pax_global_header00006660000000000000000000000064111663321100014504gustar00rootroot0000000000000052 comment=dd0caf5bdf1421ea9a8222f040b3f86cb3dd9904 python-module-decoratortools-1.7/000075500000000000000000000000001116633211000172165ustar00rootroot00000000000000python-module-decoratortools-1.7/.gear/000075500000000000000000000000001116633211000202125ustar00rootroot00000000000000python-module-decoratortools-1.7/.gear/rules000064400000000000000000000000071116633211000212640ustar00rootroot00000000000000tar: . python-module-decoratortools-1.7/README.txt000075500000000000000000000737261116633211000207360ustar00rootroot00000000000000Class, Function, and Assignment Decorators, Metaclasses, and Related Tools ========================================================================== Want to use decorators, but still need to support Python 2.3? Wish you could have class decorators, decorate arbitrary assignments, or match decorated function signatures to their original functions? Want to get metaclass features without creating metaclasses? How about synchronized methods? "DecoratorTools" gets you all of this and more. Some quick examples:: # Method decorator example from peak.util.decorators import decorate class Demo1(object): decorate(classmethod) # equivalent to @classmethod def example(cls): print "hello from", cls # Class decorator example from peak.util.decorators import decorate_class def my_class_decorator(): def decorator(cls): print "decorating", cls return cls decorate_class(decorator) class Demo2: my_class_decorator() # "decorating " will be printed when execution gets here Installing DecoratorTools (using ``"easy_install DecoratorTools"`` or ``"setup.py install"``) gives you access to the ``peak.util.decorators`` module. The tools in this module have been bundled for years inside of PEAK, PyProtocols, RuleDispatch, and the zope.interface package, so they have been widely used and tested. (Unit tests are also included, of course.) This standalone version is backward-compatible with the bundled versions, so you can mix and match decorators from this package with those provided by zope.interface, TurboGears, etc. For complete documentation, see the `DecoratorTools manual`_. Changes since version 1.6: * Added ``synchronized`` decorator to support locking objects during method execution. Changes since version 1.5: * Added ``classy`` base class that allows you to do the most often-needed metaclass behviors *without* needing an actual metaclass. Changes since version 1.4: * Added ``enclosing_frame()`` function, so that complex decorators that call DecoratorTools functions while being called *by* DecoratorTools functions, will work correctly. Changes since version 1.3: * Added support for debugging generated code, including the code generated by ``rewrap()`` and ``template_function``. Changes since version 1.2: * Added ``rewrap()`` function and ``template_function`` decorator to support signature matching for decorated functions. (These features are similar to the ones provided by Michele Simionato's "decorator" package, but do not require Python 2.4 and don't change the standard idioms for creating decorator functions.) * ``decorate_class()`` will no longer apply duplicate class decorator callbacks unless the ``allow_duplicates`` argument is true. Changes since version 1.1: * Fixed a problem where instances of different struct types could equal each other Changes since version 1.0: * The ``struct()`` decorator makes it easy to create tuple-like data structure types, by decorating a constructor function. .. _DecoratorTools Manual: http://peak.telecommunity.com/DevCenter/DecoratorTools#toc .. _toc: .. contents:: **Table of Contents** You may access any of the following APIs by importing them from ``peak.util.decorators``: Simple Decorators ----------------- decorate(\*decorators) Apply `decorators` to the subsequent function definition or assignment statement, thereby allowing you to conviently use standard decorators with Python 2.3 and up (i.e., no ``@`` syntax required), as shown in the following table of examples:: Python 2.4+ DecoratorTools ------------ -------------- @classmethod decorate(classmethod) def blah(cls): def blah(cls): pass pass @foo @bar(baz) decorate(foo, bar(baz)) def spam(bing): def spam(bing): """whee""" """whee""" decorate_class(decorator [, depth=2, frame=None]) Set up `decorator` to be passed the containing class after its creation. This function is designed to be called by a decorator factory function executed in a class suite. It is not used directly; instead you simply give your users a "magic function" to call in the body of the appropriate class. Your "magic function" (i.e. a decorator factory function) then calls ``decorate_class`` to register the decorator to be called when the class is created. Multiple decorators may be used within a single class, although they must all appear *after* the ``__metaclass__`` declaration, if there is one. The registered decorator will be given one argument: the newly created containing class. The return value of the decorator will be used in place of the original class, so the decorator should return the input class if it does not wish to replace it. Example:: >>> from peak.util.decorators import decorate_class >>> def demo_class_decorator(): ... def decorator(cls): ... print "decorating", cls ... return cls ... decorate_class(decorator) >>> class Demo: ... demo_class_decorator() decorating __builtin__.Demo In the above example, ``demo_class_decorator()`` is the decorator factory function, and its inner function ``decorator`` is what gets called to actually decorate the class. Notice that the factory function has to be called within the class body, even if it doesn't take any arguments. If you are just creating simple class decorators, you don't need to worry about the `depth` or `frame` arguments here. However, if you are creating routines that are intended to be used within other class or method decorators, you will need to pay attention to these arguments to ensure that ``decorate_class()`` can find the frame where the class is being defined. In general, the simplest way to do this is for the function that's called in the class body to get its caller's frame with ``sys._getframe(1)``, and then pass that frame down to whatever code will be calling ``decorate_class()``. Alternately, you can specify the `depth` that ``decorate_class()`` should call ``sys._getframe()`` with, but this can be a bit trickier to compute correctly. Note, by the way that ``decorate_class()`` ignores duplicate callbacks:: >>> def hello(cls): ... print "decorating", cls ... return cls >>> def do_hello(): ... decorate_class(hello) >>> class Demo: ... do_hello() ... do_hello() decorating __builtin__.Demo Unless the ``allow_duplicates`` argument is set to a true value:: >>> def do_hello(): ... decorate_class(hello, allow_duplicates=True) >>> class Demo: ... do_hello() ... do_hello() decorating __builtin__.Demo decorating __builtin__.Demo The ``synchronized`` Decorator ------------------------------ When writing multithreaded programs, it's often useful to define certain operations as being protected by a lock on an object. The ``synchronized`` decorator lets you do this by decorating object methods, e.g.:: >>> from peak.util.decorators import synchronized >>> class TryingToBeThreadSafe(object): ... synchronized() # could be just ``@synchronized`` for 2.4+ ... def method1(self, arg): ... print "in method 1" ... self.method2() ... print "back in method 1" ... return arg ... ... synchronized() # could be just ``@synchronized`` for 2.4+ ... def method2(self): ... print "in method 2" ... return 42 >>> TryingToBeThreadSafe().method1(99) in method 1 in method 2 back in method 1 99 What you can't tell from this example is that a ``__lock__`` attribute is being acquired and released around each of those calls. Let's take a closer look:: >>> class DemoLock: ... def __init__(self, name): ... self.name = name ... def acquire(self): ... print "acquiring", self.name ... def release(self): ... print "releasing", self.name >>> ts = TryingToBeThreadSafe() >>> ts.__lock__ = DemoLock("lock 1") >>> ts.method2() acquiring lock 1 in method 2 releasing lock 1 42 >>> ts.method1(27) acquiring lock 1 in method 1 acquiring lock 1 in method 2 releasing lock 1 back in method 1 releasing lock 1 27 As you can see, if an object already has a ``__lock__`` attribute, its ``acquire()`` and ``release()`` methods are called around the execution of the wrapped method. (Note that this means the lock must be re-entrant: that is, you must use a ``threading.RLock`` or something similar to it, if you explicitly create your own ``__lock__`` attribute.) If the object has no ``__lock__``, the decorator creates a ``threading.RLock`` and tries to add it to the object's ``__dict__``:: >>> del ts.__lock__ >>> ts.method1(27) in method 1 in method 2 back in method 1 27 >>> ts.__lock__ <_RLock(None, 0)> (This means, by the way, that if you want to use synchronized methods on an object with no ``__dict__``, you must explicitly include a ``__lock__`` slot and initialize it yourself when the object is created.) The ``struct()`` Decorator -------------------------- The ``struct()`` decorator creates a tuple subclass with the same name and docstring as the decorated function. The class will have read-only properties with the same names as the function's arguments, and the ``repr()`` of its instances will look like a call to the original function:: >>> from peak.util.decorators import struct >>> def X(a,b,c): ... """Demo type""" ... return a,b,c >>> X = struct()(X) # can't use decorators above functions in doctests >>> v = X(1,2,3) >>> v X(1, 2, 3) >>> v.a 1 >>> v.b 2 >>> v.c 3 >>> help(X) # doctest: +NORMALIZE_WHITESPACE Help on class X: class X(__builtin__.tuple) | Demo type | | Method resolution order: | X | __builtin__.tuple | __builtin__.object | | Methods defined here: | | __repr__(self) | | ---------------------------------------------------------------------- | Static methods defined here: | | __new__(cls, *args, **kw) | | ---------------------------------------------------------------------- | ...s defined here: | | a... | | b... | | c... | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __args__ = ['a', 'b', 'c']... | | __star__ = None | | ... The function should return a tuple of values in the same order as its argument names, as it will be used by the class' constructor. The function can perform validation, add defaults, and/or do type conversions on the values. If the function takes a ``*``, argument, it should flatten this argument into the result tuple, e.g.:: >>> def pair(first, *rest): ... return (first,) + rest >>> pair = struct()(pair) >>> p = pair(1,2,3,4) >>> p pair(1, 2, 3, 4) >>> p.first 1 >>> p.rest (2, 3, 4) Internally, ``struct`` types are actually tuples:: >>> print tuple.__repr__(X(1,2,3)) (, 1, 2, 3) The internal representation contains the struct's type object, so that structs of different types will not compare equal to each other:: >>> def Y(a,b,c): ... return a,b,c >>> Y = struct()(Y) >>> X(1,2,3) == X(1,2,3) True >>> Y(1,2,3) == Y(1,2,3) True >>> X(1,2,3) == Y(1,2,3) False Note, however, that this means that if you want to unpack them or otherwise access members directly, you must include the type entry, or use a slice:: >>> a, b, c = X(1,2,3) # wrong Traceback (most recent call last): ... ValueError: too many values to unpack >>> t, a, b, c = X(1,2,3) # right >>> a, b, c = X(1,2,3)[1:] # ok, if perhaps a bit unintuitive The ``struct()`` decorator takes optional mixin classes (as positional arguments), and dictionary entries (as keyword arguments). The mixin classes will be placed before ``tuple`` in the resulting class' bases, and the dictionary entries will be placed in the class' dictionary. These entries take precedence over any default entries (e.g. methods, properties, docstring, etc.) that are created by the ``struct()`` decorator:: >>> class Mixin(object): ... __slots__ = [] ... def foo(self): print "bar" >>> def demo(a, b): ... return a, b >>> demo = struct(Mixin, reversed=property(lambda self: self[:0:-1]))(demo) >>> demo(1,2).foo() bar >>> demo(3,4).reversed (4, 3) >>> demo.__mro__ (, , , ) Note that using mixin classes will result in your new class' instances having a ``__dict__`` attribute, unless they are new-style classes that set ``__slots__`` to an empty list. And if they have any slots other than ``__weakref__`` or ``__dict__``, this will cause a type error due to layout conflicts. In general, it's best to use mixins only for adding methods, not data. Finally, note that if your function returns a non-tuple result, it will be returned from the class' constructor. This is sometimes useful:: >>> def And(a, b): ... if a is None: return b ... return a, b >>> And = struct()(And) >>> And(1,2) And(1, 2) >>> And(None, 27) 27 Signature Matching ------------------ One of the drawbacks to using function decorators is that using ``help()`` or other documentation tools on a decorated function usually produces unhelpful results:: >>> def before_and_after(message): ... def decorator(func): ... def decorated(*args, **kw): ... print "before", message ... try: ... return func(*args, **kw) ... finally: ... print "after", message ... return decorated ... return decorator >>> def foo(bar, baz): ... """Here's some doc""" >>> foo(1,2) >>> help(foo) # doctest: -NORMALIZE_WHITESPACE Help on function foo: ... foo(bar, baz) Here's some doc ... >>> decorated_foo = before_and_after("hello")(foo) >>> decorated_foo(1,2) before hello after hello >>> help(decorated_foo) # doctest: -NORMALIZE_WHITESPACE Help on function decorated: ... decorated(*args, **kw) ... So DecoratorTools provides you with two tools to improve this situation. First, the ``rewrap()`` function provides a simple way to match the signature, module, and other characteristics of the original function:: >>> from peak.util.decorators import rewrap >>> def before_and_after(message): ... def decorator(func): ... def before_and_after(*args, **kw): ... print "before", message ... try: ... return func(*args, **kw) ... finally: ... print "after", message ... return rewrap(func, before_and_after) ... return decorator >>> decorated_foo = before_and_after("hello")(foo) >>> decorated_foo(1,2) before hello after hello >>> help(decorated_foo) # doctest: -NORMALIZE_WHITESPACE Help on function foo: ... foo(bar, baz) Here's some doc ... The ``rewrap()`` function returns you a new function object with the same attributes (including ``__doc__``, ``__dict__``, ``__name__``, ``__module__``, etc.) as the original function, but which calls the decorated function. If you want the same signature but don't want the overhead of another calling level at runtime, you can use the ``@template_function`` decorator instead. The downside to this approach, however, is that it is more complex to use. So, this approach is only recommended for more performance-intensive decorators, that you've already debugged using the ``rewrap()`` approach. But if you need to use it, the appropriate usage looks something like this:: >>> from peak.util.decorators import template_function >>> def before_and_after2(message): ... def decorator(func): ... [template_function()] # could also be @template_function in 2.4 ... def before_and_after2(__func, __message): ... ''' ... print "before", __message ... try: ... return __func($args) ... finally: ... print "after", __message ... ''' ... return before_and_after2(func, message) ... return decorator >>> decorated_foo = before_and_after2("hello")(foo) >>> decorated_foo(1,2) before hello after hello >>> help(decorated_foo) # doctest: -NORMALIZE_WHITESPACE Help on function foo: ... foo(bar, baz) Here's some doc ... As you can see, the process is somewhat more complex. Any values you wish the generated function to be able to access (aside from builtins) must be declared as arguments to the decorating function, and all arguments must be named so as not to conflict with the names of any of the decorated function's arguments. The docstring must either fit on one line, or begin with a newline and have its contents indented by at least two spaces. The string ``$args`` may be used one or more times in the docstring, whenever calling the original function. The first argument of the decorating function must always be the original function. Debugging Generated Code ------------------------ Both ``rewrap()`` and ``template_function`` are implemented using code generation and runtime compile/exec operations. Normally, such things are frowned on in Python because Python's debugging tools don't work on generated code. In particular, tracebacks and pdb don't show the source code of functions compiled from strings... or do they? Let's see:: >>> def raiser(x, y="blah"): ... raise TypeError(y) >>> def call_and_print_error(func, *args, **kw): ... # This function is necessary because we want to test the error ... # output, but doctest ignores a lot of exception detail, and ... # won't show the non-errror output unless we do it this way ... # ... try: ... func(*args, **kw) ... except: ... import sys, traceback ... print ''.join(traceback.format_exception(*sys.exc_info())) >>> call_and_print_error(before_and_after("error")(raiser), 99) before error after error Traceback (most recent call last): File "", line ..., in call_and_print_error func(*args, **kw) File "", line 3, in raiser def raiser(x, y): return __decorated(x, y) File ..., line ..., in before_and_after return func(*args, **kw) File "", line 2, in raiser raise TypeError(y) TypeError: blah >>> call_and_print_error(before_and_after2("error")(raiser), 99) before error after error Traceback (most recent call last): File "", line ..., in call_and_print_error func(*args, **kw) File "", line 6, in raiser return __func(x, y) File "", line 2, in raiser raise TypeError(y) TypeError: blah As you can see, both decorators' tracebacks include lines from the pseudo-files "" and "" (the hex id's of the corresponding objects are omitted here). This is because DecoratorTools adds information to the Python ``linecache`` module, and tracebacks and pdb both use the ``linecache`` module to get source lines. Any tools that use ``linecache``, either directly or indirectly, will therefore be able to display this information for generated code. If you'd like to be able to use this feature for your own code generation or non-file-based code (e.g. Python source loaded from a database, etc.), you can use the ``cache_source()`` function:: >>> from peak.util.decorators import cache_source >>> from linecache import getline >>> demo_source = "line 1\nline 2\nline 3" >>> cache_source("", demo_source) >>> getline("", 3) 'line 3' The function requires a dummy filename, which must be globally unique. An easy way to ensure uniqueness is to include the ``id()`` of an object that will exist at least as long as the source code being cached. Also, if you have such an object, and it is weak-referenceable, you can supply it as a third argument to ``cache_source()``, and when that object is garbage collected the source will be removed from the ``linecache`` cache. If you're generating a function from the source, the function object itself is ideal for this purpose (and it's what ``rewrap()`` and ``template_function`` do):: >>> def a_function(): pass # just an object to "own" the source >>> cache_source("", demo_source, a_function) >>> getline("", 1) 'line 1\n' >>> del a_function # GC should now clean up the cache >>> getline("", 1) '' Advanced Decorators ------------------- The ``decorate_assignment()`` function can be used to create standalone "magic" decorators that work in Python 2.3 and up, and which can also be used to decorate arbitrary assignments as well as function/method definitions. For example, if you wanted to create an ``info(**kwargs)`` decorator that could be used either with or without an ``@``, you could do something like:: from peak.util.decorators import decorate_assignment def info(**kw): def callback(frame, name, func, old_locals): func.__dict__.update(kw) return func return decorate_assignment(callback) info(foo="bar") # will set dummy.foo="bar"; @info() would also work def dummy(blah): pass As you can see, this ``info()`` decorator can be used without an ``@`` sign for backward compatibility with Python 2.3. It can also be used *with* an ``@`` sign, for forward compatibility with Python 2.4 and up. Here's a more detailed reference for the ``decorate_assignment()`` API: decorate_assignment(callback [, depth=2, frame=None]) Call `callback(frame, name, value, old_locals)` on next assign in `frame`. If a `frame` isn't supplied, a frame is obtained using ``sys._getframe(depth)``. `depth` defaults to 2 so that the correct frame is found when ``decorate_assignment()`` is called from a decorator factory that was called in the target usage context. When `callback` is invoked, `old_locals` contains the frame's local variables as they were *before* the assignment, thus allowing the callback to access the previous value of the assigned variable, if any. The callback's return value will become the new value of the variable. `name` will contain the name of the variable being created or modified, and `value` will be the thing being decorated. `frame` is the Python frame in which the assignment occurred. This function also returns a decorator function for forward-compatibility with Python 2.4 ``@`` syntax. Note, however, that if the returned decorator is used with Python 2.4 ``@`` syntax, the callback `name` argument may be ``None`` or incorrect, if the `value` is not the original function (e.g. when multiple decorators are used). "Meta-less" Classes ------------------- Sometimes, you want to create a base class in a library or program that will use the data defined in subclasses in some way, or that needs to customize the way instances are created (*without* overriding ``__new__``). Since Python 2.2, the standard way to accomplish these things is by creating a custom metaclass and overriding ``__new__``, ``__init__``, or ``__call__``. Unfortunately, however, metaclasses don't play well with others. If two frameworks define independent metaclasses, and a library or application mixes classes from those frameworks, the user will have to create a *third* metaclass to sort out the differences. For this reason, it's best to minimize the number of distinct metaclasses in use. ``peak.util.decorators`` therefore provides a kind of "one-size-fits-all" metaclass, so that most of the common use cases for metaclasses can be handled with just one metaclass. In PEAK and elsewhere, metaclasses are most commonly used to perform some sort of operations during class creation (metaclass ``__new__`` and ``__init__``), or instance creation (metaclass ``__call__``, wrapping the class-level ``__new__`` and ``__init__``). Therefore, the ``classy`` base class allows subclasses to implement one or more of the three classmethods ``__class_new__``, ``__class_init__``, and ``__class_call__``. The "one-size-fits-all" metaclass delegates these operations to the class, so that you don't need a custom metaclass for every class with these behaviors. Thus, as long as all your custom metaclasses derive from ``classy.__class__``, you can avoid any metaclass conflicts during multiple inheritance. Here's an example of ``classy`` in use:: >>> from peak.util.decorators import classy, decorate >>> class Demo(classy): ... """Look, ma! No metaclass!""" ... ... def __class_new__(meta, name, bases, cdict, supr): ... cls = supr()(meta, name, bases, cdict, supr) ... print "My metaclass is", meta ... print "And I am", cls ... return cls ... ... def __class_init__(cls, name, bases, cdict, supr): ... supr()(cls, name, bases, cdict, supr) ... print "Initializing", cls ... ... decorate(classmethod) # could be just @classmethod for 2.4+ ... def __class_call__(cls, *args, **kw): ... print "before creating instance" ... ob = super(Demo, cls).__class_call__(*args, **kw) ... print "after creating instance" ... return ob ... ... def __new__(cls, *args, **kw): ... print "new called with", args, kw ... return super(Demo, cls).__new__(cls, *args, **kw) ... ... def __init__(self, *args, **kw): ... print "init called with", args, kw My metaclass is And I am Initializing >>> d = Demo(1,2,a="b") before creating instance new called with (1, 2) {'a': 'b'} init called with (1, 2) {'a': 'b'} after creating instance Note that because ``__class_new__`` and ``__class_init__`` are called *before* the name ``Demo`` has been bound to the class under creation, ``super()`` cannot be used in these methods. So, they use a special calling convention, where the last argument (``supr``) is the ``next()`` method of an iterator that yields base class methods in mro order. In other words, calling ``supr()(..., supr)`` invokes the previous definition of the method. You MUST call this exactly *once* in your methods -- no more, no less. ``__class_call__`` is different, because it is called after the class already exists. Thus, it can be a normal ``classmethod`` and use ``super()`` in the standard way. Finally, note that any given ``classy`` subclass does NOT need to define all three methods; you can mix and match methods as needed. Just be sure to always use the ``supr`` argument (or ``super()`` in the case of ``__class_call__``). Utility/Introspection Functions ------------------------------- ``peak.util.decorators`` also exposes these additional utility and introspection functions that it uses internally: frameinfo(frame) Return a ``(kind, module, locals, globals)`` tuple for a frame The `kind` returned is a string, with one of the following values: * ``"exec"`` * ``"module"`` * ``"class"`` * ``"function call"`` * ``"unknown"`` The `module` returned is the Python module object whose globals are in effect for the frame, or ``None`` if the globals don't include a value for ``__name__``. metaclass_is_decorator(mc) Return truth if the given metaclass is a class decorator metaclass inserted into a class by ``decorate_class()``, or by another class decorator implementation that follows the same protocol (such as the one in ``zope.interface``). metaclass_for_bases(bases, explicit_mc=None) Given a sequence of 1 or more base classes and an optional explicit ``__metaclass__``, return the metaclass that should be used. This routine basically emulates what Python does to determine the metaclass when creating a class, except that it does *not* take a module-level ``__metaclass__`` into account, only the arguments as given. If there are no base classes, you should just directly use the module-level ``__metaclass__`` or ``types.ClassType`` if there is none. enclosing_frame(frame=None, level=3) Given a frame and/or stack level, skip upward past any DecoratorTools code frames. This function is used by ``decorate_class()`` and ``decorate_assignment()`` to ensure that any decorators calling them that were themselves invoked using ``decorate()``, won't end up looking at DecoratorTools code instead of the target. If you have a function that needs to be callable via ``decorate()`` and which inspects stack frames, you may need to use this function to access the right frame. Mailing List ------------ Please direct questions regarding this package to the PEAK mailing list; see http://www.eby-sarna.com/mailman/listinfo/PEAK/ for details. python-module-decoratortools-1.7/ez_setup.py000064400000000000000000000227641116633211000214410ustar00rootroot00000000000000#!python """Bootstrap setuptools installation If you want to use setuptools in your package's setup.py, just include this file in the same directory with it, and add this to the top of your setup.py:: from ez_setup import use_setuptools use_setuptools() If you want to require a specific version of setuptools, set a download mirror, or use an alternate download directory, you can do so by supplying the appropriate options to ``use_setuptools()``. This file can also be run as a script to install or upgrade setuptools. """ import sys DEFAULT_VERSION = "0.6c9" DEFAULT_URL = "http://pypi.python.org/packages/%s/s/setuptools/" % sys.version[:3] md5_data = { 'setuptools-0.6b1-py2.3.egg': '8822caf901250d848b996b7f25c6e6ca', 'setuptools-0.6b1-py2.4.egg': 'b79a8a403e4502fbb85ee3f1941735cb', 'setuptools-0.6b2-py2.3.egg': '5657759d8a6d8fc44070a9d07272d99b', 'setuptools-0.6b2-py2.4.egg': '4996a8d169d2be661fa32a6e52e4f82a', 'setuptools-0.6b3-py2.3.egg': 'bb31c0fc7399a63579975cad9f5a0618', 'setuptools-0.6b3-py2.4.egg': '38a8c6b3d6ecd22247f179f7da669fac', 'setuptools-0.6b4-py2.3.egg': '62045a24ed4e1ebc77fe039aa4e6f7e5', 'setuptools-0.6b4-py2.4.egg': '4cb2a185d228dacffb2d17f103b3b1c4', 'setuptools-0.6c1-py2.3.egg': 'b3f2b5539d65cb7f74ad79127f1a908c', 'setuptools-0.6c1-py2.4.egg': 'b45adeda0667d2d2ffe14009364f2a4b', 'setuptools-0.6c2-py2.3.egg': 'f0064bf6aa2b7d0f3ba0b43f20817c27', 'setuptools-0.6c2-py2.4.egg': '616192eec35f47e8ea16cd6a122b7277', 'setuptools-0.6c3-py2.3.egg': 'f181fa125dfe85a259c9cd6f1d7b78fa', 'setuptools-0.6c3-py2.4.egg': 'e0ed74682c998bfb73bf803a50e7b71e', 'setuptools-0.6c3-py2.5.egg': 'abef16fdd61955514841c7c6bd98965e', 'setuptools-0.6c4-py2.3.egg': 'b0b9131acab32022bfac7f44c5d7971f', 'setuptools-0.6c4-py2.4.egg': '2a1f9656d4fbf3c97bf946c0a124e6e2', 'setuptools-0.6c4-py2.5.egg': '8f5a052e32cdb9c72bcf4b5526f28afc', 'setuptools-0.6c5-py2.3.egg': 'ee9fd80965da04f2f3e6b3576e9d8167', 'setuptools-0.6c5-py2.4.egg': 'afe2adf1c01701ee841761f5bcd8aa64', 'setuptools-0.6c5-py2.5.egg': 'a8d3f61494ccaa8714dfed37bccd3d5d', 'setuptools-0.6c6-py2.3.egg': '35686b78116a668847237b69d549ec20', 'setuptools-0.6c6-py2.4.egg': '3c56af57be3225019260a644430065ab', 'setuptools-0.6c6-py2.5.egg': 'b2f8a7520709a5b34f80946de5f02f53', 'setuptools-0.6c7-py2.3.egg': '209fdf9adc3a615e5115b725658e13e2', 'setuptools-0.6c7-py2.4.egg': '5a8f954807d46a0fb67cf1f26c55a82e', 'setuptools-0.6c7-py2.5.egg': '45d2ad28f9750e7434111fde831e8372', 'setuptools-0.6c8-py2.3.egg': '50759d29b349db8cfd807ba8303f1902', 'setuptools-0.6c8-py2.4.egg': 'cba38d74f7d483c06e9daa6070cce6de', 'setuptools-0.6c8-py2.5.egg': '1721747ee329dc150590a58b3e1ac95b', 'setuptools-0.6c9-py2.3.egg': 'a83c4020414807b496e4cfbe08507c03', 'setuptools-0.6c9-py2.4.egg': '260a2be2e5388d66bdaee06abec6342a', 'setuptools-0.6c9-py2.5.egg': 'fe67c3e5a17b12c0e7c541b7ea43a8e6', 'setuptools-0.6c9-py2.6.egg': 'ca37b1ff16fa2ede6e19383e7b59245a', } import sys, os try: from hashlib import md5 except ImportError: from md5 import md5 def _validate_md5(egg_name, data): if egg_name in md5_data: digest = md5(data).hexdigest() if digest != md5_data[egg_name]: print >>sys.stderr, ( "md5 validation of %s failed! (Possible download problem?)" % egg_name ) sys.exit(2) return data def use_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=os.curdir, download_delay=15 ): """Automatically find/download setuptools and make it available on sys.path `version` should be a valid setuptools version number that is available as an egg for download under the `download_base` URL (which should end with a '/'). `to_dir` is the directory where setuptools will be downloaded, if it is not already available. If `download_delay` is specified, it should be the number of seconds that will be paused before initiating a download, should one be required. If an older version of setuptools is installed, this routine will print a message to ``sys.stderr`` and raise SystemExit in an attempt to abort the calling script. """ was_imported = 'pkg_resources' in sys.modules or 'setuptools' in sys.modules def do_download(): egg = download_setuptools(version, download_base, to_dir, download_delay) sys.path.insert(0, egg) import setuptools; setuptools.bootstrap_install_from = egg try: import pkg_resources except ImportError: return do_download() try: pkg_resources.require("setuptools>="+version); return except pkg_resources.VersionConflict, e: if was_imported: print >>sys.stderr, ( "The required version of setuptools (>=%s) is not available, and\n" "can't be installed while this script is running. Please install\n" " a more recent version first, using 'easy_install -U setuptools'." "\n\n(Currently using %r)" ) % (version, e.args[0]) sys.exit(2) else: del pkg_resources, sys.modules['pkg_resources'] # reload ok return do_download() except pkg_resources.DistributionNotFound: return do_download() def download_setuptools( version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=os.curdir, delay = 15 ): """Download setuptools from a specified location and return its filename `version` should be a valid setuptools version number that is available as an egg for download under the `download_base` URL (which should end with a '/'). `to_dir` is the directory where the egg will be downloaded. `delay` is the number of seconds to pause before an actual download attempt. """ import urllib2, shutil egg_name = "setuptools-%s-py%s.egg" % (version,sys.version[:3]) url = download_base + egg_name saveto = os.path.join(to_dir, egg_name) src = dst = None if not os.path.exists(saveto): # Avoid repeated downloads try: from distutils import log if delay: log.warn(""" --------------------------------------------------------------------------- This script requires setuptools version %s to run (even to display help). I will attempt to download it for you (from %s), but you may need to enable firewall access for this script first. I will start the download in %d seconds. (Note: if this machine does not have network access, please obtain the file %s and place it in this directory before rerunning this script.) ---------------------------------------------------------------------------""", version, download_base, delay, url ); from time import sleep; sleep(delay) log.warn("Downloading %s", url) src = urllib2.urlopen(url) # Read/write all in one block, so we don't create a corrupt file # if the download is interrupted. data = _validate_md5(egg_name, src.read()) dst = open(saveto,"wb"); dst.write(data) finally: if src: src.close() if dst: dst.close() return os.path.realpath(saveto) def main(argv, version=DEFAULT_VERSION): """Install or upgrade setuptools and EasyInstall""" try: import setuptools except ImportError: egg = None try: egg = download_setuptools(version, delay=0) sys.path.insert(0,egg) from setuptools.command.easy_install import main return main(list(argv)+[egg]) # we're done here finally: if egg and os.path.exists(egg): os.unlink(egg) else: if setuptools.__version__ == '0.0.1': print >>sys.stderr, ( "You have an obsolete version of setuptools installed. Please\n" "remove it from your system entirely before rerunning this script." ) sys.exit(2) req = "setuptools>="+version import pkg_resources try: pkg_resources.require(req) except pkg_resources.VersionConflict: try: from setuptools.command.easy_install import main except ImportError: from easy_install import main main(list(argv)+[download_setuptools(delay=0)]) sys.exit(0) # try to force an exit else: if argv: from setuptools.command.easy_install import main main(argv) else: print "Setuptools version",version,"or greater has been installed." print '(Run "ez_setup.py -U setuptools" to reinstall or upgrade.)' def update_md5(filenames): """Update our built-in md5 registry""" import re for name in filenames: base = os.path.basename(name) f = open(name,'rb') md5_data[base] = md5(f.read()).hexdigest() f.close() data = [" %r: %r,\n" % it for it in md5_data.items()] data.sort() repl = "".join(data) import inspect srcfile = inspect.getsourcefile(sys.modules[__name__]) f = open(srcfile, 'rb'); src = f.read(); f.close() match = re.search("\nmd5_data = {\n([^}]+)}", src) if not match: print >>sys.stderr, "Internal error!" sys.exit(2) src = src[:match.start(1)] + repl + src[match.end(1):] f = open(srcfile,'w') f.write(src) f.close() if __name__=='__main__': if len(sys.argv)>2 and sys.argv[1]=='--md5update': update_md5(sys.argv[2:]) else: main(sys.argv[1:]) python-module-decoratortools-1.7/peak/000075500000000000000000000000001116633211000201365ustar00rootroot00000000000000python-module-decoratortools-1.7/peak/__init__.py000075500000000000000000000000711116633211000222500ustar00rootroot00000000000000__import__('pkg_resources').declare_namespace(__name__) python-module-decoratortools-1.7/peak/util/000075500000000000000000000000001116633211000211135ustar00rootroot00000000000000python-module-decoratortools-1.7/peak/util/__init__.py000075500000000000000000000000701116633211000232240ustar00rootroot00000000000000__import__('pkg_resources').declare_namespace(__name__) python-module-decoratortools-1.7/peak/util/decorators.py000064400000000000000000000503511116633211000236360ustar00rootroot00000000000000from types import ClassType, FunctionType import sys, os __all__ = [ 'decorate_class', 'metaclass_is_decorator', 'metaclass_for_bases', 'frameinfo', 'decorate_assignment', 'decorate', 'struct', 'classy', 'template_function', 'rewrap', 'cache_source', 'enclosing_frame', 'synchronized', ] def decorate(*decorators): """Use Python 2.4 decorators w/Python 2.3+ Example:: class Foo(object): decorate(classmethod) def something(cls,etc): \"""This is a classmethod\""" You can pass in more than one decorator, and they are applied in the same order that would be used for ``@`` decorators in Python 2.4. This function can be used to write decorator-using code that will work with both Python 2.3 and 2.4 (and up). """ if len(decorators)>1: decorators = list(decorators) decorators.reverse() def callback(frame,k,v,old_locals): for d in decorators: v = d(v) return v return decorate_assignment(callback) def enclosing_frame(frame=None, level=3): """Get an enclosing frame that skips DecoratorTools callback code""" frame = frame or sys._getframe(level) while frame.f_globals.get('__name__')==__name__: frame = frame.f_back return frame def name_and_spec(func): from inspect import formatargspec, getargspec funcname = func.__name__ if funcname=='': funcname = "anonymous" args, varargs, kwargs, defaults = getargspec(func) return funcname, formatargspec(args, varargs, kwargs)[1:-1] def qname(func): m = func.__module__ return m and m+'.'+func.__name__ or func.__name__ def apply_template(wrapper, func, *args, **kw): funcname, argspec = name_and_spec(func) wrapname, wrapspec = name_and_spec(wrapper) body = wrapper.__doc__.replace('%','%%').replace('$args','%(argspec)s') d ={} body = """ def %(wrapname)s(%(wrapspec)s): def %(funcname)s(%(argspec)s): """+body+""" return %(funcname)s """ body %= locals() filename = "<%s wrapping %s at 0x%08X>" % (qname(wrapper), qname(func), id(func)) exec compile(body, filename, "exec") in func.func_globals, d f = d[wrapname](func, *args, **kw) cache_source(filename, body, f) f.func_defaults = func.func_defaults f.__doc__ = func.__doc__ f.__dict__ = func.__dict__ return f def rewrap(func, wrapper): """Create a wrapper with the signature of `func` and a body of `wrapper` Example:: def before_and_after(func): def decorated(*args, **kw): print "before" try: return func(*args, **kw) finally: print "after" return rewrap(func, decorated) The above function is a normal decorator, but when users run ``help()`` or other documentation tools on the returned wrapper function, they will see a function with the original function's name, signature, module name, etc. This function is similar in use to the ``@template_function`` decorator, but rather than generating the entire decorator function in one calling layer, it simply generates an extra layer for signature compatibility. NOTE: the function returned from ``rewrap()`` will have the same attribute ``__dict__`` as the original function, so if you need to set any function attributes you should do so on the function returned from ``rewrap()`` (or on the original function), and *not* on the wrapper you're passing in to ``rewrap()``. """ def rewrap(__original, __decorated): """return __decorated($args)""" return apply_template(rewrap, func, wrapper) if sys.version<"2.5": # We'll need this for monkeypatching linecache def checkcache(filename=None): """Discard cache entries that are out of date. (This is not checked upon each call!)""" if filename is None: filenames = linecache.cache.keys() else: if filename in linecache.cache: filenames = [filename] else: return for filename in filenames: size, mtime, lines, fullname = linecache.cache[filename] if mtime is None: continue # no-op for files loaded via a __loader__ try: stat = os.stat(fullname) except os.error: del linecache.cache[filename] continue if size != stat.st_size or mtime != stat.st_mtime: del linecache.cache[filename] def _cache_lines(filename, lines, owner=None): if owner is None: owner = filename else: from weakref import ref owner = ref(owner, lambda r: linecache and linecache.cache.__delitem__(filename)) global linecache; import linecache if sys.version<"2.5" and linecache.checkcache.__module__!=__name__: linecache.checkcache = checkcache linecache.cache[filename] = 0, None, lines, owner def cache_source(filename, source, owner=None): _cache_lines(filename, source.splitlines(True), owner) def template_function(wrapper=None): """Decorator that uses its wrapped function's docstring as a template Example:: def before_and_after(func): @template_function def wrap(__func, __message): ''' print "before", __message try: return __func($args) finally: print "after", __message ''' return wrap(func, "test") The above code will return individually-generated wrapper functions whose signature, defaults, ``__name__``, ``__module__``, and ``func_globals`` match those of the wrapped functions. You can use define any arguments you wish in the wrapping function, as long as the first argument is the function to be wrapped, and the arguments are named so as not to conflict with the arguments of the function being wrapped. (i.e., they should have relatively unique names.) Note that the function body will *not* have access to the globals of the calling module, as it is compiled with the globals of the *wrapped* function! Thus, any non-builtin values that you need in the wrapper should be passed in as arguments to the template function. """ if wrapper is None: return decorate_assignment(lambda f,k,v,o: template_function(v)) return apply_template.__get__(wrapper) def struct(*mixins, **kw): """Turn a function into a simple data structure class This decorator creates a tuple subclass with the same name and docstring as the decorated function. The class will have read-only properties with the same names as the function's arguments, and the ``repr()`` of its instances will look like a call to the original function. The function should return a tuple of values in the same order as its argument names, as it will be used by the class' constructor. The function can perform validation, add defaults, and/or do type conversions on the values. If the function takes a ``*``, argument, it should flatten this argument into the result tuple, e.g.:: @struct() def pair(first, *rest): return (first,) + rest The ``rest`` property of the resulting class will thus return a tuple for the ``*rest`` arguments, and the structure's ``repr()`` will reflect the way it was created. The ``struct()`` decorator takes optional mixin classes (as positional arguments), and dictionary entries (as keyword arguments). The mixin classes will be placed before ``tuple`` in the resulting class' bases, and the dictionary entries will be placed in the class' dictionary. These entries take precedence over any default entries (e.g. methods, properties, docstring, etc.) that are created by the ``struct()`` decorator. """ def callback(frame, name, func, old_locals): def __new__(cls, *args, **kw): result = func(*args, **kw) if type(result) is tuple: return tuple.__new__(cls, (cls,)+result) else: return result def __repr__(self): return name+tuple.__repr__(self[1:]) import inspect args, star, dstar, defaults = inspect.getargspec(func) d = dict( __new__ = __new__, __repr__ = __repr__, __doc__=func.__doc__, __module__ = func.__module__, __args__ = args, __star__ = star, __slots__ = [], ) for p,a in enumerate(args): if isinstance(a,str): d[a] = property(lambda self, p=p+1: self[p]) if star: d[star] = property(lambda self, p=len(args)+1: self[p:]) d.update(kw) return type(name, mixins+(tuple,), d) return decorate_assignment(callback) def synchronized(func=None): """Create a method synchronized by first argument's ``__lock__`` attribute If the object has no ``__lock__`` attribute at run-time, the wrapper will attempt to add one by creating a ``threading.RLock`` and adding it to the object's ``__dict__``. If ``threading`` isn't available, it will use a ``dummy_threading.RLock`` instead. Neither will be imported unless the method is called on an object that doesn't have a ``__lock__``. This decorator can be used as a standard decorator (e.g. ``@synchronized``) or as a Python 2.3-compatible decorator by calling it with no arguments (e.g. ``[synchronized()]``). """ if func is None: return decorate_assignment(lambda f,k,v,o: synchronized(v)) def wrap(__func): ''' try: lock = $self.__lock__ except AttributeError: try: from threading import RLock except ImportError: from dummy_threading import RLock lock = $self.__dict__.setdefault('__lock__',RLock()) lock.acquire() try: return __func($args) finally: lock.release()''' from inspect import getargspec first_arg = getargspec(func)[0][0] wrap.__doc__ = wrap.__doc__.replace('$self', first_arg) return apply_template(wrap, func) def frameinfo(frame): """Return (kind, module, locals, globals) tuple for a frame 'kind' is one of "exec", "module", "class", "function call", or "unknown". """ f_locals = frame.f_locals f_globals = frame.f_globals sameNamespace = f_locals is f_globals hasModule = '__module__' in f_locals hasName = '__name__' in f_globals sameName = hasModule and hasName sameName = sameName and f_globals['__name__']==f_locals['__module__'] module = hasName and sys.modules.get(f_globals['__name__']) or None namespaceIsModule = module and module.__dict__ is f_globals if not namespaceIsModule: # some kind of funky exec kind = "exec" if hasModule and not sameNamespace: kind="class" elif sameNamespace and not hasModule: kind = "module" elif sameName and not sameNamespace: kind = "class" elif not sameNamespace: kind = "function call" else: # How can you have f_locals is f_globals, and have '__module__' set? # This is probably module-level code, but with a '__module__' variable. kind = "unknown" return kind,module,f_locals,f_globals def decorate_class(decorator, depth=2, frame=None, allow_duplicates=False): """Set up `decorator` to be passed the containing class upon creation This function is designed to be called by a decorator factory function executed in a class suite. The factory function supplies a decorator that it wishes to have executed when the containing class is created. The decorator will be given one argument: the newly created containing class. The return value of the decorator will be used in place of the class, so the decorator should return the input class if it does not wish to replace it. The optional `depth` argument to this function determines the number of frames between this function and the targeted class suite. `depth` defaults to 2, since this skips the caller's frame. Thus, if you call this function from a function that is called directly in the class suite, the default will be correct, otherwise you will need to determine the correct depth value yourself. Alternately, you can pass in a `frame` argument to explicitly indicate what frame is doing the class definition. This function works by installing a special class factory function in place of the ``__metaclass__`` of the containing class. Therefore, only decorators *after* the last ``__metaclass__`` assignment in the containing class will be executed. Thus, any classes using class decorators should declare their ``__metaclass__`` (if any) *before* specifying any class decorators, to ensure that all class decorators will be applied.""" frame = enclosing_frame(frame, depth+1) kind, module, caller_locals, caller_globals = frameinfo(frame) if kind != "class": raise SyntaxError( "Class decorators may only be used inside a class statement" ) elif not allow_duplicates and has_class_decorator(decorator, None, frame): return previousMetaclass = caller_locals.get('__metaclass__') defaultMetaclass = caller_globals.get('__metaclass__', ClassType) def advise(name,bases,cdict): if '__metaclass__' in cdict: del cdict['__metaclass__'] if previousMetaclass is None: if bases: # find best metaclass or use global __metaclass__ if no bases meta = metaclass_for_bases(bases) else: meta = defaultMetaclass elif metaclass_is_decorator(previousMetaclass): # special case: we can't compute the "true" metaclass here, # so we need to invoke the previous metaclass and let it # figure it out for us (and apply its own advice in the process) meta = previousMetaclass else: meta = metaclass_for_bases(bases, previousMetaclass) newClass = meta(name,bases,cdict) # this lets the decorator replace the class completely, if it wants to return decorator(newClass) # introspection data only, not used by inner function # Note: these attributes cannot be renamed or it will break compatibility # with zope.interface and any other code that uses this decoration protocol advise.previousMetaclass = previousMetaclass advise.callback = decorator # install the advisor caller_locals['__metaclass__'] = advise def metaclass_is_decorator(ob): """True if 'ob' is a class advisor function""" return isinstance(ob,FunctionType) and hasattr(ob,'previousMetaclass') def iter_class_decorators(depth=2, frame=None): frame = enclosing_frame(frame, depth+1) m = frame.f_locals.get('__metaclass__') while metaclass_is_decorator(m): yield getattr(m, 'callback', None) m = m.previousMetaclass def has_class_decorator(decorator, depth=2, frame=None): return decorator in iter_class_decorators(0, frame or sys._getframe(depth)) def metaclass_for_bases(bases, explicit_mc=None): """Determine metaclass from 1+ bases and optional explicit __metaclass__""" meta = [getattr(b,'__class__',type(b)) for b in bases] if explicit_mc is not None: # The explicit metaclass needs to be verified for compatibility # as well, and allowed to resolve the incompatible bases, if any meta.append(explicit_mc) if len(meta)==1: # easy case return meta[0] classes = [c for c in meta if c is not ClassType] candidates = [] for m in classes: for n in classes: if issubclass(n,m) and m is not n: break else: # m has no subclasses in 'classes' if m in candidates: candidates.remove(m) # ensure that we're later in the list candidates.append(m) if not candidates: # they're all "classic" classes return ClassType elif len(candidates)>1: # We could auto-combine, but for now we won't... raise TypeError("Incompatible metatypes",bases) # Just one, return it return candidates[0] def decorate_assignment(callback, depth=2, frame=None): """Invoke 'callback(frame,name,value,old_locals)' on next assign in 'frame' The frame monitored is determined by the 'depth' argument, which gets passed to 'sys._getframe()'. When 'callback' is invoked, 'old_locals' contains a copy of the frame's local variables as they were before the assignment took place, allowing the callback to access the previous value of the assigned variable, if any. The callback's return value will become the new value of the variable. 'name' is the name of the variable being created or modified, and 'value' is its value (the same as 'frame.f_locals[name]'). This function also returns a decorator function for forward-compatibility with Python 2.4 '@' syntax. Note, however, that if the returned decorator is used with Python 2.4 '@' syntax, the callback 'name' argument may be 'None' or incorrect, if the 'value' is not the original function (e.g. when multiple decorators are used). """ frame = enclosing_frame(frame, depth+1) oldtrace = [frame.f_trace] old_locals = frame.f_locals.copy() def tracer(frm,event,arg): if event=='call': # We don't want to trace into any calls if oldtrace[0]: # ...but give the previous tracer a chance to, if it wants return oldtrace[0](frm,event,arg) else: return None try: if frm is frame and event !='exception': # Aha, time to check for an assignment... for k,v in frm.f_locals.items(): if k not in old_locals or old_locals[k] is not v: break else: # No luck, keep tracing return tracer # Got it, fire the callback, then get the heck outta here... frm.f_locals[k] = callback(frm,k,v,old_locals) finally: # Give the previous tracer a chance to run before we return if oldtrace[0]: # And allow it to replace our idea of the "previous" tracer oldtrace[0] = oldtrace[0](frm,event,arg) uninstall() return oldtrace[0] def uninstall(): # Unlink ourselves from the trace chain. frame.f_trace = oldtrace[0] sys.settrace(oldtrace[0]) # Install the trace function frame.f_trace = tracer sys.settrace(tracer) def do_decorate(f): # Python 2.4 '@' compatibility; call the callback uninstall() frame = sys._getframe(1) return callback( frame, getattr(f,'__name__',None), f, frame.f_locals ) return do_decorate def super_next(cls, attr): for c in cls.__mro__: if attr in c.__dict__: yield getattr(c, attr).im_func class classy_class(type): """Metaclass that delegates selected operations back to the class""" def __new__(meta, name, bases, cdict): cls = super(classy_class, meta).__new__(meta, name, bases, cdict) supr = super_next(cls, '__class_new__').next return supr()(meta, name, bases, cdict, supr) def __init__(cls, name, bases, cdict): supr = super_next(cls, '__class_init__').next return supr()(cls, name, bases, cdict, supr) def __call__(cls, *args, **kw): return cls.__class_call__.im_func(cls, *args, **kw) class classy(object): """Base class for classes that want to be their own metaclass""" __metaclass__ = classy_class __slots__ = () def __class_new__(meta, name, bases, cdict, supr): return type.__new__(meta, name, bases, cdict) def __class_init__(cls, name, bases, cdict, supr): return type.__init__(cls, name, bases, cdict) def __class_call__(cls, *args, **kw): return type.__call__(cls, *args, **kw) __class_call__ = classmethod(__class_call__) python-module-decoratortools-1.7/python-module-decoratortools.spec000064400000000000000000000023421116633211000257400ustar00rootroot00000000000000%define modulename decoratortools Name: python-module-%modulename Version: 1.7 Release: alt2 %setup_python_module %modulename Summary: Use class and function decorators -- even in Python 2.3 License: PSF or ZPL Group: Development/Python # svn://svn.eby-sarna.com/svnroot/DecoratorTools Url: http://pypi.python.org/pypi/DecoratorTools Packager: Vladimir V. Kamarzin BuildArch: noarch Source: %name-%version.tar BuildPreReq: %py_dependencies setuptools Requires: python-module-peak %description Want to use decorators, but still need to support Python 2.3? Wish you could have class decorators, decorate arbitrary assignments, or match decorated function signatures to their original functions? Want to get metaclass features without creating metaclasses? How about synchronized methods? "DecoratorTools" gets you all of this and more. %prep %setup %build %python_build %install %python_install %files %python_sitelibdir/peak/util/decorators* %python_sitelibdir/*.egg-info %changelog * Mon Apr 06 2009 Vladimir V. Kamarzin 1.7-alt2 - Avoid file conflict with python-module-peak and add dependency on it * Sat Apr 04 2009 Vladimir V. Kamarzin 1.7-alt1 - Initial build for Sisyphus python-module-decoratortools-1.7/setup.cfg000075500000000000000000000000601116633211000210360ustar00rootroot00000000000000[egg_info] tag_build = dev tag_svn_revision = 1 python-module-decoratortools-1.7/setup.py000075500000000000000000000021451116633211000207350ustar00rootroot00000000000000#!/usr/bin/env python """Distutils setup file""" import ez_setup ez_setup.use_setuptools() from setuptools import setup # Metadata PACKAGE_NAME = "DecoratorTools" PACKAGE_VERSION = "1.7" PACKAGES = ['peak', 'peak.util'] def get_description(): # Get our long description from the documentation f = file('README.txt') lines = [] for line in f: if not line.strip(): break # skip to first blank line for line in f: if line.startswith('.. contents::'): break # read to table of contents lines.append(line) f.close() return ''.join(lines) setup( name=PACKAGE_NAME, version=PACKAGE_VERSION, description="Class, function, and metaclass decorators -- even in Python 2.3" " (now with source debugging for generated code)!", long_description = get_description(), author="Phillip J. Eby", author_email="peak@eby-sarna.com", license="PSF or ZPL", url="http://cheeseshop.python.org/pypi/DecoratorTools", test_suite = 'test_decorators', packages = PACKAGES, namespace_packages = PACKAGES, ) python-module-decoratortools-1.7/test_decorators.py000064400000000000000000000112151116633211000227740ustar00rootroot00000000000000from unittest import TestCase, makeSuite, TestSuite from peak.util.decorators import * import sys def ping(log, value): """Class decorator for testing""" def pong(klass): log.append((value,klass)) return [klass] decorate_class(pong) def additional_tests(): import doctest return doctest.DocFileSuite( 'README.txt', optionflags=doctest.ELLIPSIS|doctest.NORMALIZE_WHITESPACE, ) class DecoratorTests(TestCase): def testAssignAdvice(self): log = [] def track(f,k,v,d): log.append((f,k,v)) if k in f.f_locals: del f.f_locals[k] # simulate old-style advisor decorate_assignment(track,frame=sys._getframe()) test_var = 1 self.assertEqual(log, [(sys._getframe(),'test_var',1)]) log = [] decorate_assignment(track,1) test2 = 42 self.assertEqual(log, [(sys._getframe(),'test2',42)]) # Try doing double duty, redefining an existing variable... log = [] decorate_assignment(track,1) decorate_assignment(track,1) test2 = 42 self.assertEqual(log, [(sys._getframe(),'test2',42)]*2) def testAs(self): def f(): pass [decorate(lambda x: [x])] f1 = f self.assertEqual(f1, [f]) [decorate(list, lambda x: (x,))] f1 = f self.assertEqual(f1, [f]) def test24DecoratorMode(self): log = [] def track(f,k,v,d): log.append((f,k,v)) return v def foo(x): pass decorate_assignment(track,1)(foo) x = 1 self.assertEqual(log, [(sys._getframe(),'foo',foo)]) moduleLevelFrameInfo = frameinfo(sys._getframe()) class FrameInfoTest(TestCase): classLevelFrameInfo = frameinfo(sys._getframe()) def testModuleInfo(self): kind,module,f_locals,f_globals = moduleLevelFrameInfo assert kind=="module" for d in module.__dict__, f_locals, f_globals: assert d is globals() def testClassInfo(self): kind,module,f_locals,f_globals = self.classLevelFrameInfo assert kind=="class" assert f_locals['classLevelFrameInfo'] is self.classLevelFrameInfo for d in module.__dict__, f_globals: assert d is globals() def testCallInfo(self): kind,module,f_locals,f_globals = frameinfo(sys._getframe()) assert kind=="function call" assert f_locals is locals() # ??? for d in module.__dict__, f_globals: assert d is globals() def testClassExec(self): d = {'sys':sys, 'frameinfo':frameinfo} exec "class Foo: info=frameinfo(sys._getframe())" in d kind,module,f_locals,f_globals = d['Foo'].info assert kind=="class", kind class ClassDecoratorTests(TestCase): def testOrder(self): log = [] class Foo: ping(log, 1) ping(log, 2) ping(log, 3) # Strip the list nesting for i in 1,2,3: assert isinstance(Foo,list) Foo, = Foo assert log == [ (1, Foo), (2, [Foo]), (3, [[Foo]]), ] def testOutside(self): try: ping([], 1) except SyntaxError: pass else: raise AssertionError( "Should have detected advice outside class body" ) def testDoubleType(self): if sys.hexversion >= 0x02030000: return # you can't duplicate bases in 2.3 class aType(type,type): ping([],1) aType, = aType assert aType.__class__ is type def testSingleExplicitMeta(self): class M(type): pass class C(M): __metaclass__ = M ping([],1) C, = C assert C.__class__ is M def testMixedMetas(self): class M1(type): pass class M2(type): pass class B1: __metaclass__ = M1 class B2: __metaclass__ = M2 try: class C(B1,B2): ping([],1) except TypeError: pass else: raise AssertionError("Should have gotten incompatibility error") class M3(M1,M2): pass class C(B1,B2): __metaclass__ = M3 ping([],1) assert isinstance(C,list) C, = C assert isinstance(C,M3) def testMetaOfClass(self): class metameta(type): pass class meta(type): __metaclass__ = metameta assert metaclass_for_bases((meta,type))==metameta python-module-decoratortools-1.7/wikiup.cfg000064400000000000000000000000431116633211000212040ustar00rootroot00000000000000[PEAK] DecoratorTools = README.txt