Lines Matching refs:extension
26 'debugger': '\n``pdb`` --- The Python Debugger\n*******************************\n\nThe module ``pdb`` defines an interactive source code debugger for\nPython programs. It supports setting (conditional) breakpoints and\nsingle stepping at the source line level, inspection of stack frames,\nsource code listing, and evaluation of arbitrary Python code in the\ncontext of any stack frame. It also supports post-mortem debugging\nand can be called under program control.\n\nThe debugger is extensible --- it is actually defined as the class\n``Pdb``. This is currently undocumented but easily understood by\nreading the source. The extension interface uses the modules ``bdb``\nand ``cmd``.\n\nThe debugger\'s prompt is ``(Pdb)``. Typical usage to run a program\nunder control of the debugger is:\n\n >>> import pdb\n >>> import mymodule\n >>> pdb.run(\'mymodule.test()\')\n > <string>(0)?()\n (Pdb) continue\n > <string>(1)?()\n (Pdb) continue\n NameError: \'spam\'\n > <string>(1)?()\n (Pdb)\n\n``pdb.py`` can also be invoked as a script to debug other scripts.\nFor example:\n\n python -m pdb myscript.py\n\nWhen invoked as a script, pdb will automatically enter post-mortem\ndebugging if the program being debugged exits abnormally. After post-\nmortem debugging (or after normal exit of the program), pdb will\nrestart the program. Automatic restarting preserves pdb\'s state (such\nas breakpoints) and in most cases is more useful than quitting the\ndebugger upon program\'s exit.\n\nNew in version 2.4: Restarting post-mortem behavior added.\n\nThe typical usage to break into the debugger from a running program is\nto insert\n\n import pdb; pdb.set_trace()\n\nat the location you want to break into the debugger. You can then\nstep through the code following this statement, and continue running\nwithout the debugger using the ``c`` command.\n\nThe typical usage to inspect a crashed program is:\n\n >>> import pdb\n >>> import mymodule\n >>> mymodule.test()\n Traceback (most recent call last):\n File "<stdin>", line 1, in ?\n File "./mymodule.py", line 4, in test\n test2()\n File "./mymodule.py", line 3, in test2\n print spam\n NameError: spam\n >>> pdb.pm()\n > ./mymodule.py(3)test2()\n -> print spam\n (Pdb)\n\nThe module defines the following functions; each enters the debugger\nin a slightly different way:\n\npdb.run(statement[, globals[, locals]])\n\n Execute the *statement* (given as a string) under debugger control.\n The debugger prompt appears before any code is executed; you can\n set breakpoints and type ``continue``, or you can step through the\n statement using ``step`` or ``next`` (all these commands are\n explained below). The optional *globals* and *locals* arguments\n specify the environment in which the code is executed; by default\n the dictionary of the module ``__main__`` is used. (See the\n explanation of the ``exec`` statement or the ``eval()`` built-in\n function.)\n\npdb.runeval(expression[, globals[, locals]])\n\n Evaluate the *expression* (given as a string) under debugger\n control. When ``runeval()`` returns, it returns the value of the\n expression. Otherwise this function is similar to ``run()``.\n\npdb.runcall(function[, argument, ...])\n\n Call the *function* (a function or method object, not a string)\n with the given arguments. When ``runcall()`` returns, it returns\n whatever the function call returned. The debugger prompt appears\n as soon as the function is entered.\n\npdb.set_trace()\n\n Enter the debugger at the calling stack frame. This is useful to\n hard-code a breakpoint at a given point in a program, even if the\n code is not otherwise being debugged (e.g. when an assertion\n fails).\n\npdb.post_mortem([traceback])\n\n Enter post-mortem debugging of the given *traceback* object. If no\n *traceback* is given, it uses the one of the exception that is\n currently being handled (an exception must be being handled if the\n default is to be used).\n\npdb.pm()\n\n Enter post-mortem debugging of the traceback found in\n ``sys.last_traceback``.\n\nThe ``run*`` functions and ``set_trace()`` are aliases for\ninstantiating the ``Pdb`` class and calling the method of the same\nname. If you want to access further features, you have to do this\nyourself:\n\nclass class pdb.Pdb(completekey=\'tab\', stdin=None, stdout=None, skip=None)\n\n ``Pdb`` is the debugger class.\n\n The *completekey*, *stdin* and *stdout* arguments are passed to the\n underlying ``cmd.Cmd`` class; see the description there.\n\n The *skip* argument, if given, must be an iterable of glob-style\n module name patterns. The debugger will not step into frames that\n originate in a module that matches one of these patterns. [1]\n\n Example call to enable tracing with *skip*:\n\n import pdb; pdb.Pdb(skip=[\'django.*\']).set_trace()\n\n New in version 2.7: The *skip* argument.\n\n run(statement[, globals[, locals]])\n runeval(expression[, globals[, locals]])\n runcall(function[, argument, ...])\n set_trace()\n\n See the documentation for the functions explained above.\n',
67 'types': '\nThe standard type hierarchy\n***************************\n\nBelow is a list of the types that are built into Python. Extension\nmodules (written in C, Java, or other languages, depending on the\nimplementation) can define additional types. Future versions of\nPython may add types to the type hierarchy (e.g., rational numbers,\nefficiently stored arrays of integers, etc.).\n\nSome of the type descriptions below contain a paragraph listing\n\'special attributes.\' These are attributes that provide access to the\nimplementation and are not intended for general use. Their definition\nmay change in the future.\n\nNone\n This type has a single value. There is a single object with this\n value. This object is accessed through the built-in name ``None``.\n It is used to signify the absence of a value in many situations,\n e.g., it is returned from functions that don\'t explicitly return\n anything. Its truth value is false.\n\nNotImplemented\n This type has a single value. There is a single object with this\n value. This object is accessed through the built-in name\n ``NotImplemented``. Numeric methods and rich comparison methods may\n return this value if they do not implement the operation for the\n operands provided. (The interpreter will then try the reflected\n operation, or some other fallback, depending on the operator.) Its\n truth value is true.\n\nEllipsis\n This type has a single value. There is a single object with this\n value. This object is accessed through the built-in name\n ``Ellipsis``. It is used to indicate the presence of the ``...``\n syntax in a slice. Its truth value is true.\n\n``numbers.Number``\n These are created by numeric literals and returned as results by\n arithmetic operators and arithmetic built-in functions. Numeric\n objects are immutable; once created their value never changes.\n Python numbers are of course strongly related to mathematical\n numbers, but subject to the limitations of numerical representation\n in computers.\n\n Python distinguishes between integers, floating point numbers, and\n complex numbers:\n\n ``numbers.Integral``\n These represent elements from the mathematical set of integers\n (positive and negative).\n\n There are three types of integers:\n\n Plain integers\n These represent numbers in the range -2147483648 through\n 2147483647. (The range may be larger on machines with a\n larger natural word size, but not smaller.) When the result\n of an operation would fall outside this range, the result is\n normally returned as a long integer (in some cases, the\n exception ``OverflowError`` is raised instead). For the\n purpose of shift and mask operations, integers are assumed to\n have a binary, 2\'s complement notation using 32 or more bits,\n and hiding no bits from the user (i.e., all 4294967296\n different bit patterns correspond to different values).\n\n Long integers\n These represent numbers in an unlimited range, subject to\n available (virtual) memory only. For the purpose of shift\n and mask operations, a binary representation is assumed, and\n negative numbers are represented in a variant of 2\'s\n complement which gives the illusion of an infinite string of\n sign bits extending to the left.\n\n Booleans\n These represent the truth values False and True. The two\n objects representing the values False and True are the only\n Boolean objects. The Boolean type is a subtype of plain\n integers, and Boolean values behave like the values 0 and 1,\n respectively, in almost all contexts, the exception being\n that when converted to a string, the strings ``"False"`` or\n ``"True"`` are returned, respectively.\n\n The rules for integer representation are intended to give the\n most meaningful interpretation of shift and mask operations\n involving negative integers and the least surprises when\n switching between the plain and long integer domains. Any\n operation, if it yields a result in the plain integer domain,\n will yield the same result in the long integer domain or when\n using mixed operands. The switch between domains is transparent\n to the programmer.\n\n ``numbers.Real`` (``float``)\n These represent machine-level double precision floating point\n numbers. You are at the mercy of the underlying machine\n architecture (and C or Java implementation) for the accepted\n range and handling of overflow. Python does not support single-\n precision floating point numbers; the savings in processor and\n memory usage that are usually the reason for using these is\n dwarfed by the overhead of using objects in Python, so there is\n no reason to complicate the language with two kinds of floating\n point numbers.\n\n ``numbers.Complex``\n These represent complex numbers as a pair of machine-level\n double precision floating point numbers. The same caveats apply\n as for floating point numbers. The real and imaginary parts of a\n complex number ``z`` can be retrieved through the read-only\n attributes ``z.real`` and ``z.imag``.\n\nSequences\n These represent finite ordered sets indexed by non-negative\n numbers. The built-in function ``len()`` returns the number of\n items of a sequence. When the length of a sequence is *n*, the\n index set contains the numbers 0, 1, ..., *n*-1. Item *i* of\n sequence *a* is selected by ``a[i]``.\n\n Sequences also support slicing: ``a[i:j]`` selects all items with\n index *k* such that *i* ``<=`` *k* ``<`` *j*. When used as an\n expression, a slice is a sequence of the same type. This implies\n that the index set is renumbered so that it starts at 0.\n\n Some sequences also support "extended slicing" with a third "step"\n parameter: ``a[i:j:k]`` selects all items of *a* with index *x*\n where ``x = i + n*k``, *n* ``>=`` ``0`` and *i* ``<=`` *x* ``<``\n *j*.\n\n Sequences are distinguished according to their mutability:\n\n Immutable sequences\n An object of an immutable sequence type cannot change once it is\n created. (If the object contains references to other objects,\n these other objects may be mutable and may be changed; however,\n the collection of objects directly referenced by an immutable\n object cannot change.)\n\n The following types are immutable sequences:\n\n Strings\n The items of a string are characters. There is no separate\n character type; a character is represented by a string of one\n item. Characters represent (at least) 8-bit bytes. The\n built-in functions ``chr()`` and ``ord()`` convert between\n characters and nonnegative integers representing the byte\n values. Bytes with the values 0-127 usually represent the\n corresponding ASCII values, but the interpretation of values\n is up to the program. The string data type is also used to\n represent arrays of bytes, e.g., to hold data read from a\n file.\n\n (On systems whose native character set is not ASCII, strings\n may use EBCDIC in their internal representation, provided the\n functions ``chr()`` and ``ord()`` implement a mapping between\n ASCII and EBCDIC, and string comparison preserves the ASCII\n order. Or perhaps someone can propose a better rule?)\n\n Unicode\n The items of a Unicode object are Unicode code units. A\n Unicode code unit is represented by a Unicode object of one\n item and can hold either a 16-bit or 32-bit value\n representing a Unicode ordinal (the maximum value for the\n ordinal is given in ``sys.maxunicode``, and depends on how\n Python is configured at compile time). Surrogate pairs may\n be present in the Unicode object, and will be reported as two\n separate items. The built-in functions ``unichr()`` and\n ``ord()`` convert between code units and nonnegative integers\n representing the Unicode ordinals as defined in the Unicode\n Standard 3.0. Conversion from and to other encodings are\n possible through the Unicode method ``encode()`` and the\n built-in function ``unicode()``.\n\n Tuples\n The items of a tuple are arbitrary Python objects. Tuples of\n two or more items are formed by comma-separated lists of\n expressions. A tuple of one item (a \'singleton\') can be\n formed by affixing a comma to an expression (an expression by\n itself does not create a tuple, since parentheses must be\n usable for grouping of expressions). An empty tuple can be\n formed by an empty pair of parentheses.\n\n Mutable sequences\n Mutable sequences can be changed after they are created. The\n subscription and slicing notations can be used as the target of\n assignment and ``del`` (delete) statements.\n\n There are currently two intrinsic mutable sequence types:\n\n Lists\n The items of a list are arbitrary Python objects. Lists are\n formed by placing a comma-separated list of expressions in\n square brackets. (Note that there are no special cases needed\n to form lists of length 0 or 1.)\n\n Byte Arrays\n A bytearray object is a mutable array. They are created by\n the built-in ``bytearray()`` constructor. Aside from being\n mutable (and hence unhashable), byte arrays otherwise provide\n the same interface and functionality as immutable bytes\n objects.\n\n The extension module ``array`` provides an additional example of\n a mutable sequence type.\n\nSet types\n These represent unordered, finite sets of unique, immutable\n objects. As such, they cannot be indexed by any subscript. However,\n they can be iterated over, and the built-in function ``len()``\n returns the number of items in a set. Common uses for sets are fast\n membership testing, removing duplicates from a sequence, and\n computing mathematical operations such as intersection, union,\n difference, and symmetric difference.\n\n For set elements, the same immutability rules apply as for\n dictionary keys. Note that numeric types obey the normal rules for\n numeric comparison: if two numbers compare equal (e.g., ``1`` and\n ``1.0``), only one of them can be contained in a set.\n\n There are currently two intrinsic set types:\n\n Sets\n These represent a mutable set. They are created by the built-in\n ``set()`` constructor and can be modified afterwards by several\n methods, such as ``add()``.\n\n Frozen sets\n These represent an immutable set. They are created by the\n built-in ``frozenset()`` constructor. As a frozenset is\n immutable and *hashable*, it can be used again as an element of\n another set, or as a dictionary key.\n\nMappings\n These represent finite sets of objects indexed by arbitrary index\n sets. The subscript notation ``a[k]`` selects the item indexed by\n ``k`` from the mapping ``a``; this can be used in expressions and\n as the target of assignments or ``del`` statements. The built-in\n function ``len()`` returns the number of items in a mapping.\n\n There is currently a single intrinsic mapping type:\n\n Dictionaries\n These represent finite sets of objects indexed by nearly\n arbitrary values. The only types of values not acceptable as\n keys are values containing lists or dictionaries or other\n mutable types that are compared by value rather than by object\n identity, the reason being that the efficient implementation of\n dictionaries requires a key\'s hash value to remain constant.\n Numeric types used for keys obey the normal rules for numeric\n comparison: if two numbers compare equal (e.g., ``1`` and\n ``1.0``) then they can be used interchangeably to index the same\n dictionary entry.\n\n Dictionaries are mutable; they can be created by the ``{...}``\n notation (see section *Dictionary displays*).\n\n The extensionextensionextension\n modules). The objects ``sys.stdin``, ``sys.stdout`` and\n ``sys.stderr`` are initialized to file objects corresponding to the\n interpreter\'s standard input, output and error streams. See *File\n Objects* for complete documentation of file objects.\n\nInternal types\n A few types used internally by the interpreter are exposed to the\n user. Their definitions may change with future versions of the\n interpreter, but they are mentioned here for completeness.\n\n Code objects\n Code objects represent *byte-compiled* executable Python code,\n or *bytecode*. The difference between a code object and a\n function object is that the function object contains an explicit\n reference to the function\'s globals (the module in which it was\n defined), while a code object contains no context; also the\n default argument values are stored in the function object, not\n in the code object (because they represent values calculated at\n run-time). Unlike function objects, code objects are immutable\n and contain no references (directly or indirectly) to mutable\n objects.\n\n Special read-only attributes: ``co_name`` gives the function\n name; ``co_argcount`` is the number of positional arguments\n (including arguments with default values); ``co_nlocals`` is the\n number of local variables used by the function (including\n arguments); ``co_varnames`` is a tuple containing the names of\n the local variables (starting with the argument names);\n ``co_cellvars`` is a tuple containing the names of local\n variables that are referenced by nested functions;\n ``co_freevars`` is a tuple containing the names of free\n variables; ``co_code`` is a string representing the sequence of\n bytecode instructions; ``co_consts`` is a tuple containing the\n literals used by the bytecode; ``co_names`` is a tuple\n containing the names used by the bytecode; ``co_filename`` is\n the filename from which the code was compiled;\n ``co_firstlineno`` is the first line number of the function;\n ``co_lnotab`` is a string encoding the mapping from bytecode\n offsets to line numbers (for details see the source code of the\n interpreter); ``co_stacksize`` is the required stack size\n (including local variables); ``co_flags`` is an integer encoding\n a number of flags for the interpreter.\n\n The following flag bits are defined for ``co_flags``: bit\n ``0x04`` is set if the function uses the ``*arguments`` syntax\n to accept an arbitrary number of positional arguments; bit\n ``0x08`` is set if the function uses the ``**keywords`` syntax\n to accept arbitrary keyword arguments; bit ``0x20`` is set if\n the function is a generator.\n\n Future feature declarations (``from __future__ import\n division``) also use bits in ``co_flags`` to indicate whether a\n code object was compiled with a particular feature enabled: bit\n ``0x2000`` is set if the function was compiled with future\n division enabled; bits ``0x10`` and ``0x1000`` were used in\n earlier versions of Python.\n\n Other bits in ``co_flags`` are reserved for internal use.\n\n If a code object represents a function, the first item in\n ``co_consts`` is the documentation string of the function, or\n ``None`` if undefined.\n\n Frame objects\n Frame objects represent execution frames. They may occur in\n traceback objects (see below).\n\n Special read-only attributes: ``f_back`` is to the previous\n stack frame (towards the caller), or ``None`` if this is the\n bottom stack frame; ``f_code`` is the code object being executed\n in this frame; ``f_locals`` is the dictionary used to look up\n local variables; ``f_globals`` is used for global variables;\n ``f_builtins`` is used for built-in (intrinsic) names;\n ``f_restricted`` is a flag indicating whether the function is\n executing in restricted execution mode; ``f_lasti`` gives the\n precise instruction (this is an index into the bytecode string\n of the code object).\n\n Special writable attributes: ``f_trace``, if not ``None``, is a\n function called at the start of each source code line (this is\n used by the debugger); ``f_exc_type``, ``f_exc_value``,\n ``f_exc_traceback`` represent the last exception raised in the\n parent frame provided another exception was ever raised in the\n current frame (in all other cases they are None); ``f_lineno``\n is the current line number of the frame --- writing to this from\n within a trace function jumps to the given line (only for the\n bottom-most frame). A debugger can implement a Jump command\n (aka Set Next Statement) by writing to f_lineno.\n\n Traceback objects\n Traceback objects represent a stack trace of an exception. A\n traceback object is created when an exception occurs. When the\n search for an exception handler unwinds the execution stack, at\n each unwound level a traceback object is inserted in front of\n the current traceback. When an exception handler is entered,\n the stack trace is made available to the program. (See section\n *The try statement*.) It is accessible as ``sys.exc_traceback``,\n and also as the third item of the tuple returned by\n ``sys.exc_info()``. The latter is the preferred interface,\n since it works correctly when the program is using multiple\n threads. When the program contains no suitable handler, the\n stack trace is written (nicely formatted) to the standard error\n stream; if the interpreter is interactive, it is also made\n available to the user as ``sys.last_traceback``.\n\n Special read-only attributes: ``tb_next`` is the next level in\n the stack trace (towards the frame where the exception\n occurred), or ``None`` if there is no next level; ``tb_frame``\n points to the execution frame of the current level;\n ``tb_lineno`` gives the line number where the exception\n occurred; ``tb_lasti`` indicates the precise instruction. The\n line number and last instruction in the traceback may differ\n from the line number of its frame object if the exception\n occurred in a ``try`` statement with no matching except clause\n or with a finally clause.\n\n Slice objects\n Slice objects are used to represent slices when *extended slice\n syntax* is used. This is a slice using two colons, or multiple\n slices or ellipses separated by commas, e.g., ``a[i:j:step]``,\n ``a[i:j, k:l]``, or ``a[..., i:j]``. They are also created by\n the built-in ``slice()`` function.\n\n Special read-only attributes: ``start`` is the lower bound;\n ``stop`` is the upper bound; ``step`` is the step value; each is\n ``None`` if omitted. These attributes can have any type.\n\n Slice objects support one method:\n\n slice.indices(self, length)\n\n This method takes a single integer argument *length* and\n computes information about the extended slice that the slice\n object would describe if applied to a sequence of *length*\n items. It returns a tuple of three integers; respectively\n these are the *start* and *stop* indices and the *step* or\n stride length of the slice. Missing or out-of-bounds indices\n are handled in a manner consistent with regular slices.\n\n New in version 2.3.\n\n Static method objects\n Static method objects provide a way of defeating the\n transformation of function objects to method objects described\n above. A static method object is a wrapper around any other\n object, usually a user-defined method object. When a static\n method object is retrieved from a class or a class instance, the\n object actually returned is the wrapped object, which is not\n subject to any further transformation. Static method objects are\n not themselves callable, although the objects they wrap usually\n are. Static method objects are created by the built-in\n ``staticmethod()`` constructor.\n\n Class method objects\n A class method object, like a static method object, is a wrapper\n around another object that alters the way in which that object\n is retrieved from classes and class instances. The behaviour of\n class method objects upon such retrieval is described above,\n under "User-defined methods". Class method objects are created\n by the built-in ``classmethod()`` constructor.\n',
