@node Background, Whats New, Conventions, Introduction
@section Background and Evolution of the NetCDF Interface
@cindex XDR, introduction into netCDF
@cindex workshop, CDF
@cindex CANDIS
@cindex NASA CDF format
@cindex SNIDE
@cindex New Mexico Institute of Mining
@cindex SeaSpace, Inc
@cindex University of Miami
@cindex Terascan data format
@cindex FAN
@cindex NCO
@cindex DODS
@cindex OpenDAP
@cindex NcML
@cindex Northwestern University
@cindex Argonne National Laboratory
@cindex 64-bit offsets, history
@cindex ruby API, history
@cindex python API, history
@cindex Tcl/Tk API, history
@cindex Java API, history
@cindex Matlab API, history
@cindex WetCDF, history
@cindex ADA API, history
@cindex 64-bit offsets, history

The development of the netCDF interface began with a modest goal
related to Unidata's needs: to provide a common interface between
Unidata applications and real-time meteorological data. Since Unidata
software was intended to run on multiple hardware platforms with
access from both C and FORTRAN, achieving Unidata's goals had the
potential for providing a package that was useful in a broader
context. By making the package widely available and collaborating with
other organizations with similar needs, we hoped to improve the then
current situation in which software for scientific data access was
only rarely reused by others in the same discipline and almost never
reused between disciplines (Fulker, 1988).

Important concepts employed in the netCDF software originated in a
paper (Treinish and Gough, 1987) that described data-access software
developed at the NASA Goddard National Space Science Data Center
(NSSDC). The interface provided by this software was called the Common
Data Format (CDF). The NASA CDF was originally developed as a
platform-specific FORTRAN library to support an abstraction for
storing arrays.

The NASA CDF package had been used for many different kinds of data in
an extensive collection of applications. It had the virtues of
simplicity (only 13 subroutines), independence from storage format,
generality, ability to support logical user views of data, and support
for generic applications.

Unidata held a workshop on CDF in Boulder in August 1987. We proposed
exploring the possibility of collaborating with NASA to extend the CDF
FORTRAN interface, to define a C interface, and to permit the access
of data aggregates with a single call, while maintaining compatibility
with the existing NASA interface.

Independently, Dave Raymond at the New Mexico Institute of Mining and
Technology had developed a package of C software for UNIX that
supported sequential access to self-describing array-oriented data and
a "pipes and filters" (or "data flow") approach to processing,
analyzing, and displaying the data. This package also used the "Common
Data Format" name, later changed to C-Based Analysis and Display
System (CANDIS). Unidata learned of Raymond's work (Raymond, 1988),
and incorporated some of his ideas, such as the use of named
dimensions and variables with differing shapes in a single data
object, into the Unidata netCDF interface.

In early 1988, Glenn Davis of Unidata developed a prototype netCDF
package in C that was layered on XDR. This prototype proved that a
single-file, XDR-based implementation of the CDF interface could be
achieved at acceptable cost and that the resulting programs could be
implemented on both UNIX and VMS systems. However, it also
demonstrated that providing a small, portable, and NASA CDF-compatible
FORTRAN interface with the desired generality was not
practical. NASA's CDF and Unidata's netCDF have since evolved
separately, but recent CDF versions share many characteristics with
netCDF.

In early 1988, Joe Fahle of SeaSpace, Inc. (a commercial software
development firm in San Diego, California), a participant in the 1987
Unidata CDF workshop, independently developed a CDF package in C that
extended the NASA CDF interface in several important ways (Fahle,
1989). Like Raymond's package, the SeaSpace CDF software permitted
variables with unrelated shapes to be included in the same data object
and permitted a general form of access to multidimensional
arrays. Fahle's implementation was used at SeaSpace as the
intermediate form of storage for a variety of steps in their
image-processing system. This interface and format have subsequently
evolved into the Terascan data format.

After studying Fahle's interface, we concluded that it solved many of
the problems we had identified in trying to stretch the NASA interface
to our purposes. In August 1988, we convened a small workshop to agree
on a Unidata netCDF interface, and to resolve remaining open
issues. Attending were Joe Fahle of SeaSpace, Michael Gough of Apple
(an author of the NASA CDF software), Angel Li of the University of
Miami (who had implemented our prototype netCDF software on VMS and
was a potential user), and Unidata systems development
staff. Consensus was reached at the workshop after some further
simplifications were discovered. A document incorporating the results
of the workshop into a proposed Unidata netCDF interface specification
was distributed widely for comments before Glenn Davis and Russ Rew
implemented the first version of the software. Comparison with other
data-access interfaces and experience using netCDF are discussed in
Rew and Davis (1990a), Rew and Davis (1990b), Jenter and Signell
(1992), and Brown, Folk, Goucher, and Rew (1993).

In October 1991, we announced version 2.0 of the netCDF software
distribution. Slight modifications to the C interface (declaring
dimension lengths to be long rather than int) improved the usability
of netCDF on inexpensive platforms such as MS-DOS computers, without
requiring recompilation on other platforms. This change to the
interface required no changes to the associated file format.

Release of netCDF version 2.3 in June 1993 preserved the same file
format but added single call access to records, optimizations for
accessing cross-sections involving non-contiguous data, subsampling
along specified dimensions (using 'strides'), accessing non-contiguous
data (using 'mapped array sections'), improvements to the ncdump and
ncgen utilities, and an experimental C++ interface.

In version 2.4, released in February 1996, support was added for new
platforms and for the C++ interface, significant optimizations
were implemented for supercomputer architectures, and the file format
was formally specified in an appendix to the User's Guide.

FAN (File Array Notation), software providing a high-level interface
to netCDF data, was made available in May 1996. The capabilities of
the FAN utilities include extracting and manipulating array data from
netCDF datasets, printing selected data from netCDF arrays, copying
ASCII data into netCDF arrays, and performing various operations (sum,
mean, max, min, product, and others) on netCDF arrays.

In 1996 and 1997, Joe Sirott implemented and made available the first
implementation of a read-only netCDF interface for Java, Bill Noon
made a Python module available for netCDF, and Konrad Hinsen
contributed another netCDF interface for Python.

In May 1997, Version 3.3 of netCDF was released. This included a new
type-safe interface for C and Fortran, as well as many other
improvements.  A month later, Charlie Zender released version 1.0 of
the NCO (netCDF Operators) package, providing command-line utilities
for general purpose operations on netCDF data.

Version 3.4 of Unidata's netCDF software, released in March 1998,
included initial large file support, performance enhancements, and
improved Cray platform support.  Later in 1998, Dan Schmitt provided a
Tcl/Tk interface, and Glenn Davis provided version 1.0 of netCDF for
Java.

In May 1999, Glenn Davis, who was instrumental in creating and
developing netCDF, died in a small plane crash during a
thunderstorm. The memory of Glenn's passions and intellect continue to
inspire those of us who worked with him.

In February 2000, an experimental Fortran 90 interface developed by
Robert Pincus was released.

John Caron released netCDF for Java, version 2.0 in February 2001.
This version incorporated a new high-performance package for
multidimensional arrays, simplified the interface, and included
OpenDAP (known previously as DODS) remote access, as well as remote
netCDF access via HTTP contributed by Don Denbo.

In March 2001, netCDF 3.5.0 was released. This release fully
integrated the new Fortran 90 interface, enhanced portability,
improved the C++ interface, and added a few new tuning functions.

Also in 2001, Takeshi Horinouchi and colleagues made a netCDF
interface for Ruby available, as did David Pierce for the R language
for statistical computing and graphics.  Charles Denham released
WetCDF, an independent implementation of the netCDF interface for
Matlab, as well as updates to the popular netCDF Toolbox for Matlab.

In 2002, Unidata and collaborators developed NcML, an XML
representation for netCDF data useful for cataloging data holdings,
aggregation of data from multiple datasets, augmenting metadata in
existing datasets, and support for alternative views of data.  The
Java interface currently provides access to netCDF data through NcML.

Additional developments in 2002 included translation of C and Fortran
User Guides into Japanese by Masato Shiotani and colleagues, creation
of a ``Best Practices'' guide for writing netCDF files, and provision
of an Ada-95 interface by Alexandru Corlan.

In July 2003 a group of researchers at Northwestern University and
Argonne National Laboratory (Jianwei Li, Wei-keng Liao, Alok
Choudhary, Robert Ross, Rajeev Thakur, William Gropp, and Rob Latham)
contributed a new parallel interface for writing and reading netCDF
data, tailored for use on high performance platforms with parallel
I/O. The implementation built on the MPI-IO interface, providing
portability to many platforms.

In October 2003, Greg Sjaardema contributed support for an alternative
format with 64-bit offsets, to provide more complete support for very
large files. These changes, with slight modifications at Unidata, were
incorporated into version 3.6.0, released in December, 2004.

In 2004, thanks to a NASA grant, Unidata and NCSA began a
collaboration to increase the interoperability of netCDF and HDF5, and
bring some advanced HDF5 features to netCDF users. 

In February, 2006, release 3.6.1 fixed some minor bugs.

In March, 2007, release 3.6.2 introduced an improved build system that
used automake and libtool, and an upgrade to the most recent autoconf
release, to support shared libraries and the netcdf-4 builds. This
release also introduced the NetCDF Tutorial and example programs.

The first beta release of netCDF-4.0 was celebrated with a giant party
at Unidata in April, 2007. Over 2000 people danced 'til dawn at the
NCAR Mesa Lab, listening to the Flaming Lips and the Denver Gilbert &
Sullivan repertory company.

In June, 2008, netCDF-4.0 was released. Version 3.6.3, the same code
but with netcdf-4 features turned off, was released at the same
time. The 4.0 release uses HDF5 1.8.1 as the data storage layer for
netcdf, and introduces many new features including groups and
user-defined types. The 3.6.3/4.0 releases also introduced handling of
UTF8-encoded Unicode names.

NetCDF-4.1.1 was released in April, 2010, provided built-in client
support for the DAP protocol for accessing data from remote OPeNDAP
servers, full support for the enhanced netCDF-4 data model in the
ncgen utility, a new nccopy utility for copying and conversion among
netCDF format variants, ability to read some HDF4/HDF5 data archives
through the netCDF C or Fortran interfaces, support for parallel I/O
on netCDF classic and 64-bit offset files using the PnetCDF
library from Argonne/Northwestern, a new nc-config
utility to help compile and link programs that use netCDF, inclusion
of the UDUNITS library for handling ``units'' attributes, and inclusion
of libcf to assist in creating data compliant with the Climate and
Forecast (CF) metadata conventions.

In September, 2010, the Netcdf-Java/CDM (Common Data Model) version
4.2 library was declared stable and made available to users.  This
100%-Java implementation provides a read-write interface to netCDF-3
classic and 64-bit offset data, as well as a read-onlt interface to
netCDF-4 enhanced model data and many other formats of scientific data
through a common (CDM) interface.  The NetCDF-Java library also
implements NcML, which allows you to add metadata to CDM datasets, as
well as to create virtual datasets through aggregation.  A ToolsUI
application is also included that provides a graphical user interface
to capabilities similar to the C-based ncdump and ncgen utilities, as
well as CF-compliance checking and many other features.

@node Whats New, Limitations, Background, Introduction
@section What's New Since the Previous Release?
@cindex new netCDF features in 4.0

This Guide documents the @value{VERSION} release of netCDF, which
introduces a new storage format, netCDF-4/HDF5, while maintaining full
backward compatibility.

New features available with netCDF-4/HDF5 files include:

@itemize

@item
The use of groups to organize datasets.

@item
New unsigned integer data types, 64-bit integer types, and a string
type.

@item
A user defined compound type, which can be constructed by users to
match a C struct or other arbitrary organization of types.

@item
A variable length array type.

@item
Multiple unlimited dimensions.

@item
Support for parallel I/O.

@end itemize

More information about netCDF-4 can be found at the netCDF web
page @uref{@value{netcdf-4-url}}.

@node Limitations, Future , Whats New, Introduction
@section Limitations of NetCDF
@cindex arrays, ragged
@cindex writers, multiple
@cindex limitations of netCDF
@cindex classic netCDF format
@cindex GiBytes
@cindex GBytes

The netCDF classic data model is widely applicable to data that can be
organized into a collection of named array variables with named
attributes, but there are some important limitations to the model and
its implementation in software. Some of these limitations have been
removed or relaxed in netCDF-4 files, but still apply to netCDF
classic and netCDF 64-bit offset files.

Currently, netCDF classic and 64-bit offset formats offer a limited
number of external numeric data types: 8-, 16-, 32-bit integers, or
32- or 64-bit floating-point numbers. (The netCDF-4 format adds 64-bit
integer types and unsigned integer types.)

With the netCDF-4/HDF5 format, new unsigned integers (of various
sizes), 64-bit integers, and the string type allow improved expression
of meaning in scientific data. The new VLEN (variable length) and
COMPOUND types allow users to organize data in new ways.

With the classic netCDF file format, there are constraints that limit
how a dataset is structured to store more than 2 @dfn{GiBytes} (a
GiByte is 2^30
or 1,073,741,824 bytes, as compared to a @dfn{Gbyte}, which is
1,000,000,000 bytes.)  of data in a single netCDF
dataset. (@pxref{Classic Limitations}).  This limitation
is a result of 32-bit offsets used for storing relative offsets within
a classic netCDF format file. Since one of the goals of netCDF is
portable data, and some file systems still can't deal with files
larger than 2 GiB, it is best to keep files that must be portable
below this limit. Nevertheless, it is possible to create and access
netCDF files larger than 2 GiB on platforms that provide support for
such files (@pxref{Large File Support}).

The new 64-bit offset format allows large files, and makes it easy to
create to create fixed variables of about 4 GiB, and record variables
of about 4 GiB per record. (@pxref{64 bit Offset
Limitations}). However, old netCDF applications will not be able to
read the 64-bit offset files until they are upgraded to at least
version 3.6.0 of netCDF (i.e. the version in which 64-bit offset
format was introduced).

With the netCDF-4/HDF5 format, size limitations are further relaxed,
and files can be as large as the underlying file system
supports. NetCDF-4/HDF5 files are unreadable to the netCDF library
before version 4.0.

Another limitation of the classic (and 64-bit offset) model is that
only one unlimited (changeable) dimension is permitted for each netCDF
data set. Multiple variables can share an unlimited dimension, but
then they must all grow together. Hence the classic netCDF model does
not permit variables with several unlimited dimensions or the use of
multiple unlimited dimensions in different variables within the same
dataset. Variables that have non-rectangular shapes (for example,
ragged arrays) cannot be represented conveniently.

In netCDF-4/HDF5 files, multiple unlimited dimensions are fully
supported. Any variable can be defined with any combination of limited
and unlimited dimensions.

The extent to which data can be completely self-describing is limited:
there is always some assumed context without which sharing and
archiving data would be impractical. NetCDF permits storing meaningful
names for variables, dimensions, and attributes; units of measure in a
form that can be used in computations; text strings for attribute
values that apply to an entire data set; and simple kinds of
coordinate system information. But for more complex kinds of metadata
(for example, the information necessary to provide accurate
georeferencing of data on unusual grids or from satellite images), it
is often necessary to develop conventions.

Specific additions to the netCDF data model might make some of these
conventions unnecessary or allow some forms of metadata to be
represented in a uniform and compact way. For example, adding explicit
georeferencing to the netCDF data model would simplify elaborate
georeferencing conventions at the cost of complicating the model. The
problem is finding an appropriate trade-off between the richness of
the model and its generality (i.e., its ability to encompass many
kinds of data). A data model tailored to capture the shared context
among researchers within one discipline may not be appropriate for
sharing or combining data from multiple disciplines.

The classic netCDF data model (which is used for classic-format and
64-bit offset format data) does not support nested data structures
such as trees, nested arrays, or other recursive structures.  Through use of
indirection and conventions it is possible to represent some kinds of
nested structures, but the result may fall short of the netCDF goal of
self-describing data.

In netCDF-4/HDF5 format files, the introduction of the compound type
allows the creation of complex data types, involving any combination
of types. The VLEN type allows efficient storage of ragged arrays, and
the introduction of hierarchical groups allows users new ways to organize data.

Finally, using the netCDF-3 programming interfaces, concurrent access to a
netCDF dataset is limited. One writer and multiple readers may access
data in a single dataset simultaneously, but there is no support for
multiple concurrent writers.

NetCDF-4 supports parallel read/write access to netCDF-4/HDF5 files,
using the underlying HDF5 library and parallel read/write access to
classic and 64-bit offset files using the PnetCDF library.

For more information about HDF5, see the HDF5 web site:
@uref{@value{hdf5-url}}.

For more information about PnetCDF see their web site:
@uref{@value{pnetcdf-url}}.

@node Future , References, Limitations, Introduction
@section Plans for NetCDF
@cindex future plans for netCDF
@cindex plans for netCDF
@cindex netCDF 5.0
@cindex pong

Future versions of netCDF will include the following features:

@enumerate

@item Extensions of netCDF-4 features to C++ API and to tools
ncgen/ncdump.

@item Better documentation and more examples.

@end enumerate

@node References,  , Future , Introduction
@section References
@cindex references

@enumerate

@item
Brown, S. A, M. Folk, G. Goucher, and R. Rew, "Software for
Portable Scientific Data Management," Computers in Physics, American
Institute of Physics, Vol. 7, No. 3, May/June 1993.

@item
Davies, H. L., "FAN - An array-oriented query language," Second
Workshop on Database Issues for Data Visualization (Visualization
1995), Atlanta, Georgia, IEEE, October 1995.

@item
Fahle, J., TeraScan Applications Programming Interface, SeaSpace,
San Diego, California, 1989.

@item
Fulker, D. W., "The netCDF: Self-Describing, Portable Files---a
Basis for 'Plug-Compatible' Software Modules Connectable by Networks,"
ICSU Workshop on Geophysical Informatics, Moscow, USSR, August 1988.

@item
Fulker, D. W., "Unidata Strawman for Storing Earth-Referencing
Data," Seventh International Conference on Interactive Information and
Processing Systems for Meteorology, Oceanography, and Hydrology, New
Orleans, La., American Meteorology Society, January 1991.

@item
Gough, M. L., NSSDC CDF Implementer's Guide (DEC VAX/VMS) Version
1.1, National Space Science Data Center, 88-17, NASA/Goddard Space
Flight Center, 1988.

@item
Jenter, H. L. and R. P. Signell, "NetCDF: A Freely-Available
Software-Solution to Data-Access Problems for Numerical Modelers,"
Proceedings of the American Society of Civil Engineers Conference on
Estuarine and Coastal Modeling, Tampa, Florida, 1992.

@item
Raymond, D. J., "A C Language-Based Modular System for Analyzing
and Displaying Gridded Numerical Data," Journal of Atmospheric and
Oceanic Technology, 5, 501-511, 1988.

@item
Rew, R. K. and G. P. Davis, "The Unidata netCDF: Software for
Scientific Data Access," Sixth International Conference on Interactive
Information and Processing Systems for Meteorology, Oceanography, and
Hydrology, Anaheim, California, American Meteorology Society, February
1990.

@item
Rew, R. K. and G. P. Davis, "NetCDF: An Interface for Scientific
Data Access," Computer Graphics and Applications, IEEE, pp. 76-82,
July 1990.

@item
Rew, R. K. and G. P. Davis, "Unidata's netCDF Interface for Data
Access: Status and Plans," Thirteenth International Conference on
Interactive Information and Processing Systems for Meteorology,
Oceanography, and Hydrology, Anaheim, California, American Meteorology
Society, February 1997.

@item
Treinish, L. A. and M. L. Gough, "A Software Package for the Data
Independent Management of Multi-Dimensional Data," EOS Transactions,
American Geophysical Union, 68, 633-635, 1987.
@end enumerate

@node Dataset Components, Data, Introduction, Top
@chapter Components of a NetCDF Dataset

@menu
* Data Model::                  How NetCDF Sees Data
* Dimensions::                  Specifying Data Shape
* Variables::                   Storing Data
* Attributes::                  Storing Metadata
* Attributes and Variables::    Attributes vs. Variables
@end menu

@node Data Model, Dimensions, Dataset Components, Dataset Components
@section The NetCDF Data Model
@cindex netCDF data model
@cindex data model, netCDF
@cindex naming conventions
@cindex conventions, naming
@cindex CDL, example
@cindex common data form language
@cindex ncdump, introduction
@cindex groups
@cindex user defined types


A netCDF dataset contains dimensions, variables, and attributes, which
all have both a name and an ID number by which they are
identified. These components can be used together to capture the
meaning of data and relations among data fields in an array-oriented
dataset. The netCDF library allows simultaneous access to multiple
netCDF datasets which are identified by dataset ID numbers, in
addition to ordinary file names.

@subsection Enhanced Model in NetCDF-4 Files

Files created with the netCDF-4 format have access to an enhanced data
model, which includes named groups. Groups, like directories in a Unix
file system, are hierarchically organized, to arbitrary depth. They
can be used to organize large numbers of variables.

Each group acts as an entire netCDF dataset in the classic model. That
is, each group may have attributes, dimensions, and variables, as well
as other groups. 

The default group is the root group, which allows the classic netCDF
data model to fit neatly into the new model.

Dimensions are scoped such that they can be seen in all descendant
groups. That is, dimensions can be shared between variables in
different groups, if they are defined in a parent group.

In netCDF-4 files, the user may also define a type. For example a
compound type may hold information from an array of C structures, or a
variable length type allows the user to read and write arrays of
variable length values.

Variables, groups, and types share a namespace. Within the same group,
variables, groups, and types must have unique names. (That is, a type
and variable may not have the same name within the same group, and
similarly for sub-groups of that group.)

Groups and user-defined types are only available in files created in
the netCDF-4/HDF5 format. They are not available for classic or 64-bit
offset format files.

@subsection Naming Conventions

The names of dimensions, variables and attributes (and, in netCDF-4
files, groups, user-defined types, compound member names, and
enumeration symbols) consist of arbitrary sequences of alphanumeric
characters, underscore '_', period '.', plus '+', hyphen '-', or at
sign '@@', but beginning with an alphanumeric character or underscore.  However names
commencing with underscore are reserved for system use.  Case is
significant in netCDF names. A zero-length name is not allowed.  Some
widely used conventions restrict names to only alphanumeric characters
or underscores.  Beginning with versions 3.6.3 and 4.0, names may also
include UTF-8 encoded Unicode characters as well as other special
characters, except for the character '/', which may not appear in a
name.  Names that have trailing space characters are also
not permitted.

@subsection Network Common Data Form Language (CDL)

We will use a small netCDF example to illustrate the concepts of the
netCDF classic data model. This includes dimensions, variables, and
attributes. The notation used to describe this simple netCDF object is
called CDL (network Common Data form Language), which provides a
convenient way of describing netCDF datasets. The netCDF system
includes the ncdump utility for producing human-oriented CDL text
files from binary netCDF datasets and vice versa using the ncgen
utility.  
(The ncdump utility
accommodates netCDF-4 features in the CDL
output, but the example here is restricted to netCDF-3 CDL.)

@example
netcdf example_1 @{  // example of CDL notation for a netCDF dataset

dimensions:         // dimension names and lengths are declared first
        lat = 5, lon = 10, level = 4, time = unlimited;

variables:          // variable types, names, shapes, attributes
        float   temp(time,level,lat,lon);
                    temp:long_name     = "temperature";
                    temp:units         = "celsius";
        float   rh(time,lat,lon);
                    rh:long_name = "relative humidity";
                    rh:valid_range = 0.0, 1.0;      // min and max
        int     lat(lat), lon(lon), level(level);
                    lat:units       = "degrees_north";
                    lon:units       = "degrees_east";
                    level:units     = "millibars";
        short   time(time);
                    time:units      = "hours since 1996-1-1";
        // global attributes
                    :source = "Fictional Model Output";

data:                // optional data assignments
        level   = 1000, 850, 700, 500;
        lat     = 20, 30, 40, 50, 60;
        lon     = -160,-140,-118,-96,-84,-52,-45,-35,-25,-15;
        time    = 12;
        rh      =.5,.2,.4,.2,.3,.2,.4,.5,.6,.7,
                 .1,.3,.1,.1,.1,.1,.5,.7,.8,.8,
                 .1,.2,.2,.2,.2,.5,.7,.8,.9,.9,
                 .1,.2,.3,.3,.3,.3,.7,.8,.9,.9,
                  0,.1,.2,.4,.4,.4,.4,.7,.9,.9;
@}
@end example

The CDL notation for a netCDF dataset can be generated automatically
by using ncdump, a utility program described later
(@pxref{ncdump}). Another netCDF utility, ncgen, generates a netCDF
dataset (or optionally C or FORTRAN source code containing calls
needed to produce a netCDF dataset) from CDL input (@pxref{ncgen}).
This version of ncgen can produce netcdf-3 or netcdf-4
files and can utilize CDL input that includes the netcdf-4 data model
constructs. The older ncgen program is still available under the name
ncgen3.

The CDL notation is simple and largely self-explanatory. It will be
explained more fully as we describe the components of a netCDF
dataset. For now, note that CDL statements are terminated by a
semicolon. Spaces, tabs, and newlines can be used freely for
readability. Comments in CDL follow the characters '//' on any line. A
CDL description of a netCDF dataset takes the form

@example
  netCDF name @{
    types: [netcdf-4 only]
    dimensions: @dots{} 
    variables: @dots{} 
    data: @dots{} 
  @}
@end example

where the name is used only as a default in constructing file names by
the ncgen utility. The CDL description consists of three optional
parts, introduced by the keywords dimensions, variables, and
data. NetCDF dimension declarations appear after the dimensions
keyword, netCDF variables and attributes are defined after the
variables keyword, and variable data assignments appear after the data
keyword.

The ncgen utility provides a command line option which indicates the
desired output format. Limitations are enforced for the selected
format - that is, some CDL files may be expressible only in 64-bit
offset or netCDF-4 format. 

For example, trying to create a file with very large variables in
classic format may result in an error because size limits are
violated. 

@node Dimensions, Variables, Data Model, Dataset Components
@section Dimensions
@cindex appending data along unlimited dimension
@cindex dimensions, introduction
@cindex dimensions, unlimited
@cindex unlimited dimensions
@cindex multiple unlimited dimensions

A dimension may be used to represent a real physical dimension, for
example, time, latitude, longitude, or height. A dimension might also
be used to index other quantities, for example station or
model-run-number.

A netCDF dimension has both a name and a length. 

@c As a convenience, in netCDF-4 format files, a name is no longer
@c necessary for every dimension. Dimensions without a name are called
@c anonymous dimensions, and function in every other way just as other
@c dimensions do.

A dimension length is an arbitrary positive integer, except that one
dimension in a classic or 64-bit offset netCDF dataset can have the
length UNLIMITED. In a netCDF-4 dataset, any number of unlimited
dimensions can be used.

Such a dimension is called the unlimited dimension or the record
dimension. A variable with an unlimited dimension can grow to any
length along that dimension. The unlimited dimension index is like a
record number in conventional record-oriented files. 

A netCDF classic or 64-bit offset dataset can have at most one
unlimited dimension, but need not have any. If a variable has an
unlimited dimension, that dimension must be the most significant
(slowest changing) one. Thus any unlimited dimension must be the first
dimension in a CDL shape and the first dimension in corresponding C
array declarations.

A netCDF-4 dataset may have multiple unlimited dimensions, and there
are no restrictions on their order in the list of a variables
dimensions. 

To grow variables along an unlimited dimension, write the data using
any of the netCDF data writing functions, and specify the index of the
unlimited dimension to the desired record number. The netCDF library
will write however many records are needed (using the fill value,
unless that feature is turned off, to fill in any intervening
records).

CDL dimension declarations may appear on one or more lines following
the CDL keyword dimensions. Multiple dimension declarations on the
same line may be separated by commas. Each declaration is of the form
name = length. Use the ``/'' character to include group information
(netCDF-4 output only).

There are four dimensions in the above example: lat, lon, level, and
time (@pxref{Data Model}). The first three are assigned fixed lengths;
time is assigned the length UNLIMITED, which means it is the unlimited
dimension.

The basic unit of named data in a netCDF dataset is a variable. When a
variable is defined, its shape is specified as a list of
dimensions. These dimensions must already exist. The number of
dimensions is called the rank (a.k.a. dimensionality). A scalar
variable has rank 0, a vector has rank 1 and a matrix has rank 2.

It is possible (since version 3.1 of netCDF) to use the same dimension
more than once in specifying a variable shape. For example,
correlation(instrument, instrument) could be a matrix giving
correlations between measurements using different instruments. But
data whose dimensions correspond to those of physical space/time
should have a shape comprising different dimensions, even if some of
these have the same length.

@node Variables, Attributes, Dimensions, Dataset Components
@section Variables
@cindex variables, defined
@cindex variable types
@cindex netCDF data types
@vindex NC_BYTE
@vindex NC_INT
@vindex NC_SHORT
@vindex NC_LONG
@vindex NC_CHAR
@vindex NC_FLOAT
@vindex NC_DOUBLE
@vindex NC_UBYTE
@vindex NC_UINT
@vindex NC_USHORT
@vindex NC_UINT
@vindex NC_INT64
@vindex NC_UINT64
@vindex NC_STRING
@vindex nf_byte
@vindex nf_char
@vindex nf_short
@vindex nf_int1
@vindex nf_int2
@vindex nf_real
@vindex nf_double
@vindex nf_float
@cindex attributes associated with a variable
@cindex primary variables
@cindex variables, primary
@cindex coordinate variables
@cindex variables, coordinate

Variables are used to store the bulk of the data in a netCDF
dataset. A variable represents an array of values of the same type. A
scalar value is treated as a 0-dimensional array. A variable has a
name, a data type, and a shape described by its list of dimensions
specified when the variable is created. A variable may also have
associated attributes, which may be added, deleted or changed after
the variable is created.

A variable external data type is one of a small set of netCDF
types. In classic and 64-bit offset files, only the original six types
are available (byte, character, short, int, float, and
double). Variables in netCDF-4 files may also use unsigned short,
unsigned int, 64-bit int, unsigned 64-bit int, or string. Or the user
may define a type, as an opaque blob of bytes, as an array of variable
length arrays, or as a compound type, which acts like a C struct.

For more information on types for the C interface, see @ref{Variable
Types,,, netcdf-c, @value{c-man}} in @value{c-man}.

For more information on types for the Fortran interface, see
@ref{Variable Types,,, netcdf-f77, @value{f77-man}} in
@value{f77-man}.

In the CDL notation, classic and 64-bit offset type can be
used. They are given the simpler names byte, char, short, int, float,
and double. The name real may be used as a synonym for float in the CDL
notation. The name long is a deprecated synonym for int. For the exact meaning
of each of the types see @ref{External Types}.  The ncgen utility
supports new primitive types with names ubyte, ushort, uint,
int64, uint64, and string.

CDL variable declarations appear after the variable keyword in a CDL
unit. They have the form

@example
     type variable_name ( dim_name_1, dim_name_2, @dots{} );
@end example

for variables with dimensions, or

@example
     type variable_name;
@end example

for scalar variables.

In the above CDL example there are six variables. As discussed below,
four of these are coordinate variables. The remaining variables
(sometimes called primary variables), temp and rh, contain what is
usually thought of as the data. Each of these variables has the
unlimited dimension time as its first dimension, so they are called
record variables. A variable that is not a record variable has a fixed
length (number of data values) given by the product of its dimension
lengths. The length of a record variable is also the product of its
dimension lengths, but in this case the product is variable because it
involves the length of the unlimited dimension, which can vary. The
length of the unlimited dimension is the number of records.

@subsection Coordinate Variables

It is legal for a variable to have the same name as a dimension. Such
variables have no special meaning to the netCDF library. However there
is a convention that such variables should be treated in a special way
by software using this library.

A variable with the same name as a dimension is called a coordinate
variable. It typically defines a physical coordinate corresponding to
that dimension. The above CDL example includes the coordinate
variables lat, lon, level and time, defined as follows:

@example
        int     lat(lat), lon(lon), level(level);
        short   time(time);
@dots{} 
data:
        level   = 1000, 850, 700, 500;
        lat     = 20, 30, 40, 50, 60;
        lon     = -160,-140,-118,-96,-84,-52,-45,-35,-25,-15;
        time    = 12;
@end example

These define the latitudes, longitudes, barometric pressures and times
corresponding to positions along these dimensions. Thus there is data
at altitudes corresponding to 1000, 850, 700 and 500 millibars; and at
latitudes 20, 30, 40, 50 and 60 degrees north. Note that each
coordinate variable is a vector and has a shape consisting of just the
dimension with the same name.

A position along a dimension can be specified using an index. This is
an integer with a minimum value of 0 for C programs, 1 in Fortran
programs. Thus the 700 millibar level would have an index value of 2
in the example above in a C program, and 3 in a Fortran program.

If a dimension has a corresponding coordinate variable, then this
provides an alternative, and often more convenient, means of
specifying position along it. Current application packages that make
use of coordinate variables commonly assume they are numeric vectors
and strictly monotonic (all values are different and either increasing
or decreasing).

@node Attributes, Attributes and Variables, Variables, Dataset Components
@section Attributes
@cindex attributes, defined
@cindex attributes, defining in CDL
@cindex attributes, adding to existing dataset
@cindex CDL, defining attributes
@cindex CDL, defining global attributes
@cindex ancillary data, storing
@cindex storing ancillary data
@cindex applications, generic
@cindex generic applications
@cindex attributes, data type
@cindex attributes, global
@cindex attributes, operations on
@cindex global attributes
@cindex operations on attributes

NetCDF attributes are used to store data about the data (ancillary
data or metadata), similar in many ways to the information stored in
data dictionaries and schema in conventional database systems. Most
attributes provide information about a specific variable. These are
identified by the name (or ID) of that variable, together with the
name of the attribute.

Some attributes provide information about the dataset as a whole and
are called global attributes. These are identified by the attribute
name together with a blank variable name (in CDL) or a special null
"global variable" ID (in C or Fortran).

In netCDF-4 file, attributes can also be added at the group level.

An attribute has an associated variable (the null "global variable"
for a global or group-level attribute), a name, a data type, a length,
and a value. The current version treats all attributes as vectors;
scalar values are treated as single-element vectors.

Conventional attribute names should be used where applicable. New
names should be as meaningful as possible.

The external type of an attribute is specified when it is created. The
types permitted for attributes are the same as the netCDF external
data types for variables. Attributes with the same name for different
variables should sometimes be of different types. For example, the
attribute valid_max specifying the maximum valid data value for a
variable of type int should be of type int, whereas the attribute
valid_max for a variable of type double should instead be of type
double.

Attributes are more dynamic than variables or dimensions; they can be
deleted and have their type, length, and values changed after they are
created, whereas the netCDF interface provides no way to delete a
variable or to change its type or shape.

The CDL notation for defining an attribute is

@example
    variable_name:attribute_name = list_of_values;
@end example

for a variable attribute, or

@example
    :attribute_name = list_of_values;
@end example

for a global attribute.

For the netCDF classic model,
the type and length of each attribute are not explicitly declared in
CDL; they are derived from the values assigned to the attribute. All
values of an attribute must be of the same type. The notation used for
constant values of the various netCDF types is discussed later
(@pxref{CDL Constants}).

The extended CDL syntax for the enhanced
data model supported by netCDF-4 allows optional type specifications,
including user-defined types, for
attributes of user-defined types.  See ncdump output or the reference
documentation for ncgen for details of the extended CDL syntax.

In the netCDF example (@pxref{Data Model}), units is an attribute for
the variable lat that has a 13-character array value
'degrees_north'. And valid_range is an attribute for the variable rh
that has length 2 and values '0.0' and '1.0'.

One global attribute, called ``source'', is defined for the example
netCDF dataset. This is a character array intended for documenting the
data. Actual netCDF datasets might have more global attributes to
document the origin, history, conventions, and other characteristics
of the dataset as a whole.

Most generic applications that process netCDF datasets assume standard
attribute conventions and it is strongly recommended that these be
followed unless there are good reasons for not doing so. For
information about units, long_name, valid_min, valid_max, valid_range,
scale_factor, add_offset, _FillValue, and other conventional
attributes, see @ref{Attribute Conventions}.

Attributes may be added to a netCDF dataset long after it is first
defined, so you don't have to anticipate all potentially useful
attributes. However adding new attributes to an existing classic or
64-bit offset format dataset can incur the same expense as copying the
dataset. For a more extensive discussion see @ref{Structure}.

@node Attributes and Variables,  , Attributes, Dataset Components
@section Differences between Attributes and Variables
@cindex ancillary data as attributes
@cindex attributes vs. variables
@cindex variables vs. attributes
@cindex differences between attributes and variables

In contrast to variables, which are intended for bulk data, attributes
are intended for ancillary data, or information about the data. The
total amount of ancillary data associated with a netCDF object, and
stored in its attributes, is typically small enough to be
memory-resident. However variables are often too large to entirely fit
in memory and must be split into sections for processing.

Another difference between attributes and variables is that variables
may be multidimensional. Attributes are all either scalars
(single-valued) or vectors (a single, fixed dimension).

Variables are created with a name, type, and shape before they are
assigned data values, so a variable may exist with no values.  The
value of an attribute is specified when it is created, unless it is a
zero-length attribute.

A variable may have attributes, but an attribute cannot have
attributes. Attributes assigned to variables may have the same units
as the variable (for example, valid_range) or have no units (for
example, scale_factor). If you want to store data that requires units
different from those of the associated variable, it is better to use a
variable than an attribute. More generally, if data require ancillary
data to describe them, are multidimensional, require any of the
defined netCDF dimensions to index their values, or require a
significant amount of storage, that data should be represented using
variables rather than attributes.

@node Data, Structure, Dataset Components, Top
@chapter Data

This chapter discusses the primitive netCDF external data types, the
kinds of data access supported by the netCDF interface, and how data
structures other than arrays may be implemented in a netCDF dataset.

@menu
* External Types::              Integers, Floats, and so on
* Classic Data Structures::     Complex Data in Classic Format
* User Defined Types::          Complex Data in NetCDF-4/HDF5 Format
* Data Access::                 Reading and Writing Data
* Type Conversion::             Changing Type of Numeric Data
@end menu

@node External Types, Classic Data Structures, Data, Data
@section NetCDF External Data Types
@cindex ASCII characters
@cindex data types, external
@cindex conversion of data types, introduction
@cindex external data types
@cindex byte, signed vs. unsigned

The atomic external types supported by the netCDF interface are:

@multitable @columnfractions .15 .15 .70

@item C name @tab Fortran name @tab storage

@item NC_BYTE @tab nf_byte @tab 8-bit signed integer

@item NC_CHAR @tab nf_char @tab 8-bit unsigned integer

@item NC_SHORT @tab nf_short @tab 16-bit signed integer

@item NC_USHORT @tab nf_ushort @tab 16-bit unsigned integer *

@item NC_INT (or NC_LONG) @tab nf_int @tab 32-bit signed integer

@item NC_UINT @tab nf_uint @tab 32-bit unsigned integer *

@item NC_INT64 @tab nf_int64 @tab 64-bit signed integer *

@item NC_UINT64 @tab nf_uint64 @tab 64-bit unsigned integer *

@item NC_FLOAT @tab nf_float @tab 32-bit floating point

@item NC_DOUBLE @tab nf_double @tab 64-bit floating point 

@item NC_STRING @tab nf_string @tab variable length character string *

@end multitable

* These types are available only for netCDF-4 format files. All the
unsigned ints, the 64-bit ints, and string
type are for netCDF-4 files only.
 
These types were chosen to provide a reasonably wide range of
trade-offs between data precision and number of bits required for each
value. These external data types are independent from whatever
internal data types are supported by a particular machine and language
combination.

These types are called "external", because they correspond to the
portable external representation for netCDF data. When a program reads
external netCDF data into an internal variable, the data is converted,
if necessary, into the specified internal type. Similarly, if you
write internal data into a netCDF variable, this may cause it to be
converted to a different external type, if the external type for the
netCDF variable differs from the internal type.

The separation of external and internal types and automatic type
conversion have several advantages. You need not be aware of the
external type of numeric variables, since automatic conversion to or
from any desired numeric type is available. You can use this feature
to simplify code, by making it independent of external types, using a
sufficiently wide internal type, e.g., double precision, for numeric
netCDF data of several different external types. Programs need not be
changed to accommodate a change to the external type of a variable.

If conversion to or from an external numeric type is necessary, it is
handled by the library.

Converting from one numeric type to another may result in an error if
the target type is not capable of representing the converted
value. For example, an internal short integer type may not be able to
hold data stored externally as an integer. When accessing an array of
values, a range error is returned if one or more values are out of the
range of representable values, but other values are converted
properly.

Note that mere loss of precision in type conversion does not return an
error. Thus, if you read double precision values into a
single-precision floating-point variable, for example, no error
results unless the magnitude of the double precision value exceeds the
representable range of single-precision floating point numbers on your
platform. Similarly, if you read a large integer into a float
incapable of representing all the bits of the integer in its mantissa,
this loss of precision will not result in an error. If you want to
avoid such precision loss, check the external types of the variables
you access to make sure you use an internal type that has adequate
precision.

The names for the primitive external data types (byte, char, short,
ushort, int, uint, int64, uint64, float or real, double, string)
are reserved words in CDL, so the names of variables, dimensions, and
attributes must not be type names.

It is possible to interpret byte data as either signed (-128 to 127)
or unsigned (0 to 255). However, when reading byte data to be
converted into other numeric types, it is interpreted as signed.

For the correspondence between netCDF external data types and the data
types of a language see @ref{Variables}.

@node Classic Data Structures, User Defined Types, External Types, Data
@section Data Structures in Classic and 64-bit Offset Files
@cindex data structures
@cindex structures, data

The only kind of data structure directly supported by the netCDF
classic (and 64-bit offset) abstraction is a collection of named
arrays with attached vector attributes. NetCDF is not particularly
well-suited for storing linked lists, trees, sparse matrices, ragged
arrays or other kinds of data structures requiring pointers.

It is possible to build other kinds of data structures in netCDF
classic or 64-bit offset formats, from sets of arrays by adopting
various conventions regarding the use of data in one array as pointers
into another array. The netCDF library won't provide much help or
hindrance with constructing such data structures, but netCDF provides
the mechanisms with which such conventions can be designed.

The following netCDF classic example stores a ragged array ragged_mat using an
attribute row_index to name an associated index variable giving the
index of the start of each row. In this example, the first row
contains 12 elements, the second row contains 7 elements (19 - 12),
and so on. (NetCDF-4 includes native support for variable length
arrays. See below.)

@example
        float   ragged_mat(max_elements);
                ragged_mat:row_index = "row_start";
        int     row_start(max_rows);
data:
        row_start   = 0, 12, 19, @dots{} 
@end example

As another example, netCDF variables may be grouped within a netCDF
classic or 64-bit offset dataset by defining attributes that list the
names of the variables in each group, separated by a conventional
delimiter such as a space or comma. Using a naming convention for
attribute names for such groupings permits any number of named groups
of variables. A particular conventional attribute for each variable
might list the names of the groups of which it is a member. Use of
attributes, or variables that refer to other attributes or variables,
provides a flexible mechanism for representing some kinds of complex
structures in netCDF datasets.

@node User Defined Types, Data Access, Classic Data Structures, Data
@section NetCDF-4 User Defined Data Types
@cindex compound type
@cindex vlen type
@cindex variable length array type
@cindex opaque type
@cindex enum type

NetCDF supported six data types through version 3.6.0 (char, byte,
short, int, float, and double). Starting with version 4.0, many new
data types are supported (unsigned int types, strings, compound types,
variable length arrays, enums, opaque).

In addition to the new atomic types the user may define types.

Types are defined in define mode, and must be fully defined before
they are used. New types may be added to a file by re-entering define
mode.

Once defined the type may be used to create a variable or attribute.

Types may be nested in complex ways. For example, a compound type
containing an array of VLEN types, each containing variable length
arrays of some other compound type, etc. Users are cautioned to keep
types simple. Reading data of complex types can be challenging for
Fortran users.

Types may be defined in any group in the data file, but they are
always available globally in the file. 

Types cannot have attributes (but variables of the type may have
attributes).

Only files created with the netCDF-4/HDF5 mode flag (NC_NETCDF4,
NF_NETCDF4, or NF90_NETCDF4), but without the classic model flag
(NC_CLASSIC_MODEL, NF_CLASSIC_MODEL, or NF90_CLASSIC_MODEL.)

Once types are defined, use their ID like any other type ID when
defining variables or attributes. Each API has functions to read and
write variables and attributes of any type. Use these functions to
read and write variables and attributes of user defined type. In C use
nc_put_att/nc_get_att and the nc_put_var/nc_get_var,
nc_put_var1/nc_get_var1, nc_put_vara/nc_get_vara, or
nc_put_vars/nc_get_vars functions to access attribute and variable data
of user defined type.

@subsection Compound Types

Compound types allow the user to combine atomic and user-defined types
into C-like structs. Since users defined types may be used within a
compound type, they can contain nested compound types. 

Users define a compound type, and (in their C code) a corresponding C
struct. They can then use the nc_put_var[1asm] calls to write
multi-dimensional arrays of these structs, and nc_get_var[1asm] calls
to read them. (For example, the nc_put_varm function will write mapped
arrays of these structs.)

While structs, in general, are not portable from platform to platform,
the HDF5 layer (when installed) performs the magic required to figure
out your platform's idiosyncrasies, and adjust to them. The end result
is that HDF5 compound types (and therefore, netCDF-4 compound types),
are portable.

For more information on creating and using compound types, see
@ref{Compound Types,,, netcdf-c, @value{c-man}} in @value{c-man}.

@subsection VLEN Types

Variable length arrays can be used to create a ragged array of data,
in which one of the dimensions varies in size from point to point.

An example of VLEN use would the to store a 1-D array of dropsonde
data, in which the data at each drop point is of variable length.

There is no special restriction on the dimensionality of VLEN
variables. It's possible to have 2D, 3D, 4D, etc. data, in which each
point contains a VLEN.

A VLEN has a base type (that is, the type that it is a VLEN of). This
may be one of the atomic types (forming, for example, a variable
length array of NC_INT), or it can be another user defined type, like
a compound type.

With VLEN data, special memory allocation and deallocation procedures
must be followed, or memory leaks may occur.

Compression is permitted but may not be effective for VLEN data,
because the compression is applied to structures containing lengths
and pointers to the data, rather than the actual data.

For more information on creating and using variable length arrays, see
@ref{Variable Length Arrays,,, netcdf-c, @value{c-man}} in
@value{c-man}.

@subsection Opaque Types

Opaque types allow the user to store arrays of data blobs of a fixed
size.

For more information on creating and using opaque types, see
@ref{Opaque Type,,, netcdf-c, @value{c-man}} in @value{c-man}.

@subsection Enum Types

Enum types allow the user to specify an enumeration.

For more information on creating and using enum types, see @ref{Enum
Type,,, netcdf-c, @value{c-man}} in @value{c-man}.

@subsection Groups

Although not a type of data, groups can help organize data within a
dataset. Like a directory structure on a Unix file-system, the
grouping feature allows users to organize variables and dimensions
into distinct, named, hierarchical areas, called groups. For more
information on groups types, see @ref{Groups,,, netcdf-c,
@value{c-man}} in @value{c-man}.

@node Data Access, Type Conversion, User Defined Types, Data
@section Data Access
@cindex data, reading
@cindex data, writing
@cindex access random

To access (read or write) netCDF data you specify an open netCDF
dataset, a netCDF variable, and information (e.g., indices)
identifying elements of the variable. The name of the access function
corresponds to the internal type of the data. If the internal type has
a different representation from the external type of the variable, a
conversion between the internal type and external type will take place
when the data is read or written.

Access to data in classic and 64-bit offset format is direct. Access
to netCDF-4 data is buffered by the HDF5 layer. In either case you can
access a small subset of data from a large dataset efficiently,
without first accessing all the data that precedes it. 

Reading and writing data by specifying a variable, instead of a
position in a file, makes data access independent of how many other
variables are in the dataset, making programs immune to data format
changes that involve adding more variables to the data.

In the C and FORTRAN interfaces, datasets are not specified by name
every time you want to access data, but instead by a small integer
called a dataset ID, obtained when the dataset is first created or
opened.

Similarly, a variable is not specified by name for every data access
either, but by a variable ID, a small integer used to identify each
variable in a netCDF dataset.

@subsection Forms of Data Access
@cindex array section, corner
@cindex array section, definition
@cindex array section, edges
@cindex array section, mapped

The netCDF interface supports several forms of direct access to data
values in an open netCDF dataset. We describe each of these forms of
access in order of increasing generality:

@itemize
@item
access to all elements; 

@item
access to individual elements, specified with an index vector; 

@item
access to array sections, specified with an index vector, and count
vector;

@item
access to sub-sampled array sections, specified with an index vector,
count vector, and stride vector; and

@item
access to mapped array sections, specified with an index vector, count
vector, stride vector, and an index mapping vector.

@end itemize

The four types of vector (index vector, count vector, stride vector
and index mapping vector) each have one element for each dimension of
the variable. Thus, for an n-dimensional variable (rank = n),
n-element vectors are needed. If the variable is a scalar (no
dimensions), these vectors are ignored.

An array section is a "slab" or contiguous rectangular block that is
specified by two vectors. The index vector gives the indices of the
element in the corner closest to the origin. The count vector gives
the lengths of the edges of the slab along each of the variable's
dimensions, in order. The number of values accessed is the product of
these edge lengths.

A subsampled array section is similar to an array section, except that
an additional stride vector is used to specify sampling. This vector
has an element for each dimension giving the length of the strides to
be taken along that dimension. For example, a stride of 4 means every
fourth value along the corresponding dimension. The total number of
values accessed is again the product of the elements of the count
vector.

A mapped array section is similar to a subsampled array section except
that an additional index mapping vector allows one to specify how data
values associated with the netCDF variable are arranged in memory. The
offset of each value from the reference location, is given by the sum
of the products of each index (of the imaginary internal array which
would be used if there were no mapping) by the corresponding element
of the index mapping vector. The number of values accessed is the same
as for a subsampled array section.

The use of mapped array sections is discussed more fully below, but
first we present an example of the more commonly used array-section
access.

@menu
* C Section Access::            A C Example
* Fortran Section Access::      A Fortran Example
@end menu

@node C Section Access, Fortran Section Access, Data Access, Data Access
@subsection A C Example of Array-Section Access
@cindex access C example of array section
@cindex array section, C example 

Assume that in our earlier example of a netCDF dataset (@pxref{Data
Model,, Network Common Data Form Language (CDL)}), we wish to read a
cross-section of all the data for the temp variable at one level (say,
the second), and assume that there are currently three records (time
values) in the netCDF dataset. Recall that the dimensions are defined
as

@example
  lat = 5, lon = 10, level = 4, time = unlimited;
@end example

and the variable temp is declared as

@example
  float   temp(time, level, lat, lon);
@end example

in the CDL notation.

A corresponding C variable that holds data for only one level might be
declared as

@example
#define LATS  5
#define LONS 10
#define LEVELS 1
#define TIMES 3                 /* currently */
    @dots{} 
float   temp[TIMES*LEVELS*LATS*LONS];

to keep the data in a one-dimensional array, or

    @dots{} 
float   temp[TIMES][LEVELS][LATS][LONS];
@end example

using a multidimensional array declaration.

To specify the block of data that represents just the second level,
all times, all latitudes, and all longitudes, we need to provide a
start index and some edge lengths. The start index should be (0, 1, 0,
0) in C, because we want to start at the beginning of each of the
time, lon, and lat dimensions, but we want to begin at the second
value of the level dimension. The edge lengths should be (3, 1, 5, 10)
in C, (since we want to get data for all three time values, only one
level value, all five lat values, and all 10 lon values. We should
expect to get a total of 150 floating-point values returned (3 * 1 * 5
* 10), and should provide enough space in our array for this many. The
order in which the data will be returned is with the last dimension,
lon, varying fastest:

@example
     temp[0][1][0][0]
     temp[0][1][0][1]
     temp[0][1][0][2]
     temp[0][1][0][3]

           @dots{}

     temp[2][1][4][7]
     temp[2][1][4][8]
     temp[2][1][4][9]
@end example

Different dimension orders for the C, FORTRAN, or other language
interfaces do not reflect a different order for values stored on the
disk, but merely different orders supported by the procedural
interfaces to the languages. In general, it does not matter whether a
netCDF dataset is written using the C, FORTRAN, or another language
interface; netCDF datasets written from any supported language may be
read by programs written in other supported languages.

@subsection More on General Array Section Access for C

The use of mapped array sections allows non-trivial relationships
between the disk addresses of variable elements and the addresses
where they are stored in memory. For example, a matrix in memory could
be the transpose of that on disk, giving a quite different order of
elements. In a regular array section, the mapping between the disk and
memory addresses is trivial: the structure of the in-memory values
(i.e., the dimensional lengths and their order) is identical to that
of the array section. In a mapped array section, however, an index
mapping vector is used to define the mapping between indices of netCDF
variable elements and their memory addresses.

With mapped array access, the offset (number of array elements) from
the origin of a memory-resident array to a particular point is given
by the inner product[1] of the index mapping vector with the point's
coordinate offset vector. A point's coordinate offset vector gives,
for each dimension, the offset from the origin of the containing array
to the point.In C, a point's coordinate offset vector is the same as
its coordinate vector.

The index mapping vector for a regular array section would have--in
order from most rapidly varying dimension to most slowly--a constant
1, the product of that value with the edge length of the most rapidly
varying dimension of the array section, then the product of that value
with the edge length of the next most rapidly varying dimension, and
so on. In a mapped array, however, the correspondence between netCDF
variable disk locations and memory locations can be different.

For example, the following C definitions

@example
struct vel @{
    int flags;
    float u;
    float v;
@} vel[NX][NY];
ptrdiff_t imap[2] = @{
    sizeof(struct vel),
    sizeof(struct vel)*NY
@};
@end example

where imap is the index mapping vector, can be used to access the
memory-resident values of the netCDF variable, vel(NY,NX), even though
the dimensions are transposed and the data is contained in a 2-D array
of structures rather than a 2-D array of floating-point values.

A detailed example of mapped array access is presented in the
description of the interfaces for mapped array
access. @xref{nc_put_varm_ type, Write a Mapped Array of Values -
nc_put_varm_ type, , netcdf-c, @value{c-man}}.

Note that, although the netCDF abstraction allows the use of
subsampled or mapped array-section access there use is not
required. If you do not need these more general forms of access, you
may ignore these capabilities and use single value access or regular
array section access instead.

@node Fortran Section Access,  , C Section Access, Data Access
@subsection A Fortran Example of Array-Section Access
@cindex access Fortran example of array section
@cindex array section, Fortran example 

Assume that in our earlier example of a netCDF dataset (@pxref{Data
Model}), we wish to read a cross-section of all the data for the temp
variable at one level (say, the second), and assume that there are
currently three records (time values) in the netCDF dataset. Recall
that the dimensions are defined as

@example
  lat = 5, lon = 10, level = 4, time = unlimited;
@end example

and the variable temp is declared as

@example
  float   temp(time, level, lat, lon);
@end example

in the CDL notation.

In FORTRAN, the dimensions are reversed from the CDL declaration with
the first dimension varying fastest and the record dimension as the
last dimension of a record variable. Thus a FORTRAN declarations for a
variable that holds data for only one level is

@example
INTEGER LATS, LONS, LEVELS, TIMES
PARAMETER (LATS=5, LONS=10, LEVELS=1, TIMES=3)
   @dots{} 
REAL TEMP(LONS, LATS, LEVELS, TIMES)
@end example

To specify the block of data that represents just the second level,
all times, all latitudes, and all longitudes, we need to provide a
start index and some edge lengths. The start index should be (1, 1, 2,
1) in FORTRAN, because we want to start at the beginning of each of
the time, lon, and lat dimensions, but we want to begin at the second
value of the level dimension. The edge lengths should be (10, 5, 1, 3)
in FORTRAN, since we want to get data for all three time values, only
one level value, all five lat values, and all 10 lon values. We should
expect to get a total of 150 floating-point values returned (3 * 1 * 5
* 10), and should provide enough space in our array for this many. The
order in which the data will be returned is with the first dimension,
LON, varying fastest:

@example
     TEMP( 1, 1, 2, 1)
     TEMP( 2, 1, 2, 1)
     TEMP( 3, 1, 2, 1)
     TEMP( 4, 1, 2, 1)

           @dots{} 

     TEMP( 8, 5, 2, 3)
     TEMP( 9, 5, 2, 3)
     TEMP(10, 5, 2, 3)
@end example

Different dimension orders for the C, FORTRAN, or other language
interfaces do not reflect a different order for values stored on the
disk, but merely different orders supported by the procedural
interfaces to the languages. In general, it does not matter whether a
netCDF dataset is written using the C, FORTRAN, or another language
interface; netCDF datasets written from any supported language may be
read by programs written in other supported languages.

@subsection More on General Array Section Access for Fortran

The use of mapped array sections allows non-trivial relationships
between the disk addresses of variable elements and the addresses
where they are stored in memory. For example, a matrix in memory could
be the transpose of that on disk, giving a quite different order of
elements. In a regular array section, the mapping between the disk and
memory addresses is trivial: the structure of the in-memory values
(i.e., the dimensional lengths and their order) is identical to that
of the array section. In a mapped array section, however, an index
mapping vector is used to define the mapping between indices of netCDF
variable elements and their memory addresses.

With mapped array access, the offset (number of array elements) from
the origin of a memory-resident array to a particular point is given
by the inner product[1] of the index mapping vector with the point's
coordinate offset vector. A point's coordinate offset vector gives,
for each dimension, the offset from the origin of the containing array
to the point. In FORTRAN, the values of a point's coordinate offset
vector are one less than the corresponding values of the point's
coordinate vector, e.g., the array element A(3,5) has coordinate
offset vector [2, 4].

The index mapping vector for a regular array section would have--in
order from most rapidly varying dimension to most slowly--a constant
1, the product of that value with the edge length of the most rapidly
varying dimension of the array section, then the product of that value
with the edge length of the next most rapidly varying dimension, and
so on. In a mapped array, however, the correspondence between netCDF
variable disk locations and memory locations can be different.

A detailed example of mapped array access is presented in the
description of the interfaces for mapped array
access. @xref{nf_put_varm_ type,,, netcdf-f77, @value{f77-man}}.

Note that, although the netCDF abstraction allows the use of
subsampled or mapped array-section access there use is not
required. If you do not need these more general forms of access, you
may ignore these capabilities and use single value access or regular
array section access instead.

@node Type Conversion,  , Data Access, Data
@section Type Conversion
@cindex type conversion
@cindex byte array vs. text string
@cindex data types, conversion

Each netCDF variable has an external type, specified when the variable
is first defined. This external type determines whether the data is
intended for text or numeric values, and if numeric, the range and
precision of numeric values.

If the netCDF external type for a variable is char, only character
data representing text strings can be written to or read from the
variable. No automatic conversion of text data to a different
representation is supported.

If the type is numeric, however, the netCDF library allows you to
access the variable data as a different type and provides automatic
conversion between the numeric data in memory and the data in the
netCDF variable. For example, if you write a program that deals with
all numeric data as double-precision floating point values, you can
read netCDF data into double-precision arrays without knowing or
caring what the external type of the netCDF variables are. On reading
netCDF data, integers of various sizes and single-precision
floating-point values will all be converted to double-precision, if
you use the data access interface for double-precision values. Of
course, you can avoid automatic numeric conversion by using the netCDF
interface for a value type that corresponds to the external data type
of each netCDF variable, where such value types exist.

The automatic numeric conversions performed by netCDF are easy to
understand, because they behave just like assignment of data of one
type to a variable of a different type. For example, if you read
floating-point netCDF data as integers, the result is truncated
towards zero, just as it would be if you assigned a floating-point
value to an integer variable. Such truncation is an example of the
loss of precision that can occur in numeric conversions.

Converting from one numeric type to another may result in an error if
the target type is not capable of representing the converted
value. For example, an integer may not be able to hold data stored
externally as an IEEE floating-point number. When accessing an array
of values, a range error is returned if one or more values are out of
the range of representable values, but other values are converted
properly.

Note that mere loss of precision in type conversion does not result in
an error. For example, if you read double precision values into an
integer, no error results unless the magnitude of the double precision
value exceeds the representable range of integers on your
platform. Similarly, if you read a large integer into a float
incapable of representing all the bits of the integer in its mantissa,
this loss of precision will not result in an error. If you want to
avoid such precision loss, check the external types of the variables
you access to make sure you use an internal type that has a compatible
precision.

Whether a range error occurs in writing a large floating-point value
near the boundary of representable values may be depend on the
platform. The largest floating-point value you can write to a netCDF
float variable is the largest floating-point number representable on
your system that is less than 2 to the 128th power. The largest double
precision value you can write to a double variable is the largest
double-precision number representable on your system that is less than
2 to the 1024th power.

@node Structure, NetCDF Utilities, Data, Top
@chapter File Structure and Performance
@cindex file structure, overview
@cindex performance of NetCDF

This chapter describes the file structure of a netCDF classic or
64-bit offset dataset in enough detail to aid in understanding netCDF
performance issues.

NetCDF is a data abstraction for array-oriented data access and a
software library that provides a concrete implementation of the
interfaces that support that abstraction. The implementation provides
a machine-independent format for representing arrays. Although the
netCDF file format is hidden below the interfaces, some understanding
of the current implementation and associated file structure may help
to make clear why some netCDF operations are more expensive than
others.

Knowledge of the format is not needed for reading and writing netCDF
data or understanding most efficiency issues. Programs that use only
the documented interfaces and that make no assumptions about the
format will continue to work even if the netCDF format is changed in
the future, because any such change will be made below the documented
interfaces and will support earlier versions of the netCDF file
format.

@menu
* Classic File Parts::          The Classic and 64-bit Offset File
* NetCDF-4 File Parts::         The NetCDF-4/HDF5 File
* XDR Layer::                   Classic Machine Interoperability
* Large File Support::          Files that Exceed 2 GiBytes
* 64 bit Offset Limitations::   Limitations on File and Data Size
* Classic Limitations::         Limitations on File and Data Size
* The NetCDF-3 IO Layer::       Classic I/O Described
* UNICOS Optimization::         Some Cray Optimizations
* Chunking::                    NetCDF-4/HDF5 Files Read/Write Chunks
* Parallel Access::             Parallel I/O with NetCDF-4
* Interoperability with HDF5::  Using HDF5 with NetCDF-4
* DAP Support::                 
@end menu

@node Classic File Parts, NetCDF-4 File Parts, Structure, Structure
@section Parts of a NetCDF Classic File
@cindex classic file format
@cindex file format, classic
@cindex file format, 64-bit offset
@cindex 64-bit offset file format

A netCDF classic or 64-bit offset dataset is stored as a single file
comprising two parts:

a header, containing all the information about dimensions, attributes,
and variables except for the variable data;

a data part, comprising fixed-size data, containing the data for
variables that don't have an unlimited dimension; and variable-size
data, containing the data for variables that have an unlimited
dimension.

Both the header and data parts are represented in a
machine-independent form. This form is very similar to XDR (eXternal
Data Representation), extended to support efficient storage of arrays
of non-byte data.

The header at the beginning of the file contains information about the
dimensions, variables, and attributes in the file, including their
names, types, and other characteristics. The information about each
variable includes the offset to the beginning of the variable's data
for fixed-size variables or the relative offset of other variables
within a record. The header also contains dimension lengths and
information needed to map multidimensional indices for each variable
to the appropriate offsets.

By default, this header has little usable extra space; it is only as
large as it needs to be for the dimensions, variables, and attributes
(including all the attribute values) in the netCDF dataset, with a
small amount of extra space from rounding up to the nearest disk block
size. This has the advantage that netCDF files are compact, requiring
very little overhead to store the ancillary data that makes the
datasets self-describing. A disadvantage of this organization is that
any operation on a netCDF dataset that requires the header to grow
(or, less likely, to shrink), for example adding new dimensions or new
variables, requires moving the data by copying it. This expense is
incurred when the enddef function is called: nc_enddef in C
(@pxref{nc_enddef,,, netcdf-c, @value{c-man}}), NF_ENDDEF in Fortran
(@pxref{NF_ENDDEF,,, netcdf-f77, @value{f77-man}}), after a previous
call to the redef function: nc_redef in C (@pxref{nc_redef,,,
netcdf-c, @value{c-man}}) or NF_REDEF in Fortran (@pxref{NF_REDEF,,,
netcdf-f77, @value{f77-man}}). If you create all necessary dimensions,
variables, and attributes before writing data, and avoid later
additions and renamings of netCDF components that require more space
in the header part of the file, you avoid the cost associated with
later changing the header.

Alternatively, you can use an alternative version of the enddef
function with two underbar characters instead of one to explicitly
reserve extra space in the file header when the file is created: in C
nc__enddef (@pxref{nc__enddef,,, netcdf-c, @value{c-man}}), in Fortran
NF__ENDDEF (@pxref{NF__ENDDEF,,, netcdf-f77, @value{f77-man}}), after
a previous call to the redef function.  This avoids the expense of
moving all the data later by reserving enough extra space in the
header to accommodate anticipated changes, such as the addition of new
attributes or the extension of existing string attributes to hold
longer strings.

When the size of the header is changed, data in the file is moved, and
the location of data values in the file changes. If another program is
reading the netCDF dataset during redefinition, its view of the file
will be based on old, probably incorrect indexes. If netCDF datasets
are shared across redefinition, some mechanism external to the netCDF
library must be provided that prevents access by readers during
redefinition, and causes the readers to call nc_sync/NF_SYNC before
any subsequent access.

The fixed-size data part that follows the header contains all the
variable data for variables that do not employ an unlimited
dimension. The data for each variable is stored contiguously in this
part of the file. If there is no unlimited dimension, this is the last
part of the netCDF file.

The record-data part that follows the fixed-size data consists of a
variable number of fixed-size records, each of which contains data for
all the record variables. The record data for each variable is stored
contiguously in each record.

The order in which the variable data appears in each data section is
the same as the order in which the variables were defined, in
increasing numerical order by netCDF variable ID. This knowledge can
sometimes be used to enhance data access performance, since the best
data access is currently achieved by reading or writing the data in
sequential order.

For more detail see @ref{File Format}.

@node NetCDF-4 File Parts, XDR Layer, Classic File Parts, Structure
@section Parts of a NetCDF-4 HDF5 File
@cindex netcdf-4 file format
@cindex file format, netcdf-4

NetCDF-4 files are created with the HDF5 library, and are HDF5 files
in every way, and can be read without the netCDF-4 interface. (Note
that modifying these files with HDF5 will almost certainly make them
unreadable to netCDF-4.)

Groups in a netCDF-4 file correspond with HDF5 groups (although the
netCDF-4 tree is rooted not at the HDF5 root, but in group
``_netCDF'').

Variables in netCDF coo-respond with identically named datasets in
HDF5. Attributes similarly.

Since there is more metadata in a netCDF file than an HDF5 file,
special datasets are used to hold netCDF metadata. 

The _netcdf_dim_info dataset (in group _netCDF) contains the ids of
the shared dimensions, and their length (0 for unlimited dimensions).

The _netcdf_var_info dataset (in group _netCDF) holds an array of
compound types which contain the variable ID, and the associated
dimension ids.

@node XDR Layer, Large File Support, NetCDF-4 File Parts, Structure
@section The Extended XDR Layer
@cindex XDR layer

XDR is a standard for describing and encoding data and a library of
functions for external data representation, allowing programmers to
encode data structures in a machine-independent way. Classic or 64-bit
offset netCDF employs an extended form of XDR for representing
information in the header part and the data parts. This extended XDR
is used to write portable data that can be read on any other machine
for which the library has been implemented.

The cost of using a canonical external representation for data varies
according to the type of data and whether the external form is the
same as the machine's native form for that type.

For some data types on some machines, the time required to convert
data to and from external form can be significant. The worst case is
reading or writing large arrays of floating-point data on a machine
that does not use IEEE floating-point as its native representation.

@node Large File Support, 64 bit Offset Limitations, XDR Layer, Structure
@section Large File Support
@cindex large file support
@cindex 64-bit offset format, introduction
@cindex classic format, introduction
@cindex LFS
@cindex CDF1
@cindex CDF2

It is possible to write netCDF files that exceed 2 GiByte on platforms
that have "Large File Support" (LFS). Such files are
platform-independent to other LFS platforms, but trying to open them
on an older platform without LFS yields a "file too large" error. 

Without LFS, no files larger than 2 GiBytes can be used. The rest of
this section applies only to systems with LFS.

The original binary format of netCDF (classic format) limits the size
of data files by using a signed 32-bit offset within its internal
structure. Files larger than 2 GiB can be created, with certain
limitations. @xref{Classic Limitations}.

In version 3.6.0, netCDF included its first-ever variant of the underlying
data format.  The new format introduced in 3.6.0 uses 64-bit file offsets
in place of the 32-bit offsets. There are still some limits on the sizes
of variables, but the new format can create very large
datasets. @xref{64 bit Offset Limitations}.

NetCDF-4 variables and files can be any size supported by the
underlying file system.

The original data format (netCDF classic), is still the default data
format for the netCDF library.  

The following table summarizes the size limitations of various
permutations of LFS support, netCDF version, and data format. Note
that 1 GiB = 2^30 bytes or about 1.07e+9 bytes, 1 EiB = 2^60 bytes or
about 1.15e+18 bytes. Note also that all sizes
are really 4 bytes less than the ones given below. For example the
maximum size of a fixed variable in netCDF 3.6 classic format is
really 2 GiB - 4 bytes.

@multitable @columnfractions .25 .15 .15 .15 .15 .15

@item Limit @tab No LFS @tab v3.5 @tab v3.6/classic @tab v3.6/64-bit
offset @tab v4.0/netCDF-4

@item Max File Size @tab 2 GiB @tab 8 EiB @tab 8 EiB @tab 8 EiB @tab ??

@item Max Number of Fixed Vars > 2 GiB @tab 0 @tab 1 (last) @tab 1
(last) @tab 2^32 @tab ??

@item  Max Record Vars w/ Rec Size > 2 GiB @tab 0 @tab 1 (last) @tab 1
(last) @tab 2^32 @tab ??

@item  Max Size of Fixed/Record Size of Record Var @tab 2 GiB @tab 2
GiB @tab 2 GiB @tab 4 GiB @tab ??

@item  Max Record Size @tab 2 GiB/nrecs @tab 4 GiB @tab 8 EiB/nrecs
@tab 8 EiB/nrecs @tab ??

@end multitable

For more information about the different file formats of netCDF
@xref{Which Format}.

@node 64 bit Offset Limitations, Classic Limitations, Large File Support, Structure
@section NetCDF 64-bit Offset Format Limitations
@cindex 64-bit offset format, limitations

Although the 64-bit offset format allows the creation of much larger
netCDF files than was possible with the classic format, there are
still some restrictions on the size of variables.

It's important to note that without Large File Support (LFS) in the
operating system, it's impossible to create any file larger than 2
GiBytes.  Assuming an operating system with LFS, the following
restrictions apply to the netCDF 64-bit offset format.

No fixed-size variable can require more than 2^32 - 4 bytes (i.e. 4GiB
- 4 bytes, or 4,294,967,292 bytes) of storage for its data, unless it is the
last fixed-size variable and there are no record variables. When there
are no record variables, the last fixed-size variable can be any size
supported by the file system, e.g. terabytes.

A 64-bit offset format netCDF file can have up to 2^32 - 1 fixed sized
variables, each under 4GiB in size. If there are no record variables
in the file the last fixed variable can be any size.
 
No record variable can require more than 2^32 - 4 bytes of storage for
each record's worth of data, unless it is the last record variable.
A 64-bit offset format netCDF file can have up to 2^32 - 1 records, of
up to 2^32 - 1 variables, as long as the size of one record's data for
each record variable except the last is less than 4 GiB - 4.

Note also that all netCDF variables and records are padded to 4 byte
boundaries.

@node Classic Limitations, The NetCDF-3 IO Layer, 64 bit Offset Limitations, Structure
@section NetCDF Classic Format Limitations
@cindex classic format, limitations

There are important constraints on the structure of large netCDF
classic files that result from the 32-bit relative offsets that are
part of the netCDF classic file format:

The maximum size of a record in the classic format in versions 3.5.1
and earlier is 2^32 - 4 bytes, or about 4 GiB.  In versions 3.6.0 and
later, there is no such restriction on total record size for the classic
format or 64-bit offset format.

If you don't use the unlimited dimension, only one variable can exceed
2 GiB in size, but it can be as large as the underlying file system
permits. It must be the last variable in the dataset, and the offset
to the beginning of this variable must be less than about 2
GiB. 

The limit is really 2^31 - 4.  If you were to specify a variable
size of 2^31 -3, for example, it would be rounded up to the nearest
multiple of 4 bytes, which would be 2^31, which is larger than the
largest signed integer, 2^31 - 1.

For example, the structure of the data might be something like:

@example
netcdf bigfile1 @{
    dimensions: 
       x=2000;
       y=5000;
       z=10000;
    variables:
       double x(x);         // coordinate variables
       double y(y);
       double z(z);
       double var(x, y, z); // 800 Gbytes
    @}
@end example

If you use the unlimited dimension, record variables may
exceed 2 GiB in size, as long as the offset of the start of each
record variable within a record is less than 2 GiB - 4. For
example, the structure of the data in a 2.4 Tbyte file might be
something like:

@example
netcdf bigfile2 @{
    dimensions: 
       x=2000;
       y=5000;
       z=10;
       t=UNLIMITED;         // 1000 records, for example
    variables:
       double x(x);         // coordinate variables
       double y(y);
       double z(z);
       double t(t);
                            // 3 record variables, 2400000000 bytes per record
       double var1(t, x, y, z);
       double var2(t, x, y, z);
       double var3(t, x, y, z);
    @}
@end example

@node The NetCDF-3 IO Layer, UNICOS Optimization, Classic Limitations, Structure
@section The NetCDF-3 I/O Layer
@cindex I/O layer
@cindex access shared dataset I/O
@cindex shared dataset I/O access
@cindex buffers, I/O
@vindex NC_SHARE
@vindex NF_SHARE
@cindex share flag
@findex NF_SYNC
@findex nc_sync
@cindex fflush
@cindex flushing buffers
@vindex _IONBF flag


The following discussion applies only to netCDF classic and 64-bit
offset files. For netCDF-4 files, the I/O layer is the HDF5 library. 

For netCDF classic and 64-bit offset files, an I/O layer implemented
much like the C standard I/O (stdio) library is used by netCDF to read
and write portable data to netCDF datasets. Hence an understanding of
the standard I/O library provides answers to many questions about
multiple processes accessing data concurrently, the use of I/O
buffers, and the costs of opening and closing netCDF files. In
particular, it is possible to have one process writing a netCDF
dataset while other processes read it.

Data reads and writes are no more atomic than calls to stdio fread()
and fwrite(). An nc_sync/NF_SYNC call is analogous to the fflush call
in the C standard I/O library, writing unwritten buffered data so
other processes can read it; The C function nc_sync (@pxref{nc_sync,,,
netcdf-c, @value{c-man}}), or the Fortran function NF_SYNC
(@pxref{NF_SYNC,,, netcdf-f77, @value{f77-man}}), also brings header
changes up-to-date (for example, changes to attribute values). Opening
the file with the NC_SHARE (in C) or the NF_SHARE (in Fortran) is
analogous to setting a stdio stream to be unbuffered with the _IONBF
flag to setvbuf.

As in the stdio library, flushes are also performed when "seeks" occur
to a different area of the file. Hence the order of read and write
operations can influence I/O performance significantly. Reading data
in the same order in which it was written within each record will
minimize buffer flushes.

You should not expect netCDF classic or 64-bit offset format data
access to work with multiple writers having the same file open for
writing simultaneously.

It is possible to tune an implementation of netCDF for some platforms
by replacing the I/O layer with a different platform-specific I/O
layer. This may change the similarities between netCDF and standard
I/O, and hence characteristics related to data sharing, buffering, and
the cost of I/O operations.

The distributed netCDF implementation is meant to be
portable. Platform-specific ports that further optimize the
implementation for better I/O performance are practical in some cases.

@node UNICOS Optimization, Chunking, The NetCDF-3 IO Layer, Structure
@section UNICOS Optimization
@cindex UNICOS
@vindex NETCDF_FFIOSPEC

It should be noted that no UNICOS platform has been available at
Unidata for netCDF testing for some years. The following information
is left here for historical reasons.

As was mentioned in the previous section, it is possible to replace
the I/O layer in order to increase I/O efficiency. This has been done
for UNICOS, the operating system of Cray computers similar to the Cray
Y-MP.

Additionally, it is possible for the user to obtain even greater I/O
efficiency through appropriate setting of the NETCDF_FFIOSPEC
environment variable. This variable specifies the Flexible File I/O
buffers for netCDF I/O when executing under the UNICOS operating
system (the variable is ignored on other operating systems). An
appropriate specification can greatly increase the efficiency of
netCDF I/O--to the extent that it can surpass default FORTRAN binary
I/O. Possible specifications include the following:

@table @code

@item bufa:336:2
2, asynchronous, I/O buffers of 336 blocks each (i.e., double
buffering). This is the default specification and favors sequential
I/O.
 
@item cache:256:8
8, synchronous, 256-block buffers. This favors larger random accesses.
 
@item cachea:256:8:2
8, asynchronous, 256-block buffers with a 2 block
read-ahead/write-behind factor. This also favors larger random
accesses.
 
@item cachea:8:256:0
256, asynchronous, 8-block buffers without
read-ahead/write-behind. This favors many smaller pages without
read-ahead for more random accesses as typified by slicing netCDF
arrays.
 
@item cache:8:256,cachea.sds:1024:4:1
This is a two layer cache. The first (synchronous) layer is composed
of 256 8-block buffers in memory, the second (asynchronous) layer is
composed of 4 1024-block buffers on the SSD. This scheme works well
when accesses proceed through the dataset in random waves roughly
2x1024-blocks wide.

@end table

All of the options/configurations supported in CRI's FFIO library are
available through this mechanism. We recommend that you look at CRI's
I/O optimization guide for information on using FFIO to its
fullest. This mechanism is also compatible with CRI's EIE I/O library.

Tuning the NETCDF_FFIOSPEC variable to a program's I/O pattern can
dramatically improve performance. Speedups of two orders of magnitude
have been seen.

@node Chunking, Parallel Access, UNICOS Optimization, Structure
@section Improving Performance With Chunking
@cindex chunking
@cindex deflation
@cindex shuffle filter

NetCDF may use HDF5 as a storage format (when files are created with
NC_NETCDF4/NF_NETCDF4/NF90_NETCDF4). For those files, the writer may
control the size of the chunks of data that are written to the HDF5,
along with other aspects of the data, such as endianness, a shuffle
and checksum filter, on-the-fly compression/decompression of the data.

The chunk sizes of a variable are specified after the variable is
defined, but before any data are written. If chunk sizes are not
specified for a variable, default chunk sizes are chosen by the
library.

The selection of good chunk sizes is a complex topic, and one that data
writers must grapple with. Once the data are written, there is no way
to change the chunk sizes except to copy the data to a new variable.

Chunks should match read access patterns; the best chunk performance
can be achieved by writing chunks which exactly match the size of the
subsets of data that will be read. When multiple read access patterns
are to be used, there is no one way to best set the chunk sizes. 

Some good discussion of chunking can be found in the HDF5-EOS XIII
workshop presentation (@uref{@value{hdf5-chunking-advice-url}}).

@menu
* Chunk Cache::                 
* Default Chunking::            
* Default Chunking 4_0_1::      
* Parallel Chunking::           
* bm_file::                     
@end menu

@node Chunk Cache, Default Chunking, Chunking, Chunking
@subsection The Chunk Cache

When data are first read or written to a netCDF-4/HDF5 variable, the
HDF5 library opens a cache for that variable. The default size of that
cache (settable with the --with-chunk-cache-size at netCDF build
time).

For good performance your chunk cache must be larger than one chunk of
your data - preferably that it be large enough to hold multiple chunks
of data.

In addition, when a file is opened (or a variable created in an open
file), the netCDF-4 library checks to make sure the default chunk
cache size will work for that variable. The cache will be large enough
to hold N chunks, up to a maximum size of M bytes. (Both N and M are
settable at configure time with the --with-default-chunks-in-cache and
the --with-max-default-cache-size options to the configure
script. Currently they are set to 10 and 64 MB.)

To change the default chunk cache size, use the set_chunk_cache
function before opening the file. C programmers see
@ref{nc_set_chunk_cache,,, netcdf-c, @value{c-man}}, Fortran 77
programmers see @ref{NF_SET_CHUNK_CACHE,,, netcdf-f77,
@value{f77-man}}). Fortran 90 programmers use the optional cache_size,
cache_nelems, and cache_preemption parameters to nf90_open/nf90_create
to change the chunk size before opening the file.

To change the per-variable cache size, use the set_var_chunk_cache
function at any time on an open file. C programmers see
@ref{nc_set_var_chunk_cache,,, netcdf-c, @value{c-man}}, Fortran 77
programmers see @ref{NF_SET_VAR_CHUNK_CACHE,,, netcdf-f77,
@value{f77-man}}, @c Fortran 90 programmers see
@c @ref{NF90_SET_VAR_CHUNK_CACHE,,, netcdf-f90, @value{f90-man}}
).

@node Default Chunking, Default Chunking 4_0_1, Chunk Cache, Chunking
@subsection The Default Chunking Scheme in version 4.1 (and 4.1.1)

When the data writer does not specify chunk sizes for variable, the
netCDF library has to come up with some default values.

The C code below determines the default chunks sizes.

For unlimited dimensions, a chunk size of one is always used. Users
are advised to set chunk sizes for large data sets with one or more
unlimited dimensions, since a chunk size of one is quite inefficient.

For fixed dimensions, the algorithm below finds a size for the chunk
sizes in each dimension which results in chunks of DEFAULT_CHUNK_SIZE
(which can be modified in the netCDF configure script).

@example
		     /* Unlimited dim always gets chunksize of 1. */
		     if (dim->unlimited)
			chunksize[d] = 1;
		     else
			chunksize[d] = pow((double)DEFAULT_CHUNK_SIZE/type_size, 
					   1/(double)(var->ndims - unlimdim));
@end example


@node Default Chunking 4_0_1, Parallel Chunking, Default Chunking, Chunking
@subsection The Default Chunking Scheme in version 4.0.1

In the 4.0.1 release, the default chunk sizes were chosen with a
different scheme, as demonstrated in the following C code:

@example
/* These are limits for default chunk sizes. (2^16 and 2^20). */
#define NC_LEN_TOO_BIG 65536 
#define NC_LEN_WAY_TOO_BIG 1048576

      /* Now we must determine the default chunksize. */
      if (dim->unlimited)
         chunksize[d] = 1;
      else if (dim->len < NC_LEN_TOO_BIG)
         chunksize[d] = dim->len;
      else if (dim->len > NC_LEN_TOO_BIG && dim->len <= NC_LEN_WAY_TOO_BIG)
         chunksize[d] = dim->len / 2 + 1;
      else
         chunksize[d] = NC_LEN_WAY_TOO_BIG;
@end example

As can be seen from this code, the default chunksize is 1 for
unlimited dimensions, otherwise it is the full length of the dimension
(if it is under NC_LEN_TOO_BIG), or half the size of the dimension (if
it is between NC_LEN_TOO_BIG and NC_LEN_WAY_TOO_BIG), and, if it's
longer than NC_LEN_WAY_TOO_BIG, it is set to NC_LEN_WAY_TOO_BIG.

Our experience is that these defaults work well for small data sets,
but once variable size reaches the GB range, the user is better off
determining chunk sizes for their read access patterns.

In particular, the idea of using 1 for the chunksize of an unlimited
dimension works well if the data are being read a record at a
time. Any other read access patterns will result in slower
performance.

@node Parallel Chunking, bm_file, Default Chunking 4_0_1, Chunking
@subsection Chunking and Parallel I/O

When files are opened for read/write parallel I/O access, the chunk
cache is not used. Therefore it is important to open parallel files
with read only access when possible, to achieve the best performance.

@node bm_file,  , Parallel Chunking, Chunking
@subsection A Utility to Help Benchmark Results: bm_file

The bm_file utility may be used to copy files, from one netCDF format to
another, changing chunking, filter, parallel I/O, and other
parameters. This program may be used for benchmarking netCDF
performance for user data files with a range of choices, allowing data
producers to pick settings that best serve their user base.

NetCDF must have been configured with --enable-benchmarks at build
time for the bm_file program to be built. When built with
--enable-benchmarks, netCDF will include tests (run with ``make
check'') that will run the bm_file program on sample data files.

Since data files and their access patterns vary from case to case,
these benchmark tests are intended to suggest further use of the
bm_file program for users.

Here's an example of a call to bm_file:
@example
./bm_file -d -f 3 -o  tst_elena_out.nc -c 0:-1:0:1024:256:256 tst_elena_int_3D.nc
@end example

Generally a range of settings must be tested. This is best done with a
shell script, which calls bf_file repeatedly, to create output like
this:

@example
*** Running benchmarking program bm_file for simple shorts test files, 1D to 6D...
input format, output_format, input size, output size, meta read time, meta write time, data read time, data write time, enddianness, metadata reread time, data reread time, read rate, write rate, reread rate, deflate, shuffle, chunksize[0], chunksize[1], chunksize[2], chunksize[3]
1, 4, 200092, 207283, 1613, 1054, 409, 312, 0, 1208, 1551, 488.998, 641.026, 128.949, 0, 0, 100000, 0, 0, 0 
1, 4, 199824, 208093, 1545, 1293, 397, 284, 0, 1382, 1563, 503.053, 703.211, 127.775, 0, 0, 316, 316, 0, 0 
1, 4, 194804, 204260, 1562, 1611, 390, 10704, 0, 1627, 2578, 499.159, 18.1868, 75.5128, 0, 0, 46, 46, 46, 0 
1, 4, 167196, 177744, 1531, 1888, 330, 265, 0, 12888, 1301, 506.188, 630.347, 128.395, 0, 0, 17, 17, 17, 17 
1, 4, 200172, 211821, 1509, 2065, 422, 308, 0, 1979, 1550, 473.934, 649.351, 129.032, 0, 0, 10, 10, 10, 10 
1, 4, 93504, 106272, 1496, 2467, 191, 214, 0, 32208, 809, 488.544, 436.037, 115.342, 0, 0, 6, 6, 6, 6 
*** SUCCESS!!!
@end example

Such tables are suitable for import into spreadsheets, for easy
graphing of results.

Several test scripts are run during the ``make check'' of the netCDF
build, in the nc_test4 directory. The following example may be found
in nc_test4/run_bm_elena.sh.

@example
#!/bin/sh

# This shell runs some benchmarks that Elena ran as described here:
# http://hdfeos.org/workshops/ws06/presentations/Pourmal/HDF5_IO_Perf.pdf

# $Id: netcdf.texi,v 1.82 2010/05/15 20:43:13 dmh Exp $

set -e
echo ""

echo "*** Testing the benchmarking program bm_file for simple float file, no compression..."
./bm_file -h -d -f 3 -o  tst_elena_out.nc -c 0:-1:0:1024:16:256 tst_elena_int_3D.nc
./bm_file -d -f 3 -o  tst_elena_out.nc -c 0:-1:0:1024:256:256 tst_elena_int_3D.nc
./bm_file -d -f 3 -o  tst_elena_out.nc -c 0:-1:0:512:64:256 tst_elena_int_3D.nc
./bm_file -d -f 3 -o  tst_elena_out.nc -c 0:-1:0:512:256:256 tst_elena_int_3D.nc
./bm_file -d -f 3 -o  tst_elena_out.nc -c 0:-1:0:256:64:256 tst_elena_int_3D.nc
./bm_file -d -f 3 -o  tst_elena_out.nc -c 0:-1:0:256:256:256 tst_elena_int_3D.nc
echo '*** SUCCESS!!!'

exit 0
@end example

The reading that bm_file does can be tailored to match the expected
access pattern.  

The bm_file program is controlled with command line options.

@example
./bm_file
bm_file -v [-s N]|[-t V:S:S:S -u V:C:C:C -r V:I:I:I] -o file_out -f N -h -c V:C:C,V:C:C:C -d -m -p -i -e 1|2 file
  [-v]        Verbose
  [-o file]   Output file name
  [-f N]      Output format (1 - classic, 2 - 64-bit offset, 3 - netCDF-4, 4 - netCDF4/CLASSIC)
  [-h]        Print output header
  [-c V:Z:S:C:C:C[,V:Z:S:C:C:C, etc.]] Deflate, shuffle, and chunking parameters for vars
  [-t V:S:S:S[,V:S:S:S, etc.]] Starts for reads/writes
  [-u V:C:C:C[,V:C:C:C, etc.]] Counts for reads/writes
  [-r V:I:I:I[,V:I:I:I, etc.]] Incs for reads/writes
  [-d]        Doublecheck output by rereading each value
  [-m]        Do compare of each data value during doublecheck (slow for large files!)
  [-p]        Use parallel I/O
  [-s N]      Denom of fraction of slowest varying dimension read.
  [-i]        Use MPIIO (only relevant for parallel builds).
  [-e 1|2]    Set the endianness of output (1=little 2=big).
  file        Name of netCDF file
@end example

@node Parallel Access, Interoperability with HDF5, Chunking, Structure
@section Parallel Access with NetCDF-4
@cindex parallel access

Use the special parallel open (or create) calls to open (or create) a
file, and then to use parallel I/O to read or write that file. C
programmers see @ref{nc_open_par,,, netcdf-c, @value{c-man}}, Fortran 77
programmers see @ref{NF_OPEN_PAR,,, netcdf-f77,
@value{f77-man}}). Fortran 90 programmers use the optional comm and
info parameters to nf90_open/nf90_create to initiate parallel access.

Note that the chunk cache is turned off if a file is opened for
parallel I/O in read/write mode. Open the file in read-only mode to
engage the chunk cache.

NetCDF uses the HDF5 parallel programming model for parallel I/O
with netCDF-4/HDF5 files. The HDF5 tutorial
(@uref{@value{hdf5-tutorial-url}}) is a good reference.

For classic and 64-bit offset files, netCDF uses the PnetCDF
library from Argonne National Labs/Nortwestern
University. For parallel access of classic and 64-bit offset files,
netCDF must be configured with the --with-pnetcdf option at build
time. See the PnetCDF site for more information
(@uref{@value{pnetcdf-url}}).

@node Interoperability with HDF5, DAP Support, Parallel Access, Structure
@section Interoperability with HDF5
@cindex interoperability with HDF5

To create HDF5 files that can be read by netCDF-4, use HDF5 1.8, which
is not yet released. However most (but not all) of the necessary
features can be found in their latest development snapshot.

HDF5 has some features that will not be supported by netCDF-4, and
will cause problems for interoperability:

@itemize

@item
HDF5 allows a Group to be both an ancestor and a descendant of
another Group, creating cycles in the subgroup graph. HDF5 also
permits multiple parents for a Group.  In the netCDF-4 data model,
Groups form a tree with no cycles, so each Group (except the top-level
unnamed Group) has a unique parent.

@item
HDF5 supports "references" which are like pointers to objects and data
regions within a file.  The netCDF-4 data model omits references.

@item
HDF5 supports some primitive types that are not included in the
netCDF-4 data model, including H5T_TIME and H5T_BITFIELD.

@item
HDF5 supports multiple names for data objects like Datasets (netCDF-4
variables) with no distinguished name.  The netCDF-4 data model
requires that each variable, attribute, dimension, and group have a
single distinguished name.

@item
HDF5 (like netCDF) supports scalar attributes, but netCDF-4 cannot
read scalar HDF5 attributes (unless it is a string attribute). This
limitation will be removed in a future release of netCDF.

@end itemize

These are fairly easy requirements to meet, but there is one relating
to shared dimensions which is a little more challenging. Every HDF5
dataset must have a dimension scale attached to each dimension.

Dimension scales are a new feature for HF 1.8, which allow
specification of shared dimensions.

(In the future netCDF-4 will be able to deal with HDF5 files which do
not have dimension scales. However, this is not expected before netCDF
4.1.)

Finally, there is one feature which is missing from all current HDF5
releases, but which will be in 1.8 - the ability to track object
creation order. As you may know, netCDF keeps track of the creation
order of variables, dimensions, etc. HDF5 (currently) does not.

There is a bit of a hack in place in netCDF-4 files for this, but that
hack will go away when HDF5 1.8 comes out.

Without creation order, the files will still be readable to netCDF-4,
it's just that netCDF-4 will number the variables in alphabetical,
rather than creation, order.

Interoperability is a complex task, and all of this is in the alpha
release stage. It is tested in libsrc4/tst_interops.c, which contains
some examples of how to create HDF5 files, modify them in netCDF-4,
and then verify them in HDF5. (And vice versa).

@node DAP Support,  , Interoperability with HDF5, Structure
@section DAP Support
@cindex DAP support

Beginning with netCDF version 4.1,
optional support is provided for accessing data through
OPeNDAP servers using the DAP protocol.
Currently, only DAP protocol version 2 is supported; DAP protocol
version 4 support is under development.

DAP support is automatically enabled if a usable curl library can be
set using the LDFLAGS environment variable (similar to the way
that the HDF5 libraries are referenced).
DAP support can forcibly be enabled or disabled using the --enable-dap
flag or the --disable-dap flag, respectively.  If enabled,
then DAP2 support requires access to the curl library.
Refer to the installation manual for details
@ref{Top,@value{i-man},, netcdf-install, @value{i-man}}.

DAP uses a data model that is different from that supported by netCDF,
either classic or enhanced. Generically, the DAP data model
is encoded textually in a @acronym{DDS, Dataset Descriptor Structure}.
There is a second data model for DAP attributes, which
is encoded textually in a @acronym{DAS, Dataset Attribute Structure}.
For detailed information about the DAP DDS and DAS, refer to
the OPeNDAP web site @uref{@value{opendap-url}}.

@subsection Accessing OPeNDAP Data
In order to access an OPeNDAP data source through the netCDF
API, the file name normally used is replaced with a
URL with a specific format.
The URL is composed of three parts.
@enumerate
@item URL - this is a standard form URL such as
http://remotetest.unidata.ucar.edu/dts/test.01
@item Constraints - these are suffixed to the URL and
take the form ``?<projections>&selections''.
The meaning of the terms projection and selection is somewhat complicated;
and the OPeNDAP web site,
@uref{http://www.opendap.org},
should be consulted.
The interaction of DAP constraints with netCDF is complex and
at the moment requires an understanding of how DAP is translated
to netCDF.
@item Client parameters -
these may be specified in either of two ways.
The older, deprecated form prefixes
text to the front of the url and is of
the general form [<name>] or [<name>=value].
Examples include [show=fetch] and [noprefetch].
The newer, preferred form prefixes the
parameters to the end of the url using the
semi-standard '#' format:
``http://....#show=fetch&noprefetch''.
@end enumerate

It is possible to see what the translation does to a particular DAP
data source in either of two ways.
First, one can examine the DDS source through a web browser
and then examine the translation using the ncdump -h command
to see the netCDF Classic translation.
The ncdump output will actually be the union of the
DDS with the DAS, so to see the complete translation, it is necessary
to view both.

For example, if a web browser is given the following,
the first URL will return the DDS for the specified dataset,
and the second URL will return the DAS for the specified dataset.
@example
http://remotetest.unidata.ucar.edu/dts/test.01.dds
http://remotetest.unidata.ucar.edu/dts/test.01.das
@end example
Then by using the following ncdump command, it is possible to see the
equivalent netCDF Classic translation.
@example
ncdump -h http://remotetest.unidata.ucar.edu/dts/test.01
@end example

The DDS output from the web server should look like this.
@verbatim
Dataset {
    Byte b;
    Int32 i32;
    UInt32 ui32;
    Int16 i16;
    UInt16 ui16;
    Float32 f32;
    Float64 f64;
    String s;
    Url u;
} SimpleTypes;
@end verbatim

The DAS output from the web server should look like this.
@verbatim
Attributes {
    Facility {
        String PrincipleInvestigator ``Mark Abort'', ``Ph.D'';
        String DataCenter ``COAS Environmental Computer Facility'';
        String DrifterType ``MetOcean WOCE/OCM'';
    }
    b {
        String Description ``A test byte'';
        String units ``unknown'';
    }
    i32 {
        String Description ``A 32 bit test server int'';
        String units ``unknown'';
    }
}
@end verbatim

The output from ncdump should look like this.
@verbatim
netcdf test {
dimensions:
	stringdim64 = 64 ;
variables:
	byte b ;
		b:Description = "A test byte" ;
		b:units = "unknown" ;
	int i32 ;
		i32:Description = "A 32 bit test server int" ;
		i32:units = "unknown" ;
	int ui32 ;
	short i16 ;
	short ui16 ;
	float f32 ;
	double f64 ;
	char s(stringdim64) ;
	char u(stringdim64) ;
}
@end verbatim
Note that the fields of type String and type URL
have suddenly acquired a dimension. This is because strings
are translated to arrays of char, which requires adding
an extra dimension.  The size of the dimension
is determined in a variety of ways and can be specified.
It defaults to 64 and when read, the underlying string is
either padded or truncated to that length.

Also note that the @code{Facility} attributes do not appear
in the translation because they are neither global nor
associated with a variable in the DDS.

Alternately, one can get the text of the DDS as a global attribute
by using the client parameters
mechanism . In this case, the parameter ``show=dds'' 
can be prefixed to the URL and the data retrieved using
the following command
@example
ncdump -h http://remotetest.unidata.ucar.edu/dts/test.01.dds#show=dds
@end example
The ncdump -h command will then show both the translation and the original
DDS. In the above example, the DDS would appear as the global
attribute ``_DDS'' as follows.
@verbatim
netcdf test {
...
variables:
        :_DDS = "Dataset { Byte b; Int32 i32; UInt32 ui32; Int16 i16;
                 UInt16 ui16; Float32 f32; Float64 f64;
                 Strings; Url u; } SimpleTypes;"

	byte b ;
...
}
@end verbatim

@subsection DAP to NetCDF Translation Rules
Currently only one translation available: DAP 2 Protocol to netCDF-3.
There used to be a DAP 2 Protocol to netCDF-4 translation
but that has been removed until the DAP4 protocol is available.

@subsubsection netCDF-3 Translation Rules
The current default translation code
translates the OPeNDAP protocol to netCDF-3 (classic).
This netCDF-3 translation converts an OPeNDAP
DAP protocol version 2 DDS to netCDF-3 and is designed to mimic
as closely as possible the translation provided by the libnc-dap
system, except that some errors in that older translation have
been fixed.

For illustrative purposes, the following example will be used.
@verbatim
Dataset {
  Int32 f1;
  Structure {
    Int32 f11;        
    Structure {
      Int32 f1[3];
      Int32 f2;
    } FS2[2]; 
  } S1; 
  Structure {
    Grid {
      Array:
        Float32 temp[lat=2][lon=2];
      Maps:
        Int32 lat[lat=2];
        Int32 lon[lon=2];
    } G1;
  } S2;
  Grid {
      Array:
        Float32 G2[lat=2][lon=2];
      Maps:
        Int32 lat[2];
        Int32 lon[2];
  } G2;
  Int32 lat[lat=2];
  Int32 lon[lon=2];
} D1;
@end verbatim

@subsubsection Variable Definition
The set of netCDF variables is derived from the fields with
primitive base types as they occur in
Sequences, Grids, and Structures.
The field names are modified to be fully qualified initially.
For the above, the set of variables are as follows.
The coordinate variables within grids are left out
in order to mimic the behavior of libnc-dap.
@enumerate
@item f1
@item S1.f11
@item S1.FS2.f1
@item S1.FS2.f2
@item S2.G1.temp
@item S2.G2.G2
@item lat
@item lon
@end enumerate

@subsubsection Variable Dimension Translation
A variable's rank is determined from three sources.
@enumerate
@item 
The variable has the dimensions associated with the field
it represents (e.g. S1.FS2.f1[3] in the above example).
@item 
The variable inherits the dimensions associated with any containing
structure that has a rank greater than zero.
These dimensions precede those of case 1.
Thus, we have in our example, f1[2][3], where the first dimension
comes from the containing Structure FS2[2].
@item 
The variable's set of dimensions are altered
if any of its containers is a DAP DDS Sequence.
This is discussed more fully below.
@item 
If the type of the netCDF variable is char, then an extra
string dimension is added as the last dimension.
@end enumerate

@subsubsection Dimension translation
For dimensions, the rules are as follows.
@enumerate
@item  Fields in dimensioned structures inherit the dimension
of the structure; thus the above list would have the following
dimensioned variables.
@itemize
@item S1.FS2.f1 -> S1.FS2.f1[2][3]
@item S1.FS2.f2 -> S1.FS2.f2[2]
@item S2.G1.temp -> S2.G1.temp[lat=2][lon=2]
@item S2.G1.lat -> S2.G1.lat[lat=2]
@item S2.G1.lon -> S2.G1.lon[lon=2]
@item S2.G2.G2 -> S2.G2.lon[lat=2][lon=2]
@item S2.G2.lat -> S2.G2.lat[lat=2]
@item S2.G2.lon -> S2.G2.lon[lon=2]
@item lat -> lat[lat=2]
@item lon -> lon[lon=2]
@end itemize

@item 
Collect all of the dimension specifications from the DDS, both
named and anonymous (unnamed)
For each unique anonymous dimension with value NN
create a netCDF dimension of the form "XX_<i>=NN",
where XX is the fully qualified name of the variable and i is the
i'th (inherited) dimension of the array where the anonymous dimension occurs.
For our example, this would create the following dimensions.
@itemize
@item S1.FS2.f1_0 = 2 ;
@item S1.FS2.f1_1 = 3 ;
@item S1.FS2.f2_0 = 2 ;
@item S2.G2.lat_0 = 2 ;
@item S2.G2.lon_0 = 2 ;
@end itemize

@item 
If however, the anonymous dimension is the single dimension
of a MAP vector in a Grid then the dimension is given the
same name as the map vector This leads to the following.
@itemize
@item S2.G2.lat_0 -> S2.G2.lat
@item S2.G2.lon_0 -> S2.G2.lon
@end itemize

@item 
For each unique named dimension "<name>=NN",
create a netCDF dimension of the form "<name>=NN",
where name has the qualifications removed.
If this leads to duplicates (i.e. same name and same value),
then the duplicates are ignored.
This produces the following.
@itemize
@item S2.G2.lat -> lat
@item S2.G2.lon -> lon
@end itemize
Note that this produces duplicates that will be ignored later.

@item 
At this point the only dimensions left to process should be named
dimensions with the same name as some dimension from step number 3,
but with a different value.  For those dimensions create a dimension
of the form "<name>M=NN" where M is a counter starting at 1.
The example has no instances of this.

@item 
Finally and if needed, define a single UNLIMITED dimension named "unlimited"
with value zero.
Unlimited will be used to handle certain kinds of DAP sequences (see below).
@end enumerate
This leads to the following set of dimensions.
@verbatim
dimensions:
  unlimited = UNLIMITED;
  lat = 2 ;
  lon = 2 ;
  S1.FS2.f1_0 = 2 ;
  S1.FS2.f1_1 = 3 ;
  S1.FS2.f2_0 = 2 ;
@end verbatim

@subsubsection Variable Name Translation
The steps for variable name translation are as follows.

@enumerate
@item Take the set of variables captured above.
Thus for the above DDS, the following fields would be collected.
@itemize
@item f1
@item S1.f11
@item S1.FS2.f1
@item S1.FS2.f2
@item S2.G1.temp
@item S2.G2.G2
@item lat
@item lon
@end itemize

@item All grid array variables are renamed to be the same as the containing
grid and the grid prefix is removed.
In the above DDS, this results in the following changes.
@enumerate
@item  G1.temp -> G1
@item  G2.G2 -> G2
@end enumerate
@end enumerate

It is important to note that this process could produce duplicate
variables (i.e. with the same name); in that case they are all assumed
to have the same content and the duplicates are ignored.
If it turns out that the duplicates have different content, then
the translation will not detect this. YOU HAVE BEEN WARNED.

The final netCDF-3 schema (minus attributes) is then as follows.
@verbatim
netcdf t {
dimensions:
        unlimited = UNLIMITED ;
        lat = 2 ;
        lon = 2 ;
        S1.FS2.f1_0 = 2 ;
        S1.FS2.f1_1 = 3 ;
        S1.FS2.f2_0 = 2 ;
variables:
        int f1 ;
        int lat(lat) ;
        int lon(lon) ;
        int S1.f11 ;
	int S1.FS2.f1(S1.FS2.f1_0, S1.FS2.f1_1) ;
        int S1.FS2.f2(S1_FS2_f2_0) ;
        float S2.G1(lat, lon) ;
        float G2(lat, lon) ;
}
@end verbatim
In actuality, the unlimited dimension is dropped because
it is unused.

There are differences with the original libnc-dap here
because libnc-dap technically was incorrect.  The original
would have said this, for example.
@verbatim
int S1.FS2.f1(lat, lat) ;
@end verbatim
Note that this is incorrect because it dimensions
S1.FS2.f1(2,2) rather than S1.FS2.f1(2,3).

@subsubsection Translating DAP DDS Sequences
Any variable (as determined above) that is contained
directly or indirectly by a Sequence is subject to revision
of its rank using the following rules.
@enumerate
@item  
Let the variable be contained in Sequence Q1, where Q1 is the
innermost containing sequence. If Q1 is itself contained
(directly or indirectly) in a sequence,
or Q1 is contained (again directly or indirectly)
in a structure that has rank greater than 0,
then the variable will have an initial UNLIMITED
dimension.  Further, all dimensions coming from "above" and including (in
the containment sense) the innermost Sequence, Q1, will be
removed and replaced by that single UNLIMITED dimension.  The
size associated with that UNLIMITED is zero, which means
that its contents are inaccessible through the netCDF-3 API.
Again, this differs from libnc-dap, which leaves out such variables.
Again, however, this difference is backward compatible.

@item  
If the variable is contained in a single Sequence (i.e. not nested)
and all containing structures have rank 0, then the variable will
have an initial dimension whose size is the record count for that
Sequence. The name of the new dimension will be the name of the
Sequence.
@end enumerate

Consider this example.
@verbatim
Dataset {
  Structure {
    Sequence {
      Int32 f1[3];
      Int32 f2;
    } SQ1;
  } S1[2]; 
  Sequence {
    Structure {
      Int32 x1[7];
    } S2[5];
  } Q2;
} D;
@end verbatim
The corresponding netCDF-3 translation is pretty much as follows
(the value for dimension Q2 may differ).
@verbatim
dimensions:
    unlimited = UNLIMITED ; // (0 currently)
    S1.SQ1.f1_0 = 2 ;
    S1.SQ1.f1_1 = 3 ;
    S1.SQ1.f2_0 = 2 ;
    Q2.S2.x1_0 = 5 ;
    Q2.S2.x1_1 = 7 ;
    Q2 = 5 ;
variables:
    int S1.SQ1.f1(unlimited, S1.SQ1.f1_1) ;
    int S1.SQ1.f2(unlimited) ;
    int Q2.S2.x1(Q2, Q2.S2.x1_0, Q2.S2.x1_1) ;
@end verbatim
Note that for example S1.SQ1.f1_0
is not actually used because it has been folded
into the unlimited dimension.

Note that for sequences without a leading unlimited dimension,
there is a performance cost
because the translation code has to walk the data to determine
how many records are associated with the sequence.
Since libnc-dap did essentially the same thing, it can be assumed that
the cost is not prohibitive.

@ignore
@subsubsection netCDF-4 Translation Rules
A DAP to netCDF-4 translation also exists, but is not the
default and in any case is only available if the
"--enable-netcdf-4" option is specified at configure time.
This translation includes some elements of the libnc-dap
translation, but attempts to provide a simpler (but not,
unfortunately, simple) set of translation rules than is used
for the netCDF-3 translation.  Please note that the
translation is still experimental and will change to respond
to unforeseen problems or to suggested improvements.

This text will use this running example.
@verbatim
Dataset {
  Int32 f1[fdim=10];
  Structure {
    Int32 f11;        
    Structure {
      Int32 f1[3];
      Int32 f2;
    } FS2[2]; 
  } S1; 
  Grid {
    Array:
      Float32 temp[lat=2][lon=2];
    Maps:
      Int32 lat[2];
      Int32 lon[2];
  } G1;
  Sequence {
    Float64 depth;
  } Q1;
} D
@end verbatim

@subsubsection Variable Definition
The rule for choosing variables is relatively simple.
Start with the names of the top-level fields of the DDS.
The term top-level means that the object is a direct subnode
of the Dataset object. In our example, this produces the set
[f1, S1, G1, Q1].

@ignore
@enumerate
@item  Start with the names of the top-level fields of the DDS.
The term top-level means that the object is a direct subnode
of the Dataset object. In our example, this produces the set
[f1, S1, G1, Q1].
@item 
Replace all Grid objects with the fully qualified list of array
and map fields of the grid.
Our variable set then becomes [f1, S1, G1.temp, G1.lat, G1.lon, Q1].
Note that the libnc-dap practice of re-naming the array variable to be
that of the Grid is not used.
@item 
Attempt to remove the prefix Grid name from the top-level Grid array and map
variables. If that eventually conflicts with some other name,
then leave the conflicting Grids alone.
Our variable set then becomes [f1, S1, temp, lat, lon, Q1].
@item 
If the Grid array name is the same as the Grid name, then
remove the prefix Grid name (not shown).
@end enumerate
@end ignore

@subsubsection Dimension Definition
The rules for choosing and defining dimensions is as follows.
@enumerate
@item 
Collect the set of dimensions (named and anonymous) directly
associated with the  variables as defined above.
This means that dimensions
within user-defined types are ignored.  From our example,
the dimension set is [fdim=10,lat=2,lon=2,2,2].  Note that the
unqualified names are used.

@ignore
@item 
If an anonymous dimension is associated with a Grid Map variable,
then give the dimension the name of the map.
Our dimension set now becomes 
[fdim=10,lat=2,lon=2,lat=2,lon=2].
@end ignore

 @item 
All remaining anonymous dimensions are given the
name "<var>_NN", where "<var>" is the
unqualified name of the variable in which the anonymous
dimension appears and NN is the relative position of that
dimension in the dimensions associated with that array.
No instances of this rule occur in the running example.

@item 
Remove duplicate dimensions (those with same name and value).
Our dimension set now becomes 
[fdim=10,lat=2,lon=2].

@item 
The final case occurs when there are dimensions with the same
name but with different values. For this case,
the size of the dimension is appended to the dimension name.
@end enumerate

@subsubsection Type Definition
The rules for choosing user-defined types are as follows.
@enumerate
@item For every Structure, Grid, and Sequence, a netCDF-4
compound type is created whose fields are the fields of the
Structure, Sequence, or Grid. With one exception, the name
of the type is the same as the Structure or Grid name
suffixed with "_t".  The exception is that the compound
types derived from Sequences are instead suffixed with
"_record_t".

The types of the fields are the types of the corresponding field
of the Structure, Sequence, or Grid. Note that this type
might be itself a user-defined type.

From the example, we get the following compound types.
@verbatim
compound FS2_t {
    int f1(3);
    int f2;
};
compound S1_t {
    int f11;
    FS2_t FS2(2);  
};
compound G1_t {
    float temp(2,2);
    int lat(2);
    int lon(2);
}
compound Q1_record_t {
    double depth;
};
@end verbatim

@item 
For all sequences of name X,
also create this type.
@verbatim
    X_record_t (*) X_t
@end verbatim
In our example, this produces the following type.
@verbatim
    Q1_record_t (*) Q1_t
@end verbatim

@item 
If a Sequence, Q has a single field F,
whose type is a primitive type, T,
(e.g., int, float, string), then
do not apply the previous rule, but instead replace the whole
sequence with the the following field.
@verbatim
    T (*) Q.f
@end verbatim

@ignore
@item 
Attempt to maximally shorten the type names as long as there is no conflict.
@end ignore
@end enumerate

@subsubsection Choosing a Translation
The decision about whether to translate to netCDF-3
or netCDF-4 is determined by applying the
following rules in order.
@enumerate
@item 
If the NC_CLASSIC_MODEL flag is set on nc_open(), then
netCDF-3 translation is used.
@item 
If the NC_NETCDF4 flag is set on nc_open(), then netCDF-4
translation is used.
@item 
If the URL is prefixed with the client parameter
"[netcdf3]" or "[netcdf-3]"
then netCF-3 translation is used.
@item 
If the URL is prefixed with the client parameter
"[netcdf4]" or "[netcdf-4]"
then netCF-4 translation is used.
@item
If none of the above holds, then default to netCDF-3 classic
translation.
@end enumerate
@end

@subsubsection Caching
In an effort to provide better
performance for some access patterns,
client-side caching of data is available.
The default is no caching, but it may
be enabled by the parameter "cache".

Caching operates basically as follows.
@enumerate
@item When a URL is first accessed using nc_open(),
netCDF automatically does a pre-fetch
of selected variables. These include all
variables smaller than a specified (and user definable)
size. This allows, for example, quick access to
coordinate variables. This can be suppressed
with the parameter "noprefetch".

@item
Whenever a request is made using some variant
of the nc_get_var() API procedures, the complete
variable is fetched and stored in the cache as a new
cache entry. Subsequence requests for any part of that
variable will access the cache entry to obtain the data.

@item
The cache may become too full, either because there are
too many entries or because it is taking up too much disk space.
In this case cache entries are purged until the cache size
limits are reached.
The cache purge algorithm is LRU (least recently used) so that
variables that are repeatedly referenced will tend to stay
in the cache.

@item
The cache is completely purged when nc_close() is invoked.
@end enumerate

In order to decide if you should enable caching,
you will need to have some understanding of the
access patterns  of your program.
@itemize
@item
The ncdump program always dumps one or more
whole variables so it turns on caching.

@item
If your program accesses only parts of a number of variables,
then caching should probably not be used since fetching
whole variables will probably slow down your program
for no purpose.
@end itemize

Unfortunately, caching is currently an all or nothing proposition,
so for more complex access patterns, the decision to cache or not
may not have an obvious answer. Probably a good rule of thumb
is to avoid caching initially and later turn it on to see
its effect on performance.

@subsubsection Defined Client Parameters
Currently, a limited set of client parameters is recognized.
Parameters not listed here are ignored, but no error is signalled.
@table @emph
@item  Parameter Name Legal Values Semantics
@item netcdf-3 | netcdf-3
Specify translation to netCDF-3.
@item "log | log=<file>"
Turn on logging and send the log output to the specified file.
If no file is specified, then output is sent to standard error.
@item  "show=..." das|dds|url
This causes information to appear as specific global attributes.
The currently recognized tags are "dds" to
display the underlying DDS, "das" similarly, and "url" to display
the url used to retrieve the data.
This parameter may be specified multiple times (e.g. ``show=dds&show=url'').
@item  "show=fetch"
This parameter causes the netCDF code to log a copy of the complete
url for every HTTP get request. If logging is enabled, then
this can be helpful in checking to see the access behavior of the
netCDF code.
@item "stringlength=NN"
Specify the default string length to use for string dimensions.
The default is 64.
@item "stringlength_<var>=NN"
Specify the default string length to use for a string dimension
for the specified variable.
The default is 64.
@item  "cache"
This enables caching.
@item  "cachelimit=NN"
Specify the maximum amount of space allowed for the cache.
@item  "cachecount=NN"
Specify the maximum number of entries in the cache.
@item  "noprefetch"
Disable prefetch of small variables
@end table

@subsection Notes on Debugging OPeNDAP Access

The OPeNDAP support makes use of the logging facility of the
underlying oc system (see http://www.opendap.org/oc).
Note that this is currently
separate from the existing netCDF logging facility.
Turning on this logging can sometimes give
important information. Logging can be enabled
using the client parameter "log" or "log=filename", where the first
case will send log output to standard error and the second
will send log output to the specified file.

Users should also be aware that if you one is
accessing data over an NFS mount, you may see some .nfsxxxxx files;
those can be ignored.

@subsection HTTP Configuration.

Limited support for configuring the http connection
is provided via parameters in the
``.dodsrc'' configuration file.
The relevant .dodsrc file is located by first looking in the
current working directory, and if not found, then looking in the
directory specified by the ``$HOME'' environment variable.

Entries in the .dodsrc file are of the form:
@example
['['<url>']']<key>=<value>
@end example
That is, it consists of a key name and value pair
and optionally preceded by a url enclosed in square
brackets.

For given KEY and URL strings, the value chosen is as follows:
@enumerate
@item If URL is null, then look for the .dodsrc entry that has no url prefix and whose key is same as the KEY for which we are looking.

@item If the URL is not null, then look for all the .dodsrc entries
that have a url, URL1, say, and for which URL1 is a prefix (in the
string sense) of URL. For example, if URL = http//x.y/a, then it will
match entries of the form
@example
1. [http//x.y/a]KEY=VALUE
2. [http//x.y/a/b]KEY=VALUE
@end example
It will not match an entry of the form
@example
[http//x.y/b]KEY=VALUE
@end example
because ``http://x.y/b'' is not a string prefix of ``http://x.y/a''.
Finally from the set so constructed, choose the entry with the longest
url prefix: ``http//x.y/a/b]KEY=VALUE'' in this case.
@end enumerate

Currently, the supported set of keys (with descriptions) are as follows. 
@itemize
@item HTTP.VERBOSE
    @enumerate
    @item Type: boolean ("1"/"0")
    @item Description:
Produce verbose output, especially using SSL.
    @item Related CURL Flags: CURLOPT_VERBOSE
    @end enumerate

@item HTTP.DEFLATE
    @enumerate
    @item Type: boolean ("1"/"0")
    @item Description:
Allow use of compression by the server.
    @item Related CURL Flags: CURLOPT_ENCODING
    @end enumerate

@item HTTP.COOKIEJAR
    @enumerate
    @item Type: String representing file path
    @item Description:
Specify the name of file into which to store cookies.
Defaults to in-memory storage.
    @item Related CURL Flags:CURLOPT_COOKIEJAR
    @end enumerate

@item HTTP.COOKIEFILE
    @enumerate
    @item Type: String representing file path
    @item Description:
Same as HTTP.COOKIEJAR.
    @item Related CURL Flags: CURLOPT_COOKIEFILE
    @end enumerate

@item HTTP.CREDENTIALS.USER
    @enumerate
    @item Type: String representing user name
    @item Description:
Specify the user name for Digest and Basic authentication.
    @item Related CURL Flags:
    @end enumerate

@item HTTP.CREDENTIALS.PASSWORD
    @enumerate
    @item Type: String representing password
    @item Type: boolean ("1"/"0")
    @item Description:
Specify the password for Digest and Basic authentication.
    @item Related CURL Flags:
    @end enumerate

@item HTTP.SSL.CERTIFICATE
    @enumerate
    @item Type: String representing file path
    @item Description:
Path to a file containing a PEM cerficate.
    @item Related CURL Flags: CURLOPT_CERT
    @end enumerate

@item HTTP.SSL.KEY
    @enumerate
    @item Type: String representing file path
    @item Description:
Same as HTTP.SSL.CERTIFICATE, and should usually have the same value.
    @item Related CURL Flags: CURLOPT_SSLKEY
    @end enumerate

@item HTTP.SSL.KEYPASSWORD
    @enumerate
    @item Type: String representing password
    @item Description:
Password for accessing the HTTP.SSL.KEY/HTTP.SSL.CERTIFICATE
    @item Related CURL Flags: CURLOPT_KEYPASSWORD
    @end enumerate

@item HTTP.SSL.CAPATH
    @enumerate
    @item Type: String representing directory
    @item Description:
Path to a directory containing trusted certificates for validating
server certificates.
    @item Related CURL Flags: CURLOPT_CAPATH
    @end enumerate

@item HTTP.SSL.VALIDATE
    @enumerate
    @item Type: boolean ("1"/"0")
    @item Description:
Cause the client to verify the server's presented certificate.
    @item Related CURL Flags: CURLOPT_SSL_VERIFYPEER, CURLOPT_SSL_VERIFYHOST
    @end enumerate

@item HTTP.TIMEOUT
    @enumerate
    @item Type: String ("dddddd")
    @item Description:
Specify the maximum time in seconds that you allow the http
transfer operation to take.
    @item Related CURL Flags:
CURLOPT_TIMEOUT, CURLOPT_NOSIGNAL
    @end enumerate

@item HTTP.PROXY_SERVER
    @enumerate
    @item Type: String representing url to access the proxy:
                (e.g.http://[username:password@@]host[:port])
    @item Description:
Specify the needed information for accessing a proxy.
    @item Related CURL Flags: CURLOPT_PROXY, CURLOPT_PROXYHOST, CURLOPT_PROXYUSERPWD
    @end enumerate

@end itemize

The related curl flags line indicates the curl flags modified
by this key.  See the libcurl documentation of the curl_easy_setopt()
function for more detail
@uref{http://curl.haxx.se/libcurl/c/curl_easy_setopt.html}.

For ESG client side key support, the following entries must be specified:
@itemize
@item HTTP.SSL.VALIDATE
@item HTTP.COOKIEJAR
@item HTTP.SSL.CERTIFICATE
@item HTTP.SSL.KEY
@item HTTP.SSL.CAPATH 
@end itemize
Additionally, for ESG, the HTTP.SSL.CERTIFICATE and HTTP.SSL.KEY
entries should have same value, which is the file path for the
certificate produced by MyProxyLogon.  The HTTP.SSL.CAPATH entry
should be the path to the "certificates" directory produced by
MyProxyLogon.

@node NetCDF Utilities, Units, Structure, Top
@chapter NetCDF Utilities
@cindex utilities
@cindex ncgen and ncgen3, overview
@cindex ncdump, overview
@cindex nccopy, overview
@cindex software list
@cindex applications, generic, reasons for netCDF

One of the primary reasons for using the netCDF interface for
applications that deal with arrays is to take advantage of
higher-level netCDF utilities and generic applications for netCDF
data. Currently three netCDF utilities are available as part of the
netCDF software distribution:

@table @code

@item ncdump 
reads a netCDF dataset and prints a textual representation of
the information in the dataset

@item ncgen
reads a textual representation of a netCDF dataset and generates
the corresponding binary netCDF file or a program to
create the netCDF dataset

@item nccopy
reads a netCDF dataset using the netCDF programming interface and
copies it, optionally to a different kind of netCDF dataset, and
optionally with compression or chunking

@end table

Users have contributed other netCDF utilities, and various
visualization and analysis packages are available that access netCDF
data. For an up-to-date list of freely-available and commercial
software that can access or manipulate netCDF data, see the netCDF
Software list,
@uref{@value{netcdf-url}/software.html}.

This chapter describes the ncgen, ncdump, and nccopy utilities.  These
tools convert between binary netCDF datasets and a text representation
of netCDF datasets, or between netCDF data format variants. The output
of ncdump and the input to ncgen is a text description of a netCDF
dataset in a tiny language known as CDL (network Common data form
Description Language).

@menu
* CDL Syntax::                  Creating a File without Code
* CDL Data Types::              Describing Types in CDL
* CDL Constants::               Constant Values in CDL
* ncgen::                       Turning CDL into Classic or Enhanced Data Files
* ncdump::                      Turning Data Files into CDL (or XML)
* nccopy::                      Copying, Converting, Compressing, and Chunking Data Files
* ncgen3::                      Turning CDL into Classic Data Files
@end menu

@node CDL Syntax, CDL Data Types, NetCDF Utilities, NetCDF Utilities
@section CDL Syntax
@cindex CDL syntax
@cindex attributes, CDL, defining
@cindex attributes, CDL, global
@cindex dimensions, CDL, defining
@cindex variables, CDL, defining
@cindex CDL attributes, defining
@cindex CDL dimensions, defining
@cindex CDL variables, defining

Below is an example of CDL, describing a netCDF dataset with several
named dimensions (lat, lon, time), variables (z, t, p, rh, lat, lon,
time), variable attributes (units, _FillValue, valid_range), and some
data.

@example
netcdf foo @{    // example netCDF specification in CDL

dimensions:
lat = 10, lon = 5, time = unlimited;

variables:
  int     lat(lat), lon(lon), time(time);
  float   z(time,lat,lon), t(time,lat,lon);
  double  p(time,lat,lon);
  int     rh(time,lat,lon);

  lat:units = "degrees_north";
  lon:units = "degrees_east";
  time:units = "seconds";
  z:units = "meters";
  z:valid_range = 0., 5000.;
  p:_FillValue = -9999.;
  rh:_FillValue = -1;

data:
  lat   = 0, 10, 20, 30, 40, 50, 60, 70, 80, 90;
  lon   = -140, -118, -96, -84, -52;
@}
@end example

All CDL statements are terminated by a semicolon. Spaces, tabs, and
newlines can be used freely for readability. Comments may follow the
double slash characters '//' on any line.

A CDL description for a classic model file consists of three optional
parts: dimensions, variables, and data. The variable part may contain
variable declarations and attribute assignments.  For the enhanced
model supported by netCDF-4, a CDL description may also includes
groups, subgroups, and user-defined types.

A dimension is used to define the shape of one or more of the
multidimensional variables described by the CDL description. A
dimension has a name and a length. At most one dimension in a classic CDL
description can have the unlimited length, which means a variable
using this dimension can grow to any length (like a record number in a
file).  Any number of dimensions can be declared of unlimited length
in CDL for an enhanced model file.

A variable represents a multidimensional array of values of the same
type. A variable has a name, a data type, and a shape described by its
list of dimensions. Each variable may also have associated attributes
(see below) as well as data values. The name, data type, and shape of
a variable are specified by its declaration in the variable section of
a CDL description. A variable may have the same name as a dimension;
by convention such a variable contains coordinates of the dimension it
names.

An attribute contains information about a variable or about the whole
netCDF dataset or containing group. Attributes may be used to specify
such properties as
units, special values, maximum and minimum valid values, and packing
parameters. Attribute information is represented by single values or
one-dimensional arrays of values. For example, ``units'' might be an attribute
represented by a string such as ``celsius''. An attribute has an associated
variable, a name, a data type, a length, and a value. In contrast to
variables that are intended for data, attributes are intended for
ancillary data or metadata (data about data).

In CDL, an attribute is designated by a variable and attribute name,
separated by a colon (':'). It is possible to assign global attributes
to the netCDF dataset as a whole by omitting the variable name and
beginning the attribute name with a colon (':'). The data type of an
attribute in CDL, if not explicitly specified, is derived from the
type of the value assigned to 
it. The length of an attribute is the number of data values or the
number of characters in the character string assigned to it. Multiple
values are assigned to non-character attributes by separating the
values with commas (','). All values assigned to an attribute must be
of the same type.  In the netCDF-4 enhanced model, attributes may be declared
to be of user-defined type, like variables.

In CDL, just as for netCDF, the names of dimensions, variables and
attributes (and, in netCDF-4 files, groups, user-defined types,
compound member names, and enumeration symbols) consist of arbitrary
sequences of alphanumeric characters, underscore '_', period '.', plus
'+', hyphen '-', or at sign '@@', but beginning with a letter or
underscore.  However names commencing with underscore are reserved for
system use.  Case is significant in netCDF names. A zero-length name
is not allowed.  Some widely used conventions restrict names to only
alphanumeric characters or underscores.  Names that have trailing
space characters are also not permitted.

Beginning with versions 3.6.3 and 4.0, names may also include UTF-8
encoded Unicode characters as well as other special characters, except
for the character '/', which may not appear in a name (because it is
reserved for path names of nested groups).  In CDL, most special
characters are escaped with a backslash '\' character, but that
character is not actually part of the netCDF name.  The special
characters that do not need to be escaped in CDL names are underscore
'_', period '.', plus '+', hyphen '-', or at sign '@@'.  For the
formal specification of CDL name syntax @xref{Format}.  Note that by
using special characters in names, you may make your data not
compliant with conventions that have more stringent requirements on
valid names for netCDF components, for example the CF Conventions.

The names for the primitive data types are reserved words in CDL, so
names of variables, dimensions, and attributes must not be primitive
type names.

The optional data section of a CDL description is where netCDF
variables may be initialized. The syntax of an initialization is
simple:

@example
variable = value_1, value_2, @dots{};
@end example

The comma-delimited list of constants may be separated by spaces,
tabs, and newlines. For multidimensional arrays, the last dimension
varies fastest. Thus, row-order rather than column order is used for
matrices. If fewer values are supplied than are needed to fill a
variable, it is extended with the fill value. The types of constants
need not match the type declared for a variable; coercions are done to
convert integers to floating point, for example. All meaningful type
conversions among primitive types are supported.

A special notation for fill values is supported: the @samp{_} character
designates a fill value for variables.


@node CDL Data Types, CDL Constants, CDL Syntax, NetCDF Utilities
@section CDL Data Types
@cindex CDL data types
@cindex byte, CDL data type
@cindex char, CDL data type
@cindex short, CDL data type
@cindex int, CDL data type
@cindex long, CDL data type
@cindex float, CDL data type
@cindex real, CDL data type
@cindex double, CDL data type

The CDL primitive data types for the classic model are:

@vtable @code
@item char
Characters.
 
@item byte
Eight-bit integers.
 
@item short
16-bit signed integers.
 
@item int
32-bit signed integers.
 
@item long
(Deprecated, synonymous with int) 
 
@item float
IEEE single-precision floating point (32 bits).
 
@item real
(Synonymous with float).
 
@item double
IEEE double-precision floating point (64 bits).
@end vtable

NetCDF-4 supports the additional primitive types:

@vtable @code
@item ubyte
Unsigned eight-bit integers.
 
@item ushort
Unsigned 16-bit integers.
 
@item uint
Unsigned 32-bit integers.
 
@item int64
64-bit signed integers.
 
@item uint64
Unsigned 64-bit signed integers.
 
@item string
Variable-length string of characters
@end vtable

Except for the added data-type byte, CDL supports the same primitive
data types as C. For
backward compatibility, in declarations primitive type names may be
specified in either upper or lower case.

The byte type differs from the char type in that it is intended for
numeric data, and the zero byte has no special significance, as it may
for character data.  The short type holds values between -32768 and
32767.  The ushort type holds values between 0 and 65536.  The int
type can hold values between -2147483648 and 2147483647.  The uint
type holds values between 0 and 4294967296.  The int64 type can hold
values between -9223372036854775808 and 9223372036854775807.  The
uint64 type can hold values between 0 and 18446744073709551616.

The float type can hold values between about -3.4+38 and 3.4+38, with
external representation as 32-bit IEEE normalized single-precision
floating-point numbers.  The double type can hold values between about
-1.7+308 and 1.7+308, with external representation as 64-bit IEEE
standard normalized double-precision, floating-point numbers. 
The string type holds variable length strings.

@node CDL Constants, ncgen, CDL Data Types, NetCDF Utilities
@section CDL Notation for Data Constants
@cindex CDL constants
@cindex attributes, CDL, initializing
@cindex dimensions, CDL, initializing
@cindex variables, CDL, initializing
@cindex attributes, data types, CDL
@cindex variables, data types, CDL
@cindex attributes, length, CDL
@cindex dimensions, length, CDL
@cindex byte CDL constant
@cindex initializing CDL

This section describes the CDL notation for constants.

Attributes are initialized in the variables section of a CDL
description by providing a list of constants that determines the
attribute's length and type (if primitive and not explicitly declared).
CDL defines a syntax
for constant values that permits distinguishing among different netCDF
primitive types. The syntax for CDL constants is similar to C syntax, with
type suffixes appended to bytes, shorts, and floats to distinguish
them from ints and doubles.

A byte constant is represented by a single character or multiple
character escape sequence enclosed in single quotes. For example:

@example
'a'     // ASCII a
'\0'    // a zero byte
'\n'    // ASCII newline character
'\33'   // ASCII escape character (33 octal)
'\x2b'  // ASCII plus (2b hex)
'\376'  // 377 octal = -127 (or 254) decimal
@end example

Character constants are enclosed in double quotes. A character array
may be represented as a string enclosed in double quotes. Multiple
strings are concatenated into a single array of characters, permitting
long character arrays to appear on multiple lines. To support multiple
variable-length string values, a conventional delimiter such as ','
may be used, but interpretation of any such convention for a string
delimiter must be implemented in software above the netCDF library
layer. The usual escape conventions for C strings are honored. For
example:

@example
"a"            // ASCII 'a'
"Two\nlines\n" // a 10-character string with two embedded newlines
"a bell:\007"  // a string containing an ASCII bell
"ab","cde"     // the same as "abcde"
@end example

The form of a short constant is an integer constant with an 's' or 'S'
appended. If a short constant begins with '0', it is interpreted as
octal. When it begins with '0x', it is interpreted as a hexadecimal
constant. For example:

@example
2s      // a short 2
0123s   // octal
0x7ffs  // hexadecimal
@end example

The form of an int constant is an ordinary integer constant. If an int
constant begins with '0', it is interpreted as octal. When it begins
with '0x', it is interpreted as a hexadecimal constant. Examples of
valid int constants include:

@example
-2
0123            // octal
0x7ff           // hexadecimal
1234567890L     // deprecated, uses old long suffix
@end example

The float type is appropriate for representing data with about seven
significant digits of precision. The form of a float constant is the
same as a C floating-point constant with an 'f' or 'F' appended. A
decimal point is required in a CDL float to distinguish it from an
integer. For example, the following are all acceptable float
constants:

@example
-2.0f
3.14159265358979f       // will be truncated to less precision
1.f
.1f
@end example

The double type is appropriate for representing floating-point data
with about 16 significant digits of precision. The form of a double
constant is the same as a C floating-point constant. An optional 'd'
or 'D' may be appended. A decimal point is required in a CDL double to
distinguish it from an integer. For example, the following are all
acceptable double constants:

@example
-2.0
3.141592653589793
1.0e-20
1.d
@end example


@node ncgen, ncdump, CDL Constants, NetCDF Utilities
@section ncgen
@cindex ncgen
@cindex C code via ncgen, generating
@cindex generating C code via ncgen

The ncgen tool generates a netCDF file or a C or FORTRAN program that
creates a netCDF dataset. If no options are specified in invoking
ncgen, the program merely checks the syntax of the CDL input,
producing error messages for any violations of CDL syntax.

The ncgen tool is now is capable of producing netcdf-4 files.
It operates essentially identically to the original ncgen.

The CDL input to ncgen may include data model constructs
from the netcdf- data model. In particular, it includes
new primitive types such as unsigned integers and strings,
opaque data, enumerations, and user-defined constructs using
vlen and compound types.
The ncgen man page should be consulted for more detailed information.

UNIX syntax for invoking ncgen:

@example
ncgen [-b] [-o netcdf-file] [-c] [-f] [-k<kind>] [-l<language>] [-x] [input-file]
@end example

where:

@table @code
@item -b
Create a (binary) netCDF file. If the '-o' option is absent, a default
file name will be constructed from the netCDF name (specified after
the netcdf keyword in the input) by appending the '.nc'
extension. Warning: if a file already exists with the specified name
it will be overwritten.
 
@item -o netcdf-file
Name for the netCDF file created. If this option is specified, it
implies the '-b' option. (This option is necessary because netCDF
files are direct-access files created with seek calls, and hence
cannot be written to standard output.)
 
@item -c
Generate C source code that will create a netCDF dataset matching the
netCDF specification. The C source code is written to standard
output. This is only useful for relatively small CDL files, since all
the data is included in variable initializations in the generated
program.
The -c flag is deprecated and the -lc flag should be used instead.
 
@item -f
Generate FORTRAN source code that will create a netCDF dataset
matching the netCDF specification. The FORTRAN source code is written
to standard output. This is only useful for relatively small CDL
files, since all the data is included in variable initializations in
the generated program.
The -f flag is deprecated and the -lf77 flag should be used instead.

@item -k
The -k file specifies the kind of netCDF file to generate.
The arguments to the -k flag can be as follows.
@itemize
@item 1, classic
-- Produce a netcdf classic file format file."
@item 2, 64-bit-offset, '64-bit offset'
-- Produce a netcdf 64 bit classic file format file.
@item 3, hdf5, netCDF-4, enhanced
-- Produce a netcdf-4 format file.
@item 4, hdf5-nc3, 'netCDF-4 classic model', enhanced-nc3
-- Produce a netcdf-4 file format, but restricted to
netcdf-3 classic CDL input.
@end itemize
Note that the -v flag is a deprecated alias for -k.

@item -l
The -l file specifies that ncgen should output (to
standard output) the text of a program that, when
compiled and executed, will produce the corresponding
binary .nc file.
The arguments to the -l flag can be as follows.
@itemize
@item c|C
=> C language output.
@item f77|fortran77
=> FORTRAN 77 language output; note that currently
only the classic model is supported for fortran output.
@item cml|CML
=> (experimental) NcML language output
@item j|java
=> (experimental) Java language output; the generated
java code targets the existing Unidata Java interface,
which means that only the classic model is supported.
@end itemize
@item -x
Use ``no fill'' mode, omitting the initialization of variable values
with fill values.  This can make the creation of large files much
faster, but it will also eliminate the possibility of detecting the
inadvertent reading of values that haven't been written.
@end table

@heading Examples
Check the syntax of the CDL file foo.cdl:

@example
ncgen foo.cdl
@end example

From the CDL file foo.cdl, generate an equivalent binary netCDF file
named bar.nc:

@example
ncgen -o bar.nc foo.cdl
@end example

From the CDL file foo.cdl, generate a C program containing netCDF
function invocations that will create an equivalent binary netCDF
dataset:

@example
ncgen -c foo.cdl > foo.c
@end example
@node ncdump, nccopy, ncgen, NetCDF Utilities
@section ncdump
@cindex ncdump

The ncdump tool generates the CDL text representation of a netCDF
dataset on standard output, optionally excluding some or all of the
variable data in the output. The output from ncdump is intended to be
acceptable as input to ncgen. Thus ncdump and ncgen can be used as
inverses to transform data representation between binary and text
representations.

As of netCDF version 4.1, ncdump can also access DAP data sources
if DAP support is enabled in the underlying netCDF library.
Instead of specifying a file name as argument to ncdump, the user
specifies a URL to a DAP source.

ncdump may also be used as a simple browser for netCDF datasets, to
display the dimension names and lengths; variable names, types, and
shapes; attribute names and values; and optionally, the values of data
for all variables or selected variables in a netCDF dataset.

ncdump defines a default format used for each type of netCDF variable
data, but this can be overridden if a C_format attribute is defined
for a netCDF variable. In this case, ncdump will use the C_format
attribute to format values for that variable. For example, if
floating-point data for the netCDF variable Z is known to be accurate
to only three significant digits, it might be appropriate to use this
variable attribute:

@example
Z:C_format = "%.3g"
@end example

Ncdump uses '_' to represent data values that are equal to the
_FillValue attribute for a variable, intended to represent data that
has not yet been written. If a variable has no _FillValue attribute,
the default fill value for the variable type is used unless the
variable is of byte type.

UNIX syntax for invoking ncdump:

@example
ncdump  [ -c | -h]  [-v var1,@dots{}]  [-b lang]  [-f lang]
[-l len]  [ -p fdig[,ddig]] [ -s ] [ -n name]  [input-file]
@end example

where:

@table @code
@item -c 
Show the values of coordinate variables (variables that are also
dimensions) as well as the declarations of all dimensions, variables,
and attribute values. Data values of non-coordinate variables are not
included in the output. This is often the most suitable option to use
for a brief look at the structure and contents of a netCDF dataset.
 
@item -h
Show only the header information in the output, that is, output only
the declarations for the netCDF dimensions, variables, and attributes
of the input file, but no data values for any variables. The output is
identical to using the '-c' option except that the values of
coordinate variables are not included. (At most one of '-c' or '-h'
options may be present.)
 
@item -v var1,@dots{} 
The output will include data values for the specified variables, in
addition to the declarations of all dimensions, variables, and
attributes. One or more variables must be specified by name in the
comma-delimited list following this option. The list must be a single
argument to the command, hence cannot contain blanks or other white
space characters. The named variables must be valid netCDF variables
in the input-file. The default, without this option and in the absence
of the '-c' or '-h' options, is to include data values for all
variables in the output.
 
@item -b lang
A brief annotation in the form of a CDL comment (text beginning with
the characters '//') will be included in the data section of the
output for each 'row' of data, to help identify data values for
multidimensional variables. If lang begins with 'C' or 'c', then C
language conventions will be used (zero-based indices, last dimension
varying fastest). If lang begins with 'F' or 'f', then FORTRAN
language conventions will be used (one-based indices, first dimension
varying fastest). In either case, the data will be presented in the
same order; only the annotations will differ. This option may be
useful for browsing through large volumes of multidimensional data.
 
@item -f lang
Full annotations in the form of trailing CDL comments (text beginning
with the characters '//') for every data value (except individual
characters in character arrays) will be included in the data
section. If lang begins with 'C' or 'c', then C language conventions
will be used (zero-based indices, last dimension varying fastest). If
lang begins with 'F' or 'f', then FORTRAN language conventions will be
used (one-based indices, first dimension varying fastest). In either
case, the data will be presented in the same order; only the
annotations will differ. This option may be useful for piping data
into other filters, since each data value appears on a separate line,
fully identified. (At most one of '-b' or '-f' options may be
present.)
 
@item -l len
Changes the default maximum line length (80) used in formatting lists
of non-character data values.
 
@item -p float_digits[,double_digits]
Specifies default precision (number of significant digits) to use in
displaying floating-point or double precision data values for
attributes and variables. If specified, this value overrides the value
of the C_format attribute, if any, for a variable. Floating-point data
will be displayed with float_digits significant digits. If
double_digits is also specified, double-precision values will be
displayed with that many significant digits. In the absence of any
'-p' specifications, floating-point and double-precision data are
displayed with 7 and 15 significant digits respectively. CDL files can
be made smaller if less precision is required. If both floating-point
and double precisions are specified, the two values must appear
separated by a comma (no blanks) as a single argument to the command.
 
@item -n name
CDL requires a name for a netCDF dataset, for use by 'ncgen -b' in
generating a default netCDF dataset name. By default, ncdump
constructs this name from the last component of the file name of the
input netCDF dataset by stripping off any extension it has. Use the
'-n' option to specify a different name. Although the output file name
used by 'ncgen -b' can be specified, it may be wise to have ncdump
change the default name to avoid inadvertently overwriting a valuable
netCDF dataset when using ncdump, editing the resulting CDL file, and
using 'ncgen -b' to generate a new netCDF dataset from the edited CDL
file.

@item -s
Specifies that special virtual attributes should be output for the
file format variant and for variable properties such as compression,
chunking, and other properties specific to the format implementation
that are primarily related to performance rather than the logical
schema of the data.  All the special virtual attributes begin with '_'
followed by an upper-case letter.  Currently they include the global
attribute ``_Format'' and the variable attributes ``_Fletcher32'',
``_ChunkSizes'', ``_Endianness'', ``_DeflateLevel'', ``_Shuffle'',
``_Storage'', and ``_NoFill''.  The ncgen utility recognizes these
attributes and supports them appropriately.

@item -t
Controls display of time data, if stored in a variable that uses a
udunits compliant time representation such as ``days since
1970-01-01'' or ``seconds since 2009-03-15 12:01:17''.  If this option
is specified, time values are displayed as human-readable date-time
strings rather than numerical values, interpreted in terms of a
``calendar'' variable attribute, if specified.  Calendar attribute
values interpreted with this option include the CF Conventions values
``gregorian'' or ``standard'', ``proleptic_gregorian'', ``noleap'' or
``365_day'', ``all_leap'' or ``366_day'', ``360_day'', and ``julian''.

@end table

@heading Examples

Look at the structure of the data in the netCDF dataset foo.nc:

ncdump -c foo.nc

Produce an annotated CDL version of the structure and data in the
netCDF dataset foo.nc, using C-style indexing for the annotations:

ncdump -b c foo.nc > foo.cdl

Output data for only the variables uwind and vwind from the netCDF
dataset foo.nc, and show the floating-point data with only three
significant digits of precision:

ncdump -v uwind,vwind -p 3 foo.nc

Produce a fully-annotated (one data value per line) listing of the
data for the variable omega, using FORTRAN conventions for indices,
and changing the netCDF dataset name in the resulting CDL file to
omega:

ncdump -v omega -f fortran -n omega foo.nc > Z.cdl

Examine the translated DDS for the DAP source from the specified URL.

ncdump -h http://remotetest.unidata.ucar.edu/dts/test.01

@node nccopy, ncgen3, ncdump, NetCDF Utilities
@section nccopy
@cindex nccopy

The nccopy utility copies an input netCDF file to an output netCDF
file, in any of the four format variants, if possible.  For example,
if built with the netCDF-3 library, a netCDF classic file may be
copied to a netCDF 64-bit offset file, permitting larger variables.
If built with the netCDF-4 library, a netCDF classic file may be
copied to a netCDF-4 file or to a netCDF-4 classic model file as well,
permitting data compression, data chunking, efficient schema changes, larger
variable sizes, and use of other netCDF-4 features in case the output
uses the enhanced netCDF model.

The nccopy source also serves as an example of a @dfn{generic}
netCDF-4 program, with its ability to read any valid netCDF file and
handle strings, arbitrarily nested groups, any user-defined types,
(including arbitrarily nested compound types and variable-length
types), and data of any valid netCDF-4 type.  Functions from and
fragments of the nccopy source may be useful to other developers for
writing generic programs that handle or write arbitrary netCDF data.

As of netCDF version 4.1, and if DAP support was enabled when nccopy
was built, the input file name on which nccopy is invoked may specify
a DAP URL. This allows nccopy to convert data on DAP servers to local
netCDF files.

UNIX syntax for invoking nccopy:

@example
nccopy  [ -k kind]  [ -d n]  [ -s ]  [ -u ]  [ -c chunkspec ]
        [ -m bufsize] input  output
@end example

where:

@table @code
@item -k kind 
Specifies the kind of file to be created
and, by inference, 
the data model (i.e. netcdf-3 (classic) versus
netcdf-4 (enhanced)).
The possible arguments are as follows.
@itemize
@item ``1'' or ``classic''
write a netCDF classic file format file
@item ``2'', ``64-bit-offset'', or ``64-bit offset''
write a netCDF 64 bit classic file format file
@item ``3'', ``hdf5'', ``netCDF-4'', or ``enhanced''
write a netCDF-4 format file
@item ``4'', ``hdf5-nc3'', ``netCDF-4 classic model'', or ``enhanced-nc3''
write a netCDF-4 classic model file format, which is restricted to
netCDF-3 classic data model but may use netCDF-4/HDF5 features such as
compression and chunking.
@end itemize
If no value for -k is specified, then the output will use the same
format as the input.  However, if the input is classic or 64-bit
offset and a compression level is specified with the -d option or
chunking is specified with the -c option, then
the output will be netCDF-4 classic model format.  Note that attempting some
kinds of format conversion will result in an error, if the conversion
is not possible.  For example, an attempt to copy a netCDF-4 file that
uses features of the enhanced model to any of the other kinds of
netCDF formats that use the classic model will result in an error.

@item -d n
Specify deflation level (level of compression) for variable data in
output.  0 corresponds to no compression and 9 to maximum compression,
with higher levels of compression requiring marginally more time to
compress or uncompress than lower levels.  Compression achieved may
also depend on chunking parameters, which will use default chunking in
the current nccopy implementation.  If this option is specified for a
classic format or 64-bit offset format input file, it is not necessary
to also specify that the output should be netCDF-4 classic model, as
that will be the default.  If this option is not specified and the
input file has compressed variables, the compression will still be
preserved in the output, using the same chunking as in the input
unless the -c option is used to specify different chunking.

Note that nccopy requires all variables to be compressed using
the same compression level, but the API has no such restriction.  With
a program you can customize compression for each variable independently.

@item -s
Specify shuffling of variable data bytes before compression or after
decompression.  This option is ignored unless a non-zero deflation
level is specified.  Turning shuffling on sometimes improves
compression. 

@item -u
Convert any unlimited size dimensions in the input to fixed size
dimensions in the output.

@item -c chunkspec
Specify chunking (multidimensional tiling) for variable data in the
output, useful to specify the units of disk access, compression, or
other filters such as checksums.  The chunkspec argument is a string
of comma-separated associations, each specifying a dimension name, a
`/' character, and optionally the corresponding chunk length for that
dimension.  No blanks should appear in the chunkspec string, except
possibly escaped blanks that are part of a dimension name.  A
chunkspec must name at least one dimension, and may omit dimensions
which are not to be chunked or for which the default chunk length is
desired.  If a dimension name is followed by a `/' character but no
subsequent chunk length, the actual dimension length is assumed.  If
copying a classic model file to a netCDF-4 output file and not naming
all dimensions in the chunkspec, unnamed dimensions will also use the
actual dimension length for the chunk length.

By default, fixed-size variables smaller than 1024 bytes of data will
not be chunked even if they use dimensions mentioned in the chunkspec
string.

An example of a chunkspec for variables that use the `m' and `n'
dimensions might be `m/100,n/200' to specify 100 by 200 chunks.  To
see the chunking resulting from copying with a chunkspec, use ncdump
with the `-s' option (and typically with the `-h' option) on the
output file.

Note that nccopy requires variables that share a dimension to
also share the chunk size associated with that dimension, but the API
has no such restriction.  With a program you can customize chunking
for each variable independently.

@item -m bufsize
Specifies the size, in bytes, of the copy buffer used to
to copy large variables, by copying them in smaller pieces, each no
larger than @dfn{bufsize}.  A suffix of k, m, or g multiplies
the copy buffer size by one thousand, million, or billion, respectively.
The default is 5 million bytes,
but will be increased if necessary to hold at least one chunk of
netCDF-4 chunked variables in the input file.  You may want to specify
a value larger than the default for OPeNDAP copies of large files over high
latency networks. 
@end table

@heading Examples

@subsubheading Simple copy, check for corruption or truncation

Make a copy of foo1.nc, a netCDF file of any type, to foo2.nc, a
netCDF file of the same type:

@example
nccopy foo1.nc foo2.nc
@end example

This also can be used to check foo1.nc for corruption of metadata
or for truncation, because such problems will usually be detected in
trying to parse and read through the data.

Note that the above copy will not be as fast as use of a simple copy
utility, because the file is copied using only the netCDF API.  If the
input file has extra bytes after the end of the netCDF data, those
will not be copied, because they are not accessible through the netCDF
interface.  If the original file was generated in `No fill' mode so
that fill values are not stored for padding for data alignment, the
output file may have different padding bytes.

@subsubheading Uncompress and convert to classic format

Convert a netCDF-4 classic model file that uses compression (compressed.nc)
to a netCDF-3 file (classic.nc):

@example
nccopy -k classic compressed.nc classic.nc
@end example

Note that ``1'' could be used instead of ``classic''.

@subsubheading Copy a subset of data on an OPeNDAP server to a netCDF file

Download the variable `time_bnds' and it's associated attributes from
an OPeNDAP server and copy the result to a netCDF file named `tb.nc':

@example
nccopy 'http://test.opendap.org/opendap/data/nc/sst.mnmean.nc.gz?time_bnds' tb.nc
@end example

Note that URLs that name specific variables as command-line arguments
should generally be quoted, to avoid the shell interpreting special
characters in OPeNDAP syntax, such as `?'.

@subsubheading Apply compression to a netCDF file

Compress all the variables in the input file foo.nc, a netCDF file of any
type, to the output file bar.nc:

@example
nccopy -d1 foo.nc bar.nc
@end example

If foo.nc was a classic or 64-bit offset netCDF file, bar.nc will be a
netCDF-4 classic model netCDF file, because the classic and 64-bit
offset format variants don't support compression.  If foo.nc was a
netCDF-4 file with some variables compressed using various deflation
levels, the output will also be a netCDF-4 file of the same type, but
all the variables, including any uncompressed variables in the input,
will now use deflation level 1.

@node ncgen3,  , nccopy, NetCDF Utilities
@section ncgen3
@cindex ncgen3
@cindex C code via ncgen3, generating
@cindex generating C code via ncgen3

The ncgen3 tool is the new name for the older, original
ncgen utility.

The ncgen3 tool generates a netCDF file or a C or FORTRAN program that
creates a netCDF dataset. If no options are specified in invoking
ncgen3, the program merely checks the syntax of the CDL input,
producing error messages for any violations of CDL syntax.

The ncgen3 utility can only generate
classic-model netCDF-4 files or programs.

UNIX syntax for invoking ncgen3:

@example
ncgen3 [-b] [-o netcdf-file] [-c] [-f] [-v2|-v3] [-x] [input-file]
@end example

where:

@table @code
@item -b
Create a (binary) netCDF file. If the '-o' option is absent, a default
file name will be constructed from the netCDF name (specified after
the netcdf keyword in the input) by appending the '.nc'
extension. Warning: if a file already exists with the specified name
it will be overwritten.
 
@item -o netcdf-file
Name for the netCDF file created. If this option is specified, it
implies the '-b' option. (This option is necessary because netCDF
files are direct-access files created with seek calls, and hence
cannot be written to standard output.)
 
@item -c
Generate C source code that will create a netCDF dataset matching the
netCDF specification. The C source code is written to standard
output. This is only useful for relatively small CDL files, since all
the data is included in variable initializations in the generated
program.
 
@item -f
Generate FORTRAN source code that will create a netCDF dataset
matching the netCDF specification. The FORTRAN source code is written
to standard output. This is only useful for relatively small CDL
files, since all the data is included in variable initializations in
the generated program.

@item -v2
The generated netCDF file or program will use the version of the
format with 64-bit offsets, to allow for the creation of very large
files.  These files are not as portable as classic format netCDF
files, because they require version 3.6.0 or later of the netCDF
library.

@item -v3
The generated netCDF file will be in netCDF-4/HDF5 format. These files
are not as portable as classic format netCDF files, because they
require version 4.0 or later of the netCDF library.

@item -x
Use ``no fill'' mode, omitting the initialization of variable values
with fill values.  This can make the creation of large files much
faster, but it will also eliminate the possibility of detecting the
inadvertent reading of values that haven't been written.
@end table

@node Units, Attribute Conventions, NetCDF Utilities, Top
@appendix Units
@cindex units library
@cindex udunits
@cindex applications, generic, units

The Unidata Program Center has developed a units library to convert
between formatted and binary forms of units specifications and perform
unit algebra on the binary form. Though the units library is
self-contained and there is no dependency between it and the netCDF
library, it is nevertheless useful in writing generic netCDF programs
and we suggest you obtain it. The library and associated documentation
is available from @uref{http://www.unidata.ucar.edu/software/udunits/}.

The following are examples of units strings that can be interpreted by
the utScan() function of the Unidata units library:

@example
10 kilogram.meters/seconds2
10 kg-m/sec2
10 kg m/s^2
10 kilogram meter second-2
(PI radian)2
degF
100rpm
geopotential meters
33 feet water
milliseconds since 1992-12-31 12:34:0.1 -7:00
@end example

A unit is specified as an arbitrary product of constants and
unit-names raised to arbitrary integral powers. Division is indicated
by a slash '/'. Multiplication is indicated by white space, a period
'.', or a hyphen '-'. Exponentiation is indicated by an integer suffix
or by the exponentiation operators '^' and '**'. Parentheses may be
used for grouping and disambiguation. The time stamp in the last
example is handled as a special case.

Arbitrary Galilean transformations (i.e., y = ax + b) are allowed. In
particular, temperature conversions are correctly handled. The
specification:

@example
degF @ 32
@end example

indicates a Fahrenheit scale with the origin shifted to thirty-two
degrees Fahrenheit (i.e., to zero Celsius). Thus, the Celsius scale is
equivalent to the following unit:

@example
1.8 degF @ 32
@end example

Note that the origin-shift operation takes precedence over
multiplication. In order of increasing precedence, the operations are
division, multiplication, origin-shift, and exponentiation.

utScan() understands all the SI prefixes (e.g. "mega" and "milli")
plus their abbreviations (e.g. "M" and "m")

The function utPrint() always encodes a unit specification one way. To
reduce misunderstandings, it is recommended that this encoding style
be used as the default. In general, a unit is encoded in terms of
basic units, factors, and exponents. Basic units are separated by
spaces, and any exponent directly appends its associated unit. The
above examples would be encoded as follows:

@example
10 kilogram meter second-2
9.8696044 radian2
0.555556 kelvin @ 255.372
10.471976 radian second-1
9.80665 meter2 second-2
98636.5 kilogram meter-1 second-2
0.001 seconds since 1992-12-31 19:34:0.1000 UTC
@end example

(Note that the Fahrenheit unit is encoded as a deviation, in
fractional kelvins, from an origin at 255.372 kelvin, and that the
time in the last example has been referenced to UTC.)

The database for the units library is a formatted file containing unit
definitions and is used to initialize this package. It is the first
place to look to discover the set of valid names and symbols.

The format for the units-file is documented internally and the file
may be modified by the user as necessary. In particular, additional
units and constants may be easily added (including variant spellings
of existing units or constants).

utScan() is case-sensitive. If this causes difficulties, you might try
making appropriate additional entries to the units-file.

Some unit abbreviations in the default units-file might seem
counter-intuitive. In particular, note the following:

@multitable @columnfractions .25 .25 .25 .25

@item For @tab Use @tab Not @tab Which Instead Means
 
@item Celsius @tab Celsius @tab C @tab coulomb
 
@item gram @tab gram @tab g @tab <standard free fall>
 
@item gallon @tab gallon @tab gal @tab <acceleration>
 
@item radian @tab radian @tab rad @tab <absorbed dose>
 
@item Newton @tab newton or N @tab nt @tab nit (unit of photometry) 

@end multitable

For additional information on this units library, please consult the
manual pages that come with its distribution.

@node Attribute Conventions, File Format, Units, Top
@appendix Attribute Conventions
@cindex attribute conventions
@cindex applications, generic, conventions
@cindex conventions, attributes

Names commencing with underscore ('_') are reserved for use by the
netCDF library. Most generic applications that process netCDF datasets
assume standard attribute conventions and it is strongly recommended
that these be followed unless there are good reasons for not doing
so. Below we list the names and meanings of recommended standard
attributes that have proven useful. Note that some of these
(e.g. units, valid_range, scale_factor) assume numeric data and should
not be used with character data.

@vtable @code

@item units
A character string that specifies the units used for the variable's
data. Unidata has developed a freely-available library of routines to
convert between character string and binary forms of unit
specifications and to perform various useful operations on the binary
forms. This library is used in some netCDF applications. Using the
recommended units syntax permits data represented in conformable units
to be automatically converted to common units for arithmetic
operations. For more information see @ref{Units}.

@item long_name
A long descriptive name. This could be used for labeling plots, for
example. If a variable has no long_name attribute assigned, the
variable name should be used as a default.
 
@item _FillValue
The _FillValue attribute specifies the fill value used to pre-fill
disk space allocated to the variable. Such pre-fill occurs unless
nofill mode is set using nc_set_fill in C (@pxref{nc_set_fill,,,
netcdf-c, @value{c-man}}) or NF_SET_FILL in Fortran
(@pxref{NF_SET_FILL,,, netcdf-f77, @value{f77-man}}). The fill value
is returned when reading values that were never written. If _FillValue
is defined then it should be scalar and of the same type as the
variable. If the variable is packed using scale_factor and
add_offset attributes (see below), the _FillValue attribute should
have the data type of the packed data.

It is not necessary to define your own _FillValue attribute for a
variable if the default fill value for the type of the variable is
adequate. However, use of the default fill value for data type byte is
not recommended. Note that if you change the value of this attribute,
the changed value applies only to subsequent writes; previously
written data are not changed.

Generic applications often need to write a value to represent
undefined or missing values. The fill value provides an appropriate
value for this purpose because it is normally outside the valid range
and therefore treated as missing when read by generic applications. It
is legal (but not recommended) for the fill value to be within the
valid range.

For more information for C programmers see @ref{Fill
Values,,,netcdf-c, @value{c-man}}. For more information for Fortran
programmers see @ref{Fill Values,,,netcdf-f77, @value{f77-man}}.

@item missing_value 
This attribute is not treated in any special way by the
library or conforming generic applications, but is often useful
documentation and may be used by specific applications. The
missing_value attribute can be a scalar or vector containing values
indicating missing data. These values should all be outside the valid
range so that generic applications will treat them as missing.

When scale_factor and add_offset are used for packing, the value(s) of
the missing_value attribute should be specified in the domain of the
data in the file (the packed data), so that missing values can be
detected before the scale_factor and add_offset are applied.
 
@item valid_min
A scalar specifying the minimum valid value for this variable.
 
@item valid_max
A scalar specifying the maximum valid value for this variable.
 
@item valid_range
A vector of two numbers specifying the minimum and maximum valid
values for this variable, equivalent to specifying values for both
valid_min and valid_max attributes. Any of these attributes define the
valid range. The attribute valid_range must not be defined if either
valid_min or valid_max is defined.

Generic applications should treat values outside the valid range as
missing. The type of each valid_range, valid_min and valid_max
attribute should match the type of its variable (except that for byte
data, these can be of a signed integral type to specify the intended
range).

If neither valid_min, valid_max nor valid_range is defined then
generic applications should define a valid range as follows. If the
data type is byte and _FillValue is not explicitly defined, then the
valid range should include all possible values. Otherwise, the valid
range should exclude the _FillValue (whether defined explicitly or by
default) as follows. If the _FillValue is positive then it defines a
valid maximum, otherwise it defines a valid minimum. For integer
types, there should be a difference of 1 between the _FillValue and
this valid minimum or maximum. For floating point types, the
difference should be twice the minimum possible (1 in the least
significant bit) to allow for rounding error.

If the variable is packed using scale_factor and add_offset attributes
(see below), the _FillValue, missing_value, valid_range, valid_min, or
valid_max attributes should have the data type of the packed data.
 
@item scale_factor
If present for a variable, the data are to be multiplied by this
factor after the data are read by the application that accesses the
data.

If valid values are specified using the valid_min, valid_max,
valid_range, or _FillValue attributes, those values should be
specified in the domain of the data in the file (the packed data),
so that they can be interpreted before the scale_factor and add_offset
are applied.
 
@item add_offset
If present for a variable, this number is to be added to the data
after it is read by the application that accesses the data. If both
scale_factor and add_offset attributes are present, the data are first
scaled before the offset is added. The attributes scale_factor and
add_offset can be used together to provide simple data compression to
store low-resolution floating-point data as small integers in a netCDF
dataset. When scaled data are written, the application should first
subtract the offset and then divide by the scale factor, rounding the
result to the nearest integer to avoid a bias caused by truncation
towards zero.

When scale_factor and add_offset are used for packing, the associated
variable (containing the packed data) is typically of type byte or
short, whereas the unpacked values are intended to be of type float or
double. The attributes scale_factor and add_offset should both be of
the type intended for the unpacked data, e.g. float or double.
 
@item signedness
Deprecated attribute, originally designed to indicate whether byte
values should be treated as signed or unsigned. The attributes
valid_min and valid_max may be used for this purpose. For example, if
you intend that a byte variable store only non-negative values, you can
use valid_min = 0 and valid_max = 255. This attribute is ignored by
the netCDF library.
 
@item C_format
A character array providing the format that should be used by C
applications to print values for this variable. For example, if you
know a variable is only accurate to three significant digits, it would
be appropriate to define the C_format attribute as "%.3g". The ncdump
utility program uses this attribute for variables for which it is
defined. The format applies to the scaled (internal) type and value,
regardless of the presence of the scaling attributes scale_factor and
add_offset.
 
@item FORTRAN_format
A character array providing the format that should be used by FORTRAN
applications to print values for this variable. For example, if you
know a variable is only accurate to three significant digits, it would
be appropriate to define the FORTRAN_format attribute as "(G10.3)".
 
@item title
A global attribute that is a character array providing a succinct
description of what is in the dataset.
 
@item history
A global attribute for an audit trail. This is a character array with
a line for each invocation of a program that has modified the
dataset. Well-behaved generic netCDF applications should append a line
containing: date, time of day, user name, program name and command
arguments.
 
@item Conventions
If present, 'Conventions' is a global attribute that is a character
array for the name of the conventions followed by the dataset.
Originally, these conventions were named by a string that was
interpreted as a directory name relative to the directory
/pub/netcdf/Conventions/ on the host ftp.unidata.ucar.edu.  The web
page @value{netcdf-url}/conventions.html is now the preferred and
authoritative location for registering a URI reference to a set of
conventions maintained elsewhere.  The FTP site will be preserved for
compatibility with existing references, but authors of new conventions
should submit a request to @value{netcdf-support-email} for listing
on the Unidata conventions web page.

It may be convenient for defining institutions and groups to use a
hierarchical structure for general conventions and more specialized
conventions. 
For example, if a group named NUWG agrees upon a set of conventions
for dimension names, variable names, required attributes, and netCDF
representations for certain discipline-specific data structures, they
may store a document describing the agreed-upon conventions in a
dataset in the NUWG/ subdirectory of the Conventions
directory. Datasets that followed these conventions would contain a
global Conventions attribute with value "NUWG".
 
Later, if the group agrees upon some additional conventions for a
specific subset of NUWG data, for example time series data, the
description of the additional conventions might be stored in the
NUWG/Time_series/ subdirectory, and datasets that adhered to these
additional conventions would use the global Conventions attribute with
value "NUWG/Time_series", implying that this dataset adheres to the
NUWG conventions and also to the additional NUWG time-series
conventions.

It is possible for a netCDF file to adhere to more than one set of
conventions, even when there is no inheritance relationship among the
conventions.  In this case, the value of the `Conventions' attribute
may be a single text string containing a list of the convention names
separated by blank space (recommended) or commas (if a convention name
contains blanks).

Typical conventions web sites will include references to documents in
some form agreed upon by the community that supports the conventions
and examples of netCDF file structures that follow the conventions.
@end vtable

@node File Format, Internal Dispatch Table, Attribute Conventions, Top
@appendix File Format Specification
@cindex file format

In different contexts, ``netCDF'' may refer to an abstract data model,
a software implementation with associated application program
interfaces (APIs), or a data format. Confusion may easily arise in
discussions of different versions of the data models, software, and
formats, because the relationships among versions of these entities is
more complex than a simple one-to-one correspondence by version. For
example, compatibility commitments require that new versions of the
software support all previous variants of the format and data model.

To avoid this potential confusion, we assign distinct names to
versions of the formats, data models, and software releases that will
be used consistently in the remainder of this appendix.

In this appendix, two format variants are specified formally, the
@dfn{classic format} and the @dfn{64-bit offset format} for netCDF
data. Two additional format variants are discussed less formally, the
@dfn{netCDF-4 format} and the @dfn{netCDF-4 classic model format}.

The classic format was the only format for netCDF data created between
1989 and 2004 by various versions of the reference software from
Unidata. In 2004, the 64-bit offset format variant was introduced for
creation of and access to much larger files. The reference software,
available for C-based and Java-based programs, supported use of the
same APIs for accessing either classic or 64-bit offset files, so
programs reading the files would not have to depend on which format
was used.

There are only two netCDF data models, the @dfn{classic model} and the
@dfn{enhanced model}. The classic model is the simpler of the two, and is
used for all data stored in classic format, 64-bit offset format, or
netCDF-4 classic model format. The enhanced model (also referred to as
the netCDF- 4 data model) was introduced in 2008 as an extension of
the classic model that adds more powerful forms of data representation
and data types at the expense of some additional complexity. Although
data represented with the classic model can also be represented using
the enhanced model, datasets that use features of the enhanced model,
such as user-defined nested data types, cannot be represented with the
classic model. Use of added features of the enhanced model requires
that data be stored in the netCDF-4 format.  

Versions 1.0 through 3.5 of the Unidata C-based reference software,
released between 1989 and 2000, supported only the classic data model
and classic format. Version 3.6, released in late 2004, first provided
support for the 64-bit offset format, but still used the classic data
model.  With version 4.0, released in 2008, the enhanced data model
was introduced along with the two new HDF5-based format variants, the
netCDF-4 format and the netCDF-4 classic model format.  Evolution of
the data models, formats, and APIs will continue the commitment to support
all previous netCDF data models, data format variants, and APIs in
future software releases.

Use of the HDF5 storage layer in netCDF-4 software provides features
for improved performance, independent of the data model used, for
example compression and dynamic schema changes. Such performance
improvements are available for data stored in the netCDF-4 classic
model format, even when accessed by programs that only support the
classic model.

Related formats not discussed in this appendix include
CDL (``Common Data Language'', the original ASCII form of binary netCDF
data), and NcML (NetCDF Markup Language, an XML-based representation
for netCDF metadata and data).

Knowledge of format details is not required to read or write netCDF
datasets. Software that reads netCDF data using the reference
implementation automatically detects and uses the correct version of
the format for accessing data. Understanding details may be helpful
for understanding performance issues related to disk or server access.

The netCDF reference library, developed and supported by Unidata, is
written in C, with Fortran77, Fortran90, and C++ interfaces. A number
of community and commercially supported interfaces to other languages
are also available, including IDL, Matlab, Perl, Python, and Ruby.  An
independent implementation, also developed and supported by Unidata,
is written entirely in Java.

@menu
* NetCDF Classic Format::       The Original Binary Format
* 64-bit Offset Format::        Supporting Larger Variables
* NetCDF-4 Format::             Uses HDF5 
* NetCDF-4 Classic Model Format::  HDF5 with NetCDF Limitations
* HDF4 SD Format::              
@end menu

@node NetCDF Classic Format, 64-bit Offset Format, File Format, File Format
@section The NetCDF Classic Format Specification

To present the format more formally, we use a BNF grammar notation. In
this notation:

@itemize
@item
Non-terminals (entities defined by grammar rules) are in lower case.  
@item
Terminals (atomic entities in terms of which the format specification
is written) are in upper case, and are specified literally as US-ASCII
characters within single-quote characters or are described with text
between angle brackets (@samp{<} and @samp{>}).  
@item
Optional entities are enclosed between braces (@samp{[} and @samp{]}).  
@item
A sequence of zero or more occurrences of an entity is denoted by
@samp{[entity ...]}.
@item
A vertical line character (@samp{|}) separates alternatives. Alternation
has lower precedence  than concatenation.
@item
Comments follow @samp{//} characters.
@item
A single byte that is not a printable character is denoted using a
hexadecimal number with the notation @samp{\xDD}, where each D is a
hexadecimal digit.
@item
A literal single-quote character is denoted by @samp{\'}, and a literal
back-slash character is denoted by @samp{\\}. 
@end itemize

Following the grammar, a few additional notes are included to specify
format characteristics that are impractical to capture in a BNF
grammar, and to note some special cases for implementers.  Comments in
the grammar point to the notes and special cases, and help to clarify
the intent of elements of the format.

@menu
* Classic Format Spec::         Detailed Format Information
* Computing Offsets::           How to Get the Data You Want
* Examples::                    The Binary Layout of some Simple Files
@end menu

@node Classic Format Spec, Computing Offsets, NetCDF Classic Format, NetCDF Classic Format
@unnumberedsubsec The Format in Detail

@example
netcdf_file  = header  data
header       = magic  numrecs  dim_list  gatt_list  var_list
magic        = 'C'  'D'  'F'  VERSION
VERSION      = \x01 |                      // classic format
               \x02                        // 64-bit offset format
numrecs      = NON_NEG | STREAMING         // length of record dimension
dim_list     = ABSENT | NC_DIMENSION  nelems  [dim ...]
gatt_list    = att_list                    // global attributes
att_list     = ABSENT | NC_ATTRIBUTE  nelems  [attr ...]
var_list     = ABSENT | NC_VARIABLE   nelems  [var ...]
ABSENT       = ZERO  ZERO                  // Means list is not present
ZERO         = \x00 \x00 \x00 \x00         // 32-bit zero
NC_DIMENSION = \x00 \x00 \x00 \x0A         // tag for list of dimensions
NC_VARIABLE  = \x00 \x00 \x00 \x0B         // tag for list of variables
NC_ATTRIBUTE = \x00 \x00 \x00 \x0C         // tag for list of attributes
nelems       = NON_NEG       // number of elements in following sequence
dim          = name  dim_length
name         = nelems  namestring
                    // Names a dimension, variable, or attribute.
                    // Names should match the regular expression
                    // ([a-zA-Z0-9_]|@{MUTF8@})([^\x00-\x1F/\x7F-\xFF]|@{MUTF8@})*
                    // For other constraints, see "Note on names", below.
namestring   = ID1 [IDN ...] padding
ID1          = alphanumeric | '_'
IDN          = alphanumeric | special1 | special2
alphanumeric = lowercase | uppercase | numeric | MUTF8
lowercase    = 'a'|'b'|'c'|'d'|'e'|'f'|'g'|'h'|'i'|'j'|'k'|'l'|'m'|
               'n'|'o'|'p'|'q'|'r'|'s'|'t'|'u'|'v'|'w'|'x'|'y'|'z'
uppercase    = 'A'|'B'|'C'|'D'|'E'|'F'|'G'|'H'|'I'|'J'|'K'|'L'|'M'|
               'N'|'O'|'P'|'Q'|'R'|'S'|'T'|'U'|'V'|'W'|'X'|'Y'|'Z'
numeric      = '0'|'1'|'2'|'3'|'4'|'5'|'6'|'7'|'8'|'9'
                             // special1 chars have traditionally been
                             // permitted in netCDF names.
special1     = '_'|'.'|'@@'|'+'|'-'
                             // special2 chars are recently permitted in
                             // names (and require escaping in CDL).
                             // Note: '/' is not permitted.
special2     = ' ' | '!' | '"' | '#'  | '$' | '%' | '&' | '\'' |
               '(' | ')' | '*' | ','  | ':' | ';' | '<' | '='  |
               '>' | '?' | '[' | '\\' | ']' | '^' | '`' | '@{'  |
               '|' | '@}' | '~'
MUTF8        = <multibyte UTF-8 encoded, NFC-normalized Unicode character>
dim_length   = NON_NEG       // If zero, this is the record dimension.
                             // There can be at most one record dimension.
attr         = name  nc_type  nelems  [values ...]
nc_type      = NC_BYTE   |
               NC_CHAR   |
               NC_SHORT  |
               NC_INT    |
               NC_FLOAT  |
               NC_DOUBLE
var          = name  nelems  [dimid ...]  vatt_list  nc_type  vsize  begin
                             // nelems is the dimensionality (rank) of the
                             // variable: 0 for scalar, 1 for vector, 2
                             // for matrix, ...
dimid        = NON_NEG       // Dimension ID (index into dim_list) for
                             // variable shape.  We say this is a "record
                             // variable" if and only if the first
                             // dimension is the record dimension.
vatt_list    = att_list      // Variable-specific attributes
vsize        = NON_NEG       // Variable size.  If not a record variable,
                             // the amount of space in bytes allocated to
                             // the variable's data.  If a record variable,
                             // the amount of space per record.  See "Note
                             // on vsize", below.
begin        = OFFSET        // Variable start location.  The offset in
                             // bytes (seek index) in the file of the
                             // beginning of data for this variable.
data         = non_recs  recs
non_recs     = [vardata ...] // The data for all non-record variables,
                             // stored contiguously for each variable, in
                             // the same order the variables occur in the
                             // header.
vardata      = [values ...]  // All data for a non-record variable, as a
                             // block of values of the same type as the
                             // variable, in row-major order (last
                             // dimension varying fastest).
recs         = [record ...]  // The data for all record variables are
                             // stored interleaved at the end of the
                             // file.
record       = [varslab ...] // Each record consists of the n-th slab
                             // from each record variable, for example
                             // x[n,...], y[n,...], z[n,...] where the
                             // first index is the record number, which
                             // is the unlimited dimension index.
varslab      = [values ...]  // One record of data for a variable, a
                             // block of values all of the same type as
                             // the variable in row-major order (last
                             // index varying fastest).
values       = bytes | chars | shorts | ints | floats | doubles
string       = nelems  [chars]
bytes        = [BYTE ...]  padding
chars        = [CHAR ...]  padding
shorts       = [SHORT ...]  padding
ints         = [INT ...]
floats       = [FLOAT ...]
doubles      = [DOUBLE ...]
padding      = <0, 1, 2, or 3 bytes to next 4-byte boundary>
                             // Header padding uses null (\x00) bytes.  In
                             // data, padding uses variable's fill value.
                             // See "Note on padding", below, for a special
                             // case.
NON_NEG      = <non-negative INT>
STREAMING    = \xFF \xFF \xFF \xFF   // Indicates indeterminate record
                                     // count, allows streaming data
OFFSET       = <non-negative INT> |  // For classic format or
               <non-negative INT64>  // for 64-bit offset format
BYTE         = <8-bit byte>          // See "Note on byte data", below.
CHAR         = <8-bit byte>          // See "Note on char data", below.
SHORT        = <16-bit signed integer, Bigendian, two's complement>
INT          = <32-bit signed integer, Bigendian, two's complement>
INT64        = <64-bit signed integer, Bigendian, two's complement>
FLOAT        = <32-bit IEEE single-precision float, Bigendian>
DOUBLE       = <64-bit IEEE double-precision float, Bigendian>
                             // following type tags are 32-bit integers
NC_BYTE      = \x00 \x00 \x00 \x01       // 8-bit signed integers
NC_CHAR      = \x00 \x00 \x00 \x02       // text characters
NC_SHORT     = \x00 \x00 \x00 \x03       // 16-bit signed integers
NC_INT       = \x00 \x00 \x00 \x04       // 32-bit signed integers
NC_FLOAT     = \x00 \x00 \x00 \x05       // IEEE single precision floats
NC_DOUBLE    = \x00 \x00 \x00 \x06       // IEEE double precision floats
                             // Default fill values for each type, may be
                             // overridden by variable attribute named
                             // `_FillValue'. See "Note on fill values",
                             // below.
FILL_CHAR    = \x00                      // null byte
FILL_BYTE    = \x81                      // (signed char) -127
FILL_SHORT   = \x80 \x01                 // (short) -32767
FILL_INT     = \x80 \x00 \x00 \x01       // (int) -2147483647
FILL_FLOAT   = \x7C \xF0 \x00 \x00       // (float) 9.9692099683868690e+36
FILL_DOUBLE  = \x47 \x9E \x00 \x00 \x00 \x00 //(double)9.9692099683868690e+36
@end example

Note on @code{vsize}: This number is the product of the dimension lengths
(omitting the record dimension) and the number of bytes per value
(determined from the type), increased to the next multiple of 4, for
each variable.  If a record variable, this is the amount of space per
record (except that, for backward compatibility, it always includes
padding to the next multiple of 4 bytes, even in the exceptional case
noted below under ``Note on padding'').  The netCDF ``record size'' is
calculated as the sum of the @code{vsize}'s of all the record variables.

The @code{vsize} field is actually redundant, because its value may be
computed from other information in the header. The 32-bit @code{vsize} field
is not large enough to contain the size of variables that require more
than @math{2^32 - 4} bytes, so @math{2^32 - 1} is used in the @code{vsize} field for such
variables.

Note on names: Earlier versions of the netCDF C-library reference
implementation enforced a more restricted set of characters in
creating new names, but permitted reading names containing arbitrary
bytes.  This specification extends the permitted characters in names
to include multi-byte UTF-8 encoded Unicode and additional printing
characters from the US-ASCII alphabet. The first character of a name
must be alphanumeric, a multi-byte UTF-8 character, or '_'
(reserved for special names with meaning to implementations,
such as the ``_FillValue'' attribute).  Subsequent characters may also
include printing special characters, except for '/' which is not
allowed in names.  Names that have trailing space characters are also
not permitted.

Implementations of the netCDF classic and 64-bit offset format must
ensure that names are normalized according to Unicode NFC
normalization rules during encoding as UTF-8 for storing in the file
header.  This is necessary to ensure that gratuitous differences in
the representation of Unicode names do not cause anomalies in
comparing files and querying data objects by name.

Note on streaming data: The largest possible record count, @math{2^32
- 1}, is reserved to indicate an indeterminate number of records.
This means that the number of records in the file must be determined
by other means, such as reading them or computing the current number
of records from the file length and other information in the header.
It also means that the numrecs field in the header will not be updated
as records are added to the file.  [This feature is not yet
implemented].

Note on padding: In the special case when there is only one record
variable and it is of type character, byte, or short, no padding is
used between record slabs, so records after the first record do not
necessarily start on four-byte boundaries.  However, as noted above
under ``Note on @code{vsize}'', the @code{vsize} field is computed to
include padding to the next multiple of 4 bytes.  In this case,
readers should ignore @code{vsize} and assume no padding.  Writers
should store @code{vsize} as if padding were included.

Note on byte data: It is possible to interpret byte data as either
signed (-128 to 127) or unsigned (0 to 255). When reading byte data
through an interface that converts it into another numeric type, the
default interpretation is signed.  There are various attribute
conventions for specifying whether bytes represent signed or unsigned
data, but no standard convention has been established.  The variable
attribute ``_Unsigned'' is reserved for this purpose in future
implementations.

Note on char data: Although the characters used in netCDF names must
be encoded as UTF-8, character data may use other encodings. The
variable attribute ``_Encoding'' is reserved for this purpose in future
implementations.

Note on fill values: Because data variables may be created before
their values are written, and because values need not be written
sequentially in a netCDF file, default ``fill values'' are defined for
each type, for initializing data values before they are explicitly
written.  This makes it possible to detect reading values that were
never written.  The variable attribute ``_FillValue'', if present,
overrides the default fill value for a variable. If _FillValue is
defined then it should be scalar and of the same type as the variable.

Fill values are not required, however, because netCDF libraries have
traditionally supported a ``no fill'' mode when writing, omitting the
initialization of variable values with fill values. This makes the
creation of large files faster, but also eliminates the possibility of
detecting the inadvertent reading of values that haven't been written.

@node Computing Offsets, Examples, Classic Format Spec, NetCDF Classic Format
@unnumberedsubsec Notes on Computing File Offsets

The offset (position within the file) of a specified data value in a
classic format or 64-bit offset data file is completely determined by
the variable start location (the offset in the @code{begin} field), the
external type of the variable (the @code{nc_type} field), and the
dimension indices (one for each of the variable's dimensions) of the
value desired.

The external size in bytes of one data value for each possible
netCDF type, denoted @code{extsize} below, is:

@multitable @columnfractions .01 .10
@item @code{NC_BYTE}
@tab 1
@item @code{NC_CHAR}
@tab 1
@item @code{NC_SHORT}
@tab 2
@item @code{NC_INT}
@tab 4
@item @code{NC_FLOAT}
@tab 4
@item @code{NC_DOUBLE}
@tab 8
@end multitable

The record size, denoted by @code{recsize} below, is the sum of the @code{vsize}
fields of record variables (variables that use the unlimited
dimension), using the actual value determined by dimension sizes and
variable type in case the @code{vsize} field is too small for the variable
size.

To compute the offset of a value relative to the beginning of a
variable, it is helpful to precompute a ``product vector'' from the
dimension lengths.  Form the products of the dimension lengths for the
variable from right to left, skipping the leftmost (record) dimension
for record variables, and storing the results as the product vector
for each variable.

For example:

Non-record variable:

        dimension lengths:      [  5  3  2 7]
        product vector:         [210 42 14 7]

Record variable:

        dimension lengths:      [0  2  9 4]
        product vector:         [0 72 36 4]

At this point, the leftmost product, when rounded up to the next
multiple of 4, is the variable size, @code{vsize}, in the grammar above. For
example, in the non-record variable above, the value of the @code{vsize}
field is 212 (210 rounded up to a multiple of 4). For the record
variable, the value of @code{vsize} is just 72, since this is already a
multiple of 4.

Let coord be the array of coordinates (dimension indices, zero-based)
of the desired data value.  Then the offset of the value from the
beginning of the file is just the file offset of the first data value
of the desired variable (its @code{begin} field) added to the inner product
of the coord and product vectors times the size, in bytes, of each
datum for the variable. Finally, if the variable is a record variable,
the product of the record number, 'coord[0]', and the record size,
@code{recsize}, is added to yield the final offset value.

A special case: Where there is exactly one record variable, we drop
the requirement that each record be four-byte aligned, so in this case
there is no record padding.

@node Examples,  , Computing Offsets, NetCDF Classic Format
@unnumberedsubsec Examples

By using the grammar above, we can derive the smallest valid netCDF
file, having no dimensions, no variables, no attributes, and hence, no
data. A CDL representation of the empty netCDF file is

netcdf empty @{ @}

This empty netCDF file has 32 bytes. It begins with the four-byte
``magic number'' that identifies it as a netCDF version 1 file:
@samp{C}, @samp{D}, @samp{F}, @samp{\x01}. Following are seven 32-bit
integer zeros representing the number of records, an empty list of
dimensions, an empty list of global attributes, and an empty list of
variables.

Below is an (edited) dump of the file produced using the Unix command

od -xcs empty.nc

Each 16-byte portion of the file is displayed with 4 lines. The first
line displays the bytes in hexadecimal. The second line displays the
bytes as characters. The third line displays each group of two
bytes interpreted as a signed 16-bit integer. The fourth line (added
by human) presents the interpretation of the bytes in terms of netCDF
components and values.

@example
   4344    4601    0000    0000    0000    0000    0000    0000
  C   D   F 001  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0
  17220   17921   00000   00000   00000   00000   00000   00000
[magic number ] [  0 records  ] [  0 dimensions   (ABSENT)    ]

   0000    0000    0000    0000    0000    0000    0000    0000
 \0  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0
  00000   00000   00000   00000   00000   00000   00000   00000
[  0 global atts  (ABSENT)    ] [  0 variables    (ABSENT)    ]
@end example

As a less trivial example, consider the CDL

@example
netcdf tiny @{
dimensions:
        dim = 5;
variables:
        short vx(dim);
data:
        vx = 3, 1, 4, 1, 5 ;
@}
@end example

which corresponds to a 92-byte netCDF file. The following is an edited
dump of this file:

@example
   4344    4601    0000    0000    0000    000a    0000    0001
  C   D   F 001  \0  \0  \0  \0  \0  \0  \0  \n  \0  \0  \0 001
  17220   17921   00000   00000   00000   00010   00000   00001
[magic number ] [  0 records  ] [NC_DIMENSION ] [ 1 dimension ]

   0000    0003    6469    6d00    0000    0005    0000    0000
 \0  \0  \0 003   d   i   m  \0  \0  \0  \0 005  \0  \0  \0  \0
  00000   00003   25705   27904   00000   00005   00000   00000
[  3 char name = "dim"        ] [ size = 5    ] [ 0 global atts

   0000    0000    0000    000b    0000    0001    0000    0002
 \0  \0  \0  \0  \0  \0  \0 013  \0  \0  \0 001  \0  \0  \0 002
  00000   00000   00000   00011   00000   00001   00000   00002
 (ABSENT)     ] [NC_VARIABLE  ] [ 1 variable  ] [ 2 char name =

   7678    0000    0000    0001    0000    0000    0000    0000
  v   x  \0  \0  \0  \0  \0 001  \0  \0  \0  \0  \0  \0  \0  \0
  30328   00000   00000   00001   00000   00000   00000   00000
 "vx"         ] [1 dimension  ] [ with ID 0   ] [ 0 attributes

   0000    0000    0000    0003    0000    000c    0000    0050
 \0  \0  \0  \0  \0  \0  \0 003  \0  \0  \0  \f  \0  \0  \0   P
  00000   00000   00000   00003   00000   00012   00000   00080
 (ABSENT)     ] [type NC_SHORT] [size 12 bytes] [offset:    80]

   0003    0001    0004    0001    0005    8001
 \0 003  \0 001  \0 004  \0 001  \0 005 200 001
  00003   00001   00004   00001   00005  -32767
[    3] [    1] [    4] [    1] [    5] [fill ]
@end example

@node 64-bit Offset Format, NetCDF-4 Format, NetCDF Classic Format, File Format
@section The 64-bit Offset Format

The netCDF 64-bit offset format differs from the classic format
only in the VERSION byte, @samp{\x02} instead of @samp{\x01}, and the OFFSET
entity, a 64-bit instead of a 32-bit offset from the beginning of the
file.  This small format change permits much larger files, but there
are still some practical size restrictions.  Each fixed-size variable
and the data for one record's worth of each record variable are still
limited in size to a little less that 4 GiB.  The rationale for this
limitation is to permit aggregate access to all the data in a netCDF
variable (or a record's worth of data) on 32-bit platforms.

@node NetCDF-4 Format, NetCDF-4 Classic Model Format, 64-bit Offset Format, File Format
@section The NetCDF-4 Format

The netCDF-4 format implements and expands the netCDF-3 data model by
using an enhanced version of HDF5 as the storage layer.  Use is
made of features that are only available in HDF5 version 1.8 and
later.

Using HDF5 as the underlying storage layer, netCDF-4 files remove many
of the restrictions for classic and 64-bit offset files.  The richer
enhanced model supports user-defined types and data structures,
hierarchical scoping of names using groups, additional primitive types
including strings, larger variable sizes, and multiple unlimited
dimensions.  The underlying HDF5 storage layer also supports
per-variable compression, multidimensional tiling, and efficient
dynamic schema changes, so that data need not be copied when adding
new variables to the file schema.

Creating a netCDF-4/HDF5 file with netCDF-4 results in an HDF5
file. The features of netCDF-4 are a subset of the features of HDF5,
so the resulting file can be used by any existing HDF5 application.

Although every file in netCDF-4 format is an HDF5 file, there are HDF5
files that are not netCDF-4 format files, because the netCDF-4 format
intentionally uses a limited subset of the HDF5 data model and file
format features.  Some HDF5 features not supported in the netCDF
enhanced model and netCDF-4 format include non-hierarchical group
structures, HDF5 reference types, multiple links to a data object,
user-defined atomic data types, stored property lists, more permissive
rules for data object names, the HDF5 date/time type, and attributes
associated with user-defined types.

A complete specification of HDF5 files is beyond the scope of this
document.  For more information about HDF5, see the HDF5 web site:
@uref{http://hdf.ncsa.uiuc.edu/HDF5/}. 

The specification that follows is sufficient to allow HDF5 users to
create files that will be accessible from netCDF-4.

@subsection Creation Order 

The netCDF API maintains the creation order of objects that are
created in the file. The same is not true in HDF5, which maintains the
objects in alphabetical order. Starting in version 1.8 of HDF5, the
ability to maintain creation order was added. This must be explicitly
turned on in the HDF5 data file in several ways.

Each group must have link and attribute creation order set. The
following code (from libsrc4/nc4hdf.c) shows how the netCDF-4 library
sets these when creating a group.

@example
      /* Create group, with link_creation_order set in the group
       * creation property list. */
      if ((gcpl_id = H5Pcreate(H5P_GROUP_CREATE)) < 0)
         return NC_EHDFERR;
      if (H5Pset_link_creation_order(gcpl_id, H5P_CRT_ORDER_TRACKED|H5P_CRT_ORDER_INDEXED) < 0)
         BAIL(NC_EHDFERR);
      if (H5Pset_attr_creation_order(gcpl_id, H5P_CRT_ORDER_TRACKED|H5P_CRT_ORDER_INDEXED) < 0)
         BAIL(NC_EHDFERR);
      if ((grp->hdf_grpid = H5Gcreate2(grp->parent->hdf_grpid, grp->name,  
                                       H5P_DEFAULT, gcpl_id, H5P_DEFAULT)) < 0)
         BAIL(NC_EHDFERR);
      if (H5Pclose(gcpl_id) < 0)
         BAIL(NC_EHDFERR);
@end example

Each dataset in the HDF5 file must be created with a property list for
which the attribute creation order has been set to creation
ordering. The H5Pset_attr_creation_order function is used to set the
creation ordering of attributes of a variable.

The following example code (from libsrc4/nc4hdf.c) shows how the
creation ordering is turned on by the netCDF library.

@example
   /* Turn on creation order tracking. */
   if (H5Pset_attr_creation_order(plistid, H5P_CRT_ORDER_TRACKED|
                                  H5P_CRT_ORDER_INDEXED) < 0)
      BAIL(NC_EHDFERR);
@end example

@subsection Groups

NetCDF-4 groups are the same as HDF5 groups, but groups in a netCDF-4
file must be strictly hierarchical. In general, HDF5 permits
non-hierarchical structuring of groups (for example, a group that is
its own grandparent). These non-hierarchical relationships are not
allowed in netCDF-4 files.

In the netCDF API, the global attribute becomes a group-level
attribute. That is, each group may have its own global
attributes.

The root group of a file is named ``/'' in the netCDF API, where names
of groups are used. It should be noted that the netCDF API (like the
HDF5 API) makes little use of names, and refers to entities by number.

@subsection Dimensions with HDF5 Dimension Scales

Until version 1.8, HDF5 did not have any capability to represent
shared dimensions. With the 1.8 release, HDF5 introduced the dimension
scale feature to allow shared dimensions in HDF5 files.

The dimension scale is unfortunately not exactly equivalent to the
netCDF shared dimension, and this leads to a number of compromises in
the design of netCDF-4.

A netCDF shared dimension consists solely of a length and a name. An
HDF5 dimension scale also includes values for each point along the
dimension, information that is (optionally) included in a netCDF
coordinate variable.

To handle the case of a netCDF dimension without a coordinate
variable, netCDF-4 creates dimension scales of type char, and leaves
the contents of the dimension scale empty. Only the name and length of
the scale are significant. To distinguish this case, netCDF-4 takes
advantage of the NAME attribute of the dimension scale. (Not to be
confused with the name of the scale itself.) In the case of dimensions
without coordinate data, the HDF5 dimension scale NAME attribute is
set to the string: "This is a netCDF dimension but not a netCDF
variable."

In the case where a coordinate variable is defined for a dimension,
the HDF5 dimscale matches the type of the netCDF coordinate variable,
and contains the coordinate data.

A further difficulty arrises when an n-dimensional coordinate
variable is defined, where n is greater than one. NetCDF allows such
coordinate variables, but the HDF5 model does not allow dimension
scales to be attached to other dimension scales, making it impossible
to completely represent the multi-dimensional coordinate variables of
the netCDF model.

To capture this information, multidimensional coordinate variables
have an attribute named _Netcdf4Coordinates. The attribute is an array
of H5T_NATIVE_INT, with the netCDF dimension IDs of each of its
dimensions.

The _Netcdf4Coordinates attribute is otherwise hidden by the netCDF
API. It does not appear as one of the attributes for the netCDF
variable involved, except through the HDF5 API.

@subsection Dimensions without HDF5 Dimension Scales

Starting with the netCDF-4.1 release, netCDF can read HDF5 files which
do not use dimension scales. In this case the netCDF library assigns
dimensions to the HDF5 dataset as needed, based on the length of the
dimension. 

When an HDF5 file is opened, each dataset is examined in
turn. The lengths of all the dimensions involved in the shape of the
dataset are determined. Each new (i.e. previously unencountered)
length results in the creation of a phony dimension in the netCDF API.

This will not accurately detect a shared, unlimited dimension in the
HDF5 file, if different datasets have different lengths along this
dimension (possible in HDF5, but not in netCDF). 

Note that this is a read-only capability for the netCDF library. When
the netCDF library writes HDF5 files, they always use a dimension
scale for every dimension.

Datasets must have either dimension scales for every dimension, or no
dimension scales at all. Partial dimension scales are not, at this
time, understood by the netCDF library.

@subsection Dimension and Coordinate Variable Ordering

In order to preserve creation order, the netCDF-4 library writes
variables in their creation order. Since some variables are also
dimension scales, their order reflects both the order of the
dimensions and the order of the coordinate variables.

However, these may be different. Consider the following code:

@example
      /* Create a test file. */
      if (nc_create(FILE_NAME, NC_CLASSIC_MODEL|NC_NETCDF4, &ncid)) ERR;

      /* Define dimensions in order. */
      if (nc_def_dim(ncid, DIM0, NC_UNLIMITED, &dimids[0])) ERR;
      if (nc_def_dim(ncid, DIM1, 4, &dimids[1])) ERR;

      /* Define coordinate variables in a different order. */
      if (nc_def_var(ncid, DIM1, NC_DOUBLE, 1, &dimids[1], &varid[1])) ERR;
      if (nc_def_var(ncid, DIM0, NC_DOUBLE, 1, &dimids[0], &varid[0])) ERR;
@end example

In this case the order of the coordinate variables will be different
from the order of the dimensions.

In practice, this should make little difference in user code, but if
the user is writing code that depends on the ordering of dimensions,
the netCDF library was updated in version 4.1 to detect this
condition, and add the attribute _Netcdf4Dimid to the dimension scales
in the HDF5 file. This attribute holds a scalar H5T_NATIVE_INT which
is the (zero-based) dimension ID for this dimension.

If this attribute is present on any dimension scale, it must be
present on all dimension scales in the file.

@subsection Variables

Variables in netCDF-4/HDF5 files exactly correspond to HDF5
datasets. The data types match naturally between netCDF and HDF5.

In netCDF classic format, the problem of endianness is solved by
writing all data in big-endian order. The HDF5 library allows data to
be written as either big or little endian, and automatically reorders
the data when it is read, if necessary. 

By default, netCDF uses the native types on the machine which writes
the data. Users may change the endianness of a variable (before any
data are written). In that case the specified endian type will be used
in HDF5 (for example, a H5T_STD_I16LE will be used for NC_SHORT, if
little-endian has been specified for that variable.)

@table @code

@item NC_BYTE
H5T_NATIVE_SCHAR

@item NC_UBYTE
H5T_NATIVE_SCHAR

@item NC_CHAR
H5T_C_S1

@item NC_STRING
variable length array of H5T_C_S1

@item NC_SHORT
H5T_NATIVE_SHORT

@item NC_USHORT
H5T_NATIVE_USHORT

@item NC_INT
H5T_NATIVE_INT

@item NC_UINT
H5T_NATIVE_UINT

@item NC_INT64
H5T_NATIVE_LLONG

@item NC_UINT64
H5T_NATIVE_ULLONG

@item NC_FLOAT
H5T_NATIVE_FLOAT

@item NC_DOUBLE
H5T_NATIVE_DOUBLE

@end table

The NC_CHAR type represents a single character, and the NC_STRING an
array of characters. This can be confusing because a one-dimensional
array of NC_CHAR is used to represent a string (i.e. a scalar
NC_STRING).

An odd case may arise in which the user defines a variable with the
same name as a dimension, but which is not intended to be the
coordinate variable for that dimension. In this case the string
"_nc4_non_coord_" is pre-pended to the name of the HDF5 dataset, and
stripped from the name for the netCDF API.

@subsection Attributes

Attributes in HDF5 and netCDF-4 correspond very closely. Each
attribute in an HDF5 file is represented as an attribute in the netCDF-4
file, with the exception of the attributes below, which are ignored by
the netCDF-4 API.

@table @code

@item _Netcdf4Coordinates
An integer array containing the dimension IDs of a variable which is a
multi-dimensional coordinate variable.

@item _nc3_strict
When this (scalar, H5T_NATIVE_INT) attribute exists in the root group
of the HDF5 file, the netCDF API will enforce the netCDF classic model
on the data file.

@item REFERENCE_LIST
This attribute is created and maintained by the HDF5 dimension scale
API.

@item CLASS
This attribute is created and maintained by the HDF5 dimension scale
API.
@item DIMENSION_LIST
This attribute is created and maintained by the HDF5 dimension scale
API.
@item NAME
This attribute is created and maintained by the HDF5 dimension scale
API.

@end table

@subsection User-Defined Data Types

Each user-defined data type in an HDF5 file exactly corresponds to a
user-defined data type in the netCDF-4 file. Only base data types which
correspond to netCDF-4 data types may be used. (For example, no
HDF5 reference data types may be used.)

@subsection Compression

The HDF5 library provides data compression using the zlib library and
the szlib library. NetCDF-4 only allows users to create data with the
zlib library (due to licensing restrictions on the szlib
library). Since HDF5 supports the transparent reading of the data with
either compression filter, the netCDF-4 library can read data
compressed with szlib (if the underlying HDF5 library is built to
support szlib), but has no way to write data with szlib compression.

With zlib compression (a.k.a. deflation) the user may set a deflation
factor from 0 to 9. In our measurements the zero deflation level does
not compress the data, but does incur the performance penalty of
compressing the data. The netCDF API does not allow the user to write
a variable with zlib deflation of 0 - when asked to do so, it turns
off deflation for the variable instead. NetCDF can read an HDF5 file
with deflation of zero, and correctly report that to the user.

@node NetCDF-4 Classic Model Format, HDF4 SD Format, NetCDF-4 Format, File Format
@section The NetCDF-4 Classic Model Format

Every classic and 64-bit offset file can be represented as a netCDF-4
file, with no loss of information.  There are some significant
benefits to using the simpler netCDF classic model with the netCDF-4
file format.  For example, software that writes or reads classic model
data can write or read netCDF-4 classic model format data by
recompiling/relinking to a netCDF-4 API library, with no or only
trivial changes needed to the program source code.  The netCDF-4
classic model format supports this usage by enforcing rules on what
functions may be called to store data in the file, to make sure its
data can be read by older netCDF applications (when relinked to a netCDF-4
library).

Writing data in this format prevents use of enhanced model features
such as groups, added primitive types not available in the classic
model, and user-defined types.  However performance features of the
netCDF-4 formats that do not require additional features of the
enhanced model, such as per-variable compression and chunking,
efficient dynamic schema changes, and larger variable size limits,
offer potentially significant performance improvements to readers of
data stored in this format, without requiring program changes.

When a file is created via the netCDF API with a CLASSIC_MODEL mode
flag, the library creates an attribute (_nc3_strict) in the root
group. This attribute is hidden by the netCDF API, but is read when
the file is later opened, and used to ensure that no enhanced model
features are written to the file.

@node HDF4 SD Format,  , NetCDF-4 Classic Model Format, File Format
@section HDF4 SD Format

Starting with version 4.1, the netCDF libraries can read HDF4 SD
(Scientific Dataset) files. Access is limited to those HDF4 files
created with the Scientific Dataset API. Access is read-only.

Dataset types are translated between HDF4 and netCDF in a
straightforward manner.

@table @code
@item DFNT_CHAR
NC_CHAR
@item DFNT_UCHAR, DFNT_UINT8
NC_UBYTE
@item DFNT_INT8
NC_BYTE
@item DFNT_INT16
NC_SHORT
@item DFNT_UINT16
NC_USHORT
@item DFNT_INT32
NC_INT
@item DFNT_UINT32
NC_UINT
@item DFNT_FLOAT32
NC_FLOAT
@item DFNT_FLOAT64
NC_DOUBLE
@end table

@node Internal Dispatch Table, Combined Index, File Format, Top
@appendix Internal Dispatch Table
@subheading Draft 3: 5/15/2010
@unnumberedsec netCDF Dispatch Mechanism

This document describes the architecture and details of the
new netCDF internal dispatch mechanism. The idea is that
when a user opens or creates a netcdf file, that a specific
dispatch table is chosen. Subsequent netcdf API calls are
then channeled through that dispatch table to the
appropriate function for implementing that API call.

Currently, the following four dispatch tables are supported.
@enumerate
@item netcdf classic files (netcdf-3) 
@item netcdf enhanced files (netcdf-4) 
@item OPeNDAP to netcdf-3 
@item OPeNDAP to netcdf-4 
@end enumerate

The dispatch table represents a distillation of the
netcdf API down to a minimal set of internal operations. The
format of the dispatch table is defined in the file
libdispatch/ncdispatch.h.  Every new dispatch table must
define this minimal set of operations.

@unnumberedsec Adding a New Dispatch Table
In order to make this process concrete, let us assume we
plan to add an in-memory implementation of netcdf-3.

@unnumberedsec Step 1.
Define a --enable flag and an AM_CONFIGURE flag in
configure.ac. We will use the flags --enable-netcdfm and
USE_NETCDFM respectively.

@unnumberedsec Step 2
Choose some prefix of characters to identify the new
dispatch system. In effect we are defining a name-space. For
our in-memory system, we will choose "NCM" and "ncm". NCM is
used for non-static procedures to be entered into the
dispatch table and ncm for all other non-static procedures.

@unnumberedsec Step 3.
Modify file libdispatch/ncdispatch.h as follows.
@itemize
@item Add a index for this implementation: 
@example
#define NC_DISPATCH_NCM  5
@end example
@item Define an external reference to the in-memory dispatch table. 
@example
#ifdef USE_NETCDFM
extern NC_Dispatch* NCM_dispatch_table;
#endif
@end example
@end itemize

@unnumberedsec Step 4.
Modify file libdispatch/netcdf.c as follows.
@itemize
@item Add a ptr to the in-memory dispatch table. 
@example
#ifdef USE_NETCDFM
NC_Dispatch* NCM_dispatch_table = NULL;
#endif
@end example

@item Add any necessary #include files as needed. 
@end itemize

@unnumberedsec Step 5.
Define the functions necessary to fill in the dispatch
table. As a rule, we assume that a new directory is defined,
libsrcm, say.  Within this directory, we need to define
Makefile.am, the source files containing the dispatch table
and the functions to be placed in the dispatch table -- call
them ncmdispatch.c and ncmdispatch.h. Look at
libsrc/nc3dispatch.[ch] for an example.

As part of the ncmdispatch.c file, you must define the following.
@example
NC_Dispatch NCM_dispatcher = @{
NC_DISPATCH_NCM,
NCM_create,
NCM_open,
...
@};

int
NCM_initialize(void)
@{
    NCM_dispatch_table = &NCM_dispatcher;
    return NC_NOERR;
@}
@end example

Assuming that the in-memory library does not require any
external libraries, then the Makefile.am will look something
like this.
@example
NCM_SOURCES = ncmdispatch.c ncmdispatch.h ...
AM_CPPFLAGS +=  -I$(top_srcdir)/libsrc -I$(top_srcdir)/libdispatch
libnetcdfm_la_SOURCES = $(NCM_SOURCES)
noinst_LTLIBRARIES = libnetcdfm.la
@end example

@unnumberedsec Step 6.
Provide for the inclusion of this library in the final
libnetcdf library. This is accomplished by modifying
liblib/Makefile.am by adding something like the following.
@example
if USE_NETCDFM
   libnetcdf_la_LIBADD += $(top_builddir)/libsrcm/libnetcdfm.la
endif
@end example

@unnumberedsec Step 7.
Modify the NC_initialize function in liblib/stub.c by
adding appropriate references to the NCM dispatch function.
@example
#ifdef USE_NETCDFM
extern int NCM_initialize(void);
#endif
...
int NC_initialize(void)
@{
...
#ifdef USE_DAP
    if((stat = NCM_initialize())) return stat;
#endif
...
@}
@end example

@unnumberedsec Step 8.
Add a directory of tests; ncm_test, say. The file
ncm_test/Makefile.am will look something like this.
@example
# These files are created by the tests.
CLEANFILES = ...
# These are the tests which are always run.
TESTPROGRAMS = test1 test2 ...
test1_SOURCES = test1.c ...
...
# Set up the tests.
check_PROGRAMS = $(TESTPROGRAMS)
TESTS = $(TESTPROGRAMS)
# Any extra files required by the tests
EXTRA_DIST = ...
@end example

@unnumberedsec Step 9.
Provide for libnetcdfm to be constructed by adding the
following to the top-level Makefile.am.
@example
if USE_NETCDFM
NCM=libsrcm
NCMTESTDIR=ncm_test
endif
...
SUBDIRS = ... $(DISPATCHDIR)  $(NCM) ... $(NCMTESTDIR)
@end example

@subsection Choosing a Dispatch Table
The dispatch table is chosen in the NC_create and the
NC_open procedures in libdispatch/netcdf.c.  The decision is
currently based on the following pieces of information.
@itemize
@item
The file path -- this can be used to detect, for example, a DAP url versus a normal file system file. 
@item
The mode argument -- this can be used to detect, for example, what kind of file to create: netcdf-3, netcdf-4, 64-bit netcdf-3, etc. 
@item
For nc_open and when the file path references a real file, the contents of the file can also be used to determine the dispatch table. 
@item
Although currently not used, this code could be modified to also use other pieces of information such as environment variables.
@end itemize

In addition to the above, there is one additional mechanism
to force the use of a specific dispatch table. The procedure
"NC_set_dispatch_override()" can be invoked to specify a
dispatch table.

When adding a new dispatcher, it is necessary to modify
NC_create and NC_open in libdispatch/netcdf.c to detect when
it is appropriate to use the NCM dispatcher.
Some possibilities are as follows.
@enumerate
@item  Add a new mode flag: say NC_NETCDFM. 
@item  Use an environment variable. 
@item  Define a special file path format that indicates the need to use a special dispatch table.
@end enumerate

@subsection Special Dispatch Table Signatures.
Several of the entries in the dispatch table are
significantly different than those of the external API.

@subsubsection Create/Open
The create table entry and the open table entry have the
following signatures respectively.
@example
int (*create)(const char *path, int cmode,
           size_t initialsz, int basepe, size_t *chunksizehintp,
           int useparallel, MPI_Comm comm, MPI_Info info,
           struct NC_Dispatch*, struct NC** ncp);

int (*open)(const char *path, int mode,
         int basepe, size_t *chunksizehintp,
         int use_parallel, MPI_Comm comm, MPI_Info info,
         NC_Dispatch*, NC** ncp);
@end example

The key difference is that these are the union of all the
possible create/open signatures from the netcdf.h API. Note
especially the last two parameters. The dispatch table is
included in case the create function (e.g. NCM_create) needs
to invoke other dispatch functions. The very last parameter
is a pointer to a pointer to an NC instance. It is expected
that the create function will allocate and fill in an
instance of an "NC" object and return a pointer to it in the
ncp parameter.

@subsubsection Notes:
@itemize
@item
As with the existing code, and when MPI is not being used, the comm
and info parameters should be passed in as 0.  This is taken care of in the
nc_open and nc_create API procedures in libdispatch/netcdf.c.

@item
In fact, the object returned in the ncp parameter does
not actually have to be an instance of struct NC.  It only
needs to "look like it for the first few fields.  This is,
in effect, a fake version of subclassing.  Let us suppose
that the NCM_create function uses a struct NCM object. The
initial part of the definition of NCM must match the fields
at the beginning of struct NC between the comments
BEGIN_COMMON and END_COMMON. So, we would have the
following.
@example
typedef struct NCM @{
/*BEGIN COMMON*/
        int ext_ncid; /* uid «« 16 */
        int int_ncid; /* unspecified other id */
        struct NC_Dispatch* dispatch;
#ifdef USE_DAP
        struct NCDRNO* drno;
#endif
/*END COMMON*/
...
@} NCM;
@end example
This allows the pointer to the NCM object to be cast as an instance of NC* and its pointer returned in the ncp file.
Eventually, this will be replaced with a separate structure containing the common fields.
@end itemize

@subsubsection put_vara/get_vara
@example
int (*put_vara)(int ncid, int varid, const size_t *start, const size_t *count,
                     const void *value, nc_type memtype);

int (*get_vara)(int ncid, int varid, const size_t *start, const size_t *count,
                void *value, nc_type memtype);
@end example

Most of the parameters are similar to the netcdf API
parameters. The last parameter, however, is the type of the
data in memory. Additionally, instead of using an "int
islong" parameter, the memtype will be either NC_INT or
NC_INT64, depending on the value of sizeof(long). This means
that even netcdf-3 code must be prepared to encounter the
NC_INT64 type.

@subsubsection put_attr/get_attr
@example
int (*get_att)(int ncid, int varid, const char *name,
                    void *value, nc_type memtype);

int (*put_att)(int ncid, int varid, const char *name, nc_type datatype, size_t len,
               const void *value, nc_type memtype);
@end example

Again, the key difference is the memtype parameter. As with
put/get_vara, it used NC_INT64 to encode the long case.

@subsection NetCDF Library Assembly
The assembly of the final libnetcdf library occurs in the directory
liblib. The Makefile uses all of the available configuration flags
to decide which component libraries will be added to libnetcdf to produce
the final library. In addition, the proper version of netcdf.h will have
been placed in liblib: either the version from libsrc or the version from
libsrc4 depending on the USE_NETCDF4 flag.

@subsection Utility Construction
All of the utilities and the test directories (nctest, nc_test, ...)
are expected to obtain their libnetcdf library and their netcdf.h
from the ones in liblib.

@subsection Miscellaneous Notes
@enumerate
@item
It may be desirable to include a few test cases in the
libsrcm directory. Libsrc4, for example, has quite a number
of such tests. In order to do this, it is necessary to
create a number of stub definitions so that the library will
compile and load with the test cases. The file
libsrc/stub3.c shows a typical stub file.

@end enumerate

@node Combined Index,  , Internal Dispatch Table, Top
@unnumbered Index

@printindex cp

@bye
End:
