8. Initialization, Finalization, and Threads
-
Initialize the Python interpreter. In an application embedding
Python, this should be called before using any other Python/C API
functions; with the exception of
Py_SetProgramName(),
PyEval_InitThreads(),
PyEval_ReleaseLock(),
and PyEval_AcquireLock().
This initializes the table of loaded modules (
sys.modules
), and creates the fundamental modules __builtin__, __main__ and sys. It also initializes the module search path (sys.path
). It does not setsys.argv
; use PySys_SetArgv() for that. This is a no-op when called for a second time (without calling Py_Finalize() first). There is no return value; it is a fatal error if the initialization fails.
- This function works like Py_Initialize() if initsigs is 1. If initsigs is 0, it skips initialization registration of signal handlers, which might be useful when Python is embedded. New in version 2.4.
- Return true (nonzero) when the Python interpreter has been initialized, false (zero) if not. After Py_Finalize() is called, this returns false until Py_Initialize() is called again.
-
Undo all initializations made by Py_Initialize() and
subsequent use of Python/C API functions, and destroy all
sub-interpreters (see Py_NewInterpreter() below) that
were created and not yet destroyed since the last call to
Py_Initialize(). Ideally, this frees all memory
allocated by the Python interpreter. This is a no-op when called
for a second time (without calling Py_Initialize() again
first). There is no return value; errors during finalization are
ignored.
This function is provided for a number of reasons. An embedding application might want to restart Python without having to restart the application itself. An application that has loaded the Python interpreter from a dynamically loadable library (or DLL) might want to free all memory allocated by Python before unloading the DLL. During a hunt for memory leaks in an application a developer might want to free all memory allocated by Python before exiting from the application.
Bugs and caveats: The destruction of modules and objects in modules is done in random order; this may cause destructors (__del__() methods) to fail when they depend on other objects (even functions) or modules. Dynamically loaded extension modules loaded by Python are not unloaded. Small amounts of memory allocated by the Python interpreter may not be freed (if you find a leak, please report it). Memory tied up in circular references between objects is not freed. Some memory allocated by extension modules may not be freed. Some extensions may not work properly if their initialization routine is called more than once; this can happen if an application calls Py_Initialize() and Py_Finalize() more than once.
-
Create a new sub-interpreter. This is an (almost) totally separate
environment for the execution of Python code. In particular, the
new interpreter has separate, independent versions of all imported
modules, including the fundamental modules
__builtin__,
__main__ and
sys. The table of loaded modules
(
sys.modules
) and the module search path (sys.path
) are also separate. The new environment has nosys.argv
variable. It has new standard I/O stream file objectssys.stdin
,sys.stdout
andsys.stderr
(however these refer to the same underlying FILE structures in the C library).The return value points to the first thread state created in the new sub-interpreter. This thread state is made in the current thread state. Note that no actual thread is created; see the discussion of thread states below. If creation of the new interpreter is unsuccessful, NULL is returned; no exception is set since the exception state is stored in the current thread state and there may not be a current thread state. (Like all other Python/C API functions, the global interpreter lock must be held before calling this function and is still held when it returns; however, unlike most other Python/C API functions, there needn't be a current thread state on entry.)
Extension modules are shared between (sub-)interpreters as follows: the first time a particular extension is imported, it is initialized normally, and a (shallow) copy of its module's dictionary is squirreled away. When the same extension is imported by another (sub-)interpreter, a new module is initialized and filled with the contents of this copy; the extension's
init
function is not called. Note that this is different from what happens when an extension is imported after the interpreter has been completely re-initialized by calling Py_Finalize() and Py_Initialize(); in that case, the extension'sinitmodule
function is called again.Bugs and caveats: Because sub-interpreters (and the main interpreter) are part of the same process, the insulation between them isn't perfect -- for example, using low-level file operations like os.close() they can (accidentally or maliciously) affect each other's open files. Because of the way extensions are shared between (sub-)interpreters, some extensions may not work properly; this is especially likely when the extension makes use of (static) global variables, or when the extension manipulates its module's dictionary after its initialization. It is possible to insert objects created in one sub-interpreter into a namespace of another sub-interpreter; this should be done with great care to avoid sharing user-defined functions, methods, instances or classes between sub-interpreters, since import operations executed by such objects may affect the wrong (sub-)interpreter's dictionary of loaded modules. (XXX This is a hard-to-fix bug that will be addressed in a future release.)
- Destroy the (sub-)interpreter represented by the given thread state. The given thread state must be the current thread state. See the discussion of thread states below. When the call returns, the current thread state is NULL. All thread states associated with this interpreter are destroyed. (The global interpreter lock must be held before calling this function and is still held when it returns.) Py_Finalize() will destroy all sub-interpreters that haven't been explicitly destroyed at that point.
-
This function should be called before
Py_Initialize() is called
for the first time, if it is called at all. It tells the
interpreter the value of the
argv[0]
argument to the main() function of the program. This is used by Py_GetPath() and some other functions below to find the Python run-time libraries relative to the interpreter executable. The default value is'python'
. The argument should point to a zero-terminated character string in static storage whose contents will not change for the duration of the program's execution. No code in the Python interpreter will change the contents of this storage.
- Return the program name set with Py_SetProgramName(), or the default. The returned string points into static storage; the caller should not modify its value.
-
Return the prefix for installed platform-independent files.
This is derived through a number of complicated rules from the
program name set with Py_SetProgramName() and some
environment variables; for example, if the program name is
'/usr/local/bin/python'
, the prefix is'/usr/local'
. The returned string points into static storage; the caller should not modify its value. This corresponds to the prefix variable in the top-level Makefile and the --prefix argument to the configure script at build time. The value is available to Python code assys.prefix
. It is only useful on Unix. See also the next function.
-
Return the exec-prefix for installed
platform-dependent files. This is derived through a number
of complicated rules from the program name set with
Py_SetProgramName() and some environment variables; for
example, if the program name is
'/usr/local/bin/python'
, the exec-prefix is'/usr/local'
. The returned string points into static storage; the caller should not modify its value. This corresponds to the exec_prefix variable in the top-level Makefile and the --exec-prefix argument to the configure script at build time. The value is available to Python code assys.exec_prefix
. It is only useful on Unix.Background: The exec-prefix differs from the prefix when platform dependent files (such as executables and shared libraries) are installed in a different directory tree. In a typical installation, platform dependent files may be installed in the /usr/local/plat subtree while platform independent may be installed in /usr/local.
Generally speaking, a platform is a combination of hardware and software families, e.g. Sparc machines running the Solaris 2.x operating system are considered the same platform, but Intel machines running Solaris 2.x are another platform, and Intel machines running Linux are yet another platform. Different major revisions of the same operating system generally also form different platforms. Non-Unix operating systems are a different story; the installation strategies on those systems are so different that the prefix and exec-prefix are meaningless, and set to the empty string. Note that compiled Python bytecode files are platform independent (but not independent from the Python version by which they were compiled!).
System administrators will know how to configure the mount or automount programs to share /usr/local between platforms while having /usr/local/plat be a different filesystem for each platform.
-
Return the full program name of the Python executable; this is
computed as a side-effect of deriving the default module search path
from the program name (set by
Py_SetProgramName() above).
The returned string points into static storage; the caller should
not modify its value. The value is available to Python code as
sys.executable
.
-
Return the default module search path; this is computed from the
program name (set by Py_SetProgramName() above) and some
environment variables. The returned string consists of a series of
directory names separated by a platform dependent delimiter
character. The delimiter character is ":" on Unixand Mac OS X,
";" on Windows. The returned string points into
static storage; the caller should not modify its value. The value
is available to Python code as the list
sys.path
, which may be modified to change the future search path for loaded modules.
-
Return the version of this Python interpreter. This is a string
that looks something like
"1.5 (#67, Dec 31 1997, 22:34:28) [GCC 2.7.2.2]"
The first word (up to the first space character) is the current Python version; the first three characters are the major and minor version separated by a period. The returned string points into static storage; the caller should not modify its value. The value is available to Python code as
sys.version
.
-
Return the platform identifier for the current platform. On Unix,
this is formed from the ``official'' name of the operating system,
converted to lower case, followed by the major revision number;
e.g., for Solaris 2.x, which is also known as SunOS 5.x, the value
is
'sunos5'
. On Mac OS X, it is'darwin'
. On Windows, it is'win'
. The returned string points into static storage; the caller should not modify its value. The value is available to Python code assys.platform
.
-
Return the official copyright string for the current Python version,
for example
'Copyright 1991-1995 Stichting Mathematisch Centrum, Amsterdam'
The returned string points into static storage; the caller should not modify its value. The value is available to Python code as
sys.copyright
.
-
Return an indication of the compiler used to build the current
Python version, in square brackets, for example:
"[GCC 2.7.2.2]"
The returned string points into static storage; the caller should not modify its value. The value is available to Python code as part of the variable
sys.version
.
-
Return information about the sequence number and build date and time
of the current Python interpreter instance, for example
"#67, Aug 1 1997, 22:34:28"
The returned string points into static storage; the caller should not modify its value. The value is available to Python code as part of the variable
sys.version
.
-
Set
sys.argv
based on argc and argv. These parameters are similar to those passed to the program's main() function with the difference that the first entry should refer to the script file to be executed rather than the executable hosting the Python interpreter. If there isn't a script that will be run, the first entry in argv can be an empty string. If this function fails to initializesys.argv
, a fatal condition is signalled using Py_FatalError().
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