Core components of the Climate Data Analysis tools. This software is based on CDAT-6.0.alpha-g82e6c52 and cdunfpp0.13.
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-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.
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
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.
- If you want to run the short test suite you will need nose
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.
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
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
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.
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=”
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:
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.
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.
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.
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
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.