Log message:
Extend PYTHON_VERSIONS_INCOMPATIBLE to 35

Log message:
Add SHA512 digests for distfiles for devel category

Issues found with existing distfiles:
	distfiles/eclipse-sourceBuild-srcIncluded-3.0.1.zip
	distfiles/fortran-utils-1.1.tar.gz
	distfiles/ivykis-0.39.tar.gz
	distfiles/enum-1.11.tar.gz
	distfiles/pvs-3.2-libraries.tgz
	distfiles/pvs-3.2-linux.tgz
	distfiles/pvs-3.2-solaris.tgz
	distfiles/pvs-3.2-system.tgz
No changes made to these distinfo files.

Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden).  All existing
SHA1 digests retained for now as an audit trail.

Log message:
Mark packages that are not ready for python-3.3 also not ready for 3.4,
until proven otherwise.

Log message:
Mark packages as not ready for python-3.x where applicable;
either because they themselves are not ready or because a
dependency isn't. This is annotated by
PYTHON_VERSIONS_INCOMPATIBLE=  33 # not yet ported as of x.y.z
or
PYTHON_VERSIONS_INCOMPATIBLE=  33 # py-foo, py-bar
respectively, please use the same style for other packages,
and check during updates.

Use versioned_dependencies.mk where applicable.
Use REPLACE_PYTHON instead of handcoded alternatives, where applicable.
Reorder Makefile sections into standard order, where applicable.

Remove PYTHON_VERSIONS_INCLUDE_3X lines since that will be default
with the next commit.

Whitespace cleanups and other nits corrected, where necessary.

Log message:
Import Python module "tabular" version 0.1 as py-tabular.

Tabular data can be easily represented in Python using the language's
native objects. These representations typically do not support
important tabular data manipulations, like efficient column selection,
matrix mathematics, or spreadsheet-style operations.

Tabular is a package of Python modules for working with tabular
data. Its main object is the tabarray class, a data structure for
holding and manipulating tabular data. By putting data into a tabarray
object, you'll get a representation of the data that is more flexible
and powerful than a native Python representation. Tabarray provides:

 . ultra-fast filtering, selection, and numerical analysis methods,
   using convenient Matlab-style matrix operation syntax

 . spreadsheet-style operations, including row & column operations,
   'sort', 'replace', 'aggregate', 'pivot', and 'join'

 . flexible load and save methods for a variety of file formats,
   including delimited text (CSV), binary, and HTML

 . sophisticated inference algorithms for determining formatting
   parameters and data types of input files