What's new in PyTables 0.8
----------------------------

On this release, many enhancements has been added and some bugs has
been fixed. Here is the (non-exhaustive) list:

	- The new VLArray class enables you to store large lists of rows 
	  containing variable numbers of elements. The elements can 
	  be scalars or fully multimensional objects, in the PyTables 
	  tradition. This class supports two special objects as rows: 
	  Unicode strings (UTF-8 codification is used internally) and 
	  generic Python objects (through the use of cPickle).

	- The new EArray class allows you to enlarge already existing
	  multidimensional homogeneous data objects. Consider it
	  an extension of the already existing Array class, but 
	  with more functionality. Online compression or other filters 
	  can be applied to EArray instances, for example.

	  Another nice feature of EA's is their support for fully
	  multidimensional data selection with extended slices.  You
	  can write "earray[1,2:3,...,4:200]", for example, to get the
	  desired dataset slice from the disk. This is implemented
	  using the powerful selection capabilities of the HDF5
	  library, which results in very highly efficient I/O
	  operations. The same functionality has been added to Array
	  objects as well.

	- New UnImplemented class. If a dataset contains unsupported
	  datatypes, it will be associated with an UnImplemented
	  instance, then inserted into to the object tree as usual.
	  This allows you to continue to work with supported objects
	  while retaining access to attributes of unsupported
	  datasets.  This has changed from previous versions, where a
	  RuntimeError occurred when an unsupported object was
	  encountered.

	  The combination of the new UnImplemented class with the 
	  support for new datatypes will enable PyTables to greatly 
	  increase the number of types of native HDF5 files that can
	  be read and modified.

	- Boolean support has been added for all the Leaf objects.

	- The Table class has now an append() method that allows you
	  to save large buffers of data in one go (i.e. bypassing the
	  Row accessor). This can greatly improve data gathering
	  speed.

	- The standard HDF5 shuffle filter (to further enhance the
          compression level) is supported.

	- The standard HDF5 fletcher32 checksum filter is supported.

	- As the supported number of filters is growing (and may be
          further increased in the future), a Filters() class has been
          introduced to handle filters more easily.  In order to add
          support for this class, it was necessary to make a change in
          the createTable() method that is not backwards compatible:
          the "compress" and "complib" parameters are deprecated now
          and the "filters" parameter should be used in their
          place. You will be able to continue using the old parameters
          (only a Deprecation warning will be issued) for the next few
          releases, but you should migrate to the new version as soon
          as possible. In general, you can easily migrate old code by
          substituting code in its place:

 table = fileh.createTable(group, 'table', Test, '',
 complevel, complib)
	 should be replaced by

 table = fileh.createTable(group, 'table', Test, '',
 Filters(complevel, complib))


	- A copy() method that supports slicing and modification of
 filtering capabilities has been added for all the Leaf
 objects. See the User's Manual for more information.

	- A couple of new methods, namely copyFile() and copyChilds(),
 have been added to File class, to permit easy replication
 of complete hierarchies or sub-hierarchies, even to
 other files. You can change filters during the copy
 process as well.

	- Two new utilities has been added: ptdump and
 ptrepack. The utility ptdump allows the user to examine 
          thecontents of PyTables files (both metadata and actual
 data). The powerful ptrepack utility lets you 
 selectively copy (portions of) hierarchies to specific
 locations in other files. It can be also used as an
 importer for generic HDF5 files.

 - The meaning of the stop parameter in read() methods has
 changed. Now a value of 'None' means the last row, and a
 value of 0 (zero) means the first row. This is more
 consistent with the range() function in python and the
 __getitem__() special method in numarray.

	- The method Table.removeRows() is no longer limited by table 
	  size.  You can now delete rows regardless of the size of the 
	  table.

	- The "numarray" value has been added to the flavor parameter
 in the Table.read() method for completeness.

	- The attributes (.attr instance variable) are Python
 properties now. Access to their values is no longer
 lazy, i.e. you will be able to see both system or user
 attributes from the command line using the tab-completion
 capability of your python console (if enabled).

	- Documentation has been greatly improved to explain all the
 new functionality. In particular, the internal format of
 PyTables is now fully described. You can now build
 "native" PyTables files using any generic HDF5software 
          by just duplicating their format.

	- Many new tests have been added, not only to check new
 functionality but also to more stringently check 
 existing functionality. There are more than 800 different
 tests now (and the number is increasing :).

        - PyTables has a new record in the data size that fits in one
          single file: more than 5 TB (yeah, more than 5000 GB), that
          accounts for 11 GB compressed, has been created on an AMD
          Opteron machine running Linux-64 (the 64 bits version of the
          Linux kernel). See the gory details in:
          http://pytables.sf.net/html/HowFast.html.

	- New platforms supported: PyTables has been compiled and tested
	  under Linux32 (Intel), Linux64 (AMD Opteron and Alpha), Win32
	  (Intel), MacOSX (PowerPC), FreeBSD (Intel), Solaris (6, 7, 8
	  and 9 with UltraSparc), IRIX64 (IRIX 6.5 with R12000) and it
	  probably works in many more architectures. In particular,
	  release 0.8 is the first one that provides a relatively clean
	  porting to 64-bit platforms.

	- As always, some bugs have been solved (especially bugs that
 occur when deleting and/or overwriting attributes).

	- And last, but definitely not least, a new donations section
	  has beenadded to the PyTables web site
	  (http://sourceforge.net/projects/pytables, then follow the
	  "Donations" tag). If you like PyTables and want this effort
	  to continue, please, donate!

Enjoy!,

-- Francesc Alted
falted@pytables.org

