Improved tests and docs

author: Michele Simionato <michele.simionato@gmail.com> 2015-07-23 16:33:46 +0200
committer: Michele Simionato <michele.simionato@gmail.com> 2015-07-23 16:33:46 +0200
commit: 8b7b3aba63d278eb391c3c9b0ccbe065559f1b93 (patch)
tree: 5438712066b25f00151e80a0f4741a53d2516193
parent: bf1748df8d229bbfe40de6944c48349228e5b5bc (diff)
download: python-decorator-git-8b7b3aba63d278eb391c3c9b0ccbe065559f1b93.tar.gz
4 files changed, 658 insertions, 376 deletions
diff --git a/documentation.pdf b/documentation.pdf
index dd1b347..8fde909 100644
--- a/documentation.pdf
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-BT 1 0 0 1 0 26 Tm .20561 Tw 12 TL /F1 10 Tf 0 0 0 rg (The ) Tj /F3 10 Tf 0 0 0 rg (ba ) Tj /F1 10 Tf 0 0 0 rg (decorator is basically inserting a ) Tj /F3 10 Tf 0 0 0 rg (with) Tj ( ) Tj (ba: ) Tj /F1 10 Tf 0 0 0 rg (block inside the function. However there two issues: ) Tj T* 0 Tw 1.402126 Tw (the first is that ) Tj /F3 10 Tf 0 0 0 rg (GeneratorContextManager ) Tj /F1 10 Tf 0 0 0 rg (objects are callable only in Python 3.2, so the previous ) Tj T* 0 Tw .356905 Tw (example will break in older versions of Python; the second is that ) Tj /F3 10 Tf 0 0 0 rg (GeneratorContextManager ) Tj /F1 10 Tf 0 0 0 rg (objects) Tj T* 0 Tw ET
+BT 1 0 0 1 0 26 Tm .20561 Tw 12 TL /F1 10 Tf 0 0 0 rg (The ) Tj /F3 10 Tf 0 0 0 rg (ba ) Tj /F1 10 Tf 0 0 0 rg (decorator is basically inserting a ) Tj /F3 10 Tf 0 0 0 rg (with) Tj ( ) Tj (ba: ) Tj /F1 10 Tf 0 0 0 rg (block inside the function. However there two issues: ) Tj T* 0 Tw 1.402126 Tw (the first is that ) Tj /F3 10 Tf 0 0 0 rg (GeneratorContextManager ) Tj /F1 10 Tf 0 0 0 rg (objects are callable only in Python 3.2, so the previous ) Tj T* 0 Tw 2.323984 Tw (example will break in older versions of Python \(you can solve this by installing ) Tj /F3 10 Tf 0 0 0 rg (contextlib2) Tj /F1 10 Tf 0 0 0 rg (\); the) Tj T* 0 Tw ET
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-BT 1 0 0 1 0 74 Tm .57561 Tw 12 TL /F1 10 Tf 0 0 0 rg (do not preserve the signature of the decorated functions: the decorated ) Tj /F3 10 Tf 0 0 0 rg (hello ) Tj /F1 10 Tf 0 0 0 rg (function here will have a) Tj T* 0 Tw 3.990814 Tw (generic signature ) Tj /F3 10 Tf 0 0 0 rg (hello\(*args,) Tj ( ) Tj (**kwargs\) ) Tj /F1 10 Tf 0 0 0 rg (but will break when called with more than zero) Tj T* 0 Tw 7.708314 Tw (arguments. For such reasons the decorator module, starting with release 3.4, offers a) Tj T* 0 Tw 1.027251 Tw /F3 10 Tf 0 0 0 rg (decorator.contextmanager ) Tj /F1 10 Tf 0 0 0 rg (decorator that solves both problems and works in all supported Python) Tj T* 0 Tw 2.85816 Tw (versions. The usage is the same and factories decorated with ) Tj /F3 10 Tf 0 0 0 rg (decorator.contextmanager ) Tj /F1 10 Tf 0 0 0 rg (will) Tj T* 0 Tw .17122 Tw (returns instances of ) Tj /F3 10 Tf 0 0 0 rg (ContextManager) Tj /F1 10 Tf 0 0 0 rg (, a subclass of ) Tj /F3 10 Tf 0 0 0 rg (contextlib.GeneratorContextManager ) Tj /F1 10 Tf 0 0 0 rg (with) Tj T* 0 Tw (a ) Tj /F3 10 Tf 0 0 0 rg (__call__ ) Tj /F1 10 Tf 0 0 0 rg (method acting as a signature-preserving decorator.) Tj T* ET
+BT 1 0 0 1 0 86 Tm 1.819147 Tw 12 TL /F1 10 Tf 0 0 0 rg (second is that ) Tj /F3 10 Tf 0 0 0 rg (GeneratorContextManager ) Tj /F1 10 Tf 0 0 0 rg (objects do not preserve the signature of the decorated) Tj T* 0 Tw 13.76298 Tw (functions: the decorated ) Tj /F3 10 Tf 0 0 0 rg (hello ) Tj /F1 10 Tf 0 0 0 rg (function here will have a generic signature) Tj T* 0 Tw 2.777674 Tw /F3 10 Tf 0 0 0 rg (hello\(*args,) Tj ( ) Tj (**kwargs\) ) Tj /F1 10 Tf 0 0 0 rg (but will break when called with more than zero arguments. For such) Tj T* 0 Tw 3.697976 Tw (reasons the decorator module, starting with release 3.4, offers a ) Tj /F3 10 Tf 0 0 0 rg (decorator.contextmanager) Tj T* 0 Tw .70936 Tw /F1 10 Tf 0 0 0 rg (decorator that solves both problems and works in all supported Python versions. The usage is the same) Tj T* 0 Tw 11.01622 Tw (and factories decorated with ) Tj /F3 10 Tf 0 0 0 rg (decorator.contextmanager ) Tj /F1 10 Tf 0 0 0 rg (will returns instances of) Tj T* 0 Tw 4.152823 Tw /F3 10 Tf 0 0 0 rg (ContextManager) Tj /F1 10 Tf 0 0 0 rg (, a subclass of ) Tj /F3 10 Tf 0 0 0 rg (contextlib.GeneratorContextManager ) Tj /F1 10 Tf 0 0 0 rg (with a ) Tj /F3 10 Tf 0 0 0 rg (__call__) Tj T* 0 Tw /F1 10 Tf 0 0 0 rg (method acting as a signature-preserving decorator.) Tj T* ET
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-BT 1 0 0 1 0 74 Tm .11936 Tw 12 TL /F1 10 Tf 0 0 0 rg (There has been talk of implementing multiple dispatch \(i.e. generic\) functions in Python for over ten years.) Tj T* 0 Tw 3.96881 Tw (Last year for the first time something was done and now in Python 3.4 we have a decorator) Tj T* 0 Tw .294147 Tw /F3 10 Tf 0 0 0 rg (functools.singledispatch ) Tj /F1 10 Tf 0 0 0 rg (which can be used to implement generic functions. As the name implies,) Tj T* 0 Tw .063322 Tw (it has the restriction of being limited to single dispatch, i.e. it is able to dispatch on the first argument of the) Tj T* 0 Tw 1.423555 Tw (function only. The decorator module provide a decorator factory ) Tj /F3 10 Tf 0 0 0 rg (dispatch_on ) Tj /F1 10 Tf 0 0 0 rg (which can be used to) Tj T* 0 Tw .616905 Tw (implement generic functions dispatching on any argument; moreover it can manage dispatching on more) Tj T* 0 Tw (than one argument and, of course, it is signature-preserving.) Tj T* ET
+BT 1 0 0 1 0 74 Tm .11936 Tw 12 TL /F1 10 Tf 0 0 0 rg (There has been talk of implementing multiple dispatch \(i.e. generic\) functions in Python for over ten years.) Tj T* 0 Tw 1.46332 Tw (Last year for the first time something concrete was done and now in Python 3.4 we have a decorator) Tj T* 0 Tw .294147 Tw /F3 10 Tf 0 0 0 rg (functools.singledispatch ) Tj /F1 10 Tf 0 0 0 rg (which can be used to implement generic functions. As the name implies,) Tj T* 0 Tw .063322 Tw (it has the restriction of being limited to single dispatch, i.e. it is able to dispatch on the first argument of the) Tj T* 0 Tw 1.423555 Tw (function only. The decorator module provide a decorator factory ) Tj /F3 10 Tf 0 0 0 rg (dispatch_on ) Tj /F1 10 Tf 0 0 0 rg (which can be used to) Tj T* 0 Tw .616905 Tw (implement generic functions dispatching on any argument; moreover it can manage dispatching on more) Tj T* 0 Tw (than one argument and, of course, it is signature-preserving.) Tj T* ET
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-BT 1 0 0 1 0 26 Tm .129461 Tw 12 TL /F1 10 Tf 0 0 0 rg (Since in Python it is possible to consider any instance of ABCMeta as a virtual ancestor of any other class) Tj T* 0 Tw .21152 Tw (\(it is enough to register it as ) Tj /F3 10 Tf 0 0 0 rg (ancestor.register\(cls\)) Tj /F1 10 Tf 0 0 0 rg (\), any implementation of generic functions must) Tj T* 0 Tw (take this feature into account. Let me give an example.) Tj T* ET
+BT 1 0 0 1 0 26 Tm .129461 Tw 12 TL /F1 10 Tf 0 0 0 rg (Since in Python it is possible to consider any instance of ABCMeta as a virtual ancestor of any other class) Tj T* 0 Tw .21152 Tw (\(it is enough to register it as ) Tj /F3 10 Tf 0 0 0 rg (ancestor.register\(cls\)) Tj /F1 10 Tf 0 0 0 rg (\), any implementation of generic functions must) Tj T* 0 Tw (take virtual ancestors into account. Let me give an example.) Tj T* ET
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-BT 1 0 0 1 0 50 Tm 12 TL /F5 10 Tf 0 .501961 0 rg (class) Tj /F3 10 Tf 0 0 0 rg ( ) Tj /F5 10 Tf 0 0 1 rg (SomeSet) Tj /F3 10 Tf 0 0 0 rg (\() Tj 0 0 0 rg (collections) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (Sized) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (    ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# methods that make SomeSet set-like) Tj /F3 10 Tf 0 0 0 rg  T* (    ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# not shown ...) Tj /F3 10 Tf 0 0 0 rg  T* (    ) Tj /F5 10 Tf 0 .501961 0 rg (def) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 1 rg (__len__) Tj 0 0 0 rg (\() Tj 0 .501961 0 rg (self) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (        ) Tj /F5 10 Tf 0 .501961 0 rg (return) Tj /F3 10 Tf 0 0 0 rg ( ) Tj .4 .4 .4 rg (0) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# in reality one would return more than zero) Tj T* ET
+BT 1 0 0 1 0 50 Tm 12 TL /F5 10 Tf 0 .501961 0 rg (class) Tj /F3 10 Tf 0 0 0 rg ( ) Tj /F5 10 Tf 0 0 1 rg (SomeSet) Tj /F3 10 Tf 0 0 0 rg (\() Tj 0 0 0 rg (collections) Tj .4 .4 .4 rg (.) Tj 0 0 0 rg (Sized) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (    ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# methods that make SomeSet set-like) Tj /F3 10 Tf 0 0 0 rg  T* (    ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# not shown ...) Tj /F3 10 Tf 0 0 0 rg  T* (    ) Tj /F5 10 Tf 0 .501961 0 rg (def) Tj /F3 10 Tf 0 0 0 rg ( ) Tj 0 0 1 rg (__len__) Tj 0 0 0 rg (\() Tj 0 .501961 0 rg (self) Tj 0 0 0 rg (\):) Tj 0 0 0 rg  T* (        ) Tj /F5 10 Tf 0 .501961 0 rg (return) Tj /F3 10 Tf 0 0 0 rg ( ) Tj .4 .4 .4 rg (0) Tj T* ET
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-BT 1 0 0 1 0 14 Tm .210697 Tw 12 TL /F1 10 Tf 0 0 0 rg (The current implementation, as the one used by ) Tj /F3 10 Tf 0 0 0 rg (functools.singledispatch) Tj /F1 10 Tf 0 0 0 rg (, is able to discern that a) Tj T* 0 Tw /F3 10 Tf 0 0 0 rg (Set ) Tj /F1 10 Tf 0 0 0 rg (is a ) Tj /F3 10 Tf 0 0 0 rg (Sized ) Tj /F1 10 Tf 0 0 0 rg (object, so the implementation for ) Tj /F3 10 Tf 0 0 0 rg (Set ) Tj /F1 10 Tf 0 0 0 rg (is taken:) Tj T* ET
+BT 1 0 0 1 0 14 Tm .210697 Tw 12 TL /F1 10 Tf 0 0 0 rg (The current implementation, as the one used by ) Tj /F3 10 Tf 0 0 0 rg (functools.singledispatch) Tj /F1 10 Tf 0 0 0 rg (, is able to discern that a) Tj T* 0 Tw /F3 10 Tf 0 0 0 rg (Set ) Tj /F1 10 Tf 0 0 0 rg (is a ) Tj /F3 10 Tf 0 0 0 rg (Sized ) Tj /F1 10 Tf 0 0 0 rg (object, so the more specific implementation for ) Tj /F3 10 Tf 0 0 0 rg (Set ) Tj /F1 10 Tf 0 0 0 rg (is taken:) Tj T* ET
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-BT 1 0 0 1 0 14 Tm 12 TL /F3 10 Tf .4 .4 .4 rg (>) Tj (>) Tj (>) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (get_length) Tj 0 0 0 rg (\() Tj 0 0 0 rg (SomeSet) Tj 0 0 0 rg (\(\)\)) Tj 0 0 0 rg  T* .4 .4 .4 rg (1) Tj T* ET
+BT 1 0 0 1 0 14 Tm 12 TL /F3 10 Tf .4 .4 .4 rg (>) Tj (>) Tj (>) Tj 0 0 0 rg ( ) Tj 0 0 0 rg (get_length) Tj 0 0 0 rg (\() Tj 0 0 0 rg (SomeSet) Tj 0 0 0 rg (\(\)\)) Tj 0 0 0 rg (  ) Tj /F6 10 Tf .25098 .501961 .501961 rg (# NB: the implementation for Sized would give 0) Tj /F3 10 Tf 0 0 0 rg  T* .4 .4 .4 rg (1) Tj T* ET
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-BT 1 0 0 1 0 50 Tm 3.134104 Tw 12 TL /F1 10 Tf 0 0 0 rg (Some times it is not clear how to dispatch. For instance, consider a class ) Tj /F3 10 Tf 0 0 0 rg (C ) Tj /F1 10 Tf 0 0 0 rg (registered both as) Tj T* 0 Tw 6.519974 Tw /F3 10 Tf 0 0 0 rg (collections.Iterable ) Tj /F1 10 Tf 0 0 0 rg (and ) Tj /F3 10 Tf 0 0 0 rg (collections.Sized ) Tj /F1 10 Tf 0 0 0 rg (and define a generic function ) Tj /F3 10 Tf 0 0 0 rg (g ) Tj /F1 10 Tf 0 0 0 rg (with) Tj T* 0 Tw 3.101085 Tw (implementations both for ) Tj /F3 10 Tf 0 0 0 rg (collections.Iterable ) Tj /F1 10 Tf 0 0 0 rg (and ) Tj /F3 10 Tf 0 0 0 rg (collections.Sized) Tj /F1 10 Tf 0 0 0 rg (. It is impossible to) Tj T* 0 Tw .496651 Tw (decide which implementation to use, since the ancestors are independent, and the following code will fail) Tj T* 0 Tw (with a RuntimeError:) Tj T* ET
+BT 1 0 0 1 0 50 Tm 3.503735 Tw 12 TL /F1 10 Tf 0 0 0 rg (Sometimes it is not clear how to dispatch. For instance, consider a class ) Tj /F3 10 Tf 0 0 0 rg (C ) Tj /F1 10 Tf 0 0 0 rg (registered both as) Tj T* 0 Tw 6.519974 Tw /F3 10 Tf 0 0 0 rg (collections.Iterable ) Tj /F1 10 Tf 0 0 0 rg (and ) Tj /F3 10 Tf 0 0 0 rg (collections.Sized ) Tj /F1 10 Tf 0 0 0 rg (and define a generic function ) Tj /F3 10 Tf 0 0 0 rg (g ) Tj /F1 10 Tf 0 0 0 rg (with) Tj T* 0 Tw 3.101085 Tw (implementations both for ) Tj /F3 10 Tf 0 0 0 rg (collections.Iterable ) Tj /F1 10 Tf 0 0 0 rg (and ) Tj /F3 10 Tf 0 0 0 rg (collections.Sized) Tj /F1 10 Tf 0 0 0 rg (. It is impossible to) Tj T* 0 Tw .690697 Tw (decide which implementation to use, since the ancestors are independent, and the following function will) Tj T* 0 Tw (raise a RuntimeError when called:) Tj T* ET
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-n 24 96 132 12 re f*
+n 24 96 138 12 re f*
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-n 156 96 18 12 re f*
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@@ -7283,7 +7287,7 @@ n 90 0 6 12 re f*
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diff --git a/documentation.rst b/documentation.rst
index 8927d4a..427487d 100644
--- a/documentation.rst
+++ b/documentation.rst
@@ -586,19 +586,20 @@ method, so that they can be used as decorators as in this example:
  hello
  AFTER
 
-The ``ba`` decorator is basically inserting a ``with ba:``
-block inside the function.
-However there two issues: the first is that ``GeneratorContextManager``
-objects are callable only in Python 3.2, so the previous example will break
-in older versions of Python; the second is that
-``GeneratorContextManager`` objects do not preserve the signature
-of the decorated functions: the decorated ``hello`` function here will have
-a generic signature ``hello(*args, **kwargs)`` but will break when
-called with more than zero arguments. For such reasons the decorator
-module, starting with release 3.4, offers a ``decorator.contextmanager``
-decorator that solves both problems and works in all supported Python versions.
-The usage is the same and factories decorated with ``decorator.contextmanager``
-will returns instances of ``ContextManager``, a subclass of
+The ``ba`` decorator is basically inserting a ``with ba:`` block
+inside the function.  However there two issues: the first is that
+``GeneratorContextManager`` objects are callable only in Python 3.2,
+so the previous example will break in older versions of Python (you
+can solve this by installing ``contextlib2``); the second is that
+``GeneratorContextManager`` objects do not preserve the signature of
+the decorated functions: the decorated ``hello`` function here will
+have a generic signature ``hello(*args, **kwargs)`` but will break
+when called with more than zero arguments. For such reasons the
+decorator module, starting with release 3.4, offers a
+``decorator.contextmanager`` decorator that solves both problems and
+works in all supported Python versions.  The usage is the same and
+factories decorated with ``decorator.contextmanager`` will returns
+instances of ``ContextManager``, a subclass of
 ``contextlib.GeneratorContextManager`` with a ``__call__`` method
 acting as a signature-preserving decorator.
 
@@ -856,7 +857,7 @@ Multiple dispatch
 
 There has been talk of implementing multiple dispatch (i.e. generic)
 functions in Python for over ten years. Last year for the first time
-something was done and now in Python 3.4 we have a decorator
+something concrete was done and now in Python 3.4 we have a decorator
 ``functools.singledispatch`` which can be used to implement generic
 functions. As the name implies, it has the restriction of being
 limited to single dispatch, i.e. it is able to dispatch on the first
@@ -1054,7 +1055,7 @@ builtin ``len``, but you should get the idea.
 Since in Python it is possible to consider any instance of ABCMeta
 as a virtual ancestor of any other class (it is enough to register it
 as ``ancestor.register(cls)``), any implementation of generic functions
-must take this feature into account. Let me give an example.
+must take virtual ancestors into account. Let me give an example.
 
 Suppose you are using a third party set-like class like
 the following:
@@ -1065,7 +1066,7 @@ the following:
      # methods that make SomeSet set-like
      # not shown ...
      def __len__(self):
-         return 0  # in reality one would return more than zero
+         return 0
 
 
 Here the author of ``SomeSet`` made a mistake by not inheriting
@@ -1090,25 +1091,25 @@ Now, let us define an implementation of ``get_length`` specific to set:
 
 
 The current implementation, as the one used by ``functools.singledispatch``,
-is able to discern that a ``Set`` is a ``Sized`` object, so the
+is able to discern that a ``Set`` is a ``Sized`` object, so the more specific
 implementation for ``Set`` is taken:
 
 .. code-block:: python
 
- >>> get_length(SomeSet())
+ >>> get_length(SomeSet())  # NB: the implementation for Sized would give 0
  1
 
-Some times it is not clear how to dispatch. For instance, consider a
+Sometimes it is not clear how to dispatch. For instance, consider a
 class ``C`` registered both as ``collections.Iterable`` and
 ``collections.Sized`` and define a generic function ``g`` with
 implementations both for ``collections.Iterable`` and
 ``collections.Sized``. It is impossible to decide which implementation
-to use, since the ancestors are independent, and the following code
-will fail with a RuntimeError:
+to use, since the ancestors are independent, and the following function
+will raise a RuntimeError when called:
 
 .. code-block:: python
 
- def singledispatch_example():
+ def singledispatch_example1():
      singledispatch = dispatch_on('obj')
  
      @singledispatch
@@ -1123,8 +1124,7 @@ will fail with a RuntimeError:
      def g_iterable(object):
          return "iterable"
  
-     with assertRaises(RuntimeError):
-         g(C())  # Ambiguous dispatch: Iterable or Sized?
+     g(C())  # RuntimeError: Ambiguous dispatch: Iterable or Sized?
 
 
 This is consistent with the "refuse the temptation to guess"
@@ -1137,10 +1137,51 @@ implementation could be taken. If implementations of the generic
 functions are distributed across modules, and you change the import
 order, a different implementation could be taken. So the decorator
 module prefers to raise an error in the face of ambiguity. This is the
-same approach taken by the standard library. Notice that the dispatch
+same approach taken by the standard library.
+
+However, it should be noticed that the dispatch
 algorithm used by the decorator module is different from the one used
-by ``functools.singledispatch``, so there are cases where you will get
-different answers.
+by the standard library, so there are cases where you will get
+different answers. The difference is that ``functools.singledispatch``
+tries to insert the virtual ancestors *before* the base classes, whereas
+``decorator.dispatch_on`` tries to insert them *after* the base classes.
+I will give an example showing the difference:
+
+.. code-block:: python
+
+ def singledispatch_example2():
+     # adapted from functools.singledispatch test case
+     singledispatch = dispatch_on('arg')
+ 
+     class S(object):
+         pass
+ 
+     class V(c.Sized, S):
+         def __len__(self):
+             return 0
+ 
+     @singledispatch
+     def j(arg):
+         return "base"
+ 
+     @j.register(S)
+     def j_s(arg):
+         return "s"
+ 
+     @j.register(c.Container)
+     def j_container(arg):
+         return "container"
+ 
+     v = V()
+     assert j(v) == "s"
+     c.Container.register(V)  # add c.Container to the virtual mro of V
+     return j(v)  # "s", since the virtual mro is V, Sized, S, Container
+
+
+If you play with this example and replace the ``singledispatch`` definition
+with ``functools.singledispatch``, you will see the output change from ``"s"``
+to ``"container"``, because ``functools.singledispatch``
+will insert the ``Container`` class right before ``S``.
 
 Finally let me notice that the decorator module implementation does
 not use any cache, whereas the one in ``singledispatch`` has a cache.
@@ -1148,8 +1189,7 @@ not use any cache, whereas the one in ``singledispatch`` has a cache.
 Caveats and limitations
 -------------------------------------------
 
-The first thing you should be aware of, it the fact that decorators
-have a performance penalty.
+One thing you should be aware of, is the performance penalty of decorators.
 The worse case is shown by the following example::
 
  $ cat performance.sh
diff --git a/src/tests/documentation.py b/src/tests/documentation.py
index c5f8c7d..e0bc070 100644
--- a/src/tests/documentation.py
+++ b/src/tests/documentation.py
@@ -455,19 +455,20 @@ method, so that they can be used as decorators as in this example:
  hello
  AFTER
 
-The ``ba`` decorator is basically inserting a ``with ba:``
-block inside the function.
-However there two issues: the first is that ``GeneratorContextManager``
-objects are callable only in Python 3.2, so the previous example will break
-in older versions of Python; the second is that
-``GeneratorContextManager`` objects do not preserve the signature
-of the decorated functions: the decorated ``hello`` function here will have
-a generic signature ``hello(*args, **kwargs)`` but will break when
-called with more than zero arguments. For such reasons the decorator
-module, starting with release 3.4, offers a ``decorator.contextmanager``
-decorator that solves both problems and works in all supported Python versions.
-The usage is the same and factories decorated with ``decorator.contextmanager``
-will returns instances of ``ContextManager``, a subclass of
+The ``ba`` decorator is basically inserting a ``with ba:`` block
+inside the function.  However there two issues: the first is that
+``GeneratorContextManager`` objects are callable only in Python 3.2,
+so the previous example will break in older versions of Python (you
+can solve this by installing ``contextlib2``); the second is that
+``GeneratorContextManager`` objects do not preserve the signature of
+the decorated functions: the decorated ``hello`` function here will
+have a generic signature ``hello(*args, **kwargs)`` but will break
+when called with more than zero arguments. For such reasons the
+decorator module, starting with release 3.4, offers a
+``decorator.contextmanager`` decorator that solves both problems and
+works in all supported Python versions.  The usage is the same and
+factories decorated with ``decorator.contextmanager`` will returns
+instances of ``ContextManager``, a subclass of
 ``contextlib.GeneratorContextManager`` with a ``__call__`` method
 acting as a signature-preserving decorator.
 
@@ -656,7 +657,7 @@ Multiple dispatch
 
 There has been talk of implementing multiple dispatch (i.e. generic)
 functions in Python for over ten years. Last year for the first time
-something was done and now in Python 3.4 we have a decorator
+something concrete was done and now in Python 3.4 we have a decorator
 ``functools.singledispatch`` which can be used to implement generic
 functions. As the name implies, it has the restriction of being
 limited to single dispatch, i.e. it is able to dispatch on the first
@@ -789,7 +790,7 @@ builtin ``len``, but you should get the idea.
 Since in Python it is possible to consider any instance of ABCMeta
 as a virtual ancestor of any other class (it is enough to register it
 as ``ancestor.register(cls)``), any implementation of generic functions
-must take this feature into account. Let me give an example.
+must take virtual ancestors into account. Let me give an example.
 
 Suppose you are using a third party set-like class like
 the following:
@@ -813,23 +814,23 @@ Now, let us define an implementation of ``get_length`` specific to set:
 $$get_length_set
 
 The current implementation, as the one used by ``functools.singledispatch``,
-is able to discern that a ``Set`` is a ``Sized`` object, so the
+is able to discern that a ``Set`` is a ``Sized`` object, so the more specific
 implementation for ``Set`` is taken:
 
 .. code-block:: python
 
- >>> get_length(SomeSet())
+ >>> get_length(SomeSet())  # NB: the implementation for Sized would give 0
  1
 
-Some times it is not clear how to dispatch. For instance, consider a
+Sometimes it is not clear how to dispatch. For instance, consider a
 class ``C`` registered both as ``collections.Iterable`` and
 ``collections.Sized`` and define a generic function ``g`` with
 implementations both for ``collections.Iterable`` and
 ``collections.Sized``. It is impossible to decide which implementation
-to use, since the ancestors are independent, and the following code
-will fail with a RuntimeError:
+to use, since the ancestors are independent, and the following function
+will raise a RuntimeError when called:
 
-$$singledispatch_example
+$$singledispatch_example1
 
 This is consistent with the "refuse the temptation to guess"
 philosophy. ``functools.singledispatch`` would raise a similar error.
@@ -841,10 +842,22 @@ implementation could be taken. If implementations of the generic
 functions are distributed across modules, and you change the import
 order, a different implementation could be taken. So the decorator
 module prefers to raise an error in the face of ambiguity. This is the
-same approach taken by the standard library. Notice that the dispatch
+same approach taken by the standard library.
+
+However, it should be noticed that the dispatch
 algorithm used by the decorator module is different from the one used
-by ``functools.singledispatch``, so there are cases where you will get
-different answers.
+by the standard library, so there are cases where you will get
+different answers. The difference is that ``functools.singledispatch``
+tries to insert the virtual ancestors *before* the base classes, whereas
+``decorator.dispatch_on`` tries to insert them *after* the base classes.
+I will give an example showing the difference:
+
+$$singledispatch_example2
+
+If you play with this example and replace the ``singledispatch`` definition
+with ``functools.singledispatch``, you will see the output change from ``"s"``
+to ``"container"``, because ``functools.singledispatch``
+will insert the ``Container`` class right before ``S``.
 
 Finally let me notice that the decorator module implementation does
 not use any cache, whereas the one in ``singledispatch`` has a cache.
@@ -852,8 +865,7 @@ not use any cache, whereas the one in ``singledispatch`` has a cache.
 Caveats and limitations
 -------------------------------------------
 
-The first thing you should be aware of, it the fact that decorators
-have a performance penalty.
+One thing you should be aware of, is the performance penalty of decorators.
 The worse case is shown by the following example::
 
  $ cat performance.sh
@@ -1072,6 +1084,7 @@ import time
 import functools
 import itertools
 import collections
+import collections as c
 from decorator import (decorator, decorate, FunctionMaker, contextmanager,
                        dispatch_on, __version__)
 
@@ -1434,7 +1447,7 @@ class SomeSet(collections.Sized):
     # methods that make SomeSet set-like
     # not shown ...
     def __len__(self):
-        return 0  # in reality one would return more than zero
+        return 0
 
 
 @dispatch_on('obj')
@@ -1452,24 +1465,13 @@ def get_length_set(obj):
     return 1
 
 
-@contextmanager
-def assertRaises(etype):
-    """This works in Python 2.6 too"""
-    try:
-        yield
-    except etype:
-        pass
-    else:
-        raise Exception('Expected %s' % etype.__name__)
-
-
 class C(object):
     "Registered as Sized and Iterable"
 collections.Sized.register(C)
 collections.Iterable.register(C)
 
 
-def singledispatch_example():
+def singledispatch_example1():
     singledispatch = dispatch_on('obj')
 
     @singledispatch
@@ -1484,9 +1486,36 @@ def singledispatch_example():
     def g_iterable(object):
         return "iterable"
 
-    with assertRaises(RuntimeError):
-        g(C())  # Ambiguous dispatch: Iterable or Sized?
+    g(C())  # RuntimeError: Ambiguous dispatch: Iterable or Sized?
+
+
+def singledispatch_example2():
+    # adapted from functools.singledispatch test case
+    singledispatch = dispatch_on('arg')
+
+    class S(object):
+        pass
+
+    class V(c.Sized, S):
+        def __len__(self):
+            return 0
+
+    @singledispatch
+    def j(arg):
+        return "base"
+
+    @j.register(S)
+    def j_s(arg):
+        return "s"
+
+    @j.register(c.Container)
+    def j_container(arg):
+        return "container"
 
+    v = V()
+    assert j(v) == "s"
+    c.Container.register(V)  # add c.Container to the virtual mro of V
+    return j(v)  # "s", since the virtual mro is V, Sized, S, Container
 
 if __name__ == '__main__':
     import doctest
diff --git a/src/tests/test.py b/src/tests/test.py
index 2de144e..1e1cb3f 100644
--- a/src/tests/test.py
+++ b/src/tests/test.py
@@ -6,21 +6,37 @@ import decimal
 import inspect
 import functools
 import collections
-from decorator import dispatch_on
+from decorator import dispatch_on, contextmanager
 try:
     from . import documentation as doc
 except (SystemError, ValueError):
     import documentation as doc
 
 
+@contextmanager
+def assertRaises(etype):
+    """This works in Python 2.6 too"""
+    try:
+        yield
+    except etype:
+        pass
+    else:
+        raise Exception('Expected %s' % etype.__name__)
+
+
 class DocumentationTestCase(unittest.TestCase):
     def test(self):
         err = doctest.testmod(doc)[0]
         self.assertEqual(err, 0)
 
-    def test_singledispatch(self):
+    def test_singledispatch1(self):
+        if hasattr(functools, 'singledispatch'):
+            with assertRaises(RuntimeError):
+                doc.singledispatch_example1()
+
+    def test_singledispatch2(self):
         if hasattr(functools, 'singledispatch'):
-            doc.singledispatch_example()
+            self.assertEqual(doc.singledispatch_example2(), "s")
 
 
 class ExtraTestCase(unittest.TestCase):
@@ -94,7 +110,7 @@ class TestSingleDispatch(unittest.TestCase):
         def g(obj):
             return "base"
 
-        with doc.assertRaises(TypeError):
+        with assertRaises(TypeError):
             # wrong number of arguments
             @g.register(int)
             def g_int():
@@ -259,7 +275,7 @@ class TestSingleDispatch(unittest.TestCase):
         c.Iterable.register(O)
         self.assertEqual(g(o), "sized")
         c.Container.register(O)
-        with doc.assertRaises(RuntimeError):
+        with assertRaises(RuntimeError):  # was "sized" because in mro
             self.assertEqual(g(o), "sized")
         c.Set.register(O)
         self.assertEqual(g(o), "set")
@@ -272,7 +288,7 @@ class TestSingleDispatch(unittest.TestCase):
         self.assertEqual(g(p), "iterable")
         c.Container.register(P)
 
-        with doc.assertRaises(RuntimeError):
+        with assertRaises(RuntimeError):
             self.assertEqual(g(p), "iterable")
 
         class Q(c.Sized):
@@ -301,7 +317,7 @@ class TestSingleDispatch(unittest.TestCase):
         # this ABC is implicitly registered on defaultdict which makes all of
         # MutableMapping's bases implicit as well from defaultdict's
         # perspective.
-        with doc.assertRaises(RuntimeError):
+        with assertRaises(RuntimeError):
             self.assertEqual(h(c.defaultdict(lambda: 0)), "sized")
 
         class R(c.defaultdict):
@@ -320,8 +336,8 @@ class TestSingleDispatch(unittest.TestCase):
         def i_sequence(arg):
             return "sequence"
         r = R()
-        with doc.assertRaises(RuntimeError):
-            self.assertEqual(i(r), "mapping")
+        with assertRaises(RuntimeError):  # was no error
+            self.assertEqual(i(r), "sequence")
 
         class S(object):
             pass
@@ -338,37 +354,14 @@ class TestSingleDispatch(unittest.TestCase):
             def __len__(self):
                 return 0
         u = U()
-        if sys.version >= '3':
-            self.assertEqual(h(u), "sized")
-            # implicit Sized subclass inferred
-            # from the existence of __len__()
+        self.assertEqual(h(u), "sized")
+        # implicit Sized subclass inferred
+        # from the existence of __len__()
 
         c.Container.register(U)
-        # There is preference for registered versus inferred ABCs.
-        with doc.assertRaises(RuntimeError):
-            self.assertEqual(h(u), "sized")
-
-        class V(c.Sized, S):
-            def __len__(self):
-                return 0
-
-        @singledispatch
-        def j(obj):
-            return "base"
-
-        @j.register(S)
-        def j_s(arg):
-            return "s"
-
-        @j.register(c.Container)
-        def j_container(arg):
-            return "container"
-        v = V()
-        self.assertEqual(j(v), "s")
-        c.Container.register(V)
-        self.assertEqual(j(v), "s")  # could be "container"
-        # because it ends up right after
-        # Sized in the MRO
+        # There is no preference for registered versus inferred ABCs.
+        with assertRaises(RuntimeError):
+            h(u)
 
 if __name__ == '__main__':
     unittest.main()
author	Michele Simionato <michele.simionato@gmail.com>	2015-07-23 16:33:46 +0200
committer	Michele Simionato <michele.simionato@gmail.com>	2015-07-23 16:33:46 +0200
commit	8b7b3aba63d278eb391c3c9b0ccbe065559f1b93 (patch)
tree	5438712066b25f00151e80a0f4741a53d2516193
parent	bf1748df8d229bbfe40de6944c48349228e5b5bc (diff)
download	python-decorator-git-8b7b3aba63d278eb391c3c9b0ccbe065559f1b93.tar.gz