ViTables is a member of the PyTables family. It’s a graphical tool for browsing and editing files in both PyTables and HDF5 formats. With ViTables you can easily navigate through data hierarchies, request metadata, view real data and much more.
ViTables is being developed using Python and PyQt, the bindings of Qt libraries, so it can run on any platform that supports these components (which includes Windows, Mac OS X, Linux and many other Unices). The interface and features will remain the same on all platforms.
Efficiency and low memory requirements are guaranteed by the fact that data is loaded only when the object that contains it is opened and by the use of data buffers for dealing with large datasets.
The current release provides browsing, displaying, editing and querying capabilities. Some of them are listed below. Details are discussed in the related chapters.
These editing features have been implemented for the object tree [1]:
All these changes automatically update the database (i.e. the file) to which the nodes belong.
Other nice features include:
We have paid special attention to usability issues so making use of these features is intuitive and pleasant. Nevertheless, and just in case, we are providing this guide :-).
To run ViTables you need to install Python 2.6 or Python 2.7, PyTables >= 2.2 (so you have to fulfil its own requirements) and PyQt4 >= 4.8.3.
At the moment, ViTables has been fully tested on Linux and Windows XP and Vista platforms. Other Unices should run just fine when using the Linux version because all the software that ViTables relies on (i.e. Python, Qt, PyQt, HDF5 and PyTables) is known to run fine on many Unix platforms as well.
The Distutils module (part of the standard Python distribution) has been used to prepare an installer for ViTables. It makes easy to get the application up and running.
At the moment no binary versions of the installer exist, so the software has to be installed from sources.
Provided that your system fulfills the requirements listed in the above sections, installing the package is really easy. Just uncompress the package, change to the distribution directory and execute
$ python setup.py install
By default ViTables will be installed in the system-protected area where your system installs third party Python packages, so you will need superuser privileges. If you prefer to install the package in a different location (for instance, your home directory, so that you can complete the installation as a non-privileged user), you can do it using the –prefix tag:
$ python setup.py install --prefix=/home/myuser/mystuff
Please remember that installing Python modules in non-standard locations makes it necessary to set the PYTHONPATH environment variable properly so that the Python interpreter can find the installed modules.
If you need further customizations, please have a look at the output of the command
$python setup.py install --help
to see the available options. Complete information about these options can be found in the Distutils documentation.
A binary installer is available for Windows platforms. Just double click the installer icon and follow the wizard instructions. ViTables will be installed in a few clicks.
Beware that the installer is not a superpackage containing all ViTables requirements. You need PyTables and PyQt4 already installed on your system (excellent installers for both packages are available) in order to install ViTables.
You can use the general Unix procedure to install ViTables on Mac OS X, but generating a double-clickable application bundle is recommended. Simply untar the source package, change to the distribution directory and execute
$ cd macosxapp
$ ./make.sh
If you have problems with this please, refer to the FAQ page in the ViTables website.
General information about PyTables can be found at the project site <www.pytables.com>. For more information on HDF5, please visit its web site <www.hdfgroup.org/HDF5>. Information about ViTables is available at <www.vitables.org>.
Questions and feedback can be mailed to the developers.
Footnotes
[1] | Dataset editing capabilities have not yet been implemented. |