./devel/py-wrapt, Python module for decorators, wrappers and monkey patching

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Branch: CURRENT, Version: 1.10.8, Package name: py27-wrapt-1.10.8, Maintainer: pkgsrc-users

A Python module for decorators, wrappers and monkey patching.

The aim of the wrapt module is to provide a transparent object proxy for
Python, which can be used as the basis for the construction of function
wrappers and decorator functions.

The wrapt module focuses very much on correctness. It therefore goes way
beyond existing mechanisms such as functools.wraps() to ensure that
decorators preserve introspectability, signatures, type checking abilities
etc. The decorators that can be constructed using this module will work in
far more scenarios than typical decorators and provide more predictable and
consistent behaviour.

To ensure that the overhead is as minimal as possible, a C extension module
is used for performance critical components. An automatic fallback to a pure
Python implementation is also provided where a target system does not have a
compiler to allow the C extension to be compiled.


Required to run:
[lang/python27]

Master sites:

SHA1: bf61e605eb54383666e3a403b99f49c4dee406d3
RMD160: 55882ba07e05aed13ceb301fd545aa9f891bb8a9
Filesize: 24.593 KB

Version history: (Expand)


CVS history: (Expand)


   2016-05-05 13:15:33 by Richard PALO | Files touched by this commit (4)
Log message:
add devel/py-wrapt (wrapt-1.10.8)

A Python module for decorators, wrappers and monkey patching.

The aim of the wrapt module is to provide a transparent object proxy for
Python, which can be used as the basis for the construction of function
wrappers and decorator functions.

The wrapt module focuses very much on correctness. It therefore goes way
beyond existing mechanisms such as functools.wraps() to ensure that
decorators preserve introspectability, signatures, type checking abilities
etc. The decorators that can be constructed using this module will work in
far more scenarios than typical decorators and provide more predictable and
consistent behaviour.

To ensure that the overhead is as minimal as possible, a C extension module
is used for performance critical components. An automatic fallback to a pure
Python implementation is also provided where a target system does not have a
compiler to allow the C extension to be compiled.