This is /home/pdm/install/Python-2.1/Doc/ref/python-ref.info, produced by makeinfo version 4.0 from ref.texi. April 15, 2001 2.1  File: python-ref.info, Node: standard type hierarchy, Next: Special method names, Prev: Objects, Up: Data model The standard type hierarchy =========================== Below is a list of the types that are built into Python. Extension modules written in C can define additional types. Future versions of Python may add types to the type hierarchy (e.g., rational numbers, efficiently stored arrays of integers, etc.). Some of the type descriptions below contain a paragraph listing `special attributes.' These are attributes that provide access to the implementation and are not intended for general use. Their definition may change in the future. There are also some `generic' special attributes, not listed with the individual objects: `__methods__' is a list of the method names of a built-in object, if it has any; `__members__' is a list of the data attribute names of a built-in object, if it has any. `None' This type has a single value. There is a single object with this value. This object is accessed through the built-in name `None'. It is used to signify the absence of a value in many situations, e.g., it is returned from functions that don't explicitly return anything. Its truth value is false. `NotImplemented' This type has a single value. There is a single object with this value. This object is accessed through the built-in name `NotImplemented'. Numeric methods and rich comparison methods may return this value if they do not implement the operation for the operands provided. (The interpreter will then try the reflected operation, or some other fallback, depending on the operator.) Its truth value is true. `Ellipsis' This type has a single value. There is a single object with this value. This object is accessed through the built-in name `Ellipsis'. It is used to indicate the presence of the `...' syntax in a slice. Its truth value is true. `Numbers' These are created by numeric literals and returned as results by arithmetic operators and arithmetic built-in functions. Numeric objects are immutable; once created their value never changes. Python numbers are of course strongly related to mathematical numbers, but subject to the limitations of numerical representation in computers. Python distinguishes between integers and floating point numbers: `Integers' These represent elements from the mathematical set of whole numbers. There are two types of integers: `Plain integers' These represent numbers in the range -2147483648 through 2147483647. (The range may be larger on machines with a larger natural word size, but not smaller.) When the result of an operation would fall outside this range, the exception `OverflowError' is raised. For the purpose of shift and mask operations, integers are assumed to have a binary, 2's complement notation using 32 or more bits, and hiding no bits from the user (i.e., all 4294967296 different bit patterns correspond to different values). `Long integers' These represent numbers in an unlimited range, subject to available (virtual) memory only. For the purpose of shift and mask operations, a binary representation is assumed, and negative numbers are represented in a variant of 2's complement which gives the illusion of an infinite string of sign bits extending to the left. The rules for integer representation are intended to give the most meaningful interpretation of shift and mask operations involving negative integers and the least surprises when switching between the plain and long integer domains. For any operation except left shift, if it yields a result in the plain integer domain without causing overflow, it will yield the same result in the long integer domain or when using mixed operands. `Floating point numbers' These represent machine-level double precision floating point numbers. You are at the mercy of the underlying machine architecture and C implementation for the accepted range and handling of overflow. Python does not support single-precision floating point numbers; the savings in CPU and memory usage that are usually the reason for using these is dwarfed by the overhead of using objects in Python, so there is no reason to complicate the language with two kinds of floating point numbers. `Complex numbers' These represent complex numbers as a pair of machine-level double precision floating point numbers. The same caveats apply as for floating point numbers. The real and imaginary value of a complex number `z' can be retrieved through the attributes `z.real' and `z.imag'. `Sequences' These represent finite ordered sets indexed by non-negative numbers. The built-in function `len()' returns the number of items of a sequence. When the length of a sequence is N, the index set contains the numbers 0, 1, ..., N-1. Item I of sequence A is selected by `A[I]'. Sequences also support slicing: `A[I:J]' selects all items with index K such that I `<=' K `<' J. When used as an expression, a slice is a sequence of the same type. This implies that the index set is renumbered so that it starts at 0. Sequences are distinguished according to their mutability: `Immutable sequences' An object of an immutable sequence type cannot change once it is created. (If the object contains references to other objects, these other objects may be mutable and may be changed; however, the collection of objects directly referenced by an immutable object cannot change.) The following types are immutable sequences: `Strings' The items of a string are characters. There is no separate character type; a character is represented by a string of one item. Characters represent (at least) 8-bit bytes. The built-in functions `chr()' and `ord()' convert between characters and nonnegative integers representing the byte values. Bytes with the values 0-127 usually represent the corresponding ASCII values, but the interpretation of values is up to the program. The string data type is also used to represent arrays of bytes, e.g., to hold data read from a file. (On systems whose native character set is not ASCII, strings may use EBCDIC in their internal representation, provided the functions `chr()' and `ord()' implement a mapping between ASCII and EBCDIC, and string comparison preserves the ASCII order. Or perhaps someone can propose a better rule?) `Unicode' The items of a Unicode object are Unicode characters. A Unicode character is represented by a Unicode object of one item and can hold a 16-bit value representing a Unicode ordinal. The built-in functions `unichr()' and `ord()' convert between characters and nonnegative integers representing the Unicode ordinals as defined in the Unicode Standard 3.0. Conversion from and to other encodings are possible through the Unicode method `encode' and the built-in function `unicode()'. `Tuples' The items of a tuple are arbitrary Python objects. Tuples of two or more items are formed by comma-separated lists of expressions. A tuple of one item (a `singleton') can be formed by affixing a comma to an expression (an expression by itself does not create a tuple, since parentheses must be usable for grouping of expressions). An empty tuple can be formed by an empty pair of parentheses. `Mutable sequences' Mutable sequences can be changed after they are created. The subscription and slicing notations can be used as the target of assignment and `del' (delete) statements. There is currently a single mutable sequence type: `Lists' The items of a list are arbitrary Python objects. Lists are formed by placing a comma-separated list of expressions in square brackets. (Note that there are no special cases needed to form lists of length 0 or 1.) The extension module `array' provides an additional example of a mutable sequence type. `Mappings' These represent finite sets of objects indexed by arbitrary index sets. The subscript notation `a[k]' selects the item indexed by `k' from the mapping `a'; this can be used in expressions and as the target of assignments or `del' statements. The built-in function `len()' returns the number of items in a mapping. There is currently a single intrinsic mapping type: `Dictionaries' These represent finite sets of objects indexed by nearly arbitrary values. The only types of values not acceptable as keys are values containing lists or dictionaries or other mutable types that are compared by value rather than by object identity, the reason being that the efficient implementation of dictionaries requires a key's hash value to remain constant. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (e.g., `1' and `1.0') then they can be used interchangeably to index the same dictionary entry. Dictionaries are mutable; they are created by the `{...}' notation (see section *Note Dictionary displays::, "Dictionary Displays"). The extension modules `dbm', `gdbm', `bsddb'provide additional examples of mapping types. `Callable types' These are the types to which the function call operation (see section *Note Calls::, "Calls") can be applied: `User-defined functions' A user-defined function object is created by a function definition (see section *Note Function definitions::, "Function definitions"). It should be called with an argument list containing the same number of items as the function's formal parameter list. Special attributes: `func_doc' or `__doc__' is the function's documentation string, or None if unavailable; `func_name' or `__name__' is the function's name; `func_defaults' is a tuple containing default argument values for those arguments that have defaults, or `None' if no arguments have a default value; `func_code' is the code object representing the compiled function body; `func_globals' is (a reference to) the dictionary that holds the function's global variables -- it defines the global namespace of the module in which the function was defined; `func_dict' or `__dict__' contains the namespace supporting arbitrary function attributes; `func_closure' is `None' or a tuple of cells that contain binding for the function's free variables. Of these, `func_code', `func_defaults', `func_closure', `func_doc'/`__doc__', and `func_dict'/`__dict__' may be writable; the others can never be changed. Additional information about a function's definition can be retrieved from its code object; see the description of internal types below. In Python 2.1, the `func_closure' slot is always `None' unless nested scopes are enabled. (See the appendix.) `User-defined methods' A user-defined method object combines a class, a class instance (or `None') and a user-defined function. Special read-only attributes: `im_self' is the class instance object, `im_func' is the function object; `im_class' is the class that defined the method (which may be a base class of the class of which `im_self' is an instance); `__doc__' is the method's documentation (same as `im_func.__doc__'); `__name__' is the method name (same as `im_func.__name__'). Methods also support accessing (but not setting) the arbitrary function attributes on the underlying function object. User-defined method objects are created in two ways: when getting an attribute of a class that is a user-defined function object, or when getting an attribute of a class instance that is a user-defined function object defined by the class of the instance. In the former case (class attribute), the `im_self' attribute is `None', and the method object is said to be unbound; in the latter case (instance attribute), `im_self' is the instance, and the method object is said to be bound. For instance, when `C' is a class which contains a definition for a function `f()', `C.f' does not yield the function object `f'; rather, it yields an unbound method object `m' where `m.im_class' is `C', `m.im_func' is `f()', and `m.im_self' is `None'. When `x' is a `C' instance, `x.f' yields a bound method object `m' where `m.im_class' is `C', `m.im_func' is `f()', and `m.im_self' is `x'. When an unbound user-defined method object is called, the underlying function (`im_func') is called, with the restriction that the first argument must be an instance of the proper class (`im_class') or of a derived class thereof. When a bound user-defined method object is called, the underlying function (`im_func') is called, inserting the class instance (`im_self') in front of the argument list. For instance, when `C' is a class which contains a definition for a function `f()', and `x' is an instance of `C', calling `x.f(1)' is equivalent to calling `C.f(x, 1)'. Note that the transformation from function object to (unbound or bound) method object happens each time the attribute is retrieved from the class or instance. In some cases, a fruitful optimization is to assign the attribute to a local variable and call that local variable. Also notice that this transformation only happens for user-defined functions; other callable objects (and all non-callable objects) are retrieved without transformation. It is also important to note that user-defined functions which are attributes of a class instance are not converted to bound methods; this _only_ happens when the function is an attribute of the class. `Built-in functions' A built-in function object is a wrapper around a C function. Examples of built-in functions are `len()' and `math.sin()' (`math' is a standard built-in module). The number and type of the arguments are determined by the C function. Special read-only attributes: `__doc__' is the function's documentation string, or `None' if unavailable; `__name__' is the function's name; `__self__' is set to `None' (but see the next item). `Built-in methods' This is really a different disguise of a built-in function, this time containing an object passed to the C function as an implicit extra argument. An example of a built-in method is `LIST.append()', assuming LIST is a list object. In this case, the special read-only attribute `__self__' is set to the object denoted by `list'. `Classes' Class objects are described below. When a class object is called, a new class instance (also described below) is created and returned. This implies a call to the class's `__init__()' method if it has one. Any arguments are passed on to the `__init__()' method. If there is no `__init__()' method, the class must be called without arguments. `Class instances' Class instances are described below. Class instances are callable only when the class has a `__call__()' method; `x(arguments)' is a shorthand for `x.__call__(arguments)'. `Modules' Modules are imported by the `import' statement (see section *Note import statement::, "The `import' statement"). A module object has a namespace implemented by a dictionary object (this is the dictionary referenced by the func_globals attribute of functions defined in the module). Attribute references are translated to lookups in this dictionary, e.g., `m.x' is equivalent to `m.__dict__["x"]'. A module object does not contain the code object used to initialize the module (since it isn't needed once the initialization is done). Attribute assignment updates the module's namespace dictionary, e.g., `m.x = 1' is equivalent to `m.__dict__["x"] = 1'. Special read-only attribute: `__dict__' is the module's namespace as a dictionary object. Predefined (writable) attributes: `__name__' is the module's name; `__doc__' is the module's documentation string, or `None' if unavailable; `__file__' is the pathname of the file from which the module was loaded, if it was loaded from a file. The `__file__' attribute is not present for C{} modules that are statically linked into the interpreter; for extension modules loaded dynamically from a shared library, it is the pathname of the shared library file. `Classes' Class objects are created by class definitions (see section *Note Class definitions::, "Class definitions"). A class has a namespace implemented by a dictionary object. Class attribute references are translated to lookups in this dictionary, e.g., `C.x' is translated to `C.__dict__["x"]'. When the attribute name is not found there, the attribute search continues in the base classes. The search is depth-first, left-to-right in the order of occurrence in the base class list. When a class attribute reference would yield a user-defined function object, it is transformed into an unbound user-defined method object (see above). The `im_class' attribute of this method object is the class in which the function object was found, not necessarily the class for which the attribute reference was initiated. Class attribute assignments update the class's dictionary, never the dictionary of a base class. A class object can be called (see above) to yield a class instance (see below). Special attributes: `__name__' is the class name; `__module__' is the module name in which the class was defined; `__dict__' is the dictionary containing the class's namespace; `__bases__' is a tuple (possibly empty or a singleton) containing the base classes, in the order of their occurrence in the base class list; `__doc__' is the class's documentation string, or None if undefined. `Class instances' A class instance is created by calling a class object (see above). A class instance has a namespace implemented as a dictionary which is the first place in which attribute references are searched. When an attribute is not found there, and the instance's class has an attribute by that name, the search continues with the class attributes. If a class attribute is found that is a user-defined function object (and in no other case), it is transformed into an unbound user-defined method object (see above). The `im_class' attribute of this method object is the class in which the function object was found, not necessarily the class of the instance for which the attribute reference was initiated. If no class attribute is found, and the object's class has a `__getattr__()' method, that is called to satisfy the lookup. Attribute assignments and deletions update the instance's dictionary, never a class's dictionary. If the class has a `__setattr__()' or `__delattr__()' method, this is called instead of updating the instance dictionary directly. Class instances can pretend to be numbers, sequences, or mappings if they have methods with certain special names. See section *Note Special method names::, "Special method names." Special attributes: `__dict__' is the attribute dictionary; `__class__' is the instance's class. `Files' A file object represents an open file. File objects are created by the `open()' built-in function, and also by `os.popen()', `os.fdopen()', and the `makefile()'method of socket objects (and perhaps by other functions or methods provided by extension modules). The objects `sys.stdin', `sys.stdout' and `sys.stderr' are initialized to file objects corresponding to the interpreter's standard input, output and error streams. See the for complete documentation of file objects. `Internal types' A few types used internally by the interpreter are exposed to the user. Their definitions may change with future versions of the interpreter, but they are mentioned here for completeness. `Code objects' Code objects represent _byte-compiled_ executable Python code, or _bytecode_. The difference between a code object and a function object is that the function object contains an explicit reference to the function's globals (the module in which it was defined), while a code object contains no context; also the default argument values are stored in the function object, not in the code object (because they represent values calculated at run-time). Unlike function objects, code objects are immutable and contain no references (directly or indirectly) to mutable objects. Special read-only attributes: `co_name' gives the function name; `co_argcount' is the number of positional arguments (including arguments with default values); `co_nlocals' is the number of local variables used by the function (including arguments); `co_varnames' is a tuple containing the names of the local variables (starting with the argument names); `co_cellvars' is a tuple containing the names of local variables that are referenced by nested functions; `co_freevars' is a tuple containing the names of local variables that are neither local nor global; `co_code' is a string representing the sequence of bytecode instructions; `co_consts' is a tuple containing the literals used by the bytecode; `co_names' is a tuple containing the names used by the bytecode; `co_filename' is the filename from which the code was compiled; `co_firstlineno' is the first line number of the function; `co_lnotab' is a string encoding the mapping from byte code offsets to line numbers (for details see the source code of the interpreter); `co_stacksize' is the required stack size (including local variables); `co_flags' is an integer encoding a number of flags for the interpreter. The `co_cellvars' and `co_freevars' are present in Python 2.1 when nested scopes are not enabled, but the code itself does not use or create cells. The following flag bits are defined for `co_flags': bit `0x04' is set if the function uses the `*arguments' syntax to accept an arbitrary number of positional arguments; bit `0x08' is set if the function uses the `**keywords' syntax to accept arbitrary keyword arguments; other bits are used internally or reserved for future use; bit `0x10' is set if the function was compiled with nested scopes enabled. If a code object represents a function, the first item in `co_consts' is the documentation string of the function, or `None' if undefined. `Frame objects' Frame objects represent execution frames. They may occur in traceback objects (see below). Special read-only attributes: `f_back' is to the previous stack frame (towards the caller), or `None' if this is the bottom stack frame; `f_code' is the code object being executed in this frame; `f_locals' is the dictionary used to look up local variables; `f_globals' is used for global variables; `f_builtins' is used for built-in (intrinsic) names; `f_restricted' is a flag indicating whether the function is executing in restricted execution mode; `f_lineno' gives the line number and `f_lasti' gives the precise instruction (this is an index into the bytecode string of the code object). Special writable attributes: `f_trace', if not `None', is a function called at the start of each source code line (this is used by the debugger); `f_exc_type', `f_exc_value', `f_exc_traceback' represent the most recent exception caught in this frame. `Traceback objects' Traceback objects represent a stack trace of an exception. A traceback object is created when an exception occurs. When the search for an exception handler unwinds the execution stack, at each unwound level a traceback object is inserted in front of the current traceback. When an exception handler is entered, the stack trace is made available to the program. (See section *Note try statement::, "The `try' statement.") It is accessible as `sys.exc_traceback', and also as the third item of the tuple returned by `sys.exc_info()'. The latter is the preferred interface, since it works correctly when the program is using multiple threads. When the program contains no suitable handler, the stack trace is written (nicely formatted) to the standard error stream; if the interpreter is interactive, it is also made available to the user as `sys.last_traceback'. Special read-only attributes: `tb_next' is the next level in the stack trace (towards the frame where the exception occurred), or `None' if there is no next level; `tb_frame' points to the execution frame of the current level; `tb_lineno' gives the line number where the exception occurred; `tb_lasti' indicates the precise instruction. The line number and last instruction in the traceback may differ from the line number of its frame object if the exception occurred in a `try' statement with no matching except clause or with a finally clause. `Slice objects' Slice objects are used to represent slices when _extended slice syntax_ is used. This is a slice using two colons, or multiple slices or ellipses separated by commas, e.g., `a[i:j:step]', `a[i:j, k:l]', or `a[..., i:j])'. They are also created by the built-in `slice()' function. Special read-only attributes: `start' is the lower bound; `stop' is the upper bound; `step' is the step value; each is `None' if omitted. These attributes can have any type.  File: python-ref.info, Node: Special method names, Prev: standard type hierarchy, Up: Data model Special method names ==================== A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. For instance, if a class defines a method named `__getitem__()', and `x' is an instance of this class, then `x[i]' is equivalent to `x.__getitem__(i)'. (The reverse is not true -- if `x' is a list object, `x.__getitem__(i)' is not equivalent to `x[i]'.) Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined. When implementing a class that emulates any built-in type, it is important that the emulation only be implemented to the degree that it makes sense for the object being modelled. For example, some sequences may work well with retrieval of individual elements, but extracting a slice may not make sense. (One example of this is the `NodeList' interface in the W3C's Document Object Model.) * Menu: * Basic customization:: * Customizing attribute access:: * Emulating callable objects:: * Emulating sequence and mapping types:: * Additional methods for emulation of sequence types:: * Emulating numeric types::  File: python-ref.info, Node: Basic customization, Next: Customizing attribute access, Prev: Special method names, Up: Special method names Basic customization ------------------- `__init__(self[, args...])' Called when the instance is created. The arguments are those passed to the class constructor expression. If a base class has an `__init__()' method the derived class's `__init__()' method must explicitly call it to ensure proper initialization of the base class part of the instance, e.g., `BaseClass.__init__(SELF, [ARGS...])'. `__del__(self)' Called when the instance is about to be destroyed. This is also called a destructor. If a base class has a `__del__()' method, the derived class's `__del__()' method must explicitly call it to ensure proper deletion of the base class part of the instance. Note that it is possible (though not recommended!) for the `__del__()' method to postpone destruction of the instance by creating a new reference to it. It may then be called at a later time when this new reference is deleted. It is not guaranteed that `__del__()' methods are called for objects that still exist when the interpreter exits. *Programmer's note:* `del x' doesn't directly call `x.__del__()' -- the former decrements the reference count for `x' by one, and the latter is only called when its reference count reaches zero. Some common situations that may prevent the reference count of an object to go to zero include: circular references between objects (e.g., a doubly-linked list or a tree data structure with parent and child pointers); a reference to the object on the stack frame of a function that caught an exception (the traceback stored in `sys.exc_traceback' keeps the stack frame alive); or a reference to the object on the stack frame that raised an unhandled exception in interactive mode (the traceback stored in `sys.last_traceback' keeps the stack frame alive). The first situation can only be remedied by explicitly breaking the cycles; the latter two situations can be resolved by storing None in `sys.exc_traceback' or `sys.last_traceback'. *Warning:* due to the precarious circumstances under which `__del__()' methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to `sys.stderr' instead. Also, when `__del__()' is invoked is response to a module being deleted (e.g., when execution of the program is done), other globals referenced by the `__del__()' method may already have been deleted. For this reason, `__del__()' methods should do the absolute minimum needed to maintain external invariants. Python 1.5 guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the `__del__()' method is called. `__repr__(self)' Called by the `repr()' built-in function and by string conversions (reverse quotes) to compute the "official" string representation of an object. If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value (given an appropriate environment). If this is not possible, a string of the form `<...SOME USEFUL DESCRIPTION...>' should be returned. The return value must be a string object. This is typically used for debugging, so it is important that the representation is information-rich and unambiguous. `__str__(self)' Called by the `str()' built-in function and by the `print' statement to compute the "informal" string representation of an object. This differs from `__repr__()' in that it does not have to be a valid Python expression: a more convenient or concise representation may be used instead. The return value must be a string object. `__lt__(self, other)' `__le__(self, other)' `__eq__(self, other)' `__ne__(self, other)' `__gt__(self, other)' `__ge__(self, other)' _Added in Python version 2.1_ These are the so-called "rich comparison" methods, and are called for comparison operators in preference to `__cmp__()' below. The correspondence between operator symbols and method names is as follows: `XY' call `X.__ne__(Y)', `X>Y' calls `X.__gt__(Y)', and `X>=Y' calls `X.__ge__(Y)'. These methods can return any value, but if the comparison operator is used in a Boolean context, the return value should be interpretable as a Boolean value, else a `TypeError' will be raised. By convention, `0' is used for false and `1' for true. There are no reflected (swapped-argument) versions of these methods (to be used when the left argument does not support the operation but the right argument does); rather, `__lt__()' and `__gt__()' are each other's reflection, `__le__()' and `__ge__()' are each other's reflection, and `__eq__()' and `__ne__()' are their own reflection. Arguments to rich comparison methods are never coerced. A rich comparison method may return `NotImplemented' if it does not implement the operation for a given pair of arguments. `__cmp__(self, other)' Called by comparison operations if rich comparison (see above) is not defined. Should return a negative integer if `self < other', zero if `self == other', a positive integer if `self > other'. If no `__cmp__()' operation is defined, class instances are compared by object identity ("address"). (Note: the restriction that exceptions are not propagated by `__cmp__()' has been removed in Python 1.5.) `__rcmp__(self, other)' _Changed in Python version 2.1_ `__hash__(self)' Called for the key object for dictionaryoperations, and by the built-in function `hash()'. Should return a 32-bit integer usable as a hash value for dictionary operations. The only required property is that objects which compare equal have the same hash value; it is advised to somehow mix together (e.g., using exclusive or) the hash values for the components of the object that also play a part in comparison of objects. If a class does not define a `__cmp__()' method it should not define a `__hash__()' operation either; if it defines `__cmp__()' but not `__hash__()' its instances will not be usable as dictionary keys. If a class defines mutable objects and implements a `__cmp__()' method it should not implement `__hash__()', since the dictionary implementation requires that a key's hash value is immutable (if the object's hash value changes, it will be in the wrong hash bucket). `__nonzero__(self)' Called to implement truth value testing; should return `0' or `1'. When this method is not defined, `__len__()' is called, if it is defined (see below). If a class defines neither `__len__()' nor `__nonzero__()', all its instances are considered true.  File: python-ref.info, Node: Customizing attribute access, Next: Emulating callable objects, Prev: Basic customization, Up: Special method names Customizing attribute access ---------------------------- The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of `x.name') for class instances. For performance reasons, these methods are cached in the class object at class definition time; therefore, they cannot be changed after the class definition is executed. `__getattr__(self, name)' Called when an attribute lookup has not found the attribute in the usual places (i.e. it is not an instance attribute nor is it found in the class tree for `self'). `name' is the attribute name. This method should return the (computed) attribute value or raise an `AttributeError' exception. Note that if the attribute is found through the normal mechanism, `__getattr__()' is not called. (This is an intentional asymmetry between `__getattr__()' and `__setattr__()'.) This is done both for efficiency reasons and because otherwise `__setattr__()' would have no way to access other attributes of the instance. Note that at least for instance variables, you can fake total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). `__setattr__(self, name, value)' Called when an attribute assignment is attempted. This is called instead of the normal mechanism (i.e. store the value in the instance dictionary). NAME is the attribute name, VALUE is the value to be assigned to it. If `__setattr__()' wants to assign to an instance attribute, it should not simply execute `self.NAME = value' -- this would cause a recursive call to itself. Instead, it should insert the value in the dictionary of instance attributes, e.g., `self.__dict__[NAME] = value'. `__delattr__(self, name)' Like `__setattr__()' but for attribute deletion instead of assignment. This should only be implemented if `del obj.NAME' is meaningful for the object.  File: python-ref.info, Node: Emulating callable objects, Next: Emulating sequence and mapping types, Prev: Customizing attribute access, Up: Special method names Emulating callable objects -------------------------- `__call__(self[, args...])' Called when the instance is "called" as a function; if this method is defined, `X(arg1, arg2, ...)' is a shorthand for `X.__call__(arg1, arg2, ...)'.  File: python-ref.info, Node: Emulating sequence and mapping types, Next: Additional methods for emulation of sequence types, Prev: Emulating callable objects, Up: Special method names Emulating sequence and mapping types ------------------------------------ The following methods can be defined to emulate sequence or mapping objects. The first set of methods is used either to emulate a sequence or to emulate a mapping; the difference is that for a sequence, the allowable keys should be the integers K for which `0 <= K < N' where N is the length of the sequence, or slice objects, which define a range of items. (For backwards compatibility, the method `__getslice__()' (see below) can also be defined to handle simple, but not extended slices.) It is also recommended that mappings provide the methods `keys()', `values()', `items()', `has_key()', `get()', `clear()', `copy()', and `update()' behaving similar to those for Python's standard dictionary objects; mutable sequences should provide methods `append()', `count()', `index()', `insert()', `pop()', `remove()', `reverse()' and `sort()', like Python standard list objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods `__add__()', `__radd__()', `__iadd__()', `__mul__()', `__rmul__()' and `__imul__()' described below; they should not define `__coerce__()' or other numerical operators. `__len__(self)' Called to implement the built-in function `len()'. Should return the length of the object, an integer `>=' 0. Also, an object that doesn't define a `__nonzero__()' method and whose `__len__()' method returns zero is considered to be false in a Boolean context. `__getitem__(self, key)' Called to implement evaluation of `SELF[KEY]'. For sequence types, the accepted keys should be integers and slice objects. Note that the special interpretation of negative indexes (if the class wishes to emulate a sequence type) is up to the `__getitem__()' method. If KEY is of an inappropriate type, `TypeError' may be raised; if of a value outside the set of indexes for the sequence (after any special interpretation of negative values), `IndexError' should be raised. *Note:* `for' loops expect that an `IndexError' will be raised for illegal indexes to allow proper detection of the end of the sequence. `__setitem__(self, key, value)' Called to implement assignment to `SELF[KEY]'. Same note as for `__getitem__()'. This should only be implemented for mappings if the objects support changes to the values for keys, or if new keys can be added, or for sequences if elements can be replaced. The same exceptions should be raised for improper KEY values as for the `__getitem__()' method. `__delitem__(self, key)' Called to implement deletion of `SELF[KEY]'. Same note as for `__getitem__()'. This should only be implemented for mappings if the objects support removal of keys, or for sequences if elements can be removed from the sequence. The same exceptions should be raised for improper KEY values as for the `__getitem__()' method.  File: python-ref.info, Node: Additional methods for emulation of sequence types, Next: Emulating numeric types, Prev: Emulating sequence and mapping types, Up: Special method names Additional methods for emulation of sequence types -------------------------------------------------- The following methods can be defined to further emulate sequence objects. Immutable sequences methods should only define `__getslice__()'; mutable sequences, should define all three three methods. `__getslice__(self, i, j)' _This is deprecated in Python 2.0. Support slice objects as parameters to the `__getitem__()' method._ Called to implement evaluation of `SELF[I:J]'. The returned object should be of the same type as SELF. Note that missing I or J in the slice expression are replaced by zero or `sys.maxint', respectively. If negative indexes are used in the slice, the length of the sequence is added to that index. If the instance does not implement the `__len__()' method, an `AttributeError' is raised. No guarantee is made that indexes adjusted this way are not still negative. Indexes which are greater than the length of the sequence are not modified. If no `__getslice__()' is found, a slice object is created instead, and passed to `__getitem__()' instead. `__setslice__(self, i, j, sequence)' Called to implement assignment to `SELF[I:J]'. Same notes for I and J as for `__getslice__()'. This method is deprecated. If no `__setslice__()' is found, a slice object is created instead, and passed to `__setitem__()' instead. `__delslice__(self, i, j)' Called to implement deletion of `SELF[I:J]'. Same notes for I and J as for `__getslice__()'. This method is deprecated. If no `__delslice__()' is found, a slice object is created instead, and passed to `__delitem__()' instead. Notice that these methods are only invoked when a single slice with a single colon is used, and the slice method is available. For slice operations involving extended slice notation, or in absence of the slice methods, `__getitem__()', `__setitem__()' or `__delitem__()' is called with a slice object as argument. The following example demonstrate how to make your program or module compatible with earlier versions of Python (assuming that methods `__getitem__()', `__setitem__()' and `__delitem__()' support slice objects as arguments): class MyClass: ... def __getitem__(self, index): ... def __setitem__(self, index, value): ... def __delitem__(self, index): ... if sys.version_info < (2, 0): # They won't be defined if version is at least 2.0 final def __getslice__(self, i, j): return self[max(0, i):max(0, j):] def __setslice__(self, i, j, seq): self[max(0, i):max(0, j):] = seq def __delslice__(self, i, j): del self[max(0, i):max(0, j):] ... Note the calls to `max()'; these are actually necessary due to the handling of negative indices before the `__*slice__()' methods are called. When negative indexes are used, the `__*item__()' methods receive them as provided, but the `__*slice__()' methods get a "cooked" form of the index values. For each negative index value, the length of the sequence is added to the index before calling the method (which may still result in a negative index); this is the customary handling of negative indexes by the built-in sequence types, and the `__*item__()' methods are expected to do this as well. However, since they should already be doing that, negative indexes cannot be passed in; they must be be constrained to the bounds of the sequence before being passed to the `__*item__()' methods. Calling `max(0, i)' conveniently returns the proper value. The membership test operators (`in' and `not in') are normally implemented as iteration loop through the sequence. However, sequence objects can supply the following special method with a more efficient implementation: `__contains__(self, item)' Called to implement membership test operators. Should return true if ITEM is in SELF, false otherwise.