This file documents NCO, a collection of utilities to manipulate and
analyze netCDF files.
Copyright (C) 1995-2010 Charlie Zender
This is the first edition of the `NCO User's Guide',
and is consistent with version 2 of `texinfo.tex'.
Permission is granted to copy, distribute and/or modify this document
under the terms of the GNU Free Documentation License, Version 1.3 or
any later version published by the Free Software Foundation; with no
Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. The
license is available online at `http://www.gnu.org/copyleft/fdl.html'
The original author of this software, Charlie Zender, wants to
improve it with the help of your suggestions, improvements,
bug-reports, and patches.
Charlie Zender <surname at uci dot edu> (yes, my surname is zender)
3200 Croul Hall
Department of Earth System Science
University of California, Irvine
Irvine, CA 92697-3100
NCO User's Guide
****************
_Note to readers of the NCO User's Guide in Info format_: _The NCO
User's Guide in PDF format (./nco.pdf) (also on SourceForge
(http://nco.sf.net/nco.pdf)) contains the complete NCO documentation._
This Info documentation is equivalent except it refers you to the
printed (i.e., DVI, PostScript, and PDF) documentation for description
of complex mathematical expressions.
The netCDF Operators, or NCO, are a suite of programs known as
operators. The operators facilitate manipulation and analysis of data
stored in the self-describing netCDF format, available from
(`http://www.unidata.ucar.edu/packages/netcdf'). Each NCO operator
(e.g., ncks) takes netCDF input file(s), performs an operation (e.g.,
averaging, hyperslabbing, or renaming), and outputs a processed netCDF
file. Although most users of netCDF data are involved in scientific
research, these data formats, and thus NCO, are generic and are equally
useful in fields from agriculture to zoology. The NCO User's Guide
illustrates NCO use with examples from the field of climate modeling
and analysis. The NCO homepage is `http://nco.sf.net', and there is a
mirror at `http://dust.ess.uci.edu/nco'.
This documentation is for NCO version 4.0.1. It was last updated
5 April 2010. Corrections, additions, and rewrites of this
documentation are very welcome.
Enjoy,
Charlie Zender
Foreword
********
NCO is the result of software needs that arose while I worked on
projects funded by NCAR, NASA, and ARM. Thinking they might prove
useful as tools or templates to others, it is my pleasure to provide
them freely to the scientific community. Many users (most of whom I
have never met) have encouraged the development of NCO. Thanks
espcially to Jan Polcher, Keith Lindsay, Arlindo da Silva, John
Sheldon, and William Weibel for stimulating suggestions and
correspondence. Your encouragment motivated me to complete the `NCO
User's Guide'. So if you like NCO, send me a note! I should mention
that NCO is not connected to or officially endorsed by Unidata, ACD,
ASP, CGD, or Nike.
Charlie Zender
May 1997
Boulder, Colorado
Major feature improvements entitle me to write another Foreword. In
the last five years a lot of work has been done to refine NCO. NCO is
now an open source project and appears to be much healthier for it.
The list of illustrious institutions that do not endorse NCO continues
to grow, and now includes UCI.
Charlie Zender
October 2000
Irvine, California
The most remarkable advances in NCO capabilities in the last few years
are due to contributions from the Open Source community. Especially
noteworthy are the contributions of Henry Butowsky and Rorik Peterson.
Charlie Zender
January 2003
Irvine, California
NCO has been generously supported from 2004-2008 by US National Science
Foundation (NSF) grant IIS-0431203
(http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0431203).
This support allowed me to maintain and extend core NCO code, and
others to advance NCO in new directions: Gayathri Venkitachalam helped
implement MPI; Harry Mangalam improved regression testing and
benchmarking; Daniel Wang developed the server-side capability, SWAMP;
and Henry Butowsky, a long-time contributor, developed `ncap2'. This
support also led NCO to debut in professional journals and meetings.
The personal and professional contacts made during this evolution have
been immensely rewarding.
Charlie Zender
March 2008
Grenoble, France
Summary
*******
This manual describes NCO, which stands for netCDF Operators. NCO is a
suite of programs known as "operators". Each operator is a standalone,
command line program executed at the shell-level like, e.g., `ls' or
`mkdir'. The operators take netCDF files (including HDF5 files
constructed using the netCDF API) as input, perform an operation (e.g.,
averaging or hyperslabbing), and produce a netCDF file as output. The
operators are primarily designed to aid manipulation and analysis of
data. The examples in this documentation are typical applications of
the operators for processing climate model output. This stems from
their origin, though the operators are as general as netCDF itself.
1 Introduction
**************
1.1 Availability
================
The complete NCO source distribution is currently distributed as a
"compressed tarfile" from `http://sf.net/projects/nco' and from
`http://dust.ess.uci.edu/nco/nco.tar.gz'. The compressed tarfile must
be uncompressed and untarred before building NCO. Uncompress the file
with `gunzip nco.tar.gz'. Extract the source files from the resulting
tarfile with `tar -xvf nco.tar'. GNU `tar' lets you perform both
operations in one step with `tar -xvzf nco.tar.gz'.
The documentation for NCO is called the `NCO User's Guide'. The
`User's Guide' is available in Postscript, HTML, DVI, TeXinfo, and Info
formats. These formats are included in the source distribution in the
files `nco.ps', `nco.html', `nco.dvi', `nco.texi', and `nco.info*',
respectively. All the documentation descends from a single source file,
`nco.texi' (1). Hence the documentation in every format is very
similar. However, some of the complex mathematical expressions needed
to describe `ncwa' can only be displayed in DVI, Postscript, and PDF
formats.
A complete list of papers and publications on/about NCO is available
on the NCO homepage. Most of these are freely available. The primary
refereed publications are fxm ZeM06 and fxm Zen07. These contain
copyright restrictions which limit their redistribution, but they are
freely available in preprint form from the NCO.
If you want to quickly see what the latest improvements in NCO are
(without downloading the entire source distribution), visit the NCO
homepage at `http://nco.sf.net'. The HTML version of the `User's
Guide' is also available online through the World Wide Web at URL
`http://nco.sf.net/nco.html'. To build and use NCO, you must have
netCDF installed. The netCDF homepage is
`http://www.unidata.ucar.edu/packages/netcdf'.
New NCO releases are announced on the netCDF list and on the
`nco-announce' mailing list
`http://lists.sf.net/mailman/listinfo/nco-announce'.
---------- Footnotes ----------
(1) To produce these formats, `nco.texi' was simply run through the
freely available programs `texi2dvi', `dvips', `texi2html', and
`makeinfo'. Due to a bug in TeX, the resulting Postscript file,
`nco.ps', contains the Table of Contents as the final pages. Thus if
you print `nco.ps', remember to insert the Table of Contents after the
cover sheet before you staple the manual.
1.2 Operating systems compatible with NCO
=========================================
NCO has been successfully ported and tested and is known to work on the
following 32- and 64-bit platforms: IBM AIX 4.x, 5.x, FreeBSD 4.x,
GNU/Linux 2.x, LinuxPPC, LinuxAlpha, LinuxARM, LinuxSparc64, SGI IRIX
5.x and 6.x, MacOS X 10.x, NEC Super-UX 10.x, DEC OSF, Sun SunOS 4.1.x,
Solaris 2.x, Cray UNICOS 8.x-10.x, and MS Windows95 and all later
versions. If you port the code to a new operating system, please send
me a note and any patches you required.
The major prerequisite for installing NCO on a particular platform
is the successful, prior installation of the netCDF library (and, as of
2003, the UDUnits library). Unidata has shown a commitment to
maintaining netCDF and UDUnits on all popular UNIX platforms, and is
moving towards full support for the Microsoft Windows operating system
(OS). Given this, the only difficulty in implementing NCO on a
particular platform is standardization of various C and Fortran
interface and system calls. NCO code is tested for ANSI compliance by
compiling with C compilers including those from GNU (`gcc -std=c99
-pedantic -D_BSD_SOURCE -D_POSIX_SOURCE' -Wall) (1), Comeau Computing
(`como --c99'), Cray (`cc'), HP/Compaq/DEC (`cc'), IBM (`xlc -c
-qlanglvl=extc99'), Intel (`icc -std=c99'), NEC (`cc'), PathScale
(QLogic) (`pathcc -std=c99'), PGI (`pgcc -c9x'), SGI (`cc -c99'), and
Sun (`cc'). NCO (all commands and the `libnco' library) and the C++
interface to netCDF (called `libnco_c++') comply with the ISO C++
standards as implemented by Comeau Computing (`como'), Cray (`CC'), GNU
(`g++ -Wall'), HP/Compaq/DEC (`cxx'), IBM (`xlC'), Intel (`icc'), NEC
(`c++'), PathScale (Qlogic) (`pathCC'), PGI (`pgCC'), SGI (`CC
-LANG:std'), and Sun (`CC -LANG:std'). See `nco/bld/Makefile' and
`nco/src/nco_c++/Makefile.old' for more details and exact settings.
Until recently (and not even yet), ANSI-compliant has meant
compliance with the 1989 ISO C-standard, usually called C89 (with minor
revisions made in 1994 and 1995). C89 lacks variable-size arrays,
restricted pointers, some useful `printf' formats, and many
mathematical special functions. These are valuable features of C99,
the 1999 ISO C-standard. NCO is C99-compliant where possible and
C89-compliant where necessary. Certain branches in the code are
required to satisfy the native SGI and SunOS C compilers, which are
strictly ANSI C89 compliant, and cannot benefit from C99 features.
However, C99 features are fully supported by modern AIX, GNU, Intel,
NEC, Solaris, and UNICOS compilers. NCO requires a C99-compliant
compiler as of NCO version 2.9.8, released in August, 2004.
The most time-intensive portion of NCO execution is spent in
arithmetic operations, e.g., multiplication, averaging, subtraction.
These operations were performed in Fortran by default until August,
1999. This was a design decision based on the relative speed of
Fortran-based object code vs. C-based object code in late 1994.
C compiler vectorization capabilities have dramatically improved since
1994. We have accordingly replaced all Fortran subroutines with
C functions. This greatly simplifies the task of building NCO on
nominally unsupported platforms. As of August 1999, NCO built entirely
in C by default. This allowed NCO to compile on any machine with an
ANSI C compiler. In August 2004, the first C99 feature, the `restrict'
type qualifier, entered NCO in version 2.9.8. C compilers can obtain
better performance with C99 restricted pointers since they inform the
compiler when it may make Fortran-like assumptions regarding pointer
contents alteration. Subsequently, NCO requires a C99 compiler to
build correctly (2).
In January 2009, NCO version 3.9.6 was the first to link to the GNU
Scientific Library (GSL). GSL must be version 1.4 or later. NCO, in
particular `ncap2', uses the GSL special function library to evaluate
geoscience-relevant mathematics such as Bessel functions, Legendre
polynomials, and incomplete gamma functions (*note GSL special
functions::).
In June 2005, NCO version 3.0.1 began to take advantage of C99
mathematical special functions. These include the standarized gamma
function (called `tgamma()' for "true gamma"). NCO automagically takes
advantage of some GNU Compiler Collection (GCC) extensions to ANSI C.
As of July 2000 and NCO version 1.2, NCO no longer performs
arithmetic operations in Fortran. We decided to sacrifice executable
speed for code maintainability. Since no objective statistics were
ever performed to quantify the difference in speed between the Fortran
and C code, the performance penalty incurred by this decision is
unknown. Supporting Fortran involves maintaining two sets of routines
for every arithmetic operation. The `USE_FORTRAN_ARITHMETIC' flag is
still retained in the `Makefile'. The file containing the Fortran
code, `nco_fortran.F', has been deprecated but a volunteer
(Dr. Frankenstein?) could resurrect it. If you would like to volunteer
to maintain `nco_fortran.F' please contact me.
---------- Footnotes ----------
(1) The `_BSD_SOURCE' token is required on some Linux platforms where
`gcc' dislikes the network header files like `netinet/in.h').
(2) NCO may still build with an ANSI or ISO C89 or C94/95-compliant
compiler if the C pre-processor undefines the `restrict' type
qualifier, e.g., by invoking the compiler with `-Drestrict='''.
1.2.1 Compiling NCO for Microsoft Windows OS
--------------------------------------------
NCO has been successfully ported and tested on the Microsoft Windows
(95/98/NT/2000/XP) operating systems. The switches necessary to
accomplish this are included in the standard distribution of NCO.
Using the freely available Cygwin (formerly gnu-win32) development
environment (1), the compilation process is very similar to installing
NCO on a UNIX system. Set the `PVM_ARCH' preprocessor token to `WIN32'.
Note that defining `WIN32' has the side effect of disabling Internet
features of NCO (see below). NCO should now build like it does on UNIX.
The least portable section of the code is the use of standard UNIX
and Internet protocols (e.g., `ftp', `rcp', `scp', `sftp', `getuid',
`gethostname', and header files `<arpa/nameser.h>' and `<resolv.h>'). Fortunately,
these UNIX-y calls are only invoked by the single NCO subroutine which
is responsible for retrieving files stored on remote systems (*note
Remote storage::). In order to support NCO on the Microsoft Windows
platforms, this single feature was disabled (on Windows OS only). This
was required by Cygwin 18.x--newer versions of Cygwin may support these
protocols (let me know if this is the case). The NCO operators should
behave identically on Windows and UNIX platforms in all other respects.
---------- Footnotes ----------
(1) The Cygwin package is available from
`http://sourceware.redhat.com/cygwin'
Currently, Cygwin 20.x comes with the GNU C/C++/Fortran compilers
(`gcc', `g++', `g77'). These GNU compilers may be used to build the
netCDF distribution itself.
1.3 Libraries
=============
Like all executables, the NCO operators can be built using dynamic
linking. This reduces the size of the executable and can result in
significant performance enhancements on multiuser systems.
Unfortunately, if your library search path (usually the
`LD_LIBRARY_PATH' environment variable) is not set correctly, or if the
system libraries have been moved, renamed, or deleted since NCO was
installed, it is possible NCO operators will fail with a message that
they cannot find a dynamically loaded (aka "shared object" or `.so')
library. This will produce a distinctive error message, such as
`ld.so.1: /usr/local/bin/ncea: fatal: libsunmath.so.1: can't open file:
errno=2'. If you received an error message like this, ask your system
administrator to diagnose whether the library is truly missing (1), or
whether you simply need to alter your library search path. As a final
remedy, you may re-compile and install NCO with all operators
statically linked.
---------- Footnotes ----------
(1) The `ldd' command, if it is available on your system, will tell
you where the executable is looking for each dynamically loaded
library. Use, e.g., `ldd `which ncea`'.
1.4 netCDF2/3/4 and HDF4/5 Support
==================================
netCDF version 2 was released in 1993. NCO (specifically `ncks') began
soon after this in 1994. netCDF 3.0 was released in 1996, and we were
eager to reap the performance advantages of the newer netCDF
implementation. One netCDF3 interface call (`nc_inq_libvers') was
added to NCO in January, 1998, to aid in maintainance and debugging.
In March, 2001, the final conversion of NCO to netCDF3 was completed
(coincidentally on the same day netCDF 3.5 was released). NCO
versions 2.0 and higher are built with the `-DNO_NETCDF_2' flag to
ensure no netCDF2 interface calls are used.
However, the ability to compile NCO with only netCDF2 calls is worth
maintaining because HDF version 4 (1) (available from HDF
(http://hdf.ncsa.uiuc.edu)) supports only the netCDF2 library calls
(see `http://hdf.ncsa.uiuc.edu/UG41r3_html/SDS_SD.fm12.html#47784').
Note that there are multiple versions of HDF. Currently HDF
version 4.x supports netCDF2 and thus NCO version 1.2.x. If NCO
version 1.2.x (or earlier) is built with only netCDF2 calls then all
NCO operators should work with HDF4 files as well as netCDF files (2). The
preprocessor token `NETCDF2_ONLY' exists in NCO version 1.2.x to
eliminate all netCDF3 calls. Only versions of NCO numbered 1.2.x and
earlier have this capability. The NCO 1.2.x branch will be maintained
with bugfixes only (no new features) until HDF begins to fully support
the netCDF3 interface (which is employed by NCO 2.x). If, at
compilation time, `NETCDF2_ONLY' is defined, then NCO version 1.2.x
will not use any netCDF3 calls and, if linked properly, the resulting
NCO operators will work with HDF4 files. The `Makefile' supplied with
NCO 1.2.x is written to simplify building in this HDF capability. When
NCO is built with `make HDF4=Y', the `Makefile' sets all required
preprocessor flags and library links to build with the HDF4 libraries
(which are assumed to reside under `/usr/local/hdf4', edit the
`Makefile' to suit your installation).
HDF version 5 became available in 1999, but did not support netCDF
(or, for that matter, Fortran) as of December 1999. By early 2001,
HDF5 did support Fortran90. In 2004, Unidata and NCSA began a project
to implement the HDF5 features necessary to support the netCDF API.
NCO version 3.0.3 added support for reading/writing netCDF4-formatted
HDF5 files in October, 2005. See *note Selecting Output File Format::
for more details.
HDF support for netCDF was completed with HDF5 version version 1.8
in 2007. The netCDF front-end that uses this HDF5 back-end was
completed and released soon after as netCDF version 4. Download it
from the netCDF4
(http://my.unidata.ucar.edu/content/software/netcdf/netcdf-4) website.
NCO version 3.9.0 added support for all netCDF4 atomic data types
except `NC_STRING' in May, 2007. Support for additional netCDF4
features has been incremental. We add one netCDF4 feature at a time.
You must build NCO with netCDF4 to obtain this support.
The main netCDF4 features that NCO currently supports are the new
atomic data types, Lempel-Ziv compression, and chunking. The new
atomic data types are `NC_UBYTE', `NC_USHORT', `NC_UINT', `NC_INT64',
and `NC_UINT64'. Eight-byte integer support is an especially useful
improvement from netCDF3. All NCO operators support these types, e.g.,
`ncks' copies and prints them, `ncra' averages them, and `ncap2'
processes algebraic scripts with them. `ncks' prints compression
information, if any, to screen.
NCO version 3.9.9 (June, 2009) added support for the `NC_STRING'
netCDF4 atomic data type. Ragged arrays of strings are supported.
NCO version 3.9.1 (June, 2007) added support for netCDF4 Lempel-Ziv
deflation. Lempel-Ziv deflation is a lossless compression technique.
See *note Deflation:: for more details.
NCO version 3.9.9 (June, 2009) added support for netCDF4 chunking.
See *note Chunking:: for more details.
netCDF4-enabled NCO handles netCDF3 files without change. In
addition, it automagically handles netCDF4 (HDF5) files: If you feed
NCO netCDF3 files, it produces netCDF3 output. If you feed NCO netCDF4
files, it produces netCDF4 output. Use the handy-dandy `-4' switch to
request netCDF4 output from netCDF3 input, i.e., to convert netCDF3 to
netCDF4. See *note Selecting Output File Format:: for more details.
Use appropriate caution while netCDF4 is beta software. Problems
with netCDF4 and HDF libraries are still being fixed. NCO support for
netCDF4 atomic types is relatively untested. Binary NCO distributions
(RPMs and debs) still use netCDF3.
For now you must build NCO from source to get netCDF4 support.
Typically, one specifies the root of the netCDF4-beta installation
directory. Do this with the `NETCDF4_ROOT' variable. Then use your
preferred NCO build mechanism, e.g.,
export NETCDF4_ROOT=/usr/local/netcdf4 # Set netCDF4 location
cd ~/nco;./configure --enable-netcdf4 # Configure mechanism -or-
cd ~/nco/bld;./make NETCDF4=Y allinone # Old Makefile mechanism
Our short term goal is to track the netCDF4-beta releases, keep the
new netCDF4 atomic type support working, and iron out any problems.
Our long term goal is to utilize more of the extensive new netCDF4
feature set. The next major netCDF4 feature we are likely to utilize is
parallel I/O. We will enable this in the MPI netCDF operators.
---------- Footnotes ----------
(1) The Hierarchical Data Format, or HDF, is another self-describing
data format similar to, but more elaborate than, netCDF.
(2) One must link the NCO code to the HDF4 MFHDF library instead of
the usual netCDF library. Does `MF' stands for Mike Folk? Perhaps.
In any case, the MFHDF library only supports netCDF2 calls. Thus I
will try to keep this capability in NCO as long as it is not too much
trouble.
1.5 Help Requests and Bug Reports
=================================
We generally receive three categories of mail from users: help requests,
bug reports, and feature requests. Notes saying the equivalent of
"Hey, NCO continues to work great and it saves me more time everyday
than it took to write this note" are a distant fourth.
There is a different protocol for each type of request. The
preferred etiquette for all communications is via NCO Project Forums.
Do not contact project members via personal e-mail unless your request
comes with money or you have damaging information about our personal
lives. _Please use the Forums_--they preserve a record of the questions
and answers so that others can learn from our exchange. Also, since
NCO is government-funded, this record helps us provide program officers
with information they need to evaluate our project.
Before posting to the NCO forums described below, you might first
register (https://sf.net/account/register.php) your name and email
address with SourceForge.net or else all of your postings will be
attributed to "nobody". Once registered you may choose to "monitor"
any forum and to receive (or not) email when there are any postings
including responses to your questions. We usually reply to the forum
message, not to the original poster.
If you want us to include a new feature in NCO, check first to see
if that feature is already on the TODO (file:./TODO) list. If it is,
why not implement that feature yourself and send us the patch? If the
feature is not yet on the list, then send a note to the NCO Discussion
forum (http://sf.net/forum/forum.php?forum_id=9829).
Read the manual before reporting a bug or posting a help request.
Sending questions whose answers are not in the manual is the best way
to motivate us to write more documentation. We would also like to
accentuate the contrapositive of this statement. If you think you have
found a real bug _the most helpful thing you can do is simplify the
problem to a manageable size and then report it_. The first thing to
do is to make sure you are running the latest publicly released version
of NCO.
Once you have read the manual, if you are still unable to get NCO to
perform a documented function, submit a help request. Follow the same
procedure as described below for reporting bugs (after all, it might be
a bug). That is, describe what you are trying to do, and include the
complete commands (run with `-D 5'), error messages, and version of NCO
(with `-r'). Post your help request to the NCO Help forum
(http://sf.net/forum/forum.php?forum_id=9830).
If you think you used the right command when NCO misbehaves, then
you might have found a bug. Incorrect numerical answers are the
highest priority. We usually fix those within one or two days. Core
dumps and sementation violations receive lower priority. They are
always fixed, eventually.
How do you simplify a problem that reveal a bug? Cut out extraneous
variables, dimensions, and metadata from the offending files and re-run
the command until it no longer breaks. Then back up one step and
report the problem. Usually the file(s) will be very small, i.e., one
variable with one or two small dimensions ought to suffice. Run the
operator with `-r' and then run the command with `-D 5' to increase the
verbosity of the debugging output. It is very important that your
report contain the exact error messages and compile-time environment.
Include a copy of your sample input file, or place one on a publically
accessible location, of the file(s). Post the full bug report to the
NCO Project buglist (http://sf.net/bugs/?group_id=3331).
Build failures count as bugs. Our limited machine access means we
cannot fix all build failures. The information we need to diagnose,
and often fix, build failures are the three files output by GNU build
tools, `nco.config.log.${GNU_TRP}.foo', `nco.configure.${GNU_TRP}.foo',
and `nco.make.${GNU_TRP}.foo'. The file `configure.eg' shows how to
produce these files. Here `${GNU_TRP}' is the "GNU architecture
triplet", the CHIP-VENDOR-OS string returned by `config.guess'. Please
send us your improvements to the examples supplied in `configure.eg'. The
regressions archive at `http://dust.ess.uci.edu/nco/rgr' contains the
build output from our standard test systems. You may find you can
solve the build problem yourself by examining the differences between
these files and your own.
2 Operator Strategies
*********************
2.1 Philosophy
==============
The main design goal is command line operators which perform useful,
scriptable operations on netCDF files. Many scientists work with
models and observations which produce too much data to analyze in
tabular format. Thus, it is often natural to reduce and massage this
raw or primary level data into summary, or second level data, e.g.,
temporal or spatial averages. These second level data may become the
inputs to graphical and statistical packages, and are often more
suitable for archival and dissemination to the scientific community.
NCO performs a suite of operations useful in manipulating data from the
primary to the second level state. Higher level interpretive languages
(e.g., IDL, Yorick, Matlab, NCL, Perl, Python), and lower level
compiled languages (e.g., C, Fortran) can always perform any task
performed by NCO, but often with more overhead. NCO, on the other
hand, is limited to a much smaller set of arithmetic and metadata
operations than these full blown languages.
Another goal has been to implement enough command line switches so
that frequently used sequences of these operators can be executed from a
shell script or batch file. Finally, NCO was written to consume the
absolute minimum amount of system memory required to perform a given
job. The arithmetic operators are extremely efficient; their exact
memory usage is detailed in *note Memory Requirements::.
2.2 Climate Model Paradigm
==========================
NCO was developed at NCAR to aid analysis and manipulation of datasets
produced by General Circulation Models (GCMs). Datasets produced by
GCMs share many features with all gridded scientific datasets and so
provide a useful paradigm for the explication of the NCO operator set.
Examples in this manual use a GCM paradigm because latitude, longitude,
time, temperature and other fields related to our natural environment
are as easy to visualize for the layman as the expert.
2.3 Temporary Output Files
==========================
NCO operators are designed to be reasonably fault tolerant, so that if
there is a system failure or the user aborts the operation (e.g., with
`C-c'), then no data are lost. The user-specified OUTPUT-FILE is only
created upon successful completion of the operation (1). This is
accomplished by performing all operations in a temporary copy of
OUTPUT-FILE. The name of the temporary output file is constructed by
appending `.pid<PROCESS ID>.<OPERATOR NAME>.tmp' to the user-specified
OUTPUT-FILE name. When the operator completes its task with no fatal
errors, the temporary output file is moved to the user-specified
OUTPUT-FILE. Note the construction of a temporary output file uses
more disk space than just overwriting existing files "in place"
(because there may be two copies of the same file on disk until the NCO
operation successfully concludes and the temporary output file
overwrites the existing OUTPUT-FILE). Also, note this feature
increases the execution time of the operator by approximately the time
it takes to copy the OUTPUT-FILE. Finally, note this feature allows
the OUTPUT-FILE to be the same as the INPUT-FILE without any danger of
"overlap".
Other safeguards exist to protect the user from inadvertently
overwriting data. If the OUTPUT-FILE specified for a command is a
pre-existing file, then the operator will prompt the user whether to
overwrite (erase) the existing OUTPUT-FILE, attempt to append to it, or
abort the operation. However, in processing large amounts of data, too
many interactive questions slows productivity. Therefore NCO also
implements two ways to override its own safety features, the `-O' and
`-A' switches. Specifying `-O' tells the operator to overwrite any
existing OUTPUT-FILE without prompting the user interactively.
Specifying `-A' tells the operator to attempt to append to any existing
OUTPUT-FILE without prompting the user interactively. These switches
are useful in batch environments because they suppress interactive
keyboard input.
---------- Footnotes ----------
(1) The `ncrename' operator is an exception to this rule. *Note
ncrename netCDF Renamer::.
2.4 Appending Variables
=======================
Adding variables from one file to another is often desirable. This is
referred to as "appending", although some prefer the terminology
"merging" (1) or "pasting". Appending is often confused with what NCO
calls "concatenation". In NCO, concatenation refers to splicing a
variable along the record dimension. Appending, on the other hand,
refers to adding variables from one file to another (2). In this
sense, `ncks' can append variables from one file to another file. This
capability is invoked by naming two files on the command line,
INPUT-FILE and OUTPUT-FILE. When OUTPUT-FILE already exists, the user
is prompted whether to "overwrite", "append/replace", or "exit" from
the command. Selecting "overwrite" tells the operator to erase the
existing OUTPUT-FILE and replace it with the results of the operation.
Selecting "exit" causes the operator to exit--the OUTPUT-FILE will not
be touched in this case. Selecting "append/replace" causes the
operator to attempt to place the results of the operation in the
existing OUTPUT-FILE, *Note ncks netCDF Kitchen Sink::.
The simplest way to create the union of two files is
ncks -A fl_1.nc fl_2.nc
This puts the contents of `fl_1.nc' into `fl_2.nc'. The `-A' is
optional. On output, `fl_2.nc' is the union of the input files,
regardless of whether they share dimensions and variables, or are
completely disjoint. The append fails if the input files have
differently named record dimensions (since netCDF supports only one),
or have dimensions of the same name but different sizes.
---------- Footnotes ----------
(1) The terminology "merging" is reserved for an (unwritten)
operator which replaces hyperslabs of a variable in one file with
hyperslabs of the same variable from another file
(2) Yes, the terminology is confusing. By all means mail me if you
think of a better nomenclature. Should NCO use "paste" instead of
"append"?
2.5 Simple Arithmetic and Interpolation
=======================================
Users comfortable with NCO semantics may find it easier to perform some
simple mathematical operations in NCO rather than higher level
languages. `ncbo' (*note ncbo netCDF Binary Operator::) does file
addition, subtraction, multiplication, division, and broadcasting.
`ncflint' (*note ncflint netCDF File Interpolator::) does file
addition, subtraction, multiplication and interpolation. Sequences of
these commands can accomplish simple but powerful operations from the
command line.
2.6 Averagers vs. Concatenators
===============================
The most frequently used operators of NCO are probably the averagers
and concatenators. Because there are so many permutations of averaging
(e.g., across files, within a file, over the record dimension, over
other dimensions, with or without weights and masks) and of
concatenating (across files, along the record dimension, along other
dimensions), there are currently no fewer than five operators which
tackle these two purposes: `ncra', `ncea', `ncwa', `ncrcat', and
`ncecat'. These operators do share many capabilities (1), but each has
its unique specialty. Two of these operators, `ncrcat' and `ncecat',
are for concatenating hyperslabs across files. The other two
operators, `ncra' and `ncea', are for averaging hyperslabs across files
(2). First, let's describe the concatenators, then the averagers.
---------- Footnotes ----------
(1) Currently `ncea' and `ncrcat' are symbolically linked to the
`ncra' executable, which behaves slightly differently based on its
invocation name (i.e., `argv[0]'). These three operators share the
same source code, but merely have different inner loops.
(2) The third averaging operator, `ncwa', is the most sophisticated
averager in NCO. However, `ncwa' is in a different class than `ncra'
and `ncea' because it can only operate on a single file per invocation
(as opposed to multiple files). On that single file, however, `ncwa'
provides a richer set of averaging options--including weighting,
masking, and broadcasting.
2.6.1 Concatenators `ncrcat' and `ncecat'
-----------------------------------------
Joining independent files together along a record dimension is called
"concatenation". `ncrcat' is designed for concatenating record
variables, while `ncecat' is designed for concatenating fixed length
variables. Consider five files, `85.nc', `86.nc', ... `89.nc' each
containing a year's worth of data. Say you wish to create from them a
single file, `8589.nc' containing all the data, i.e., spanning all five
years. If the annual files make use of the same record variable, then
`ncrcat' will do the job nicely with, e.g., `ncrcat 8?.nc 8589.nc'.
The number of records in the input files is arbitrary and can vary from
file to file. *Note ncrcat netCDF Record Concatenator::, for a
complete description of `ncrcat'.
However, suppose the annual files have no record variable, and thus
their data are all fixed length. For example, the files may not be
conceptually sequential, but rather members of the same group, or
"ensemble". Members of an ensemble may have no reason to contain a
record dimension. `ncecat' will create a new record dimension (named
RECORD by default) with which to glue together the individual files
into the single ensemble file. If `ncecat' is used on files which
contain an existing record dimension, that record dimension is
converted to a fixed-length dimension of the same name and a new record
dimension (named `record') is created. Consider five realizations,
`85a.nc', `85b.nc', ... `85e.nc' of 1985 predictions from the same
climate model. Then `ncecat 85?.nc 85_ens.nc' glues the individual
realizations together into the single file, `85_ens.nc'. If an input
variable was dimensioned [`lat',`lon'], it will have dimensions
[`record',`lat',`lon'] in the output file. A restriction of `ncecat'
is that the hyperslabs of the processed variables must be the same from
file to file. Normally this means all the input files are the same
size, and contain data on different realizations of the same variables.
*Note ncecat netCDF Ensemble Concatenator::, for a complete description
of `ncecat'.
`ncpdq' makes it possible to concatenate files along any dimension,
not just the record dimension. First, use `ncpdq' to convert the
dimension to be concatenated (i.e., extended with data from other
files) into the record dimension. Second, use `ncrcat' to concatenate
these files. Finally, if desirable, use `ncpdq' to revert to the
original dimensionality. As a concrete example, say that files
`x_01.nc', `x_02.nc', ... `x_10.nc' contain time-evolving datasets from
spatially adjacent regions. The time and spatial coordinates are
`time' and `x', respectively. Initially the record dimension is `time'.
Our goal is to create a single file that contains joins all the
spatially adjacent regions into one single time-evolving dataset.
for idx in 01 02 03 04 05 06 07 08 09 10; do # Bourne Shell
ncpdq -a x,time x_${idx}.nc foo_${idx}.nc # Make x record dimension
done
ncrcat foo_??.nc out.nc # Concatenate along x
ncpdq -a time,x out.nc out.nc # Revert to time as record dimension
Note that `ncrcat' will not concatenate fixed-length variables,
whereas `ncecat' concatenates both fixed-length and record variables
along a new record variable. To conserve system memory, use `ncrcat'
where possible.
2.6.2 Averagers `ncea', `ncra', and `ncwa'
------------------------------------------
The differences between the averagers `ncra' and `ncea' are analogous
to the differences between the concatenators. `ncra' is designed for
averaging record variables from at least one file, while `ncea' is
designed for averaging fixed length variables from multiple files.
`ncra' performs a simple arithmetic average over the record dimension
of all the input files, with each record having an equal weight in the
average. `ncea' performs a simple arithmetic average of all the input
files, with each file having an equal weight in the average. Note that
`ncra' cannot average fixed-length variables, but `ncea' can average
both fixed-length and record variables. To conserve system memory, use
`ncra' rather than `ncea' where possible (e.g., if each INPUT-FILE is
one record long). The file output from `ncea' will have the same
dimensions (meaning dimension names as well as sizes) as the input
hyperslabs (*note ncea netCDF Ensemble Averager::, for a complete
description of `ncea'). The file output from `ncra' will have the same
dimensions as the input hyperslabs except for the record dimension,
which will have a size of 1 (*note ncra netCDF Record Averager::, for a
complete description of `ncra').
2.6.3 Interpolator `ncflint'
----------------------------
`ncflint' can interpolate data between or two files. Since no other
operators have this ability, the description of interpolation is given
fully on the `ncflint' reference page (*note ncflint netCDF File
Interpolator::). Note that this capability also allows `ncflint' to
linearly rescale any data in a netCDF file, e.g., to convert between
differing units.
2.7 Large Numbers of Files
==========================
Occasionally one desires to digest (i.e., concatenate or average)
hundreds or thousands of input files. Unfortunately, data archives
(e.g., NASA EOSDIS) may not name netCDF files in a format understood by
the `-n LOOP' switch (*note Specifying Input Files::) that
automagically generates arbitrary numbers of input filenames. The `-n
LOOP' switch has the virtue of being concise, and of minimizing the
command line. This helps keeps output file small since the command
line is stored as metadata in the `history' attribute (*note History
Attribute::). However, the `-n LOOP' switch is useless when there is no
simple, arithmetic pattern to the input filenames (e.g., `h00001.nc',
`h00002.nc', ... `h90210.nc'). Moreover, filename globbing does not
work when the input files are too numerous or their names are too
lengthy (when strung together as a single argument) to be passed by the
calling shell to the NCO operator (1). When this occurs, the ANSI
C-standard `argc'-`argv' method of passing arguments from the calling
shell to a C-program (i.e., an NCO operator) breaks down. There are
(at least) three alternative methods of specifying the input filenames
to NCO in environment-limited situations.
The recommended method for sending very large numbers (hundreds or
more, typically) of input filenames to the multi-file operators is to
pass the filenames with the UNIX "standard input" feature, aka `stdin':
# Pipe large numbers of filenames to stdin
/bin/ls | grep ${CASEID}_'......'.nc | ncecat -o foo.nc
This method avoids all constraints on command line size imposed by
the operating system. A drawback to this method is that the `history'
attribute (*note History Attribute::) does not record the name of any
input files since the names were not passed on the command line. This
makes determining the data provenance at a later date difficult. To
remedy this situation, multi-file operators store the number of input
files in the `nco_input_file_number' global attribute and the input
file list itself in the `nco_input_file_list' global attribute (*note
File List Attributes::). Although this does not preserve the exact
command used to generate the file, it does retains all the information
required to reconstruct the command and determine the data provenance.
A second option is to use the UNIX `xargs' command. This simple
example selects as input to `xargs' all the filenames in the current
directory that match a given pattern. For illustration, consider a
user trying to average millions of files which each have a six
character filename. If the shell buffer can not hold the results of
the corresponding globbing operator, `??????.nc', then the filename
globbing technique will fail. Instead we express the filename pattern
as an extended regular expression, `......\.nc' (*note Subsetting
Variables::). We use `grep' to filter the directory listing for this
pattern and to pipe the results to `xargs' which, in turn, passes the
matching filenames to an NCO multi-file operator, e.g., `ncecat'.
# Use xargs to transfer filenames on the command line
/bin/ls | grep ${CASEID}_'......'.nc | xargs -x ncecat -o foo.nc
The single quotes protect the only sensitive parts of the extended
regular expression (the `grep' argument), and allow shell interpolation
(the `${CASEID}' variable substitution) to proceed unhindered on the
rest of the command. `xargs' uses the UNIX pipe feature to append the
suitably filtered input file list to the end of the `ncecat' command
options. The `-o foo.nc' switch ensures that the input files supplied
by `xargs' are not confused with the output file name. `xargs' does,
unfortunately, have its own limit (usually about 20,000 characters) on
the size of command lines it can pass. Give `xargs' the `-x' switch to
ensure it dies if it reaches this internal limit. When this occurs,
use either the `stdin' method above, or the symbolic link presented
next.
Even when its internal limits have not been reached, the `xargs'
technique may not be sophisticated enough to handle all situations. A
full scripting language like Perl can handle any level of complexity of
filtering input filenames, and any number of filenames. The technique
of last resort is to write a script that creates symbolic links between
the irregular input filenames and a set of regular, arithmetic
filenames that the `-n LOOP' switch understands. For example, the
following Perl script a monotonically enumerated symbolic link to up to
one million `.nc' files in a directory. If there are 999,999 netCDF
files present, the links are named `000001.nc' to `999999.nc':
# Create enumerated symbolic links
/bin/ls | grep \.nc | perl -e \
'$idx=1;while(<STDIN>){chop;symlink $_,sprintf("%06d.nc",$idx++);}'
ncecat -n 999999,6,1 000001.nc foo.nc
# Remove symbolic links when finished
/bin/rm ??????.nc
The `-n LOOP' option tells the NCO operator to automatically
generate the filnames of the symbolic links. This circumvents any OS
and shell limits on command line size. The symbolic links are easily
removed once NCO is finished. One drawback to this method is that the
`history' attribute (*note History Attribute::) retains the filename
list of the symbolic links, rather than the data files themselves.
This makes it difficult to determine the data provenance at a later
date.
---------- Footnotes ----------
(1) The exact length which exceeds the operating system internal
limit for command line lengths varies from OS to OS and from shell to
shell. GNU `bash' may not have any arbitrary fixed limits to the size
of command line arguments. Many OSs cannot handle command line
arguments (including results of file globbing) exceeding 4096
characters.
2.8 Large Datasets
==================
"Large datasets" are those files that are comparable in size to the
amount of random access memory (RAM) in your computer. Many users of
NCO work with files larger than 100 MB. Files this large not only push
the current edge of storage technology, they present special problems
for programs which attempt to access the entire file at once, such as
`ncea' and `ncecat'. If you work with a 300 MB files on a machine with
only 32 MB of memory then you will need large amounts of swap space
(virtual memory on disk) and NCO will work slowly, or even fail. There
is no easy solution for this. The best strategy is to work on a
machine with sufficient amounts of memory and swap space. Since about
2004, many users have begun to produce or analyze files exceeding 2 GB
in size. These users should familiarize themselves with NCO's Large
File Support (LFS) capabilities (*note Large File Support::). The next
section will increase your familiarity with NCO's memory requirements.
With this knowledge you may re-design your data reduction approach to
divide the problem into pieces solvable in memory-limited situations.
If your local machine has problems working with large files, try
running NCO from a more powerful machine, such as a network server.
Certain machine architectures, e.g., Cray UNICOS, have special commands
which allow one to increase the amount of interactive memory. On Cray
systems, try to increase the available memory with the `ilimit' command. If
you get a memory-related core dump (e.g., `Error exit (core dumped)')
on a GNU/Linux system, try increasing the process-available memory with
`ulimit'.
The speed of the NCO operators also depends on file size. When
processing large files the operators may appear to hang, or do nothing,
for large periods of time. In order to see what the operator is
actually doing, it is useful to activate a more verbose output mode.
This is accomplished by supplying a number greater than 0 to the `-D
DEBUG-LEVEL' (or `--debug-level', or `--dbg_lvl') switch. When the
DEBUG-LEVEL is nonzero, the operators report their current status to
the terminal through the STDERR facility. Using `-D' does not slow the
operators down. Choose a DEBUG-LEVEL between 1 and 3 for most
situations, e.g., `ncea -D 2 85.nc 86.nc 8586.nc'. A full description
of how to estimate the actual amount of memory the multi-file NCO
operators consume is given in *note Memory Requirements::.
2.9 Memory Requirements
=======================
Many people use NCO on gargantuan files which dwarf the memory
available (free RAM plus swap space) even on today's powerful machines.
These users want NCO to consume the least memory possible so that their
scripts do not have to tediously cut files into smaller pieces that fit
into memory. We commend these greedy users for pushing NCO to its
limits!
This section describes the memory NCO requires during operation.
The required memory is based on the underlying algorithms. The
description below is the memory usage per thread. Users with shared
memory machines may use the threaded NCO operators (*note OpenMP
Threading::). The peak and sustained memory usage will scale
accordingly, i.e., by the number of threads. Memory consumption
patterns of all operators are similar, with the exception of `ncap2'.
2.9.1 Single and Multi-file Operators
-------------------------------------
The multi-file operators currently comprise the record operators,
`ncra' and `ncrcat', and the ensemble operators, `ncea' and `ncecat'.
The record operators require _much less_ memory than the ensemble
operators. This is because the record operators operate on one single
record (i.e., time-slice) at a time, wherease the ensemble operators
retrieve the entire variable into memory. Let MS be the peak sustained
memory demand of an operator, FT be the memory required to store the
entire contents of all the variables to be processed in an input file,
FR be the memory required to store the entire contents of a single
record of each of the variables to be processed in an input file, VR be
the memory required to store a single record of the largest record
variable to be processed in an input file, VT be the memory required to
store the largest variable to be processed in an input file, VI be the
memory required to store the largest variable which is not processed,
but is copied from the initial file to the output file. All operators
require MI = VI during the initial copying of variables from the first
input file to the output file. This is the _initial_ (and transient)
memory demand. The _sustained_ memory demand is that memory required
by the operators during the processing (i.e., averaging, concatenation)
phase which lasts until all the input files have been processed. The
operators have the following memory requirements: `ncrcat' requires MS
<= VR. `ncecat' requires MS <= VT. `ncra' requires MS = 2FR + VR.
`ncea' requires MS = 2FT + VT. `ncbo' requires MS <= 3VT (both input
variables and the output variable). `ncflint' requires MS <= 3VT (both
input variables and the output variable). `ncpdq' requires MS <= 2VT
(one input variable and the output variable). `ncwa' requires MS <=
8VT (see below). Note that only variables that are processed, e.g.,
averaged, concatenated, or differenced, contribute to MS. Variables
which do not appear in the output file (*note Subsetting Variables::)
are never read and contribute nothing to the memory requirements.
`ncwa' consumes between two and seven times the memory of a variable
in order to process it. Peak consumption occurs when storing
simultaneously in memory one input variable, one tally array, one input
weight, one conformed/working weight, one weight tally, one input mask,
one conformed/working mask, and one output variable. When invoked, the
weighting and masking features contribute up to three-sevenths and
two-sevenths of these requirements apiece. If weights and masks are
_not_ specified (i.e., no `-w' or `-a' options) then `ncwa'
requirements drop to MS <= 3VT (one input variable, one tally array,
and the output variable).
The above memory requirements must be multiplied by the number of
threads THR_NBR (*note OpenMP Threading::). If this causes problems
then reduce (with `-t THR_NBR') the number of threads.
2.9.2 Memory for `ncap2'
------------------------
`ncap2' has unique memory requirements due its ability to process
arbitrarily long scripts of any complexity. All scripts acceptable to
`ncap2' are ultimately processed as a sequence of binary or unary
operations. `ncap2' requires MS <= 2VT under most conditions. An
exception to this is when left hand casting (*note Left hand casting::)
is used to stretch the size of derived variables beyond the size of any
input variables. Let VC be the memory required to store the largest
variable defined by left hand casting. In this case, MS <= 2VC.
`ncap2' scripts are complete dynamic and may be of arbitrary length.
A script that contains many thousands of operations, may uncover a slow
memory leak even though each single operation consumes little
additional memory. Memory leaks are usually identifiable by their
memory usage signature. Leaks cause peak memory usage to increase
monotonically with time regardless of script complexity. Slow leaks
are very difficult to find. Sometimes a `malloc()' (or `new[]')
failure is the only noticeable clue to their existance. If you have
good reasons to believe that a memory allocation failure is ultimately
due to an NCO memory leak (rather than inadequate RAM on your system),
then we would be very interested in receiving a detailed bug report.
2.10 Performance
================
An overview of NCO capabilities as of about 2006 is in Zender, C. S.
(2008), "Analysis of Self-describing Gridded Geoscience Data with
netCDF Operators (NCO)", Environ. Modell. Softw.,
doi:10.1016/j.envsoft.2008.03.004. This paper is also available at
`http://dust.ess.uci.edu/ppr/ppr_Zen08_ems.pdf'.
NCO performance and scaling for arithmetic operations is described in
Zender, C. S., and H. J. Mangalam (2007), "Scaling Properties of Common
Statistical Operators for Gridded Datasets", Int. J. High Perform.
Comput. Appl., 21(4), 485-498, doi:10.1177/1094342007083802. This
paper is also available at
`http://dust.ess.uci.edu/ppr/ppr_ZeM07_ijhpca.pdf'.
It is helpful to be aware of the aspects of NCO design that can
limit its performance:
1. No data buffering is performed during `nc_get_var' and
`nc_put_var' operations. Hyperslabs too large too hold in core
memory will suffer substantial performance penalties because of
this.
2. Since coordinate variables are assumed to be monotonic, the search
for bracketing the user-specified limits should employ a quicker
algorithm, like bisection, than the two-sided incremental search
currently implemented.
3. C_FORMAT, FORTRAN_FORMAT, SIGNEDNESS, SCALE_FORMAT and ADD_OFFSET
attributes are ignored by `ncks' when printing variables to screen.
4. In the late 1990s it was discovered that some random access
operations on large files on certain architectures (e.g., UNICOS)
were much slower with NCO than with similar operations performed
using languages that bypass the netCDF interface (e.g., Yorick).
This may have been a penalty of unnecessary byte-swapping in the
netCDF interface. It is unclear whether such problems exist in
present day (2007) netCDF/NCO environments, where unnecessary
byte-swapping has been reduced or eliminated.
3 NCO Features
**************
Many features have been implemented in more than one operator and are
described here for brevity. The description of each feature is
preceded by a box listing the operators for which the feature is
implemented. Command line switches for a given feature are consistent
across all operators wherever possible. If no "key switches" are
listed for a feature, then that particular feature is automatic and
cannot be controlled by the user.
3.1 Internationalization
========================
Availability: All operators
NCO support for "internationalization" of textual input and output
(e.g., Warning messages) is nascent. We hope to produce foreign
language string catalogues in 2004.
3.2 Metadata Optimization
=========================
Availability: `ncatted', `ncks', `ncrename'
Short options: None
Long options: `--hdr_pad', `--header_pad'
NCO supports padding headers to improve the speed of future metadata
operations. Use the `--hdr_pad' and `--header_pad' switches to request
that HDR_PAD bytes be inserted into the metadata section of the output
file. Future metadata expansions will not incur the performance
penalty of copying the entire output file unless the expansion exceeds
the amount of header padding exceeded. This can be beneficial when it
is known that some metadata will be added at a future date.
This optimization exploits the netCDF library `nc__enddef()'
function, which behaves differently with different versions of netCDF.
It will improve speed of future metadata expansion with `CLASSIC' and
`64bit' netCDF files, but not necessarily with `NETCDF4' files, i.e.,
those created by the netCDF interface to the HDF5 library (*note
Selecting Output File Format::).
3.3 OpenMP Threading
====================
Availability: `ncap2', `ncbo', `ncea', `ncecat', `ncflint', `ncpdq',
`ncra', `ncrcat', `ncwa'
Short options: `-t'
Long options: `--thr_nbr', `--threads', `--omp_num_threads'
NCO supports shared memory parallelism (SMP) when compiled with an
OpenMP-enabled compiler. Threads requests and allocations occur in two
stages. First, users may request a specific number of threads THR_NBR
with the `-t' switch (or its long option equivalents, `--thr_nbr',
`--threads', and `--omp_num_threads'). If not user-specified, OpenMP
obtains THR_NBR from the `OMP_NUM_THREADS' environment variable, if
present, or from the OS, if not.
NCO may modify THR_NBR according to its own internal settings before
it requests any threads from the system. Certain operators contain
hard-code limits to the number of threads they request. We base these
limits on our experience and common sense, and to reduce potentially
wasteful system usage by inexperienced users. For example, `ncrcat' is
extremely I/O-intensive so we restrict THR_NBR <= 2 for `ncrcat'. This
is based on the notion that the best performance that can be expected
from an operator which does no arithmetic is to have one thread reading
and one thread writing simultaneously. In the future (perhaps with
netCDF4), we hope to demonstrate significant threading improvements
with operators like `ncrcat' by performing multiple simultaneous writes.
Compute-intensive operators (`ncap', `ncwa' and `ncpdq') benefit
most from threading. The greatest increases in throughput due to
threading occur on large datasets where each thread performs millions,
at least, of floating point operations. Otherwise, the system overhead
of setting up threads probably outweighs the speed enhancements due to
SMP parallelism. However, we have not yet demonstrated that the SMP
parallelism scales well beyond four threads for these operators. Hence
we restrict THR_NBR <= 4 for all operators. We encourage users to play
with these limits (edit file `nco_omp.c') and send us their feedback.
Once the initial THR_NBR has been modified for any operator-specific
limits, NCO requests the system to allocate a team of THR_NBR threads
for the body of the code. The operating system then decides how many
threads to allocate based on this request. Users may keep track of
this information by running the operator with DBG_LVL > 0.
By default, threaded operators attach one global attribute,
`nco_openmp_thread_number', to any file they create or modify. This
attribute contains the number of threads the operator used to process
the input files. This information helps to verify that the answers
with threaded and non-threaded operators are equal to within machine
precision. This information is also useful for benchmarking.
3.4 Command Line Options
========================
Availability: All operators
NCO achieves flexibility by using "command line options". These
options are implemented in all traditional UNIX commands as single
letter "switches", e.g., `ls -l'. For many years NCO used only single
letter option names. In late 2002, we implemented GNU/POSIX extended
or long option names for all options. This was done in a backward
compatible way such that the full functionality of NCO is still
available through the familiar single letter options. In the future,
however, some features of NCO may require the use of long options,
simply because we have nearly run out of single letter options. More
importantly, mnemonics for single letter options are often
non-intuitive so that long options provide a more natural way of
expressing intent.
Extended options, also called long options, are implemented using the
system-supplied `getopt.h' header file, if possible. This provides the
`getopt_long' function to NCO (1).
The syntax of "short options" (single letter options) is `-KEY
VALUE' (dash-key-space-value). Here, KEY is the single letter option
name, e.g., `-D 2'.
The syntax of "long options" (multi-letter options) is `--LONG_NAME
VALUE' (dash-dash-key-space-value), e.g., `--dbg_lvl 2' or
`--LONG_NAME=VALUE' (dash-dash-key-equal-value), e.g., `--dbg_lvl=2'.
Thus the following are all valid for the `-D' (short version) or
`--dbg_lvl' (long version) command line option.
ncks -D 3 in.nc # Short option
ncks --dbg_lvl=3 in.nc # Long option, preferred form
ncks --dbg_lvl 3 in.nc # Long option, alternate form
The last example is preferred for two reasons. First, `--dbg_lvl'
is more specific and less ambiguous than `-D'. The long option form
makes scripts more self documenting and less error prone. Often long
options are named after the source code variable whose value they carry.
Second, the equals sign `=' joins the key (i.e., LONG_NAME) to the
value in an uninterruptible text block. Experience shows that users
are less likely to mis-parse commands when restricted to this form.
GNU implements a superset of the POSIX standard which allows any
unambiguous truncation of a valid option to be used.
ncks -D 3 in.nc # Short option
ncks --dbg_lvl=3 in.nc # Long option, full form
ncks --dbg=3 in.nc # Long option, unambiguous truncation
ncks --db=3 in.nc # Long option, unambiguous truncation
ncks --d=3 in.nc # Long option, ambiguous truncation
The first four examples are equivalent and will work as expected.
The final example will exit with an error since `ncks' cannot
disambiguate whether `--d' is intended as a truncation of `--dbg_lvl',
of `--dimension', or of some other long option.
NCO provides many long options for common switches. For example,
the debugging level may be set in all operators with any of the
switches `-D', `--debug-level', or `--dbg_lvl'. This flexibility
allows users to choose their favorite mnemonic. For some, it will be
`--debug' (an unambiguous truncation of `--debug-level', and other will
prefer `--dbg'. Interactive users usually prefer the minimal amount of
typing, i.e., `-D'. We recommend that scripts which are re-usable
employ some form of the long options for future maintainability.
This manual generally uses the short option syntax. This is for
historical reasons and to conserve space. The remainder of this manual
specifies the full LONG_NAME of each option. Users are expected to
pick the unambiguous truncation of each option name that most suits
their taste.
---------- Footnotes ----------
(1) If a `getopt_long' function cannot be found on the system, NCO
will use the `getopt_long' from the `my_getopt' package by Benjamin
Sittler <bsittler@iname.com>. This is BSD-licensed software available
from `http://www.geocities.com/ResearchTriangle/Node/9405/#my_getopt'.
3.5 Specifying Input Files
==========================
Availability (`-n'): `ncea', `ncecat', `ncra', `ncrcat'
Availability (`-p'): All operators
Short options: `-n', `-p'
Long options: `--nintap', `--pth', `--path'
It is important that users be able to specify multiple input files
without typing every filename in full, often a tedious task even by
graduate student standards. There are four different ways of
specifying input files to NCO: explicitly typing each, using UNIX shell
wildcards, and using the NCO `-n' and `-p' switches (or their long
option equivalents, `--nintap' or `--pth' and `--path', respectively).
To illustrate these methods, consider the simple problem of using
`ncra' to average five input files, `85.nc', `86.nc', ... `89.nc', and
store the results in `8589.nc'. Here are the four methods in order.
They produce identical answers.
ncra 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
ncra 8[56789].nc 8589.nc
ncra -p INPUT-PATH 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
ncra -n 5,2,1 85.nc 8589.nc
The first method (explicitly specifying all filenames) works by brute
force. The second method relies on the operating system shell to "glob"
(expand) the "regular expression" `8[56789].nc'. The shell passes
valid filenames which match the expansion to `ncra'. The third method
uses the `-p INPUT-PATH' argument to specify the directory where all
the input files reside. NCO prepends INPUT-PATH (e.g.,
`/data/usrname/model') to all INPUT-FILES (but not to OUTPUT-FILE).
Thus, using `-p', the path to any number of input files need only be
specified once. Note INPUT-PATH need not end with `/'; the `/' is
automatically generated if necessary.
The last method passes (with `-n') syntax concisely describing the
entire set of filenames (1). This option is only available with the
"multi-file operators": `ncra', `ncrcat', `ncea', and `ncecat'. By
definition, multi-file operators are able to process an arbitrary
number of INPUT-FILES. This option is very useful for abbreviating
lists of filenames representable as
ALPHANUMERIC_PREFIX+NUMERIC_SUFFIX+`.'+FILETYPE where
ALPHANUMERIC_PREFIX is a string of arbitrary length and composition,
NUMERIC_SUFFIX is a fixed width field of digits, and FILETYPE is a
standard filetype indicator. For example, in the file `ccm3_h0001.nc',
we have ALPHANUMERIC_PREFIX = `ccm3_h', NUMERIC_SUFFIX = `0001', and
FILETYPE = `nc'.
NCO is able to decode lists of such filenames encoded using the `-n'
option. The simpler (3-argument) `-n' usage takes the form `-n
FILE_NUMBER,DIGIT_NUMBER,NUMERIC_INCREMENT' where FILE_NUMBER is the
number of files, DIGIT_NUMBER is the fixed number of numeric digits
comprising the NUMERIC_SUFFIX, and NUMERIC_INCREMENT is the constant,
integer-valued difference between the NUMERIC_SUFFIX of any two
consecutive files. The value of ALPHANUMERIC_PREFIX is taken from the
input file, which serves as a template for decoding the filenames. In
the example above, the encoding `-n 5,2,1' along with the input file
name `85.nc' tells NCO to construct five (5) filenames identical to the
template `85.nc' except that the final two (2) digits are a numeric
suffix to be incremented by one (1) for each successive file.
Currently FILETYPE may be either be empty, `nc', `cdf', `hdf', or `hd5'.
If present, these FILETYPE suffixes (and the preceding `.') are ignored
by NCO as it uses the `-n' arguments to locate, evaluate, and compute
the NUMERIC_SUFFIX component of filenames.
Recently the `-n' option has been extended to allow convenient
specification of filenames with "circular" characteristics. This means
it is now possible for NCO to automatically generate filenames which
increment regularly until a specified maximum value, and then wrap back
to begin again at a specified minimum value. The corresponding `-n'
usage becomes more complex, taking one or two additional arguments for
a total of four or five, respectively: `-n
FILE_NUMBER,DIGIT_NUMBER,NUMERIC_INCREMENT[,NUMERIC_MAX[,NUMERIC_MIN]]'
where NUMERIC_MAX, if present, is the maximum integer-value of
NUMERIC_SUFFIX and NUMERIC_MIN, if present, is the minimum
integer-value of NUMERIC_SUFFIX. Consider, for example, the problem of
specifying non-consecutive input files where the filename suffixes end
with the month index. In climate modeling it is common to create
summertime and wintertime averages which contain the averages of the
months June-July-August, and December-January-February, respectively:
ncra -n 3,2,1 85_06.nc 85_0608.nc
ncra -n 3,2,1,12 85_12.nc 85_1202.nc
ncra -n 3,2,1,12,1 85_12.nc 85_1202.nc
The first example shows that three arguments to the `-n' option
suffice to specify consecutive months (`06, 07, 08') which do not
"wrap" back to a minimum value. The second example shows how to use
the optional fourth and fifth elements of the `-n' option to specify a
wrap value to NCO. The fourth argument to `-n', if present, specifies
the maximum integer value of NUMERIC_SUFFIX. In this case the maximum
value is 12, and will be formatted as `12' in the filename string. The
fifth argument to `-n', if present, specifies the minimum integer value
of NUMERIC_SUFFIX. The default minimum filename suffix is 1, which is
formatted as `01' in this case. Thus the second and third examples
have the same effect, that is, they automatically generate, in order,
the filenames `85_12.nc', `85_01.nc', and `85_02.nc' as input to NCO.
---------- Footnotes ----------
(1) The `-n' option is a backward compatible superset of the
`NINTAP' option from the NCAR CCM Processor.
3.6 Specifying Output Files
===========================
Availability: All operators
Short options: `-o'
Long options: `--fl_out', `--output'
NCO commands produce no more than one output file, FL_OUT.
Traditionally, users specify FL_OUT as the final argument to the
operator, following all input file names. This is the "positional
argument" method of specifying input and ouput file names. The
positional argument method works well in most applications. NCO also
supports specifying FL_OUT using the command line switch argument
method, `-o FL_OUT'.
Specifying FL_OUT with a switch, rather than as a positional
argument, allows FL_OUT to precede input files in the argument list. This
is particularly useful with multi-file operators for three reasons.
Multi-file operators may be invoked with hundreds (or more) filenames.
Visual or automatic location of FL_OUT in such a list is difficult when
the only syntactic distinction between input and output files is their
position. Second, specification of a long list of input files may be
difficult (*note Large Numbers of Files::). Making the input file list
the final argument to an operator facilitates using `xargs' for this
purpose. Some alternatives to `xargs' are very ugly and undesirable.
Finally, many users are more comfortable specifying output files with
`-o FL_OUT' near the beginning of an argument list. Compilers and
linkers are usually invoked this way.
Users should specify FL_OUT using either but not both methods. If
FL_OUT is specified twice (once with the switch and once as the last
positional argument), then the positional argument takes precedence.
3.7 Accessing Remote Files
==========================
Availability: All operators
Short options: `-p', `-l'
Long options: `--pth', `--path', `--lcl', `--local'
All NCO operators can retrieve files from remote sites as well as from
the local file system. A remote site can be an anonymous FTP server, a
machine on which the user has `rcp', `scp', or `sftp' privileges, or
NCAR's Mass Storage System (MSS), or an OPeNDAP server. Examples of
each are given below, following a brief description of the particular
access protocol.
To access a file via an anonymous FTP server, supply the remote
file's URL. FTP is an intrinsically insecure protocol because it
transfers passwords in plain text format. Users should access sites
using anonymous FTP when possible. Some FTP servers require a
login/password combination for a valid user account. NCO allows these
transactions so long as the required information is stored in the
`.netrc' file. Usually this information is the remote machine name,
login, and password, in plain text, separated by those very keywords,
e.g.,
machine dust.ess.uci.edu login zender password bushlied
Eschew using valuable passwords for FTP transactions, since `.netrc'
passwords are potentially exposed to eavesdropping software (1).
SFTP, i.e., secure FTP, uses SSH-based security protocols that solve
the security issues associated with plain FTP. NCO supports SFTP
protocol access to files specified with a homebrew syntax of the form
sftp://machine.domain.tld:/path/to/filename
Note the second colon following the top-level-domain (tld). This
syntax is a hybrid between an FTP URL and a standard remote file syntax.
To access a file using `rcp' or `scp', specify the Internet address
of the remote file. Of course in this case you must have `rcp' or `scp'
privileges which allow transparent (no password entry required) access
to the remote machine. This means that `~/.rhosts' or
`~/ssh/authorized_keys' must be set accordingly on both local and
remote machines.
To access a file on NCAR's MSS, specify the full MSS pathname of the
remote file. NCO will attempt to detect whether the local machine has
direct (synchronous) MSS access. In this case, NCO attempts to use the
NCAR `msrcp' command (2), or, failing that, `/usr/local/bin/msread'.
Otherwise NCO attempts to retrieve the MSS file through the
(asynchronous) Masnet Interface Gateway System (MIGS) using the `nrnet'
command.
The following examples show how one might analyze files stored on
remote systems.
ncks -l . ftp://dust.ess.uci.edu/pub/zender/nco/in.nc
ncks -l . sftp://dust.ess.uci.edu:/home/ftp/pub/zender/nco/in.nc
ncks -l . dust.ess.uci.edu:/home/zender/nco/data/in.nc
ncks -l . /ZENDER/nco/in.nc
ncks -l . mss:/ZENDER/nco/in.nc
ncks -l . http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata/in.nc
The first example works verbatim if your system is connected to the
Internet and is not behind a firewall. The second example works if you
have `sftp' access to the machine `dust.ess.uci.edu'. The third
example works if you have `rcp' or `scp' access to the machine
`dust.ess.uci.edu'. The fourth and fifth examples work on NCAR
computers with local access to the `msrcp', `msread', or `nrnet'
commands. The sixth command works if your local version of NCO is
OPeNDAP-enabled (this is fully described in *note OPeNDAP::). The
above commands can be rewritten using the `-p INPUT-PATH' option as
follows:
ncks -p ftp://dust.ess.uci.edu/pub/zender/nco -l . in.nc
ncks -p sftp://dust.ess.uci.edu:/home/ftp/pub/zender/nco -l . in.nc
ncks -p dust.ess.uci.edu:/home/zender/nco -l . in.nc
ncks -p /ZENDER/nco -l . in.nc
ncks -p mss:/ZENDER/nco -l . in.nc
ncks -p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata \
-l . in.nc
Using `-p' is recommended because it clearly separates the
INPUT-PATH from the filename itself, sometimes called the "stub". When
INPUT-PATH is not explicitly specified using `-p', NCO internally
generates an INPUT-PATH from the first input filename. The
automatically generated INPUT-PATH is constructed by stripping the
input filename of everything following the final `/' character (i.e.,
removing the stub). The `-l OUTPUT-PATH' option tells NCO where to
store the remotely retrieved file and the output file. Often the path
to a remotely retrieved file is quite different than the path on the
local machine where you would like to store the file. If `-l' is not
specified then NCO internally generates an OUTPUT-PATH by simply
setting OUTPUT-PATH equal to INPUT-PATH stripped of any machine names.
If `-l' is not specified and the remote file resides on the NCAR MSS
system, then the leading character of INPUT-PATH, `/', is also stripped
from OUTPUT-PATH. Specifying OUTPUT-PATH as `-l ./' tells NCO to store
the remotely retrieved file and the output file in the current
directory. Note that `-l .' is equivalent to `-l ./' though the latter
is recommended as it is syntactically more clear.
---------- Footnotes ----------
(1) NCO does not implement command line options to specify FTP
logins and passwords because copying those data into the `history'
global attribute in the output file (done by default) poses an
unacceptable security risk.
(2) The `msrcp' command must be in the user's path and located in
one of the following directories: `/usr/local/bin', `/usr/bin',
`/opt/local/bin', or `/usr/local/dcs/bin'.
3.7.1 OPeNDAP
-------------
The Distributed Oceanographic Data System (DODS) provides useful
replacements for common data interface libraries like netCDF. The DODS
versions of these libraries implement network transparent access to
data via a client-server data access protocol that uses the HTTP
protocol for communication. Although DODS-technology originated with
oceanography data, it applyies to virtually all scientific data. In
recognition of this, the data access protocol underlying DODS (which is
what NCO cares about) has been renamed the Open-source Project for a
Network Data Access Protocol, OPeNDAP. We use the terms DODS and
OPeNDAP interchangeably, and often write OPeNDAP/DODS for now. In the
future we will deprecate DODS in favor of DAP or OPeNDAP, as appropriate
(1).
NCO may be DAP-enabled by linking NCO to the OPeNDAP libraries. This
is described in the OPeNDAP documentation and automagically implemented
in NCO build mechanisms (2). The `./configure' mechanism automatically
enables NCO as OPeNDAP clients if it can find the required OPeNDAP
libraries (3). in the usual locations. The `$DODS_ROOT' environment
variable may be used to override the default OPeNDAP library location
at NCO compile-time. Building NCO with `bld/Makefile' and the command
`make DODS=Y' adds the (non-intuitive) commands to link to the OPeNDAP
libraries installed in the `$DODS_ROOT' directory. The file
`doc/opendap.sh' contains a generic script intended to help users
install OPeNDAP before building NCO. The documentation at the OPeNDAP
Homepage (http://www.opendap.org) is voluminous. Check there and on the
DODS mail lists
(http://www.unidata.ucar.edu/packages/dods/home/mailLists/). to learn
more about the extensive capabilities of OPeNDAP (4).
Once NCO is DAP-enabled the operators are OPeNDAP clients. All
OPeNDAP clients have network transparent access to any files controlled
by a OPeNDAP server. Simply specify the input file path(s) in URL
notation and all NCO operations may be performed on remote files made
accessible by a OPeNDAP server. This command tests the basic
functionality of OPeNDAP-enabled NCO clients:
% ncks -o ~/foo.nc -C -H -v one -l /tmp \
-p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata in.nc
one = 1
% ncks -H -v one ~/foo.nc
one = 1
The `one = 1' outputs confirm (first) that `ncks' correctly
retrieved data via the OPeNDAP protocol and (second) that `ncks'
created a valid local copy of the subsetted remote file.
The next command is a more advanced example which demonstrates the
real power of OPeNDAP-enabled NCO clients. The `ncwa' client requests
an equatorial hyperslab from remotely stored NCEP reanalyses data of
the year 1969. The NOAA OPeNDAP server (hopefully!) serves these data.
The local `ncwa' client then computes and stores (locally) the regional
mean surface pressure (in Pa).
ncwa -C -a lat,lon,time -d lon,-10.,10. -d lat,-10.,10. -l /tmp -p \
http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface \
pres.sfc.1969.nc ~/foo.nc
All with one command! The data in this particular input file also
happen to be packed (*note Methods and functions::), although this is
completely transparent to the user since NCO automatically unpacks data
before attempting arithmetic.
NCO obtains remote files from the OPeNDAP server (e.g.,
`www.cdc.noaa.gov') rather than the local machine. Input files are
first copied to the local machine, then processed. The OPeNDAP server
performs data access, hyperslabbing, and transfer to the local machine. This
allows the I/O to appear to NCO as if the input files were local. The
local machine performs all arithmetic operations. Only the
hyperslabbed output data are transferred over the network (to the local
machine) for the number-crunching to begin. The advantages of this are
obvious if you are examining small parts of large files stored at
remote locations.
---------- Footnotes ----------
(1) DODS is being deprecated because it is ambiguous, referring both
to a protocol and to a collection of (oceanography) data. It is
superceded by two terms. DAP is the discipline-neutral Data Access
Protocol at the heart of DODS. The National Virtual Ocean Data System
(NVODS) refers to the collection of oceanography data and oceanographic
extensions to DAP. In other words, NVODS is implemented with OPeNDAP.
OPeNDAP is _also_ the open source project which maintains, develops,
and promulgates the DAP standard. OPeNDAP and DAP really are
interchangeable. Got it yet?
(2) Automagic support for DODS version 3.2.x was deprecated in
December, 2003 after NCO version 2.8.4. NCO support for OPeNDAP
versions 3.4.x commenced in December, 2003, with NCO version 2.8.5.
NCO support for OPeNDAP versions 3.5.x commenced in June, 2005, with
NCO version 3.0.1. NCO support for OPeNDAP versions 3.6.x commenced in
June, 2006, with NCO version 3.1.3. NCO support for OPeNDAP versions
3.7.x commenced in January, 2007, with NCO version 3.1.9.
(3) The minimal set of libraries required to build NCO as OPeNDAP
clients are, in link order, `libnc-dap.a', `libdap.a', and `libxml2'
and `libcurl.a'.
(4) We are most familiar with the OPeNDAP ability to enable
network-transparent data access. OPeNDAP has many other features,
including sophisticated hyperslabbing and server-side processing via
"constraint expressions". If you know more about this, please consider
writing a section on "OPeNDAP Capabilities of Interest to NCO Users"
for incorporation in the `NCO User's Guide'.
3.8 Retaining Retrieved Files
=============================
Availability: All operators
Short options: `-R'
Long options: `--rtn', `--retain'
In order to conserve local file system space, files retrieved from
remote locations are automatically deleted from the local file system
once they have been processed. Many NCO operators were constructed to
work with numerous large (e.g., 200 MB) files. Retrieval of multiple
files from remote locations is done serially. Each file is retrieved,
processed, then deleted before the cycle repeats. In cases where it is
useful to keep the remotely-retrieved files on the local file system
after processing, the automatic removal feature may be disabled by
specifying `-R' on the command line.
Invoking `-R' disables the default printing behavior of `ncks'.
This allows `ncks' to retrieve remote files without automatically
trying to print them. See *note ncks netCDF Kitchen Sink::, for more
details.
Note that the remote retrieval features of NCO can always be used to
retrieve _any_ file, including non-netCDF files, via `SSH', anonymous
FTP, or `msrcp'. Often this method is quicker than using a browser, or
running an FTP session from a shell window yourself. For example, say
you want to obtain a JPEG file from a weather server.
ncks -R -p ftp://weather.edu/pub/pix/jpeg -l . storm.jpg
In this example, `ncks' automatically performs an anonymous FTP
login to the remote machine and retrieves the specified file. When
`ncks' attempts to read the local copy of `storm.jpg' as a netCDF file,
it fails and exits, leaving `storm.jpg' in the current directory.
If your NCO is DAP-enabled (*note OPeNDAP::), then you may use NCO
to retrieve any files (including netCDF, HDF, etc.) served by an
OPeNDAP server to your local machine. For example,
ncks -R -l . -p \
http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface \
pres.sfc.1969.nc
Note that NCO is never the preffered way to transport files from
remote machines. For large jobs, that is best handled by FTP, SSH, or
`wget'. It may occasionally be useful to use NCO to transfer files
when your other preferred methods are not available locally.
3.9 Selecting Output File Format
================================
Availability: `ncap2', `ncbo', `ncea', `ncecat', `ncflint', `ncks',
`ncpdq', `ncra', `ncrcat', `ncwa'
Short options: `-3', `-4'
Long options: `--3', `--4', `--64bit', `--fl_fmt', `--netcdf4'
All NCO operators support (read and write) all three (or four,
depending on how one counts) file formats supported by netCDF4. The
default output file format for all operators is the input file format.
The operators listed under "Availability" above allow the user to
specify the output file format independent of the input file format.
These operators allow the user to convert between the various file
formats. (The operators `ncatted' and `ncrename' do not support these
switches so they always write the output netCDF file in the same format
as the input netCDF file.)
netCDF supports four types of files: `CLASSIC', `64BIT', `NETCDF4',
and `NETCDF4_CLASSIC', The `CLASSIC' format is the traditional 32-bit
offset written by netCDF2 and netCDF3. As of 2005, most netCDF
datasets are in `CLASSIC' format. The `64BIT' format was added in
Fall, 2004.
The `NETCDF4' format uses HDF5 as the file storage layer. The files
are (usually) created, accessed, and manipulated using the traditional
netCDF3 API (with numerous extensions). The `NETCDF4_CLASSIC' format
refers to netCDF4 files created with the `NC_CLASSIC_MODEL' mask. Such
files use HDF5 as the back-end storage format (unlike netCDF3), though
they incorporate only netCDF3 features. Hence `NETCDF4_CLASSIC' files
are perfectly readable by applications which use only the netCDF3 API
and library. NCO must be built with netCDF4 to write files in the new
`NETCDF4' and `NETCDF4_CLASSIC' formats, and to read files in the new
`NETCDF4' format. Users are advised to use the default `CLASSIC'
format or the `NETCDF4_CLASSIC' format until netCDF4 is more widespread.
Widespread support for `NETCDF4' format files is not expected for a few
more years, 2010-2011, say. If performance or coolness are issues,
then use `NETCDF4_CLASSIC' instead of `CLASSIC' format files.
As mentioned above, all operators write use the input file format for
output files unless told otherwise. Toggling the long option `--64bit'
switch (or its KEY-VALUE equivalent `--fl_fmt=64bit') produces the
netCDF3 64-bit offset format named `64BIT'. NCO must be built with
netCDF 3.6 or higher to produce a `64BIT' file. Using the `-4' switch
(or its long option equivalents `--4' or `--netcdf4'), or setting its
KEY-VALUE equivalent `--fl_fmt=netcdf4' produces a `NETCDF4' file
(i.e., HDF). Casual users are advised to use the default (netCDF3)
`CLASSIC' format until netCDF 3.6 and netCDF 4.0 are more widespread.
Conversely, operators given the `-3' (or `--3') switch without
arguments will (attempt to) produce netCDF3 `CLASSIC' output, even from
netCDF4 input files.
These examples demonstrate converting a file from any netCDF format
into any other netCDF format (subject to limits of the format):
ncks --fl_fmt=classic in.nc foo_3c.nc # netCDF3 classic
ncks --fl_fmt=64bit in.nc foo_364.nc # netCDF3 64bit
ncks --fl_fmt=netcdf4_classic in.nc foo_4c.nc # netCDF4 classic
ncks --fl_fmt=netcdf4 in.nc foo_4.nc # netCDF4
ncks -3 in.nc foo_3c.nc # netCDF3 classic
ncks --3 in.nc foo_3c.nc # netCDF3 classic
ncks -4 in.nc foo_4.nc # netCDF4
ncks --4 in.nc foo_4.nc # netCDF4
ncks --64 in.nc foo364.nc # netCDF3 64bit
Of course since most operators support these switches, the
"conversions" can be done at the output stage of arithmetic or metadata
processing rather than requiring a separate step. Producing (netCDF3)
`CLASSIC' or `64BIT' files from `NETCDF4_CLASSIC' files will always
work. However, producing netCDF3 files from `NETCDF4' files will only
work if the output files are not required to contain netCDF4-specific
features.
Note that `NETCDF4' and `NETCDF4_CLASSIC' are the same binary format.
The latter simply causes a writing application to fail if it attempts to
write a `NETCDF4' file that cannot be completely read by the netCDF3
library. Conversely, `NETCDF4_CLASSIC' indicates to a reading
application that all of the file contents are readable with the netCDF3
library. As of October, 2005, NCO writes no netCDF4-specific data
structures and so always succeeds at writing `NETCDF4_CLASSIC' files.
There are at least three ways to discover the format of a netCDF
file, i.e., whether it is a classic (32-bit offset) or newer 64-bit
offset netCDF3 format, or is netCDF4 format. Each method returns the
information using slightly different terminology that becomes easier to
understand with practice.
First, examine the end of the first line of global metadata output by
`ncks -M':
% ncks -M foo_3c.nc
Opened file foo_3c.nc: dimensions = 19, variables = 261, global atts. = 4,
id = 65536, type = NC_FORMAT_CLASSIC
% ncks -M foo_364.nc
Opened file foo_364.nc: dimensions = 19, variables = 261, global atts. = 4,
id = 65536, type = NC_FORMAT_64BIT
% ncks -M foo_4c.nc
Opened file foo_4c.nc: dimensions = 19, variables = 261, global atts. = 4,
id = 65536, type = NC_FORMAT_NETCDF4_CLASSIC
% ncks -M foo_4.nc
Opened file foo_4.nc: dimensions = 19, variables = 261, global atts. = 4,
id = 65536, type = NC_FORMAT_NETCDF4
This method requires a netCDF4-enabled NCO version 3.9.0+ (i.e.,
from 2007 or later).
Second, query the file with `ncdump -k':
% ncdump -k foo_3.nc
classic
% ncdump -k foo_364.nc
64-bit-offset
% ncdump -k foo_4c.nc
netCDF-4 classic model
% ncdump -k foo_4.nc
netCDF-4
This method requires a netCDF4-enabled netCDF 3.6.2+ (i.e., from
2007 or later).
The third option uses the POSIX-standard `od' (octal dump) command:
% od -An -c -N4 foo_3c.nc
C D F 001
% od -An -c -N4 foo_364.nc
C D F 002
% od -An -c -N4 foo_4c.nc
211 H D F
% od -An -c -N4 foo_4.nc
211 H D F
This option works without NCO and `ncdump'. Values of `C D F 001'
and `C D F 002' indicate 32-bit (classic) and 64-bit netCDF3 formats,
respectively, while values of `211 H D F' indicate the newer netCDF4
file format.
3.10 Large File Support
=======================
Availability: All operators
Short options: none
Long options: none
NCO has Large File Support (LFS), meaning that NCO can write files
larger than 2 GB on some 32-bit operating systems with netCDF libraries
earlier than version 3.6. If desired, LFS support must be configured
when both netCDF and NCO are installed. netCDF versions 3.6 and higher
support 64-bit file addresses as part of the netCDF standard. We
recommend that users ignore LFS support which is difficult to configure
and is implemented in NCO only to support netCDF versions prior to 3.6.
This obviates the need for configuring explicit LFS support in
applications (such as NCO) which now support 64-bit files directly
through the netCDF interface. See *note Selecting Output File Format::
for instructions on accessing the different file formats, including
64-bit files, supported by the modern netCDF interface.
If you are still interesting in explicit LFS support for netCDF
versions prior to 3.6, know that LFS support depends on a complex,
interlocking set of operating system (1) and netCDF suppport issues.
The netCDF LFS FAQ at
`http://my.unidata.ucar.edu/content/software/netcdf/faq-lfs.html'
describes the various file size limitations imposed by different
versions of the netCDF standard. NCO and netCDF automatically attempt
to configure LFS at build time.
---------- Footnotes ----------
(1) Linux and AIX are known to support LFS.
3.11 Subsetting Variables
=========================
Availability: (`ncap2'), `ncbo', `ncea', `ncecat', `ncflint', `ncks',
`ncpdq', `ncra', `ncrcat', `ncwa'
Short options: `-v', `-x'
Long options: `--variable', `--exclude' or `--xcl'
Subsetting variables refers to explicitly specifying variables to be
included or excluded from operator actions. Subsetting is implemented
with the `-v VAR[,...]' and `-x' options. A list of variables to
extract is specified following the `-v' option, e.g., `-v time,lat,lon'.
Not using the `-v' option is equivalent to specifying all variables.
The `-x' option causes the list of variables specified with `-v' to be
_excluded_ rather than _extracted_. Thus `-x' saves typing when you
only want to extract fewer than half of the variables in a file.
Variables explicitly specified for extraction with `-v VAR[,...]'
_must_ be present in the input file or an error will result. Variables
explicitly specified for _exclusion_ with `-x -v VAR[,...]' need not be
present in the input file. Remember, if averaging or concatenating
large files stresses your systems memory or disk resources, then the
easiest solution is often to use the `-v' option to retain only the
most important variables (*note Memory Requirements::).
Due to its special capabilities, `ncap2' interprets the `-v' switch
differently (*note ncap2 netCDF Arithmetic Processor::). For `ncap2',
the `-v' switch takes no arguments and indicates that _only_
user-defined variables should be output. `ncap2' neither accepts nor
understands the -X switch.
As of NCO 2.8.1 (August, 2003), variable name arguments of the `-v'
switch may contain "extended regular expressions". As of NCO 3.9.6
(January, 2009), variable names arguments to `ncatted' may contain
"extended regular expressions". For example, `-v '^DST'' selects all
variables beginning with the string `DST'. Extended regular
expressions are defined by the GNU `egrep' command. The
meta-characters used to express pattern matching operations are
`^$+?.*[]{}|'. If the regular expression pattern matches _any_ part of
a variable name then that variable is selected. This capability is
called "wildcarding", and is very useful for sub-setting large data
files.
Because of its wide availability, NCO uses the POSIX regular
expression library `regex'. Regular expressions of arbitary complexity
may be used. Since netCDF variable names are relatively simple
constructs, only a few varieties of variable wildcards are likely to be
useful. For convenience, we define the most useful pattern matching
operators here:
`^'
Matches the beginning of a string
`$'
Matches the end of a string
`.'
Matches any single character
The most useful repetition and combination operators are
`?'
The preceding regular expression is optional and matched at most
once
`*'
The preceding regular expression will be matched zero or more times
`+'
The preceding regular expression will be matched one or more times
`|'
The preceding regular expression will be joined to the following
regular expression. The resulting regular expression matches any
string matching either subexpression.
To illustrate the use of these operators in extracting variables,
consider a file with variables `Q', `Q01'-`Q99', `Q100', `QAA'-`QZZ',
`Q_H2O', `X_H2O', `Q_CO2', `X_CO2'.
ncks -v 'Q.?' in.nc # Variables that contain Q
ncks -v '^Q.?' in.nc # Variables that start with Q
ncks -v '^Q+.?.' in.nc # Q, Q0--Q9, Q01--Q99, QAA--QZZ, etc.
ncks -v '^Q..' in.nc # Q01--Q99, QAA--QZZ, etc.
ncks -v '^Q[0-9][0-9]' in.nc # Q01--Q99, Q100
ncks -v '^Q[[:digit:]]{2}' in.nc # Q01--Q99
ncks -v 'H2O$' in.nc # Q_H2O, X_H2O
ncks -v 'H2O$|CO2$' in.nc # Q_H2O, X_H2O, Q_CO2, X_CO2
ncks -v '^Q[0-9][0-9]$' in.nc # Q01--Q99
ncks -v '^Q[0-6][0-9]|7[0-3]' in.nc # Q01--Q73, Q100
ncks -v '(Q[0-6][0-9]|7[0-3])$' in.nc # Q01--Q73
ncks -v '^[a-z]_[a-z]{3}$' in.nc # Q_H2O, X_H2O, Q_CO2, X_CO2
Beware--two of the most frequently used repetition pattern matching
operators, `*' and `?', are also valid pattern matching operators for
filename expansion (globbing) at the shell-level. Confusingly, their
meanings in extended regular expressions and in shell-level filename
expansion are significantly different. In an extended regular
expression, `*' matches zero or more occurences of the preceding
regular expression. Thus `Q*' selects all variables, and `Q+.*'
selects all variables containing `Q' (the `+' ensures the preceding item
matches at least once). To match zero or one occurence of the
preceding regular expression, use `?'. Documentation for the UNIX
`egrep' command details the extended regular expressions which NCO
supports.
One must be careful to protect any special characters in the regular
expression specification from being interpreted (globbed) by the shell.
This is accomplish by enclosing special characters within single or
double quotes
ncra -v Q?? in.nc out.nc # Error: Shell attempts to glob wildcards
ncra -v '^Q+..' in.nc out.nc # Correct: NCO interprets wildcards
ncra -v '^Q+..' in*.nc out.nc # Correct: NCO interprets, Shell globs
The final example shows that commands may use a combination of
variable wildcarding and shell filename expansion (globbing). For
globbing, `*' and `?' _have nothing to do_ with the preceding regular
expression! In shell-level filename expansion, `*' matches any string,
including the null string and `?' matches any single character.
Documentation for `bash' and `csh' describe the rules of filename
expansion (globbing).
3.12 Subsetting Coordinate Variables
====================================
Availability: `ncap2', `ncbo', `ncea', `ncecat', `ncflint', `ncks',
`ncpdq', `ncra', `ncrcat', `ncwa'
Short options: `-C', `-c'
Long options: `--no-coords', `--no-crd', `--crd', `--coords'
By default, coordinates variables associated with any variable appearing
in the INPUT-FILE will also appear in the OUTPUT-FILE, even if they are
not explicitly specified, e.g., with the `-v' switch. Thus variables
with a latitude coordinate `lat' always carry the values of `lat' with
them into the OUTPUT-FILE. This feature can be disabled with `-C',
which causes NCO to not automatically add coordinates to the variables
appearing in the OUTPUT-FILE. However, using `-C' does not preclude
the user from including some coordinates in the output files simply by
explicitly selecting the coordinates with the -V option. The `-c'
option, on the other hand, is a shorthand way of automatically
specifying that _all_ coordinate variables in the INPUT-FILES should
appear in the OUTPUT-FILE. Thus `-c' allows the user to select all the
coordinate variables without having to know their names. Both `-c' and
`-C' honor the CF `coordinates' convention described in *note CF
Conventions::.
3.13 C and Fortran Index conventions
====================================
Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncks', `ncpdq',
`ncra', `ncrcat', `ncwa'
Short options: `-F'
Long options: `--fortran'
The `-F' switch changes NCO to read and write with the Fortran index
convention. By default, NCO uses C-style (0-based) indices for all I/O.
In C, indices count from 0 (rather than 1), and dimensions are ordered
from slowest (inner-most) to fastest (outer-most) varying. In Fortran,
indices count from 1 (rather than 0), and dimensions are ordered from
fastest (inner-most) to slowest (outer-most) varying. Hence C and
Fortran data storage conventions represent mathematical transposes of
eachother. Note that record variables contain the record dimension as
the most slowly varying dimension. See *note ncpdq netCDF Permute
Dimensions Quickly:: for techniques to re-order (including transpose)
dimensions and to reverse data storage order.
Consider a file `85.nc' containing 12 months of data in the record
dimension `time'. The following hyperslab operations produce identical
results, a June-July-August average of the data:
ncra -d time,5,7 85.nc 85_JJA.nc
ncra -F -d time,6,8 85.nc 85_JJA.nc
Printing variable THREE_DMN_VAR in file `in.nc' first with the
C indexing convention, then with Fortran indexing convention results in
the following output formats:
% ncks -v three_dmn_var in.nc
lat[0]=-90 lev[0]=1000 lon[0]=-180 three_dmn_var[0]=0
...
% ncks -F -v three_dmn_var in.nc
lon(1)=0 lev(1)=100 lat(1)=-90 three_dmn_var(1)=0
...
3.14 Hyperslabs
===============
Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncks', `ncpdq',
`ncra', `ncrcat', `ncwa'
Short options: `-d DIM,[MIN][,[MAX][,[STRIDE]]]'
Long options: `--dimension DIM,[MIN][,[MAX][,[STRIDE]]]',
`--dmn DIM,[MIN][,[MAX][,[STRIDE]]]'
A "hyperslab" is a subset of a variable's data. The coordinates of a
hyperslab are specified with the `-d DIM,[MIN][,[MAX][,[STRIDE]]]' short
option (or with the same arguments to the `--dimension' or `--dmn' long
options). At least one hyperslab argument (MIN, MAX, or STRIDE) must
be present. The bounds of the hyperslab to be extracted are specified
by the associated MIN and MAX values. A half-open range is specified
by omitting either the MIN or MAX parameter. The separating comma must
be present to indicate the omission of one of these arguments. The
unspecified limit is interpreted as the maximum or minimum value in the
unspecified direction. A cross-section at a specific coordinate is
extracted by specifying only the MIN limit and omitting a trailing
comma. Dimensions not mentioned are passed with no reduction in range.
The dimensionality of variables is not reduced (in the case of a
cross-section, the size of the constant dimension will be one). If
values of a coordinate-variable are used to specify a range or
cross-section, then the coordinate variable must be monotonic (values
either increasing or decreasing). In this case, command-line values
need not exactly match coordinate values for the specified dimension.
Ranges are determined by seeking the first coordinate value to occur in
the closed range [MIN,MAX] and including all subsequent values until
one falls outside the range. The coordinate value for a cross-section
is the coordinate-variable value closest to the specified value and
must lie within the range or coordinate-variable values.
Coordinate values should be specified using real notation with a
decimal point required in the value, whereas dimension indices are
specified using integer notation without a decimal point. This
convention serves only to differentiate coordinate values from
dimension indices. It is independent of the type of any netCDF
coordinate variables. For a given dimension, the specified limits must
both be coordinate values (with decimal points) or dimension indices
(no decimal points). The STRIDE option, if any, must be a dimension
index, not a coordinate value. *Note Stride::, for more information on
the STRIDE option.
User-specified coordinate limits are promoted to double precision
values while searching for the indices which bracket the range. Thus,
hyperslabs on coordinates of type `NC_BYTE' and `NC_CHAR' are computed
numerically rather than lexically, so the results are unpredictable.
The relative magnitude of MIN and MAX indicate to the operator
whether to expect a "wrapped coordinate" (*note Wrapped Coordinates::),
such as longitude. If MIN > MAX, the NCO expects the coordinate to be
wrapped, and a warning message will be printed. When this occurs, NCO
selects all values outside the domain [MAX < MIN], i.e., all the values
exclusive of the values which would have been selected if MIN and MAX
were swapped. If this seems confusing, test your command on just the
coordinate variables with `ncks', and then examine the output to ensure
NCO selected the hyperslab you expected (coordinate wrapping is
currently only supported by `ncks').
Because of the way wrapped coordinates are interpreted, it is very
important to make sure you always specify hyperslabs in the
monotonically increasing sense, i.e., MIN < MAX (even if the underlying
coordinate variable is monotonically decreasing). The only exception
to this is when you are indeed specifying a wrapped coordinate. The
distinction is crucial to understand because the points selected by,
e.g., `-d longitude,50.,340.', are exactly the complement of the points
selected by `-d longitude,340.,50.'.
Not specifying any hyperslab option is equivalent to specifying full
ranges of all dimensions. This option may be specified more than once
in a single command (each hyperslabbed dimension requires its own `-d'
option).
3.15 Stride
===========
Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncks', `ncpdq',
`ncra', `ncrcat', `ncwa'
Short options: `-d DIM,[MIN][,[MAX][,[STRIDE]]]'
Long options: `--dimension DIM,[MIN][,[MAX][,[STRIDE]]]',
`--dmn DIM,[MIN][,[MAX][,[STRIDE]]]'
All data operators support specifying a "stride" for any and all
dimensions at the same time. The STRIDE is the spacing between
consecutive points in a hyperslab. A STRIDE of 1 picks all the
elements of the hyperslab, and a STRIDE of 2 skips every other element,
etc.. `ncks' multislabs support strides, and are more powerful than
the regular hyperslabs supported by the other operators (*note
Multislabs::). Using the STRIDE option for the record dimension with
`ncra' and `ncrcat' makes it possible, for instance, to average or
concatenate regular intervals across multi-file input data sets.
The STRIDE is specified as the optional fourth argument to the `-d'
hyperslab specification: `-d DIM,[MIN][,[MAX][,[STRIDE]]]'. Specify
STRIDE as an integer (i.e., no decimal point) following the third comma
in the `-d' argument. There is no default value for STRIDE. Thus
using `-d time,,,2' is valid but `-d time,,,2.0' and `-d time,,,' are
not. When STRIDE is specified but MIN is not, there is an ambiguity as
to whether the extracted hyperslab should begin with (using C-style,
0-based indexes) element 0 or element `stride-1'. NCO must resolve
this ambiguity and it chooses element 0 as the first element of the
hyperslab when MIN is not specified. Thus `-d time,,,STRIDE' is
syntactically equivalent to `-d time,0,,STRIDE'. This means, for
example, that specifying the operation `-d time,,,2' on the array
`1,2,3,4,5' selects the hyperslab `1,3,5'. To obtain the hyperslab
`2,4' instead, simply explicitly specify the starting index as 1, i.e.,
`-d time,1,,2'.
For example, consider a file `8501_8912.nc' which contains 60
consecutive months of data. Say you wish to obtain just the March data
from this file. Using 0-based subscripts (*note C and Fortran Index
Conventions::) these data are stored in records 2, 14, ... 50 so the
desired STRIDE is 12. Without the STRIDE option, the procedure is very
awkward. One could use `ncks' five times and then use `ncrcat' to
concatenate the resulting files together:
for idx in 02 14 26 38 50; do # Bourne Shell
ncks -d time,${idx} 8501_8912.nc foo.${idx}
done
foreach idx (02 14 26 38 50) # C Shell
ncks -d time,${idx} 8501_8912.nc foo.${idx}
end
ncrcat foo.?? 8589_03.nc
rm foo.??
With the STRIDE option, `ncks' performs this hyperslab extraction in
one operation:
ncks -d time,2,,12 8501_8912.nc 8589_03.nc
*Note ncks netCDF Kitchen Sink::, for more information on `ncks'.
Applying the STRIDE option to the record dimension in `ncra' and
`ncrcat' makes it possible, for instance, to average or concatenate
regular intervals across multi-file input data sets.
ncra -F -d time,3,,12 85.nc 86.nc 87.nc 88.nc 89.nc 8589_03.nc
ncrcat -F -d time,3,,12 85.nc 86.nc 87.nc 88.nc 89.nc 8503_8903.nc
3.16 Multislabs
===============
Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncks', `ncpdq',
`ncra', `ncrcat'
Short options: `-d DIM,[MIN][,[MAX][,[STRIDE]]]'
Long options: `--dimension DIM,[MIN][,[MAX][,[STRIDE]]]',
`--dmn DIM,[MIN][,[MAX][,[STRIDE]]]'
A multislab is a union of one or more hyperslabs. One defines
multislabs by chaining together hyperslab commands, i.e., `-d' options
(*note Hyperslabs::). Support for specifying a "multi-hyperslab" or
"multislab" for any variable was first added to `ncks' in late 2002.
The other operators received MSA capabilities in April 2008. Sometimes
multi-slabbing is referred to by the acronym MSA, which stands for
"Multi-Slabbing Algorithm".
Multislabs overcome some restraints that limit hyperslabs. A single
`-d' option can only specify a contiguous and/or a regularly spaced
multi-dimensional data array. Multislabs are constructed from multiple
`-d' options and may therefore have non-regularly spaced arrays. For
example, suppose it is desired to operate on all longitudes from 10.0
to 20.0 and from 80.0 to 90.0 degrees. The combined range of
longitudes is not selectable in a single hyperslab specfication of the
form `-d DIMENSION,MIN,MAX' or `-d DIMENSION,MIN,MAX,STRIDE' because its
elements are irregularly spaced in coordinate space (and presumably in
index space too). The multislab specification for obtaining these
values is simply the union of the hyperslabs specifications that
comprise the multislab, i.e.,
ncks -d lon,10.,20. -d lon,80.,90. in.nc out.nc
ncks -d lon,10.,15. -d lon,15.,20. -d lon,80.,90. in.nc out.nc
Any number of hyperslabs specifications may be chained together to
specify the multislab.
Users may specify redundant ranges of indices in a multislab, e.g.,
ncks -d lon,0,4 -d lon,2,9,2 in.nc out.nc
This command retrieves the first five longitudes, and then every
other longitude value up to the tenth. Elements 0, 2, and 4 are
specified by both hyperslab arguments (hence this is redundant) but
will count only once if an arithmetic operation is being performed.
This example uses index-based (not coordinate-based) multislabs because
the STRIDE option only supports index-based hyper-slabbing. *Note
Stride::, for more information on the STRIDE option.
Multislabs are more efficient than the alternative of sequentially
performing hyperslab operations and concatenating the results. This is
because NCO employs a novel multislab algorithm to minimize the number
of I/O operations when retrieving irregularly spaced data from disk.
The NCO multislab algorithm retrieves each element from disk once and
only once. Thus users may take some shortcuts in specifying multislabs
and the algorithm will obtain the intended values. Specifying
redundant ranges is not encouraged, but may be useful on occasion and
will not result in unintended consequences.
A final example shows the real power of multislabs. Suppose the Q
variable contains three dimensional arrays of distinct chemical
constituents in no particular order. We are interested in the NOy
species in a certain geographic range. Say that NO, NO2, and N2O5 are
elements 0, 1, and 5 of the SPECIES dimension of Q. The multislab
specification might look something like
ncks -d species,0,1 -d species,5 -d lon,0,4 -d lon,2,9,2 in.nc out.nc
Multislabs are powerful because they may be specified for every
dimension at the same time. Thus multislabs obsolete the need to
execute multiple `ncks' commands to gather the desired range of data.
3.17 Wrapped Coordinates
========================
Availability: `ncks'
Short options: `-d DIM,[MIN][,[MAX][,[STRIDE]]]'
Long options: `--dimension DIM,[MIN][,[MAX][,[STRIDE]]]',
`--dmn DIM,[MIN][,[MAX][,[STRIDE]]]'
A "wrapped coordinate" is a coordinate whose values increase or
decrease monotonically (nothing unusual so far), but which represents a
dimension that ends where it begins (i.e., wraps around on itself).
Longitude (i.e., degrees on a circle) is a familiar example of a wrapped
coordinate. Longitude increases to the East of Greenwich, England,
where it is defined to be zero. Halfway around the globe, the
longitude is 180 degrees East (or West). Continuing eastward,
longitude increases to 360 degrees East at Greenwich. The longitude
values of most geophysical data are either in the range [0,360), or
[-180,180). In either case, the Westernmost and Easternmost longitudes
are numerically separated by 360 degrees, but represent contiguous
regions on the globe. For example, the Saharan desert stretches from
roughly 340 to 50 degrees East. Extracting the hyperslab of data
representing the Sahara from a global dataset presents special problems
when the global dataset is stored consecutively in longitude from 0 to
360 degrees. This is because the data for the Sahara will not be
contiguous in the INPUT-FILE but is expected by the user to be
contiguous in the OUTPUT-FILE. In this case, `ncks' must invoke
special software routines to assemble the desired output hyperslab from
multiple reads of the INPUT-FILE.
Assume the domain of the monotonically increasing longitude
coordinate `lon' is 0 < LON < 360. `ncks' will extract a hyperslab
which crosses the Greenwich meridian simply by specifying the
westernmost longitude as MIN and the easternmost longitude as MAX. The
following commands extract a hyperslab containing the Saharan desert:
ncks -d lon,340.,50. in.nc out.nc
ncks -d lon,340.,50. -d lat,10.,35. in.nc out.nc
The first example selects data in the same longitude range as the
Sahara. The second example further constrains the data to having the
same latitude as the Sahara. The coordinate `lon' in the OUTPUT-FILE,
`out.nc', will no longer be monotonic! The values of `lon' will be,
e.g., `340, 350, 0, 10, 20, 30, 40, 50'. This can have serious
implications should you run `out.nc' through another operation which
expects the `lon' coordinate to be monotonically increasing.
Fortunately, the chances of this happening are slim, since `lon' has
already been hyperslabbed, there should be no reason to hyperslab `lon'
again. Should you need to hyperslab `lon' again, be sure to give
dimensional indices as the hyperslab arguments, rather than coordinate
values (*note Hyperslabs::).
3.18 Auxiliary Coordinates
==========================
Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncks', `ncpdq',
`ncra', `ncrcat'
Short options: `-X LON_MIN,LON_MAX,LAT_MIN,LAT_MAX'
Long options: `--auxiliary LON_MIN,LON_MAX,LAT_MIN,LAT_MAX'
Utilize auxiliary coordinates specified in values of coordinate
variable's `standard_name' attributes, if any, when interpreting
hyperslab and multi-slab options. Also `--auxiliary'. This switch
supports hyperslabbing cell-based grids over coordinate ranges. This
works on datasets that associate coordinate variables to grid-mappings
using the CF-convention (*note CF Conventions::) `coordinates' and
`standard_name' attributes described here
(http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.1/cf-conventions.html#coordinate-system).
Currently, NCO understands auxiliary coordinate variables pointed to by
the `standard_name' attributes for LATITUDE and LONGITUDE. Cells that
contain a value within the user-specified range
[LON_MIN,LON_MAX,LAT_MIN,LAT_MAX] are included in the output hyperslab.
A cell-based grid collapses the horizontal spatial information
(latitude and longitude) and stores it along a one-dimensional
coordinate that has a one-to-one mapping to both latitude and longitude
coordinates. Rectangular (in longitude and latitude) horizontal
hyperslabs cannot be selected using the typical procedure (*note
Hyperslabs::) of separately specifying `-d' arguments for longitude and
latitude. Instead, when the `-X' is used, NCO learns the names of the
latitude and longitude coordinates by searching the `standard_name'
attribute of all variables until it finds the two variables whose
`standard_name''s are "latitude" and "longitude", respectively. This
`standard_name' attribute for latitude and longitude coordinates
follows the CF-convention (*note CF Conventions::).
Putting it all together, consider a variable GDS_3DVAR output from
simulations on a cell-based geodesic grid. Although the variable
contains three dimensions of data (time, latitude, and longitude), it
is stored in the netCDF file with only two dimensions, `time' and
`gds_crd'.
% ncks -m -C -v gds_3dvar ~/nco/data/in.nc
gds_3dvar: # dim. = 2, NC_FLOAT, # att. = 4, ID = 38
gds_3dvar dimension 0: time, size = 10 NC_DOUBLE, dim. ID = 18 (CRD)(REC)
gds_3dvar dimension 1: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD)
gds_3dvar memory size is 10*8*nco_typ_lng(NC_FLOAT) = 80*4 = 320 bytes
gds_3dvar attribute 0: long_name, size = 17 NC_CHAR, value = Geodesic variable
gds_3dvar attribute 1: units, size = 5 NC_CHAR, value = meter
gds_3dvar attribute 2: coordinates, size = 15 NC_CHAR, value = lat_gds lon_gds
The `coordinates' attribute lists the names of the latitude and
longitude coordinates, `lat_gds' and `lon_gds', respectively. The
`coordinates' attribute is recommended though optional. With it, the
user can immediately identify which variables contain the latitude and
longitude coordinates. Without a `coordinates' attribute it would be
unclear at first glance whether a variable is on a cell-based grid. In
this example, `time' is a normal record dimension and `gds_crd' is the
cell-based dimension.
The cell-based grid file must contain two variables whose
`standard_name' attributes are "latitude", and "longitude":
% ncks -m -C -v lat_gds,lon_gds ~/nco/data/in.nc
lat_gds: # dim. = 1, NC_DOUBLE, # att. = 4, ID = 34
lat_gds dimension 0: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD)
lat_gds memory size is 8*nco_typ_lng(NC_DOUBLE) = 8*8 = 64 bytes
lat_gds attribute 0: long_name, size = 8 NC_CHAR, value = Latitude
lat_gds attribute 1: standard_name, size = 8 NC_CHAR, value = latitude
lat_gds attribute 2: units, size = 6 NC_CHAR, value = degree
lon_gds: # dim. = 1, NC_DOUBLE, # att. = 4, ID = 35
lon_gds dimension 0: gds_crd, size = 8 NC_FLOAT, dim. ID = 17 (CRD)
lon_gds memory size is 8*nco_typ_lng(NC_DOUBLE) = 8*8 = 64 bytes
lon_gds attribute 0: long_name, size = 9 NC_CHAR, value = Longitude
lon_gds attribute 1: standard_name, size = 9 NC_CHAR, value = longitude
lon_gds attribute 2: units, size = 6 NC_CHAR, value = degree
In this example `lat_gds' and `lon_gds' represent the latitude or
longitude, respectively, of cell-based variables. These coordinates
(must) have the same single dimension (`gds_crd', in this case) as the
cell-based variables. And the coordinates must be
one-dimensional--multidimensional coordinates will not work.
This infrastructure allows NCO to identify, interpret, and process
(e.g., hyperslab) the variables on cell-based grids as easily as it
works with regular grids. To time-average all the values between zero
and 180 degrees longitude and between plus and minus 30 degress
latitude, we use
ncra -O -X 0.,180.,-30.,30. -v gds_3dvar in.nc out.nc
NCO accepts multiple `-X' arguments for cell-based grids
multi-slabs, just as it accepts multiple `-d' arguments for multi-slabs
of regular coordinates.
ncra -O -X 0.,180.,-30.,30. -X 270.,315.,45.,90. in.nc out.nc
The arguments to `-X' are always interpreted as floating point
numbers, i.e., as coordinate values rather than dimension indices so
that these two commands produce identical results
ncra -X 0.,180.,-30.,30. in.nc out.nc
ncra -X 0,180,-30,30 in.nc out.nc
In contrast, arguments to `-d' require decimal places to be
recognized as coordinates not indices (*note Hyperslabs::). We
recommend always using decimal points with `-X' arguments to avoid
confusion.
3.19 UDUnits Support
====================
Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncks', `ncpdq',
`ncra', `ncrcat', `ncwa'
Short options: `-d DIM,[MIN][,[MAX][,[STRIDE]]]'
Long options: `--dimension DIM,[MIN][,[MAX][,[STRIDE]]]',
`--dmn DIM,[MIN][,[MAX][,[STRIDE]]]'
There is more than one way to hyperskin a cat. The UDUnits
(http://www.unidata.ucar.edu/packages/udunits) package provides a
library which, if present, NCO uses to translate user-specified
physical dimensions into the physical dimensions of data stored in
netCDF files. Unidata provides UDUnits under the same terms as netCDF,
so sites should install both. Compiling NCO with UDUnits support is
currently optional but may become required in a future version of NCO.
Two examples suffice to demonstrate the power and convenience of
UDUnits support. First, consider extraction of a variable containing
non-record coordinates with physical dimensions stored in MKS units.
In the following example, the user extracts all wavelengths in the
visible portion of the spectrum in terms of the units very frequently
used in visible spectroscopy, microns:
% ncks -C -H -v wvl -d wvl,"0.4 micron","0.7 micron" in.nc
wvl[0]=5e-07 meter
The hyperslab returns the correct values because the WVL variable is
stored on disk with a length dimension that UDUnits recognizes in the
`units' attribute. The automagical algorithm that implements this
functionality is worth describing since understanding it helps one
avoid some potential pitfalls. First, the user includes the physical
units of the hyperslab dimensions she supplies, separated by a simple
space from the numerical values of the hyperslab limits. She encloses
each coordinate specifications in quotes so that the shell does not
break the _value-space-unit_ string into separate arguments before
passing them to NCO. Double quotes (`"foo"') or single quotes
(`'foo'') are equally valid for this purpose. Second, NCO recognizes
that units translation is requested because each hyperslab argument
contains text characters and non-initial spaces. Third, NCO determines
whether the WVL is dimensioned with a coordinate variable that has a
`units' attribute. In this case, WVL itself is a coordinate variable.
The value of its `units' attribute is `meter'. Thus WVL passes this
test so UDUnits conversion is attempted. If the coordinate associated
with the variable does not contain a `units' attribute, then NCO aborts.
Fourth, NCO passes the specified and desired dimension strings (microns
are specified by the user, meters are required by NCO) to the UDUnits
library. Fifth, the UDUnits library that these dimension are
commensurate and it returns the appropriate linear scaling factors to
convert from microns to meters to NCO. If the units are incommensurate
(i.e., not expressible in the same fundamental MKS units), or are not
listed in the UDUnits database, then NCO aborts since it cannot
determine the user's intent. Finally, NCO uses the scaling information
to convert the user-specified hyperslab limits into the same physical
dimensions as those of the corresponding cooridinate variable on disk.
At this point, NCO can perform a coordinate hyperslab using the same
algorithm as if the user had specified the hyperslab without requesting
units conversion.
The translation and dimensional innterpretation of time coordinates
shows a more powerful, and probably more common, UDUnits application.
In this example, the user prints all data between the eighth and ninth
of December, 1999, from a variable whose time dimension is hours since
the year 1900:
% ncks -H -C -v time_udunits -d time_udunits,"1999-12-08 \
12:00:0.0","1999-12-09 00:00:0.0",2 in.nc foo2.nc
time_udunits[1]=876018 hours since 1900-01-01 00:00:0.0
Here, the user invokes the stride (*note Stride::) capability to
obtain every other timeslice. This is possible because the UDUnits
feature is additive, not exclusive--it works in conjunction with all
other hyperslabbing (*note Hyperslabs::) options and in all operators
which support hyperslabbing. The following example shows how one might
average data in a time period spread across multiple input files
ncra -d time,"1939-09-09 12:00:0.0","1945-05-08 00:00:0.0" \
in1.nc in2.nc in3.nc out.nc
Note that there is no excess whitespace before or after the
individual elements of the `-d' argument. This is important since, as
far as the shell knows, `-d' takes only _one_ command-line argument.
Parsing this argument into its component `DIM,[MIN][,[MAX][,[STRIDE]]]'
elements (*note Hyperslabs::) is the job of NCO. When unquoted
whitespace is present between these elements, the shell passes NCO
arugment fragments which will not parse as intended.
NCO implemented support for the UDUnits2 library with version 3.9.2
(August, 2007). The UDUnits2
(http://www.unidata.ucar.edu/software/udunits/udunits-2/udunits2.html)
package supports non-ASCII characters and logarithmic units. We are
interested in user-feedback on these features, which are relatively
un-tested with NCO.
The UDUnits (http://www.unidata.ucar.edu/packages/udunits) package
documentation describes the supported formats of time dimensions.
Among the metadata conventions which adhere to these formats are the
Climate and Forecast (CF) Conventions (http://cf-pcmdi.llnl.gov) and the
Cooperative Ocean/Atmosphere Research Data Service (COARDS) Conventions
(http://ferret.wrc.noaa.gov/noaa_coop/coop_cdf_profile.html). The
following `-d arguments' extract the same data using commonly
encountered time dimension formats:
-d time,"1918-11-11 11:00:0.0","1939-09-09 00:00:0.0"
All of these formats include at least one dash `-' in a non-leading
character position (a dash in a leading character position is a
negative sign). NCO assumes that a non-leading dash in a limit string
indicates that a UDUnits date conversion is requested.
As of NCO 4.0.0 some of calendar attributes as specified by the CF
conventions are supported. The unsupported types default to mixed
Gregorian/Julian as defined by UDUunits.
*Supported types: *
"365_day"/"no_leap", "360_day", "gregorian", "standard"
*Unsupported types:*
"366_day"/"all_leap","proleptic_gregorian","julian","none"
An Example: Consider the following netcdf variable
variables:
double lon_cal(lon_cal) ;
lon_cal:long_name = "lon_cal" ;
lon_cal:units = "days since 1964-2-28 0:0:0" ;
lon_cal:calendar = "365_day" ;
data:
lon_cal = 1,2,3,4,5,6,7,8,9,10;
So the command
"ncks -v lon_cal -d lon_cal,'1964-3-1 0:00:0.0','1964-3-4 00:00:0.0' in.nc out.nc"
Results in the hyperslab lon_cal=1,2,3,4
netCDF variables should always be stored with MKS (i.e., God's)
units, so that application programs may assume MKS dimensions apply to
all input variables. The UDUnits feature is intended to alleviate some
of the NCO user's pain when handling MKS units. It connects users who
think in human-friendly units (e.g., miles, millibars, days) to extract
data which are always stored in God's units, MKS (e.g., meters,
Pascals, seconds). The feature is not intended to encourage writers to
store data in esoteric units (e.g., furlongs, pounds per square inch,
fortnights).
3.20 Rebasing Time Coordinate
=============================
Availability: `ncra', `ncrcat' Short options: None
Time rebasing seeks to fix the following problem. we have a bunch files
to concatenate or average along a common record dimension/ coordinate.
The thing is although the record coordinate is in the same time units
in each file the date offset is different.
For example suppose the time co-ordinate is in hours and we have 31
files for each day in january. Witin each file is the variable
temperature *temp(time)*; and a time co-ordinate that goes from 0-23
hours. The time:units attribute from each file is as follows
file01.nc -- time:units="hours since 1990-1-1"
file02.nc -- time:units="hours since 1990-1-2"
file03.nc -- time:units="hours since 1990-1-3"
file04.nc -- time:units="hours since 1990-1-4"
...
...
//Find the mean noon day temperature in january
ncra -v temp -d time,"1990-1-1 12:00:00","1990-1-31 23:59:59",24 \
file??.nc noon.nc
// concatenate day2 noon - day3 noon records
ncrcat -v temp -d time,"1990-1-2 12:00:00","1990-1-3 11:59:59" \
file01.nc file02.nc file03.nc noon.nc
// As you can see the time has been rebased to the time units in the first file
time=36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, 55, 56, 57, 58, 59 ;
// If we repeat the above command but with only two input files
ncrcat -v temp -d time,"1990-1-2 12:00:00","1990-1-3 11:59:59" \
file02.nc file03 noon.nc
// then the output time coordinate is "based" on the units in the second file
time = 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35 ;
3.21 Missing values
===================
Availability: `ncap2', `ncbo', `ncea', `ncflint', `ncpdq', `ncra',
`ncwa'
Short options: None
The phrase "missing data" refers to data points that are missing,
invalid, or for any reason not intended to be arithmetically processed
in the same fashion as valid data. The NCO arithmetic operators
attempt to handle missing data in an intelligent fashion. There are
four steps in the NCO treatment of missing data:
1. Identifying variables that may contain missing data.
NCO follows the convention that missing data should be stored with
the _FILLVALUE specified in the variable's `_FillValue' attributes.
The _only_ way NCO recognizes that a variable _may_ contain
missing data is if the variable has a `_FillValue' attribute. In
this case, any elements of the variable which are numerically equal
to the _FILLVALUE are treated as missing data.
NCO adopted the behavior that the default attribute name, if any,
assumed to specify the value of data to ignore is `_FillValue'
with version 3.9.2 (August, 2007). Prior to that, the
`missing_value' attribute, if any, was assumed to specify the
value of data to ignore. Supporting both of these attributes
simultaneously is not practical. Hence the behavior NCO once
applied to MISSING_VALUE it now applies to any _FILLVALUE. NCO
now treats any MISSING_VALUE as normal data (1).
It has been and remains most advisable to create both `_FillValue'
and `missing_value' attributes with identical values in datasets.
Many legacy datasets contain only `missing_value' attributes. NCO
can help migrating datasets between these conventions. One may
use `ncrename' (*note ncrename netCDF Renamer::) to rename all
`missing_value' attributes to `_FillValue':
ncrename -a .missing_value,_FillValue inout.nc
Alternatively, one may use `ncatted' (*note ncatted netCDF
Attribute Editor::) to add a `_FillValue' attribute to all
variables
ncatted -O -a _FillValue,,o,f,1.0e36 inout.nc
2. Converting the _FILLVALUE to the type of the variable, if
neccessary.
Consider a variable VAR of type VAR_TYPE with a `_FillValue'
attribute of type ATT_TYPE containing the value _FILLVALUE. As a
guideline, the type of the `_FillValue' attribute should be the
same as the type of the variable it is attached to. If VAR_TYPE
equals ATT_TYPE then NCO straightforwardly compares each value of
VAR to _FILLVALUE to determine which elements of VAR are to be
treated as missing data. If not, then NCO converts _FILLVALUE from
ATT_TYPE to VAR_TYPE by using the implicit conversion rules of C,
or, if ATT_TYPE is `NC_CHAR' (2), by typecasting the results of
the C function `strtod(_FILLVALUE)'. You may use the NCO operator
`ncatted' to change the `_FillValue' attribute and all data whose
data is _FILLVALUE to a new value (*note ncatted netCDF Attribute
Editor::).
3. Identifying missing data during arithmetic operations.
When an NCO arithmetic operator processes a variable VAR with a
`_FillValue' attribute, it compares each value of VAR to
_FILLVALUE before performing an operation. Note the _FILLVALUE
comparison imposes a performance penalty on the operator.
Arithmetic processing of variables which contain the `_FillValue'
attribute always incurs this penalty, even when none of the data
are missing. Conversely, arithmetic processing of variables which
do not contain the `_FillValue' attribute never incurs this
penalty. In other words, do not attach a `_FillValue' attribute
to a variable which does not contain missing data. This
exhortation can usually be obeyed for model generated data, but it
may be harder to know in advance whether all observational data
will be valid or not.
4. Treatment of any data identified as missing in arithmetic
operators.
NCO averagers (`ncra', `ncea', `ncwa') do not count any element
with the value _FILLVALUE towards the average. `ncbo' and
`ncflint' define a _FILLVALUE result when either of the input
values is a _FILLVALUE. Sometimes the _FILLVALUE may change from
file to file in a multi-file operator, e.g., `ncra'. NCO is
written to account for this (it always compares a variable to the
_FILLVALUE assigned to that variable in the current file).
Suffice it to say that, in all known cases, NCO does "the right
thing".
It is impossible to determine and store the correct result of a
binary operation in a single variable. One such corner case
occurs when both operands have differing _FILLVALUE attributes,
i.e., attributes with different numerical values. Since the
output (result) of the operation can only have one _FILLVALUE,
some information may be lost. In this case, NCO always defines
the output variable to have the same _FILLVALUE as the first input
variable. Prior to performing the arithmetic operation, all
values of the second operand equal to the second _FILLVALUE are
replaced with the first _FILLVALUE. Then the arithmetic operation
proceeds as normal, comparing each element of each operand to a
single _FILLVALUE. Comparing each element to two distinct
_FILLVALUE's would be much slower and would be no likelier to
yield a more satisfactory answer. In practice, judicious choice
of _FILLVALUE values prevents any important information from being
lost.
---------- Footnotes ----------
(1) The old functionality, i.e., where the ignored values are
indicated by `missing_value' not `_FillValue', may still be selected
_at NCO build time_ by compiling NCO with the token definition
`CPPFLAGS='-DNCO_MSS_VAL_SNG=missing_value''.
(2) For example, the DOE ARM program often uses ATT_TYPE = `NC_CHAR'
and _FILLVALUE = `-99999.'.
3.22 Chunking
=============
Availability: `ncap2', `ncbo', `ncea', `ncecat', `ncflint', `ncks',
`ncpdq', `ncra', `ncrcat', `ncwa'
Short options: none
Long options: `--cnk_dmn DMN_NM,CNK_SZ', `--chunk_dimension
DMN_NM,CNK_SZ'
, `--cnk_map CNK_MAP', `--chunk_map CNK_MAP',
`--cnk_plc CNK_PLC', `--chunk_policy CNK_PLC',
`--cnk_scl CNK_SZ', `--chunk_scalar CNK_SZ'
All netCDF4-enabled NCO operators that define variables support a
plethora of chunksize options. Chunking can significantly accelerate
or degrade read/write access to large datasets. Dataset chunking
issues are described in detail here
(http://www.hdfgroup.org/HDF5/doc/H5.user/Chunking.html).
The NCO chunking implementation is designed to be flexible. Users
control three aspects of the chunking implementation. These are known
as the "chunking policy", "chunking map", and "chunksize". The first
two are high-level mechanisms that apply to an entire file, while the
third allows per-dimension specification of parameters. The
implementation is a hybrid of the `ncpdq' packing policies (*note ncpdq
netCDF Permute Dimensions Quickly::), and the hyperslab specifications
(*note Hyperslabs::). Each aspect is intended to have a sensible
default, so that most users will only need to set one switch to obtain
sensible chunking. Power users can tune the three switches in tandem
to obtain optimal performance.
The user specifies the desired chunking policy with the `-P' switch
(or its long option equivalents, `--cnk_plc' and `--chunk_policy') and
its CNK_PLC argument. Five chunking policies are currently implemented:
"Chunk All Variables [_default_]"
Definition: Chunk all variables possible
Alternate invocation: `ncchunk'
CNK_PLC key values: `all', `cnk_all', `plc_all'
Mnemonic: All
"Chunk Variables with at least Two Dimensions"
Definition: Chunk all variables possible with at least two
dimensions
Alternate invocation: none
CNK_PLC key values: `g2d', `cnk_g2d', `plc_g2d'
Mnemonic: _G_reater than or equal to _2_ _D_imensions
"Chunk Variables with at least Three Dimensions"
Definition: Chunk all variables possible with at least three
dimensions
Alternate invocation: none
CNK_PLC key values: `g3d', `cnk_g3d', `plc_g3d'
Mnemonic: _G_reater than or equal to _3_ _D_imensions
"Chunk Variables Containing Explicitly Chunked Dimensions"
Definition: Chunk all variables possible that contain at least one
dimension whose chunksize was explicitly set with the `--cnk_dmn'
option. Alternate invocation: none
CNK_PLC key values: `xpl', `cnk_xpl', `plc_xpl'
Mnemonic: E_XPL_icitly specified dimensions
"Unchunking"
Definition: Unchunk all variables
Alternate invocation: `ncunchunk'
CNK_PLC key values: `uck', `cnk_uck', `plc_uck', `unchunk'
Mnemonic: _U_n_C_hun_K_
Equivalent key values are fully interchangeable. Multiple equivalent
options are provided to satisfy disparate needs and tastes of NCO users
working with scripts and from the command line.
The chunking algorithms must know the chunksizes of each dimension of
each variable to be chunked. The correspondence between the input
variable shape and the chunksizes is called the "chunking map". The
user specifies the desired chunking map with the `-M' switch (or its
long option equivalents, `--cnk_map' and `--chunk_map') and its CNK_MAP
argument. Four chunking maps are currently implemented:
"Chunksize Equals Dimension Size [_default_]"
Definition: Chunksize defaults to dimension size. Explicitly
specify chunksizes for particular dimensions with `--cnk_dmn'
option.
CNK_MAP key values: `dmn', `cnk_dmn', `map_dmn'
Mnemonic: _D_i_M_e_N_sion
"Chunksize Equals Dimension Size except Record Dimension"
Definition: Chunksize equals dimension size except record
dimension has size one. Explicitly specify chunksizes for
particular dimensions with `--cnk_dmn' option.
CNK_MAP key values: `rd1', `cnk_rd1', `map_rd1'
Mnemonic: _R_ecord _D_imension size _1_
"Chunksize Equals Scalar Size Specified"
Definition: Chunksize for all dimensions is set with the
`--cnk_scl' option.
CNK_MAP key values: `xpl', `cnk_xpl', `map_xpl'
Mnemonic: E_XPL_icitly specified dimensions
"Chunksize Product Equals Scalar Size Specified"
Definition: The product of the chunksizes for each variable
(approximately) equals the size specified with the `--cnk_scl'
option. For a variable of rank R (i.e., with R non-degenerate
dimensions), the chunksize in each non-degenerate dimension is the
Rth root of CNK_SCL.
CNK_MAP key values: `prd', `cnk_prd', `map_prd'
Mnemonic: _PR_o_D_uct
It is possible to combine the above chunking map algorithms with
user-specified per-dimension (but not per-variable) chunksizes that
override specific chunksizes determined by the maps above. The user
specifies the per-dimension chunksizes with the (equivalent) long
options `--cnk_dmn' or `--chunk_dimension'). The option takes two
comma-separated arguments, DMN_NM,CNK_SZ, which are the dimension name
and its chunksize, respectively. The `--cnk_dmn' option may be us as
many times as necessary.
# Debugging
ncks -O -4 -D 4 --cnk_scl=8 ~/nco/data/in.nc ~/foo.nc
ncks -O -4 -D 4 --cnk_scl=8 /data/zender/dstmch90/dstmch90_clm.nc ~/foo.nc
ncks -O -4 -D 4 --cnk_dmn lat,64 --cnk_dmn lon,128 /data/zender/dstmch90/dstmch90_clm.nc ~/foo.nc
ncks -O -4 -D 4 --cnk_plc=uck ~/foo.nc ~/foo.nc
ncks -O -4 -D 4 --cnk_plc=g2d --cnk_map=rd1 --cnk_dmn lat,64 --cnk_dmn lon,128 /data/zender/dstmch90/dstmch90_clm.nc ~/foo.nc
# Chunk data then unchunk it back to its original state:
ncks -O -4 -D 4 --cnk_plc=all ~/nco/data/in.nc ~/foo.nc
ncks -O -4 -D 4 --cnk_plc=uck ~/foo.nc ~/foo.nc
# Final, cleaner examples for manual
ncks --cnk_plc=all in.nc out.nc # Chunk in.nc
ncks --cnk_plc=unchunk in.nc out.nc # Unchunk in.nc
3.23 Deflation
==============
Availability: `ncap2', `ncbo', `ncea', `ncecat', `ncflint', `ncks',
`ncpdq', `ncra', `ncrcat', `ncwa'
Short options: `-L'
Long options: `--dfl_lvl', `--deflate'
All NCO operators that define variables support the netCDF4 feature
of storing variables compressed with Lempel-Ziv deflation. The
Lempel-Ziv algorithm is a lossless data compression technique.
Activate this deflation with the `-L DFL_LVL' short option (or with the
same argument to the `--dfl_lvl' or `--deflate' long options). Specify
the deflation level DFL_LVL on a scale from no deflation (DFL_LVL = 0)
to maximum deflation (DFL_LVL = 9). Minimal deflation (DFL_LVL = 1)
achieves considerable storage compression with little time penalty.
Higher deflation levels require more time for compression. File sizes
resulting from minimal (DFL_LVL = 1) and maximal (DFL_LVL = 9)
deflation levels typically differ by a few percent in size.
To compress an entire file using deflation, use
ncks -4 -L 0 in.nc out.nc # No deflation (fast, no time penalty)
ncks -4 -L 1 in.nc out.nc # Minimal deflation (little time penalty)
ncks -4 -L 9 in.nc out.nc # Maximal deflation (much slower)
Unscientific testing shows that deflation compresses typical climate
datasets by 30-60%. Packing, a lossy compression technique available
for all netCDF files (see *note Packed data::), can easily compress
files by 50%. Packed data may be deflated to squeeze datasets by about
80%.
ncks -4 -L 1 in.nc out.nc # Minimal deflation (~30-60% compression)
ncks -4 -L 9 in.nc out.nc # Maximal deflation (~31-63% compression)
ncpdq in.nc out.nc # Standard packing (~50% compression)
ncpdq -4 -L 9 in.nc out.nc # Deflated packing (~80% compression)
`ncks' prints deflation parameters, if any, to screen (*note ncks
netCDF Kitchen Sink::).
3.24 Packed data
================
Availability: `ncap2', `ncbo', `ncea', `ncflint', `ncpdq', `ncra',
`ncwa'
Short options: None
The phrase "packed data" refers to data which are stored in the
standard netCDF3 packing format which employs a lossy algorithm. See
*note ncks netCDF Kitchen Sink:: for a description of deflation, a
lossless compression technique available with netCDF4 only. Packed
data may be deflated to save additional space.
Packing Algorithm
-----------------
"Packing" The standard netCDF packing algorithm is lossy, and produces
data with the same dynamic range as the original but which requires no
more than half the space to store. The packed variable is stored
(usually) as type `NC_SHORT' with the two attributes required to unpack
the variable, `scale_factor' and `add_offset', stored at the original
(unpacked) precision of the variable (1). Let MIN and MAX be the
minimum and maximum values of X.
SCALE_FACTOR = (MAX-MIN)/NDRV
ADD_OFFSET = 0.5*(MIN+MAX)
PCK = (UPK-ADD_OFFSET)/SCALE_FACTOR = (UPK-0.5*(MIN+MAX))*NDRV/(MAX-MIN)
where NDRV is the number of discrete representable values for given
type of packed variable. The theoretical maximum value for NDRV is two
raised to the number of bits used to store the packed variable. Thus
if the variable is packed into type `NC_SHORT', a two-byte datatype,
then there are at most 2^16 = 65536 distinct values representible. In
practice, the number of discretely representible values is taken to be
one less than the theoretical maximum. This leaves extra space and
solves potential problems with rounding which can occur during the
unpacking of the variable. Thus for `NC_SHORT', ndrv = 65536 - 1 =
65535. Less often, the variable may be packed into type `NC_CHAR',
where ndrv = 256 - 1 = 255, or type `NC_INT' where where ndrv =
4294967295 - 1 = 4294967294. One useful feature of (lossy) netCDF
packing algorithm is that additional, loss-less packing algorithms
perform well on top of it.
Unpacking Algorithm
-------------------
"Unpacking" The unpacking algorithm depends on the presence of two
attributes, `scale_factor' and `add_offset'. If `scale_factor' is
present for a variable, the data are multiplied by the value
SCALE_FACTOR after the data are read. If `add_offset' is present for a
variable, then the ADD_OFFSET value is added to the data after the data
are read. If both `scale_factor' and `add_offset' attributes are
present, the data are first scaled by SCALE_FACTOR before the offset
ADD_OFFSET is added.
UPK = SCALE_FACTOR*PCK + ADD_OFFSET = (MAX-MIN)*PCK/NDRV +
0.5*(MIN+MAX)
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 `int', `float', or `double'. An attribute's `scale_factor' and
`add_offset' and `_FillValue', if any, should all be of the type
intended for the unpacked data, i.e., `int', `float' or `double'.
Default Handling of Packed Data
-------------------------------
All NCO arithmetic operators understand packed data. The operators
automatically unpack any packed variable in the input file which will
be arithmetically processed. For example, `ncra' unpacks all record
variables, and `ncwa' unpacks all variable which contain a dimension to
be averaged. These variables are stored unpacked in the output file.
On the other hand, arithmetic operators do not unpack non-processed
variables. For example, `ncra' leaves all non-record variables packed,
and `ncwa' leaves packed all variables lacking an averaged dimension.
These variables (called fixed variables) are passed unaltered from the
input to the output file. Hence fixed variables which are packed in
input files remain packed in output files. Completely packing and
unpacking files is easily accomplished with `ncpdq' (*note ncpdq netCDF
Permute Dimensions Quickly::). Packing and unpacking individual
variables may be done with `ncpdq' and the `ncap2' `pack()' and
`unpack()' functions (*note Methods and functions::).
---------- Footnotes ----------
(1) Although not a part of the standard, NCO enforces the policy
that the `_FillValue' attribute, if any, of a packed variable is also
stored at the original precision.
3.25 Operation Types
====================
Availability: `ncap2', `ncra', `ncea', `ncwa'
Short options: `-y'
Long options: `--operation', `--op_typ'
The `-y OP_TYP' switch allows specification of many different types of
operations Set OP_TYP to the abbreviated key for the corresponding
operation:
`avg'
Mean value
`sqravg'
Square of the mean
`avgsqr'
Mean of sum of squares
`max'
Maximium value
`min'
Minimium value
`rms'
Root-mean-square (normalized by N)
`rmssdn'
Root-mean square (normalized by N-1)
`sqrt'
Square root of the mean
`ttl'
Sum of values
NCO assumes coordinate variables represent grid axes, e.g.,
longitude. The only rank-reduction which makes sense for coordinate
variables is averaging. Hence NCO implements the operation type
requested with `-y' on all non-coordinate variables, but not on
coorniate variables. When an operation requires a coordinate variable
to be reduced in rank, i.e., from one dimension to a scalar or from one
dimension to a degenerate (single value) array, then NCO _always
averages_ the coordinate variable regardless of the arithmetic
operation type performed on the non-coordinate variables.
The mathematical definition of each arithmetic operation is given
below. *Note ncwa netCDF Weighted Averager::, for additional
information on masks and normalization. If an operation type is not
specified with `-y' then the operator performs an arithmetic average by
default. Averaging is described first so the terminology for the other
operations is familiar.
_Note for Info users_: The definition of mathematical operations
involving rank reduction (e.g., averaging) relies heavily on
mathematical expressions which cannot be easily represented in Info.
_See the printed manual (./nco.pdf) for much more detailed and complete
documentation of this subject._
The definitions of some of these operations are not universally
useful. Mostly they were chosen to facilitate standard statistical
computations within the NCO framework. We are open to redefining and
or adding to the above. If you are interested in having other
statistical quantities defined in NCO please contact the NCO project
(*note Help Requests and Bug Reports::).
EXAMPLES
Suppose you wish to examine the variable `prs_sfc(time,lat,lon)' which
contains a time series of the surface pressure as a function of
latitude and longitude. Find the minimium value of `prs_sfc' over all
dimensions:
ncwa -y min -v prs_sfc in.nc foo.nc
Find the maximum value of `prs_sfc' at each time interval for each
latitude:
ncwa -y max -v prs_sfc -a lon in.nc foo.nc
Find the root-mean-square value of the time-series of `prs_sfc' at
every gridpoint:
ncra -y rms -v prs_sfc in.nc foo.nc
ncwa -y rms -v prs_sfc -a time in.nc foo.nc
The previous two commands give the same answer but `ncra' is
preferred because it has a smaller memory footprint. Also, by default,
`ncra' leaves the (degenerate) `time' dimension in the output file
(which is usually useful) whereas `ncwa' removes the `time' dimension
(unless `-b' is given).
These operations work as expected in multi-file operators. Suppose
that `prs_sfc' is stored in multiple timesteps per file across multiple
files, say `jan.nc', `feb.nc', `march.nc'. We can now find the three
month maximium surface pressure at every point.
ncea -y max -v prs_sfc jan.nc feb.nc march.nc out.nc
It is possible to use a combination of these operations to compute the
variance and standard deviation of a field stored in a single file or
across multiple files. The procedure to compute the temporal standard
deviation of the surface pressure at all points in a single file
`in.nc' involves three steps.
ncwa -O -v prs_sfc -a time in.nc out.nc
ncbo -O -v prs_sfc in.nc out.nc out.nc
ncra -O -y rmssdn out.nc out.nc
First construct the temporal mean of `prs_sfc' in the file `out.nc'.
Next overwrite `out.nc' with the anomaly (deviation from the mean).
Finally overwrite `out.nc' with the root-mean-square of itself. Note
the use of `-y rmssdn' (rather than `-y rms') in the final step. This
ensures the standard deviation is correctly normalized by one fewer
than the number of time samples. The procedure to compute the variance
is identical except for the use of `-y var' instead of `-y rmssdn' in
the final step.
`ncap2' can also compute statistics like standard deviations.
Brute-force implementation of formulae is one option, e.g.,
ncap2 -s 'prs_sfc_sdn=sqrt((prs_sfc-prs_sfc.avg($time)^2).total($time))/($time.size-1)'
in.nc out.nc
The operation may, of course, be broken into multiple steps in order
to archive intermediate quantities, such as the time-anomalies
ncap2 -s 'prs_sfc_anm=prs_sfc-prs_sfc.avg($time)' \
-s 'prs_sfc_sdn=sqrt((prs_sfc_anm^2).total($time))/($time.size-1)' \
in.nc out.nc
`ncap2' supports intrinsic standard deviation functions (*note
Operation Types::) which simplify the above expression to
ncap2 -s 'prs_sfc_sdn=(prs_sfc-prs_sfc.avg($time)).rmssdn($time)' in.nc out.nc
These instrinsic functions compute the answer quickly and concisely.
The procedure to compute the spatial standard deviation of a field
in a single file `in.nc' involves three steps.
ncwa -O -v prs_sfc,gw -a lat,lon -w gw in.nc out.nc
ncbo -O -v prs_sfc,gw in.nc out.nc out.nc
ncwa -O -y rmssdn -v prs_sfc -a lat,lon -w gw out.nc out.nc
First the appropriately weighted (with `-w gw') spatial mean values
are written to the output file. This example includes the use of a
weighted variable specified with `-w gw'. When using weights to
compute standard deviations one must remember to include the weights in
the initial output files so that they may be used again in the final
step. The initial output file is then overwritten with the gridpoint
deviations from the spatial mean. Finally the root-mean-square of the
appropriately weighted spatial deviations is taken.
The `ncap2' solution to the spatially-weighted standard deviation
problem is
ncap2 -s 'prs_sfc_sdn=(prs_sfc*gw-prs_sfc*gw.avg($lat,$lon)).rmssdn($lat,$lon)' \
in.nc out.nc
Be sure to multiply the variable by the weight prior to computing the
the anomalies and the standard deviation.
The procedure to compute the standard deviation of a time-series
across multiple files involves one extra step since all the input must
first be collected into one file.
ncrcat -O -v tpt in.nc in.nc foo1.nc
ncwa -O -a time foo1.nc foo2.nc
ncbo -O -v tpt foo1.nc foo2.nc foo2.nc
ncra -O -y rmssdn foo2.nc out.nc
The first step assembles all the data into a single file. This may
require a lot of temporary disk space, but is more or less required by
the `ncbo' operation in the third step.
3.26 Type Conversion
====================
Availability: `ncap2', `ncbo', `ncea', `ncra', `ncwa'
Short options: None
Type conversion (often called "promotion" or "demotion") refers to the
casting of one fundamental data type to another, e.g., converting
`NC_SHORT' (two bytes) to `NC_DOUBLE' (eight bytes). Type conversion
is automatic when the language carries out this promotion according to
an internal set of rules without explicit user intervention. In
contrast, manual type conversion refers to explicit user commands to
change the type of a variable or attribute. Most type conversion
happens automatically, yet there are situations in which manual type
conversion is advantageous.
3.26.1 Automatic type conversion
--------------------------------
As a general rule, automatic type conversions should be avoided for at
least two reasons. First, type conversions are expensive since they
require creating (temporary) buffers and casting each element of a
variable from the type it was stored at to some other type. Second,
the dataset's creator probably had a good reason for storing data as,
say, `NC_FLOAT' rather than `NC_DOUBLE'. In a scientific framework
there is no reason to store data with more precision than the
observations were made. Thus NCO tries to avoid performing automatic
type conversions when performing arithmetic.
Automatic type conversion during arithmetic in the languages C and
Fortran is performed only when necessary. All operands in an operation
are converted to the most precise type before the operation takes place.
However, following this parsimonious conversion rule dogmatically
results in numerous headaches. For example, the average of the two
`NC_SHORT's `17000s' and `17000s' results in garbage since the
intermediate value which holds their sum is also of type `NC_SHORT' and
thus cannot represent values greater than 32,767 (1). There are valid
reasons for expecting this operation to succeed and the NCO philosophy
is to make operators do what you want, not what is most pure. Thus,
unlike C and Fortran, but like many other higher level interpreted
languages, NCO arithmetic operators will perform automatic type
conversion when all the following conditions are met (2):
1. The operator is `ncea', `ncra', or `ncwa'. `ncbo' is not yet
included in this list because subtraction did not benefit from
type conversion. This will change in the future
2. The arithmetic operation could benefit from type conversion.
Operations that could benefit (e.g., from larger representable
sums) include averaging, summation, or any "hard" arithmetic.
Type conversion does not benefit searching for minima and maxima
(`-y min', or `-y max').
3. The variable on disk is of type `NC_BYTE', `NC_CHAR', `NC_SHORT',
or `NC_INT'. Type `NC_DOUBLE' is not type converted because there
is no type of higher precision to convert to. Type `NC_FLOAT' is
not type converted because, in our judgement, the performance
penalty of always doing so would outweigh the (extremely rare)
potential benefits.
When these criteria are all met, the operator promotes the variable
in question to type `NC_DOUBLE', performs all the arithmetic
operations, casts the `NC_DOUBLE' type back to the original type, and
finally writes the result to disk. The result written to disk may not
be what you expect, because of incommensurate ranges represented by
different types, and because of (lack of) rounding. First, continuing
the above example, the average (e.g., `-y avg') of `17000s' and
`17000s' is written to disk as `17000s'. The type conversion feature
of NCO makes this possible since the arithmetic and intermediate values
are stored as `NC_DOUBLE's, i.e., `34000.0d' and only the final result
must be represented as an `NC_SHORT'. Without the type conversion
feature of NCO, the average would have been garbage (albeit predictable
garbage near `-15768s'). Similarly, the total (e.g., `-y ttl') of
`17000s' and `17000s' written to disk is garbage (actually `-31536s')
since the final result (the true total) of 34000 is outside the range
of type `NC_SHORT'.
Type conversions use the `floor' function to convert floating point
number to integers. Type conversions do not attempt to round floating
point numbers to the nearest integer. Thus the average of `1s' and
`2s' is computed in double precisions arithmetic as (`1.0d' +
`1.5d')/2) = `1.5d'. This result is converted to `NC_SHORT' and stored
on disk as `floor(1.5d)' = `1s' (3). Thus no "rounding up" is
performed. The type conversion rules of C can be stated as follows: If
N is an integer then any floating point value X satisfying
n <= x < n+1
will have the value N when converted to an integer.
---------- Footnotes ----------
(1)
32767 = 2^15-1
(2) Operators began performing type conversions before arithmetic in
NCO version 1.2, August, 2000. Previous versions never performed
unnecessary type conversion for arithmetic.
(3) The actual type conversions are handled by intrinsic C-language
type conversion, so the `floor()' function is not explicitly called,
though the results would be the same if it were.
3.26.2 Manual type conversion
-----------------------------
`ncap2' provides intrinsic functions for performing manual type
conversions. This, for example, converts variable `tpt' to external
type `NC_SHORT' (a C-type `short'), and variable `prs' to external type
`NC_DOUBLE' (a C-type `double').
ncap2 -s 'tpt=short(tpt);prs=double(prs)' in.nc out.nc
*Note ncap2 netCDF Arithmetic Processor::, for more details.
3.27 Batch Mode
===============
Availability: All operators
Short options: `-O', `-A'
Long options: `--ovr', `--overwrite', `--apn', `--append'
If the OUTPUT-FILE specified for a command is a pre-existing file, then
the operator will prompt the user whether to overwrite (erase) the
existing OUTPUT-FILE, attempt to append to it, or abort the operation.
However, interactive questions reduce productivity when processing large
amounts of data. Therefore NCO also implements two ways to override
its own safety features, the `-O' and `-A' switches. Specifying `-O'
tells the operator to overwrite any existing OUTPUT-FILE without
prompting the user interactively. Specifying `-A' tells the operator
to attempt to append to any existing OUTPUT-FILE without prompting the
user interactively. These switches are useful in batch environments
because they suppress interactive keyboard input.
3.28 History Attribute
======================
Availability: All operators
Short options: `-h'
Long options: `--hst', `--history'
All operators automatically append a `history' global attribute to any
file they create or modify. The `history' attribute consists of a
timestamp and the full string of the invocation command to the
operator, e.g., `Mon May 26 20:10:24 1997: ncks in.nc foo.nc'. The
full contents of an existing `history' attribute are copied from the
first INPUT-FILE to the OUTPUT-FILE. The timestamps appear in reverse
chronological order, with the most recent timestamp appearing first in
the `history' attribute. Since NCO and many other netCDF operators
adhere to the `history' convention, the entire data processing path of
a given netCDF file may often be deduced from examination of its
`history' attribute. As of May, 2002, NCO is case-insensitive to the
spelling of the `history' attribute name. Thus attributes named
`History' or `HISTORY' (which are non-standard and not recommended)
will be treated as valid history attributes. When more than one global
attribute fits the case-insensitive search for "history", the first one
found will be used. `history' attribute To avoid information overkill,
all operators have an optional switch (`-h', `--hst', or `--history')
to override automatically appending the `history' attribute (*note
ncatted netCDF Attribute Editor::). Note that the `-h' switch also
turns off writing the `nco_input_file_list' attribute for multi-file
operators (*note File List Attributes::).
3.29 File List Attributes
=========================
Availability: `ncea', `ncecat', `ncra', `ncrcat'
Short options: `-H'
Long options: `--fl_lst_in', `--file_list'
Many methods of specifying large numbers of input file names pass these
names via pipes, encodings, or argument transfer programs (*note Large
Numbers of Files::). When these methods are used, the input file list
is not explicitly passed on the command line. This results in a loss
of information since the `history' attribute no longer contains the
exact command by which the file was created.
NCO solves this dilemma by archiving input file list attributes.
When the input file list to a multi-file operator is specified via
`stdin', the operator, by default, attaches two global attributes to
any file they create or modify. The `nco_input_file_number' global
attribute contains the number of input files, and `nco_input_file_list'
contains the file names, specified as standard input to the multi-file
operator. This information helps to verify that all input files the
user thinks were piped through `stdin' actually arrived. Without the
`nco_input_file_list' attribute, the information is lost forever and
the "chain of evidence" would be broken.
The `-H' switch overrides (turns off) the default behavior of
writing the input file list global attributes when input is from
`stdin'. The `-h' switch does this too, and turns off the `history'
attribute as well (*note History Attribute::). Hence both switches
allows space-conscious users to avoid storing what may amount to many
thousands of filenames in a metadata attribute.
3.30 CF Conventions
===================
Availability: `ncbo', `ncea', `ncecat', `ncflint', `ncra', `ncwa'
Short options: None
NCO recognizes the Climate and Forecast (CF) metadata conventions, and
treats such data (often called history tapes), specially. NCO handles
older NCAR model datasets, such as CCM and early CCSM datasets, with
its CF rules even though the earlier data may not contain an explicit
`Conventions' attribute (e.g., `CF-1.0'). We refer to all such data
collectively as CF data. Skip this section if you never work with CF
data.
The CF netCDF conventions are described at
`http://www.cgd.ucar.edu/cms/eaton/cf-metadata/CF-1.0.html'. Most CF
netCDF conventions are transparent to NCO (1). There are no known
pitfalls associated with using any NCO operator on files adhering to
these conventions (2). However, to facilitate maximum user
friendliness, NCO does treat certain variables in some CF files
specially. The special functions are not required by the CF netCDF
conventions, but experience shows they simplify data analysis.
Currently, NCO determines whether a datafile is a CF output datafile
simply by checking whether value of the global attribute `Conventions'
(if it exists) equals `CF-1.0' or `NCAR-CSM'. Should `Conventions'
equal either of these in the (first) INPUT-FILE, NCO will attempt to
treat certain variables specially, because of their meaning in CF files.
NCO will not average the following variables often found in CF files:
`ntrm', `ntrn', `ntrk', `ndbase', `nsbase', `nbdate', `nbsec', `mdt',
`mhisf'. These variables contain scalar metadata such as the
resolution of the host geophysical model and it makes no sense to
change their values.
Furthermore, the `ncbo' operator does not operate on (i.e., add,
subtract, etc.) the following variables: `ORO', `area', `datesec',
`date', `gw', `hyai', `hyam', `hybi'. `hybm', `lat_bnds', `lon_bnds',
`msk_*'. These variables represent the Gaussian weights, the orography
field, time fields, hybrid pressure coefficients, and
latititude/longitude boundaries. We call these fields non-coordinate
"grid properties". Coordinate grid properties are easy to identify
because they are coordinate variables such as `latitude' and
`longitude'.
Users usually want _all_ grid properties to remain unaltered in the
output file. To be treated as a grid property, the variable name must
_exactly_ match a name in the above list, or be a coordinate variable.
The handling of `msk_*' is exceptional in that _any_ variable name
beginning with the string `msk_' is considered to be a "mask" and is
thus preserved (not operated on arithmetically).
You must spoof NCO if you would like any grid properties or other
special CF fields processed normally. For example rename the variables
first with `ncrename', or alter the `Conventions' attribute.
NCO supports the CF `coordinates' convention described here
(http://cf-pcmdi.llnl.gov/documents/cf-conventions/1.1/cf-conventions.html#coordinate-system).
This convention allows variables to specify additional coordinates
(including multidimensional coordinates) in a space-separated string
attribute named `coordinates'. NCO attaches any such coordinates to
the extraction list along with variable and its usual (one-dimensional)
coordinates, if any. These auxiliary coordinates are subject to the
user-specified overrides described in *note Subsetting Coordinate
Variables::.
---------- Footnotes ----------
(1) The exception is appending/altering the attributes `x_op',
`y_op', `z_op', and `t_op' for variables which have been averaged
across space and time dimensions. This feature is scheduled for future
inclusion in NCO.
(2) The CF conventions recommend `time' be stored in the format TIME
since BASE_TIME, e.g., the `units' attribute of `time' might be `days
since 1992-10-8 15:15:42.5 -6:00'. A problem with this format occurs
when using `ncrcat' to concatenate multiple files together, each with a
different BASE_TIME. That is, any `time' values from files following
the first file to be concatenated should be corrected to the BASE_TIME
offset specified in the `units' attribute of `time' from the first file.
The analogous problem has been fixed in ARM files (*note ARM
Conventions::) and could be fixed for CF files if there is sufficient
lobbying.
3.31 ARM Conventions
====================
Availability: `ncrcat'
Short options: None
`ncrcat' has been programmed to correctly handle data files which
utilize the Atmospheric Radiation Measurement (ARM) Program convention
(http://www.arm.gov/data/time.stm) for time and time offsets. If you
do not work with ARM data then you may skip this section. ARM data
files store time information in two variables, a scalar, `base_time',
and a record variable, `time_offset'. Subtle but serious problems can
arise when these type of files are just blindly concatenated.
Therefore `ncrcat' has been specially programmed to be able to chain
together consecutive ARM INPUT-FILES and produce and an OUTPUT-FILE
which contains the correct time information. Currently, `ncrcat'
determines whether a datafile is an ARM datafile simply by testing for
the existence of the variables `base_time', `time_offset', and the
dimension `time'. If these are found in the INPUT-FILE then `ncrcat'
will automatically perform two non-standard, but hopefully useful,
procedures. First, `ncrcat' will ensure that values of `time_offset'
appearing in the OUTPUT-FILE are relative to the `base_time' appearing
in the first INPUT-FILE (and presumably, though not necessarily, also
appearing in the OUTPUT-FILE). Second, if a coordinate variable named
`time' is not found in the INPUT-FILES, then `ncrcat' automatically
creates the `time' coordinate in the OUTPUT-FILE. The values of `time'
are defined by the ARM conventions TIME = BASE_TIME + TIME_OFFSET.
Thus, if OUTPUT-FILE contains the `time_offset' variable, it will also
contain the `time' coordinate. A short message is added to the
`history' global attribute whenever these ARM-specific procedures are
executed.
3.32 Operator Version
=====================
Availability: All operators
Short options: `-r'
Long options: `--revision', `--version', or `--vrs'
All operators can be told to print their version information, library
version, copyright notice, and compile-time configuration with the `-r'
switch, or its long-option equivalent `revision'. The `--version' or
`--vrs' switches print the operator version information only. The
internal version number varies between operators, and indicates the
most recent change to a particular operator's source code. This is
useful in making sure you are working with the most recent operators.
The version of NCO you are using might be, e.g., `3.9.5'. Using `-r'
on, say, `ncks', produces something like `NCO netCDF Operators version
"3.9.5" last modified 2008/05/11 built May 12 2008 on neige by zender
Copyright (C) 1995--2008 Charlie Zender ncks version 20090918'. This
tells you that `ncks' contains all patches up to version `3.9.5', which
dates from May 11, 2008.
4 Operator Reference Manual
***************************
This chapter presents reference pages for each of the operators
individually. The operators are presented in alphabetical order. All
valid command line switches are included in the syntax statement.
Recall that descriptions of many of these command line switches are
provided only in *note Common features::, to avoid redundancy. Only
options specific to, or most useful with, a particular operator are
described in any detail in the sections below.
4.1 `ncap2' netCDF Arithmetic Processor
=======================================
`ncap2' understands a relatively full-featured language of operations,
including loops, conditionals, arrays, and math functions. `ncap2' is
the most rapidly changing NCO operator and its documentation is
incomplete. The distribution file `data/ncap2_tst.nco' contains an
up-to-date overview of its syntax and capabilities. The `data/*.nco'
distribution files (especially `bin_cnt.nco', `psd_wrf.nco', and
`rgr.nco') contain in-depth examples of `ncap2' solutions to complex
problems.
SYNTAX
ncap2 [-3] [-4] [-6] [-A] [-C] [-c] [-D DBG] [-F] [-f] [-L DFL_LVL]
[-l PATH] [-O] [-o OUTPUT-FILE] [-p PATH] [-R] [-r]
[-s ALGEBRA] [-S FL.NCO] [-t THR_NBR] [-v]
INPUT-FILE [OUTPUT-FILE]
DESCRIPTION
`ncap2' arithmetically processes netCDF files (1). The processing
instructions are contained either in the NCO script file `fl.nco' or in
a sequence of command line arguments. The options `-s' (or long
options `--spt' or `--script') are used for in-line scripts and `-S'
(or long options `--fl_spt' or `--script-file') are used to provide the
filename where (usually multiple) scripting commands are pre-stored.
`ncap2' was written to perform arbitrary algebraic transformations of
data and archive the results as easily as possible. *Note Missing
Values::, for treatment of missing values. The results of the
algebraic manipulations are called "derived fields".
Unlike the other operators, `ncap2' does not accept a list of
variables to be operated on as an argument to `-v' (*note Subsetting
Variables::). Rather, the `-v' switch takes no arguments and indicates
that `ncap2' should output _only_ user-defined variables. `ncap2'
neither accepts nor understands the -X switch.
Defining new variables in terms of existing variables is a powerful
feature of `ncap2'. Derived fields inherit the metadata (i.e.,
attributes) of their ancestors, if any, in the script or input file.
When the derived field is completely new (no identically-named ancestors
exist), then it inherits the metadata (if any) of the left-most variable
on the right hand side of the defining expression. This metadata
inheritance is called "attribute propagation". Attribute propagation
is intended to facilitate well-documented data analysis, and we welcome
suggestions to improve this feature.
---------- Footnotes ----------
(1) `ncap2' is the successor to `ncap' which was put into
maintenance mode in November, 2006. This documentation refers to
`ncap2', which has a superset of the `ncap' functionality. Eventually
`ncap' will be deprecated in favor `ncap2'. `ncap2' may be renamed
`ncap' in 2010 or 2011.
4.1.1 Syntax of `ncap2' statements
----------------------------------
Mastering `ncap2' is relatively simple. Each valid statement STATEMENT
consists of standard forward algebraic expression. The `fl.nco', if
present, is simply a list of such statements, whitespace, and comments. The
syntax of statements is most like the computer language C. The
following characteristics of C are preserved:
Array syntax
Arrays elements are placed within `[]' characters;
Array indexing
Arrays are 0-based;
Array storage
Last dimension is most rapidly varying;
Assignment statements
A semi-colon `;' indicates the end of an assignment statement.
Comments
Multi-line comments are enclosed within `/* */' characters.
Single line comments are preceded by `//' characters.
Nesting
Files may be nested in scripts using `#include SCRIPT'. Note that
the `#include' command is not followed by a semi-colon because it
is a pre-processor directive, not an assignment statement. The
filename `script' is interpreted relative to the run directory.
Attribute syntax
The at-sign `@' is used to delineate an attribute name from a
variable name.
4.1.2 Expressions
-----------------
Expressions are the fundamental building block of `ncap2'. Expressions
are composed of variables, numbers, literals, and attributes. The
following C operators are "overloaded" and work with scalars and
multi-dimensional arrays:
Arithmetic Operators: * / % + - ^
Binary Operators: > >= < <= == != == || && >> <<
Unary Operators: + - ++ -- !
Conditional Operator: exp1 ? exp2 : exp3
Assign Operators: = += -= /= *=
In the following section a "variable" also refers to a number
literal which is read in as a scalar variable:
*Arithmetic and Binary Operators *
Consider _var1 'op' var2_
*Precision*
* When both operands are variables, the result has the precision of
the higher precision operand
* When one operand is a variable and the other an attribute, the
result has the precision of the variable.
* When both operands are attributes, the result has the precision of
the more precise attribute.
* The exponentiation operator "^" is an exception to the above rules.
When both operands have type less than `NC_FLOAT', the result is
`NC_FLOAT'. When either type is `NC_DOUBLE', the result is also
`NC_DOUBLE'.
*Rank*
* The Rank of the result is generally equal to Rank of the operand
that has the greatest number of dimensions.
* If the dimensions in var2 are a subset of the dimensions in var1
then its possible to make var2 conform to var1 through
broadcasting and or dimension reordering.
* Broadcasting a variable means creating data in non-existing
dimensions from the data in existing dimensions.
* More specifically: If the numbers of dimensions in var1 is greater
than or equal to the number of dimensions in var2 then an attempt
is made to make var2 conform to var1 ,else var1 is made to conform
to var2. If conformance is not possible then an error message
will be emitted and script execution will cease.
Even though the logical operators return True(1) or False(0) they are
treated in the same way as the arithmetic operators with regard to
precision and rank.
examples:
dimensions: time=10, lat=2, lon=4
Suppose we have the two variables:
double P(time,lat,lon);
float PZ0(lon,lat); // PZ0=1,2,3,4,5,6,7,8;
Consider now the expression:
PZ=P-PZ0
PZ0 is made to conform to P and the result is
PZ0 =
1,3,5,7,2,4,6,8,
1,3,5,7,2,4,6,8,
1,3,5,7,2,4,6,8,
1,3,5,7,2,4,6,8,
1,3,5,7,2,4,6,8,
1,3,5,7,2,4,6,8,
1,3,5,7,2,4,6,8,
1,3,5,7,2,4,6,8,
1,3,5,7,2,4,6,8,
1,3,5,7,2,4,6,8,
Once the expression is evaluated then PZ will be of type double;
Consider now
start=four-att_var@double_att; // start =-69 and is of type intger;
four_pow=four^3.0f // four_pow=64 and is of type float
three_nw=three_dmn_var_sht*1.0f; // type is now float
start@n1=att_var@short_att*att_var@int_att;
// start@n1=5329 and is type int
*Binary Operators*
Unlike C the binary operators return an array of values. There is no
such thing as short circuiting with the AND/OR operators. Missing
values are carried into the result in the same way they are with the
arithmetic operators. When an expression is evaluated in an if() the
missing values are treated as true.
The Binary operators are,in order of precedence:
! Logical Not
----------------------------
<< Less Than Selection
>> Greater Than Selection
----------------------------
> Greater than
>= Greater than or equal to
< Less than
<= Less than or equal to
----------------------------
== Equal to
!= Not equal to
----------------------------
&& Logical AND
----------------------------
|| Logical OR
----------------------------
To see all operators: *note Operators precedence and associativity::
examples:
tm1= time>2 && time <7; // tm1 = 0, 0, 1, 1, 1, 1, 0, 0, 0, 0 ; type double;
tm2= time==3 || time>=6; // tm2 = 0, 0, 1, 0, 0, 1, 1, 1, 1, 1 ; type double
tm3= int(!tm1); // tm3= 1, 1, 0, 0, 0, 0, 1, 1, 1, 1 ; type int
tm4= tm1 && tm2; // tm4= 0, 0, 1, 0, 0, 1, 0, 0, 0, 0 ; type double;
tm5= !tm4; // tm5= 1, 1, 0, 1, 1, 0, 1, 1, 1, 1 ; type double;
*Regular Assign Operator*
_var1 '=' exp1_
If var1 doesn't already exist in Output then var1 is written to Output
with the values and dimensions from expr1. If var1 already exists in
Output, then the only requirement on expr1 is that the number of
elements must match the number already on disk. The type of expr1 is
converted if necessary to the disk type.
* Other Assign Operators +=,-=,*=./= *
_var1 'ass_op' exp1 _
if exp1 is a variable and it doesn't conform to var1 then an attempt is
made to make it conform to var1. If exp1 is an attribute it must have
unity size or else have the same number of elements as var1. If expr1
has a different type to var1 the it is converted to the var1 type.
example:
z1=four+=one*=10 // z1=14 four=14 one=10;
time-=2 // time= -1,0,1,2,3,4,5,6,7,8
*Increment/ Decrement Operators
* These work in a similar fashion to their regular C counterparts. If
say the variable "four" is input only then the statement "++four"
effectively means -read four from input increment each element by one ,
then write the new values to Output;
example:
n2=++four; n2=5, four=5
n3=one--+20; n3=21 one=0;
n4=--time; n4=time=0.,1.,2.,3.,4.,5.,6.,7.,8.,9.;
*Conditional Operator ?: *
_exp1 ? exp2 : exp3 _
The conditional operator ( or ternary Operator) is nice and succinct
way of writing an if/then/else. If exp1 evaluates to true then exp2 is
returned else exp3 is returned.
example
weight_avg= weight.avg();
weight_avg@units= (weight_avg ==1 ? "kilo" : "kilos");
PS_nw= PS - (PS.min() >100000 ? 100000 : 0 );
*Clipping Operators*
<< Less Than Selection
For arrays, the less-than selection operator selects all values in
the left operand that are less than the corresponding value in the
right operand. If the value of the left side is greater than or
equal to the corresponding value of the right side, then the right
side value is placed in the result
>> Greater Than Selection
For arrays, the greater-than selection operator selects all values
in the left operand that are greater than the corresponding value
in the right operand. If the value of the left side is less than
or equal to the corresponding value of the right side, then the
right side value is placed in the result.
example:
RDM2= RDM >>100.0; RDM2=100,100,100,100,126,126,100,100,100, 100 ; // type double
RDM2= RDM <<90s; RDM3=1, 9, 36, 84, 90, 90, 84, 36, 9, 1 ; // type int
4.1.3 Dimensions
----------------
Dimensions can be defined in Output using the `defdim()' function.
defdim("cnt",10);
This dimension can then be subsequently referred to in method
arguments and a left hand cast by prefixing it with a dollar e.g
new_var[$cnt]=time;
temperature[$time,$lat,$lon]=35.5;
temp_avg=temperature.avg($time);
To refer to the dimension size in an expression use the `size'
method.
time_avg=time.total() / $time.size;
Increase size of new var by one and set new member to zero;
defdim("cnt_grw", $cnt.size+1);
new_var_grw[$cnt_grw]=0.0;
new_var_grw( 0:($cnt_grw.size-2))=new_var;
*Dimension Abbreviations
* Its possible to use dimension abbreviations as method arguments.
`$0' is the first dimension of the variable
`$1' is the second dimension of the variable
`$n' is the n+1 dimension of the variable
consider the variables:
float four_dmn_rec_var(time,lat,lev,lon);
double three_dmn_var_dbl(time,lat,lon);
four_nw=four_dmn_rev_var.reverse($time,$lon)
four_nw=four_dmn_rec_var.reverse($0,$3);
four_avg=four_dmn_rec_var.avg($lat,$lev);
four_avg=four_dmn_rec_var.avg($1,$2);
three_mw=three_dmn_var_dbl.permute($time,$lon,$lat);
three_mw=three_dmn_var_dbl.permute($0,$2,$1);
*ID Quoting
* If the dim name contains non-regular characters use ID quoting. See
*note ID Quoting::
defdim("a--list.A",10);
A1['$a--list.A']=30.0;
*GOTCHA
* It is not possible to manually define in Output any dimensions that
exist in Input. When a variable from Input appears in an expression or
statement its dimensions in Input are automagically copied to Output
(if they are not already present)
4.1.4 Left hand casting
-----------------------
The following examples demonstrate the utility of the "left hand
casting" ability of `ncap2'. Consider first this simple, artificial,
example. If LAT and LON are one dimensional coordinates of dimensions
LAT and LON, respectively, then addition of these two one-dimensional
arrays is intrinsically ill-defined because whether LAT_LON should be
dimensioned LAT by LON or LON by LAT is ambiguous (assuming that
addition is to remain a "commutative" procedure, i.e., one that does
not depend on the order of its arguments). Differing dimensions are
said to be "orthogonal" to one another, and sets of dimensions which
are mutually exclusive are orthogonal as a set and any arithmetic
operation between variables in orthogonal dimensional spaces is
ambiguous without further information.
The ambiguity may be resolved by enumerating the desired dimension
ordering of the output expression inside square brackets on the left
hand side (LHS) of the equals sign. This is called "left hand casting"
because the user resolves the dimensional ordering of the RHS of the
expression by specifying the desired ordering on the LHS.
ncap2 -s 'lat_lon[lat,lon]=lat+lon' in.nc out.nc
ncap2 -s 'lon_lat[lon,lat]=lat+lon' in.nc out.nc
The explicit list of dimensions on the LHS, `[lat,lon]' resolves the
otherwise ambiguous ordering of dimensions in LAT_LON. In effect, the
LHS "casts" its rank properties onto the RHS. Without LHS casting, the
dimensional ordering of LAT_LON would be undefined and, hopefully,
`ncap2' would print an error message.
Consider now a slightly more complex example. In geophysical
models, a coordinate system based on a blend of terrain-following and
density-following surfaces is called a "hybrid coordinate system". In
this coordinate system, four variables must be manipulated to obtain
the pressure of the vertical coordinate: PO is the domain-mean surface
pressure offset (a scalar), PS is the local (time-varying) surface
pressure (usually two horizontal spatial dimensions, i.e. latitude by
longitude), HYAM is the weight given to surfaces of constant density
(one spatial dimension, pressure, which is orthogonal to the horizontal
dimensions), and HYBM is the weight given to surfaces of constant
elevation (also one spatial dimension). This command constructs a
four-dimensional pressure `prs_mdp' from the four input variables of
mixed rank and orthogonality:
ncap2 -s 'prs_mdp[time,lat,lon,lev]=P0*hyam+PS*hybm' in.nc out.nc
Manipulating the four fields which define the pressure in a hybrid
coordinate system is easy with left hand casting.
4.1.5 Arrays and hyperslabs
---------------------------
Hyperslabs in `ncap2' are a bit more limited than hyperslabs with the
other nco operators. There is no per-se multi-slabs, wrapped
co-ordinates, negative stride or co-ordinate value limits. However with
a bit of syntactic magic they are all are possible.
var1( hyper_arg1, hyper_arg2 .. hyper_argN)
A hyperslab argument is specified using the following notation
start:end:stride
if "start" is omitted - then default = 0
if "end" is omitted - default = dimension size less one
if "stride" is omitted - default = 1
If a single value is present then it is assumed that that dimension
collapses to a single value (ie a cross-section). The number of
hyperslab arguments MUST be equal to the number of dimensions of the
variable.
*Hyperslabs on the Right Hand Side of an assign
*
A simple 1D example:
($time.size=10)
od[$time]={20,22,24,26,28,30,32,34,36,38};
od(7); // 34
od(7:); // 34,36,38
od(:7); // 20, 22, 24, 26, 28, 30, 32, 34
od(::4); // 20.28,36
od(1:6:2) // 22,26,30
od(:) // 20,22,24,26,28,30,32,34,36,38
A more complex 3D example
($lat.size=2, $lon.size=4 )
th[$time,$lat,$lon]=
{1, 2, 3, 4, 5, 6, 7, 8,
9,10,11,12,13,14,15,16,
17,18,19,20,21,22,23,24,
-99,-99,-99,-99,-99,-99,-99,-99,
33,34,35,36,37,38,39,40,
41,42,43,44,45,46,47,48,
49,50,51,52,53,54,55,56,
-99,58,59,60,61,62,63,64,
65,66,67,68,69,70,71,72,
-99,74,75,76,77,78,79,-99 };
th(1,1,3); // 16
th(2,0,:); // { 17, 18, 19, 20 };
th(:,1,3); // 8, 16, 24, -99, 40, 48, 56, 64, 72, -99
th(::5 ,:,0:3:2); // 1, 3, 5, 7, 41, 43, 45, 47 ;
If any of the hyperslab arguments collapse to a single value ( a
cross-section has been specified), then that dimension is removed from
the returned variable. If all the values collapse then a scalar
variable is returned
So for example: the following is valid:
th_nw=th(0,:,:) +th(9,:,:);
th_nw has dimensions $lon,$lat
nb the time dim has become degenerate
The following is not valid:
th_nw=th(0,:,0:1) +th(9,:,0:1);
As the _$lon_ now only has two elements. The above can be
calculated by using a LHS cast with _$lon_nw_ as replacement dim for
_$lon_.
defdim("lon_nw",2);
th_nw[$lat,$lon_nw]=th(0,:,0:1) +th(9,:,0:1);
*Hyperslabs on the Left Hand Side of an assign
* When hyperslabing on the LHS ,the expression on the RHS must evaluate
to a scalar or a variable/attribute with the same number of elements as
the LHS hyperslab
Sets all elements of the last record to zero.
th(9,:,:)=0.0;
Set first element of each lon element to 1.0.
th(:,:,0)=1.0;
Can hyperslab on both sides of an assign.
Sets the last record to the same as the first record
th(9,:,:)=th(0,:,:);
th0 represents pressure at height=0
th1 represents pressure at height=1
Then its possible to hyperslab in the records
P[$time,$height,$lat,$lon]=0.0;
P(:,0,:,:)=th0;
P(:,1,:,:)=th1
*Reverse method*
If you want to reverse a dimension's elements in an variable use the
`reverse()' method with at least one dimension argument (this is
equivalent to applying a negative stride) e.g
th_rv=th(1 ,:,:).reverse($lon); // { 12,11,10,9 } ,{16,15,14,13 }
od_rv=od.reverse($time); // {38, 36, 34, 32, 30, 28, 26, 24, 22, 20 }
*Permute method*
If you want to swap about the dimensions of a variable use the the
`permute()' method. The number and names of dimension arguments must
match the dimensions in the variable. If the first dimension in the
variable is of record type then this must remain the first dimension.
If you want to change the record dimension consider using `ncpdq' .
Consider the variable:
float three_dmn_var(lat,lev,lon);
three_dmn_var_pm=three_dmn_var.permute($lon,$lat,$lev);
three_dmn_var_pm=
0,4,8,
12,16,20,
1,5,9,
13,17,21,
2,6,10,
14,18,22,
3,7,11,
15,19,23;
4.1.6 Attributes
----------------
Attributes are referred to by _var_nm@att_nm_
All the following are valid statements
global@text="Test Attributes"; /* Assign a global variable attribute */
a1[$time]=time*20;
a1@long_name="Kelvin";
a1@min=a1.min();
a1@max=a1.max();
a1@min++;
--a1@max; q
a1(0)=a1@min;
a1($time.size-1)=a1@max;
A _value list_ can be used on the RHS of an assign...
a1@trip1={ 1,2,3 } ;
a1@triplet={ a1@min, (a1@min+a1@max)/2, a1@max };
The netcdf specification allows all attribute types to have a size
greater than one. The maximum is defined by `NC_MAX_ATTRS' -The
following is an ncdump of the meta-data for variable a1
double a1(time) ;
a1:long_name = "Kelvin" ;
a1:max = 199. ;
a1:min = 21. ;
a1:trip1 = 1, 2, 3 ;
a1:triplet = 21., 110., 199. ;
The `size()' method can be used with attributes -for example to save
an attribute text string in a variable..
defdim("sng_len", a1@long_name.size());
sng_arr[$sng_len]=a1@long_name; // sng_arr now contains "Kelvin"
Attributes defined in a script are stored in memory and are written
to Output after script completion. To stop the attribute being
written use the ram_delete() method or use a bogus variable name
*Attribute Propagation & Inheritance*
* Attribute propagation occurs in a regular assign statement. The
variable being defined on the LHS gets copies of the attributes
from the leftermost variable on the RHS
* Attribute Inheritance: The LHS variable "inherits" attributes from
an Input variable with the same name
* It is possible to have a regular assign statement for which both
propagation and inheritance occur.
prs_mdp[time,lat,lon,lev]=P0*hyam+hybm*PS; //prs_mdp get attributes from PO
th_min=1.0 + 2*three_dmn_var_dbl.min($time); //th_min get attributes from three_dmn_var_dbl
If the attribute name contains non-regular characters use ID
quoting. See *note ID Quoting::
'b..m1@c--lost'=23;
4.1.7 Number literals
---------------------
The table below lists the postfix character(s) to add to a number
literal for type cohesion. To use the new netcdf4 types nco must be
compiled/linked to the netcdf4 library and the Output file must be hdf5.
n1[$time]=1UL; // n1 will now by type `NC_UINT'
n2[$lon]=4b; // n2 will be of type `NC_BYTE'
n3[$lat]=5ull; // n3 will be of type `NC_UINT64'
n3@a1=6.0d; // attribute will be type `NC_DOUBLE'
n3@a2=-666L; // attribute will be type `NC_INT'
A floating point number without a postfix will default to
`NC_DOUBLE'. An integer without a postfix will default to type
`NC_INT'. Thre is no postfix for characters. Use a quoted string.
n4[$rlev]=.1 // n4 will be of type `NC_DOUBLE'
n5[$lon_grd]=2. // n5 will be of type `NC_DOUBLE'
n6[$gds_crd]=2e3; // n6 will be of type `NC_DOUBLE'
n6@a1=41; // attribute will be type `NC_INT'
n6@a2=-21; // attribute will be type `NC_INT'
n6@units="kelvin" // attribute will be type `NC_CHAR'
*netCDF3/4 Types*
b|B
`NC_BYTE' a signed 1 byte integer
none
`NC_CHAR' a ISO/ASCII character
s|S
`NC_SHORT' a signed 2 byte integer
l|L
`NC_INT' a signed 4 byte integer
f|F
`NC_FLOAT' a single precision floating point number
d|D
`NC_DOUBLE' a double precision floating point number
*netCDF4 Types*
ub|UB
`NC_UBYTE' a unsigned 1 byte int
us|US
`NC_USHORT' a unsigned 2-byte int
u|U|ul|UL
`NC_UINT' a unsigned 4-byte int
ll|LL
`NC_INT64' a signed 8-byte int
ull|ULL
`NC_UINT64' a unsigned 8-byte int
4.1.8 if statement
------------------
The synatax of the if statement is similar to its C counterpart. The
_Conditional Operator (ternary operator)_ has also been implemented.
if(exp1)
stmt1;
else if(exp2)
stmt2
else
stmt3
Can use code blocks as well
if(exp1){
stmt1;
stmt1a;
stmt1b;
} else if(exp2)
stmt2
else {
stmt3;
stmt3a;
stmt3b;
}
For a variable or attribute expression to be logically true all its
non-missing value elements must be logically true. (i.e non-zero). The
expression can be of any type. Unlike C there is no short-circuiting of
an expression with the OR (||) AND (&&) operators. The whole expression
is evaluated regardless if one of the AND/OR operands are true/false.
A simple example
if(time>0)
print("All values of time are greater than zero\n");
else if( time<0)
print("All values of time are less than zero\n");
else {
time_max=time.max();
time_min=time.min();
print("min value of time=");print(time_min,"%f");
print("max value of time=");print(time_max,"%f");
}
A real example from ddra.nco
if(fl_typ==fl_typ_gcm){
var_nbr_apx=32;
lmn_nbr=1.0*var_nbr_apx*varsz_gcm_4D; /* [nbr] Variable size */
if(nco_op_typ==nco_op_typ_avg){
lmn_nbr_avg=1.0*var_nbr_apx*varsz_gcm_4D; /* [nbr] Averaging block size */
lmn_nbr_wgt=dmnsz_gcm_lat; /* [nbr] Weight size */
} // !nco_op_typ_avg
}else if(fl_typ==fl_typ_stl){
var_nbr_apx=8;
lmn_nbr=1.0*var_nbr_apx*varsz_stl_2D; /* [nbr] Variable size */
if(nco_op_typ==nco_op_typ_avg){
lmn_nbr_avg=1.0*var_nbr_apx*varsz_stl_2D; /* [nbr] Averaging block size */
lmn_nbr_wgt=dmnsz_stl_lat; /* [nbr] Weight size */
} // !nco_op_typ_avg
} // !fl_typ
*Conditional Operator
*
// nb you need netCDF4 to run this example
th_nw=(three_dmn_var_sht >= 0 ? three_dmn_var_sht.uint(): three_dmn_var_sht.int() );
4.1.9 print statement
---------------------
print( variable_name/attribute name/string, format string);
The print function takes a variable name or attribute name or a quoted
string and prints the contents in a in a similar fashion to `ncks -H '.
There is also an optional C style format string argument. Currently
the print function can't print RAM variables or expressions e.g
`'print(var_msk*3+4)'' is invalid. So if you want to print an
expression first assign a variable with the expression; then print the
variable.
examples
print(lon);
lon[0]=0
lon[1]=90
lon[2]=180
lon[3]=270
print(lon_2D_rrg,"%3.2f,");
0.00,0.00,180.00,0.00,180.00,0.00,180.00,0.00,
print(mss_val_fst@_FillValue);
mss_val_fst@_FillValue, size = 1 NC_FLOAT, value = -999
print("This function \t is monotonic\n");
This function is monotonic
4.1.10 Missing values ncap2
---------------------------
Missing values operate slightly differently in `ncap2' Consider the
expression where op is any of the following operators (excluding '=')
Arithmetic operators ( * / % + - ^ )
Binary Operators ( >, >= <, <= ==, !=,==,||,&&, >>,<< )
Assign Operators ( +=,-=,/=, *= )
var1 'op' var2
if var1 has a missing value then this is the value used in the
operation else the missing value for var2 is used. if during the
element by element operation an element from either operand is equal to
the missing value then the missing value is carried through. In this
way missing values 'percolate' through an expression.
Missing values associated with Output variables are stored in memory
and are written to disk after the script finishes. During script
execution its possible (and legal) for the missing value of a variable
to take on several different values.
Consider the variable:
int rec_var_int_mss_val_int(time); =-999,2,3,4,5,6,7,8,-999,-999;
rec_var_int_mss_val_int:_FillValue = -999;
n2=rec_var_int_mss_val_int + rec_var_int_mss_val_int.reverse($time);
n2=-999,-999,11,11,11,11,11,11,999,-999;
The following methods are used to edit the missing value associated
with a variable. They only work on variables in Output.
`set_miss(expr)'
Takes one argument the missing value. Sets or overwrites the
existing missing value. The argument given is converted if
necessary to the variable type
`change_miss(expr)'
Changes the missing value elements of the variable to the new
missing value (nb an expensive function).
`get_miss()'
Returns the missing value of a variable. If the variable exists in
Input and Output then the missing value of the variable in Output
is returned. If the variable has no missing value then an error is
returned.
`delete_miss()'
Deletes the missing value associated with a variable.
th=three_dmn_var_dbl;
th.change_miss(-1e10d);
/* set values less than 0 or greater than 50 to missing value */
where( th <0.0 || th > 50.0)
th=th.get_miss();
Another example
new[$time,$lat,$lon]=1.0;
new.set_miss(-997.0);
/* extract only elements divisible by 3 */
where ( three_dmn_var_dbl%3 == 0 )
new=three_dmn_var_dbl;
elsewhere
new=new.get_miss();
4.1.11 Methods and functions
----------------------------
The convention within this document is that methods can be used as
functions. However, functions are not and cannot be used as methods.
Methods can be daisy changed together and their synatax is cleaner than
functions. Method names are reserved words and CANNOT be used as
variable names. The command `ncap2 -f' shows the complete list of
methods available on your build.
n2=sin(theta) or n2=theta.sin()
n2=sin(theta)^2 +cos(theta)^2 or n2=theta.sin().pow(2) + theta.cos()^2
The below statement converts three_dmn_var_sht to type double, finds
the average, then converts this average back to type short.
three_avg=three_dmn_var_sht.double().avg().short();
* Aggregate Methods
* These methods mirror the averaging types available in `ncwa'. The
arguments to the methods are the dimensions to average over. Specifying
no dimensions is equivalent to specifying all dimensions i.e.
averaging over all dimensions. A masking variable and a weighting
variable can be manually created and applied as needed.
`avg()'
Mean value
`sqravg()'
Square of the mean
`avgsqr()'
Mean of sum of squares
`max()'
Maximum value
`min()'
Minimum value
`rms()'
Root-mean-square (normalized by N)
`rmssdn()'
Root-mean square (normalized by N-1)
`ttl() or total()'
Sum of values
// Average a variable over time
four_time_avg=four_dmn_rec_var($time);
* Packing Methods
* For more information see *note Packed data:: and *note ncpdq netCDF
Permute Dimensions Quickly::
`pack() & pack_short()'
The default packing algorithm is applied and variable is packed to
`NC_SHORT'
`pack_byte()'
Variable is packed to `NC_BYTE'
`pack_short()'
Variable is packed to `NC_SHORT'
`pack_int()'
Variable is packed to `NC_INT'
`unpack()'
The standard unpacking algorithm is applied.
*Basic Methods
* These methods work with variables and attributes. They have no
arguments
`size()'
Total number of elements
`ndims()'
Number of dimensions in variable
`type()'
Returns the netcdf type (see previous section)
*Utility Methods
* These functions are used to manipulate missing values and RAM
variables. *note Missing values ncap2::
`set_miss(expr)'
Takes one argument the missing value. Sets or overwrites the
existing missing value. The argument given is converted if
necessary to the variable type
`change_miss(expr)'
Changes the missing value elements of the variable to the new
missing value (n.b. an expensive function).
`get_miss()'
Returns the missing value of a variable in Input or Output
`delete_miss()'
Deletes the missing value associated with a variable.
`ram_write()'
Writes a RAM variable to disk i.e. converts it to a regular disk
type variable
`ram_delete()'
Deletes a RAM variable or an attribute
*PDQ Methods
* See *note ncpdq netCDF Permute Dimensions Quickly::
`reverse(dim args)'
Reverses the dimension ordering of elements in a variable.
`permute(dim args)'
Re-shapes variables by re-ordering the dimensions. All the dims of
the variable must be specified in the arguments. A limitation of
this permute (unlike ncpdq) is that the record dimension cannot be
re-assigned.
// Swap dimensions about and reorder along lon
lat_2D_rrg_new=lat_2D_rrg.permute($lon,$lat).reverse($lon);
lat_2D_rrg_new=0,90,-30,30,-30,30,-90,0
*Type Conversion Methods
* These methods allow `ncap2' to convert variables and attributes to
the different netcdf types. For more details on automatic and manual
type conversion see (*note Type Conversion::). You can only use the new
netcdf4 types if you have compiled/links nco with the netcdf4 library
and the Output file is hdf5.
`'
*netCDF3/4 Types*
`byte()'
convert to `NC_BYTE' a signed 1 byte integer
`char()'
convert to `NC_CHAR' a ISO/ASCII character
`short()'
convert to `NC_SHORT' a signed 2 byte integer
`int()'
convert to `NC_INT' a signed 4 byte integer
`float()'
convert to `NC_FLOAT' a single precision floating point number
`double()'
convert to `NC_DOUBLE' a double precision floating point number
`'
`'
*netCDF4 Types*
`ubyte()'
convert to `NC_UBYTE' a unsigned 1 byte int
`ushort()'
convert to `NC_USHORT' a unsigned 2-byte int
`uint()'
convert to `NC_UINT' a unsigned 4-byte int
`int64()'
convert to `NC_INT64' a signed 8-byte int
`uint64()'
convert to `NC_UINT64' a unsigned 8-byte int *
*Intrinsic Mathematical Methods
* The list of mathematical methods is system dependant. For the full
list *note Intrinsic mathematical methods::
All the mathematical methods take a single operand ,with the
exception of `atan2' and `pow' which take two. If the operand type is
less than _float_ then the result will be of type _float_. If the
operand is type _double_ then the result will be type _double_. Like
the other methods, you are free to use the mathematical methods as
functions.
n1=pow(2,3.0f) // n1 type float
n2=atan2(2,3.0) // n2 type double
n3=1/(three_dmn_var_dbl.cos().pow(2))-tan(three_dmn_var_dbl)^2; // n3 type double
4.1.12 RAM variables
--------------------
RAM variables are used in place of regular variables to speed things
up. For example in a loop or where a variable is very frequently
referenced. To declare and define a RAM variable simply prefix the
variable name with * when the variable is declared/initialized.
To delete a RAM variable (recover some memory) use the ram_delete()
method. To convert a RAM variable to a regular disk variable in output
use ram_write() method.
The following is valid:
*temp[$time,$lat,lon]=10.0; // Cast
*temp_avg=temp.avg($time); // Regular assign
....
temp.ram_delete(); // Delete RAM variable
temp_avg.ram_write(); // Write Variable to output
Other Assigns
// Create a RAM variable from the variable "one" in Input and increment its elements
*one++;
// Create a RAM variable from the variable three in Input and multiply its contents by 10
// Create a RAM variable from the variable four in Input and then add the variable "three" to
// its contents.
*four+=*three*=10; // three=30, four=34
4.1.13 Where statement
----------------------
A `where()' combines the definition and application of a mask all in
one go and can lead to succinct code. The full syntax of a `where()'
statement is as follows:
// Single assign (nb the else block is optional)
where (mask)
var1=expr1;
elsewhere
var1=expr2;
// Multiple assigns
where( mask) {
var1=expr1;
var2=expr2;
...
} elsewhere {
var1=expr3
var2=expr4
var3=expr5;
...
}
* The only expression allowed within a where is the assign
'var=expr'. This is different from a regular `ncap2' assign. The
LHS var must already exist in Input or Output. The expression on
the RHS must evaluate to a scalar or a variable/attribute of the
same size as the LHS variable.
* Taking the general case of a variable on the LHS side and RHS. For
every element of the mask which is True , the corresponding LHS
variable element is re-assigned with its partner element on the
RHS. In the elsewhere part the mask is logically inverted and the
assign process continues
* if the mask dimensions are a subset of the LHS variable's
dimensions , then it is made to conform; if it can't be made to
conform then script execution halts.
* Missing values in the mask evaluate to False in the first
code/block statement and True in the elsewhere block/statement.
LHS variable elements set to missing value are not re-assigned
example:
Consider the variables:
`float lon_2D_rct(lat,lon);
' `float var_msk(lat,lon);
' Suppose we want to multiply by two the elements for which var_msk is
equal to 1;
where(var_msk==1)
lon_2D_rct=2*lon_2D_rct;
Another example
Suppose we have the variable
`int RDM(time);'
And we want to set the values less than 8 or greater than 80 to 0.
where(RDM <8 || RDM >80)
RDM=0;
A more complex example.
Consider the situation where we have irregularly gridded data,
described using rank 2 variables:
` double lat(south_north,east_west)
double lon(south_north,east_west)
double temperature(south_north,east west)
' To find the average temperature in a region [lat_min,lat_max] and
[lon_min,lon_max]:
temperature_msk[$south_north,$east_west]=0.0;
where(lat >= lat_min && lat <= lat_max) && (lon >= lon_min && > lon <= lon_max)
temperature_msk=temperature;
elsewhere
temperature_msk=temperature@_FillValue;
temp_avg=temperature_msk.avg();
temp_max=temperature.max();
4.1.14 Loops
------------
In `ncap' there are for() loops and while() loops. They are currently
completely unoptimized So use them with RAM variables unless you want
thrash your disk to death. To break out of a loop use "break" command.
To iterate to the next cycle use the "continue" command.
// Follwing sets elements in variable double temp(time,lat)
// If element < 0 set to 0, if element >100 set to 100
*sz_idx=$time.size;
*sz_jdx=$lat.size;
for(*idx=0 ; idx<sz_idx ; idx++)
for(*jdx=0 ; jdx<sz_jdx; jdx++)
if( temp(idx,jdx) >100 ) temp(idx,jdx)=100.0;
else if( temp(idx,jdx) <0 ) temp(idx,jdx)=0.0;
// See if values of of a co-ordinate variable double lat(lat) are monotonic
*sz=$lat.size;
for(*idx=1 ; idx<sz;idx++)
if( lat(idx)-lat(idx-1) < 0.0)
break;
if(idx==sz)
print("lat co-ordinate is monotonic\n");
else
print("lat co-ordinate is NOT monotonic\n");
// Sum odd elements
*idx=0;
*sz=$lat_nw.size;
*sum=0.0;
while(idx<sz){
if( lat(idx) % 2) sum+=lat(idx);
idx++;
}
ram_write(sum);
print("Total of odd elements ");print(sum);print("\n");
4.1.15 Include files
--------------------
The synatax of an include file is:
#include "script"
The script filename is searched relative to the run directory. Its
possible to nest include files to an arbitrary depth. A handy use of
inlcude files is to store often used constants. Use RAM variables of
you don't want these constants written to Output.
*pi=3.1415926535;
*h=6.62607095e-34;
e=2.71828;
4.1.16 sort methods
-------------------
In ncap there are two ways to sort. The first is a regular sort. This
sorts ALL the elements of a variable or attribute without regard to any
dimensions. The second method applies a sort map to a variable. To
apply this sort map the size of the variable must be exactly divisible
by the size of the sort map. The method `sort(var_in,&var_map) ' is
overloaded. The second optional argument is a call_by_ref variable
which will hold the sort map.
a1[$time]={10,2,3,4,6,5,7,3,4,1};
a1_sort=sort(a1);
print(a1_sort);
// 1, 2, 3, 3, 4, 4, 5, 6, 7, 10 ;
a2[$lon]={2,1,4,3};
a2_sort=sort(a2,&a2_map);
print(a2);
// 1, 2, 3, 4
print(a2_map);
// 1, 0, 3, 2 ;
If the map variable doesn't exist prior to the sort call, then it
will be created with the same shape as the input variable and be of
type `NC_INT'. If the map variable already exists, then the only
restriction is that it be of at least the same size as the input
variable. To apply a map use ` dsort(var_in,var_map)'.
defdim("nlat",5);
a3[$lon]={2,5,3,7};
a4[$nlat,$lon]={
1, 2, 3, 4,
5, 6, 7, 8,
9,10,11,12,
13,14,15,16,
17,18,19,20};
a3_sort=sort(a3,&a3_map);
print(a3_map);
// 0, 2, 1, 3 ;
a5_sort=dsort(a5,a3_map);
print(a5_sort);
// 1, 3, 2, 4,
// 5, 7, 6, 8,
// 9,11,10,12,
// 13,15,14,16,
// 17,19,18,20 ;
a3_map2[$nlat]={4,3,0,2,1 };
a5_sort2=dsort(a5,a3_map2);
print(a5_sort2);
// 3, 5, 4, 2, 1
// 8, 10, 9,7, 6,
// 13,15,14,12,11,
// 18,20,19,17,16
As in the above example you a free to create your own mask. If you
wish to sort in decending order then use the `reverse()' method after
the sort.
4.1.17 Irregular Grids
----------------------
NCO is capable of analyzing datasets for many different underlying
coordinate grid types. netCDF was developed for and initially used
with grids comprised of orthogonal dimensions forming a rectangular
coordinate system. We call such grids _standard_ grids. It is
increasingly common for datasets to use metadata to describe much more
complex grids. Let us first define three important coordinate grid
properties: rectangularity, regularity, and fxm.
Grids are _regular_ if the spacing between adjacent is constant.
For example, a 4-by-5 degree latitude-longitude grid is regular because
the spacings between adjacent latitudes (4 degrees) are constant as are
the (5 degrees) spacings between adjacent longitudes. Spacing in
_irregular_ grids depends on the location along the coordinate. Grids
such as Gaussian grids have uneven spacing in latitude (points cluster
near the equator) and so are irregular.
Grids are _rectangular_ if the number of elements in any dimension
is not a function of any other dimension. For example, a T42 Gaussian
latitude-longitude grid is rectangular because there are the same
number of longitudes (128) for each of the (64) latitudes. Grids are
_non-rectangular_ if the elements in any dimension depend on another
dimension. Non-rectangular grids present many special challenges to
analysis software like NCO.
Wrapped coordinates (*note Wrapped Coordinates::), such as longitude,
are independent of these grid properties (regularity, rectangularity).
The preferred NCO technique to analyze data on non-standard
coordinate grids is to create a region mask with `ncap2', and then to
use the mask within `ncap2' for variable-specific processing, and/or
with other operators (e.g., `ncwa', `ncdiff') for entire file
processing.
Before describing the construction of masks, let us review how
irregularly gridded geoscience data are described. Say that latitude
and longitude are stored as R-dimensional arrays and the product of the
dimension sizes is the total number of elements N in the other
variables. Geoscience applications tend to use R=1, R=2, and R=3.
If the grid is has no simple representation (e.g., discontinuous)
then it makes sense to store all coordinates as 1D arrays with the same
size as the number of grid points. These gridpoints can be completely
independent of all the other (own weight, area, etc.).
R=1: lat(number_of_gridpoints) and lon(number_of_gridpoints)
If the horizontal grid is time-invariant then R=2 is common:
R=2: lat(south_north,east_west) and lon(south_north,east_west)
The WRF (Weather and Research Forecast) model uses R=3
R=3: lat(time,south_north,east_west), lon(time,south_north,east_west)
and so supports grids that change with time.
Grids with R > 1 often use missing values to indicated empty points.
For example, so-called "staggered grids" will use fewer east_west
points near the poles and more near the equator. netCDF only accepts
rectangular arrays so space must be allocated for the maximum number of
east_west points at all latitudes. Then the application writes missing
values into the unused points near the poles.
Let's demonstrate the recommended `ncap2' analysis technique by
constructing a region mask for an R=2 grid. We wish to find, say, the
mean temperature within [LAT_MIN,LAT_MAX] and [LON_MIN,LON_MAX]:
ncap2 -s 'mask= (lat >= lat_min && lat <= lat_max) && \
(lon >= lon_min && lon <= lon_max);' in.nc out.nc
Once you have a mask, you can use it on specific variables:
ncap2 -s 'temperature_avg=(temperature*mask).avg()' in.nc out.nc
and you can apply it to entire files:
ncwa -a lat,lon -m mask -w area in.nc out.nc
You can put this altogether on the command line or in a script, e.g.,
cleaner.
cat > ncap2.in << EOF
mask = (lat >= lat_min && lat <= lat_max) && (lon >= lon_min && > lon <= lon_max);
if(mask.total() > 0){ // Check that mask contains some valid values
temperature_avg=(temperature*mask).avg(); // Average temperature
temperature_max=(temperature*mask).max(); // Maximum temperature
}
EOF
ncap2 -S ncap2.in in.nc out.nc
For the WRF file creating the mask looks like
mask = (XLAT >= lat_min && XLAT <= lat_max) && (XLONG >= lon_min && > XLONG <= lon_max);
In practice with WRF it's a bit more complicated because you must
use the global metadata to determine the grid staggering and offsets to
translate XLAT and XLONG into real latitudes and longitudes and missing
points. The WRF grid documentation should describe this.
A few notes: Irregular regions are the union of arrays
lat/lon_min/max's. The mask procedure is identical for all R.
4.1.18 bilinear interpolation
-----------------------------
As of version 4.0.0 NCO has internal routines to perform bilinear
interpolation on gridded data sets.
In mathematics, bilinear interpolation is an extension of linear
interpolation for interpolating functions of two variables on a regular
grid. The idea is to perform linear interpolation first in one
direction, and then again in the other direction.
Suppose we have an irregular grid of data `temperature[lat,lon]', with
co-ordinate vars `lat[lat], lon[lon]'. And we wish to find the
temperature at an arbitary point [X,Y] within the grid. If we can
locate lat_min,lat_max and lon_min,lon_max such that `lat_min <= X <=
lat_max' and `lon_min <=Y <=lon_max' then we can interpolate in two
dimensions to find the temperature at [X,Y].
the general form of the ncap interpolation function is as follows:-
`var_out=bilinear_interp(grid_in, grid_out, grid_out_x, grid_out_y,
grid_in_x, grid_in_y) '
`grid_in'
Input function data. Usually a 2D variable. It must be of size
`grid_in_x.size()*grid_in_y.size()'
`grid_out'
This variable is the shape of var_out. Usually a 2D variable. It
must be of size `grid_out_x.size()*grid_out_y.size()'
`grid_out_x'
X output values
`grid_out_y'
Y output values
`grid_in_x'
X input values values. Must be montonic (increasing or decreasing).
`grid_in_y'
Y input values values. Must be montonic (increasing or decreasing).
Prior to calculations all arguments are converted to type `NC_DOUBLE'.
After calculations `var_out' is converted to the input type of
`grid_in'.
Suppose the first part of an ncap2 script is:
/****************************************/
defdim("X",4);
defdim("Y",5);
//Temperature
T_in[$X,$Y]=
{100, 200, 300, 400, 500,
101, 202, 303, 404, 505,
102, 204, 306, 408, 510,
103, 206, 309, 412, 515.0 };
//Co-ordinate Vars
x_in[$X]={ 0.0,1.0,2.0,3.01 };
y_in[$Y]={ 1.0,2.0,3,4,5 };
/***************************************/
Now we interpolate with the following variables:
/***************************************/
defdim("Xn",3);
defdim("Yn",4);
T_out[$Xn,$Yn]=0.0;
x_out[$Xn]={0.0,0.02,3.01 };
y_out[$Yn]={1.1,2.0,3,4 };
var_out=bilinear_interp(T_in,T_out,x_out,y_out,x_in,y_in);
print(var_out);
// 110, 200, 300, 400,
// 110.022, 200.04, 300.06, 400.08,
// 113.3, 206, 309, 412 ;
/***************************************/
Its possible to use the call to interpolate a single point:
/***************************************/
var_out=bilinear_interp(T_in,0.0,3.0,4.99,x_in,y_in);
print(var_out);
// 513.920594059406
/***************************************/
*Wrapping and Extrapolation*
The function `bilinear_interp_wrap()' takes the same arguments as
`bilinear_interp()' but performs wrapping (Y) and extrapolation (X) for
points off the edge of the grid. If the given range of longitude is say
(25-335) and we have a point at 20 degrees- then the end points of the
range are used for the interpolation. This is what wrapping means. For
wrapping to occur Y must be longitude and must be in the range (0,360)
or (-180,180). There are no restrictions on the longitude (X) values ,
but typically these are in the range (-90,90).
The follwing ncap script illustrates both wrapping and extrapolation of
end points.
/****************************************/
defdim("lat_in",6);
defdim("lon_in",5);
// co-ordinate in vars
lat_in[$lat_in]={ -80,-40,0,30,60.0,85.0 };
lon_in[$lon_in]={ 30, 110, 190, 270, 350.0 };
T_in[$lat_in,$lon_in]=
{ 10,40,50,30,15,
12,43,52,31,16,
14,46,54,32,17,
16,49,56,33,18,
18,52,58,34,19,
20,55,60,35,20.0 };
defdim("lat_out",4);
defdim("lon_out",3);
// co-ordinate vars
lat_out[$lat_out]={ -90, 0, 70, 88.0 };
lon_out[$lon_out]={ 0, 190, 355.0 };
T_out[$lat_out,$lon_out]=0.0;
T_out=bilinear_interp_wrap(T_in,T_out,lat_out,lon_out,lat_in,lon_in);
print(T_out);
// 13.4375, 49.5, 14.09375,
// 16.25, 54, 16.625,
// 19.25, 58.8, 19.325,
// 20.15, 60.24, 20.135 ;
/****************************************/
4.1.19 GSL special functions
----------------------------
As of version 3.9.6 (released January, 2009), NCO can link to the GNU
Scientific Library (GSL). `ncap' can access most GSL special functions
including Airy, Bessel, error, gamma, beta, hypergeometric, and
Legendre functions and elliptical integrals. GSL must be version 1.4
or later. To list the GSL functions available with your NCO build, use
`ncap2 -f | grep ^gsl'.
The function names used by ncap2 mirror their GSL names. The NCO
wrappers for GSL functions automatically call the error-handling
version of the GSL function when available (1). This allows NCO to
return a missing value when the GSL library encounters a domain error
or a floating point exception. The slow-down due to calling the
error-handling version of the GSL numerical functions was found to be
negligible (please let us know if you find otherwise).
Consider the gamma function.
The GSL function prototype is
`int gsl_sf_gamma_e(const double x, gsl_sf_result * result)' The `ncap'
script would be:
lon_in[lon]={-1,0.1,0,2,0.3};
lon_out=gsl_sf_gamma(lon_in);
lon_out= _, 9.5135, 4.5908, 2.9915
The first value is set to `_FillValue' since the gamma function is
undefined for negative integers. If the input variable has a missing
value then this value is used. Otherwise, the default double fill
value is used (defined in the netCDF header `netcdf.h' as
`NC_FILL_DOUBLE = 9.969e+36').
Consider a call to a Bessel function with GSL prototype
`int gsl_sf_bessel_Jn_e(int n, double x, gsl_sf_result * result)'
An `ncap' script would be
lon_out=gsl_sf_bessel_Jn(2,lon_in);
lon_out=0.11490, 0.0012, 0.00498, 0.011165
This computes the Bessel function of order N=2 for every value in
`lon_in'. The Bessel order argument, an integer, can also be a
non-scalar variable, i.e., an array.
n_in[lon]={0,1,2,3};
lon_out=gsl_sf_bessel_Jn(n_in,0.5);
lon_out= 0.93846, 0.24226, 0.03060, 0.00256
Arguments to GSL wrapper functions in `ncap' must conform to one
another, i.e., they must share the same sub-set of dimensions. For
example: `three_out=gsl_sf_bessel_Jn(n_in,three_dmn_var_dbl)' is valid
because the variable `three_dmn_var_dbl' has a LON dimension, so `n_in'
in can be broadcast to conform to `three_dmn_var_dbl'. However
`time_out=gsl_sf_bessel_Jn(n_in,time)' is invalid.
Consider the elliptical integral with prototype `int
gsl_sf_ellint_RD_e(double x, double y, double z, gsl_mode_t mode,
gsl_sf_result * result)'
three_out=gsl_sf_ellint_RD(0.5,time,three_dmn_var_dbl);
The three arguments are all conformable so the above `ncap' call is
valid. The mode argument in the function prototype controls the
convergence of the algorithm. It also appears in the Airy Function
prototypes. It can be set by defining the environment variable
`GSL_PREC_MODE'. If unset it defaults to the value `GSL_PREC_DOUBLE'.
See the GSL manual for more details.
export GSL_PREC_MODE=0 // GSL_PREC_DOUBLE
export GSL_PREC_MODE=1 // GSL_PREC_SINGLE
export GSL_PREC_MODE=2 // GSL_PREC_APPROX
The `ncap' wrappers to the array functions are slightly different. Lets
consider the following gsl prototype
`int gsl_sf_bessel_Jn_array(int nmin, int nmax, double x, double
*result_array)'
b1=lon.double();
x=0.5;
status=gsl_sf_bessel_Jn_array(1,4,x,&b1);
print(status);
b1=0.24226, 0.0306, 0.00256, 0.00016 ;
This calculates the bessel function of x=0.5 for n=1 to 4. The first
three arguments are scalar values. if a non-scalar variable is supplied
as an argument then only the first value is used.The final argument is
the variable where the results go ( note the '&' this indicates a call
by reference). This final argument must be of type `double' and must be
of least size (nmax-nmin+1). If either of these conditions are not met
then then the function will blow out with an error message. The
function/wrapper returns a status flag. Zero indicates success.
Lets look at another array function
`int gsl_sf_legendre_Pl_array( int lmax, double x, double
*result_array);'
a1=time.double();
x=0.3;
status=gsl_sf_legendre_Pl_array(a1.size()-1, x,&a1);
print(status);
This call calculates P_l(0.3) for l=0..9. Note |x|<=1, otherwise there
will be a domain error. See the GSL documentation for more details.
Below is table detailing what GSL functions have been implemented. This
table is correct for GSL version 1.10. To see what functions are
available on your build run the command `ncap2 -f |grep ^gsl' . To see
this table along with the GSL C function prototypes look at the
spreadsheet *doc/nco_gsl.ods*.
*GSL NAME* *I* *NCAP FUNCTION CALL*
gsl_sf_airy_Ai_e Y gsl_sf_airy_Ai(dbl_expr)
gsl_sf_airy_Bi_e Y gsl_sf_airy_Bi(dbl_expr)
gsl_sf_airy_Ai_scaled_e Y gsl_sf_airy_Ai_scaled(dbl_expr)
gsl_sf_airy_Bi_scaled_e Y gsl_sf_airy_Bi_scaled(dbl_expr)
gsl_sf_airy_Ai_deriv_e Y gsl_sf_airy_Ai_deriv(dbl_expr)
gsl_sf_airy_Bi_deriv_e Y gsl_sf_airy_Bi_deriv(dbl_expr)
gsl_sf_airy_Ai_deriv_scaled_eY gsl_sf_airy_Ai_deriv_scaled(dbl_expr)
gsl_sf_airy_Bi_deriv_scaled_eY gsl_sf_airy_Bi_deriv_scaled(dbl_expr)
gsl_sf_airy_zero_Ai_e Y gsl_sf_airy_zero_Ai(uint_expr)
gsl_sf_airy_zero_Bi_e Y gsl_sf_airy_zero_Bi(uint_expr)
gsl_sf_airy_zero_Ai_deriv_eY gsl_sf_airy_zero_Ai_deriv(uint_expr)
gsl_sf_airy_zero_Bi_deriv_eY gsl_sf_airy_zero_Bi_deriv(uint_expr)
gsl_sf_bessel_J0_e Y gsl_sf_bessel_J0(dbl_expr)
gsl_sf_bessel_J1_e Y gsl_sf_bessel_J1(dbl_expr)
gsl_sf_bessel_Jn_e Y gsl_sf_bessel_Jn(int_expr,dbl_expr)
gsl_sf_bessel_Jn_array Y status=gsl_sf_bessel_Jn_array(int,int,double,&var_out)
gsl_sf_bessel_Y0_e Y gsl_sf_bessel_Y0(dbl_expr)
gsl_sf_bessel_Y1_e Y gsl_sf_bessel_Y1(dbl_expr)
gsl_sf_bessel_Yn_e Y gsl_sf_bessel_Yn(int_expr,dbl_expr)
gsl_sf_bessel_Yn_array Y gsl_sf_bessel_Yn_array
gsl_sf_bessel_I0_e Y gsl_sf_bessel_I0(dbl_expr)
gsl_sf_bessel_I1_e Y gsl_sf_bessel_I1(dbl_expr)
gsl_sf_bessel_In_e Y gsl_sf_bessel_In(int_expr,dbl_expr)
gsl_sf_bessel_In_array Y status=gsl_sf_bessel_In_array(int,int,double,&var_out)
gsl_sf_bessel_I0_scaled_e Y gsl_sf_bessel_I0_scaled(dbl_expr)
gsl_sf_bessel_I1_scaled_e Y gsl_sf_bessel_I1_scaled(dbl_expr)
gsl_sf_bessel_In_scaled_e Y gsl_sf_bessel_In_scaled(int_expr,dbl_expr)
gsl_sf_bessel_In_scaled_arrayY staus=gsl_sf_bessel_In_scaled_array(int,int,double,&var_out)
gsl_sf_bessel_K0_e Y gsl_sf_bessel_K0(dbl_expr)
gsl_sf_bessel_K1_e Y gsl_sf_bessel_K1(dbl_expr)
gsl_sf_bessel_Kn_e Y gsl_sf_bessel_Kn(int_expr,dbl_expr)
gsl_sf_bessel_Kn_array Y status=gsl_sf_bessel_Kn_array(int,int,double,&var_out)
gsl_sf_bessel_K0_scaled_e Y gsl_sf_bessel_K0_scaled(dbl_expr)
gsl_sf_bessel_K1_scaled_e Y gsl_sf_bessel_K1_scaled(dbl_expr)
gsl_sf_bessel_Kn_scaled_e Y gsl_sf_bessel_Kn_scaled(int_expr,dbl_expr)
gsl_sf_bessel_Kn_scaled_arrayY status=gsl_sf_bessel_Kn_scaled_array(int,int,double,&var_out)
gsl_sf_bessel_j0_e Y gsl_sf_bessel_J0(dbl_expr)
gsl_sf_bessel_j1_e Y gsl_sf_bessel_J1(dbl_expr)
gsl_sf_bessel_j2_e Y gsl_sf_bessel_j2(dbl_expr)
gsl_sf_bessel_jl_e Y gsl_sf_bessel_jl(int_expr,dbl_expr)
gsl_sf_bessel_jl_array Y status=gsl_sf_bessel_jl_array(int,double,&var_out)
gsl_sf_bessel_jl_steed_arrayY gsl_sf_bessel_jl_steed_array
gsl_sf_bessel_y0_e Y gsl_sf_bessel_Y0(dbl_expr)
gsl_sf_bessel_y1_e Y gsl_sf_bessel_Y1(dbl_expr)
gsl_sf_bessel_y2_e Y gsl_sf_bessel_y2(dbl_expr)
gsl_sf_bessel_yl_e Y gsl_sf_bessel_yl(int_expr,dbl_expr)
gsl_sf_bessel_yl_array Y status=gsl_sf_bessel_yl_array(int,double,&var_out)
gsl_sf_bessel_i0_scaled_e Y gsl_sf_bessel_I0_scaled(dbl_expr)
gsl_sf_bessel_i1_scaled_e Y gsl_sf_bessel_I1_scaled(dbl_expr)
gsl_sf_bessel_i2_scaled_e Y gsl_sf_bessel_i2_scaled(dbl_expr)
gsl_sf_bessel_il_scaled_e Y gsl_sf_bessel_il_scaled(int_expr,dbl_expr)
gsl_sf_bessel_il_scaled_arrayY status=gsl_sf_bessel_il_scaled_array(int,double,&var_out)
gsl_sf_bessel_k0_scaled_e Y gsl_sf_bessel_K0_scaled(dbl_expr)
gsl_sf_bessel_k1_scaled_e Y gsl_sf_bessel_K1_scaled(dbl_expr)
gsl_sf_bessel_k2_scaled_e Y gsl_sf_bessel_k2_scaled(dbl_expr)
gsl_sf_bessel_kl_scaled_e Y gsl_sf_bessel_kl_scaled(int_expr,dbl_expr)
gsl_sf_bessel_kl_scaled_arrayY status=gsl_sf_bessel_kl_scaled_array(int,double,&var_out)
gsl_sf_bessel_Jnu_e Y gsl_sf_bessel_Jnu(dbl_expr,dbl_expr)
gsl_sf_bessel_Ynu_e Y gsl_sf_bessel_Ynu(dbl_expr,dbl_expr)
gsl_sf_bessel_sequence_Jnu_eN gsl_sf_bessel_sequence_Jnu
gsl_sf_bessel_Inu_scaled_eY gsl_sf_bessel_Inu_scaled(dbl_expr,dbl_expr)
gsl_sf_bessel_Inu_e Y gsl_sf_bessel_Inu(dbl_expr,dbl_expr)
gsl_sf_bessel_Knu_scaled_eY gsl_sf_bessel_Knu_scaled(dbl_expr,dbl_expr)
gsl_sf_bessel_Knu_e Y gsl_sf_bessel_Knu(dbl_expr,dbl_expr)
gsl_sf_bessel_lnKnu_e Y gsl_sf_bessel_lnKnu(dbl_expr,dbl_expr)
gsl_sf_bessel_zero_J0_e Y gsl_sf_bessel_zero_J0(uint_expr)
gsl_sf_bessel_zero_J1_e Y gsl_sf_bessel_zero_J1(uint_expr)
gsl_sf_bessel_zero_Jnu_e N gsl_sf_bessel_zero_Jnu
gsl_sf_clausen_e Y gsl_sf_clausen(dbl_expr)
gsl_sf_hydrogenicR_1_e N gsl_sf_hydrogenicR_1
gsl_sf_hydrogenicR_e N gsl_sf_hydrogenicR
gsl_sf_coulomb_wave_FG_e N gsl_sf_coulomb_wave_FG
gsl_sf_coulomb_wave_F_arrayN gsl_sf_coulomb_wave_F_array
gsl_sf_coulomb_wave_FG_arrayN gsl_sf_coulomb_wave_FG_array
gsl_sf_coulomb_wave_FGp_arrayN gsl_sf_coulomb_wave_FGp_array
gsl_sf_coulomb_wave_sphF_arrayN gsl_sf_coulomb_wave_sphF_array
gsl_sf_coulomb_CL_e N gsl_sf_coulomb_CL
gsl_sf_coulomb_CL_array N gsl_sf_coulomb_CL_array
gsl_sf_coupling_3j_e N gsl_sf_coupling_3j
gsl_sf_coupling_6j_e N gsl_sf_coupling_6j
gsl_sf_coupling_RacahW_e N gsl_sf_coupling_RacahW
gsl_sf_coupling_9j_e N gsl_sf_coupling_9j
gsl_sf_coupling_6j_INCORRECT_eN gsl_sf_coupling_6j_INCORRECT
gsl_sf_dawson_e Y gsl_sf_dawson(dbl_expr)
gsl_sf_debye_1_e Y gsl_sf_debye_1(dbl_expr)
gsl_sf_debye_2_e Y gsl_sf_debye_2(dbl_expr)
gsl_sf_debye_3_e Y gsl_sf_debye_3(dbl_expr)
gsl_sf_debye_4_e Y gsl_sf_debye_4(dbl_expr)
gsl_sf_debye_5_e Y gsl_sf_debye_5(dbl_expr)
gsl_sf_debye_6_e Y gsl_sf_debye_6(dbl_expr)
gsl_sf_dilog_e N gsl_sf_dilog
gsl_sf_complex_dilog_xy_e N gsl_sf_complex_dilog_xy_e
gsl_sf_complex_dilog_e N gsl_sf_complex_dilog
gsl_sf_complex_spence_xy_eN gsl_sf_complex_spence_xy_e
gsl_sf_multiply_e N gsl_sf_multiply
gsl_sf_multiply_err_e N gsl_sf_multiply_err
gsl_sf_ellint_Kcomp_e Y gsl_sf_ellint_Kcomp(dbl_expr)
gsl_sf_ellint_Ecomp_e Y gsl_sf_ellint_Ecomp(dbl_expr)
gsl_sf_ellint_Pcomp_e Y gsl_sf_ellint_Pcomp(dbl_expr,dbl_expr)
gsl_sf_ellint_Dcomp_e Y gsl_sf_ellint_Dcomp(dbl_expr)
gsl_sf_ellint_F_e Y gsl_sf_ellint_F(dbl_expr,dbl_expr)
gsl_sf_ellint_E_e Y gsl_sf_ellint_E(dbl_expr,dbl_expr)
gsl_sf_ellint_P_e Y gsl_sf_ellint_P(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_ellint_D_e Y gsl_sf_ellint_D(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_ellint_RC_e Y gsl_sf_ellint_RC(dbl_expr,dbl_expr)
gsl_sf_ellint_RD_e Y gsl_sf_ellint_RD(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_ellint_RF_e Y gsl_sf_ellint_RF(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_ellint_RJ_e Y gsl_sf_ellint_RJ(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_elljac_e N gsl_sf_elljac
gsl_sf_erfc_e Y gsl_sf_erfc(dbl_expr)
gsl_sf_log_erfc_e Y gsl_sf_log_erfc(dbl_expr)
gsl_sf_erf_e Y gsl_sf_erf(dbl_expr)
gsl_sf_erf_Z_e Y gsl_sf_erf_Z(dbl_expr)
gsl_sf_erf_Q_e Y gsl_sf_erf_Q(dbl_expr)
gsl_sf_hazard_e Y gsl_sf_hazard(dbl_expr)
gsl_sf_exp_e Y gsl_sf_exp(dbl_expr)
gsl_sf_exp_e10_e N gsl_sf_exp_e10
gsl_sf_exp_mult_e Y gsl_sf_exp_mult(dbl_expr,dbl_expr)
gsl_sf_exp_mult_e10_e N gsl_sf_exp_mult_e10
gsl_sf_expm1_e Y gsl_sf_expm1(dbl_expr)
gsl_sf_exprel_e Y gsl_sf_exprel(dbl_expr)
gsl_sf_exprel_2_e Y gsl_sf_exprel_2(dbl_expr)
gsl_sf_exprel_n_e Y gsl_sf_exprel_n(int_expr,dbl_expr)
gsl_sf_exp_err_e Y gsl_sf_exp_err(dbl_expr,dbl_expr)
gsl_sf_exp_err_e10_e N gsl_sf_exp_err_e10
gsl_sf_exp_mult_err_e N gsl_sf_exp_mult_err
gsl_sf_exp_mult_err_e10_e N gsl_sf_exp_mult_err_e10
gsl_sf_expint_E1_e Y gsl_sf_expint_E1(dbl_expr)
gsl_sf_expint_E2_e Y gsl_sf_expint_E2(dbl_expr)
gsl_sf_expint_En_e Y gsl_sf_expint_En(int_expr,dbl_expr)
gsl_sf_expint_E1_scaled_e Y gsl_sf_expint_E1_scaled(dbl_expr)
gsl_sf_expint_E2_scaled_e Y gsl_sf_expint_E2_scaled(dbl_expr)
gsl_sf_expint_En_scaled_e Y gsl_sf_expint_En_scaled(int_expr,dbl_expr)
gsl_sf_expint_Ei_e Y gsl_sf_expint_Ei(dbl_expr)
gsl_sf_expint_Ei_scaled_e Y gsl_sf_expint_Ei_scaled(dbl_expr)
gsl_sf_Shi_e Y gsl_sf_Shi(dbl_expr)
gsl_sf_Chi_e Y gsl_sf_Chi(dbl_expr)
gsl_sf_expint_3_e Y gsl_sf_expint_3(dbl_expr)
gsl_sf_Si_e Y gsl_sf_Si(dbl_expr)
gsl_sf_Ci_e Y gsl_sf_Ci(dbl_expr)
gsl_sf_atanint_e Y gsl_sf_atanint(dbl_expr)
gsl_sf_fermi_dirac_m1_e Y gsl_sf_fermi_dirac_m1(dbl_expr)
gsl_sf_fermi_dirac_0_e Y gsl_sf_fermi_dirac_0(dbl_expr)
gsl_sf_fermi_dirac_1_e Y gsl_sf_fermi_dirac_1(dbl_expr)
gsl_sf_fermi_dirac_2_e Y gsl_sf_fermi_dirac_2(dbl_expr)
gsl_sf_fermi_dirac_int_e Y gsl_sf_fermi_dirac_int(int_expr,dbl_expr)
gsl_sf_fermi_dirac_mhalf_eY gsl_sf_fermi_dirac_mhalf(dbl_expr)
gsl_sf_fermi_dirac_half_e Y gsl_sf_fermi_dirac_half(dbl_expr)
gsl_sf_fermi_dirac_3half_eY gsl_sf_fermi_dirac_3half(dbl_expr)
gsl_sf_fermi_dirac_inc_0_eY gsl_sf_fermi_dirac_inc_0(dbl_expr,dbl_expr)
gsl_sf_lngamma_e Y gsl_sf_lngamma(dbl_expr)
gsl_sf_lngamma_sgn_e N gsl_sf_lngamma_sgn
gsl_sf_gamma_e Y gsl_sf_gamma(dbl_expr)
gsl_sf_gammastar_e Y gsl_sf_gammastar(dbl_expr)
gsl_sf_gammainv_e Y gsl_sf_gammainv(dbl_expr)
gsl_sf_lngamma_complex_e N gsl_sf_lngamma_complex
gsl_sf_taylorcoeff_e Y gsl_sf_taylorcoeff(int_expr,dbl_expr)
gsl_sf_fact_e Y gsl_sf_fact(uint_expr)
gsl_sf_doublefact_e Y gsl_sf_doublefact(uint_expr)
gsl_sf_lnfact_e Y gsl_sf_lnfact(uint_expr)
gsl_sf_lndoublefact_e Y gsl_sf_lndoublefact(uint_expr)
gsl_sf_lnchoose_e N gsl_sf_lnchoose
gsl_sf_choose_e N gsl_sf_choose
gsl_sf_lnpoch_e Y gsl_sf_lnpoch(dbl_expr,dbl_expr)
gsl_sf_lnpoch_sgn_e N gsl_sf_lnpoch_sgn
gsl_sf_poch_e Y gsl_sf_poch(dbl_expr,dbl_expr)
gsl_sf_pochrel_e Y gsl_sf_pochrel(dbl_expr,dbl_expr)
gsl_sf_gamma_inc_Q_e Y gsl_sf_gamma_inc_Q(dbl_expr,dbl_expr)
gsl_sf_gamma_inc_P_e Y gsl_sf_gamma_inc_P(dbl_expr,dbl_expr)
gsl_sf_gamma_inc_e Y gsl_sf_gamma_inc(dbl_expr,dbl_expr)
gsl_sf_lnbeta_e Y gsl_sf_lnbeta(dbl_expr,dbl_expr)
gsl_sf_lnbeta_sgn_e N gsl_sf_lnbeta_sgn
gsl_sf_beta_e Y gsl_sf_beta(dbl_expr,dbl_expr)
gsl_sf_beta_inc_e N gsl_sf_beta_inc
gsl_sf_gegenpoly_1_e Y gsl_sf_gegenpoly_1(dbl_expr,dbl_expr)
gsl_sf_gegenpoly_2_e Y gsl_sf_gegenpoly_2(dbl_expr,dbl_expr)
gsl_sf_gegenpoly_3_e Y gsl_sf_gegenpoly_3(dbl_expr,dbl_expr)
gsl_sf_gegenpoly_n_e N gsl_sf_gegenpoly_n
gsl_sf_gegenpoly_array Y gsl_sf_gegenpoly_array
gsl_sf_hyperg_0F1_e Y gsl_sf_hyperg_0F1(dbl_expr,dbl_expr)
gsl_sf_hyperg_1F1_int_e Y gsl_sf_hyperg_1F1_int(int_expr,int_expr,dbl_expr)
gsl_sf_hyperg_1F1_e Y gsl_sf_hyperg_1F1(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_U_int_e Y gsl_sf_hyperg_U_int(int_expr,int_expr,dbl_expr)
gsl_sf_hyperg_U_int_e10_e N gsl_sf_hyperg_U_int_e10
gsl_sf_hyperg_U_e Y gsl_sf_hyperg_U(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_U_e10_e N gsl_sf_hyperg_U_e10
gsl_sf_hyperg_2F1_e Y gsl_sf_hyperg_2F1(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_2F1_conj_e Y gsl_sf_hyperg_2F1_conj(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_2F1_renorm_eY gsl_sf_hyperg_2F1_renorm(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_2F1_conj_renorm_eY gsl_sf_hyperg_2F1_conj_renorm(dbl_expr,dbl_expr,dbl_expr,dbl_expr)
gsl_sf_hyperg_2F0_e Y gsl_sf_hyperg_2F0(dbl_expr,dbl_expr,dbl_expr)
gsl_sf_laguerre_1_e Y gsl_sf_laguerre_1(dbl_expr,dbl_expr)
gsl_sf_laguerre_2_e Y gsl_sf_laguerre_2(dbl_expr,dbl_expr)
gsl_sf_laguerre_3_e Y gsl_sf_laguerre_3(dbl_expr,dbl_expr)
gsl_sf_laguerre_n_e Y gsl_sf_laguerre_n(int_expr,dbl_expr,dbl_expr)
gsl_sf_lambert_W0_e Y gsl_sf_lambert_W0(dbl_expr)
gsl_sf_lambert_Wm1_e Y gsl_sf_lambert_Wm1(dbl_expr)
gsl_sf_legendre_Pl_e Y gsl_sf_legendre_Pl(int_expr,dbl_expr)
gsl_sf_legendre_Pl_array Y status=gsl_sf_legendre_Pl_array(int,double,&var_out)
gsl_sf_legendre_Pl_deriv_arrayN gsl_sf_legendre_Pl_deriv_array
gsl_sf_legendre_P1_e Y gsl_sf_legendre_P1(dbl_expr)
gsl_sf_legendre_P2_e Y gsl_sf_legendre_P2(dbl_expr)
gsl_sf_legendre_P3_e Y gsl_sf_legendre_P3(dbl_expr)
gsl_sf_legendre_Q0_e Y gsl_sf_legendre_Q0(dbl_expr)
gsl_sf_legendre_Q1_e Y gsl_sf_legendre_Q1(dbl_expr)
gsl_sf_legendre_Ql_e Y gsl_sf_legendre_Ql(int_expr,dbl_expr)
gsl_sf_legendre_Plm_e Y gsl_sf_legendre_Plm(int_expr,int_expr,dbl_expr)
gsl_sf_legendre_Plm_array Y status=gsl_sf_legendre_Plm_array(int,int,double,&var_out)
gsl_sf_legendre_Plm_deriv_arrayN gsl_sf_legendre_Plm_deriv_array
gsl_sf_legendre_sphPlm_e Y gsl_sf_legendre_sphPlm(int_expr,int_expr,dbl_expr)
gsl_sf_legendre_sphPlm_arrayY status=gsl_sf_legendre_sphPlm_array(int,int,double,&var_out)
gsl_sf_legendre_sphPlm_deriv_arrayN gsl_sf_legendre_sphPlm_deriv_array
gsl_sf_legendre_array_sizeN gsl_sf_legendre_array_size
gsl_sf_conicalP_half_e Y gsl_sf_conicalP_half(dbl_expr,dbl_expr)
gsl_sf_conicalP_mhalf_e Y gsl_sf_conicalP_mhalf(dbl_expr,dbl_expr)
gsl_sf_conicalP_0_e Y gsl_sf_conicalP_0(dbl_expr,dbl_expr)
gsl_sf_conicalP_1_e Y gsl_sf_conicalP_1(dbl_expr,dbl_expr)
gsl_sf_conicalP_sph_reg_e Y gsl_sf_conicalP_sph_reg(int_expr,dbl_expr,dbl_expr)
gsl_sf_conicalP_cyl_reg_e Y gsl_sf_conicalP_cyl_reg(int_expr,dbl_expr,dbl_expr)
gsl_sf_legendre_H3d_0_e Y gsl_sf_legendre_H3d_0(dbl_expr,dbl_expr)
gsl_sf_legendre_H3d_1_e Y gsl_sf_legendre_H3d_1(dbl_expr,dbl_expr)
gsl_sf_legendre_H3d_e Y gsl_sf_legendre_H3d(int_expr,dbl_expr,dbl_expr)
gsl_sf_legendre_H3d_array N gsl_sf_legendre_H3d_array
gsl_sf_legendre_array_sizeN gsl_sf_legendre_array_size
gsl_sf_log_e Y gsl_sf_log(dbl_expr)
gsl_sf_log_abs_e Y gsl_sf_log_abs(dbl_expr)
gsl_sf_complex_log_e N gsl_sf_complex_log
gsl_sf_log_1plusx_e Y gsl_sf_log_1plusx(dbl_expr)
gsl_sf_log_1plusx_mx_e Y gsl_sf_log_1plusx_mx(dbl_expr)
gsl_sf_mathieu_a_array N gsl_sf_mathieu_a_array
gsl_sf_mathieu_b_array N gsl_sf_mathieu_b_array
gsl_sf_mathieu_a N gsl_sf_mathieu_a
gsl_sf_mathieu_b N gsl_sf_mathieu_b
gsl_sf_mathieu_a_coeff N gsl_sf_mathieu_a_coeff
gsl_sf_mathieu_b_coeff N gsl_sf_mathieu_b_coeff
gsl_sf_mathieu_ce N gsl_sf_mathieu_ce
gsl_sf_mathieu_se N gsl_sf_mathieu_se
gsl_sf_mathieu_ce_array N gsl_sf_mathieu_ce_array
gsl_sf_mathieu_se_array N gsl_sf_mathieu_se_array
gsl_sf_mathieu_Mc N gsl_sf_mathieu_Mc
gsl_sf_mathieu_Ms N gsl_sf_mathieu_Ms
gsl_sf_mathieu_Mc_array N gsl_sf_mathieu_Mc_array
gsl_sf_mathieu_Ms_array N gsl_sf_mathieu_Ms_array
gsl_sf_pow_int_e N gsl_sf_pow_int
gsl_sf_psi_int_e Y gsl_sf_psi_int(int_expr)
gsl_sf_psi_e Y gsl_sf_psi(dbl_expr)
gsl_sf_psi_1piy_e Y gsl_sf_psi_1piy(dbl_expr)
gsl_sf_complex_psi_e N gsl_sf_complex_psi
gsl_sf_psi_1_int_e Y gsl_sf_psi_1_int(int_expr)
gsl_sf_psi_1_e Y gsl_sf_psi_1(dbl_expr)
gsl_sf_psi_n_e Y gsl_sf_psi_n(int_expr,dbl_expr)
gsl_sf_synchrotron_1_e Y gsl_sf_synchrotron_1(dbl_expr)
gsl_sf_synchrotron_2_e Y gsl_sf_synchrotron_2(dbl_expr)
gsl_sf_transport_2_e Y gsl_sf_transport_2(dbl_expr)
gsl_sf_transport_3_e Y gsl_sf_transport_3(dbl_expr)
gsl_sf_transport_4_e Y gsl_sf_transport_4(dbl_expr)
gsl_sf_transport_5_e Y gsl_sf_transport_5(dbl_expr)
gsl_sf_sin_e N gsl_sf_sin
gsl_sf_cos_e N gsl_sf_cos
gsl_sf_hypot_e N gsl_sf_hypot
gsl_sf_complex_sin_e N gsl_sf_complex_sin
gsl_sf_complex_cos_e N gsl_sf_complex_cos
gsl_sf_complex_logsin_e N gsl_sf_complex_logsin
gsl_sf_sinc_e N gsl_sf_sinc
gsl_sf_lnsinh_e N gsl_sf_lnsinh
gsl_sf_lncosh_e N gsl_sf_lncosh
gsl_sf_polar_to_rect N gsl_sf_polar_to_rect
gsl_sf_rect_to_polar N gsl_sf_rect_to_polar
gsl_sf_sin_err_e N gsl_sf_sin_err
gsl_sf_cos_err_e N gsl_sf_cos_err
gsl_sf_angle_restrict_symm_eN gsl_sf_angle_restrict_symm
gsl_sf_angle_restrict_pos_eN gsl_sf_angle_restrict_pos
gsl_sf_angle_restrict_symm_err_eN gsl_sf_angle_restrict_symm_err
gsl_sf_angle_restrict_pos_err_eN gsl_sf_angle_restrict_pos_err
gsl_sf_zeta_int_e Y gsl_sf_zeta_int(int_expr)
gsl_sf_zeta_e Y gsl_sf_zeta(dbl_expr)
gsl_sf_zetam1_e Y gsl_sf_zetam1(dbl_expr)
gsl_sf_zetam1_int_e Y gsl_sf_zetam1_int(int_expr)
gsl_sf_hzeta_e Y gsl_sf_hzeta(dbl_expr,dbl_expr)
gsl_sf_eta_int_e Y gsl_sf_eta_int(int_expr)
gsl_sf_eta_e Y gsl_sf_eta(dbl_expr)
---------- Footnotes ----------
(1) These are the GSL standard function names postfixed with `_e'.
NCO calls these functions automatically, without the NCO command having
to specifically indicate the `_e' function suffix.
4.1.20 GSL interpolation
------------------------
As of version 3.9.9 (released July, 2009), NCO has wrappers to the GSL
interpolation functions.
Given a set of data points (x1,y1)...(xn, yn) the GSL functions
computes a continuous interpolating function Y(x) such that Y(xi) = yi.
The interpolation is piecewise smooth, and its behavior at the
end-points is determined by the type of interpolation used. For more
information consult the GSL manual.
Interpolation with `ncap2' is a two stage process. In the first stage,
a ram variable is created from the chosen interpolating function and
the data set. This ram variable holds in memory a GSL interpolation
object. In the second stage, points along the interpolating function
are calculated. If you have a very large data set or are interpolating
many sets then consider deleting the ram variable when it is redundant.
Use the command `ram_delete(var_nm)'.
A simple example
x_in[$lon]={1.0,2.0,3.0,4.0};
y_in[$lon]={1.1,1.2,1.5,1.8};
// Ram variable is declared and defined here
gsl_interp_cspline(&ram_sp,x_in,y_in);
x_out[$lon_grd]={1.1,2.0,3.0,3.1,3.99};
y_out=gsl_spline_eval(ram_sp,x_out);
y2=gsl_spline_eval(ram_sp,1.3);
y3=gsl_spline_eval(ram_sp,0.0);
ram_delete(ram_sp);
print(y_out); // 1.10472, 1.2, 1.4, 1.42658, 1.69680002
print(y2); // 1.12454
print(y3); // '_'
Note in the above example y3 is set to 'missing value' because 0.0
isn't within the input X range.
* GSL Interpolation Types *
All the interpolation functions have been implemented. These are:
gsl_interp_linear()
gsl_interp_polynomial()
gsl_interp_cspline()
gsl_interp_cspline_periodic()
gsl_interp_akima()
gsl_interp_akima_periodic()
* Evaluation of Interpolating Types *
*Implemented*
gsl_spline_eval()
*Unimplemented*
gsl_spline_deriv()
gsl_spline_deriv2()
gsl_spline_integ()
4.1.21 GSL least-squares fitting
--------------------------------
Least Squares fitting is a method of calculating a straight line
through a set of experimental data points in the XY plane. The data
maybe weighted or unweighted. For more information please refer to the
GSL manual.
These GSL functions fall into three categories:
*A)* Fitting data to Y=c0+c1*X
*B)* Fitting data (through the origin) Y=c1*X
*C)* Multi-parameter fitting (not yet implemented)
*Section A*
`status=*gsl_fit_linear*(data_x,stride_x,data_y,stride_y,n,&co,&c1,&cov00,&cov01,&cov11,&sumsq)
'
*Input variables*: data_x, stride_x, data_y, stride_y, n
From the above variables an X and Y vector both of length 'n' are
derived. If data_x or data_y is less than type double then it is
converted to type `double'. It is up to you to do bounds checking on
the input data. For example if stride_x=3 and n=8 then the size of
data_x must be at least 24
*Output variables*: c0, c1, cov00, cov01, cov11,sumsq
The '&' prefix indicates that these are call-by-reference variables.
If any of the output variables don't exist prior to the call then they
are created on the fly as scalar variables of type `double'. If they
already exist then their existing value is overwritten. If the function
call is successful then `status=0'.
`status=
*gsl_fit_wlinear*(data_x,stride_x,data_w,stride_w,data_y,stride_y,n,&co,&c1,&cov00,&cov01,&cov11,&chisq)
'
Similar to the above call except it creates an additional weighting
vector from the variables data_w, stride_w, n
` data_y_out=*gsl_fit_linear_est*(data_x,c0,c1,cov00,cov01,cov11) '
This function calculates y values along the line Y=c0+c1*X
*Section B*
`status=*gsl_fit_mul*(data_x,stride_x,data_y,stride_y,n,&c1,&cov11,&sumsq)
'
*Input variables*: data_x, stride_x, data_y, stride_y, n
From the above variables an X and Y vector both of length 'n' are
derived. If data_x or data_y is less than type `double' then it is
converted to type `double'.
*Output variables*: c1,cov11,sumsq
`status=
*gsl_fit_wmul*(data_x,stride_x,data_w,stride_w,data_y,stride_y,n,&c1,&cov11,&sumsq)
'
Similar to the above call except it creates an additional weighting
vector from the variables data_w, stride_w, n
` data_y_out=*gsl_fit_mul_est*(data_x,c0,c1,cov11) '
This function calculates y values along the line Y=c1*X
The below example shows *gsl_fit_linear()* in action
defdim("d1",10);
xin[d1]={1,2,3,4,5,6,7,8,9,10.0};
yin[d1]={3.1,6.2,9.1,12.2,15.1,18.2,21.3,24.0,27.0,30.0};
gsl_fit_linear(xin,1,yin,1,$d1.size,&c0,&c1,&cov00,&cov01,&cov11,&sumsq);
print(c0); // 0.2
print(c1); // 2.98545454545
defdim("e1",4);
xout[e1]={1.0,3.0,4.0,11};
yout[e1]=0.0;
yout=gsl_fit_linear_est(xout, c0,c1, cov00,cov01, cov11, sumsq);
print(yout); // 3.18545454545 ,9.15636363636, ,12.1418181818 ,33.04
4.1.22 GSL statistics
---------------------
Wrappers for most of the GSL Statistical functions have been
implemented. The GSL function names include a type specifier (except
for type double functions). To obtain the equivalent NCO name simply
remove the type specifier; then depending on the data type the
appropriate GSL function is called. The weighed statistical functions
e.g ` gsl_stats_wvariance()' are only defined in GSL for floating point
types; so your data must of type `float' or `double' otherwise ncap2
will emit an error message. To view the implemented functions use the
shell command `ncap2 -f|grep _stats'
GSL Functions
short gsl_stats_max (short data[], size_t stride, size_t n);
double gsl_stats_int_mean (int data[], size_t stride, size_t n);
double gsl_stats_short_sd_with_fixed_mean (short data[], size_t stride, size_t n, double mean);
double gsl_stats_wmean (double w[], size_t wstride, double data[], size_t stride, size_t n);
double gsl_stats_quantile_from_sorted_data (double sorted_data[], size_t stride, size_t n, double f) ;
Equivalent ncap2 wrapper functions
short gsl_stats_max (var_data, data_stride, n);
double gsl_stats_mean (var_data, data_stride, n);
double gsl_stats_sd_with_fixed_mean (var_data, data_stride, n, var_mean);
double gsl_stats_wmean (var_weight, weight_stride, var_data, data_stride, n, var_mean);
double gsl_stats_quantile_from_sorted_data (var_sorted_data, data_stride, n, var_f) ;
GSL has no notion of missing values or dimensionality beyond one. If
your data has missing values which you want ignored in the calculations
then use the `ncap2' built in aggregate functions( *note Methods and
functions:: ). The GSL functions operate on a vector of values created
from the var_data/stride/n arguments. The ncap wrappers check that
there is no bounding error with regard to the size of the data and the
final value in the vector.
Some examples
a1[time]={1,2,3,4,5,6,7,8,9,10 };
a1_avg=gsl_stats_mean(a1,1,10);
print(a1_avg); // 5.5
a1_var=gsl_stats_variance(a1,4,3);
print(a1_var); // 16.0
// bounding error, vector attempts to access element a1(10)
a1_sd=gsl_stats_sd(a1,5,3);
For functions with the signature *func_nm(var_data, data_stride, n)*
you can omit the second or third arguments. The default value for
stride is `1'. The default value for n is ` 1+ (data.size()-1)/stride'
// the following are equvalent
n2=gsl_stats_max(a1,1,10)
n2=gsl_stats_max(a1,1);
n2=gsl_stats_max(a1);
// the following are equivalent
n3=gsl_stats_median_from_sorted_data(a1,2,5);
n3=gsl_stats_median_from_sorted_data(a1,2);
// the following are NOT equivalent
n4=gsl_stats_kurtosis(a1,3,2);
n4=gsl_stats_kurtosis(a1,3); //default n=4
The following example illustrates some of the weighted functions in
action. The data is randomly generated. In this case the value of the
weight for each datum is either 0.0 or 1.0
defdim("r1",2000);
data[r1]=1.0;
// fill with ramdon numbers [0.0,10.0)
data=10.0*gsl_rng_uniform(data);
// Create a weighting var
weight=(data>4.0);
wmean=gsl_stats_wmean(weight,1,data,1,$r1.size);
print(wmean);
wsd=gsl_stats_wsd(weight,1,data,1,$r1.size);
print(wsd);
// number of values in data that are greater than 4
weight_size=weight.total();
print(weight_size);
// print min/max of data
dmin=data.gsl_stats_min();
dmax=data.gsl_stats_max();
print(dmin);print(dmax);
4.1.23 Examples ncap2
---------------------
See the `ncap.in' and `ncap2.in' scripts released with NCO for more
complete demonstrations of `ncap' and `ncap2' functionality,
respectively (these scripts are available on-line at
`http://nco.sf.net/ncap.in' and `http://nco.sf.net/ncap2.in').
Define new attribute NEW for existing variable ONE as twice the
existing attribute DOUBLE_ATT of variable ATT_VAR:
ncap2 -s 'one@new=2*att_var@double_att' in.nc out.nc
Average variables of mixed types (result is of type `double'):
ncap2 -s 'average=(var_float+var_double+var_int)/3' in.nc out.nc
Multiple commands may be given to `ncap2' in three ways. First, the
commands may be placed in a script which is executed, e.g., `tst.nco'.
Second, the commands may be individually specified with multiple `-s'
arguments to the same `ncap2' invocation. Third, the commands may be
chained together into a single `-s' argument to `ncap2'. Assuming the
file `tst.nco' contains the commands `a=3;b=4;c=sqrt(a^2+b^2);', then
the following `ncap2' invocations produce identical results:
ncap2 -v -S tst.nco in.nc out.nc
ncap2 -v -s 'a=3' -s 'b=4' -s 'c=sqrt(a^2+b^2)' in.nc out.nc
ncap2 -v -s 'a=3;b=4;c=sqrt(a^2+b^2)' in.nc out.nc
The second and third examples show that `ncap2' does not require
that a trailing semi-colon `;' be placed at the end of a `-s' argument,
although a trailing semi-colon `;' is always allowed. However,
semi-colons are required to separate individual assignment statements
chained together as a single `-s' argument.
`ncap2' may be used to "grow" dimensions, i.e., to increase
dimension sizes without altering existing data. Say `in.nc' has
`ORO(lat,lon)' and the user wishes a new file with
`new_ORO(new_lat,new_lon)' that contains zeros in the undefined
portions of the new grid.
defdim("new_lat",$lat.size+1); // Define new dimension sizes
defdim("new_lon",$lon.size+1);
new_ORO[$new_lat,$new_lon]=0.0f; // Initialize to zero
new_ORO(0:$lat.size-1,0:$lon.size-1)=ORO; // Fill valid data
The commands to define new coordinate variables `new_lat' and
`new_lon' in the output file follow a similar pattern. One would might
store these commands in a script `grow.nco' and then execute the script
with
ncap2 -v -S grow.nco in.nc out.nc
Imagine you wish to create a binary flag based on the value of an
array. The flag should have value 1.0 where the array exceeds 1.0, and
value 0.0 elsewhere. This example creates the binary flag `ORO_flg' in
`out.nc' from the continuous array named `ORO' in `in.nc'.
ncap2 -s 'ORO_flg=(ORO > 1.0)' in.nc out.nc
Suppose your task is to change all values of `ORO' which equal 2.0
to the new value 3.0:
ncap2 -s 'ORO_msk=(ORO==2.0);ORO=ORO_msk*3.0+!ORO_msk*ORO' in.nc out.nc
This creates and uses `ORO_msk' to mask the subsequent arithmetic
operation. Values of `ORO' are only changed where `ORO_msk' is true,
i.e., where `ORO' equals 2.0
Using the `where' statement the above code simplifies to :
ncap2 -s 'where(ORO==2.0) ORO=3.0;' in.nc foo.nc
This example uses `ncap2' to compute the covariance of two variables.
Let the variables U and V be the horizontal wind components. The
"covariance" of U and V is defined as the time mean product of the
deviations of U and V from their respective time means. Symbolically,
the covariance
[U'V'] = [UV]-[U][V] where [X] denotes the time-average of X and X'
denotes the deviation from the time-mean. The covariance tells us
how much of the correlation of two signals arises from the signal
fluctuations versus the mean signals. Sometimes this is called the
"eddy covariance". We will store the covariance in the variable
`uprmvprm'.
ncwa -O -a time -v u,v in.nc foo.nc # Compute time mean of u,v
ncrename -O -v u,uavg -v v,vavg foo.nc # Rename to avoid conflict
ncks -A -v uavg,vavg foo.nc in.nc # Place time means with originals
ncap2 -O -s 'uprmvprm=u*v-uavg*vavg' in.nc in.nc # Covariance
ncra -O -v uprmvprm in.nc foo.nc # Time-mean covariance
The mathematically inclined will note that the same covariance would
be obtained by replacing the step involving `ncap2' with
ncap2 -O -s 'uprmvprm=(u-uavg)*(v-vavg)' foo.nc foo.nc # Covariance
As of NCO version 3.1.8 (December, 2006), `ncap2' can compute
averages, and thus covariances, by itself:
ncap2 -s 'uavg=u.avg($time);vavg=v.avg($time);uprmvprm=u*v-uavg*vavg' \
-s 'uprmvrpmavg=uprmvprm.avg($time)' in.nc foo.nc
We have not seen a simpler method to script and execute powerful
arithmetic than `ncap2'.
`ncap2' utilizes many meta-characters (e.g., `$', `?', `;', `()',
`[]') that can confuse the command-line shell if not quoted properly.
The issues are the same as those which arise in utilizing extended
regular expressions to subset variables (*note Subsetting Variables::).
The example above will fail with no quotes and with double quotes.
This is because shell globbing tries to "interpolate" the value of
`$time' from the shell environment unless it is quoted:
ncap2 -s 'uavg=u.avg($time)' in.nc foo.nc # Correct (recommended)
ncap2 -s uavg=u.avg('$time') in.nc foo.nc # Correct (and dangerous)
ncap2 -s uavg=u.avg($time) in.nc foo.nc # Fails ($time = '')
ncap2 -s "uavg=u.avg($time)" in.nc foo.nc # Fails ($time = '')
Without the single quotes, the shell replaces `$time' with an empty
string. The command `ncap2' receives from the shell is `uavg=u.avg()'.
This causes `ncap2' to average over all dimensions rather than just the
TIME dimension, and unintended consequence.
We recommend using single quotes to protect `ncap2' command-line
scripts from the shell, even when such protection is not strictly
necessary. Expert users may violate this rule to exploit the ability
to use shell variables in `ncap2' command-line scripts (*note CCSM
Example::). In such cases it may be necessary to use the shell
backslash character `\' to protect the `ncap2' meta-character.
Whether a degenerate record dimension is desirable or undesirable
depends on the application. Often a degenerate TIME dimension is
useful, e.g., for concatentating, but it may cause problems with
arithmetic. Such is the case in the above example, where the first
step employs `ncwa' rather than `ncra' for the time-averaging. Of
course the numerical results are the same with both operators. The
difference is that, unless `-b' is specified, `ncwa' writes no TIME
dimension to the output file, while `ncra' defaults to keeping TIME as
a degenerate (size 1) dimension. Appending `u' and `v' to the output
file would cause `ncks' to try to expand the degenerate time axis of
`uavg' and `vavg' to the size of the non-degenerate TIME dimension in
the input file. Thus the append (`ncks -A') command would be undefined
(and should fail) in this case. Equally important is the `-C' argument
(*note Subsetting Coordinate Variables::) to `ncwa' to prevent any
scalar TIME variable from being written to the output file. Knowing
when to use `ncwa -a time' rather than the default `ncra' for
time-averaging takes, well, time.
4.1.24 Intrinsic mathematical methods
-------------------------------------
`ncap2' supports the standard mathematical functions supplied with most
operating systems. Standard calculator notation is used for addition
`+', subtraction `-', multiplication `*', division `/', exponentiation
`^', and modulus `%'. The available elementary mathematical functions
are:
`abs(x)'
"Absolute value" Absolute value of X. Example: abs(-1) = 1
`acos(x)'
"Arc-cosine" Arc-cosine of X where X is specified in radians.
Example: acos(1.0) = 0.0
`acosh(x)'
"Hyperbolic arc-cosine" Hyperbolic arc-cosine of X where X is
specified in radians. Example: acosh(1.0) = 0.0
`asin(x)'
"Arc-sine" Arc-sine of X where X is specified in radians. Example:
asin(1.0) = 1.57079632679489661922
`asinh(x)'
"Hyperbolic arc-sine" Hyperbolic arc-sine of X where X is
specified in radians. Example: asinh(1.0) = 0.88137358702
`atan(x)'
"Arc-tangent" Arc-tangent of X where X is specified in radians
between -pi/2 and pi/2. Example: atan(1.0) =
0.78539816339744830961
`atan2(y,x)'
"Arc-tangent2" Arc-tangent of Y/X :Example atan2(1,3) =
0.321689857
`atanh(x)'
"Hyperbolic arc-tangent" Hyperbolic arc-tangent of X where X is
specified in radians between -pi/2 and pi/2. Example:
atanh(3.14159265358979323844) = 1.0
`ceil(x)'
"Ceil" Ceiling of X. Smallest integral value not less than
argument. Example: ceil(0.1) = 1.0
`cos(x)'
"Cosine" Cosine of X where X is specified in radians. Example:
cos(0.0) = 1.0
`cosh(x)'
"Hyperbolic cosine" Hyperbolic cosine of X where X is specified in
radians. Example: cosh(0.0) = 1.0
`erf(x)'
"Error function" Error function of X where X is specified between
-1 and 1. Example: erf(1.0) = 0.842701
`erfc(x)'
"Complementary error function" Complementary error function of X
where X is specified between -1 and 1. Example: erfc(1.0) =
0.15729920705
`exp(x)'
"Exponential" Exponential of X, e^x. Example: exp(1.0) =
2.71828182845904523536
`floor(x)'
"Floor" Floor of X. Largest integral value not greater than
argument. Example: floor(1.9) = 1
`gamma(x)'
"Gamma function" Gamma function of X, Gamma(x). The well-known
and loved continuous factorial function. Example: gamma(0.5) =
sqrt(pi)
`gamma_inc_P(x)'
"Incomplete Gamma function" Incomplete Gamma function of parameter
A and variable X, gamma_inc_P(a,x). One of the four incomplete
gamma functions. Example: gamma_inc_P(1,1) = 1-1/e
`ln(x)'
"Natural Logarithm" Natural logarithm of X, ln(x). Example:
ln(2.71828182845904523536) = 1.0
`log(x)'
"Natural Logarithm" Exact synonym for `ln(x)'.
`log10(x)'
"Base 10 Logarithm" Base 10 logarithm of X, log10(x). Example:
log(10.0) = 1.0
`nearbyint(x)'
"Round inexactly" Nearest integer to X is returned in floating
point format. No exceptions are raised for "inexact conversions".
Example: nearbyint(0.1) = 0.0
`pow(x,y)'
"Power" Value of X is raised to the power of Y. Exceptions are
raised for "domain errors". Due to type-limitations in the
C language `pow' function, integer arguments are promoted (*note
Type Conversion::) to type `NC_FLOAT' before evaluation. Example:
pow(2,3) = 8
`rint(x)'
"Round exactly" Nearest integer to X is returned in floating point
format. Exceptions are raised for "inexact conversions". Example:
rint(0.1) = 0
`round(x)'
"Round" Nearest integer to X is returned in floating point format.
Round halfway cases away from zero, regardless of current IEEE
rounding direction. Example: round(0.5) = 1.0
`sin(x)'
"Sine" Sine of X where X is specified in radians. Example:
sin(1.57079632679489661922) = 1.0
`sinh(x)'
"Hyperbolic sine" Hyperbolic sine of X where X is specified in
radians. Example: sinh(1.0) = 1.1752
`sqrt(x)'
"Square Root" Square Root of X, sqrt(x). Example: sqrt(4.0) = 2.0
`tan(x)'
"Tangent" Tangent of X where X is specified in radians. Example:
tan(0.78539816339744830961) = 1.0
`tanh(x)'
"Hyperbolic tangent" Hyperbolic tangent of X where X is specified
in radians. Example: tanh(1.0) = 0.761594155956
`trunc(x)'
"Truncate" Nearest integer to X is returned in floating point
format. Round halfway cases toward zero, regardless of current
IEEE rounding direction. Example: trunc(0.5) = 0.0
The complete list of mathematical functions supported is
platform-specific. Functions mandated by ANSI C are _guaranteed_ to be
present and are indicated with an asterisk (1). and are indicated with
an asterisk. Use the `-f' (or `fnc_tbl' or `prn_fnc_tbl') switch to
print a complete list of functions supported on your platform. (2)
---------- Footnotes ----------
(1) ANSI C compilers are guaranteed to support double precision
versions of these functions. These functions normally operate on
netCDF variables of type `NC_DOUBLE' without having to perform
intrinsic conversions. For example, ANSI compilers provide `sin' for
the sine of C-type `double' variables. The ANSI standard does not
require, but many compilers provide, an extended set of mathematical
functions that apply to single (`float') and quadruple (`long double')
precision variables. Using these functions (e.g., `sinf' for `float',
`sinl' for `long double'), when available, is (presumably) more
efficient than casting variables to type `double', performing the
operation, and then re-casting. NCO uses the faster intrinsic
functions when they are available, and uses the casting method when
they are not.
(2) Linux supports more of these intrinsic functions than other OSs.
4.1.25 Operators precedence and associativity
---------------------------------------------
This page lists the `ncap' operators in order of precedence (highest to
lowest). Their associativity indicates in what order operators of equal
precedence in an expression are applied.
Operator Description Associativity
---------------------------------------------------------------------------
`++ --' Postfix Increment/Decrement Right to Left
`()' Parentheses (function call)
`.' Method call
`++ --' Prefix Increment/Decrement Right to Left
`+ -' Unary Plus/Minus
`!' Logical Not
`^' Power of Operator Right to Left
`* / %' Multiply/Divide/Modulus Left To Right
`+ -' Addition/Subtraction Left To Right
`>> <<' Fortran style array clipping Left to Right
`< <=' Less than/Less than or equal to Left to Right
`> >=' Greater than/Greater than or equal to
`== !=' Equal to/Not equal to Left to Right
`&&' Logical AND Left to Right
`||' Logical OR Left to Right
`?:' Ternary Operator Right to Left
`=' Assignment Right to Left
`+= -=' Addition/subtraction assignment
`*= /=' Multiplication/division assignment
4.1.26 ID Quoting
-----------------
In this section when I refer to a name I mean a variable name,
attribute name or a dimension name The allowed characters in a valid
netCDF name vary from release to release. (See end section). If you
want to use metacharacters in a name or use a method name as a variable
name then the name has to be quoted wherever it occurs.
The default nco name is specified by the regular expressions:
DGT: ('0'..'9');
LPH: ( 'a'..'z' | 'A'..'Z' | '_' );
name: (LPH)(LPH|DGT)+
The first character of a valid name must be alphabetic or the
underscore. Any subsequent characters must be alphanumeric or
underscore. ( e.g a1,_23, hell_is_666 )
The valid characters in a quoted name are specified by the regular
expressions:
LPHDGT: ( 'a'..'z' | 'A'..'Z' | '_' | '0'..'9');
name: (LPHDGT|'-'|'+'|'.'|'('|')'|':' )+ ;
Quote a variable:
'avg' , '10_+10','set_miss' '+-90field' , '-test'=10.0d
Quote a attribute:
'three@10', 'set_mss@+10', '666@hell', 't1@+units'="kelvin"
Quote a dimension:
'$10', '$t1-', '$-odd', c1['$10','$t1-']=23.0d
The following comments are lifted directly from the netcdf libraries
and detail the naming conventions for each release.
netcdf-3.5.1
netcdf-3.6.0-p1
netcdf-3.6.1
netcdf-3.6.2
/*
* ( [a-zA-Z]|[0-9]|'_'|'-'|'+'|'.'|'|':'|'@'|'('|')' )+
* Verify that a name string is valid
* CDL syntax, eg, all the characters are
* alphanumeric, '-', '_', '+', or '.'.
* Also permit ':', '@', '(', or ')' in names for chemists currently making
* use of these characters, but don't document until ncgen and ncdump can
* also handle these characters in names.
*/
netcdf-3.6.3
netcdf-4.0 Final 2008/08/28
/*
* Verify that a name string is valid syntax. The allowed name
* syntax (in RE form) is:
*
* ([a-zA-Z_]|{UTF8})([^\x00-\x1F\x7F/]|{UTF8})*
*
* where UTF8 represents a multibyte UTF-8 encoding. Also, no
* trailing spaces are permitted in names. This definition
* must be consistent with the one in ncgen.l. We do not allow '/'
* because HDF5 does not permit slashes in names as slash is used as a
* group separator. If UTF-8 is supported, then a multi-byte UTF-8
* character can occur anywhere within an identifier. We later
* normalize UTF-8 strings to NFC to facilitate matching and queries.
*/
4.2 `ncatted' netCDF Attribute Editor
=====================================
SYNTAX
ncatted [-a ATT_DSC] [-a ...] [-D DBG] [-h] [--hdr_pad NBR]
[-l PATH] [-O] [-o OUTPUT-FILE] [-p PATH] [-R] [-r]
INPUT-FILE [[OUTPUT-FILE]]
DESCRIPTION
`ncatted' edits attributes in a netCDF file. If you are editing
attributes then you are spending too much time in the world of
metadata, and `ncatted' was written to get you back out as quickly and
painlessly as possible. `ncatted' can "append", "create", "delete",
"modify", and "overwrite" attributes (all explained below).
Furthermore, `ncatted' allows each editing operation to be applied to
every variable in a file. This saves time when changing attribute
conventions throughout a file. Note that `ncatted' interprets
character attributes (i.e., attributes of type `NC_CHAR') as strings.
Because repeated use of `ncatted' can considerably increase the size
of the `history' global attribute (*note History Attribute::), the `-h'
switch is provided to override automatically appending the command to
the `history' global attribute in the OUTPUT-FILE.
When `ncatted' is used to change the `_FillValue' attribute, it
changes the associated missing data self-consistently. If the internal
floating point representation of a missing value, e.g., 1.0e36, differs
between two machines then netCDF files produced on those machines will
have incompatible missing values. This allows `ncatted' to change the
missing values in files from different machines to a single value so
that the files may then be concatenated together, e.g., by `ncrcat',
without losing any information. *Note Missing Values::, for more
information.
The key to mastering `ncatted' is understanding the meaning of the
structure describing the attribute modification, ATT_DSC specified by
the required option `-a' or `--attribute'. Each ATT_DSC contains five
elements, which makes using `ncatted' somewhat complicated, but
powerful. The ATT_DSC argument structure contains five arguments in the
following order:
ATT_DSC = ATT_NM, VAR_NM, MODE, ATT_TYPE, ATT_VAL
ATT_NM
Attribute name. Example: `units'
VAR_NM
Variable name. Regular expressions (*note Subsetting Variables::)
are accepted and will select any matching variable names.
Example: `pressure', `'^H2O''.
MODE
Edit mode abbreviation. Example: `a'. See below for complete
listing of valid values of MODE.
ATT_TYPE
Attribute type abbreviation. Example: `c'. See below for
complete listing of valid values of ATT_TYPE.
ATT_VAL
Attribute value. Example: `pascal'.
There should be no empty space between these five consecutive arguments.
The description of these arguments follows in their order of appearance.
The value of ATT_NM is the name of the attribute you want to edit.
This meaning of this should be clear to all users of the `ncatted'
operator. If ATT_NM is omitted (i.e., left blank) and "Delete" mode is
selected, then all attributes associated with the specified variable
will be deleted.
The value of VAR_NM is the name of the variable containing the
attribute (named ATT_NM) that you want to edit. There are three very
important and useful exceptions to this rule. The value of VAR_NM can
also be used to direct `ncatted' to edit global attributes, or to
repeat the editing operation for every variable in a file. A value of
VAR_NM of "global" indicates that ATT_NM refers to a global attribute,
rather than a particular variable's attribute. This is the method
`ncatted' supports for editing global attributes. If VAR_NM is left
blank, on the other hand, then `ncatted' attempts to perform the
editing operation on every variable in the file. This option may be
convenient to use if you decide to change the conventions you use for
describing the data. Finally, as mentioned above, VAR_NM may be
specified as a regular expression.
The value of MODE is a single character abbreviation (`a', `c', `d',
`m', or `o') standing for one of five editing modes:
`a'
"Append". Append value ATT_VAL to current VAR_NM attribute ATT_NM
value ATT_VAL, if any. If VAR_NM does not have an attribute
ATT_NM, there is no effect.
`c'
"Create". Create variable VAR_NM attribute ATT_NM with ATT_VAL if
ATT_NM does not yet exist. If VAR_NM already has an attribute
ATT_NM, there is no effect.
`d'
"Delete". Delete current VAR_NM attribute ATT_NM. If VAR_NM does
not have an attribute ATT_NM, there is no effect. If ATT_NM is
omitted (left blank), then all attributes associated with the
specified variable are automatically deleted. When "Delete" mode
is selected, the ATT_TYPE and ATT_VAL arguments are superfluous
and may be left blank.
`m'
"Modify". Change value of current VAR_NM attribute ATT_NM to value
ATT_VAL. If VAR_NM does not have an attribute ATT_NM, there is no
effect.
`o'
"Overwrite". Write attribute ATT_NM with value ATT_VAL to variable
VAR_NM, overwriting existing attribute ATT_NM, if any. This is
the default mode.
The value of ATT_TYPE is a single character abbreviation (`f', `d',
`l', `i', `s', `c', `b', `u') or a short string standing for one of the
twelve primitive netCDF data types:
`f'
"Float". Value(s) specified in ATT_VAL will be stored as netCDF
intrinsic type `NC_FLOAT'.
`d'
"Double". Value(s) specified in ATT_VAL will be stored as netCDF
intrinsic type `NC_DOUBLE'.
`i, l'
"Integer" or "Long". Value(s) specified in ATT_VAL will be stored
as netCDF intrinsic type `NC_INT'.
`s'
"Short". Value(s) specified in ATT_VAL will be stored as netCDF
intrinsic type `NC_SHORT'.
`c'
"Char". Value(s) specified in ATT_VAL will be stored as netCDF
intrinsic type `NC_CHAR'.
`b'
"Byte". Value(s) specified in ATT_VAL will be stored as netCDF
intrinsic type `NC_BYTE'.
`ub'
"Unsigned Byte". Value(s) specified in ATT_VAL will be stored as
netCDF intrinsic type `NC_UBYTE'.
`us'
"Unsigned Short". Value(s) specified in ATT_VAL will be stored as
netCDF intrinsic type `NC_USHORT'.
`u, ui, ul'
"Unsigned Int". Value(s) specified in ATT_VAL will be stored as
netCDF intrinsic type `NC_UINT'.
`ll, int64'
"Int64". Value(s) specified in ATT_VAL will be stored as netCDF
intrinsic type `NC_INT64'.
`ull, uint64'
"Uint64". Value(s) specified in ATT_VAL will be stored as netCDF
intrinsic type `NC_UINT64'.
`sng'
"String". Value(s) specified in ATT_VAL will be stored as netCDF
intrinsic type `NC_STRING'.
The specification of ATT_TYPE is optional (and is ignored) in "Delete"
mode.
The value of ATT_VAL is what you want to change attribute ATT_NM to
contain. The specification of ATT_VAL is optional in "Delete" (and is
ignored) mode. Attribute values for all types besides `NC_CHAR' must
have an attribute length of at least one. Thus ATT_VAL may be a single
value or one-dimensional array of elements of type `att_type'. If the
ATT_VAL is not set or is set to empty space, and the ATT_TYPE is
`NC_CHAR', e.g., `-a units,T,o,c,""' or `-a units,T,o,c,', then the
corresponding attribute is set to have zero length. When specifying an
array of values, it is safest to enclose ATT_VAL in single or double
quotes, e.g., `-a levels,T,o,s,"1,2,3,4"' or `-a
levels,T,o,s,'1,2,3,4''. The quotes are strictly unnecessary around
ATT_VAL except when ATT_VAL contains characters which would confuse the
calling shell, such as spaces, commas, and wildcard characters.
NCO processing of `NC_CHAR' attributes is a bit like Perl in that it
attempts to do what you want by default (but this sometimes causes
unexpected results if you want unusual data storage). If the ATT_TYPE
is `NC_CHAR' then the argument is interpreted as a string and it may
contain C-language escape sequences, e.g., `\n', which NCO will
interpret before writing anything to disk. NCO translates valid escape
sequences and stores the appropriate ASCII code instead. Since two
byte escape sequences, e.g., `\n', represent one-byte ASCII codes,
e.g., ASCII 10 (decimal), the stored string attribute is one byte
shorter than the input string length for each embedded escape sequence.
The most frequently used C-language escape sequences are `\n' (for
linefeed) and `\t' (for horizontal tab). These sequences in particular
allow convenient editing of formatted text attributes. The other valid
ASCII codes are `\a', `\b', `\f', `\r', `\v', and `\\'. *Note ncks
netCDF Kitchen Sink::, for more examples of string formatting (with the
`ncks' `-s' option) with special characters.
Analogous to `printf', other special characters are also allowed by
`ncatted' if they are "protected" by a backslash. The characters `"',
`'', `?', and `\' may be input to the shell as `\"', `\'', `\?', and
`\\'. NCO simply strips away the leading backslash from these
characters before editing the attribute. No other characters require
protection by a backslash. Backslashes which precede any other
character (e.g., `3', `m', `$', `|', `&', `@', `%', `{', and `}') will
not be filtered and will be included in the attribute.
Note that the NUL character `\0' which terminates C language strings
is assumed and need not be explicitly specified. If `\0' is input, it
will not be translated (because it would terminate the string in an
additional location). Because of these context-sensitive rules, if
wish to use an attribute of type `NC_CHAR' to store data, rather than
text strings, you should use `ncatted' with care.
EXAMPLES
Append the string "Data version 2.0.\n" to the global attribute
`history':
ncatted -a history,global,a,c,"Data version 2.0\n" in.nc
Note the use of embedded C language `printf()'-style escape
sequences.
Change the value of the `long_name' attribute for variable `T' from
whatever it currently is to "temperature":
ncatted -a long_name,T,o,c,temperature in.nc
Delete all existing `units' attributes:
ncatted -a units,,d,, in.nc
The value of VAR_NM was left blank in order to select all variables
in the file. The values of ATT_TYPE and ATT_VAL were left blank because
they are superfluous in "Delete" mode.
Delete all attributes associated with the `tpt' variable:
ncatted -a ,tpt,d,, in.nc
The value of ATT_NM was left blank in order to select all attributes
associated with the variable. To delete all global attributes, simply
replace `tpt' with `global' in the above.
Modify all existing `units' attributes to "meter second-1":
ncatted -a units,,m,c,"meter second-1" in.nc
Add a `units' attribute of "kilogram kilogram-1" to all variables
whose first three characters are `H2O':
ncatted -a units,'^H2O',c,c,"kilogram kilogram-1" in.nc
Overwrite the `quanta' attribute of variable `energy' to an array of
four integers.
ncatted -O -a quanta,energy,o,s,"010,101,111,121" in.nc
As of NCO 3.9.6 (January, 2009), variable names arguments to
`ncatted' may contain "extended regular expressions". Create `isotope'
attributes for all variables containing `H2O' in their names.
ncatted -O -a isotope,'^H2O*',c,s,"18" in.nc
See *note Subsetting Variables:: for more details.
Demonstrate input of C-language escape sequences (e.g., `\n') and
other special characters (e.g., `\"')
ncatted -h -a special,global,o,c,
'\nDouble quote: \"\nTwo consecutive double quotes: \"\"\n
Single quote: Beyond my shell abilities!\nBackslash: \\\n
Two consecutive backslashes: \\\\\nQuestion mark: \?\n' in.nc
Note that the entire attribute is protected from the shell by single
quotes. These outer single quotes are necessary for interactive use,
but may be omitted in batch scripts.
4.3 `ncbo' netCDF Binary Operator
=================================
SYNTAX
ncbo [-3] [-4] [-6] [-A] [-C] [-c] [-D DBG]
[-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F] [-h]
[-L DFL_LVL] [-l PATH] [-O] [-o FILE_3] [-p PATH] [-R] [-r]
[-t THR_NBR] [-v VAR[,...]] [-X ...] [-x] [-y OP_TYP]
FILE_1 FILE_2 [FILE_3]
DESCRIPTION
`ncbo' performs binary operations on variables in FILE_1 and the
corresponding variables (those with the same name) in FILE_2 and stores
the results in FILE_3. The binary operation operates on the entire
files (modulo any excluded variables). *Note Missing Values::, for
treatment of missing values. One of the four standard arithmetic
binary operations currently supported must be selected with the `-y
OP_TYP' switch (or long options `--op_typ' or `--operation'). The
valid binary operations for `ncbo', their definitions, corresponding
values of the OP_TYP key, and alternate invocations are:
"Addition"
Definition: FILE_3 = FILE_1 + FILE_2
Alternate invocation: `ncadd'
OP_TYP key values: `add', `+', `addition'
Examples: `ncbo --op_typ=add 1.nc 2.nc 3.nc', `ncadd 1.nc 2.nc
3.nc'
"Subtraction"
Definition: FILE_3 = FILE_1 - FILE_2
Alternate invocations: `ncdiff', `ncsub', `ncsubtract'
OP_TYP key values: `sbt', `-', `dff', `diff', `sub', `subtract',
`subtraction'
Examples: `ncbo --op_typ=- 1.nc 2.nc 3.nc', `ncdiff 1.nc 2.nc 3.nc'
"Multiplication"
Definition: FILE_3 = FILE_1 * FILE_2
Alternate invocations: `ncmult', `ncmultiply'
OP_TYP key values: `mlt', `*', `mult', `multiply', `multiplication'
Examples: `ncbo --op_typ=mlt 1.nc 2.nc 3.nc', `ncmult 1.nc 2.nc
3.nc'
"Division"
Definition: FILE_3 = FILE_1 / FILE_2
Alternate invocation: `ncdivide'
OP_TYP key values: `dvd', `/', `divide', `division'
Examples: `ncbo --op_typ=/ 1.nc 2.nc 3.nc', `ncdivide 1.nc 2.nc
3.nc'
Care should be taken when using the shortest form of key values,
i.e., `+', `-', `*', and `/'. Some of these single characters may have
special meanings to the shell (1). Place these characters inside
quotes to keep them from being interpreted (globbed) by the shell (2). For
example, the following commands are equivalent
ncbo --op_typ=* 1.nc 2.nc 3.nc # Dangerous (shell may try to glob)
ncbo --op_typ='*' 1.nc 2.nc 3.nc # Safe ('*' protected from shell)
ncbo --op_typ="*" 1.nc 2.nc 3.nc # Safe ('*' protected from shell)
ncbo --op_typ=mlt 1.nc 2.nc 3.nc
ncbo --op_typ=mult 1.nc 2.nc 3.nc
ncbo --op_typ=multiply 1.nc 2.nc 3.nc
ncbo --op_typ=multiplication 1.nc 2.nc 3.nc
ncmult 1.nc 2.nc 3.nc # First do 'ln -s ncbo ncmult'
ncmultiply 1.nc 2.nc 3.nc # First do 'ln -s ncbo ncmultiply'
No particular argument or invocation form is preferred. Users are
encouraged to use the forms which are most intuitive to them.
Normally, `ncbo' will fail unless an operation type is specified
with `-y' (equivalent to `--op_typ'). You may create exceptions to
this rule to suit your particular tastes, in conformance with your
site's policy on "symbolic links" to executables (files of a different
name point to the actual executable). For many years, `ncdiff' was the
main binary file operator. As a result, many users prefer to continue
invoking `ncdiff' rather than memorizing a new command (`ncbo -y SBT')
which behaves identically to the original `ncdiff' command. However,
from a software maintenance standpoint, maintaining a distinct
executable for each binary operation (e.g., `ncadd') is untenable, and
a single executable, `ncbo', is desirable. To maintain backward
compatibility, therefore, NCO automatically creates a symbolic link
from `ncbo' to `ncdiff'. Thus `ncdiff' is called an "alternate
invocation" of `ncbo'. `ncbo' supports many additional alternate
invocations which must be manually activated. Should users or system
adminitrators decide to activate them, the procedure is simple. For
example, to use `ncadd' instead of `ncbo --op_typ=add', simply create a
symbolic link from `ncbo' to `ncadd' (3). The alternatate invocations
supported for each operation type are listed above. Alternatively,
users may always define `ncadd' as an "alias" to `ncbo --op_typ=add'
(4).
It is important to maintain portability in NCO scripts. Therefore
we recommend that site-specfic invocations (e.g., `ncadd') be used only
in interactive sessions from the command-line. For scripts, we
recommend using the full invocation (e.g., `ncbo --op_typ=add'). This
ensures portability of scripts between users and sites.
`ncbo' operates (e.g., adds) variables in FILE_2 with the
corresponding variables (those with the same name) in FILE_1 and stores
the results in FILE_3. Variables in FILE_2 are "broadcast" to conform
to the corresponding variable in FILE_1 if necessary, but the reverse is
not true. Broadcasting a variable means creating data in non-existing
dimensions from the data in existing dimensions. For example, a two
dimensional variable in FILE_2 can be subtracted from a four, three, or
two (but not one or zero) dimensional variable (of the same name) in
`file_1'. This functionality allows the user to compute anomalies from
the mean. Note that variables in FILE_1 are _not_ broadcast to conform
to the dimensions in FILE_2. In the future, we will broadcast
variables in FILE_1, if necessary to conform to their counterparts in
FILE_2. Thus, presently, the number of dimensions, or "rank", of any
processed variable in FILE_1 must be greater than or equal to the rank
of the same variable in FILE_2. Furthermore, the size of all
dimensions common to both FILE_1 and FILE_2 must be equal.
When computing anomalies from the mean it is often the case that
FILE_2 was created by applying an averaging operator to a file with
initially the same dimensions as FILE_1 (often FILE_1 itself). In
these cases, creating FILE_2 with `ncra' rather than `ncwa' will cause
the `ncbo' operation to fail. For concreteness say the record
dimension in `file_1' is `time'. If FILE_2 were created by averaging
FILE_1 over the `time' dimension with the `ncra' operator rather than
with the `ncwa' operator, then FILE_2 will have a `time' dimension of
size 1 rather than having no `time' dimension at all (5). In this case
the input files to `ncbo', FILE_1 and FILE_2, will have unequally sized
`time' dimensions which causes `ncbo' to fail. To prevent this from
occuring, use `ncwa' to remove the `time' dimension from FILE_2. See
the example below.
`ncbo' never operates on coordinate variables or variables of type
`NC_CHAR' or `NC_BYTE'. This ensures that coordinates like (e.g.,
latitude and longitude) are physically meaningful in the output file,
FILE_3. This behavior is hardcoded. `ncbo' applies special rules to
some CF-defined (and/or NCAR CCSM or NCAR CCM fields) such as `ORO'.
See *note CF Conventions:: for a complete description. Finally, we
note that `ncflint' (*note ncflint netCDF File Interpolator::) is
designed for file interpolation. As such, it also performs file
subtraction, addition, multiplication, albeit in a more convoluted way
than `ncbo'.
EXAMPLES
Say files `85_0112.nc' and `86_0112.nc' each contain 12 months of
data. Compute the change in the monthly averages from 1985 to 1986:
ncbo -op_typ=sub 86_0112.nc 85_0112.nc 86m85_0112.nc
ncdiff 86_0112.nc 85_0112.nc 86m85_0112.nc
The following examples demonstrate the broadcasting feature of
`ncbo'. Say we wish to compute the monthly anomalies of `T' from the
yearly average of `T' for the year 1985. First we create the 1985
average from the monthly data, which is stored with the record
dimension `time'.
ncra 85_0112.nc 85.nc
ncwa -O -a time 85.nc 85.nc
The second command, `ncwa', gets rid of the `time' dimension of
size 1 that `ncra' left in `85.nc'. Now none of the variables in
`85.nc' has a `time' dimension. A quicker way to accomplish this is to
use `ncwa' from the beginning:
ncwa -a time 85_0112.nc 85.nc
We are now ready to use `ncbo' to compute the anomalies for 1985:
ncdiff -v T 85_0112.nc 85.nc t_anm_85_0112.nc
Each of the 12 records in `t_anm_85_0112.nc' now contains the
monthly deviation of `T' from the annual mean of `T' for each gridpoint.
Say we wish to compute the monthly gridpoint anomalies from the zonal
annual mean. A "zonal mean" is a quantity that has been averaged over
the longitudinal (or X) direction. First we use `ncwa' to average over
longitudinal direction `lon', creating `85_x.nc', the zonal mean of
`85.nc'. Then we use `ncbo' to subtract the zonal annual means from the
monthly gridpoint data:
ncwa -a lon 85.nc 85_x.nc
ncdiff 85_0112.nc 85_x.nc tx_anm_85_0112.nc
This examples works assuming `85_0112.nc' has dimensions `time' and
`lon', and that `85_x.nc' has no `time' or `lon' dimension.
As a final example, say we have five years of monthly data (i.e.,
60 months) stored in `8501_8912.nc' and we wish to create a file which
contains the twelve month seasonal cycle of the average monthly anomaly
from the five-year mean of this data. The following method is just one
permutation of many which will accomplish the same result. First use
`ncwa' to create the five-year mean:
ncwa -a time 8501_8912.nc 8589.nc
Next use `ncbo' to create a file containing the difference of each
month's data from the five-year mean:
ncbo 8501_8912.nc 8589.nc t_anm_8501_8912.nc
Now use `ncks' to group the five January anomalies together in one
file, and use `ncra' to create the average anomaly for all five
Januarys. These commands are embedded in a shell loop so they are
repeated for all twelve months:
for idx in {1..12}; do # Bash Shell (version 3.0+)
idx=`printf "%02d" ${idx}` # Zero-pad to preserve order
ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx}
ncra foo.${idx} t_anm_8589_${idx}.nc
done
for idx in 01 02 03 04 05 06 07 08 09 10 11 12; do # Bourne Shell
ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx}
ncra foo.${idx} t_anm_8589_${idx}.nc
done
foreach idx (01 02 03 04 05 06 07 08 09 10 11 12) # C Shell
ncks -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx}
ncra foo.${idx} t_anm_8589_${idx}.nc
end
Note that `ncra' understands the `stride' argument so the two
commands inside the loop may be combined into the single command
ncra -F -d time,${idx},,12 t_anm_8501_8912.nc foo.${idx}
Finally, use `ncrcat' to concatenate the 12 average monthly anomaly
files into one twelve-record file which contains the entire seasonal
cycle of the monthly anomalies:
ncrcat t_anm_8589_??.nc t_anm_8589_0112.nc
---------- Footnotes ----------
(1) A naked (i.e., unprotected or unquoted) `*' is a wildcard
character. A naked `-' may confuse the command line parser. A naked
`+' and `/' are relatively harmless.
(2) The widely used shell Bash correctly interprets all these
special characters even when they are not quoted. That is, Bash does
not prevent NCO from correctly interpreting the intended arithmetic
operation when the following arguments are given (without quotes) to
`ncbo': `--op_typ=+', `--op_typ=-', `--op_typ=*', and `--op_typ=/'
(3) The command to do this is `ln -s -f ncbo ncadd'
(4) The command to do this is `alias ncadd='ncbo --op_typ=add''
(5) This is because `ncra' collapses the record dimension to a size
of 1 (making it a "degenerate" dimension), but does not remove it,
while, unless `-b' is given, `ncwa' removes all averaged dimensions.
In other words, by default `ncra' changes variable size but not rank,
while, `ncwa' changes both variable size and rank.
4.4 `ncea' netCDF Ensemble Averager
===================================
SYNTAX
ncea [-3] [-4] [-6] [-A] [-C] [-c] [-D DBG]
[-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F] [-h] [-L DFL_LVL] [-l PATH]
[-n LOOP] [-O] [-o OUTPUT-FILE] [-p PATH] [-R] [-r]
[-t THR_NBR] [-v VAR[,...]] [-X ...] [-x] [-y OP_TYP]
[INPUT-FILES] [OUTPUT-FILE]
DESCRIPTION
`ncea' performs gridpoint averages of variables across an arbitrary
number (an "ensemble") of INPUT-FILES, with each file receiving an
equal weight in the average. `ncea' averages entire files, and weights
each file evenly. This is distinct from `ncra', which only averages
over the record dimension (e.g., TIME), and weights each record in the
record dimension evenly,
Variables in the OUTPUT-FILE are the same size as the variable in
each of the INPUT-FILES, and all INPUT-FILES must be the same size. The
only exception is that `ncea' allows files to differ in the record
dimension size if the requested record hyperslab (*note Hyperslabs::)
resolves to the same size for all files. `ncea' recomputes the record
dimension hyperslab limits for each input file so that coordinate
limits may be used to select equal length timeseries from unequal
length files. This simplifies analysis of unequal length timeseries
from simulation ensembles (e.g., the CMIP IPCC AR4 archive).
`ncea' _always averages_ coordinate variables regardless of the
arithmetic operation type performed on the non-coordinate variables.
(*note Operation Types::). All dimensions, including the record
dimension, are treated identically and preserved in the OUTPUT-FILE.
*Note Averaging vs. Concatenating::, for a description of the
distinctions between the various averagers and concatenators. As a
multi-file operator, `ncea' will read the list of INPUT-FILES from
`stdin' if they are not specified as positional arguments on the
command line (*note Large Numbers of Files::).
The file is the logical unit of organization for the results of many
scientific studies. Often one wishes to generate a file which is the
gridpoint average of many separate files. This may be to reduce
statistical noise by combining the results of a large number of
experiments, or it may simply be a step in a procedure whose goal is to
compute anomalies from a mean state. In any case, when one desires to
generate a file whose properties are the mean of all the input files,
then `ncea' is the operator to use.
`ncea' only allows coordinate variables to be processed by the
linear average, minimum, and maximum operations. `ncea' will return
the linear average of coordinates unless extrema are explicitly
requested. Other requested operations (e.g., square-root, RMS) are
applied only to non-coordinate variables. In these cases the linear
average of the coordinate variable will be returned.
EXAMPLES
Consider a model experiment which generated five realizations of one
year of data, say 1985. You can imagine that the experimenter slightly
perturbs the initial conditions of the problem before generating each
new solution. Assume each file contains all twelve months (a seasonal
cycle) of data and we want to produce a single file containing the
ensemble average (mean) seasonal cycle. Here the numeric filename
suffix denotes the experiment number (_not_ the month):
ncea 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc
ncea 85_0[1-5].nc 85.nc
ncea -n 5,2,1 85_01.nc 85.nc
These three commands produce identical answers. *Note Specifying
Input Files::, for an explanation of the distinctions between these
methods. The output file, `85.nc', is the same size as the inputs
files. It contains 12 months of data (which might or might not be
stored in the record dimension, depending on the input files), but each
value in the output file is the average of the five values in the input
files.
In the previous example, the user could have obtained the ensemble
average values in a particular spatio-temporal region by adding a
hyperslab argument to the command, e.g.,
ncea -d time,0,2 -d lat,-23.5,23.5 85_??.nc 85.nc
In this case the output file would contain only three slices of data
in the TIME dimension. These three slices are the average of the first
three slices from the input files. Additionally, only data inside the
tropics is included.
4.5 `ncecat' netCDF Ensemble Concatenator
=========================================
SYNTAX
ncecat [-3] [-4] [-6] [-A] [-C] [-c] [-D DBG]
[-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F] [-h] [-L DFL_LVL] [-l PATH]
[-M] [-n LOOP] [-O] [-o OUTPUT-FILE] [-p PATH] [-R] [-r]
[-t THR_NBR] [-u ULM_NM] [-v VAR[,...]] [-X ...] [-x]
[INPUT-FILES] [OUTPUT-FILE]
DESCRIPTION
`ncecat' concatenates an arbitrary number of input files into a
single output file. The INPUT-FILES are stored consecutively as
records in OUTPUT-FILE. Each variable in each input file becomes one
record in the same variable in the output file. All INPUT-FILES must
contain all extracted variables (or else there would be "gaps" in the
output file).
A new record dimension is the glue which binds the input file data
together. The new record dimension name is, by default, "record". Its
name can be specified with the `-u ULM_NM' short option (or the
`--ulm_nm' or `rcd_nm' long options).
Each extracted variable must be constant in size and rank across all
INPUT-FILES. The only exception is that `ncecat' allows files to
differ in the record dimension size if the requested record hyperslab
(*note Hyperslabs::) resolves to the same size for all files. This
allows easier gluing/averaging of unequal length timeseries from
simulation ensembles (e.g., the IPCC AR4 archive).
Thus, the OUTPUT-FILE size is the sum of the sizes of the extracted
variables in the input files. *Note Averaging vs. Concatenating::, for
a description of the distinctions between the various averagers and
concatenators. As a multi-file operator, `ncecat' will read the list of
INPUT-FILES from `stdin' if they are not specified as positional
arguments on the command line (*note Large Numbers of Files::).
Turn off global metadata copying. By default all NCO operators copy
the global metadata of the first input file into OUTPUT-FILE. This
helps preserve the provenance of the output data. However, the use of
metadata is burgeoning and is not uncommon to encounter files with
excessive amounts of extraneous metadata. Extracting small bits of
data from such files leads to output files which are much larger than
necessary due to the automatically copied metadata. `ncecat' supports
turning off the default copying of global metadata via the `-M' switch
(or its long option equivalents, `--glb_mtd_spr' and
`--global_metadata_suppress').
Consider five realizations, `85a.nc', `85b.nc', ... `85e.nc' of 1985
predictions from the same climate model. Then `ncecat 85?.nc
85_ens.nc' glues the individual realizations together into the single
file, `85_ens.nc'. If an input variable was dimensioned [`lat',`lon'],
it will by default have dimensions [`record',`lat',`lon'] in the output
file. A restriction of `ncecat' is that the hyperslabs of the
processed variables must be the same from file to file. Normally this
means all the input files are the same size, and contain data on
different realizations of the same variables.
Concatenating a variable packed with different scales multiple
datasets is beyond the capabilities of `ncecat' (and `ncrcat', the
other concatenator (*note Concatenation::). `ncecat' does not unpack
data, it simply _copies_ the data from the INPUT-FILES, and the
metadata from the _first_ INPUT-FILE, to the OUTPUT-FILE. This means
that data compressed with a packing convention must use the identical
packing parameters (e.g., `scale_factor' and `add_offset') for a given
variable across _all_ input files. Otherwise the concatenated dataset
will not unpack correctly. The workaround for cases where the packing
parameters differ across INPUT-FILES requires three steps: First,
unpack the data using `ncpdq'. Second, concatenate the unpacked data
using `ncecat', Third, re-pack the result with `ncpdq'.
EXAMPLES
Consider a model experiment which generated five realizations of one
year of data, say 1985. You can imagine that the experimenter slightly
perturbs the initial conditions of the problem before generating each
new solution. Assume each file contains all twelve months (a seasonal
cycle) of data and we want to produce a single file containing all the
seasonal cycles. Here the numeric filename suffix denotes the
experiment number (_not_ the month):
ncecat 85_01.nc 85_02.nc 85_03.nc 85_04.nc 85_05.nc 85.nc
ncecat 85_0[1-5].nc 85.nc
ncecat -n 5,2,1 85_01.nc 85.nc
These three commands produce identical answers. *Note Specifying
Input Files::, for an explanation of the distinctions between these
methods. The output file, `85.nc', is five times the size as a single
INPUT-FILE. It contains 60 months of data.
One often prefers that the (new) record dimension have a more
descriptive, context-based name than simply "record". This is easily
accomplished with the `-u ULM_NM' switch:
ncecat -u realization 85_0[1-5].nc 85.nc
Users are more likely to understand the data processing history when
such descriptive coordinates are used.
Consider a file with an existing record dimension named `time'. and
suppose the user wishes to convert `time' from a record dimension to a
non-record dimension. This may be useful, for example, when the user
has another use for the record variable. The procedure is to use
`ncecat' followed by `ncwa':
ncecat in.nc out.nc # Convert time to non-record dimension
ncwa -a record in.nc out.nc # Remove new degenerate record dimension
The second step removes the degenerate record dimension. See *note
ncpdq netCDF Permute Dimensions Quickly:: for other methods of changing
variable dimensionality, including the record dimension.
4.6 `ncflint' netCDF File Interpolator
======================================
SYNTAX
ncflint [-3] [-4] [-6] [-A] [-C] [-c] [-D DBG]
[-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F] [-h] [-i VAR,VAL3]
[-L DFL_LVL] [-l PATH] [-O] [-o FILE_3] [-p PATH] [-R] [-r]
[-t THR_NBR] [-v VAR[,...]] [-w WGT1[,WGT2]] [-X ...] [-x]
FILE_1 FILE_2 [FILE_3]
DESCRIPTION
`ncflint' creates an output file that is a linear combination of the
input files. This linear combination is a weighted average, a
normalized weighted average, or an interpolation of the input files.
Coordinate variables are not acted upon in any case, they are simply
copied from FILE_1.
There are two conceptually distinct methods of using `ncflint'. The
first method is to specify the weight each input file contributes to
the output file. In this method, the value VAL3 of a variable in the
output file FILE_3 is determined from its values VAL1 and VAL2 in the
two input files according to
VAL3 = WGT1*VAL1 + WGT2*VAL2
. Here at least WGT1, and, optionally, WGT2, are specified on the
command line with the `-w' (or `--weight' or `--wgt_var') switch. If
only WGT1 is specified then WGT2 is automatically computed as WGT2 = 1
- WGT1. Note that weights larger than 1 are allowed. Thus it is
possible to specify WGT1 = 2 and WGT2 = -3. One can use this
functionality to multiply all the values in a given file by a constant.
The second method of using `ncflint' is to specify the interpolation
option with `-i' (or with the `--ntp' or `--interpolate' long options).
This is really the inverse of the first method in the following sense.
When the user specifies the weights directly, `ncflint' has no work to
do besides multiplying the input values by their respective weights and
adding the results together to produce the output values. It makes
sense to use this when the weights are known _a priori_.
Another class of problems has the "arrival value" (i.e., VAL3) of a
particular variable VAR known _a priori_. In this case, the implied
weights can always be inferred by examining the values of VAR in the
input files. This results in one equation in two unknowns, WGT1 and
WGT2:
VAL3 = WGT1*VAL1 + WGT2*VAL2
. Unique determination of the weights requires imposing the
additional constraint of normalization on the weights: WGT1 + WGT2 = 1.
Thus, to use the interpolation option, the user specifies VAR and VAL3
with the `-i' option. `ncflint' then computes WGT1 and WGT2, and uses
these weights on all variables to generate the output file. Although
VAR may have any number of dimensions in the input files, it must
represent a single, scalar value. Thus any dimensions associated with
VAR must be "degenerate", i.e., of size one.
If neither `-i' nor `-w' is specified on the command line, `ncflint'
defaults to weighting each input file equally in the output file. This
is equivalent to specifying `-w 0.5' or `-w 0.5,0.5'. Attempting to
specify both `-i' and `-w' methods in the same command is an error.
`ncflint' does not interpolate variables of type `NC_CHAR' and
`NC_BYTE'. This behavior is hardcoded.
Depending on your intuition, `ncflint' may treat missing values
unexpectedly. Consider a point where the value in one input file, say
VAL1, equals the missing value MSS_VAL_1 and, at the same point, the
corresponding value in the other input file VAL2 is not misssing (i.e.,
does not equal MSS_VAL_2). There are three plausible answers, and this
creates ambiguity.
Option one is to set VAL3 = MSS_VAL_1. The rationale is that
`ncflint' is, at heart, an interpolator and interpolation involving a
missing value is intrinsically undefined. `ncflint' currently
implements this behavior since it is the most conservative and least
likely to lead to misinterpretation.
Option two is to output the weighted valid data point, i.e.,
VAL3 = WGT2*VAL2
. The rationale for this behavior is that interpolation is really a
weighted average of known points, so `ncflint' should weight the valid
point.
Option three is to return the _unweighted_ valid point, i.e., VAL3 =
VAL2. This behavior would appeal to those who use `ncflint' to
estimate data using the closest available data. When a point is not
bracketed by valid data on both sides, it is better to return the known
datum than no datum at all.
The current implementation uses the first approach, Option one. If
you have strong opinions on this matter, let us know, since we are
willing to implement the other approaches as options if there is enough
interest.
EXAMPLES
Although it has other uses, the interpolation feature was designed
to interpolate FILE_3 to a time between existing files. Consider input
files `85.nc' and `87.nc' containing variables describing the state of
a physical system at times `time' = 85 and `time' = 87. Assume each
file contains its timestamp in the scalar variable `time'. Then, to
linearly interpolate to a file `86.nc' which describes the state of the
system at time at `time' = 86, we would use
ncflint -i time,86 85.nc 87.nc 86.nc
Say you have observational data covering January and April 1985 in
two files named `85_01.nc' and `85_04.nc', respectively. Then you can
estimate the values for February and March by interpolating the
existing data as follows. Combine `85_01.nc' and `85_04.nc' in a 2:1
ratio to make `85_02.nc':
ncflint -w 0.667 85_01.nc 85_04.nc 85_02.nc
ncflint -w 0.667,0.333 85_01.nc 85_04.nc 85_02.nc
Multiply `85.nc' by 3 and by -2 and add them together to make
`tst.nc':
ncflint -w 3,-2 85.nc 85.nc tst.nc
This is an example of a null operation, so `tst.nc' should be
identical (within machine precision) to `85.nc'.
Add `85.nc' to `86.nc' to obtain `85p86.nc', then subtract `86.nc'
from `85.nc' to obtain `85m86.nc'
ncflint -w 1,1 85.nc 86.nc 85p86.nc
ncflint -w 1,-1 85.nc 86.nc 85m86.nc
ncdiff 85.nc 86.nc 85m86.nc
Thus `ncflint' can be used to mimic some `ncbo' operations. However
this is not a good idea in practice because `ncflint' does not
broadcast (*note ncbo netCDF Binary Operator::) conforming variables
during arithmetic. Thus the final two commands would produce identical
results except that `ncflint' would fail if any variables needed to be
broadcast.
Rescale the dimensional units of the surface pressure `prs_sfc' from
Pascals to hectopascals (millibars)
ncflint -C -v prs_sfc -w 0.01,0.0 in.nc in.nc out.nc
ncatted -a units,prs_sfc,o,c,millibar out.nc
4.7 `ncks' netCDF Kitchen Sink
==============================
SYNTAX
ncks [-3] [-4] [-6] [-A] [-a] [-B] [-b BINARY-FILE] [-C] [-c]
[--cnk_dmn nm,sz] [--cnk_map map] [--cnk_plc plc] [--cnk_scl sz]
[-D DBG] [-d DIM,[MIN][,[MAX][,[STRIDE]]] [--fix_rec_dmn]
[-F] [-H] [-h] [--hdr_pad NBR] [-L DFL_LVL] [-l PATH] [-M] [-m] [--mk_rec_dmn DIM]
[-O] [-o OUTPUT-FILE] [-P] [-p PATH] [-Q] [-q] [-R] [-r]
[-s FORMAT] [-u] [-v VAR[,...]] [-X ...] [-x]
INPUT-FILE [[OUTPUT-FILE]]
DESCRIPTION
`ncks' combines selected features of `ncdump', `ncextr', and the
nccut and ncpaste specifications into one versatile utility. `ncks'
extracts a subset of the data from INPUT-FILE and prints it as ASCII
text to `stdout', writes it in flat binary format to `binary-file', and
writes (or pastes) it in netCDF format to OUTPUT-FILE.
`ncks' will print netCDF data in ASCII format to `stdout', like
`ncdump', but with these differences: `ncks' prints data in a tabular
format intended to be easy to search for the data you want, one datum
per screen line, with all dimension subscripts and coordinate values
(if any) preceding the datum. Option `-s' (or long options `--sng_fmt'
and `--string') lets the user format the data using C-style format
strings.
Options `-a', `-F' , `-H', `-M', `-m', `-P', `-Q', `-q', `-s', and
`-u' (and their long option counterparts) control the formatted
appearance of the data.
`ncks' extracts (and optionally creates a new netCDF file comprised
of) only selected variables from the input file (similar to the old
`ncextr' specification). Only variables and coordinates may be
specifically included or excluded--all global attributes and any
attribute associated with an extracted variable are copied to the
screen and/or output netCDF file. Options `-c', `-C', `-v', and `-x'
(and their long option synonyms) control which variables are extracted.
`ncks' extracts hyperslabs from the specified variables (`ncks'
implements the original `nccut' specification). Option `-d' controls
the hyperslab specification. Input dimensions that are not associated
with any output variable do not appear in the output netCDF. This
feature removes superfluous dimensions from netCDF files.
`ncks' will append variables and attributes from the INPUT-FILE to
OUTPUT-FILE if OUTPUT-FILE is a pre-existing netCDF file whose relevant
dimensions conform to dimension sizes of INPUT-FILE. The append
features of `ncks' are intended to provide a rudimentary means of
adding data from one netCDF file to another, conforming, netCDF file.
If naming conflicts exist between the two files, data in OUTPUT-FILE is
usually overwritten by the corresponding data from INPUT-FILE. Thus,
when appending, the user should backup OUTPUT-FILE in case valuable
data are inadvertantly overwritten.
If OUTPUT-FILE exists, the user will be queried whether to
"overwrite", "append", or "exit" the `ncks' call completely. Choosing
"overwrite" destroys the existing OUTPUT-FILE and create an entirely
new one from the output of the `ncks' call. Append has differing
effects depending on the uniqueness of the variables and attributes
output by `ncks': If a variable or attribute extracted from INPUT-FILE
does not have a name conflict with the members of OUTPUT-FILE then it
will be added to OUTPUT-FILE without overwriting any of the existing
contents of OUTPUT-FILE. In this case the relevant dimensions must
agree (conform) between the two files; new dimensions are created in
OUTPUT-FILE as required. When a name conflict occurs, a global
attribute from INPUT-FILE will overwrite the corresponding global
attribute from OUTPUT-FILE. If the name conflict occurs for a
non-record variable, then the dimensions and type of the variable (and
of its coordinate dimensions, if any) must agree (conform) in both
files. Then the variable values (and any coordinate dimension values)
from INPUT-FILE will overwrite the corresponding variable values (and
coordinate dimension values, if any) in OUTPUT-FILE (1).
Since there can only be one record dimension in a file, the record
dimension must have the same name (but not necessarily the same size) in
both files if a record dimension variable is to be appended. If the
record dimensions are of differing sizes, the record dimension of
OUTPUT-FILE will become the greater of the two record dimension sizes,
the record variable from INPUT-FILE will overwrite any counterpart in
OUTPUT-FILE and fill values will be written to any gaps left in the
rest of the record variables (I think). In all cases variable
attributes in OUTPUT-FILE are superseded by attributes of the same name
from INPUT-FILE, and left alone if there is no name conflict.
Some users may wish to avoid interactive `ncks' queries about
whether to overwrite existing data. For example, batch scripts will
fail if `ncks' does not receive responses to its queries. Options `-O'
and `-A' are available to force overwriting existing files and
variables, respectively.
Options specific to `ncks'
--------------------------
The following list provides a short summary of the features unique to
`ncks'. Features common to many operators are described in *note
Common features::.
`-a'
Do not alphabetize extracted fields. By default, the specified
output variables are extracted, printed, and written to disk in
alphabetical order. This tends to make long output lists easier
to search for particular variables. Specifying `-a' results in
the variables being extracted, printed, and written to disk in the
order in which they were saved in the input file. Thus `-a'
retains the original ordering of the variables. Also `--abc' and
`--alphabetize'.
`-B `file''
Activate native machine binary output writing to the default binary
file, `ncks.bnr'. The `-B' switch is redundant when the
`-b' `file' option is specified, and native binary output will be
directed to the binary file `file'. Also `--bnr' and `--binary'.
Writing packed variables in binary format is not supported.
`-b `file''
Activate native machine binary output writing to binary file
`file'. Also `--fl_bnr' and `--binary-file'. Writing packed
variables in binary format is not supported.
`-d DIM,[MIN][,[MAX][,[STRIDE]]]'
Add "stride" argument to hyperslabber. For a complete description
of the STRIDE argument, *Note Stride::.
`--fix_rec_dmn'
Change all record dimensions in the input file into fixed
dimensions in the output file.
`--mk_rec_dmn'
Change dimension DIM to a record dimension in the output file.
`-H'
Print data to screen. Also activated using `--print' or `--prn'.
By default `ncks' prints all metadata and data to screen if no
netCDF output file is specified. Use `-H' to print data to screen
if a netCDF output is specified, or to restrict printing to data
(no metadata) when no netCDF output is specified. Unless
otherwise specified (with `-s'), each element of the data
hyperslab prints on a separate line containing the names, indices,
and, values, if any, of all of the variables dimensions. The
dimension and variable indices refer to the location of the
corresponding data element with respect to the variable as stored
on disk (i.e., not the hyperslab).
% ncks -C -v three_dmn_var in.nc
lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0
lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1
lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2
...
lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21
lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
Printing the same variable with the `-F' option shows the same
variable indexed with Fortran conventions
% ncks -F -C -v three_dmn_var in.nc
lon(1)=0 lev(1)=100 lat(1)=-90 three_dmn_var(1)=0
lon(2)=90 lev(1)=100 lat(1)=-90 three_dmn_var(2)=1
lon(3)=180 lev(1)=100 lat(1)=-90 three_dmn_var(3)=2
...
Printing a hyperslab does not affect the variable or dimension
indices since these indices are relative to the full variable (as
stored in the input file), and the input file has not changed.
However, if the hyperslab is saved to an output file and those
values are printed, the indices will change:
% ncks -H -d lat,90.0 -d lev,1000.0 -v three_dmn_var in.nc out.nc
...
lat[1]=90 lev[2]=1000 lon[0]=0 three_dmn_var[20]=20
lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21
lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
% ncks -C -v three_dmn_var out.nc
lat[0]=90 lev[0]=1000 lon[0]=0 three_dmn_var[0]=20
lat[0]=90 lev[0]=1000 lon[1]=90 three_dmn_var[1]=21
lat[0]=90 lev[0]=1000 lon[2]=180 three_dmn_var[2]=22
lat[0]=90 lev[0]=1000 lon[3]=270 three_dmn_var[3]=23
`-M'
Print to screen the global metadata describing the file. This
includes file summary information and global attributes. Also
`--Mtd' and `--Metadata'. By default `ncks' prints global
metadata to screen if no netCDF output file and no variable
extraction list is specified (with `-v'). Use `-M' to print
global metadata to screen if a netCDF output is specified, or if a
variable extraction list is specified (with `-v').
The various combinations of printing switches can be confusing.
In an attempt to anticipate what most users want to do, `ncks'
uses context-sensitive defaults for printing. Our goal is to
minimize the use of switches required to accomplish the common
operations. We assume that users creating a new file or
overwriting (e.g., with `-O') an existing file usually wish to
copy all global and variable-specific attributes to the new file.
In contrast, we assume that users appending (e.g., with `-A' an
explicit variable list from one file to another usually wish to
copy only the variable-specific attributes to the output file.
The switches `-H', `-M', and `-m' switches are implemented as
toggles which reverse the default behavior. The most confusing
aspect of this is that `-M' inhibits copying global metadata in
overwrite mode and causes copying of global metadata in append
mode.
ncks -O in.nc out.nc # Copy VAs and GAs
ncks -O -v one in.nc out.nc # Copy VAs and GAs
ncks -O -M -v one in.nc out.nc # Copy VAs not GAs
ncks -O -m -v one in.nc out.nc # Copy GAs not VAs
ncks -O -M -m -v one in.nc out.nc # Copy only data (no atts)
ncks -A in.nc out.nc # Append VAs and GAs
ncks -A -v one in.nc out.nc # Append VAs not GAs
ncks -A -M -v one in.nc out.nc # Append VAs and GAs
ncks -A -m -v one in.nc out.nc # Append only data (no atts)
ncks -A -M -m -v one in.nc out.nc # Append GAs not VAs
where `VAs' and `GAs' denote variable and global attributes,
respectively.
`-m'
Print variable metadata to screen (similar to `ncdump -h'). This
displays all metadata pertaining to each variable, one variable at
a time. This includes information on the compression level, if any
*Note Deflation::. Also activated using `--mtd' and `--metadata'.
The `ncks' default behavior is to print variable metadata to
screen if no netCDF output file is specified. Use `-m' to print
variable metadata to screen if a netCDF output is specified.
`-P'
Print data, metadata, and units to screen. The `-P' switch is a
convenience abbreviation for `-C -H -M -m -u'. Also activated
using `--print' or `--prn'. This set of switches is useful for
exploring file contents.
`-Q'
Toggle printing of dimension indices and coordinate values when
printing arrays. Each variable's name appears flush left in the
output. This helps locate specific variables in lists with many
variables and different dimensions.
`-q'
Turn off all printing to screen. This overrides the setting of
all print-related switches, equivalent to `-H -M -m' when in
single-file printing mode. When invoked with `-R' (*note
Retaining Retrieved Files::), `ncks' automatically sets `-q'.
This allows `ncks' to retrieve remote files without automatically
trying to print them. Also `--quiet'.
`-s FORMAT'
String format for text output. Accepts C language escape
sequences and `printf()' formats. Also `--string' and
`--sng_fmt'.
`-u'
Toggle the printing of a variable's `units' attribute, if any,
with its values. Also `--units'.
EXAMPLES
View all data in netCDF `in.nc', printed with Fortran indexing
conventions:
ncks -F in.nc
Copy the netCDF file `in.nc' to file `out.nc'.
ncks in.nc out.nc
Now the file `out.nc' contains all the data from `in.nc'. There
are, however, two differences between `in.nc' and `out.nc'. First, the
`history' global attribute (*note History Attribute::) will contain the
command used to create `out.nc'. Second, the variables in `out.nc'
will be defined in alphabetical order. Of course the internal storage
of variable in a netCDF file should be transparent to the user, but
there are cases when alphabetizing a file is useful (see description of
`-a' switch).
Copy all global attributes (and no variables) from `in.nc' to
`out.nc':
ncks -A -x ~/nco/data/in.nc ~/out.nc
The `-x' switch tells NCO to use the complement of the extraction
list (*note Subsetting Variables::). Since no extraction list is
explicitly specified (with `-v'), the default is to extract all
variables. The complement of all variables is no variables. Without
any variables to extract, the append (`-A') command (*note Appending
Variables::) has only to extract and copy (i.e., append) global
attributes to the output file.
Print variable `three_dmn_var' from file `in.nc' with default
notations. Next print `three_dmn_var' as an un-annotated text column.
Then print `three_dmn_var' signed with very high precision. Finally,
print `three_dmn_var' as a comma-separated list.
% ncks -C -v three_dmn_var in.nc
lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0
lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1
...
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
% ncks -s '%f\n' -C -v three_dmn_var in.nc
0.000000
1.000000
...
23.000000
% ncks -s '%+16.10f\n' -C -v three_dmn_var in.nc
+0.0000000000
+1.0000000000
...
+23.0000000000
% ncks -s '%f, ' -C -v three_dmn_var in.nc
0.000000, 1.000000, ..., 23.000000,
The second and third options are useful when pasting data into text
files like reports or papers. *Note ncatted netCDF Attribute Editor::,
for more details on string formatting and special characters.
One dimensional arrays of characters stored as netCDF variables are
automatically printed as strings, whether or not they are
NUL-terminated, e.g.,
ncks -v fl_nm in.nc
The `%c' formatting code is useful for printing multidimensional
arrays of characters representing fixed length strings
ncks -s '%c' -v fl_nm_arr in.nc
Using the `%s' format code on strings which are not NUL-terminated
(and thus not technically strings) is likely to result in a core dump.
Create netCDF `out.nc' containing all variables, and any associated
coordinates, except variable `time', from netCDF `in.nc':
ncks -x -v time in.nc out.nc
Extract variables `time' and `pressure' from netCDF `in.nc'. If
`out.nc' does not exist it will be created. Otherwise the you will be
prompted whether to append to or to overwrite `out.nc':
ncks -v time,pressure in.nc out.nc
ncks -C -v time,pressure in.nc out.nc
The first version of the command creates an `out.nc' which contains
`time', `pressure', and any coordinate variables associated with
PRESSURE. The `out.nc' from the second version is guaranteed to
contain only two variables `time' and `pressure'.
Create netCDF `out.nc' containing all variables from file `in.nc'.
Restrict the dimensions of these variables to a hyperslab. Print (with
`-H') the hyperslabs to the screen for good measure. The specified
hyperslab is: the fifth value in dimension `time'; the half-open range
LAT > 0. in coordinate `lat'; the half-open range LON < 330. in
coordinate `lon'; the closed interval 0.3 < BAND < 0.5 in coordinate
`band'; and cross-section closest to 1000. in coordinate `lev'. Note
that limits applied to coordinate values are specified with a decimal
point, and limits applied to dimension indices do not have a decimal
point *Note Hyperslabs::.
ncks -H -d time,5 -d lat,,0.0 -d lon,330.0, -d band,0.3,0.5
-d lev,1000.0 in.nc out.nc
Assume the domain of the monotonically increasing longitude
coordinate `lon' is 0 < LON < 360. Here, `lon' is an example of a
wrapped coordinate. `ncks' will extract a hyperslab which crosses the
Greenwich meridian simply by specifying the westernmost longitude as
MIN and the easternmost longitude as MAX, as follows:
ncks -d lon,260.0,45.0 in.nc out.nc
For more details *Note Wrapped Coordinates::.
---------- Footnotes ----------
(1) Those familiar with netCDF mechanics might wish to know what is
happening here: `ncks' does not attempt to redefine the variable in
OUTPUT-FILE to match its definition in INPUT-FILE, `ncks' merely copies
the values of the variable and its coordinate dimensions, if any, from
INPUT-FILE to OUTPUT-FILE.
4.8 `ncpdq' netCDF Permute Dimensions Quickly
=============================================
SYNTAX
ncpdq [-3] [-4] [-6] [-A] [-a [-]DIM[,...]] [-C] [-c] [-D DBG]
[-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F] [-h] [-L DFL_LVL] [-l PATH]
[-M PCK_MAP] [-O] [-o OUTPUT-FILE] [-P PCK_PLC] [-p PATH]
[-R] [-r] [-t THR_NBR] [-U] [-v VAR[,...]] [-X ...] [-x]
INPUT-FILE [OUTPUT-FILE]
DESCRIPTION
`ncpdq' performs one of two distinct functions, packing or dimension
permutation, but not both, when invoked. `ncpdq' is optimized to
perform these actions in a parallel fashion with a minimum of time and
memory. The "pdq" may stand for "Permute Dimensions Quickly", "Pack
Data Quietly", "Pillory Dan Quayle", or other silly uses.
Packing and Unpacking Functions
-------------------------------
The `ncpdq' packing (and unpacking) algorithms are described in *note
Methods and functions::, and are also implemented in `ncap2'. `ncpdq'
extends the functionality of these algorithms by providing high level
control of the "packing policy" so that users can pack (and unpack)
entire files consistently with one command. The user specifies the
desired packing policy with the `-P' switch (or its long option
equivalents, `--pck_plc' and `--pack_policy') and its PCK_PLC argument.
Four packing policies are currently implemented:
"Packing (and Re-Packing) Variables [_default_]"
Definition: Pack unpacked variables, re-pack packed variables
Alternate invocation: `ncpack'
PCK_PLC key values: `all_new', `pck_all_new_att'
"Packing (and not Re-Packing) Variables"
Definition: Pack unpacked variables, copy packed variables
Alternate invocation: none
PCK_PLC key values: `all_xst', `pck_all_xst_att'
"Re-Packing Variables"
Definition: Re-pack packed variables, copy unpacked variables
Alternate invocation: none
PCK_PLC key values: `xst_new', `pck_xst_new_att'
"Unpacking"
Definition: Unpack packed variables, copy unpacked variables
Alternate invocation: `ncunpack'
PCK_PLC key values: `upk', `unpack', `pck_upk'
Equivalent key values are fully interchangeable. Multiple equivalent
options are provided to satisfy disparate needs and tastes of NCO users
working with scripts and from the command line.
To reduce required memorization of these complex policy switches,
`ncpdq' may also be invoked via a synonym or with switches that imply a
particular policy. `ncpack' is a synonym for `ncpdq' and behaves the
same in all respects. Both `ncpdq' and `ncpack' assume a default
packing policy request of `all_new'. Hence `ncpack' may be invoked
without any `-P' switch, unlike `ncpdq'. Similarly, `ncunpack' is a
synonym for `ncpdq' except that `ncpack' implicitly assumes a request
to unpack, i.e., `-P pck_upk'. Finally, the `ncpdq' `-U' switch (or
its long option equivalents, `--upk' and `--unpack') requires no
argument. It simply requests unpacking.
Given the menagerie of synonyms, equivalent options, and implied
options, a short list of some equivalent commands is appropriate. The
following commands are equivalent for packing: `ncpdq -P all_new',
`ncpdq --pck_plc=all_new', and `ncpack'. The following commands are
equivalent for unpacking: `ncpdq -P upk', `ncpdq -U', `ncpdq
--pck_plc=unpack', and `ncunpack'. Equivalent commands for other
packing policies, e.g., `all_xst', follow by analogy. Note that
`ncpdq' synonyms are subject to the same constraints and
recommendations discussed in the secion on `ncbo' synonyms (*note ncbo
netCDF Binary Operator::). That is, symbolic links must exist from the
synonym to `ncpdq', or else the user must define an `alias'.
The `ncpdq' packing algorithms must know to which type particular
types of input variables are to be packed. The correspondence between
the input variable type and the output, packed type, is called the
"packing map". The user specifies the desired packing map with the
`-M' switch (or its long option equivalents, `--pck_map' and `--map')
and its PCK_MAP argument. Five packing maps are currently implemented:
"Pack Floating Precisions to `NC_SHORT' [_default_]"
Definition: Pack floating precision types to `NC_SHORT'
Map: Pack [`NC_DOUBLE',`NC_FLOAT'] to `NC_SHORT'
Types copied instead of packed:
[`NC_INT',`NC_SHORT',`NC_CHAR',`NC_BYTE']
PCK_MAP key values: `flt_sht', `pck_map_flt_sht'
"Pack Floating Precisions to `NC_BYTE'"
Definition: Pack floating precision types to `NC_BYTE'
Map: Pack [`NC_DOUBLE',`NC_FLOAT'] to `NC_BYTE'
Types copied instead of packed:
[`NC_INT',`NC_SHORT',`NC_CHAR',`NC_BYTE']
PCK_MAP key values: `flt_byt', `pck_map_flt_byt'
"Pack Higher Precisions to `NC_SHORT'"
Definition: Pack higher precision types to `NC_SHORT'
Map: Pack [`NC_DOUBLE',`NC_FLOAT',`NC_INT'] to `NC_SHORT'
Types copied instead of packed: [`NC_SHORT',`NC_CHAR',`NC_BYTE']
PCK_MAP key values: `hgh_sht', `pck_map_hgh_sht'
"Pack Higher Precisions to `NC_BYTE'"
Definition: Pack higher precision types to `NC_BYTE'
Map: Pack [`NC_DOUBLE',`NC_FLOAT',`NC_INT',`NC_SHORT'] to `NC_BYTE'
Types copied instead of packed: [`NC_CHAR',`NC_BYTE']
PCK_MAP key values: `hgh_byt', `pck_map_hgh_byt'
"Pack to Next Lesser Precision"
Definition: Pack each type to type of next lesser size
Map: Pack `NC_DOUBLE' to `NC_INT'. Pack [`NC_FLOAT',`NC_INT'] to
`NC_SHORT'. Pack `NC_SHORT' to `NC_BYTE'.
Types copied instead of packed: [`NC_CHAR',`NC_BYTE']
PCK_MAP key values: `nxt_lsr', `pck_map_nxt_lsr'
The default `all_new' packing policy with the default `flt_sht' packing
map reduces the typical `NC_FLOAT'-dominated file size by about 50%.
`flt_byt' packing reduces an `NC_DOUBLE'-dominated file by about 87%.
The netCDF packing algorithm (*note Methods and functions::) is
lossy--once packed, the exact original data cannot be recovered without
a full backup. Hence users should be aware of some packing caveats:
First, the interaction of packing and data equal to the _FILLVALUE is
complex. Test the `_FillValue' behavior by performing a pack/unpack
cycle to ensure data that are missing _stay_ missing and data that are
not misssing do not join the Air National Guard and go missing. This
may lead you to elect a new _FILLVALUE. Second, `ncpdq' actually
allows packing into `NC_CHAR' (with, e.g., `flt_chr'). However, the
intrinsic conversion of `signed char' to higher precision types is
tricky so for values equal to zero, i.e., `NUL'. Hence packing to
`NC_CHAR' is not documented or advertised. Pack into `NC_BYTE' (with,
e.g., `flt_byt') instead.
Dimension Permutation
---------------------
`ncpdq' re-shapes variables in INPUT-FILE by re-ordering and/or
reversing dimensions specified in the dimension list. The dimension
list is a whitespace-free, comma separated list of dimension names,
optionally prefixed by negative signs, that follows the `-a' (or long
options `--arrange', `--permute', `--re-order', or `--rdr') switch. To
re-order variables by a subset of their dimensions, specify these
dimensions in a comma-separated list following `-a', e.g., `-a lon,lat'.
To reverse a dimension, prefix its name with a negative sign in the
dimension list, e.g., `-a -lat'. Re-ordering and reversal may be
performed simultaneously, e.g., `-a lon,-lat,time,-lev'.
Users may specify any permutation of dimensions, including
permutations which change the record dimension identity. The record
dimension is re-ordered like any other dimension. This unique `ncpdq'
capability makes it possible to concatenate files along any dimension.
See *note Concatenation:: for a detailed example. The record dimension
is always the most slowly varying dimension in a record variable (*note
C and Fortran Index Conventions::). The specified re-ordering fails if
it requires creating more than one record dimension amongst all the
output variables (1).
Two special cases of dimension re-ordering and reversal deserve
special mention. First, it may be desirable to completely reverse the
storage order of a variable. To do this, include all the variable's
dimensions in the dimension re-order list in their original order, and
prefix each dimension name with the negative sign. Second, it may
useful to transpose a variable's storage order, e.g., from C to Fortran
data storage order (*note C and Fortran Index Conventions::). To do
this, include all the variable's dimensions in the dimension re-order
list in reversed order. Explicit examples of these two techniques
appear below.
EXAMPLES
Pack and unpack all variables in file `in.nc' and store the results
in `out.nc':
ncpdq in.nc out.nc # Same as ncpack in.nc out.nc
ncpdq -P all_new -M flt_sht in.nc out.nc # Defaults
ncpdq -P all_xst in.nc out.nc
ncpdq -P upk in.nc out.nc # Same as ncunpack in.nc out.nc
ncpdq -U in.nc out.nc # Same as ncunpack in.nc out.nc
The first two commands pack any unpacked variable in the input file.
They also unpack and then re-pack every packed variable. The third
command only packs unpacked variables in the input file. If a variable
is already packed, the third command copies it unchanged to the output
file. The fourth and fifth commands unpack any packed variables. If a
variable is not packed, the third command copies it unchanged.
The previous examples all utilized the default packing map. Suppose
you wish to archive all data that are currently unpacked into a form
which only preserves 256 distinct values. Then you could specify the
packing map PCK_MAP as `hgh_byt' and the packing policy PCK_PLC as
`all_xst':
ncpdq -P all_xst -M hgh_byt in.nc out.nc
Many different packing maps may be used to construct a given file by
performing the packing on subsets of variables (e.g., with `-v') and
using the append feature with `-A' (*note Appending Variables::).
Re-order file `in.nc' so that the dimension `lon' always precedes
the dimension `lat' and store the results in `out.nc':
ncpdq -a lon,lat in.nc out.nc
ncpdq -v three_dmn_var -a lon,lat in.nc out.nc
The first command re-orders every variable in the input file. The
second command extracts and re-orders only the variable `three_dmn_var'.
Suppose the dimension `lat' represents latitude and monotonically
increases increases from south to north. Reversing the `lat' dimension
means re-ordering the data so that latitude values decrease
monotonically from north to south. Accomplish this with
% ncpdq -a -lat in.nc out.nc
% ncks -C -v lat in.nc
lat[0]=-90
lat[1]=90
% ncks -C -v lat out.nc
lat[0]=90
lat[1]=-90
This operation reversed the latitude dimension of all variables.
Whitespace immediately preceding the negative sign that specifies
dimension reversal may be dangerous. Quotes and long options can help
protect negative signs that should indicate dimension reversal from
being interpreted by the shell as dashes that indicate new command line
switches.
ncpdq -a -lat in.nc out.nc # Dangerous? Whitespace before "-lat"
ncpdq -a '-lat' in.nc out.nc # OK. Quotes protect "-" in "-lat"
ncpdq -a lon,-lat in.nc out.nc # OK. No whitespace before "-"
ncpdq --rdr=-lat in.nc out.nc # Preferred. Uses "=" not whitespace
To create the mathematical transpose of a variable, place all its
dimensions in the dimension re-order list in reversed order. This
example creates the transpose of `three_dmn_var':
% ncpdq -a lon,lev,lat -v three_dmn_var in.nc out.nc
% ncks -C -v three_dmn_var in.nc
lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0
lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1
lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2
...
lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21
lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
% ncks -C -v three_dmn_var out.nc
lon[0]=0 lev[0]=100 lat[0]=-90 three_dmn_var[0]=0
lon[0]=0 lev[0]=100 lat[1]=90 three_dmn_var[1]=12
lon[0]=0 lev[1]=500 lat[0]=-90 three_dmn_var[2]=4
...
lon[3]=270 lev[1]=500 lat[1]=90 three_dmn_var[21]=19
lon[3]=270 lev[2]=1000 lat[0]=-90 three_dmn_var[22]=11
lon[3]=270 lev[2]=1000 lat[1]=90 three_dmn_var[23]=23
To completely reverse the storage order of a variable, include all
its dimensions in the re-order list, each prefixed by a negative sign.
This example reverses the storage order of `three_dmn_var':
% ncpdq -a -lat,-lev,-lon -v three_dmn_var in.nc out.nc
% ncks -C -v three_dmn_var in.nc
lat[0]=-90 lev[0]=100 lon[0]=0 three_dmn_var[0]=0
lat[0]=-90 lev[0]=100 lon[1]=90 three_dmn_var[1]=1
lat[0]=-90 lev[0]=100 lon[2]=180 three_dmn_var[2]=2
...
lat[1]=90 lev[2]=1000 lon[1]=90 three_dmn_var[21]=21
lat[1]=90 lev[2]=1000 lon[2]=180 three_dmn_var[22]=22
lat[1]=90 lev[2]=1000 lon[3]=270 three_dmn_var[23]=23
% ncks -C -v three_dmn_var out.nc
lat[0]=90 lev[0]=1000 lon[0]=270 three_dmn_var[0]=23
lat[0]=90 lev[0]=1000 lon[1]=180 three_dmn_var[1]=22
lat[0]=90 lev[0]=1000 lon[2]=90 three_dmn_var[2]=21
...
lat[1]=-90 lev[2]=100 lon[1]=180 three_dmn_var[21]=2
lat[1]=-90 lev[2]=100 lon[2]=90 three_dmn_var[22]=1
lat[1]=-90 lev[2]=100 lon[3]=0 three_dmn_var[23]=0
Consider a file with all dimensions, including `time', fixed
(non-record). Suppose the user wishes to convert `time' from a fixed
dimension to a record dimension. This may be useful, for example, when
the user wishes to append additional time slices to the data. The
procedure is to use `ncecat' followed by `ncpdq' and then `ncwa':
ncecat -O in.nc out.nc # Add degenerate record dimension named "record"
ncpdq -O -a time,record out.nc out.nc # Switch "record" and "time"
ncwa -O -a record out.nc out.nc # Remove (degenerate) "record"
The first step creates a degenerate (size equals one) record
dimension named (by default) `record'. The second step swaps the
ordering of the dimensions named `time' and `record'. Since `time' now
occupies the position of the first (least rapidly varying) dimension,
it becomes the record dimension. The dimension named `record' is no
longer a record dimension. The third step averages over this
degenerate `record' dimension. Averaging over a degenerate dimension
does not alter the data. The ordering of other dimensions in the file
(`lat', `lon', etc.) is immaterial to this procedure. See *note ncecat
netCDF Ensemble Concatenator:: for other methods of changing variable
dimensionality, including the record dimension.
---------- Footnotes ----------
(1) This limitation, imposed by the netCDF storage layer, may be
relaxed in the future with netCDF4.
4.9 `ncra' netCDF Record Averager
=================================
SYNTAX
ncra [-3] [-4] [-6] [-A] [-C] [-c] [-D DBG]
[-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F] [-h] [-L DFL_LVL] [-l PATH]
[-n LOOP] [-O] [-o OUTPUT-FILE] [-p PATH] [-R] [-r]
[-t THR_NBR] [-v VAR[,...]] [-X ...] [-x] [-y OP_TYP]
[INPUT-FILES] [OUTPUT-FILE]
DESCRIPTION
`ncra' averages record variables across an arbitrary number of
INPUT-FILES. The record dimension is, by default, retained as a
degenerate (size 1) dimension in the output variables. *Note Averaging
vs. Concatenating::, for a description of the distinctions between the
various averagers and concatenators. As a multi-file operator, `ncra'
will read the list of INPUT-FILES from `stdin' if they are not specified
as positional arguments on the command line (*note Large Numbers of
Files::).
Input files may vary in size, but each must have a record dimension.
The record coordinate, if any, should be monotonic (or else non-fatal
warnings may be generated). Hyperslabs of the record dimension which
include more than one file work correctly. `ncra' supports the STRIDE
argument to the `-d' hyperslab option (*note Hyperslabs::) for the
record dimension only, STRIDE is not supported for non-record
dimensions.
`ncra' weights each record (e.g., time slice) in the INPUT-FILES
equally. `ncra' does not attempt to see if, say, the `time' coordinate
is irregularly spaced and thus would require a weighted average in
order to be a true time average. `ncra' _always averages_ coordinate
variables regardless of the arithmetic operation type performed on the
non-coordinate variables. (*note Operation Types::).
EXAMPLES
Average files `85.nc', `86.nc', ... `89.nc' along the record
dimension, and store the results in `8589.nc':
ncra 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
ncra 8[56789].nc 8589.nc
ncra -n 5,2,1 85.nc 8589.nc
These three methods produce identical answers. *Note Specifying
Input Files::, for an explanation of the distinctions between these
methods.
Assume the files `85.nc', `86.nc', ... `89.nc' each contain a record
coordinate TIME of length 12 defined such that the third record in
`86.nc' contains data from March 1986, etc. NCO knows how to hyperslab
the record dimension across files. Thus, to average data from
December, 1985 through February, 1986:
ncra -d time,11,13 85.nc 86.nc 87.nc 8512_8602.nc
ncra -F -d time,12,14 85.nc 86.nc 87.nc 8512_8602.nc
The file `87.nc' is superfluous, but does not cause an error. The
`-F' turns on the Fortran (1-based) indexing convention. The following
uses the STRIDE option to average all the March temperature data from
multiple input files into a single output file
ncra -F -d time,3,,12 -v temperature 85.nc 86.nc 87.nc 858687_03.nc
*Note Stride::, for a description of the STRIDE argument.
Assume the TIME coordinate is incrementally numbered such that
January, 1985 = 1 and December, 1989 = 60. Assuming `??' only expands
to the five desired files, the following averages June, 1985-June, 1989:
ncra -d time,6.,54. ??.nc 8506_8906.nc
4.10 `ncrcat' netCDF Record Concatenator
========================================
SYNTAX
ncrcat [-3] [-4] [-6] [-A] [-C] [-c] [-D DBG]
[-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F] [-h] [-L DFL_LVL] [-l PATH]
[-n LOOP] [-O] [-o OUTPUT-FILE] [-p PATH] [-R] [-r]
[-t THR_NBR] [-v VAR[,...]] [-X ...] [-x]
[INPUT-FILES] [OUTPUT-FILE]
DESCRIPTION
`ncrcat' concatenates record variables across an arbitrary number of
INPUT-FILES. The final record dimension is by default the sum of the
lengths of the record dimensions in the input files. *Note Averaging
vs. Concatenating::, for a description of the distinctions between the
various averagers and concatenators. As a multi-file operator,
`ncrcat' will read the list of INPUT-FILES from `stdin' if they are not
specified as positional arguments on the command line (*note Large
Numbers of Files::).
Input files may vary in size, but each must have a record dimension.
The record coordinate, if any, should be monotonic (or else non-fatal
warnings may be generated). Hyperslabs along the record dimension that
span more than one file are handled correctly. `ncra' supports the
STRIDE argument to the `-d' hyperslab option for the record dimension
only, STRIDE is not supported for non-record dimensions.
Concatenating a variable packed with different scales multiple
datasets is beyond the capabilities of `ncrcat' (and `ncecat', the
other concatenator (*note Concatenation::). `ncrcat' does not unpack
data, it simply _copies_ the data from the INPUT-FILES, and the
metadata from the _first_ INPUT-FILE, to the OUTPUT-FILE. This means
that data compressed with a packing convention must use the identical
packing parameters (e.g., `scale_factor' and `add_offset') for a given
variable across _all_ input files. Otherwise the concatenated dataset
will not unpack correctly. The workaround for cases where the packing
parameters differ across INPUT-FILES requires three steps: First,
unpack the data using `ncpdq'. Second, concatenate the unpacked data
using `ncrcat', Third, re-pack the result with `ncpdq'.
`ncrcat' applies special rules to ARM convention time fields (e.g.,
`time_offset'). See *note ARM Conventions:: for a complete description.
EXAMPLES
Concatenate files `85.nc', `86.nc', ... `89.nc' along the record
dimension, and store the results in `8589.nc':
ncrcat 85.nc 86.nc 87.nc 88.nc 89.nc 8589.nc
ncrcat 8[56789].nc 8589.nc
ncrcat -n 5,2,1 85.nc 8589.nc
These three methods produce identical answers. *Note Specifying
Input Files::, for an explanation of the distinctions between these
methods.
Assume the files `85.nc', `86.nc', ... `89.nc' each contain a record
coordinate TIME of length 12 defined such that the third record in
`86.nc' contains data from March 1986, etc. NCO knows how to hyperslab
the record dimension across files. Thus, to concatenate data from
December, 1985-February, 1986:
ncrcat -d time,11,13 85.nc 86.nc 87.nc 8512_8602.nc
ncrcat -F -d time,12,14 85.nc 86.nc 87.nc 8512_8602.nc
The file `87.nc' is superfluous, but does not cause an error. When
`ncra' and `ncrcat' encounter a file which does contain any records
that meet the specified hyperslab criteria, they disregard the file and
proceed to the next file without failing. The `-F' turns on the
Fortran (1-based) indexing convention.
The following uses the STRIDE option to concatenate all the March
temperature data from multiple input files into a single output file
ncrcat -F -d time,3,,12 -v temperature 85.nc 86.nc 87.nc 858687_03.nc
*Note Stride::, for a description of the STRIDE argument.
Assume the TIME coordinate is incrementally numbered such that
January, 1985 = 1 and December, 1989 = 60. Assuming `??' only expands
to the five desired files, the following concatenates June, 1985-June,
1989:
ncrcat -d time,6.,54. ??.nc 8506_8906.nc
4.11 `ncrename' netCDF Renamer
==============================
SYNTAX
ncrename [-a OLD_NAME,NEW_NAME] [-a ...] [-D DBG]
[-d OLD_NAME,NEW_NAME] [-d ...] [-h] [--hdr_pad NBR] [-l PATH]
[-O] [-o OUTPUT-FILE] [-p PATH] [-R] [-r]
[-v OLD_NAME,NEW_NAME] [-v ...]
INPUT-FILE [[OUTPUT-FILE]]
DESCRIPTION
`ncrename' renames dimensions, variables, and attributes in a netCDF
file. Each object that has a name in the list of old names is renamed
using the corresponding name in the list of new names. All the new
names must be unique. Every old name must exist in the input file,
unless the old name is preceded by the character `.'. The validity of
OLD_NAME is not checked prior to the renaming. Thus, if OLD_NAME is
specified without the the `.' prefix and is not present in INPUT-FILE,
`ncrename' will abort. The NEW_NAME should never be prefixed by a `.'
(the period will be included as part of the new name). The OPTIONS and
EXAMPLES show how to select specific variables whose attributes are to
be renamed.
`ncrename' is the exception to the normal rules that the user will
be interactively prompted before an existing file is changed, and that a
temporary copy of an output file is constructed during the operation.
If only INPUT-FILE is specified, then `ncrename' will change the names
of the INPUT-FILE in place without prompting and without creating a
temporary copy of `input-file'. This is because the renaming operation
is considered reversible if the user makes a mistake. The NEW_NAME can
easily be changed back to OLD_NAME by using `ncrename' one more time.
Note that renaming a dimension to the name of a dependent variable
can be used to invert the relationship between an independent coordinate
variable and a dependent variable. In this case, the named dependent
variable must be one-dimensional and should have no missing values.
Such a variable will become a coordinate variable.
According to the `netCDF User's Guide', renaming properties in
netCDF files does not incur the penalty of recopying the entire file
when the NEW_NAME is shorter than the OLD_NAME.
OPTIONS
`-a OLD_NAME,NEW_NAME'
Attribute renaming. The old and new names of the attribute are
specified with `-a' (or `--attribute') by the associated OLD_NAME
and NEW_NAME values. Global attributes are treated no differently
than variable attributes. This option may be specified more than
once. As mentioned above, all occurrences of the attribute of a
given name will be renamed unless the `.' form is used, with one
exception. To change the attribute name for a particular
variable, specify the OLD_NAME in the format
OLD_VAR_NAME@OLD_ATT_NAME. The `@' symbol delimits the variable
and attribute names. If the attribute is uniquely named (no other
variables contain the attribute) then the
OLD_VAR_NAME@OLD_ATT_NAME syntax is redundant. The
VAR_NAME@ATT_NAME syntax is accepted, but not required, for the
NEW_NAME.
`-d OLD_NAME,NEW_NAME'
Dimension renaming. The old and new names of the dimension are
specified with `-d' (or `--dmn', `--dimension') by the associated
OLD_NAME and NEW_NAME values. This option may be specified more
than once.
`-v OLD_NAME,NEW_NAME'
Variable renaming. The old and new names of the variable are
specified with `-v' (or `--variable') by the associated OLD_NAME
and NEW_NAME values. This option may be specified more than once.
EXAMPLES
Rename the variable `p' to `pressure' and `t' to `temperature' in
netCDF `in.nc'. In this case `p' must exist in the input file (or
`ncrename' will abort), but the presence of `t' is optional:
ncrename -v p,pressure -v .t,temperature in.nc
Rename the attribute `long_name' to `largo_nombre' in the variable
`u', and no other variables in netCDF `in.nc'.
ncrename -a u:long_name,largo_nombre in.nc
`ncrename' does not automatically attach dimensions to variables of
the same name. If you want to rename a coordinate variable so that it
remains a coordinate variable, you must separately rename both the
dimension and the variable:
ncrename -d lon,longitude -v lon,longitude in.nc
Create netCDF `out.nc' identical to `in.nc' except the attribute
`_FillValue' is changed to `missing_value', the attribute `units' is
changed to `CGS_units' (but only in those variables which possess it),
the attribute `hieght' is changed to `height' in the variable `tpt',
and in the variable `prs_sfc', if it exists.
ncrename -a _FillValue,missing_value -a .units,CGS_units \
-a tpt@hieght,height -a prs_sfc@.hieght,height in.nc out.nc
The presence and absence of the `.' and `@' features cause this
command to execute successfully only if a number of conditions are met.
All variables _must_ have a `_FillValue' attribute _and_ `_FillValue'
must also be a global attribute. The `units' attribute, on the other
hand, will be renamed to `CGS_units' wherever it is found but need not
be present in the file at all (either as a global or a variable
attribute). The variable `tpt' must contain the `hieght' attribute.
The variable `prs_sfc' need not exist, and need not contain the
`hieght' attribute.
4.12 `ncwa' netCDF Weighted Averager
====================================
SYNTAX
ncwa [-3] [-4] [-6] [-A] [-a DIM[,...]] [-B MASK_COND] [-b] [-C] [-c] [-D DBG]
[-d DIM,[MIN][,[MAX][,[STRIDE]]] [-F] [-h] [-I] [-L DFL_LVL] [-l PATH]
[-M MASK_VAL] [-m MASK_VAR] [-N] [-O]
[-o OUTPUT-FILE] [-p PATH] [-R] [-r] [-T MASK_COMP]
[-t THR_NBR] [-v VAR[,...]] [-w WEIGHT] [-X ...] [-x] [-y OP_TYP]
INPUT-FILE [OUTPUT-FILE]
DESCRIPTION
`ncwa' averages variables in a single file over arbitrary
dimensions, with options to specify weights, masks, and normalization.
*Note Averaging vs. Concatenating::, for a description of the
distinctions between the various averagers and concatenators. The
default behavior of `ncwa' is to arithmetically average every numerical
variable over all dimensions and to produce a scalar result for each.
Averaged dimensions are, by default, eliminated as dimensions.
Their corresponding coordinates, if any, are output as scalars. The
`-b' switch (and its long option equivalents `--rdd' and
`--retain-degenerate-dimensions') causes `ncwa' to retain averaged
dimensions as degenerate (size 1) dimensions. This maintains the
association between a dimension (or coordinate) and variables after
averaging and simplifies, for instance, later concatenation along the
degenerate dimension.
To average variables over only a subset of their dimensions, specify
these dimensions in a comma-separated list following `-a', e.g., `-a
time,lat,lon'. As with all arithmetic operators, the operation may be
restricted to an arbitrary hypserslab by employing the `-d' option
(*note Hyperslabs::). `ncwa' also handles values matching the
variable's `_FillValue' attribute correctly. Moreover, `ncwa'
understands how to manipulate user-specified weights, masks, and
normalization options. With these options, `ncwa' can compute
sophisticated averages (and integrals) from the command line.
MASK_VAR and WEIGHT, if specified, are broadcast to conform to the
variables being averaged. The rank of variables is reduced by the
number of dimensions which they are averaged over. Thus arrays which
are one dimensional in the INPUT-FILE and are averaged by `ncwa' appear
in the OUTPUT-FILE as scalars. This allows the user to infer which
dimensions may have been averaged. Note that that it is impossible for
`ncwa' to make make a WEIGHT or MASK_VAR of rank W conform to a VAR of
rank V if W > V. This situation often arises when coordinate variables
(which, by definition, are one dimensional) are weighted and averaged.
`ncwa' assumes you know this is impossible and so `ncwa' does not
attempt to broadcast WEIGHT or MASK_VAR to conform to VAR in this case,
nor does `ncwa' print a warning message telling you this, because it is
so common. Specifying DBG > 2 does cause `ncwa' to emit warnings in
these situations, however.
Non-coordinate variables are always masked and weighted if specified.
Coordinate variables, however, may be treated specially. By default,
an averaged coordinate variable, e.g., `latitude', appears in
OUTPUT-FILE averaged the same way as any other variable containing an
averaged dimension. In other words, by default `ncwa' weights and masks
coordinate variables like all other variables. This design decision
was intended to be helpful but for some applications it may be
preferable not to weight or mask coordinate variables just like all
other variables. Consider the following arguments to `ncwa': `-a
latitude -w lat_wgt -d latitude,0.,90.' where `lat_wgt' is a weight in
the `latitude' dimension. Since, by default `ncwa' weights coordinate
variables, the value of `latitude' in the OUTPUT-FILE depends on the
weights in LAT_WGT and is not likely to be 45.0, the midpoint latitude
of the hyperslab. Option `-I' overrides this default behavior and
causes `ncwa' not to weight or mask coordinate variables (1). In the
above case, this causes the value of `latitude' in the OUTPUT-FILE to
be 45.0, an appealing result. Thus, `-I' specifies simple arithmetic
averages for the coordinate variables. In the case of latitude, `-I'
specifies that you prefer to archive the arithmetic mean latitude of
the averaged hyperslabs rather than the area-weighted mean latitude.
(2).
As explained in *Note Operation Types::, `ncwa' _always averages_
coordinate variables regardless of the arithmetic operation type
performed on the non-coordinate variables. This is independent of the
setting of the `-I' option. The mathematical definition of operations
involving rank reduction is given above (*note Operation Types::).
---------- Footnotes ----------
(1) The default behavior of (`-I') changed on 1998/12/01--before
this date the default was not to weight or mask coordinate variables.
(2) If `lat_wgt' contains Gaussian weights then the value of
`latitude' in the OUTPUT-FILE will be the area-weighted centroid of the
hyperslab. For the example given, this is about 30 degrees.
4.12.1 Mask condition
---------------------
The mask condition has the syntax MASK_VAR MASK_COMP MASK_VAL. The
preferred method to specify the mask condition is in one string with
the `-B' or `--mask_condition' switches. The older method is to use
the three switches `-m', `-T', and `-M' to specify the MASK_VAR,
MASK_COMP, and MASK_VAL, respectively. (1). The MASK_CONDITION string
is automatically parsed into its three constituents MASK_VAR,
MASK_COMP, and MASK_VAL.
Here MASK_VAR is the name of the masking variable (specified with
`-m', `--mask-variable', `--mask_variable', `--msk_nm', or `--msk_var').
The truth MASK_COMP argument (specified with `-T', `--mask_comparator',
`--msk_cmp_typ', or `--op_rlt' may be any one of the six arithmetic
comparators: `eq', `ne', `gt', `lt', `ge', `le'.
These are the Fortran-style character abbreviations for the logical
comparisons ==, !=, >, <, >=, <=.
The mask comparator defaults to `eq' (equality). The MASK_VAL
argument to `-M' (or `--mask-value', or `--msk_val') is the right hand
side of the "mask condition". Thus for the I'th element of the
hyperslab to be averaged, the mask condition is
mask(i) MASK_COMP MASK_VAL.
---------- Footnotes ----------
(1) The three switches `-m', `-T', and `-M' are maintained for
backward compatibility and may be deprecated in the future. It is
safest to write scripts using `--mask_condition'.
4.12.2 Normalization and Integration
------------------------------------
`ncwa' has one switch which controls the normalization of the averages
appearing in the OUTPUT-FILE. Short option `-N' (or long options
`--nmr' or `--numerator') prevents `ncwa' from dividing the weighted
sum of the variable (the numerator in the averaging expression) by the
weighted sum of the weights (the denominator in the averaging
expression). Thus `-N' tells `ncwa' to return just the numerator of the
arithmetic expression defining the operation (*note Operation Types::).
With this normalization option, `ncwa' can integrate variables.
Averages are first computed as sums, and then normalized to obtain the
average. The original sum (i.e., the numerator of the expression in
*note Operation Types::) is output if default normalization is turned
off (with `-N'). This sum is the integral (not the average) over the
specified (with `-a', or all, if none are specified) dimensions. The
weighting variable, if specified (with `-w'), plays the role of the
differential increment and thus permits more sophisticated integrals
(i.e., weighted sums) to be output. For example, consider the variable
`lev' where LEV = [100,500,1000] weighted by the weight `lev_wgt' where
LEV_WGT = [10,2,1]. The vertical integral of `lev', weighted by
`lev_wgt', is the dot product of LEV and LEV_WGT. That this is
is 3000.0 can be seen by inspection and verified with the integration
command
ncwa -N -a lev -v lev -w lev_wgt in.nc foo.nc;ncks foo.nc
EXAMPLES
Given file `85_0112.nc':
netcdf 85_0112 {
dimensions:
lat = 64 ;
lev = 18 ;
lon = 128 ;
time = UNLIMITED ; // (12 currently)
variables:
float lat(lat) ;
float lev(lev) ;
float lon(lon) ;
float time(time) ;
float scalar_var ;
float three_dmn_var(lat, lev, lon) ;
float two_dmn_var(lat, lev) ;
float mask(lat, lon) ;
float gw(lat) ;
}
Average all variables in `in.nc' over all dimensions and store
results in `out.nc':
ncwa in.nc out.nc
All variables in `in.nc' are reduced to scalars in `out.nc' since
`ncwa' averages over all dimensions unless otherwise specified (with
`-a').
Store the zonal (longitudinal) mean of `in.nc' in `out.nc':
ncwa -a lon in.nc out1.nc
ncwa -a lon -b in.nc out2.nc
The first command turns `lon' into a scalar and the second retains
`lon' as a degenerate dimension in all variables.
% ncks -C -H -v lon out1.nc
lon = 135
% ncks -C -H -v lon out2.nc
lon[0] = 135
In either case the tally is simply the size of `lon', i.e., for the
`85_0112.nc' file described by the sample header above.
Compute the meridional (latitudinal) mean, with values weighted by
the corresponding element of GW (1):
ncwa -w gw -a lat in.nc out.nc
Here the tally is simply the size of `lat', or 64. The sum of the
Gaussian weights is 2.0.
Compute the area mean over the tropical Pacific:
ncwa -w gw -a lat,lon -d lat,-20.,20. -d lon,120.,270. in.nc out.nc
Here the tally is
64 times 128 = 8192.
Compute the area-mean over the globe using only points for which
ORO < 0.5
(2):
ncwa -B 'ORO < 0.5' -w gw -a lat,lon in.nc out.nc
ncwa -m ORO -M 0.5 -T lt -w gw -a lat,lon in.nc out.nc
It is considerably simpler to specify the complete MASK_COND with
the single string argument to `-B' than with the three separate
switches `-m', `-T', and `-M'. If in doubt, enclose the MASK_COND with
double quotes since some of the comparators have special meanings to
the shell.
Assuming 70% of the gridpoints are maritime, then here the tally is
0.70 times 8192 = 5734.
Compute the global annual mean over the maritime tropical Pacific:
ncwa -B 'ORO < 0.5' -w gw -a lat,lon,time \
-d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc
ncwa -m ORO -M 0.5 -T lt -w gw -a lat,lon,time \
-d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc
Further examples will use the one-switch specification of MASK_COND.
Determine the total area of the maritime tropical Pacific, assuming
the variable AREA contains the area of each gridcell
ncwa -N -v area -B 'ORO < 0.5' -a lat,lon \
-d lat,-20.0,20.0 -d lon,120.0,270.0 in.nc out.nc
Weighting AREA (e.g., by GW) is not appropriate because AREA is
_already_ area-weighted by definition. Thus the `-N' switch, or,
equivalently, the `-y ttl' switch, correctly integrate the cell areas
into a total regional area.
Mask a file to contain _FILLVALUE everywhere except where THR_MIN <=
MSK_VAR <= THR_MAX:
# Set masking variable and its scalar thresholds
export msk_var='three_dmn_var_dbl' # Masking variable
export thr_max='20' # Maximum allowed value
export thr_min='10' # Minimum allowed value
ncecat -O in.nc out.nc # Wrap out.nc in degenerate "record" dimension
ncwa -O -a record -B "${msk_var} <= ${thr_max}" out.nc out.nc
ncecat -O out.nc out.nc # Wrap out.nc in degenerate "record" dimension
ncwa -O -a record -B "${msk_var} >= ${thr_min}" out.nc out.nc
After the first use of `ncwa', `out.nc' contains _FILLVALUE where
`${msk_var} >= ${thr_max}'. The process is then repeated on the
remaining data to filter out points where `${msk_var} <= ${thr_min}'.
The resulting `out.nc' contains valid data only where THR_MIN <=
MSK_VAR <= THR_MAX.
---------- Footnotes ----------
(1) `gw' stands for "Gaussian weight" in many climate models.
(2) `ORO' stands for "Orography" in some climate models and in those
models ORO < 0.5 selects ocean gridpoints.
5 Contributing
**************
We welcome contributions from anyone. The project homepage at
`https://sf.net/projects/nco' contains more information on how to
contribute.
Financial contributions to NCO development may be made through
PayPal
(https://www.paypal.com/xclick/business=zender%40uci.edu&item_name=NCO+development&item_number=nco_dnt_dvl&no_note=1&tax=0¤cy_code=USD).
NCO has been shared for over 10 years yet only two users have
contributed any money to the developers (1). So you could be the third!
---------- Footnotes ----------
(1) Happy users have sent me a few gifts, though. This includes a
box of imported chocolate. Mmm. Appreciation and gifts are definitely
better than money. Naturally, I'm too lazy to split and send gifts to
the other developers. However, unlike some NCO developers, I have a
steady "real job". My intent is to split monetary donations among the
active developers and to send them their shares via PayPal.
5.1 Contributors
================
The primary contributors to NCO development have been:
Charlie Zender
Concept, design and implementation of operators from 1995-2000.
Since then autotools, bug-squashing, chunking, documentation,
packing, NCO library redesign, `ncap2' features, `ncbo', `ncpdq',
SMP threading and MPI parallelization, netCDF4 integration,
external funding, project management, science research, releases.
Henry Butowsky
Non-linear operations and `min()', `max()', `total()' support in
`ncra' and `ncwa'. Type conversion for arithmetic. Migration to
netCDF3 API. `ncap' parser, lexer, and I/O. Multislabbing
algorithm. Variable wildcarding. Various hacks. `ncap2'
language.
Rorik Peterson
Original autotool build support. Long command-line options.
Original UDUnits support. Debianization. Numerous bug-fixes.
Daniel Wang
Script Workflow Analysis for MultiProcessing (SWAMP). RPM support.
Harry Mangalam
Benchmarking. OPeNDAP configuration.
Brian Mays
Original packaging for Debian GNU/Linux, `nroff' man pages.
George Shapovalov
Packaging for Gentoo GNU/Linux.
Bill Kocik
Memory management.
Len Makin
NEC SX architecture support.
Jim Edwards
AIX architecture support.
Juliana Rew
Compatibility with large PIDs.
Karen Schuchardt
Auxiliary coordinate support.
Gayathri Venkitachalam
MPI implementation.
Scott Capps
Large work-load testing
Martin Dix, Mark Flanner, Keith Lindsay, Mike Page, Martin Schmidt, Michael Schulz, Remik Ziemlinski
Excellent bug reports and feature requests.
Markus Liebig
Proof-read the ncap documentation
Daniel Baumann, Barry deFreese, Francesco Lovergine, Matej Vela
Debian packaging
Patrice Dumas, Ed Hill, Orion Poplawski
RedHat packaging
George Shapavalov, Patrick Kursawe
Gentoo packaging
Filipe Fernandes
OpenSuse packaging
Takeshi Enomoto, Alexander Hansen
MacIntosh packaging
Please let me know if your name was omitted!
5.2 Proposals for Institutional Funding
=======================================
NSF has funded a project (http://nco.sf.net#prp_sei) to improve
Distributed Data Reduction & Analysis (DDRA) by evolving NCO into a
suite of Scientific Data Operators called SDO. The two main components
of this project are NCO parallelism (OpenMP, MPI) and Server-Side DDRA
(SSDDRA) implemented through extensions to OPeNDAP and netCDF4. This
project will dramatically reduce bandwidth usage for NCO DDRA.
With this first NCO proposal funded, the content of the next NCO
proposal is clear. We are interested in obtaining NASA support for
HDF-specific enhancements to NCO. We plan to submit a proposal to the
next suitable NASA NRA or NSF opportunity.
We are considering a lot of interesting ideas for still more
proposals. Please contact us if you wish to be involved with any future
NCO-related proposals. Comments on the proposals and letters of
support are also very welcome.
6 CCSM Example
**************
This chapter illustrates how to use NCO to process and analyze the
results of a CCSM climate simulation.
************************************************************************
Task 0: Finding input files
************************************************************************
The CCSM model outputs files to a local directory like:
/ptmp/zender/archive/T42x1_40
Each component model has its own subdirectory, e.g.,
/ptmp/zender/archive/T42x1_40/atm
/ptmp/zender/archive/T42x1_40/cpl
/ptmp/zender/archive/T42x1_40/ice
/ptmp/zender/archive/T42x1_40/lnd
/ptmp/zender/archive/T42x1_40/ocn
within which model output is tagged with the particular model name
/ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-01.nc
/ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-02.nc
/ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-03.nc
...
/ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0001-12.nc
/ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0002-01.nc
/ptmp/zender/archive/T42x1_40/atm/T42x1_40.cam2.h0.0002-02.nc
...
or
/ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-01.nc
/ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-02.nc
/ptmp/zender/archive/T42x1_40/lnd/T42x1_40.clm2.h0.0001-03.nc
...
************************************************************************
Task 1: Regional processing
************************************************************************
The first task in data processing is often creating seasonal cycles.
Imagine a 100-year simulation with its 1200 monthly mean files.
Our goal is to create a single file containing 12 months of data.
Each month in the output file is the mean of 100 input files.
Normally, we store the "reduced" data in a smaller, local directory.
caseid='T42x1_40'
#drc_in="${DATA}/archive/${caseid}/atm"
drc_in="${DATA}/${caseid}"
drc_out="${DATA}/${caseid}"
mkdir -p ${drc_out}
cd ${drc_out}
Method 1: Assume all data in directory applies
for mth in {1..12}; do
mm=`printf "%02d" $mth`
ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc \
${drc_in}/${caseid}.cam2.h0.*-${mm}.nc
done # end loop over mth
Method 2: Use shell 'globbing' to construct input filenames
for mth in {1..12}; do
mm=`printf "%02d" $mth`
ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc \
${drc_in}/${caseid}.cam2.h0.00??-${mm}.nc \
${drc_in}/${caseid}.cam2.h0.0100-${mm}.nc
done # end loop over mth
Method 3: Construct input filename list explicitly
for mth in {1..12}; do
mm=`printf "%02d" $mth`
fl_lst_in=''
for yr in {1..100}; do
yyyy=`printf "%04d" $yr`
fl_in=${caseid}.cam2.h0.${yyyy}-${mm}.nc
fl_lst_in="${fl_lst_in} ${caseid}.cam2.h0.${yyyy}-${mm}.nc"
done # end loop over yr
ncra -O -D 1 -o ${drc_out}/${caseid}_clm${mm}.nc -p ${drc_in} \
${fl_lst_in}
done # end loop over mth
Make sure the output file averages correct input files!
ncks -M prints global metadata:
ncks -M ${drc_out}/${caseid}_clm01.nc
The input files ncra used to create the climatological monthly mean
will appear in the global attribute named 'history'.
Use ncrcat to aggregate the climatological monthly means
ncrcat -O -D 1 \
${drc_out}/${caseid}_clm??.nc ${drc_out}/${caseid}_clm_0112.nc
Finally, create climatological means for reference.
The climatological time-mean:
ncra -O -D 1 \
${drc_out}/${caseid}_clm_0112.nc ${drc_out}/${caseid}_clm.nc
The climatological zonal-mean:
ncwa -O -D 1 -a lon \
${drc_out}/${caseid}_clm.nc ${drc_out}/${caseid}_clm_x.nc
The climatological time- and spatial-mean:
ncwa -O -D 1 -a lon,lat,time -w gw \
${drc_out}/${caseid}_clm.nc ${drc_out}/${caseid}_clm_xyt.nc
This file contains only scalars, e.g., "global mean temperature",
used for summarizing global results of a climate experiment.
Climatological monthly anomalies = Annual Cycle:
Subtract climatological mean from climatological monthly means.
Result is annual cycle, i.e., climate-mean has been removed.
ncbo -O -D 1 -o ${drc_out}/${caseid}_clm_0112_anm.nc \
${drc_out}/${caseid}_clm_0112.nc ${drc_out}/${caseid}_clm_xyt.nc
************************************************************************
Task 2: Correcting monthly averages
************************************************************************
The previous step appoximates all months as being equal, so, e.g.,
February weighs slightly too much in the climatological mean.
This approximation can be removed by weighting months appropriately.
We must add the number of days per month to the monthly mean files.
First, create a shell variable dpm:
unset dpm # Days per month
declare -a dpm
dpm=(0 31 28.25 31 30 31 30 31 31 30 31 30 31) # Allows 1-based indexing
Method 1: Create dpm directly in climatological monthly means
for mth in {1..12}; do
mm=`printf "%02d" ${mth}`
ncap2 -O -s "dpm=0.0*date+${dpm[${mth}]}" \
${drc_out}/${caseid}_clm${mm}.nc ${drc_out}/${caseid}_clm${mm}.nc
done # end loop over mth
Method 2: Create dpm by aggregating small files
for mth in {1..12}; do
mm=`printf "%02d" ${mth}`
ncap2 -O -v -s "dpm=${dpm[${mth}]}" ~/nco/data/in.nc \
${drc_out}/foo_${mm}.nc
done # end loop over mth
ncecat -O -D 1 -p ${drc_out} -n 12,2,2 foo_${mm}.nc foo.nc
ncrename -O -D 1 -d record,time ${drc_out}/foo.nc
ncatted -O -h \
-a long_name,dpm,o,c,"Days per month" \
-a units,dpm,o,c,"days" \
${drc_out}/${caseid}_clm_0112.nc
ncks -A -v dpm ${drc_out}/foo.nc ${drc_out}/${caseid}_clm_0112.nc
Method 3: Create small netCDF file using ncgen
cat > foo.cdl << EOF
netcdf foo {
dimensions:
time=unlimited;
variables:
float dpm(time);
dpm:long_name="Days per month";
dpm:units="days";
data:
dpm=31,28.25,31,30,31,30,31,31,30,31,30,31;
}
EOF
ncgen -b -o foo.nc foo.cdl
ncks -A -v dpm ${drc_out}/foo.nc ${drc_out}/${caseid}_clm_0112.nc
Another way to get correct monthly weighting is to average daily
output files, if available.
************************************************************************
Task 3: Regional processing
************************************************************************
Let's say you are interested in examining the California region.
Hyperslab your dataset to isolate the appropriate latitude/longitudes.
ncks -O -D 1 -d lat,30.0,37.0 -d lon,240.0,270.0 \
${drc_out}/${caseid}_clm_0112.nc ${drc_out}/${caseid}_clm_0112_Cal.nc
The dataset is now much smaller!
To examine particular metrics.
************************************************************************
Task 4: Accessing data stored remotely
************************************************************************
OPeNDAP server examples:
UCI DAP servers:
ncks -M -p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata in.nc
ncrcat -O -C -D 3 -p http://dust.ess.uci.edu/cgi-bin/dods/nph-dods/dodsdata \
-l /tmp in.nc in.nc ~/foo.nc
NOAA DAP servers:
ncwa -O -C -a lat,lon,time -d lon,-10.,10. -d lat,-10.,10. -l /tmp -p \
http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface \
pres.sfc.1969.nc ~/foo.nc
LLNL PCMDI IPCC OPeNDAP Data Portal:
ncks -M -p http://username:password@esgcet.llnl.gov/cgi-bin/dap-cgi.py/ipcc4/sresa1b/ncar_ccsm3_0 pcmdi.ipcc4.ncar_ccsm3_0.sresa1b.run1.atm.mo.xml
Earth System Grid (ESG): http://www.earthsystemgrid.org
caseid='b30.025.ES01'
CCSM3.0 1% increasing CO2 run, T42_gx1v3, 200 years starting in year 400
Atmospheric post-processed data, monthly averages, e.g.,
/data/zender/tmp/b30.025.ES01.cam2.h0.TREFHT.0400-01_cat_0449-12.nc
/data/zender/tmp/b30.025.ES01.cam2.h0.TREFHT.0400-01_cat_0599-12.nc
ESG supports password-protected FTP access by registered users
NCO uses the .netrc file, if present, for password-protected FTP access
Syntax for accessing single file is, e.g.,
ncks -O -D 3 \
-p ftp://climate.llnl.gov/sresa1b/atm/mo/tas/ncar_ccsm3_0/run1 \
-l /tmp tas_A1.SRESA1B_1.CCSM.atmm.2000-01_cat_2099-12.nc ~/foo.nc
# Average surface air temperature tas for SRESA1B scenario
# This loop is illustrative and will not work until NCO correctly
# translates '*' to FTP 'mget' all remote files
for var in 'tas'; do
for scn in 'sresa1b'; do
for mdl in 'cccma_cgcm3_1 cccma_cgcm3_1_t63 cnrm_cm3 csiro_mk3_0 \
gfdl_cm2_0 gfdl_cm2_1 giss_aom giss_model_e_h giss_model_e_r \
iap_fgoals1_0_g inmcm3_0 ipsl_cm4 miroc3_2_hires miroc3_2_medres \
miub_echo_g mpi_echam5 mri_cgcm2_3_2a ncar_ccsm3_0 ncar_pcm1 \
ukmo_hadcm3 ukmo_hadgem1'; do
for run in '1'; do
ncks -R -O -D 3 -p ftp://climate.llnl.gov/${scn}/atm/mo/${var}/${mdl}/run${run} -l ${DATA}/${scn}/atm/mo/${var}/${mdl}/run${run} '*' ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc
done # end loop over run
done # end loop over mdl
done # end loop over scn
done # end loop over var
cd sresa1b/atm/mo/tas/ukmo_hadcm3/run1/
ncks -H -m -v lat,lon,lat_bnds,lon_bnds -M tas_A1.nc | m
bds -x 096 -y 073 -m 33 -o ${DATA}/data/dst_3.75x2.5.nc # ukmo_hadcm3
ncview ${DATA}/data/dst_3.75x2.5.nc
# msk_rgn is California mask on ukmo_hadcm3 grid
# area is correct area weight on ukmo_hadcm3 grid
ncks -A -v area,msk_rgn ${DATA}/data/dst_3.75x2.5.nc \
${DATA}/sresa1b/atm/mo/tas/ukmo_hadcm3/run1/area_msk_ukmo_hadcm3.nc
Template for standardized data:
${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc
e.g., raw data
${DATA}/sresa1b/atm/mo/tas/ukmo_hadcm3/run1/tas_A1.nc
becomes standardized data
Level 0: raw from IPCC site--no changes except for name
Make symbolic link name match raw data
Template: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc
ln -s -f tas_A1.nc sresa1b_ukmo_hadcm3_run1_tas_200101_209911.nc
area_msk_ukmo_hadcm3.nc
Level I: Add all variables (but not standardized in time)
to file containing msk_rgn and area
Template: ${scn}_${mdl}_${run}_${yyyymm}_${yyyymm}.nc
/bin/cp area_msk_ukmo_hadcm3.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc
ncks -A -v tas sresa1b_ukmo_hadcm3_run1_tas_200101_209911.nc \
sresa1b_ukmo_hadcm3_run1_200101_209911.nc
ncks -A -v pr sresa1b_ukmo_hadcm3_run1_pr_200101_209911.nc \
sresa1b_ukmo_hadcm3_run1_200101_209911.nc
If already have file then:
mv sresa1b_ukmo_hadcm3_run1_200101_209911.nc foo.nc
/bin/cp area_msk_ukmo_hadcm3.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc
ncks -A -v tas,pr foo.nc sresa1b_ukmo_hadcm3_run1_200101_209911.nc
Level II: Correct # years, months
Template: ${scn}_${mdl}_${run}_${var}_${yyyymm}_${yyyymm}.nc
ncks -d time,....... file1.nc file2.nc
ncrcat file2.nc file3.nc sresa1b_ukmo_hadcm3_run1_200001_209912.nc
Level III: Many derived products from level II, e.g.,
A. Global mean timeseries
ncwa -w area -a lat,lon \
sresa1b_ukmo_hadcm3_run1_200001_209912.nc \
sresa1b_ukmo_hadcm3_run1_200001_209912_xy.nc
B. Califoria average timeseries
ncwa -m msk_rgn -w area -a lat,lon \
sresa1b_ukmo_hadcm3_run1_200001_209912.nc \
sresa1b_ukmo_hadcm3_run1_200001_209912_xy_Cal.nc
7 References
************
[ZeM07] Zender, C. S., and H. J. Mangalam (2007), Scaling
Properties of Common Statistical Operators for Gridded Datasets,
Int. J. High Perform. Comput. Appl., 21(4), 485-498,
doi:10.1177/1094342007083802.
[Zen08] Zender, C. S. (2008), Analysis of Self-describing Gridded
Geoscience Data with netCDF Operators (NCO), Environ. Modell.
Softw., 23(10), 1338-1342, doi:10.1016/j.envsoft.2008.03.004.
[WZJ07] Wang, D. L., C. S. Zender, and S. F. Jenks (2007),
DAP-enabled Server-side Data Reduction and Analysis, Proceedings
of the 23rd AMS Conference on Interactive Information and
Processing Systems (IIPS) for Meteorology, Oceanography, and
Hydrology, Paper 3B.2, January 14-18, San Antonio, TX. American
Meteorological Society, AMS Press, Boston, MA.
[ZMW06] Zender, C. S., H. Mangalam, and D. L. Wang (2006),
Improving Scaling Properties of Common Statistical Operators for
Gridded Geoscience Datasets, Eos Trans. AGU, 87(52), Fall Meet.
Suppl., Abstract IN53B-0827.
[ZeW07] Zender, C. S., and D. L. Wang (2007), High performance
distributed data reduction and analysis with the netCDF Operators
(NCO), Proceedings of the 23rd AMS Conference on Interactive
Information and Processing Systems (IIPS) for Meteorology,
Oceanography, and Hydrology, Paper 3B.4, January 14-18, San
Antonio, TX. American Meteorological Society, AMS Press, Boston,
MA.
[WZJ06] Wang, D. L., C. S. Zender, and S. F. Jenks (2006),
Server-side netCDF Data Reduction and Analysis, Eos Trans. AGU,
87(52), Fall Meet. Suppl., Abstract IN53B-0826.
[WZJ073] Wang, D. L., C. S. Zender, and S. F. Jenks (2007),
Server-side parallel data reduction and analysis, in Advances in
Grid and Pervasive Computing, Second International Conference, GPC
2007, Paris, France, May 2-4, 2007, Proceedings. IEEE Lecture
Notes in Computer Science, vol. 4459, edited by C. Cerin and K.-C.
Li, pp. 744-750, Springer-Verlag, Berlin/Heidelberg,
doi:10.1007/978-3-540-72360-8_67.
[WZJ074] Wang, D. L., C. S. Zender and S. F. Jenks (2007), A
System for Scripted Data Analysis at Remote Data Centers, Eos
Trans. AGU, 88(52), Fall Meet. Suppl., Abstract IN11B-0469.
[WZJ081] Wang, D. L., C. S. Zender and S. F. Jenks (2008),
Cluster Workflow Execution of Retargeted Data Analysis Scripts,
Proceedings of the 8th IEEE Int'l Symposium on Cluster Computing
and the Grid (IEEE CCGRID '08), pp. 449-458, Lyon, France, May
2008.
[WZJ091] Wang, D. L., C. S. Zender, and S. F. Jenks (2009),
Efficient Clustered Server-side Data Analysis Workflows using
SWAMP, Earth Sci. Inform., 2(3), 141-155,
doi:10.1007/s12145-009-0021-z.
General Index
*************
" (double quote): Voir 4.2. (ligne 5946)
#include: Voir 4.1.1. (ligne 3360)
$ (wildcard character): Voir 3.11. (ligne 1787)
% (modulus): Voir 4.1.24. (ligne 5484)
' (end quote): Voir 4.2. (ligne 5946)
*: Voir 4.3. (ligne 6031)
* (filename expansion): Voir 3.11. (ligne 1787)
* (multiplication): Voir 4.1.24. (ligne 5484)
* (wildcard character): Voir 3.11. (ligne 1796)
+: Voir 4.3. (ligne 6031)
+ (addition): Voir 4.1.24. (ligne 5484)
+ (wildcard character): Voir 3.11. (ligne 1796)
-: Voir 4.3. (ligne 6031)
- (subtraction): Voir 4.1.24. (ligne 5484)
--3: Voir 3.9. (ligne 1584)
--4: Voir 3.9. (ligne 1584)
--64bit: Voir 3.9. (ligne 1584)
--abc: Voir 4.7. (ligne 6668)
--alphabetize: Voir 4.7. (ligne 6668)
--apn <1>: Voir 4.7. (ligne 6843)
--apn <2>: Voir 3.27. (ligne 3062)
--apn: Voir 2.3. (ligne 575)
--append <1>: Voir 4.7. (ligne 6843)
--append <2>: Voir 3.27. (ligne 3062)
--append: Voir 2.3. (ligne 575)
--auxiliary: Voir 3.18. (ligne 2160)
--auxiliary LON_MIN,LON_MAX,LAT_MIN,LAT_MAX: Voir 3.18. (ligne 2160)
--binary: Voir 4.7. (ligne 6678)
--bnr: Voir 4.7. (ligne 6678)
--chunk_dimension: Voir 3.22. (ligne 2547)
--chunk_map: Voir 3.22. (ligne 2547)
--chunk_policy: Voir 3.22. (ligne 2547)
--chunk_scalar: Voir 3.22. (ligne 2547)
--cnk_dmn: Voir 3.22. (ligne 2547)
--cnk_map: Voir 3.22. (ligne 2547)
--cnk_map CNK_MAP: Voir 3.22. (ligne 2609)
--cnk_plc: Voir 3.22. (ligne 2547)
--cnk_scl: Voir 3.22. (ligne 2547)
--coords <1>: Voir 3.30. (ligne 3190)
--coords: Voir 3.12. (ligne 1857)
--crd <1>: Voir 3.30. (ligne 3190)
--crd: Voir 3.12. (ligne 1857)
--dbg_lvl DEBUG-LEVEL <1>: Voir 3.4. (ligne 1148)
--dbg_lvl DEBUG-LEVEL <2>: Voir 2.8. (ligne 898)
--dbg_lvl DEBUG-LEVEL: Voir 1.5. (ligne 492)
--debug-level DEBUG-LEVEL <1>: Voir 2.8. (ligne 898)
--debug-level DEBUG-LEVEL: Voir 1.5. (ligne 492)
--deflate: Voir 3.23. (ligne 2667)
--dfl_lvl: Voir 3.23. (ligne 2667)
--dimension DIM,[MIN],[MAX],STRIDE: Voir 3.15. (ligne 1988)
--dimension DIM,[MIN][,[MAX][,[STRIDE]]] <1>: Voir 3.19. (ligne 2257)
--dimension DIM,[MIN][,[MAX][,[STRIDE]]] <2>: Voir 3.17. (ligne 2112)
--dimension DIM,[MIN][,[MAX][,[STRIDE]]] <3>: Voir 3.16. (ligne 2048)
--dimension DIM,[MIN][,[MAX][,[STRIDE]]]: Voir 3.14. (ligne 1915)
--dmn DIM,[MIN],[MAX],STRIDE: Voir 3.15. (ligne 1988)
--dmn DIM,[MIN][,[MAX][,[STRIDE]]] <1>: Voir 3.19. (ligne 2257)
--dmn DIM,[MIN][,[MAX][,[STRIDE]]] <2>: Voir 3.17. (ligne 2112)
--dmn DIM,[MIN][,[MAX][,[STRIDE]]] <3>: Voir 3.16. (ligne 2048)
--dmn DIM,[MIN][,[MAX][,[STRIDE]]]: Voir 3.14. (ligne 1915)
--exclude <1>: Voir 4.7. (ligne 6837)
--exclude: Voir 3.11. (ligne 1743)
--file_format: Voir 3.9. (ligne 1584)
--file_list: Voir 3.29. (ligne 3108)
--fix_rec_dmn: Voir 4.7. (ligne 6694)
--fl_bnr: Voir 4.7. (ligne 6685)
--fl_fmt: Voir 3.9. (ligne 1584)
--fl_lst_in: Voir 3.29. (ligne 3108)
--fl_out FL_OUT: Voir 3.6. (ligne 1303)
--fl_spt: Voir 4.1. (ligne 3300)
--fnc_tbl: Voir 4.1.24. (ligne 5619)
--fortran: Voir 3.13. (ligne 1880)
--glb_mtd_spr: Voir 4.5. (ligne 6360)
--hdr_pad HDR_PAD: Voir 3.2. (ligne 1059)
--header_pad HDR_PAD: Voir 3.2. (ligne 1059)
--hieronymus: Voir 4.7. (ligne 6701)
--history: Voir 3.28. (ligne 3080)
--hst: Voir 3.28. (ligne 3080)
--lcl OUTPUT-PATH: Voir 3.7. (ligne 1335)
--local OUTPUT-PATH: Voir 3.7. (ligne 1335)
--map CNK_MAP: Voir 3.22. (ligne 2609)
--map PCK_MAP: Voir 4.8. (ligne 6998)
--mask-value MASK_VAL: Voir 4.12.1. (ligne 7569)
--mask-variable MASK_VAR: Voir 4.12. (ligne 7497)
--mask_comparator MASK_COMP: Voir 4.12.1. (ligne 7552)
--mask_condition MASK_COND <1>: Voir 4.12.1. (ligne 7552)
--mask_condition MASK_COND: Voir 4.12. (ligne 7497)
--mask_value MASK_VAL: Voir 4.12.1. (ligne 7569)
--mask_variable MASK_VAR: Voir 4.12. (ligne 7497)
--metadata: Voir 4.7. (ligne 6782)
--Metadata: Voir 4.7. (ligne 6745)
--mk_rec_dmn DIM: Voir 4.7. (ligne 6698)
--msk_cmp_typ MASK_COMP: Voir 4.12.1. (ligne 7552)
--msk_cnd MASK_COND: Voir 4.12. (ligne 7497)
--msk_cnd_sng MASK_COND: Voir 4.12.1. (ligne 7552)
--msk_nm MASK_VAR: Voir 4.12. (ligne 7497)
--msk_val MASK_VAL: Voir 4.12.1. (ligne 7569)
--msk_var MASK_VAR: Voir 4.12. (ligne 7497)
--mtd: Voir 4.7. (ligne 6782)
--Mtd: Voir 4.7. (ligne 6745)
--netcdf4: Voir 3.9. (ligne 1584)
--nintap LOOP: Voir 3.5. (ligne 1205)
--no-coords <1>: Voir 3.30. (ligne 3190)
--no-coords: Voir 3.12. (ligne 1857)
--no-crd <1>: Voir 3.30. (ligne 3190)
--no-crd: Voir 3.12. (ligne 1857)
--no_rec_dmn: Voir 4.7. (ligne 6694)
--omp_num_threads THR_NBR: Voir 3.3. (ligne 1080)
--op_rlt MASK_COMP: Voir 4.12.1. (ligne 7552)
--op_typ OP_TYP <1>: Voir 4.3. (ligne 6031)
--op_typ OP_TYP: Voir 3.25. (ligne 2795)
--operation OP_TYP <1>: Voir 4.3. (ligne 6031)
--operation OP_TYP: Voir 3.25. (ligne 2795)
--output FL_OUT: Voir 3.6. (ligne 1303)
--overwrite <1>: Voir 3.27. (ligne 3062)
--overwrite: Voir 2.3. (ligne 575)
--ovr <1>: Voir 3.27. (ligne 3062)
--ovr: Voir 2.3. (ligne 575)
--pack_policy PCK_PLC: Voir 4.8. (ligne 6951)
--path INPUT-PATH <1>: Voir 3.7. (ligne 1335)
--path INPUT-PATH: Voir 3.5. (ligne 1205)
--pck_map PCK_MAP: Voir 4.8. (ligne 6998)
--pck_plc PCK_PLC: Voir 4.8. (ligne 6951)
--print: Voir 4.7. (ligne 6791)
--prn: Voir 4.7. (ligne 6791)
--prn_fnc_tbl: Voir 4.1.24. (ligne 5619)
--pth INPUT-PATH <1>: Voir 3.7. (ligne 1335)
--pth INPUT-PATH: Voir 3.5. (ligne 1205)
--quiet: Voir 4.7. (ligne 6803)
--rcd_nm ULM_NM: Voir 4.5. (ligne 6342)
--retain: Voir 3.8. (ligne 1541)
--revision <1>: Voir 3.32. (ligne 3252)
--revision: Voir 1.5. (ligne 492)
--rtn: Voir 3.8. (ligne 1541)
--script: Voir 4.1. (ligne 3300)
--script-file: Voir 4.1. (ligne 3300)
--sng_fmt: Voir 4.7. (ligne 6811)
--spt: Voir 4.1. (ligne 3300)
--string: Voir 4.7. (ligne 6811)
--thr_nbr THR_NBR: Voir 3.3. (ligne 1080)
--threads THR_NBR: Voir 3.3. (ligne 1080)
--ulm_nm ULM_NM: Voir 4.5. (ligne 6342)
--units: Voir 4.7. (ligne 6816)
--unpack: Voir 4.8. (ligne 6982)
--upk: Voir 4.8. (ligne 6982)
--variable VAR <1>: Voir 4.7. (ligne 6837)
--variable VAR: Voir 3.11. (ligne 1743)
--version <1>: Voir 3.32. (ligne 3252)
--version: Voir 1.5. (ligne 492)
--vrs <1>: Voir 3.32. (ligne 3252)
--vrs: Voir 1.5. (ligne 492)
--weight WEIGHT: Voir 4.12. (ligne 7497)
--weight WGT1[,WGT2]: Voir 4.6. (ligne 6456)
--wgt_var WEIGHT: Voir 4.12. (ligne 7497)
--wgt_var WGT1[,WGT2]: Voir 4.6. (ligne 6456)
--xcl <1>: Voir 4.7. (ligne 6837)
--xcl: Voir 3.11. (ligne 1743)
-3 <1>: Voir 3.9. (ligne 1584)
-3: Voir 1.4. (ligne 397)
-4 <1>: Voir 3.9. (ligne 1584)
-4: Voir 1.4. (ligne 397)
-A <1>: Voir 4.8. (ligne 7107)
-A: Voir 4.7. (ligne 6843)
-a: Voir 4.7. (ligne 6668)
-A <1>: Voir 3.27. (ligne 3062)
-A: Voir 2.3. (ligne 575)
-b: Voir 4.7. (ligne 6685)
-B: Voir 4.7. (ligne 6678)
-b <1>: Voir 4.3. (ligne 6128)
-b: Voir 4.1.23. (ligne 5462)
-B MASK_COND <1>: Voir 4.12.1. (ligne 7552)
-B MASK_COND: Voir 4.12. (ligne 7497)
-C: Voir 4.1.23. (ligne 5474)
-c: Voir 3.30. (ligne 3190)
-C: Voir 3.30. (ligne 3190)
-c: Voir 3.12. (ligne 1857)
-C: Voir 3.12. (ligne 1857)
-D: Voir 1.5. (ligne 477)
-D DEBUG-LEVEL <1>: Voir 3.4. (ligne 1148)
-D DEBUG-LEVEL <2>: Voir 2.8. (ligne 898)
-D DEBUG-LEVEL: Voir 1.5. (ligne 492)
-d DIM,[MIN],[MAX],STRIDE: Voir 3.15. (ligne 1988)
-d DIM,[MIN][,[MAX][,[STRIDE]]] <1>: Voir 3.19. (ligne 2257)
-d DIM,[MIN][,[MAX][,[STRIDE]]] <2>: Voir 3.17. (ligne 2112)
-d DIM,[MIN][,[MAX][,[STRIDE]]] <3>: Voir 3.16. (ligne 2048)
-d DIM,[MIN][,[MAX][,[STRIDE]]]: Voir 3.14. (ligne 1915)
-d DIM,[MIN][,[MAX]]: Voir 4.12. (ligne 7489)
-f: Voir 4.1.24. (ligne 5619)
-F: Voir 3.13. (ligne 1880)
-H: Voir 4.7. (ligne 6701)
-h: Voir 4.2. (ligne 5769)
-H: Voir 3.29. (ligne 3108)
-h: Voir 3.28. (ligne 3080)
-I: Voir 4.12. (ligne 7525)
-L: Voir 3.23. (ligne 2667)
-l OUTPUT-PATH: Voir 3.7. (ligne 1335)
-m: Voir 4.7. (ligne 6782)
-M <1>: Voir 4.7. (ligne 6745)
-M <2>: Voir 4.5. (ligne 6360)
-M: Voir 3.9. (ligne 1666)
-M CNK_MAP: Voir 3.22. (ligne 2609)
-m MASK_VAR: Voir 4.12. (ligne 7497)
-M PCK_MAP: Voir 4.8. (ligne 6998)
-N: Voir 4.12.2. (ligne 7585)
-n LOOP <1>: Voir 3.5. (ligne 1205)
-n LOOP: Voir 2.7. (ligne 768)
-O <1>: Voir 3.27. (ligne 3062)
-O: Voir 2.3. (ligne 575)
-o FL_OUT <1>: Voir 3.6. (ligne 1303)
-o FL_OUT: Voir 2.7. (ligne 822)
-P: Voir 4.7. (ligne 6791)
-p INPUT-PATH <1>: Voir 3.7. (ligne 1397)
-p INPUT-PATH: Voir 3.5. (ligne 1205)
-P PCK_PLC: Voir 4.8. (ligne 6951)
-q: Voir 4.7. (ligne 6803)
-Q: Voir 4.7. (ligne 6797)
-r: Voir 3.32. (ligne 3252)
-R: Voir 3.8. (ligne 1541)
-r: Voir 1.5. (ligne 477)
-s: Voir 4.7. (ligne 6811)
-t THR_NBR <1>: Voir 3.3. (ligne 1080)
-t THR_NBR: Voir 2.9.1. (ligne 970)
-U: Voir 4.8. (ligne 6982)
-u: Voir 4.7. (ligne 6816)
-u ULM_NM: Voir 4.5. (ligne 6342)
-v: Voir 4.8. (ligne 7107)
-v VAR <1>: Voir 4.7. (ligne 6837)
-v VAR: Voir 3.11. (ligne 1743)
-w WEIGHT: Voir 4.12. (ligne 7497)
-w WGT1[,WGT2]: Voir 4.6. (ligne 6456)
-x: Voir 4.7. (ligne 6837)
-X: Voir 3.18. (ligne 2160)
-x: Voir 3.11. (ligne 1743)
-X LON_MIN,LON_MAX,LAT_MIN,LAT_MAX: Voir 3.18. (ligne 2160)
-y OP_TYP <1>: Voir 4.3. (ligne 6031)
-y OP_TYP: Voir 3.25. (ligne 2795)
. (wildcard character): Voir 3.11. (ligne 1787)
.netrc: Voir 3.7. (ligne 1335)
.rhosts: Voir 3.7. (ligne 1335)
/: Voir 4.3. (ligne 6031)
/ (division): Voir 4.1.24. (ligne 5484)
/*...*/ (comment): Voir 4.1.1. (ligne 3356)
// (comment): Voir 4.1.1. (ligne 3356)
0 (NUL): Voir 4.2. (ligne 5955)
32-bit offset file format: Voir 3.9. (ligne 1666)
64-bit offset file format: Voir 3.9. (ligne 1666)
64BIT files: Voir 3.9. (ligne 1584)
; (end of statement): Voir 4.1.1. (ligne 3353)
<arpa/nameser.h>: Voir 1.2.1. (ligne 280)
<resolv.h>: Voir 1.2.1. (ligne 280)
? (filename expansion): Voir 3.11. (ligne 1787)
? (question mark): Voir 4.2. (ligne 5946)
? (wildcard character): Voir 3.11. (ligne 1796)
@ (attribute): Voir 4.1.1. (ligne 3366)
[] (array delimiters): Voir 4.1.1. (ligne 3344)
\ (backslash): Voir 4.2. (ligne 5946)
\" (protected double quote): Voir 4.2. (ligne 5946)
\' (protected end quote): Voir 4.2. (ligne 5946)
\? (protected question mark): Voir 4.2. (ligne 5946)
\\ (ASCII \, backslash): Voir 4.2. (ligne 5941)
\\ (protected backslash): Voir 4.2. (ligne 5946)
\a (ASCII BEL, bell): Voir 4.2. (ligne 5941)
\b (ASCII BS, backspace): Voir 4.2. (ligne 5941)
\f (ASCII FF, formfeed): Voir 4.2. (ligne 5941)
\n (ASCII LF, linefeed): Voir 4.2. (ligne 5931)
\n (linefeed): Voir 4.7. (ligne 6848)
\r (ASCII CR, carriage return): Voir 4.2. (ligne 5941)
\t (ASCII HT, horizontal tab): Voir 4.2. (ligne 5931)
\t (horizontal tab): Voir 4.7. (ligne 6848)
\v (ASCII VT, vertical tab): Voir 4.2. (ligne 5941)
^ (power): Voir 4.1.24. (ligne 5484)
^ (wildcard character): Voir 3.11. (ligne 1787)
_FillValue <1>: Voir 4.11. (ligne 7441)
_FillValue: Voir 4.8. (ligne 7036)
_FILLVALUE: Voir 4.8. (ligne 7036)
_FillValue <1>: Voir 4.6. (ligne 6495)
_FillValue <2>: Voir 4.2. (ligne 5774)
_FillValue <3>: Voir 3.24. (ligne 2704)
_FillValue: Voir 3.21. (ligne 2432)
`NCO User's Guide': Voir 1.1. (ligne 136)
`User's Guide': Voir 1.1. (ligne 136)
ABS: Voir 4.1.24. (ligne 5487)
absolute value: Voir 4.1.24. (ligne 5487)
ACOS: Voir 4.1.24. (ligne 5487)
ACOSH: Voir 4.1.24. (ligne 5487)
add: Voir 4.3. (ligne 6031)
add_offset <1>: Voir 4.10. (ligne 7301)
add_offset <2>: Voir 4.8. (ligne 6944)
add_offset <3>: Voir 4.5. (ligne 6381)
add_offset: Voir 3.24. (ligne 2704)
ADD_OFFSET: Voir 2.10. (ligne 1025)
adding data <1>: Voir 4.6. (ligne 6432)
adding data: Voir 4.3. (ligne 6016)
addition <1>: Voir 4.6. (ligne 6432)
addition <2>: Voir 4.3. (ligne 6016)
addition: Voir 4.1.24. (ligne 5484)
Alexander Hansen: Voir 5.1. (ligne 7810)
alias <1>: Voir 4.8. (ligne 6992)
alias: Voir 4.3. (ligne 6075)
all: Voir 3.22. (ligne 2577)
alphabetization: Voir 4.7. (ligne 6668)
alphabetize output: Voir 4.7. (ligne 6831)
alternate invocations: Voir 4.3. (ligne 6031)
anomalies: Voir 4.3. (ligne 6111)
ANSI: Voir 1.2. (ligne 205)
ANSI C: Voir 4.1.24. (ligne 5618)
appending data <1>: Voir 4.7. (ligne 6615)
appending data: Voir 4.1.23. (ligne 5462)
appending to files <1>: Voir 4.7. (ligne 6843)
appending to files <2>: Voir 3.27. (ligne 3062)
appending to files: Voir 2.3. (ligne 575)
appending variables <1>: Voir 4.8. (ligne 7107)
appending variables: Voir 2.4. (ligne 597)
AR4: Voir 4.4. (ligne 6263)
arccosine function: Voir 4.1.24. (ligne 5487)
arcsine function: Voir 4.1.24. (ligne 5487)
arctangent function: Voir 4.1.24. (ligne 5487)
area: Voir 3.30. (ligne 3139)
arithmetic operators <1>: Voir 4.12. (ligne 7489)
arithmetic operators: Voir 3.21. (ligne 2438)
arithmetic processor: Voir 4.1. (ligne 3283)
ARM conventions <1>: Voir 4.10. (ligne 7314)
ARM conventions: Voir 3.31. (ligne 3221)
array indexing: Voir 4.1.1. (ligne 3347)
array storage: Voir 4.1.1. (ligne 3350)
array syntax: Voir 4.1.1. (ligne 3344)
arrival value: Voir 4.6. (ligne 6470)
ASCII: Voir 4.2. (ligne 5929)
ASIN: Voir 4.1.24. (ligne 5487)
ASINH: Voir 4.1.24. (ligne 5487)
assignment statement: Voir 4.1.1. (ligne 3353)
asynchronous file access: Voir 3.7. (ligne 1335)
ATAN: Voir 4.1.24. (ligne 5487)
ATANH: Voir 4.1.24. (ligne 5487)
attribute inheritance: Voir 4.1.6. (ligne 3824)
attribute names <1>: Voir 4.11. (ligne 7355)
attribute names: Voir 4.2. (ligne 5752)
attribute propagation: Voir 4.1.6. (ligne 3824)
attribute syntax: Voir 4.1.1. (ligne 3366)
attribute, units: Voir 3.19. (ligne 2257)
attributes: Voir 4.2. (ligne 5752)
attributes, appending: Voir 4.2. (ligne 5834)
attributes, creating: Voir 4.2. (ligne 5834)
attributes, deleting: Voir 4.2. (ligne 5834)
attributes, editing: Voir 4.2. (ligne 5834)
attributes, global <1>: Voir 4.11. (ligne 7400)
attributes, global <2>: Voir 4.7. (ligne 6601)
attributes, global <3>: Voir 4.2. (ligne 5818)
attributes, global <4>: Voir 3.31. (ligne 3245)
attributes, global <5>: Voir 3.29. (ligne 3108)
attributes, global <6>: Voir 3.28. (ligne 3080)
attributes, global: Voir 2.7. (ligne 796)
attributes, modifying: Voir 4.2. (ligne 5834)
attributes, overwriting: Voir 4.2. (ligne 5834)
attributesncap: Voir 4.1.6. (ligne 3787)
autoconf: Voir 1.5. (ligne 500)
automagic <1>: Voir 2.7. (ligne 769)
automagic: Voir 1.2. (ligne 244)
automatic type conversion <1>: Voir 4.1.24. (ligne 5577)
automatic type conversion: Voir 3.26. (ligne 2952)
auxiliary coordinates: Voir 3.30. (ligne 3190)
average <1>: Voir 4.12. (ligne 7534)
average: Voir 3.25. (ligne 2795)
averaging data <1>: Voir 4.12. (ligne 7461)
averaging data <2>: Voir 4.9. (ligne 7210)
averaging data <3>: Voir 4.4. (ligne 6240)
averaging data: Voir 3.21. (ligne 2432)
avg: Voir 3.25. (ligne 2795)
avg(): Voir 4.1.11. (ligne 4101)
avgsqr: Voir 3.25. (ligne 2795)
Barry deFreese: Voir 5.1. (ligne 7798)
base_time: Voir 3.31. (ligne 3221)
bash: Voir 3.11. (ligne 1839)
Bash Shell: Voir 4.3. (ligne 6193)
Bash shell: Voir 4.3. (ligne 6060)
batch mode: Voir 3.27. (ligne 3062)
benchmarks: Voir 3.3. (ligne 1125)
Bessel function: Voir 4.1.19. (ligne 4763)
Bill Kocik: Voir 5.1. (ligne 7771)
binary format: Voir 4.7. (ligne 6678)
binary operations <1>: Voir 4.3. (ligne 6016)
binary operations: Voir 2.9.2. (ligne 976)
binary Operators: Voir 4.1.2. (ligne 3460)
Bourne Shell <1>: Voir 4.3. (ligne 6193)
Bourne Shell: Voir 3.15. (ligne 2025)
Brian Mays: Voir 5.1. (ligne 7765)
broadcasting variables <1>: Voir 4.12. (ligne 7461)
broadcasting variables <2>: Voir 4.6. (ligne 6557)
broadcasting variables: Voir 4.3. (ligne 6105)
BSD: Voir 3.4. (ligne 1145)
buffering: Voir 2.10. (ligne 1015)
bugs, reporting: Voir 1.5. (ligne 437)
byte(): Voir 4.1.11. (ligne 4214)
C index convention: Voir 3.13. (ligne 1880)
C language <1>: Voir 4.7. (ligne 6811)
C language <2>: Voir 4.2. (ligne 5955)
C language <3>: Voir 4.1.2. (ligne 3373)
C language <4>: Voir 4.1.1. (ligne 3340)
C language <5>: Voir 3.26.1. (ligne 2975)
C language <6>: Voir 3.21. (ligne 2480)
C language: Voir 1.2. (ligne 226)
C Shell <1>: Voir 4.3. (ligne 6193)
C Shell: Voir 3.15. (ligne 2025)
C++: Voir 1.2. (ligne 197)
c++: Voir 1.2. (ligne 192)
C89: Voir 1.2. (ligne 205)
C99: Voir 1.2. (ligne 209)
C_FORMAT: Voir 2.10. (ligne 1025)
cc: Voir 1.2. (ligne 192)
CC: Voir 1.2. (ligne 192)
CCM Processor <1>: Voir 4.10. (ligne 7320)
CCM Processor <2>: Voir 4.9. (ligne 7246)
CCM Processor: Voir 3.5. (ligne 1205)
CCSM <1>: Voir 6. (ligne 7839)
CCSM: Voir 5.2. (ligne 7819)
CCSM conventions: Voir 3.30. (ligne 3139)
CEIL: Voir 4.1.24. (ligne 5487)
ceiling function: Voir 4.1.24. (ligne 5487)
cell-based grids: Voir 3.18. (ligne 2177)
CF conventions <1>: Voir 4.3. (ligne 6137)
CF conventions <2>: Voir 3.30. (ligne 3139)
CF conventions <3>: Voir 3.19. (ligne 2341)
CF conventions <4>: Voir 3.18. (ligne 2160)
CF conventions: Voir 3.12. (ligne 1873)
change_miss(): Voir 4.1.10. (ligne 4044)
char(): Voir 4.1.11. (ligne 4217)
characters, special: Voir 4.2. (ligne 5931)
Charlie Zender <1>: Voir 5.1. (ligne 7743)
Charlie Zender:
Voir ``Foreword''. (ligne 56)
chocolate: Voir 5. (ligne 7732)
chunking <1>: Voir 3.22. (ligne 2547)
chunking: Voir 1.4. (ligne 394)
chunking map: Voir 3.22. (ligne 2562)
chunking policy: Voir 3.22. (ligne 2562)
chunksize: Voir 3.22. (ligne 2562)
CLASSIC files: Voir 3.9. (ligne 1584)
client-server: Voir 3.7.1. (ligne 1438)
Climate and Forecast Metadata Convention: Voir 3.19. (ligne 2341)
climate model <1>: Voir 4.12.2. (ligne 7649)
climate model <2>: Voir 4.5. (ligne 6371)
climate model <3>: Voir 3.5. (ligne 1264)
climate model <4>: Voir 2.6.1. (ligne 691)
climate model <5>: Voir 2.2. (ligne 545)
climate model: Voir 2.1. (ligne 519)
clipping operators: Voir 4.1.2. (ligne 3540)
CMIP: Voir 4.4. (ligne 6263)
cnk_all: Voir 3.22. (ligne 2577)
cnk_dmn: Voir 3.22. (ligne 2615)
cnk_g2d: Voir 3.22. (ligne 2577)
cnk_g3d: Voir 3.22. (ligne 2577)
CNK_MAP: Voir 3.22. (ligne 2609)
cnk_prd: Voir 3.22. (ligne 2615)
cnk_rd1: Voir 3.22. (ligne 2615)
cnk_scl: Voir 3.22. (ligne 2615)
cnk_xpl: Voir 3.22. (ligne 2577)
Comeau: Voir 1.2. (ligne 176)
command line options: Voir 3.4. (ligne 1130)
command line switches <1>: Voir 4. (ligne 3273)
command line switches <2>: Voir 3.6. (ligne 1303)
command line switches <3>: Voir 3. (ligne 1043)
command line switches: Voir 2.1. (ligne 535)
comments: Voir 4.1.1. (ligne 3356)
como: Voir 1.2. (ligne 192)
Compaq: Voir 1.2. (ligne 176)
comparator: Voir 4.12.1. (ligne 7560)
compatability: Voir 1.2. (ligne 176)
compilers: Voir 3.6. (ligne 1325)
complementary error function: Voir 4.1.24. (ligne 5487)
compression <1>: Voir 4.7. (ligne 6785)
compression: Voir 3.23. (ligne 2667)
concatenation <1>: Voir 4.10. (ligne 7276)
concatenation <2>: Voir 4.8. (ligne 7066)
concatenation <3>: Voir 4.5. (ligne 6325)
concatenation: Voir 2.4. (ligne 597)
conditional Operator: Voir 4.1.2. (ligne 3529)
config.guess: Voir 1.5. (ligne 500)
configure.eg: Voir 1.5. (ligne 500)
constraint expressions: Voir 3.7.1. (ligne 1532)
contributing: Voir 5. (ligne 7720)
contributors: Voir 5.1. (ligne 7742)
coordinate limits: Voir 3.14. (ligne 1915)
coordinate variable <1>: Voir 4.12. (ligne 7525)
coordinate variable <2>: Voir 4.3. (ligne 6134)
coordinate variable <3>: Voir 3.30. (ligne 3190)
coordinate variable <4>: Voir 3.25. (ligne 2827)
coordinate variable: Voir 3.19. (ligne 2292)
coordinate variables: Voir 4.11. (ligne 7435)
coordinates <1>: Voir 3.30. (ligne 3190)
coordinates: Voir 3.18. (ligne 2160)
coordinates convention: Voir 3.30. (ligne 3190)
core dump <1>: Voir 4.7. (ligne 6880)
core dump <2>: Voir 2.8. (ligne 888)
core dump: Voir 1.5. (ligne 437)
COS: Voir 4.1.24. (ligne 5487)
COSH: Voir 4.1.24. (ligne 5487)
cosine function: Voir 4.1.24. (ligne 5487)
covariance: Voir 4.1.23. (ligne 5411)
Cray <1>: Voir 2.8. (ligne 884)
Cray: Voir 1.2. (ligne 176)
csh: Voir 3.11. (ligne 1839)
cxx: Voir 1.2. (ligne 192)
Cygwin: Voir 1.2.1. (ligne 276)
Daniel Baumann: Voir 5.1. (ligne 7798)
Daniel Wang: Voir 5.1. (ligne 7759)
DAP: Voir 3.7.1. (ligne 1438)
data access protocol: Voir 3.7.1. (ligne 1438)
data safety <1>: Voir 4.11. (ligne 7376)
data safety: Voir 2.3. (ligne 556)
data, missing <1>: Voir 4.2. (ligne 5774)
data, missing: Voir 3.21. (ligne 2432)
date: Voir 3.30. (ligne 3139)
datesec: Voir 3.30. (ligne 3139)
DBG_LVL <1>: Voir 3.3. (ligne 1114)
DBG_LVL <2>: Voir 2.8. (ligne 898)
DBG_LVL: Voir 1.5. (ligne 492)
DDRA: Voir 5.2. (ligne 7819)
Debian: Voir 1.4. (ligne 404)
DEBUG-LEVEL <1>: Voir 2.8. (ligne 898)
DEBUG-LEVEL: Voir 1.5. (ligne 492)
debugging <1>: Voir 3.3. (ligne 1114)
debugging <2>: Voir 2.8. (ligne 898)
debugging: Voir 1.5. (ligne 477)
DEC: Voir 1.2. (ligne 176)
defdim(): Voir 4.1.3. (ligne 3563)
deflation <1>: Voir 4.7. (ligne 6785)
deflation <2>: Voir 3.23. (ligne 2667)
deflation: Voir 1.4. (ligne 390)
degenerate dimension <1>: Voir 4.12.2. (ligne 7640)
degenerate dimension <2>: Voir 4.12. (ligne 7478)
degenerate dimension <3>: Voir 4.9. (ligne 7220)
degenerate dimension <4>: Voir 4.8. (ligne 7186)
degenerate dimension <5>: Voir 4.6. (ligne 6484)
degenerate dimension <6>: Voir 4.5. (ligne 6422)
degenerate dimension <7>: Voir 4.3. (ligne 6128)
degenerate dimension <8>: Voir 4.1.23. (ligne 5462)
degenerate dimension: Voir 3.25. (ligne 2872)
delete_miss(): Voir 4.1.10. (ligne 4054)
demotion: Voir 3.26. (ligne 2952)
derived fields: Voir 4.1. (ligne 3307)
Digital: Voir 1.2. (ligne 176)
dimension limits: Voir 3.14. (ligne 1915)
dimension names: Voir 4.11. (ligne 7355)
dimensions, growing: Voir 4.1.23. (ligne 5381)
dimensionsncap: Voir 4.1.3. (ligne 3563)
disjoint files: Voir 2.4. (ligne 614)
Distributed Data Reduction & Analysis: Voir 5.2. (ligne 7819)
Distributed Oceanographic Data System: Voir 3.7.1. (ligne 1438)
divide: Voir 4.3. (ligne 6031)
dividing data: Voir 4.3. (ligne 6016)
division: Voir 4.1.24. (ligne 5484)
dmn: Voir 3.22. (ligne 2615)
documentation: Voir 1.1. (ligne 136)
DODS <1>: Voir 3.8. (ligne 1570)
DODS: Voir 3.7.1. (ligne 1438)
DODS_ROOT: Voir 3.7.1. (ligne 1438)
dot product: Voir 4.12.2. (ligne 7585)
double precision: Voir 4.1.24. (ligne 5618)
double(): Voir 4.1.11. (ligne 4229)
dynamic linking: Voir 1.3. (ligne 302)
Ed Hill: Voir 5.1. (ligne 7801)
eddy covariance: Voir 4.1.23. (ligne 5420)
editing attributes: Voir 4.2. (ligne 5752)
egrep: Voir 3.11. (ligne 1770)
Elliptic integrals: Voir 4.1.19. (ligne 4783)
ensemble <1>: Voir 4.4. (ligne 6251)
ensemble: Voir 2.6.1. (ligne 691)
ensemble average: Voir 4.4. (ligne 6240)
ensemble concatenation: Voir 4.5. (ligne 6325)
ERF: Voir 4.1.24. (ligne 5487)
ERFC: Voir 4.1.24. (ligne 5487)
error function: Voir 4.1.24. (ligne 5487)
error tolerance: Voir 2.3. (ligne 556)
exclusion <1>: Voir 4.7. (ligne 6837)
exclusion: Voir 3.11. (ligne 1743)
execution time <1>: Voir 4.11. (ligne 7391)
execution time <2>: Voir 3.21. (ligne 2490)
execution time <3>: Voir 3.2. (ligne 1059)
execution time <4>: Voir 2.10. (ligne 1016)
execution time <5>: Voir 2.3. (ligne 569)
execution time: Voir 1.3. (ligne 303)
EXP: Voir 4.1.24. (ligne 5487)
exponentiation: Voir 4.1.24. (ligne 5484)
exponentiation function: Voir 4.1.24. (ligne 5487)
expressions: Voir 4.1.1. (ligne 3369)
extended regular expressions <1>: Voir 4.2. (ligne 5795)
extended regular expressions <2>: Voir 4.1.23. (ligne 5439)
extended regular expressions <3>: Voir 3.11. (ligne 1770)
extended regular expressions: Voir 2.7. (ligne 804)
extraction <1>: Voir 4.7. (ligne 6837)
extraction: Voir 3.11. (ligne 1743)
f90: Voir 1.2.1. (ligne 276)
features, requesting: Voir 1.5. (ligne 437)
file deletion: Voir 3.8. (ligne 1541)
file removal: Voir 3.8. (ligne 1541)
file retention: Voir 3.8. (ligne 1541)
files, multiple: Voir 3.5. (ligne 1235)
files, numerous input: Voir 2.7. (ligne 768)
Filipe Fernandes: Voir 5.1. (ligne 7807)
fixed dimension: Voir 4.7. (ligne 6694)
flags: Voir 4.1.23. (ligne 5396)
float: Voir 4.1.24. (ligne 5618)
float(): Voir 4.1.11. (ligne 4226)
FLOOR: Voir 4.1.24. (ligne 5487)
floor: Voir 3.26.1. (ligne 3022)
floor function: Voir 4.1.24. (ligne 5487)
flt_byt: Voir 4.8. (ligne 7004)
flt_sht: Voir 4.8. (ligne 7004)
for(): Voir 4.1.14. (ligne 4382)
force append: Voir 3.27. (ligne 3062)
force overwrite: Voir 3.27. (ligne 3062)
foreword:
Voir ``Foreword''. (ligne 56)
Fortran <1>: Voir 4.10. (ligne 7328)
Fortran <2>: Voir 4.9. (ligne 7254)
Fortran: Voir 3.26.1. (ligne 2975)
Fortran index convention: Voir 3.13. (ligne 1880)
FORTRAN_FORMAT: Voir 2.10. (ligne 1025)
Francesco Lovergine: Voir 5.1. (ligne 7798)
FTP: Voir 3.8. (ligne 1559)
ftp <1>: Voir 3.7. (ligne 1335)
ftp: Voir 1.2.1. (ligne 282)
funding: Voir 5.2. (ligne 7819)
g++: Voir 1.2.1. (ligne 295)
g2d: Voir 3.22. (ligne 2577)
g3d: Voir 3.22. (ligne 2577)
g77: Voir 1.2.1. (ligne 295)
GAMMA <1>: Voir 4.1.24. (ligne 5487)
GAMMA: Voir 1.2. (ligne 242)
gamma function <1>: Voir 4.1.24. (ligne 5487)
gamma function: Voir 4.1.19. (ligne 4749)
Gaussian weights: Voir 4.12.2. (ligne 7649)
Gayathri Venkitachalam: Voir 5.1. (ligne 7786)
gcc <1>: Voir 1.2.1. (ligne 295)
gcc: Voir 1.2. (ligne 192)
GCM: Voir 2.2. (ligne 545)
George Shapavalov: Voir 5.1. (ligne 7804)
George Shapovalov: Voir 5.1. (ligne 7768)
get_miss(): Voir 4.1.10. (ligne 4048)
gethostname: Voir 1.2.1. (ligne 280)
getopt: Voir 3.4. (ligne 1145)
getopt.h: Voir 3.4. (ligne 1145)
getopt_long: Voir 3.4. (ligne 1145)
getuid: Voir 1.2.1. (ligne 280)
global attributes <1>: Voir 4.11. (ligne 7400)
global attributes <2>: Voir 4.7. (ligne 6601)
global attributes <3>: Voir 4.2. (ligne 5818)
global attributes <4>: Voir 3.31. (ligne 3245)
global attributes <5>: Voir 3.29. (ligne 3108)
global attributes <6>: Voir 3.28. (ligne 3080)
global attributes: Voir 2.7. (ligne 796)
globbing <1>: Voir 4.10. (ligne 7320)
globbing <2>: Voir 4.9. (ligne 7246)
globbing <3>: Voir 4.3. (ligne 6061)
globbing <4>: Voir 4.1.23. (ligne 5439)
globbing <5>: Voir 3.11. (ligne 1839)
globbing <6>: Voir 3.5. (ligne 1205)
globbing: Voir 2.7. (ligne 804)
GNU <1>: Voir 3.11. (ligne 1770)
GNU: Voir 3.4. (ligne 1131)
gnu-win32: Voir 1.2.1. (ligne 276)
GNU/Linux: Voir 2.8. (ligne 888)
GNUmakefile: Voir 1.2.1. (ligne 276)
God: Voir 3.19. (ligne 2378)
growing dimensions: Voir 4.1.23. (ligne 5381)
GSL <1>: Voir 4.1.20. (ligne 5126)
GSL <2>: Voir 4.1.19. (ligne 4734)
GSL: Voir 1.2. (ligne 235)
GSL_SF_BESSEL_JN: Voir 4.1.19. (ligne 4763)
GSL_SF_GAMMA: Voir 4.1.19. (ligne 4749)
gsl_sf_legendre_Pl: Voir 4.1.19. (ligne 4821)
gw <1>: Voir 4.12.2. (ligne 7649)
gw: Voir 3.30. (ligne 3139)
Harry Mangalam: Voir 5.1. (ligne 7762)
HDF <1>: Voir 5.2. (ligne 7826)
HDF <2>: Voir 3.9. (ligne 1584)
HDF: Voir 1.4. (ligne 337)
HDF5: Voir 1.4. (ligne 359)
help: Voir 1.5. (ligne 437)
Henry Butowsky: Voir 5.1. (ligne 7748)
hgh_byt: Voir 4.8. (ligne 7004)
hgh_sht: Voir 4.8. (ligne 7004)
Hierarchical Data Format: Voir 1.4. (ligne 337)
history <1>: Voir 4.7. (ligne 6829)
history <2>: Voir 4.2. (ligne 5769)
history <3>: Voir 3.31. (ligne 3245)
history <4>: Voir 3.28. (ligne 3080)
history <5>: Voir 3.7. (ligne 1335)
history: Voir 2.7. (ligne 849)
HP: Voir 1.2. (ligne 176)
HTML: Voir 1.1. (ligne 136)
HTTP protocol: Voir 3.7.1. (ligne 1438)
hyai: Voir 3.30. (ligne 3139)
hyam: Voir 3.30. (ligne 3139)
hybi: Voir 3.30. (ligne 3139)
hybm: Voir 3.30. (ligne 3139)
hybrid coordinate system: Voir 4.1.4. (ligne 3614)
hyperbolic arccosine function: Voir 4.1.24. (ligne 5487)
hyperbolic arcsine function: Voir 4.1.24. (ligne 5487)
hyperbolic arctangent function: Voir 4.1.24. (ligne 5487)
hyperbolic cosine function: Voir 4.1.24. (ligne 5487)
hyperbolic sine function: Voir 4.1.24. (ligne 5487)
hyperbolic tangent: Voir 4.1.24. (ligne 5487)
hyperslab <1>: Voir 4.12. (ligne 7489)
hyperslab <2>: Voir 4.10. (ligne 7296)
hyperslab <3>: Voir 4.9. (ligne 7230)
hyperslab <4>: Voir 4.5. (ligne 6347)
hyperslab <5>: Voir 4.4. (ligne 6257)
hyperslab <6>: Voir 3.22. (ligne 2566)
hyperslab: Voir 3.14. (ligne 1915)
hyperslabs: Voir 4.1.5. (ligne 3660)
I/O <1>: Voir 3.16. (ligne 2089)
I/O <2>: Voir 3.13. (ligne 1884)
I/O: Voir 3.7.1. (ligne 1499)
I18N: Voir 3.1. (ligne 1051)
IBM: Voir 1.2. (ligne 176)
icc: Voir 1.2. (ligne 192)
ID Quoting: Voir 4.1.26. (ligne 5686)
IDL: Voir 2.1. (ligne 528)
if(): Voir 4.1.8. (ligne 3909)
ilimit: Voir 2.8. (ligne 887)
include ncap: Voir 4.1.15. (ligne 4425)
including files: Voir 4.1.1. (ligne 3360)
index convention: Voir 3.13. (ligne 1880)
inexact conversion: Voir 4.1.24. (ligne 5573)
Info: Voir 1.1. (ligne 136)
input files <1>: Voir 3.6. (ligne 1303)
input files <2>: Voir 3.5. (ligne 1205)
input files: Voir 2.7. (ligne 822)
INPUT-PATH <1>: Voir 3.7. (ligne 1397)
INPUT-PATH: Voir 3.5. (ligne 1205)
installation <1>: Voir 1.5. (ligne 500)
installation: Voir 1.2. (ligne 176)
int(): Voir 4.1.11. (ligne 4223)
int64(): Voir 4.1.11. (ligne 4246)
integration: Voir 4.12.2. (ligne 7585)
Intel: Voir 1.2. (ligne 176)
Internationalization: Voir 3.1. (ligne 1051)
interpolation: Voir 4.6. (ligne 6432)
introduction: Voir 1. (ligne 125)
IPCC <1>: Voir 5.2. (ligne 7819)
IPCC <2>: Voir 4.5. (ligne 6349)
IPCC: Voir 4.4. (ligne 6263)
irregular grids: Voir 4.1.17. (ligne 4505)
ISO: Voir 1.2. (ligne 197)
Jim Edwards: Voir 5.1. (ligne 7777)
Juliana Rew: Voir 5.1. (ligne 7780)
Karen Schuchardt: Voir 5.1. (ligne 7783)
Keith Lindsay: Voir 5.1. (ligne 7792)
kitchen sink: Voir 4.7. (ligne 6572)
L10N: Voir 3.1. (ligne 1052)
large datasets <1>: Voir 3.3. (ligne 1092)
large datasets: Voir 2.8. (ligne 867)
Large File Support <1>: Voir 3.10. (ligne 1711)
Large File Support: Voir 2.8. (ligne 867)
lat_bnds: Voir 3.30. (ligne 3139)
LD_LIBRARY_PATH: Voir 1.3. (ligne 302)
left hand casting <1>: Voir 4.1.4. (ligne 3614)
left hand casting: Voir 2.9.2. (ligne 976)
Legendre polynomial: Voir 4.1.19. (ligne 4821)
Lempel-Ziv deflation: Voir 3.23. (ligne 2667)
Len Makin: Voir 5.1. (ligne 7774)
lexer: Voir 4.1. (ligne 3283)
LFS <1>: Voir 3.10. (ligne 1711)
LFS: Voir 2.8. (ligne 867)
LHS: Voir 4.1.4. (ligne 3614)
libnco: Voir 1.2. (ligne 197)
libraries: Voir 1.3. (ligne 302)
linkers: Voir 3.6. (ligne 1325)
Linux: Voir 4.1.24. (ligne 5620)
LN: Voir 4.1.24. (ligne 5487)
ln -s <1>: Voir 4.8. (ligne 6992)
ln -s: Voir 4.3. (ligne 6075)
LOG: Voir 4.1.24. (ligne 5487)
LOG10: Voir 4.1.24. (ligne 5487)
logarithm, base 10: Voir 4.1.24. (ligne 5487)
logarithm, natural: Voir 4.1.24. (ligne 5487)
lon_bnds: Voir 3.30. (ligne 3139)
long double: Voir 4.1.24. (ligne 5618)
long options <1>: Voir 4.8. (ligne 7131)
long options: Voir 3.4. (ligne 1144)
longitude: Voir 3.17. (ligne 2112)
Macintosh: Voir 1.2. (ligne 176)
Makefile <1>: Voir 3.7.1. (ligne 1451)
Makefile <2>: Voir 1.4. (ligne 352)
Makefile <3>: Voir 1.2.1. (ligne 276)
Makefile: Voir 1.2. (ligne 202)
malloc(): Voir 2.9.2. (ligne 985)
manual type conversion: Voir 3.26. (ligne 2952)
map_dmn: Voir 3.22. (ligne 2615)
map_prd: Voir 3.22. (ligne 2615)
map_rd1: Voir 3.22. (ligne 2615)
map_scl: Voir 3.22. (ligne 2615)
Mark Flanner: Voir 5.1. (ligne 7792)
Markus Liebig: Voir 5.1. (ligne 7795)
Martin Dix: Voir 5.1. (ligne 7792)
Martin Schmidt: Voir 5.1. (ligne 7792)
mask <1>: Voir 4.1.23. (ligne 5404)
mask: Voir 4.1.17. (ligne 4505)
mask condition <1>: Voir 4.12.2. (ligne 7694)
mask condition: Voir 4.12.1. (ligne 7552)
masked average: Voir 4.12. (ligne 7461)
Mass Store System: Voir 3.7. (ligne 1335)
Matej Vela: Voir 5.1. (ligne 7798)
mathematical functions: Voir 4.1.24. (ligne 5487)
max: Voir 3.25. (ligne 2795)
max(): Voir 4.1.11. (ligne 4110)
maximum: Voir 3.25. (ligne 2795)
mean: Voir 3.25. (ligne 2795)
memory available: Voir 2.9. (ligne 909)
memory leaks: Voir 2.9.2. (ligne 976)
memory requirements <1>: Voir 3.11. (ligne 1759)
memory requirements: Voir 2.9. (ligne 909)
merging files <1>: Voir 4.7. (ligne 6615)
merging files: Voir 2.4. (ligne 597)
metadata: Voir 4.7. (ligne 6782)
metadata optimization: Voir 3.2. (ligne 1059)
metadata, global <1>: Voir 4.7. (ligne 6745)
metadata, global: Voir 4.5. (ligne 6360)
Michael Schulz: Voir 5.1. (ligne 7792)
Microsoft <1>: Voir 1.2.1. (ligne 271)
Microsoft: Voir 1.2. (ligne 176)
Mike Folk: Voir 1.4. (ligne 337)
Mike Page: Voir 5.1. (ligne 7792)
min: Voir 3.25. (ligne 2795)
min(): Voir 4.1.11. (ligne 4113)
minimum: Voir 3.25. (ligne 2795)
missing values <1>: Voir 4.6. (ligne 6495)
missing values <2>: Voir 4.2. (ligne 5774)
missing values: Voir 3.21. (ligne 2432)
missing values ncap2: Voir 4.1.10. (ligne 4008)
missing_value <1>: Voir 4.11. (ligne 7441)
missing_value <2>: Voir 3.24. (ligne 2704)
missing_value: Voir 3.21. (ligne 2432)
MKS units: Voir 3.19. (ligne 2271)
modulus: Voir 4.1.24. (ligne 5484)
monotonic coordinates: Voir 2.10. (ligne 1020)
MSA: Voir 3.16. (ligne 2048)
msk_*: Voir 3.30. (ligne 3139)
msrcp <1>: Voir 3.8. (ligne 1559)
msrcp: Voir 3.7. (ligne 1372)
msread: Voir 3.7. (ligne 1372)
MSS: Voir 3.7. (ligne 1335)
multi-file operators <1>: Voir 4.10. (ligne 7289)
multi-file operators <2>: Voir 4.9. (ligne 7223)
multi-file operators <3>: Voir 4.5. (ligne 6356)
multi-file operators <4>: Voir 4.4. (ligne 6272)
multi-file operators <5>: Voir 3.6. (ligne 1315)
multi-file operators <6>: Voir 3.5. (ligne 1235)
multi-file operators: Voir 2.9.1. (ligne 927)
multi-hyperslab: Voir 3.16. (ligne 2048)
multiplication <1>: Voir 4.3. (ligne 6016)
multiplication: Voir 4.1.24. (ligne 5484)
multiply: Voir 4.3. (ligne 6031)
multiplying data <1>: Voir 4.6. (ligne 6432)
multiplying data: Voir 4.3. (ligne 6016)
multislab: Voir 3.16. (ligne 2048)
naked characters: Voir 4.3. (ligne 6060)
NASA: Voir 5.2. (ligne 7826)
NASA EOSDIS: Voir 2.7. (ligne 769)
National Virtual Ocean Data System: Voir 3.7.1. (ligne 1509)
nc__enddef(): Voir 3.2. (ligne 1059)
NC_BYTE <1>: Voir 4.8. (ligne 7004)
NC_BYTE <2>: Voir 4.3. (ligne 6134)
NC_BYTE: Voir 3.14. (ligne 1955)
NC_CHAR <1>: Voir 4.8. (ligne 7004)
NC_CHAR <2>: Voir 4.3. (ligne 6134)
NC_CHAR: Voir 3.14. (ligne 1955)
NC_DOUBLE <1>: Voir 4.8. (ligne 7004)
NC_DOUBLE: Voir 4.1.24. (ligne 5618)
NC_FLOAT: Voir 4.8. (ligne 7004)
NC_INT: Voir 4.8. (ligne 7004)
NC_INT64: Voir 1.4. (ligne 378)
NC_SHORT: Voir 4.8. (ligne 7004)
NC_STRING: Voir 1.4. (ligne 387)
NC_UBYTE: Voir 1.4. (ligne 378)
NC_UINT: Voir 1.4. (ligne 378)
NC_UINT64: Voir 1.4. (ligne 378)
NC_USHORT: Voir 1.4. (ligne 378)
ncadd: Voir 4.3. (ligne 6016)
ncap <1>: Voir 4.1. (ligne 3283)
ncap: Voir 3.3. (ligne 1092)
ncap2 <1>: Voir 4.8. (ligne 6944)
ncap2 <2>: Voir 4.1. (ligne 3283)
ncap2 <3>: Voir 3.26.2. (ligne 3052)
ncap2 <4>: Voir 2.9.2. (ligne 976)
ncap2: Voir 1.2. (ligne 235)
NCAR: Voir 2.2. (ligne 545)
NCAR MSS: Voir 3.7. (ligne 1335)
ncatted <1>: Voir 4.2. (ligne 5752)
ncatted <2>: Voir 3.28. (ligne 3098)
ncatted <3>: Voir 3.21. (ligne 2459)
ncatted: Voir 3.11. (ligne 1770)
ncbo <1>: Voir 4.3. (ligne 6016)
ncbo: Voir 3.21. (ligne 2507)
ncdiff: Voir 4.3. (ligne 6016)
ncdivide: Voir 4.3. (ligne 6016)
ncdump <1>: Voir 4.7. (ligne 6782)
ncdump: Voir 3.9. (ligne 1682)
ncea <1>: Voir 4.4. (ligne 6240)
ncea <2>: Voir 3.21. (ligne 2507)
ncea: Voir 2.6.2. (ligne 736)
ncecat <1>: Voir 4.5. (ligne 6325)
ncecat: Voir 2.6.1. (ligne 678)
ncextr: Voir 4.7. (ligne 6583)
ncflint <1>: Voir 4.6. (ligne 6432)
ncflint <2>: Voir 3.21. (ligne 2507)
ncflint: Voir 2.6.3. (ligne 758)
ncks <1>: Voir 4.7. (ligne 6572)
ncks <2>: Voir 4.1.23. (ligne 5462)
ncks <3>: Voir 3.23. (ligne 2698)
ncks: Voir 3.9. (ligne 1666)
NCL: Voir 2.1. (ligne 528)
ncmult: Voir 4.3. (ligne 6016)
ncmultiply: Voir 4.3. (ligne 6016)
NCO availability: Voir 1.1. (ligne 128)
NCO homepage: Voir 1.1. (ligne 152)
nco.config.log.${GNU_TRP}.foo: Voir 1.5. (ligne 500)
nco.configure.${GNU_TRP}.foo: Voir 1.5. (ligne 500)
nco.make.${GNU_TRP}.foo: Voir 1.5. (ligne 500)
nco_input_file_list <1>: Voir 3.29. (ligne 3108)
nco_input_file_list: Voir 2.7. (ligne 796)
nco_input_file_number <1>: Voir 3.29. (ligne 3108)
nco_input_file_number: Voir 2.7. (ligne 796)
nco_openmp_thread_number: Voir 3.3. (ligne 1080)
ncpack: Voir 4.8. (ligne 6929)
ncpdq <1>: Voir 4.10. (ligne 7301)
ncpdq <2>: Voir 4.8. (ligne 6929)
ncpdq <3>: Voir 4.5. (ligne 6381)
ncpdq <4>: Voir 3.22. (ligne 2566)
ncpdq <5>: Voir 3.3. (ligne 1092)
ncpdq: Voir 2.6.1. (ligne 711)
ncra <1>: Voir 4.9. (ligne 7210)
ncra <2>: Voir 4.1.23. (ligne 5462)
ncra <3>: Voir 3.21. (ligne 2507)
ncra: Voir 2.6.2. (ligne 736)
ncrcat <1>: Voir 4.10. (ligne 7276)
ncrcat <2>: Voir 3.3. (ligne 1092)
ncrcat: Voir 2.6.1. (ligne 678)
ncrename <1>: Voir 4.11. (ligne 7355)
ncrename: Voir 3.21. (ligne 2459)
NCSA: Voir 1.4. (ligne 359)
ncsub: Voir 4.3. (ligne 6016)
ncsubtract: Voir 4.3. (ligne 6016)
ncunpack: Voir 4.8. (ligne 6929)
ncwa <1>: Voir 4.12. (ligne 7461)
ncwa <2>: Voir 4.1.23. (ligne 5462)
ncwa <3>: Voir 3.21. (ligne 2507)
ncwa <4>: Voir 3.3. (ligne 1092)
ncwa: Voir 2.6.2. (ligne 736)
ndims(): Voir 4.1.11. (ligne 4155)
NEARBYINT: Voir 4.1.24. (ligne 5487)
nearest integer function (exact): Voir 4.1.24. (ligne 5487)
nearest integer function (inexact): Voir 4.1.24. (ligne 5487)
NEC: Voir 1.2. (ligne 176)
nesting: Voir 4.1.1. (ligne 3360)
netCDF: Voir 1.1. (ligne 156)
netCDF2 <1>: Voir 3.9. (ligne 1584)
netCDF2: Voir 1.4. (ligne 327)
NETCDF2_ONLY: Voir 1.4. (ligne 344)
netCDF3 <1>: Voir 3.9. (ligne 1584)
netCDF3: Voir 1.4. (ligne 327)
netCDF3 classic file format: Voir 3.9. (ligne 1666)
netCDF4 <1>: Voir 3.9. (ligne 1584)
netCDF4: Voir 1.4. (ligne 359)
netCDF4 classic file format: Voir 3.9. (ligne 1666)
netCDF4 file format: Voir 3.9. (ligne 1666)
NETCDF4 files: Voir 3.9. (ligne 1584)
NETCDF4_CLASSIC files: Voir 3.9. (ligne 1584)
NETCDF4_ROOT: Voir 1.4. (ligne 409)
NINTAP <1>: Voir 4.10. (ligne 7320)
NINTAP <2>: Voir 4.9. (ligne 7246)
NINTAP: Voir 3.5. (ligne 1205)
NO_NETCDF_2: Voir 1.4. (ligne 335)
non-coordinate grid properties: Voir 3.30. (ligne 3169)
non-rectangular grids: Voir 4.1.17. (ligne 4505)
non-standard grids: Voir 4.1.17. (ligne 4505)
normalization: Voir 4.12.2. (ligne 7585)
NRA: Voir 5.2. (ligne 7826)
nrnet: Voir 3.7. (ligne 1372)
NSF: Voir 5.2. (ligne 7819)
NT (Microsoft operating system): Voir 1.2.1. (ligne 271)
NUL <1>: Voir 4.8. (ligne 7036)
NUL: Voir 4.2. (ligne 5955)
NUL-termination: Voir 4.2. (ligne 5955)
null operation: Voir 4.6. (ligne 6549)
number literals ncap: Voir 4.1.7. (ligne 3845)
numerator: Voir 4.12.2. (ligne 7585)
NVODS: Voir 3.7.1. (ligne 1509)
nxt_lsr: Voir 4.8. (ligne 7004)
oceanography: Voir 3.7.1. (ligne 1438)
octal dump: Voir 3.9. (ligne 1694)
od: Voir 3.9. (ligne 1694)
OMP_NUM_THREADS: Voir 3.3. (ligne 1092)
on-line documentation: Voir 1.1. (ligne 136)
open source <1>: Voir 3.7.1. (ligne 1509)
open source:
Voir ``Foreword''. (ligne 74)
Open-source Project for a Network Data Access Protocol:Voir 3.7.1.
(ligne 1438)
OPeNDAP.: Voir 3.7.1. (ligne 1438)
OpenMP <1>: Voir 3.3. (ligne 1080)
OpenMP <2>: Voir 2.9.1. (ligne 969)
OpenMP: Voir 2.9. (ligne 916)
operation types <1>: Voir 4.12. (ligne 7534)
operation types <2>: Voir 4.9. (ligne 7239)
operation types <3>: Voir 4.4. (ligne 6266)
operation types: Voir 3.25. (ligne 2795)
operator speed <1>: Voir 4.11. (ligne 7391)
operator speed <2>: Voir 3.21. (ligne 2490)
operator speed <3>: Voir 3.2. (ligne 1059)
operator speed <4>: Voir 2.10. (ligne 1016)
operator speed <5>: Voir 2.3. (ligne 569)
operator speed: Voir 1.3. (ligne 303)
operators:
Voir ``Summary''. (ligne 111)
OptIPuter: Voir 5.2. (ligne 7819)
Orion Powlawski: Voir 5.1. (ligne 7801)
ORO <1>: Voir 4.12.2. (ligne 7661)
ORO: Voir 3.30. (ligne 3139)
OS: Voir 1.2. (ligne 176)
output file <1>: Voir 3.6. (ligne 1303)
output file: Voir 2.7. (ligne 822)
OUTPUT-PATH: Voir 3.7. (ligne 1397)
overview: Voir 2.10. (ligne 1000)
overwriting files <1>: Voir 3.27. (ligne 3062)
overwriting files: Voir 2.3. (ligne 575)
pack(): Voir 4.1.11. (ligne 4132)
pack(x): Voir 3.24. (ligne 2704)
pack_byte(): Voir 4.1.11. (ligne 4136)
pack_int(): Voir 4.1.11. (ligne 4142)
pack_short(): Voir 4.1.11. (ligne 4139)
packing <1>: Voir 4.10. (ligne 7301)
packing <2>: Voir 4.8. (ligne 6929)
packing <3>: Voir 4.5. (ligne 6381)
packing <4>: Voir 3.24. (ligne 2704)
packing <5>: Voir 3.22. (ligne 2566)
packing: Voir 3.7.1. (ligne 1491)
packing map: Voir 4.8. (ligne 6998)
packing policy: Voir 4.8. (ligne 6944)
papers: Voir 2.10. (ligne 1000)
parallelism <1>: Voir 5.2. (ligne 7821)
parallelism: Voir 3.3. (ligne 1080)
parser: Voir 4.1. (ligne 3283)
pasting variables: Voir 2.4. (ligne 597)
pathCC: Voir 1.2. (ligne 192)
pathcc: Voir 1.2. (ligne 192)
PathScale: Voir 1.2. (ligne 176)
Patrice Dumas: Voir 5.1. (ligne 7801)
Patrick Kursawe: Voir 5.1. (ligne 7804)
pattern matching <1>: Voir 4.2. (ligne 5795)
pattern matching <2>: Voir 3.11. (ligne 1770)
pattern matching: Voir 2.7. (ligne 804)
PayPal: Voir 5. (ligne 7724)
PCK_MAP: Voir 4.8. (ligne 6998)
PCK_PLC: Voir 4.8. (ligne 6951)
peak memory usage: Voir 2.9. (ligne 909)
performance <1>: Voir 4.11. (ligne 7391)
performance <2>: Voir 3.21. (ligne 2490)
performance <3>: Voir 3.2. (ligne 1059)
performance <4>: Voir 2.10. (ligne 1006)
performance <5>: Voir 2.3. (ligne 569)
performance: Voir 1.3. (ligne 303)
Perl <1>: Voir 4.2. (ligne 5929)
Perl <2>: Voir 2.7. (ligne 836)
Perl: Voir 2.1. (ligne 528)
permute dimensions: Voir 4.8. (ligne 6929)
permute(): Voir 4.1.5. (ligne 3763)
pgCC: Voir 1.2. (ligne 192)
pgcc: Voir 1.2. (ligne 192)
PGI: Voir 1.2. (ligne 176)
philosophy: Voir 2.1. (ligne 519)
pipes: Voir 2.7. (ligne 817)
plc_all: Voir 3.22. (ligne 2577)
plc_g2d: Voir 3.22. (ligne 2577)
plc_g3d: Voir 3.22. (ligne 2577)
plc_xpl: Voir 3.22. (ligne 2577)
portability: Voir 1.2. (ligne 176)
positional arguments: Voir 3.6. (ligne 1303)
POSIX <1>: Voir 3.11. (ligne 1782)
POSIX: Voir 3.4. (ligne 1131)
POW: Voir 4.1.24. (ligne 5487)
power: Voir 4.1.24. (ligne 5484)
power function: Voir 4.1.24. (ligne 5487)
prd: Voir 3.22. (ligne 2615)
precision: Voir 4.1.24. (ligne 5618)
preprocessor tokens: Voir 1.2.1. (ligne 276)
presentations: Voir 1.1. (ligne 146)
print()ncap: Voir 4.1.9. (ligne 3979)
printf: Voir 1.2. (ligne 205)
printf() <1>: Voir 4.7. (ligne 6811)
printf(): Voir 4.2. (ligne 5931)
printing files contents: Voir 4.7. (ligne 6572)
printing variables: Voir 4.7. (ligne 6572)
Processor <1>: Voir 4.10. (ligne 7320)
Processor: Voir 4.9. (ligne 7246)
Processor, CCM: Voir 3.5. (ligne 1205)
promotion <1>: Voir 4.1.24. (ligne 5577)
promotion: Voir 3.26. (ligne 2952)
proposals: Voir 5.2. (ligne 7819)
publications: Voir 1.1. (ligne 146)
QLogic: Voir 1.2. (ligne 176)
quadruple precision: Voir 4.1.24. (ligne 5618)
quiet: Voir 4.7. (ligne 6803)
quotes <1>: Voir 4.8. (ligne 7131)
quotes <2>: Voir 4.3. (ligne 6061)
quotes <3>: Voir 4.1.23. (ligne 5439)
quotes: Voir 3.11. (ligne 1839)
RAM: Voir 2.9. (ligne 909)
ram_delete(): Voir 4.1.12. (ligne 4273)
ram_write(): Voir 4.1.12. (ligne 4273)
rank <1>: Voir 4.12. (ligne 7498)
rank: Voir 4.3. (ligne 6115)
rcp <1>: Voir 3.7. (ligne 1335)
rcp: Voir 1.2.1. (ligne 282)
RCS: Voir 3.32. (ligne 3252)
rd1: Voir 3.22. (ligne 2615)
re-dimension: Voir 4.8. (ligne 6929)
re-order dimensions: Voir 4.8. (ligne 6929)
record average: Voir 4.9. (ligne 7210)
record concatenation: Voir 4.10. (ligne 7276)
record dimension <1>: Voir 4.10. (ligne 7286)
record dimension <2>: Voir 4.9. (ligne 7210)
record dimension <3>: Voir 4.8. (ligne 7064)
record dimension <4>: Voir 4.7. (ligne 6694)
record dimension <5>: Voir 4.5. (ligne 6342)
record dimension <6>: Voir 4.4. (ligne 6257)
record dimension <7>: Voir 3.13. (ligne 1896)
record dimension: Voir 2.4. (ligne 600)
record variable <1>: Voir 4.8. (ligne 7068)
record variable: Voir 3.13. (ligne 1891)
rectangular grids: Voir 4.1.17. (ligne 4505)
regex: Voir 3.11. (ligne 1782)
regressions archive: Voir 1.5. (ligne 507)
regular expressions <1>: Voir 4.2. (ligne 5795)
regular expressions <2>: Voir 4.1.23. (ligne 5439)
regular expressions <3>: Voir 3.11. (ligne 1770)
regular expressions <4>: Voir 3.5. (ligne 1205)
regular expressions: Voir 2.7. (ligne 804)
Remik Ziemlinski: Voir 5.1. (ligne 7792)
remote files <1>: Voir 3.7. (ligne 1335)
remote files: Voir 1.2.1. (ligne 282)
renaming attributes: Voir 4.11. (ligne 7355)
renaming dimensions: Voir 4.11. (ligne 7355)
renaming variables: Voir 4.11. (ligne 7355)
reporting bugs: Voir 1.5. (ligne 437)
reshape variables: Voir 4.8. (ligne 6929)
restrict: Voir 1.2. (ligne 228)
reverse data: Voir 4.8. (ligne 7161)
reverse dimensions: Voir 4.8. (ligne 6929)
reverse(): Voir 4.1.5. (ligne 3756)
RINT: Voir 4.1.24. (ligne 5487)
rms: Voir 3.25. (ligne 2795)
rmssdn: Voir 3.25. (ligne 2795)
rmssdn(): Voir 4.1.11. (ligne 4119)
root-mean-square: Voir 3.25. (ligne 2795)
Rorik Peterson: Voir 5.1. (ligne 7755)
ROUND: Voir 4.1.24. (ligne 5487)
rounding functions: Voir 4.1.24. (ligne 5487)
RPM: Voir 1.4. (ligne 404)
running average: Voir 4.9. (ligne 7210)
safeguards <1>: Voir 4.11. (ligne 7376)
safeguards: Voir 2.3. (ligne 556)
scale_factor <1>: Voir 4.10. (ligne 7301)
scale_factor <2>: Voir 4.8. (ligne 6944)
scale_factor <3>: Voir 4.5. (ligne 6381)
scale_factor: Voir 3.24. (ligne 2704)
SCALE_FORMAT: Voir 2.10. (ligne 1025)
scaling: Voir 2.10. (ligne 1006)
Scientific Data Operators: Voir 5.2. (ligne 7819)
scl: Voir 3.22. (ligne 2615)
Scott Capps: Voir 5.1. (ligne 7789)
scp <1>: Voir 3.7. (ligne 1335)
scp: Voir 1.2.1. (ligne 282)
script file: Voir 4.1. (ligne 3300)
SDO: Voir 5.2. (ligne 7819)
SEIII: Voir 5.2. (ligne 7819)
semi-colon: Voir 4.1.1. (ligne 3353)
server <1>: Voir 3.8. (ligne 1562)
server <2>: Voir 3.7.1. (ligne 1438)
server: Voir 2.8. (ligne 884)
Server-Side Distributed Data Reduction & Analysis:Voir 5.2. (ligne 7819)
server-side processing <1>: Voir 5.2. (ligne 7819)
server-side processing: Voir 3.7.1. (ligne 1532)
set_miss(): Voir 4.1.10. (ligne 4039)
sftp <1>: Voir 3.7. (ligne 1335)
sftp: Voir 1.2.1. (ligne 282)
SGI: Voir 1.2. (ligne 176)
shared memory machines: Voir 2.9. (ligne 916)
shared memory parallelism: Voir 3.3. (ligne 1080)
shell <1>: Voir 4.3. (ligne 6061)
shell <2>: Voir 4.1.23. (ligne 5439)
shell <3>: Voir 3.19. (ligne 2327)
shell <4>: Voir 3.11. (ligne 1839)
shell: Voir 2.7. (ligne 804)
SIGNEDNESS: Voir 2.10. (ligne 1025)
SIN: Voir 4.1.24. (ligne 5487)
sine function: Voir 4.1.24. (ligne 5487)
single precision: Voir 4.1.24. (ligne 5618)
SINH: Voir 4.1.24. (ligne 5487)
size(): Voir 4.1.11. (ligne 4152)
SMP: Voir 3.3. (ligne 1080)
sort alphabetically: Voir 4.7. (ligne 6668)
source code: Voir 1.1. (ligne 128)
special characters: Voir 4.2. (ligne 5946)
speed <1>: Voir 4.11. (ligne 7391)
speed <2>: Voir 3.21. (ligne 2490)
speed <3>: Voir 3.2. (ligne 1059)
speed <4>: Voir 2.10. (ligne 1016)
speed <5>: Voir 2.8. (ligne 893)
speed <6>: Voir 2.3. (ligne 569)
speed: Voir 1.3. (ligne 303)
sqravg: Voir 3.25. (ligne 2795)
sqravg(): Voir 4.1.11. (ligne 4104)
SQRT: Voir 4.1.24. (ligne 5487)
sqrt: Voir 3.25. (ligne 2795)
square root function: Voir 4.1.24. (ligne 5487)
SSDDRA: Voir 5.2. (ligne 7819)
SSH <1>: Voir 3.8. (ligne 1559)
SSH: Voir 1.2.1. (ligne 282)
sshort(): Voir 4.1.11. (ligne 4220)
standard deviation: Voir 3.25. (ligne 2795)
standard input <1>: Voir 4.10. (ligne 7289)
standard input <2>: Voir 4.9. (ligne 7223)
standard input <3>: Voir 4.5. (ligne 6356)
standard input <4>: Voir 4.4. (ligne 6272)
standard input: Voir 2.7. (ligne 787)
standard_name: Voir 3.18. (ligne 2160)
statement: Voir 4.1.1. (ligne 3338)
static linking: Voir 1.3. (ligne 302)
stdin <1>: Voir 4.10. (ligne 7289)
stdin <2>: Voir 4.9. (ligne 7223)
stdin <3>: Voir 4.5. (ligne 6356)
stdin <4>: Voir 4.4. (ligne 6272)
stdin <5>: Voir 3.29. (ligne 3108)
stdin: Voir 2.7. (ligne 787)
stride <1>: Voir 4.10. (ligne 7297)
stride <2>: Voir 4.9. (ligne 7231)
stride <3>: Voir 4.7. (ligne 6690)
stride <4>: Voir 3.19. (ligne 2318)
stride <5>: Voir 3.16. (ligne 2078)
stride <6>: Voir 3.15. (ligne 1988)
stride: Voir 3.14. (ligne 1944)
strings: Voir 4.2. (ligne 5955)
stub: Voir 3.7. (ligne 1406)
subsetting <1>: Voir 4.7. (ligne 6837)
subsetting <2>: Voir 3.30. (ligne 3190)
subsetting <3>: Voir 3.12. (ligne 1857)
subsetting: Voir 3.11. (ligne 1743)
subtract: Voir 4.3. (ligne 6031)
subtracting data: Voir 4.3. (ligne 6016)
subtraction <1>: Voir 4.3. (ligne 6016)
subtraction: Voir 4.1.24. (ligne 5484)
summary:
Voir ``Summary''. (ligne 111)
Sun: Voir 1.2. (ligne 176)
swap space <1>: Voir 2.9. (ligne 909)
swap space: Voir 2.8. (ligne 872)
switches: Voir 3.4. (ligne 1131)
symbolic links <1>: Voir 4.8. (ligne 6992)
symbolic links <2>: Voir 4.3. (ligne 6075)
symbolic links <3>: Voir 2.7. (ligne 830)
symbolic links: Voir 2.6. (ligne 648)
synchronous file access: Voir 3.7. (ligne 1335)
syntax: Voir 4.1.1. (ligne 3338)
Takeshi Enomoto: Voir 5.1. (ligne 7810)
TAN: Voir 4.1.24. (ligne 5487)
TANH: Voir 4.1.24. (ligne 5487)
temporary output files <1>: Voir 4.11. (ligne 7376)
temporary output files: Voir 2.3. (ligne 556)
TeXinfo: Voir 1.1. (ligne 136)
THR_NBR: Voir 3.3. (ligne 1092)
threads <1>: Voir 3.3. (ligne 1080)
threads <2>: Voir 2.9.1. (ligne 969)
threads: Voir 2.9. (ligne 916)
time <1>: Voir 3.31. (ligne 3221)
time: Voir 3.19. (ligne 2310)
time-averaging: Voir 4.1.23. (ligne 5462)
time_offset: Voir 3.31. (ligne 3221)
timestamp: Voir 3.28. (ligne 3080)
total: Voir 3.25. (ligne 2795)
transpose <1>: Voir 4.8. (ligne 7078)
transpose: Voir 3.13. (ligne 1889)
TRUNC: Voir 4.1.24. (ligne 5487)
truncation function: Voir 4.1.24. (ligne 5487)
truth condition <1>: Voir 4.12.2. (ligne 7694)
truth condition: Voir 4.12.1. (ligne 7552)
ttl: Voir 3.25. (ligne 2795)
ttl(): Voir 4.1.11. (ligne 4122)
type conversion: Voir 3.26. (ligne 2950)
type(): Voir 4.1.11. (ligne 4158)
ubyte(): Voir 4.1.11. (ligne 4237)
UDUnits <1>: Voir 3.30. (ligne 3139)
UDUnits <2>: Voir 3.19. (ligne 2257)
UDUnits: Voir 1.2. (ligne 184)
uint(): Voir 4.1.11. (ligne 4243)
ulimit: Voir 2.8. (ligne 888)
unary operations: Voir 2.9.2. (ligne 976)
UNICOS: Voir 2.8. (ligne 884)
Unidata <1>: Voir 3.19. (ligne 2257)
Unidata <2>: Voir 1.4. (ligne 359)
Unidata: Voir 1.2. (ligne 184)
union of two files: Voir 2.4. (ligne 614)
unit64(): Voir 4.1.11. (ligne 4249)
units <1>: Voir 4.6. (ligne 6564)
units <2>: Voir 4.2. (ligne 5986)
units: Voir 3.19. (ligne 2257)
UNIX <1>: Voir 3.5. (ligne 1211)
UNIX <2>: Voir 3.4. (ligne 1131)
UNIX <3>: Voir 2.7. (ligne 804)
UNIX <4>: Voir 1.2.1. (ligne 280)
UNIX: Voir 1.2. (ligne 184)
unlimited dimension: Voir 4.5. (ligne 6342)
unpack(): Voir 4.1.11. (ligne 4145)
unpack(x): Voir 3.24. (ligne 2704)
unpacking <1>: Voir 4.10. (ligne 7301)
unpacking <2>: Voir 4.8. (ligne 6929)
unpacking <3>: Voir 4.5. (ligne 6381)
unpacking <4>: Voir 3.24. (ligne 2704)
unpacking: Voir 3.7.1. (ligne 1491)
URL: Voir 3.7. (ligne 1335)
ushort(): Voir 4.1.11. (ligne 4240)
value list: Voir 4.1.6. (ligne 3800)
variable names: Voir 4.11. (ligne 7355)
variance: Voir 3.25. (ligne 2795)
version: Voir 3.32. (ligne 3252)
weighted average: Voir 4.12. (ligne 7461)
where(): Voir 4.1.13. (ligne 4297)
while(): Voir 4.1.14. (ligne 4382)
whitespace: Voir 3.19. (ligne 2318)
wildcards <1>: Voir 4.2. (ligne 5795)
wildcards <2>: Voir 3.11. (ligne 1770)
wildcards: Voir 3.5. (ligne 1205)
WIN32: Voir 1.2.1. (ligne 276)
Windows <1>: Voir 1.2.1. (ligne 271)
Windows: Voir 1.2. (ligne 176)
wrapped coordinates <1>: Voir 4.7. (ligne 6910)
wrapped coordinates <2>: Voir 4.1.17. (ligne 4532)
wrapped coordinates <3>: Voir 3.17. (ligne 2112)
wrapped coordinates: Voir 3.14. (ligne 1960)
wrapped filenames: Voir 3.5. (ligne 1264)
WWW documentation: Voir 1.1. (ligne 136)
xargs <1>: Voir 3.6. (ligne 1320)
xargs: Voir 2.7. (ligne 804)
xlc: Voir 1.2. (ligne 192)
xlC: Voir 1.2. (ligne 192)
XP (Microsoft operating system): Voir 1.2.1. (ligne 271)
xpl: Voir 3.22. (ligne 2577)
Yorick <1>: Voir 2.10. (ligne 1028)
Yorick: Voir 2.1. (ligne 528)
| (wildcard character): Voir 3.11. (ligne 1796)
Table of Contents
*****************
NCO User's Guide
Foreword
Summary
1 Introduction
1.1 Availability
1.2 Operating systems compatible with NCO
1.2.1 Compiling NCO for Microsoft Windows OS
1.3 Libraries
1.4 netCDF2/3/4 and HDF4/5 Support
1.5 Help Requests and Bug Reports
2 Operator Strategies
2.1 Philosophy
2.2 Climate Model Paradigm
2.3 Temporary Output Files
2.4 Appending Variables
2.5 Simple Arithmetic and Interpolation
2.6 Averagers vs. Concatenators
2.6.1 Concatenators `ncrcat' and `ncecat'
2.6.2 Averagers `ncea', `ncra', and `ncwa'
2.6.3 Interpolator `ncflint'
2.7 Large Numbers of Files
2.8 Large Datasets
2.9 Memory Requirements
2.9.1 Single and Multi-file Operators
2.9.2 Memory for `ncap2'
2.10 Performance
3 NCO Features
3.1 Internationalization
3.2 Metadata Optimization
3.3 OpenMP Threading
3.4 Command Line Options
3.5 Specifying Input Files
3.6 Specifying Output Files
3.7 Accessing Remote Files
3.7.1 OPeNDAP
3.8 Retaining Retrieved Files
3.9 Selecting Output File Format
3.10 Large File Support
3.11 Subsetting Variables
3.12 Subsetting Coordinate Variables
3.13 C and Fortran Index conventions
3.14 Hyperslabs
3.15 Stride
3.16 Multislabs
3.17 Wrapped Coordinates
3.18 Auxiliary Coordinates
3.19 UDUnits Support
3.20 Rebasing Time Coordinate
3.21 Missing values
3.22 Chunking
3.23 Deflation
3.24 Packed data
Packing Algorithm
Unpacking Algorithm
Default Handling of Packed Data
3.25 Operation Types
3.26 Type Conversion
3.26.1 Automatic type conversion
3.26.2 Manual type conversion
3.27 Batch Mode
3.28 History Attribute
3.29 File List Attributes
3.30 CF Conventions
3.31 ARM Conventions
3.32 Operator Version
4 Operator Reference Manual
4.1 `ncap2' netCDF Arithmetic Processor
4.1.1 Syntax of `ncap2' statements
4.1.2 Expressions
4.1.3 Dimensions
4.1.4 Left hand casting
4.1.5 Arrays and hyperslabs
4.1.6 Attributes
4.1.7 Number literals
4.1.8 if statement
4.1.9 print statement
4.1.10 Missing values ncap2
4.1.11 Methods and functions
4.1.12 RAM variables
4.1.13 Where statement
4.1.14 Loops
4.1.15 Include files
4.1.16 sort methods
4.1.17 Irregular Grids
4.1.18 bilinear interpolation
4.1.19 GSL special functions
4.1.20 GSL interpolation
4.1.21 GSL least-squares fitting
4.1.22 GSL statistics
4.1.23 Examples ncap2
4.1.24 Intrinsic mathematical methods
4.1.25 Operators precedence and associativity
4.1.26 ID Quoting
4.2 `ncatted' netCDF Attribute Editor
4.3 `ncbo' netCDF Binary Operator
4.4 `ncea' netCDF Ensemble Averager
4.5 `ncecat' netCDF Ensemble Concatenator
4.6 `ncflint' netCDF File Interpolator
4.7 `ncks' netCDF Kitchen Sink
Options specific to `ncks'
4.8 `ncpdq' netCDF Permute Dimensions Quickly
Packing and Unpacking Functions
Dimension Permutation
4.9 `ncra' netCDF Record Averager
4.10 `ncrcat' netCDF Record Concatenator
4.11 `ncrename' netCDF Renamer
4.12 `ncwa' netCDF Weighted Averager
4.12.1 Mask condition
4.12.2 Normalization and Integration
5 Contributing
5.1 Contributors
5.2 Proposals for Institutional Funding
6 CCSM Example
7 References
General Index
