This is /home/pdm/install/Python-2.1/Doc/api/python-api.info, produced by makeinfo version 4.0 from api.texi. April 15, 2001 2.1  File: python-api.info, Node: Top, Next: Front Matter, Prev: (dir), Up: (dir) Python/C API Reference Manual ***************************** * Menu: * Front Matter:: * Introduction:: * Very High Level Layer:: * Reference Counting:: * Exception Handling:: * Utilities:: * Abstract Objects Layer:: * Concrete Objects Layer:: * Initialization:: * Memory Management:: * Defining New Object Types:: * Reporting Bugs:: * Module Index:: * Class-Exception-Object Index:: * Function-Method-Variable Index:: * Miscellaneous Index::  File: python-api.info, Node: Front Matter, Next: Introduction, Prev: Top, Up: Top Front Matter ************ Copyright (C) 2001 Python Software Foundation. All rights reserved. Copyright (C) 2000 BeOpen.com. All rights reserved. Copyright (C) 1995-2000 Corporation for National Research Initiatives. All rights reserved. Copyright (C) 1991-1995 Stichting Mathematisch Centrum. All rights reserved. *BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1* 1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the Individual or Organization ("Licensee") accessing and otherwise using this software in source or binary form and its associated documentation ("the Software"). 2. Subject to the terms and conditions of this BeOpen Python License Agreement, BeOpen hereby grants Licensee a non-exclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use the Software alone or in any derivative version, provided, however, that the BeOpen Python License is retained in the Software, alone or in any derivative version prepared by Licensee. 3. BeOpen is making the Software available to Licensee on an "AS IS" basis. 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This Agreement may also be obtained from a proxy server on the Internet using the following URL: . *CWI PERMISSIONS STATEMENT AND DISCLAIMER* Copyright (C) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam, The Netherlands. All rights reserved. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Stichting Mathematisch Centrum or CWI not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. This manual documents the API used by C and C++ programmers who want to write extension modules or embed Python. It is a companion to , which describes the general principles of extension writing but does not document the API functions in detail. *Warning:* The current version of this document is incomplete. I hope that it is nevertheless useful. I will continue to work on it, and release new versions from time to time, independent from Python source code releases.  File: python-api.info, Node: Introduction, Next: Very High Level Layer, Prev: Front Matter, Up: Top Introduction ************ The Application Programmer's Interface to Python gives C and C++ programmers access to the Python interpreter at a variety of levels. The API is equally usable from C++, but for brevity it is generally referred to as the Python/C API. There are two fundamentally different reasons for using the Python/C API. The first reason is to write _extension modules_ for specific purposes; these are C modules that extend the Python interpreter. This is probably the most common use. The second reason is to use Python as a component in a larger application; this technique is generally referred to as "embedding" Python in an application. Writing an extension module is a relatively well-understood process, where a "cookbook" approach works well. There are several tools that automate the process to some extent. While people have embedded Python in other applications since its early existence, the process of embedding Python is less straightforward that writing an extension. Many API functions are useful independent of whether you're embedding or extending Python; moreover, most applications that embed Python will need to provide a custom extension as well, so it's probably a good idea to become familiar with writing an extension before attempting to embed Python in a real application. * Menu: * Include Files:: * Objects:: * Exceptions:: * Embedding Python::  File: python-api.info, Node: Include Files, Next: Objects, Prev: Introduction, Up: Introduction Include Files ============= All function, type and macro definitions needed to use the Python/C API are included in your code by the following line: #include "Python.h" This implies inclusion of the following standard headers: `', `', `', `', and `' (if available). All user visible names defined by Python.h (except those defined by the included standard headers) have one of the prefixes `Py' or `_Py'. Names beginning with `_Py' are for internal use by the Python implementation and should not be used by extension writers. Structure member names do not have a reserved prefix. *Important:* user code should never define names that begin with `Py' or `_Py'. This confuses the reader, and jeopardizes the portability of the user code to future Python versions, which may define additional names beginning with one of these prefixes. The header files are typically installed with Python. On UNIX, these are located in the directories ``prefix'/include/pythonVERSION/' and ``exec_prefix'/include/pythonVERSION/', where `prefix' and `exec_prefix' are defined by the corresponding parameters to Python's `configure' script and VERSION is `sys.version[:3]'. On Windows, the headers are installed in ``prefix'/include', where `prefix' is the installation directory specified to the installer. To include the headers, place both directories (if different) on your compiler's search path for includes. Do _not_ place the parent directories on the search path and then use `#include '; this will break on multi-platform builds since the platform independent headers under `prefix' include the platform specific headers from `exec_prefix'.  File: python-api.info, Node: Objects, Next: Exceptions, Prev: Include Files, Up: Introduction Objects, Types and Reference Counts =================================== Most Python/C API functions have one or more arguments as well as a return value of type `PyObject*'. This type is a pointer to an opaque data type representing an arbitrary Python object. Since all Python object types are treated the same way by the Python language in most situations (e.g., assignments, scope rules, and argument passing), it is only fitting that they should be represented by a single C type. Almost all Python objects live on the heap: you never declare an automatic or static variable of type `PyObject', only pointer variables of type `PyObject*' can be declared. The sole exception are the type objects; since these must never be deallocated, they are typically static `PyTypeObject' objects. All Python objects (even Python integers) have a "type" and a "reference count". An object's type determines what kind of object it is (e.g., an integer, a list, or a user-defined function; there are many more as explained in the ). For each of the well-known types there is a macro to check whether an object is of that type; for instance, `PyList_Check(A)' is true if (and only if) the object pointed to by A is a Python list. * Menu: * Reference Counts:: * Types::  File: python-api.info, Node: Reference Counts, Next: Types, Prev: Objects, Up: Objects Reference Counts ---------------- The reference count is important because today's computers have a finite (and often severely limited) memory size; it counts how many different places there are that have a reference to an object. Such a place could be another object, or a global (or static) C variable, or a local variable in some C function. When an object's reference count becomes zero, the object is deallocated. If it contains references to other objects, their reference count is decremented. Those other objects may be deallocated in turn, if this decrement makes their reference count become zero, and so on. (There's an obvious problem with objects that reference each other here; for now, the solution is "don't do that.") Reference counts are always manipulated explicitly. The normal way is to use the macro `Py_INCREF()' to increment an object's reference count by one, and `Py_DECREF()' to decrement it by one. The `Py_DECREF()' macro is considerably more complex than the incref one, since it must check whether the reference count becomes zero and then cause the object's deallocator to be called. The deallocator is a function pointer contained in the object's type structure. The type-specific deallocator takes care of decrementing the reference counts for other objects contained in the object if this is a compound object type, such as a list, as well as performing any additional finalization that's needed. There's no chance that the reference count can overflow; at least as many bits are used to hold the reference count as there are distinct memory locations in virtual memory (assuming `sizeof(long) >= sizeof(char*)'). Thus, the reference count increment is a simple operation. It is not necessary to increment an object's reference count for every local variable that contains a pointer to an object. In theory, the object's reference count goes up by one when the variable is made to point to it and it goes down by one when the variable goes out of scope. However, these two cancel each other out, so at the end the reference count hasn't changed. The only real reason to use the reference count is to prevent the object from being deallocated as long as our variable is pointing to it. If we know that there is at least one other reference to the object that lives at least as long as our variable, there is no need to increment the reference count temporarily. An important situation where this arises is in objects that are passed as arguments to C functions in an extension module that are called from Python; the call mechanism guarantees to hold a reference to every argument for the duration of the call. However, a common pitfall is to extract an object from a list and hold on to it for a while without incrementing its reference count. Some other operation might conceivably remove the object from the list, decrementing its reference count and possible deallocating it. The real danger is that innocent-looking operations may invoke arbitrary Python code which could do this; there is a code path which allows control to flow back to the user from a `Py_DECREF()', so almost any operation is potentially dangerous. A safe approach is to always use the generic operations (functions whose name begins with `PyObject_', `PyNumber_', `PySequence_' or `PyMapping_'). These operations always increment the reference count of the object they return. This leaves the caller with the responsibility to call `Py_DECREF()' when they are done with the result; this soon becomes second nature. * Menu: * Reference Count Details::  File: python-api.info, Node: Reference Count Details, Prev: Reference Counts, Up: Reference Counts Reference Count Details ....................... The reference count behavior of functions in the Python/C API is best explained in terms of _ownership of references_. Note that we talk of owning references, never of owning objects; objects are always shared! When a function owns a reference, it has to dispose of it properly -- either by passing ownership on (usually to its caller) or by calling `Py_DECREF()' or `Py_XDECREF()'. When a function passes ownership of a reference on to its caller, the caller is said to receive a _new_ reference. When no ownership is transferred, the caller is said to _borrow_ the reference. Nothing needs to be done for a borrowed reference. Conversely, when a calling function passes it a reference to an object, there are two possibilities: the function _steals_ a reference to the object, or it does not. Few functions steal references; the two notable exceptions are `PyList_SetItem()' and `PyTuple_SetItem()', which steal a reference to the item (but not to the tuple or list into which the item is put!). These functions were designed to steal a reference because of a common idiom for populating a tuple or list with newly created objects; for example, the code to create the tuple `(1, 2, "three")' could look like this (forgetting about error handling for the moment; a better way to code this is shown below): PyObject *t; t = PyTuple_New(3); PyTuple_SetItem(t, 0, PyInt_FromLong(1L)); PyTuple_SetItem(t, 1, PyInt_FromLong(2L)); PyTuple_SetItem(t, 2, PyString_FromString("three")); Incidentally, `PyTuple_SetItem()' is the _only_ way to set tuple items; `PySequence_SetItem()' and `PyObject_SetItem()' refuse to do this since tuples are an immutable data type. You should only use `PyTuple_SetItem()' for tuples that you are creating yourself. Equivalent code for populating a list can be written using `PyList_New()' and `PyList_SetItem()'. Such code can also use `PySequence_SetItem()'; this illustrates the difference between the two (the extra `Py_DECREF()' calls): PyObject *l, *x; l = PyList_New(3); x = PyInt_FromLong(1L); PySequence_SetItem(l, 0, x); Py_DECREF(x); x = PyInt_FromLong(2L); PySequence_SetItem(l, 1, x); Py_DECREF(x); x = PyString_FromString("three"); PySequence_SetItem(l, 2, x); Py_DECREF(x); You might find it strange that the "recommended" approach takes more code. However, in practice, you will rarely use these ways of creating and populating a tuple or list. There's a generic function, `Py_BuildValue()', that can create most common objects from C values, directed by a "format string". For example, the above two blocks of code could be replaced by the following (which also takes care of the error checking): PyObject *t, *l; t = Py_BuildValue("(iis)", 1, 2, "three"); l = Py_BuildValue("[iis]", 1, 2, "three"); It is much more common to use `PyObject_SetItem()' and friends with items whose references you are only borrowing, like arguments that were passed in to the function you are writing. In that case, their behaviour regarding reference counts is much saner, since you don't have to increment a reference count so you can give a reference away ("have it be stolen"). For example, this function sets all items of a list (actually, any mutable sequence) to a given item: int set_all(PyObject *target, PyObject *item) { int i, n; n = PyObject_Length(target); if (n < 0) return -1; for (i = 0; i < n; i++) { if (PyObject_SetItem(target, i, item) < 0) return -1; } return 0; } The situation is slightly different for function return values. While passing a reference to most functions does not change your ownership responsibilities for that reference, many functions that return a referece to an object give you ownership of the reference. The reason is simple: in many cases, the returned object is created on the fly, and the reference you get is the only reference to the object. Therefore, the generic functions that return object references, like `PyObject_GetItem()' and `PySequence_GetItem()', always return a new reference (i.e., the caller becomes the owner of the reference). It is important to realize that whether you own a reference returned by a function depends on which function you call only -- _the plumage_ (i.e., the type of the type of the object passed as an argument to the function) _doesn't enter into it!_ Thus, if you extract an item from a list using `PyList_GetItem()', you don't own the reference -- but if you obtain the same item from the same list using `PySequence_GetItem()' (which happens to take exactly the same arguments), you do own a reference to the returned object. Here is an example of how you could write a function that computes the sum of the items in a list of integers; once using `PyList_GetItem()', and once using `PySequence_GetItem()'. long sum_list(PyObject *list) { int i, n; long total = 0; PyObject *item; n = PyList_Size(list); if (n < 0) return -1; /* Not a list */ for (i = 0; i < n; i++) { item = PyList_GetItem(list, i); /* Can't fail */ if (!PyInt_Check(item)) continue; /* Skip non-integers */ total += PyInt_AsLong(item); } return total; } long sum_sequence(PyObject *sequence) { int i, n; long total = 0; PyObject *item; n = PySequence_Length(sequence); if (n < 0) return -1; /* Has no length */ for (i = 0; i < n; i++) { item = PySequence_GetItem(sequence, i); if (item == NULL) return -1; /* Not a sequence, or other failure */ if (PyInt_Check(item)) total += PyInt_AsLong(item); Py_DECREF(item); /* Discard reference ownership */ } return total; }  File: python-api.info, Node: Types, Prev: Reference Counts, Up: Objects Types ----- There are few other data types that play a significant role in the Python/C API; most are simple C types such as `int', `long', `double' and `char*'. A few structure types are used to describe static tables used to list the functions exported by a module or the data attributes of a new object type, and another is used to describe the value of a complex number. These will be discussed together with the functions that use them.  File: python-api.info, Node: Exceptions, Next: Embedding Python, Prev: Objects, Up: Introduction Exceptions ========== The Python programmer only needs to deal with exceptions if specific error handling is required; unhandled exceptions are automatically propagated to the caller, then to the caller's caller, and so on, until they reach the top-level interpreter, where they are reported to the user accompanied by a stack traceback. For C programmers, however, error checking always has to be explicit. All functions in the Python/C API can raise exceptions, unless an explicit claim is made otherwise in a function's documentation. In general, when a function encounters an error, it sets an exception, discards any object references that it owns, and returns an error indicator -- usually `NULL' or `-1'. A few functions return a Boolean true/false result, with false indicating an error. Very few functions return no explicit error indicator or have an ambiguous return value, and require explicit testing for errors with `PyErr_Occurred()'. Exception state is maintained in per-thread storage (this is equivalent to using global storage in an unthreaded application). A thread can be in one of two states: an exception has occurred, or not. The function `PyErr_Occurred()' can be used to check for this: it returns a borrowed reference to the exception type object when an exception has occurred, and `NULL' otherwise. There are a number of functions to set the exception state: `PyErr_SetString()' is the most common (though not the most general) function to set the exception state, and `PyErr_Clear()' clears the exception state. The full exception state consists of three objects (all of which can be `NULL'): the exception type, the corresponding exception value, and the traceback. These have the same meanings as the Python objects `sys.exc_type', `sys.exc_value', and `sys.exc_traceback'; however, they are not the same: the Python objects represent the last exception being handled by a Python `try' ... `except' statement, while the C level exception state only exists while an exception is being passed on between C functions until it reaches the Python bytecode interpreter's main loop, which takes care of transferring it to `sys.exc_type' and friends. Note that starting with Python 1.5, the preferred, thread-safe way to access the exception state from Python code is to call the function `sys.exc_info()', which returns the per-thread exception state for Python code. Also, the semantics of both ways to access the exception state have changed so that a function which catches an exception will save and restore its thread's exception state so as to preserve the exception state of its caller. This prevents common bugs in exception handling code caused by an innocent-looking function overwriting the exception being handled; it also reduces the often unwanted lifetime extension for objects that are referenced by the stack frames in the traceback. As a general principle, a function that calls another function to perform some task should check whether the called function raised an exception, and if so, pass the exception state on to its caller. It should discard any object references that it owns, and return an error indicator, but it should _not_ set another exception -- that would overwrite the exception that was just raised, and lose important information about the exact cause of the error. A simple example of detecting exceptions and passing them on is shown in the `sum_sequence()' example above. It so happens that that example doesn't need to clean up any owned references when it detects an error. The following example function shows some error cleanup. First, to remind you why you like Python, we show the equivalent Python code: def incr_item(dict, key): try: item = dict[key] except KeyError: item = 0 dict[key] = item + 1 Here is the corresponding C code, in all its glory: int incr_item(PyObject *dict, PyObject *key) { /* Objects all initialized to NULL for Py_XDECREF */ PyObject *item = NULL, *const_one = NULL, *incremented_item = NULL; int rv = -1; /* Return value initialized to -1 (failure) */ item = PyObject_GetItem(dict, key); if (item == NULL) { /* Handle KeyError only: */ if (!PyErr_ExceptionMatches(PyExc_KeyError)) goto error; /* Clear the error and use zero: */ PyErr_Clear(); item = PyInt_FromLong(0L); if (item == NULL) goto error; } const_one = PyInt_FromLong(1L); if (const_one == NULL) goto error; incremented_item = PyNumber_Add(item, const_one); if (incremented_item == NULL) goto error; if (PyObject_SetItem(dict, key, incremented_item) < 0) goto error; rv = 0; /* Success */ /* Continue with cleanup code */ error: /* Cleanup code, shared by success and failure path */ /* Use Py_XDECREF() to ignore NULL references */ Py_XDECREF(item); Py_XDECREF(const_one); Py_XDECREF(incremented_item); return rv; /* -1 for error, 0 for success */ } This example represents an endorsed use of the `goto' statement in C! It illustrates the use of `PyErr_ExceptionMatches()' and `PyErr_Clear()' to handle specific exceptions, and the use of `Py_XDECREF()' to dispose of owned references that may be `NULL' (note the `X' in the name; `Py_DECREF()' would crash when confronted with a `NULL' reference). It is important that the variables used to hold owned references are initialized to `NULL' for this to work; likewise, the proposed return value is initialized to `-1' (failure) and only set to success after the final call made is successful.  File: python-api.info, Node: Embedding Python, Prev: Exceptions, Up: Introduction Embedding Python ================ The one important task that only embedders (as opposed to extension writers) of the Python interpreter have to worry about is the initialization, and possibly the finalization, of the Python interpreter. Most functionality of the interpreter can only be used after the interpreter has been initialized. The basic initialization function is `Py_Initialize()'. This initializes the table of loaded modules, and creates the fundamental modules `__builtin__', `__main__' and `sys'. It also initializes the module search path (`sys.path'). `Py_Initialize()' does not set the "script argument list" (`sys.argv'). If this variable is needed by Python code that will be executed later, it must be set explicitly with a call to `PySys_SetArgv(ARGC, ARGV)' subsequent to the call to `Py_Initialize()'. On most systems (in particular, on UNIX and Windows, although the details are slightly different), `Py_Initialize()' calculates the module search path based upon its best guess for the location of the standard Python interpreter executable, assuming that the Python library is found in a fixed location relative to the Python interpreter executable. In particular, it looks for a directory named `lib/python2.1' relative to the parent directory where the executable named `python' is found on the shell command search path (the environment variable `PATH'). For instance, if the Python executable is found in `/usr/local/bin/python', it will assume that the libraries are in `/usr/local/lib/python2.1'. (In fact, this particular path is also the "fallback" location, used when no executable file named `python' is found along `PATH'.) The user can override this behavior by setting the environment variable `PYTHONHOME', or insert additional directories in front of the standard path by setting `PYTHONPATH'. The embedding application can steer the search by calling `Py_SetProgramName(FILE)' _before_ calling `Py_Initialize()'. Note that `PYTHONHOME' still overrides this and `PYTHONPATH' is still inserted in front of the standard path. An application that requires total control has to provide its own implementation of `Py_GetPath()', `Py_GetPrefix()', `Py_GetExecPrefix()', and `Py_GetProgramFullPath()' (all defined in `Modules/getpath.c'). Sometimes, it is desirable to "uninitialize" Python. For instance, the application may want to start over (make another call to `Py_Initialize()') or the application is simply done with its use of Python and wants to free all memory allocated by Python. This can be accomplished by calling `Py_Finalize()'. The function `Py_IsInitialized()' returns true if Python is currently in the initialized state. More information about these functions is given in a later chapter.  File: python-api.info, Node: Very High Level Layer, Next: Reference Counting, Prev: Introduction, Up: Top The Very High Level Layer ************************* The functions in this chapter will let you execute Python source code given in a file or a buffer, but they will not let you interact in a more detailed way with the interpreter. Several of these functions accept a start symbol from the grammar as a parameter. The available start symbols are `Py_eval_input', `Py_file_input', and `Py_single_input'. These are described following the functions which accept them as parameters. Note also that several of these functions take `FILE*' parameters. On particular issue which needs to be handled carefully is that the `FILE' structure for different C libraries can be different and incompatible. Under Windows (at least), it is possible for dynamically linked extensions to actually use different libraries, so care should be taken that `FILE*' parameters are only passed to these functions if it is certain that they were created by the same library that the Python runtime is using. `int PyRun_AnyFile(FILE *fp, char *filename)' If FP refers to a file associated with an interactive device (console or terminal input or UNIX pseudo-terminal), return the value of `PyRun_InteractiveLoop()', otherwise return the result of `PyRun_SimpleFile()'. If FILENAME is `NULL', this function uses `"???"' as the filename. `int PyRun_SimpleString(char *command)' Executes the Python source code from COMMAND in the `__main__' module. If `__main__' does not already exist, it is created. Returns `0' on success or `-1' if an exception was raised. If there was an error, there is no way to get the exception information. `int PyRun_SimpleFile(FILE *fp, char *filename)' Similar to `PyRun_SimpleString()', but the Python source code is read from FP instead of an in-memory string. FILENAME should be the name of the file. `int PyRun_InteractiveOne(FILE *fp, char *filename)' Read and execute a single statement from a file associated with an interactive device. If FILENAME is `NULL', `"???"' is used instead. The user will be prompted using `sys.ps1' and `sys.ps2'. Returns `0' when the input was executed successfully, `-1' if there was an exception, or an error code from the `errcode.h' include file distributed as part of Python in case of a parse error. (Note that `errcode.h' is not included by `Python.h', so must be included specifically if needed.) `int PyRun_InteractiveLoop(FILE *fp, char *filename)' Read and execute statements from a file associated with an interactive device until `EOF' is reached. If FILENAME is `NULL', `"???"' is used instead. The user will be prompted using `sys.ps1' and `sys.ps2'. Returns `0' at `EOF'. `struct _node* PyParser_SimpleParseString(char *str, int start)' Parse Python source code from STR using the start token START. The result can be used to create a code object which can be evaluated efficiently. This is useful if a code fragment must be evaluated many times. `struct _node* PyParser_SimpleParseFile(FILE *fp, char *filename, int start)' Similar to `PyParser_SimpleParseString()', but the Python source code is read from FP instead of an in-memory string. FILENAME should be the name of the file. `PyObject* PyRun_String(char *str, int start, PyObject *globals, PyObject *locals)' Execute Python source code from STR in the context specified by the dictionaries GLOBALS and LOCALS. The parameter START specifies the start token that should be used to parse the source code. Returns the result of executing the code as a Python object, or `NULL' if an exception was raised. `PyObject* PyRun_File(FILE *fp, char *filename, int start, PyObject *globals, PyObject *locals)' Similar to `PyRun_String()', but the Python source code is read from FP instead of an in-memory string. FILENAME should be the name of the file. `PyObject* Py_CompileString(char *str, char *filename, int start)' Parse and compile the Python source code in STR, returning the resulting code object. The start token is given by START; this can be used to constrain the code which can be compiled and should be `Py_eval_input', `Py_file_input', or `Py_single_input'. The filename specified by FILENAME is used to construct the code object and may appear in tracebacks or `SyntaxError' exception messages. This returns `NULL' if the code cannot be parsed or compiled. `int Py_eval_input' The start symbol from the Python grammar for isolated expressions; for use with `Py_CompileString()'. `int Py_file_input' The start symbol from the Python grammar for sequences of statements as read from a file or other source; for use with `Py_CompileString()'. This is the symbol to use when compiling arbitrarily long Python source code. `int Py_single_input' The start symbol from the Python grammar for a single statement; for use with `Py_CompileString()'. This is the symbol used for the interactive interpreter loop.  File: python-api.info, Node: Reference Counting, Next: Exception Handling, Prev: Very High Level Layer, Up: Top Reference Counting ****************** The macros in this section are used for managing reference counts of Python objects. `void Py_INCREF(PyObject *o)' Increment the reference count for object O. The object must not be `NULL'; if you aren't sure that it isn't `NULL', use `Py_XINCREF()'. `void Py_XINCREF(PyObject *o)' Increment the reference count for object O. The object may be `NULL', in which case the macro has no effect. `void Py_DECREF(PyObject *o)' Decrement the reference count for object O. The object must not be `NULL'; if you aren't sure that it isn't `NULL', use `Py_XDECREF()'. If the reference count reaches zero, the object's type's deallocation function (which must not be `NULL') is invoked. *Warning:* The deallocation function can cause arbitrary Python code to be invoked (e.g. when a class instance with a `__del__()' method is deallocated). While exceptions in such code are not propagated, the executed code has free access to all Python global variables. This means that any object that is reachable from a global variable should be in a consistent state before `Py_DECREF()' is invoked. For example, code to delete an object from a list should copy a reference to the deleted object in a temporary variable, update the list data structure, and then call `Py_DECREF()' for the temporary variable. `void Py_XDECREF(PyObject *o)' Decrement the reference count for object O. The object may be `NULL', in which case the macro has no effect; otherwise the effect is the same as for `Py_DECREF()', and the same warning applies. The following functions or macros are only for use within the interpreter core: `_Py_Dealloc()', `_Py_ForgetReference()', `_Py_NewReference()', as well as the global variable `_Py_RefTotal'.  File: python-api.info, Node: Exception Handling, Next: Utilities, Prev: Reference Counting, Up: Top Exception Handling ****************** The functions described in this chapter will let you handle and raise Python exceptions. It is important to understand some of the basics of Python exception handling. It works somewhat like the UNIX `errno' variable: there is a global indicator (per thread) of the last error that occurred. Most functions don't clear this on success, but will set it to indicate the cause of the error on failure. Most functions also return an error indicator, usually `NULL' if they are supposed to return a pointer, or `-1' if they return an integer (exception: the `PyArg_Parse*()' functions return `1' for success and `0' for failure). When a function must fail because some function it called failed, it generally doesn't set the error indicator; the function it called already set it. The error indicator consists of three Python objects corresponding to the Python variables `sys.exc_type', `sys.exc_value' and `sys.exc_traceback'. API functions exist to interact with the error indicator in various ways. There is a separate error indicator for each thread. `void PyErr_Print()' Print a standard traceback to `sys.stderr' and clear the error indicator. Call this function only when the error indicator is set. (Otherwise it will cause a fatal error!) `PyObject* PyErr_Occurred()' Test whether the error indicator is set. If set, return the exception _type_ (the first argument to the last call to one of the `PyErr_Set*()' functions or to `PyErr_Restore()'). If not set, return `NULL'. You do not own a reference to the return value, so you do not need to `Py_DECREF()' it. *Note:* Do not compare the return value to a specific exception; use `PyErr_ExceptionMatches()' instead, shown below. (The comparison could easily fail since the exception may be an instance instead of a class, in the case of a class exception, or it may the a subclass of the expected exception.) `int PyErr_ExceptionMatches(PyObject *exc)' Equivalent to `PyErr_GivenExceptionMatches(PyErr_Occurred(), EXC)'. This should only be called when an exception is actually set; a memory access violation will occur if no exception has been raised. `int PyErr_GivenExceptionMatches(PyObject *given, PyObject *exc)' Return true if the GIVEN exception matches the exception in EXC. If EXC is a class object, this also returns true when GIVEN is an instance of a subclass. If EXC is a tuple, all exceptions in the tuple (and recursively in subtuples) are searched for a match. If GIVEN is `NULL', a memory access violation will occur. `void PyErr_NormalizeException(PyObject**exc, PyObject**val, PyObject**tb)' Under certain circumstances, the values returned by `PyErr_Fetch()' below can be "unnormalized", meaning that `*EXC' is a class object but `*VAL' is not an instance of the same class. This function can be used to instantiate the class in that case. If the values are already normalized, nothing happens. The delayed normalization is implemented to improve performance. `void PyErr_Clear()' Clear the error indicator. If the error indicator is not set, there is no effect. `void PyErr_Fetch(PyObject **ptype, PyObject **pvalue, PyObject **ptraceback)' Retrieve the error indicator into three variables whose addresses are passed. If the error indicator is not set, set all three variables to `NULL'. If it is set, it will be cleared and you own a reference to each object retrieved. The value and traceback object may be `NULL' even when the type object is not. *Note:* This function is normally only used by code that needs to handle exceptions or by code that needs to save and restore the error indicator temporarily. `void PyErr_Restore(PyObject *type, PyObject *value, PyObject *traceback)' Set the error indicator from the three objects. If the error indicator is already set, it is cleared first. If the objects are `NULL', the error indicator is cleared. Do not pass a `NULL' type and non-`NULL' value or traceback. The exception type should be a string or class; if it is a class, the value should be an instance of that class. Do not pass an invalid exception type or value. (Violating these rules will cause subtle problems later.) This call takes away a reference to each object, i.e. you must own a reference to each object before the call and after the call you no longer own these references. (If you don't understand this, don't use this function. I warned you.) *Note:* This function is normally only used by code that needs to save and restore the error indicator temporarily. `void PyErr_SetString(PyObject *type, char *message)' This is the most common way to set the error indicator. The first argument specifies the exception type; it is normally one of the standard exceptions, e.g. `PyExc_RuntimeError'. You need not increment its reference count. The second argument is an error message; it is converted to a string object. `void PyErr_SetObject(PyObject *type, PyObject *value)' This function is similar to `PyErr_SetString()' but lets you specify an arbitrary Python object for the "value" of the exception. You need not increment its reference count. `PyObject* PyErr_Format(PyObject *exception, const char *format, ...)' This function sets the error indicator. EXCEPTION should be a Python exception (string or class, not an instance). FORMAT should be a string, containing format codes, similar to `printf'. The `width.precision' before a format code is parsed, but the width part is ignored. Character Meaning ------ ----- c Character, as an `int' parameter d Number in decimal, as an `int' parameter x Number in hexadecimal, as an `int' parameter x A string, as a `char *' parameter An unrecognized format character causes all the rest of the format string to be copied as-is to the result string, and any extra arguments discarded. A new reference is returned, which is owned by the caller. `void PyErr_SetNone(PyObject *type)' This is a shorthand for `PyErr_SetObject(TYPE, Py_None)'. `int PyErr_BadArgument()' This is a shorthand for `PyErr_SetString(PyExc_TypeError, MESSAGE)', where MESSAGE indicates that a built-in operation was invoked with an illegal argument. It is mostly for internal use. `PyObject* PyErr_NoMemory()' This is a shorthand for `PyErr_SetNone(PyExc_MemoryError)'; it returns `NULL' so an object allocation function can write `return PyErr_NoMemory();' when it runs out of memory. `PyObject* PyErr_SetFromErrno(PyObject *type)' This is a convenience function to raise an exception when a C library function has returned an error and set the C variable `errno'. It constructs a tuple object whose first item is the integer `errno' value and whose second item is the corresponding error message (gotten from `strerror()'), and then calls `PyErr_SetObject(TYPE, OBJECT)'. On UNIX, when the `errno' value is `EINTR', indicating an interrupted system call, this calls `PyErr_CheckSignals()', and if that set the error indicator, leaves it set to that. The function always returns `NULL', so a wrapper function around a system call can write `return PyErr_SetFromErrno();' when the system call returns an error. `void PyErr_BadInternalCall()' This is a shorthand for `PyErr_SetString(PyExc_TypeError, MESSAGE)', where MESSAGE indicates that an internal operation (e.g. a Python/C API function) was invoked with an illegal argument. It is mostly for internal use. `int PyErr_Warn(PyObject *category, char *message)' Issue a warning message. The CATEGORY argument is a warning category (see below) or `NULL'; the MESSAGE argument is a message string. This function normally prints a warning message to SYS.STDERR; however, it is also possible that the user has specified that warnings are to be turned into errors, and in that case this will raise an exception. It is also possible that the function raises an exception because of a problem with the warning machinery (the implementation imports the `warnings' module to do the heavy lifting). The return value is `0' if no exception is raised, or `-1' if an exception is raised. (It is not possible to determine whether a warning message is actually printed, nor what the reason is for the exception; this is intentional.) If an exception is raised, the caller should do its normal exception handling (e.g. `Py_DECREF()' owned references and return an error value). Warning categories must be subclasses of `Warning'; the default warning category is `RuntimeWarning'. The standard Python warning categories are available as global variables whose names are `PyExc_' followed by the Python exception name. These have the type `PyObject*'; they are all class objects. Their names are `PyExc_Warning', `PyExc_UserWarning', `PyExc_DeprecationWarning', `PyExc_SyntaxWarning', and `PyExc_RuntimeWarning'. `PyExc_Warning' is a subclass of `PyExc_Exception'; the other warning categories are subclasses of `PyExc_Warning'. For information about warning control, see the documentation for the `warnings' module and the `-W' option in the command line documentation. There is no C API for warning control. `int PyErr_WarnExplicit(PyObject *category, char *message, char *filename, int lineno, char *module, PyObject *registry)' Issue a warning message with explicit control over all warning attributes. This is a straightforward wrapper around the Python function `warnings.warn_explicit()', see there for more information. The MODULE and REGISTRY arguments may be set to `NULL' to get the default effect described there. `int PyErr_CheckSignals()' This function interacts with Python's signal handling. It checks whether a signal has been sent to the processes and if so, invokes the corresponding signal handler. If the `signal' module is supported, this can invoke a signal handler written in Python. In all cases, the default effect for `SIGINT' is to raise the `KeyboardInterrupt' exception. If an exception is raised the error indicator is set and the function returns `1'; otherwise the function returns `0'. The error indicator may or may not be cleared if it was previously set. `void PyErr_SetInterrupt()' This function is obsolete. It simulates the effect of a `SIGINT' signal arriving -- the next time `PyErr_CheckSignals()' is called, `KeyboardInterrupt' will be raised. It may be called without holding the interpreter lock. `PyObject* PyErr_NewException(char *name, PyObject *base, PyObject *dict)' This utility function creates and returns a new exception object. The NAME argument must be the name of the new exception, a C string of the form `module.class'. The BASE and DICT arguments are normally `NULL'. This creates a class object derived from the root for all exceptions, the built-in name `Exception' (accessible in C as `PyExc_Exception'). The `__module__' attribute of the new class is set to the first part (up to the last dot) of the NAME argument, and the class name is set to the last part (after the last dot). The BASE argument can be used to specify an alternate base class. The DICT argument can be used to specify a dictionary of class variables and methods. `void PyErr_WriteUnraisable(PyObject *obj)' This utility function prints a warning message to SYS.STDERR when an exception has been set but it is impossible for the interpreter to actually raise the exception. It is used, for example, when an exception occurs in an `__del__' method. The function is called with a single argument OBJ that identifies where the context in which the unraisable exception occurred. The repr of OBJ will be printed in the warning message. * Menu: * Standard Exceptions:: * Deprecation of String Exceptions::