Ports using USE_PYTHON=distutils are now flavored. They will
automatically get flavors (py27, py34, py35, py36) depending on what
versions they support.
There is also a USE_PYTHON=flavors for ports that do not use distutils
but need FLAVORS to be set. A USE_PYTHON=noflavors can be set if
using distutils but flavors are not wanted.
A new USE_PYTHON=optsuffix that will add PYTHON_PKGNAMESUFFIX has been
added to cope with Python ports that did not have the Python
PKGNAMEPREFIX but are flavored.
USES=python now also exports a PY_FLAVOR variable that contains the
current python flavor. It can be used in dependency lines when the
port itself is not python flavored. For example, deskutils/calibre.
By default, all the flavors are generated. To only generate flavors
for the versions in PYTHON2_DEFAULT and PYTHON3_DEFAULT, define
BUILD_DEFAULT_PYTHON_FLAVORS in your make.conf.
In all the ports with Python dependencies, the *_DEPENDS entries MUST
end with the flavor so that the framework knows which to build/use.
This is done by appending '@${PY_FLAVOR}' after the origin (or
@${FLAVOR} if in a Python module with Python flavors, as the content
will be the same). For example:
RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}six>0:devel/py-six@${PY_FLAVOR}
PR: 223071
Reviewed by: portmgr, python
Sponsored by: Absolight
Differential Revision: https://reviews.freebsd.org/D12464
This only affects "Created by" lines with one exception: devel/uclcmd. There the maintainer is changed. This was overlooked in r416918.
Approved by: junovitch (mentor)
GCC 4.6.4 to GCC 4.7.3. This entails updating the lang/gcc port as
well as changing the default in Mk/bsd.default-versions.mk.
Part II, Bump PORTREVISIONs.
PR: 182136
Supported by: Christoph Moench-Tegeder <cmt@burggraben.net> (fixing many ports)
Tested by: bdrewery (two -exp runs)
Fastcluster provides Python functions for hierarchical clustering. It generates
hierarchical clusters from distance matrices or from vector data.
Part of this module is intended to replace the functions
linkage, single, complete, average, weighted, centroid, median, ward
in the module scipy.cluster.hierarchy with the same functionality but much
faster algorithms. Moreover, the function 'linkage_vector' provides
memory-efficient clustering for vector data.
The interface is very similar to MATLAB's Statistics Toolbox API to make code
easier to port from MATLAB to Python/Numpy. The core implementation of this
library is in C++ for efficiency.
WWW: http://danifold.net/fastcluster.html
PR: ports/184931
Submitted by: Johannes Jost Meixner <xmj chaot.net>
