Operator Overload Mac OS

1. Operator Overload Mac Os X
2. Operator Overload Mac Os X
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In order to overload a function (and overloading operator is the same) the overloaded functions must differ by the number or/and the type of the parameters. The following operators can be overloaded: binary +,-,/,.,%,!=,=,=,+=,-=,/=,.=,%=,&=, =,^=,=,&&,&, ,^ unary +,-,-,!, assignment operator = indexing operator Operation overloading allows the use of the operating notation (written in the form of simple expressions) for complex objects - structures and classes.

11.1: a) operator overloading. C) assignment (=), address (&), comma (,).d) precedence, associativity, 'arity.' 11.2: Operator is both the right-shift operator and the stream extraction operator, depending on its context. Operator operator and the stream insertion operator, depending on its context. 11.3: For operator overloading: It would be the name. A library of over 125,000 free and free-to-try software applications for Mac OS. This is improved code after I some issue in pointed by @Edward in the last question: C operator overloading for matrix operations This work assignment in operator overloading.I need to use operators., , =, +.

Contents

• User API
  • Overloading/Generic Functions
  • Method Combination and Overriding
  • Interfaces and Adaptation

See https://mail.python.org/pipermail/python-3000/2007-July/008784.html.

This PEP proposes a new standard library module, overloading, toprovide generic programming features including dynamic overloading(aka generic functions), interfaces, adaptation, method combining (alaCLOS and AspectJ), and simple forms of aspect-oriented programming(AOP).

The proposed API is also open to extension; that is, it will bepossible for library developers to implement their own specializedinterface types, generic function dispatchers, method combinationalgorithms, etc., and those extensions will be treated as first-classcitizens by the proposed API.

The API will be implemented in pure Python with no C, but may havesome dependency on CPython-specific features such as sys._getframeand the func_code attribute of functions. It is expected thate.g. Jython and IronPython will have other ways of implementingsimilar functionality (perhaps using Java or C#).

Python has always provided a variety of built-in and standard-librarygeneric functions, such as len(), iter(), pprint.pprint(),and most of the functions in the operator module. However, itcurrently:

1. does not have a simple or straightforward way for developers tocreate new generic functions,
2. does not have a standard way for methods to be added to existinggeneric functions (i.e., some are added using registrationfunctions, others require defining __special__ methods,possibly by monkeypatching), and
3. does not allow dispatching on multiple argument types (except ina limited form for arithmetic operators, where 'right-hand'(__r*__) methods can be used to do two-argument dispatch.

In addition, it is currently a common anti-pattern for Python codeto inspect the types of received arguments, in order to decide whatto do with the objects. For example, code may wish to accept eitheran object of some type, or a sequence of objects of that type.

Currently, the 'obvious way' to do this is by type inspection, butthis is brittle and closed to extension. A developer using analready-written library may be unable to change how their objects aretreated by such code, especially if the objects they are using werecreated by a third party.

Therefore, this PEP proposes a standard library module to addressthese, and related issues, using decorators and argument annotations(PEP 3107). The primary features to be provided are:

• a dynamic overloading facility, similar to the static overloadingfound in languages such as Java and C++, but including optionalmethod combination features as found in CLOS and AspectJ.
• a simple 'interfaces and adaptation' library inspired by Haskell'stypeclasses (but more dynamic, and without any static type-checking),with an extension API to allow registering user-defined interfacetypes such as those found in PyProtocols and Zope.
• a simple 'aspect' implementation to make it easy to create statefuladapters and to do other stateful AOP.

These features are to be provided in such a way that extendedimplementations can be created and used. For example, it should bepossible for libraries to define new dispatching criteria forgeneric functions, and new kinds of interfaces, and use them inplace of the predefined features. For example, it should be possibleto use a zope.interface interface object to specify the desiredtype of a function argument, as long as the zope.interface packageregistered itself correctly (or a third party did the registration).

In this way, the proposed API simply offers a uniform way of accessingthe functionality within its scope, rather than prescribing a singleimplementation to be used for all libraries, frameworks, andapplications.

The overloading API will be implemented as a single module, namedoverloading, providing the following features:

Overloading/Generic Functions

The @overload decorator allows you to define alternateimplementations of a function, specialized by argument type(s). Afunction with the same name must already exist in the local namespace.The existing function is modified in-place by the decorator to addthe new implementation, and the modified function is returned by thedecorator. Thus, the following code:

creates a single flatten() function whose implementation roughlyequates to:

except that the flatten() function defined by overloadingremains open to extension by adding more overloads, while thehardcoded version cannot be extended.

For example, if someone wants to use flatten() with a string-liketype that doesn't subclass basestring, they would be out of luckwith the second implementation. With the overloaded implementation,however, they can either write this:

Operator Overload Mac Os X

or this (to avoid copying the implementation):

Flatout drillin mac os. (Note also that, although PEP 3119 proposes that it should be possiblefor abstract base classes like Iterable to allow classes likeMyString to claim subclass-hood, such a claim is global,throughout the application. In contrast, adding a specific overloador copying a rule is specific to an individual function, and thereforeless likely to have undesired side effects.)

@overload vs. @when

The @overload decorator is a common-case shorthand for the moregeneral @when decorator. It allows you to leave out the name ofthe function you are overloading, at the expense of requiring thetarget function to be in the local namespace. It also doesn't supportadding additional criteria besides the ones specified via argumentannotations. The following function definitions have identicaleffects, except for name binding side-effects (which will be describedbelow):

The first definition above will bind flatten to whatever it waspreviously bound to. The second will do the same, if it was alreadybound to the when decorator's first argument. If flatten isunbound or bound to something else, it will be rebound to the functiondefinition as given. The last two definitions above will always bindflatten_basestring to the function definition as given.

Using this approach allows you to both give a method a descriptivename (often useful in tracebacks!) and to reuse the method later.

Except as otherwise specified, all overloading decorators have thesame signature and binding rules as @when. They accept a functionand an optional 'predicate' object.

The default predicate implementation is a tuple of types withpositional matching to the overloaded function's arguments. However,an arbitrary number of other kinds of predicates can be created andregistered using the Extension API, and will then be usable with@when and other decorators created by this module (like@before, @after, and @around).

Method Combination and Overriding

When an overloaded function is invoked, the implementation with thesignature that most specifically matches the calling arguments isthe one used. If no implementation matches, a NoApplicableMethodserror is raised. If more than one implementation matches, but none ofthe signatures are more specific than the others, an AmbiguousMethodserror is raised.

For example, the following pair of implementations are ambiguous, ifthe foo() function is ever called with two integer arguments,because both signatures would apply, but neither signature is morespecific than the other (i.e., neither implies the other):

In contrast, the following pair of implementations can never beambiguous, because one signature always implies the other; theint/int signature is more specific than the object/objectsignature:

A signature S1 implies another signature S2, if whenever S1 wouldapply, S2 would also. Nsfb: not safe for bugs! mac os. A signature S1 is 'more specific' than anothersignature S2, if S1 implies S2, but S2 does not imply S1.

Although the examples above have all used concrete or abstract typesas argument annotations, there is no requirement that the annotationsbe such. They can also be 'interface' objects (discussed in theInterfaces and Adaptation section), including user-definedinterface types. (They can also be other objects whose types areappropriately registered via the Extension API.)

Proceeding to the 'Next' Method

If the first parameter of an overloaded function is named__proceed__, it will be passed a callable representing the nextmost-specific method. For example, this code:

Will print 'got integers!' followed by 'got objects!'.

If there is no next most-specific method, __proceed__ will bebound to a NoApplicableMethods instance. When called, a newNoApplicableMethods instance will be raised, with the argumentspassed to the first instance.

Similarly, if the next most-specific methods have ambiguous precedencewith respect to each other, __proceed__ will be bound to anAmbiguousMethods instance, and if called, it will raise a newinstance.

Thus, a method can either check if __proceed__ is an errorinstance, or simply invoke it. The NoApplicableMethods andAmbiguousMethods error classes have a common DispatchErrorbase class, so isinstance(__proceed__, overloading.DispatchError)is sufficient to identify whether __proceed__ can be safelycalled.

(Implementation note: using a magic argument name like __proceed__could potentially be replaced by a magic function that would be calledto obtain the next method. A magic function, however, would degradeperformance and might be more difficult to implement on non-CPythonplatforms. Method chaining via magic argument names, however, can beefficiently implemented on any Python platform that supports creatingbound methods from functions -- one simply recursively binds eachfunction to be chained, using the following function or error as theim_self of the bound method.)

'Before' and 'After' Methods

In addition to the simple next-method chaining shown above, it issometimes useful to have other ways of combining methods. Forexample, the 'observer pattern' can sometimes be implemented by addingextra methods to a function, that execute before or after the normalimplementation.

To support these use cases, the overloading module will supply@before, @after, and @around decorators, that roughlycorrespond to the same types of methods in the Common Lisp ObjectSystem (CLOS), or the corresponding 'advice' types in AspectJ.

Like @when, all of these decorators must be passed the function tobe overloaded, and can optionally accept a predicate as well: Slotastic no deposit bonus.

@before and @after methods are invoked either before or afterthe main function body, and are never considered ambiguous. Thatis, it will not cause any errors to have multiple 'before' or 'after'methods with identical or overlapping signatures. Ambiguities areresolved using the order in which the methods were added to thetarget function.

'Before' methods are invoked most-specific method first, withambiguous methods being executed in the order they were added. All'before' methods are called before any of the function's 'primary'methods (i.e. normal @overload methods) are executed.

'After' methods are invoked in the reverse order, after all of thefunction's 'primary' methods are executed. That is, they are executedleast-specific methods first, with ambiguous methods being executed inthe reverse of the order in which they were added.

The return values of both 'before' and 'after' methods are ignored,and any uncaught exceptions raised by any methods (primary or other)immediately end the dispatching process. 'Before' and 'after' methodscannot have __proceed__ arguments, as they are not responsiblefor calling any other methods. They are simply called as anotification before or after the primary methods.

Thus, 'before' and 'after' methods can be used to check or establishpreconditions (e.g. by raising an error if the conditions aren't met)or to ensure postconditions, without needing to duplicate any existingfunctionality.

'Around' Methods

The @around decorator declares a method as an 'around' method.'Around' methods are much like primary methods, except that theleast-specific 'around' method has higher precedence than themost-specific 'before' method.

Unlike 'before' and 'after' methods, however, 'Around' methods areresponsible for calling their __proceed__ argument, in order tocontinue the invocation process. 'Around' methods are usually usedto transform input arguments or return values, or to wrap specificcases with special error handling or try/finally conditions, e.g.:

They can also be used to replace the normal handling for a specificcase, by not invoking the __proceed__ function.

The __proceed__ given to an 'around' method will either be thenext applicable 'around' method, a DispatchError instance,or a synthetic method object that will call all the 'before' methods,followed by the primary method chain, followed by all the 'after'methods, and return the result from the primary method chain.

Thus, just as with normal methods, __proceed__ can be checked forDispatchError-ness, or simply invoked. The 'around' method shouldreturn the value returned by __proceed__, unless of course itwishes to modify or replace it with a different return value for thefunction as a whole.

Custom Combinations

The decorators described above (@overload, @when, @before,@after, and @around) collectively implement what in CLOS iscalled the 'standard method combination' -- the most common patternsused in combining methods.

Sometimes, however, an application or library may have use for a moresophisticated type of method combination. For example, if youwould like to have 'discount' methods that return a percentage off,to be subtracted from the value returned by the primary method(s),you might write something like this:

Similar techniques can be used to implement a wide variety ofCLOS-style method qualifiers and combination rules. The process ofcreating custom method combination objects and their correspondingdecorators is described in more detail under the Extension APIsection.

Note, by the way, that the @discount decorator shown will workcorrectly with any new predicates defined by other code. For example,if zope.interface were to register its interface types to workcorrectly as argument annotations, you would be able to specifydiscounts on the basis of its interface types, not just classes oroverloading-defined interface types.

Similarly, if a library like RuleDispatch or PEAK-Rules were toregister an appropriate predicate implementation and dispatch engine,one would then be able to use those predicates for discounts as well,e.g.:

The process of defining custom predicate types and dispatching enginesis also described in more detail under the Extension API section.

Overloading Inside Classes

All of the decorators above have a special additional behavior whenthey are directly invoked within a class body: the first parameter(other than __proceed__, if present) of the decorated functionwill be treated as though it had an annotation equal to the classin which it was defined.

That is, this code:

produces the same effect as this (apart from the existence of aprivate method):

This behavior is both a convenience enhancement when defining lots ofmethods, and a requirement for safely distinguishing multi-argumentoverloads in subclasses. Consider, for example, the following code:

Due to the implicit class rule, calling B().foo([]) will print'B got an iterable!' followed by 'it's iterable!', and finally,'got an object', while A().foo([]) would print only the messagesdefined in A.

Conversely, without the implicit class rule, the two 'Iterable'methods would have the exact same applicability conditions, so callingeither A().foo([]) or B().foo([]) would result in anAmbiguousMethods error.

It is currently an open issue to determine the best way to implementthis rule in Python 3.0. Under Python 2.x, a class' metaclass wasnot chosen until the end of the class body, which means thatdecorators could insert a custom metaclass to do processing of thissort. (This is how RuleDispatch, for example, implements the implicitclass rule.)

PEP 3115, however, requires that a class' metaclass be determinedbefore the class body has executed, making it impossible to use thistechnique for class decoration any more.

At this writing, discussion on this issue is ongoing.

Interfaces and Adaptation

The overloading module provides a simple implementation ofinterfaces and adaptation. The following example defines anIStack interface, and declares that list objects support it:

The Interface class is a kind of 'universal adapter'. It acceptsa single argument: an object to adapt. It then binds all its methodsto the target object, in place of itself. Thus, callingmystack.push(42) is the same as callingIStack.push(mylist, 42).

The @abstract decorator marks a function as being abstract: i.e.,having no implementation. If an @abstract function is called,it raises NoApplicableMethods. To become executable, overloadedmethods must be added using the techniques previously described. (Thatis, methods can be added using @when, @before, @after,@around, or any custom method combination decorators.)

In the example above, the list.append method is added as a methodfor IStack.push() when its arguments are a list and an arbitraryobject. Thus, IStack.push(mylist, 42) is translated tolist.append(mylist, 42), thereby implementing the desiredoperation.

Abstract and Concrete Methods

Note, by the way, that the @abstract decorator is not limited touse in interface definitions; it can be used anywhere that you wish tocreate an 'empty' generic function that initially has no methods. Inparticular, it need not be used inside a class.

Also note that interface methods need not be abstract; one could, forexample, write an interface like this:

As long as __setitem__ is defined for some type, the aboveinterface will provide a usable update() implementation. However,if some specific type (or pair of types) has a more efficient way ofhandling update() operations, an appropriate overload can stillbe registered for use in that case.

Subclassing and Re-assembly

Interfaces can be subclassed:

Or assembled by combining functions from existing interfaces:

A class can be considered to 'adapt to' an interface at a givenpoint in time, if no method defined in the interface is guaranteed toraise a NoApplicableMethods error if invoked on an instance ofthat class at that point in time.

In normal usage, however, it is 'easier to ask forgiveness thanpermission'. That is, it is easier to simply use an interface onan object by adapting it to the interface (e.g. IStack(mylist))or invoking interface methods directly (e.g. IStack.push(mylist,42)), than to try to figure out whether the object is adaptable to(or directly implements) the interface.

Implementing an Interface in a Class

It is possible to declare that a class directly implements aninterface, using the declare_implementation() function:

https://blogfree.mystrikingly.com/blog/backpacking-mac-os. The declare_implementation() call above is roughly equivalent tothe following steps:

That is, calling IStack.push() or IStack.pop() on an instanceof any subclass of Stack, will simply delegate to the actualpush() or pop() methods thereof.

For the sake of efficiency, calling IStack(s) where s is aninstance of Stack, may return s rather than an IStackadapter. (Note that calling IStack(x) where x is already anIStack adapter will always return x unchanged; this is anadditional optimization allowed in cases where the adaptee is knownto directly implement the interface, without adaptation.)

For convenience, it may be useful to declare implementations in theclass header, e.g.:

Instead of calling declare_implementation() after the end of thesuite.

Interfaces as Type Specifiers

Interface subclasses can be used as argument annotations toindicate what type of objects are acceptable to an overload, e.g.:

Note, however, that the actual arguments are not changed or adaptedin any way by the mere use of an interface as a type specifier. Youmust explicitly cast the objects to the appropriate interface, asshown above.

Note, however, that other patterns of interface use are possible.For example, other interface implementations might not supportadaptation, or might require that function arguments already beadapted to the specified interface. So the exact semantics of usingan interface as a type specifier are dependent on the interfaceobjects you actually use.

For the interface objects defined by this PEP, however, the semanticsare as described above. An interface I1 is considered 'more specific'than another interface I2, if the set of descriptors in I1'sinheritance hierarchy are a proper superset of the descriptors in I2'sinheritance hierarchy.

So, for example, ISizedStack is more specific than bothISizable and ISizedStack, irrespective of the inheritancerelationships between these interfaces. It is purely a question ofwhat operations are included within those interfaces -- and thenames of the operations are unimportant.

Interfaces (at least the ones provided by overloading) are alwaysconsidered less-specific than concrete classes. Other interfaceimplementations can decide on their own specificity rules, bothbetween interfaces and other interfaces, and between interfaces andclasses.

Non-Method Attributes in Interfaces

The Interface implementation actually treats all attributes andmethods (i.e. descriptors) in the same way: their __get__ (and__set__ and __delete__, if present) methods are called withthe wrapped (adapted) object as 'self'. For functions, this has theeffect of creating a bound method linking the generic function to thewrapped object.

For non-function attributes, it may be easiest to specify them usingthe property built-in, and the corresponding fget, fset,and fdel attributes:

Alternatively, methods such as _get_foo() and _set_foo()may be defined as part of the interface, and the property definedin terms of those methods, but this is a bit more difficult for usersto implement correctly when creating a class that directly implementsthe interface, as they would then need to match all the individualmethod names, not just the name of the property or attribute.

Aspects

The adaptation system described above assumes that adapters are 'stateless',which is to say that adapters have no attributes or state apart fromthat of the adapted object. This follows the 'typeclass/instance'model of Haskell, and the concept of 'pure' (i.e., transitivelycomposable) adapters.

However, there are occasionally cases where, to provide a completeimplementation of some interface, some sort of additional state isrequired.

One possibility of course, would be to attach monkeypatched 'private'attributes to the adaptee. But this is subject to name collisions,and complicates the process of initialization (since any code usingthese attributes has to check for their existence and initialize themif necessary). It also doesn't work on objects that don't have a__dict__ attribute.

So the Aspect class is provided to make it easy to attach extrainformation to objects that either:

1. have a __dict__ attribute (so aspect instances can be storedin it, keyed by aspect class),
2. support weak referencing (so aspect instances can be managed usinga global but thread-safe weak-reference dictionary), or
3. implement or can be adapt to the overloading.IAspectOwnerinterface (technically, #1 or #2 imply this).

Subclassing Aspect creates an adapter class whose state is tiedto the life of the adapted object.

For example, suppose you would like to count all the times a certainmethod is called on instances of Target (a classic AOP example).You might do something like:

The above code will keep track of the number of times thatTarget.some_method() is successfully called on an instance ofTarget (i.e., it will not count errors unless they occur in amore-specific 'after' method). Other code can then access the countusing Count(someTarget).count.

Aspect instances can of course have __init__ methods, toinitialize any data structures. They can use either __slots__or dictionary-based attributes for storage.

While this facility is rather primitive compared to a full-featuredAOP tool like AspectJ, persons who wish to build pointcut librariesor other AspectJ-like features can certainly use Aspect objectsand method-combination decorators as a base for building moreexpressive AOP tools.

XXX spec out full aspect API, including keys, N-to-1 aspects, manual

attach/detach/delete of aspect instances, and the IAspectOwnerinterface.

Operator Overload Mac Os X

TODO: explain how all of these work

Mac Os Download

implies(o1, o2)

declare_implementation(iface, class)

predicate_signatures(ob)

parse_rule(ruleset, body, predicate, actiontype, localdict, globaldict)

combine_actions(a1, a2)

rules_for(f)

Rule objects

ActionDef objects

RuleSet objects

Method objects

MethodList objects Deuces wild free video games no download.

IAspectOwner

In discussion on the Python-3000 list, the proposed feature of allowingarbitrary functions to be overloaded has been somewhat controversial,with some people expressing concern that this would make programs moredifficult to understand.

The general thrust of this argument is that one cannot rely on what afunction does, if it can be changed from anywhere in the program at anytime. Even though in principle this can already happen throughmonkeypatching or code substitution, it is considered poor practice todo so.

However, providing support for overloading any function (or so theargument goes), is implicitly blessing such changes as being anacceptable practice.

This argument appears to make sense in theory, but it is almost entirelymooted in practice for two reasons.

First, people are generally not perverse, defining a function to do onething in one place, and then summarily defining it to do the oppositesomewhere else! The principal reasons to extend the behavior of afunction that has not been specifically made generic are to:

• Add special cases not contemplated by the original function's author,such as support for additional types.
• Be notified of an action in order to cause some related operation tobe performed, either before the original operation is performed,after it, or both. This can include general-purpose operations likeadding logging, timing, or tracing, as well as application-specificbehavior.

None of these reasons for adding overloads imply any change to theintended default or overall behavior of the existing function, however.Just as a base class method may be overridden by a subclass for thesesame two reasons, so too may a function be overloaded to provide forsuch enhancements.

In other words, universal overloading does not equal arbitraryoverloading, in the sense that we need not expect people to randomlyredefine the behavior of existing functions in illogical orunpredictable ways. If they did so, it would be no less of a badpractice than any other way of writing illogical or unpredictable code!

However, to distinguish bad practice from good, it is perhaps necessaryto clarify further what good practice for defining overloads is. Andthat brings us to the second reason why generic functions do notnecessarily make programs harder to understand: overloading patterns inactual programs tend to follow very predictable patterns. (Both inPython and in languages that have no non-generic functions.)

If a module is defining a new generic operation, it will usually alsodefine any required overloads for existing types in the same place.Likewise, if a module is defining a new type, then it will usuallydefine overloads there for any generic functions that it knows or caresabout.

As a result, the vast majority of overloads can be found adjacent toeither the function being overloaded, or to a newly-defined type forwhich the overload is adding support. Thus, overloads are highly-discoverable in the common case, as you are either looking at thefunction or the type, or both.

It is only in rather infrequent cases that one will have overloads in amodule that contains neither the function nor the type(s) for which theoverload is added. This would be the case if, say, a third-partycreated a bridge of support between one library's types and anotherlibrary's generic function(s). In such a case, however, best practicesuggests prominently advertising this, especially by way of the modulename.

For example, PyProtocols defines such bridge support for working withZope interfaces and legacy Twisted interfaces, using modules calledprotocols.twisted_support and protocols.zope_support. (Thesebridges are done with interface adapters, rather than generic functions,but the basic principle is the same.)

To heaven and back mac os. In short, understanding programs in the presence of universaloverloading need not be any more difficult, given that the vast majorityof overloads will either be adjacent to a function, or the definition ofa type that is passed to that function.

And, in the absence of incompetence or deliberate intention to beobscure, the few overloads that are not adjacent to the relevant type(s)or function(s), will generally not need to be understood or known aboutoutside the scope where those overloads are defined. (Except in the'support modules' case, where best practice suggests naming themaccordingly.)

Most of the functionality described in this PEP is already implementedin the in-development version of the PEAK-Rules framework. Inparticular, the basic overloading and method combination framework(minus the @overload decorator) already exists there. Theimplementation of all of these features in peak.rules.core is 656lines of Python at this writing.

peak.rules.core currently relies on the DecoratorTools andBytecodeAssembler modules, but both of these dependencies can bereplaced, as DecoratorTools is used mainly for Python 2.3compatibility and to implement structure types (which can be donewith named tuples in later versions of Python). The use ofBytecodeAssembler can be replaced using an 'exec' or 'compile'workaround, given a reasonable effort. (It would be easier to do thisif the func_closure attribute of function objects was writable.)

The Interface class has been previously prototyped, but is notincluded in PEAK-Rules at the present time.

The 'implicit class rule' has previously been implemented in theRuleDispatch library. However, it relies on the __metaclass__hook that is currently eliminated in PEP 3115.

I don't currently know how to make @overload play nicely withclassmethod and staticmethod in class bodies. It's not reallyclear if it needs to, however.

This document has been placed in the public domain.