Metadata-Version: 1.1
Name: cdat-lite
Version: 6.0.1
Summary: Core components of the Climate Data Analysis tools.  This software is based on CDAT-6.0.alpha-g82e6c52 and cdunfpp0.13.
Home-page: http://proj.badc.rl.ac.uk/cedaservices/wiki/CdatLite
Author: Ag Stephens
Author-email: ag.stephens@stfc.ac.uk
License: http://www-pcmdi.llnl.gov/software-portal/cdat/docs/cdat-license
Description: =========
        cdat-lite
        =========
        
        .. contents::
        
        .. sectnum::
        
        Cdat-lite is a Python package for managing and analysing climate
        science data.  It is a subset of the Climate Data Analysis Tools
        (CDAT_) developed by PCMDI_ at Lawrence Livermore National Laboratory.
        
        Cdat-lite aims to complement CDAT by focusing on its core data
        management and analysis components and by offering a radically
        different installation system to CDAT.  As a result it is much more
        lightweight (hence the name): CDAT's source distribution is the order
        of 1Gb whereas cdat-lite is under 5Mb.
        
        Cdat-lite is designed to work with the `CF checker`_ package.
        
        .. _CDAT: http://www2-pcmdi.llnl.gov/cdat
        .. _PCMDI: http://www2-pcmdi.llnl.gov/
        .. _`CF checker`: http://pypi.python.org/pypi/cfchecker
        
        
        cdat-lite versioning
        ====================
        
        Cdat-lite is a project that tracks versions of 2 other projects (CDAT
        and cdunifpp).  From version 6.0rc1 the cdat-lite version will not be
        based directly on the CDAT version.  This is because CDAT updates its
        version very seldom and stays as an "alpha" distribution for long
        periods when the parts included in cdat-lite are generally stable.
        
        Full details of which versions of CDAT and cdunifpp a cdat-lite
        distribution includes is available in the setup.py file and the
        PKG_INFO metadata.
        
        Installing cdat-lite
        ====================
        
        cdat-lite is distributed as a tarball available from the `cdat-lite
        homepage`_ on the `NERC Data Grid wiki` .  It is also installable
        using the ``easy_install`` tool.  If you are familiar with
        ``easy_install`` try this super-quick installation recipe::
        
          $ export NETCDF_HOME=/usr/local/netcdf
          # Required if using a NetCDF4 compiled with HDF5
          $ export HDF5_HOME=/usr/local/hdf5
          $ easy_install cdat_lite 
        
        
        Dependencies
        ------------
        
        To install cdat-lite you will need:
        
         1. `Python 2.5.x`_.  cdat-lite has not been tested on 2.6 but may
         work (feedback would be gratefully received).  It is unlikely to work on 3.0.
        
         2. `setuptools`_.  cdat-lite will attempt to download and install
         setuptools if it is missing but it is safer to install it first.
        
         3. `NetCDF-3.x`_ or greater.  cdat-lite should work with any
         relatively modern NetCDF3 installation on your system provided it is
         compiled as a shared library.  It will also work with NetCDF4
         configured in various different ways, including embedded OPeNDAP
         mode.
        
         4. If you want to run the short test suite you will need nose_
        
        .. _`Python 2.5.x`: http://www.python.org/download/releases/2.5.4
        .. _`setuptools`: http://pypi.python/org/setuptools
        .. _`NetCDF-3.x`: http://www.unidata.ucar.edu/software/netcdf/
        .. _nose: http://somethingaboutorange.com/mrl/projects/nose/
        
        Selecting your NetCDF installation
        ----------------------------------
        
        cdat-lite will work with NetCDF3 or NetCDF4 but because it is
        referenced by shared libraries (the python C extension modules) it
        must be compiled as position independent code.  If you have a NetCDF4
        installation you almost certainly are using shared libraries and even
        if you wish to use NetCDF3 it is probably easiest to install NetCDF as
        a shared library (use ``--enable-shared`` in the NetCDF ``configure``
        script).  Alternatively, you can configure NetCDF with::
        
          $ ./configure --with-pic ...
        
        If you are using NetCDF4 you will also need to configure HDF5 with ``--enable-shared`` or ``--with-pic``.
        
        If you have the command ``nc-config`` in your path cdat-lite will
        detect all library and include dependencies.  Otherwise cdat-lite will
        look for a NetCDF installation in several places.
        
        If your NetCDF is installed somewhere unusual, or if you want to
        select a specific installation, set the NETCDF_HOME variable.  E.g.::
        
          # sh users
          $ export NETCDF_HOME=/usr/local/netcdf
          # csh users
          $ setenv NETCDF_HOME /usr/local/netcdf
        
        If you are using NetCDF4 cdat-lite will also look for your HDF5
        installation which you can configure in a similar way::
        
          # sh users
          $ export HDF5_HOME=/usr/local/hdf5
          # csh users
          $ setenv HDF5_HOME /usr/local/hdf5
        
        For compatibility with the ``netcdf4-python`` package cdat-lite also accepts ``NETCDF4_DIR`` AND ``HDF5_DIR`` as synonims for these environment variables.
        
        Note, you don't need these environment variables set to run cdat_lite,
        although the libraries must be findable by your system's dynamic
        linker.  This can be configured by setting ``LD_LIBRARY_PATH`` or using ``ldconfig``.
        
        Running the installer
        ---------------------
        
        If you have all the dependencies in place you can try using
        ``easy_install`` to automatically download and install cdat_lite.  Make sure you have access to the internet, with the appropriate HTTP proxy settings, and do::
        
          $ easy_install cdat-lite
        
        Alternatively you might want to see what you are installing :-).  In
        this case either download the tarball__ or use ``easy_install`` to do it for you::
        
          $ easy_install -eb . cdat-lite
          # The cdat-lite tarball will be downloaded unpacked into you current directory
        
        Now from the distribution directory run the build and install steps separately::
        
          $ python setup.py bdist_egg
          $ easy_install dist/cdat-lite*.egg
        
        __ `cdat-lite homepage`_
        
        
        .. _`installing locally`:
        
        Installing as an unprivileged user
        ----------------------------------
        
        If you don't have write access to your python distribution you can use
        the tool virtualenv_ to create a local python environment with
        its own ``easy_install`` executable which you can then use to install
        cdat-lite.  In combination with ``NETCDF_HOME``, ``HDF5_HOME`` and
        ``LD_LIBRARY_PATH`` it should be possible to install all dependencies
        of cdat-lite locally.  See the virtualenv_ for details on
        installation or try this recipe after downloading the virtualenv::
        
          # From virtualenv distribution directory
          $ ./virtualenv.py <virtualenv-path>
          $ cd <virtualenv-path>
          $ source bin/activate
          (venv)$ easy_install cdat-lite
        
        .. _virtualenv: http://pypi.python.org/pypi/virtualenv
        
        
        Platform-specific installation notes
        ------------------------------------
        
        OS X
        ''''
        
        Christopher Lee contributed the following experiences installing on OS X 10.6.7.
        
        My particular Macbook has an Intel CPU, and the default on the Mac is
        to compile for the architecture x86_64. In order to override this
        (because python is 32 bit, and the netcdf libraries I use are also 32
        bit) I needed to pass in "-arch i386" to the compiler. I also needed
        the little endian flag '-DBYTESWAP' when compiling the netcdf
        interface (inside libcdms). The -DBYTESWAP flag should be included by
        the libcdms configure script, where there is a section for 'darwin'
        (OS X), but it's currently configured without BYTESWAP (line
        6182). The problem here is that OS X used to run on PowerPC CPUs,
        which don't need the BYTESWAP flag. I'm not sure if this is your
        configure script or if it's from the cdat package.
        
        I included the -arch i386 and -DBYTESWAP in the setup.py in the
        libcdms section, and the setup works fine.
        
        After running python setup.py build ; python setup.py install ; I
        still get an error when importing cdms2. This problem is caused by the
        way that libcdms is linked to the netcdf libraries. The 'normal' Mac
        method is to link with absolute paths, but libcdms is linked with
        relative paths (the libraries are references with @rpath). The result
        is that LD_LIBRARY_PATH environment variable is often empty. I'm not
        sure how to fix this in the 'Mac' way with absolute paths, but I added
        my $NETCDF_HOME/lib directory to the variable and cdms2 now imports
        without error.
        
        
        Testing the installation
        ========================
        
        cdat-lite ships with a small set of tests designed to verify that it
        has been built successfuly.  These tests require the testing framework
        nose_.  Once cdat-lite is installed just run::
        
          $ nosetests cdat_lite
        
        When run from cdat-lite's distribution directory nosetests will run
        slightly differently, running some tests that are known to fail at the
        moment.  To disable this behaviour do:
        
          $ nosetests --config=''
        
        .. _`cdat-lite homepage`: http://proj.badc.rl.ac.uk/ndg/wiki/CdatLite
        .. _`NERC Data Grid wiki`: http://proj.badc.rl.ac.uk/ndg/wiki
        
        
        
        FAQ
        ===
        
        What is CDAT?
        -------------
        
        CDAT_ is a large suite of open source tools distributed by PCMDI_ for
        the management and analysis of climate data.  It includes several
        visualisation components and the graphical user interface VCDAT.
        
        What is the difference between CDAT and cdat-lite?
        --------------------------------------------------
        
        Differences between CDAT and cdat-lite can be classified as
        differences in scope, i.e. which packages are included, and installation system.
        
        cdat-lite contains the 'cdms2' package and a few related
        packages.  It does not include the 'vcs' visualisation package or the
        VCDAT graphical user interface.  As of v5.1.1-0.3pre3 the included
        packages are:
        
         * cdms2
        
         * cdtime
        
         * cdutil
        
         * genutil
        
         * ncml
        
         * Properties
        
         * regrid2
        
         * unidataa
        
         * xmgrace
        
        CDAT bundles virtually all dependencies together in its source
        distribution -- even Python itself.  This has its advantages as it
        simplifies satisfying dependencies and avoids version conflicts
        between dependencies.  However, if you want to integrate CDAT's data
        management components into your existing Python architecture CDAT can
        be overkill.
        
        
        What has changed between cdat-lite-4.x and cdat-lite-5.x?
        ---------------------------------------------------------
        
        If you are a cdat-lite-4 user (or a CDAT 4 user) you have a big
        migration job on your hands.  CDAT-4 uses the ``Numeric`` package for
        arrays which has been out of date and unmaintained for a long time
        now.  It is known to have problems on 64bit architectures.
        
        How does cdat-lite track changes to CDAT?
        -----------------------------------------
        
        cdat-lite tries to release major new versions shortly after new
        versions of CDAT.  Sometimes CDAT-trunk contains important fixes that
        should be applied so that the latest cdat_lite can run ahead of
        official CDAT releases (although sometimes CDAT recommends you build
        from trunk anyway).
        
        The one exception is the UK Met. Office PP file support which is
        usually updated in cdat_lite before CDAT.  In all cases the exact
        build versions of CDAT and cdunifpp will be stated in the
        distribution's ``setup.py`` file.
        
        How can I use CMOR2 with cdat-lite?
        -----------------------------------
        
        We are interested to hear any with experience of using CMOR2 with
        cdat-lite but it should be as simple as downloading the distribution
        and installing it in parallel with::
        
          # From the CMOR install directory
          $ python setup.py install
        
        How can I use OPeNDAP with cdat-lite?
        -------------------------------------
        
        OPeNDAP support is an experimental feature of cdat-lite at the moment.
        Unlike CDAT you don't select OPeNDAP explicitly during installation
        but cdat-lite will inherit any OPeNDAP support embedded into the
        NetCDF4 library.  Recent beta releases of NetCDF4 provides a switch to
        transparently use OPeNDAP.
        
        How do I install cdat-lite as an unprivileged user?
        ---------------------------------------------------
        
        See `installing locally`_
        
        Which versions of NetCDF does cdat-lite support?
        ------------------------------------------------
        
        TODO
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
