Using the C API: Assorted topics
********************************

The tutorial walked you through creating a C API extension module, but
left many areas unexplained. This document looks at several concepts
that you'll need to learn in order to write more complex extensions.


Błędy i wyjątki
===============

An important convention throughout the Python interpreter is the
following: when a function fails, it should set an exception condition
and return an error value (usually "-1" or a "NULL" pointer).
Exception information is stored in three members of the interpreter's
thread state.  These are "NULL" if there is no exception.  Otherwise
they are the C equivalents of the members of the Python tuple returned
by "sys.exc_info()".  These are the exception type, exception
instance, and a traceback object.  It is important to know about them
to understand how errors are passed around.

Sprzęg języka pytonowskiego określa pewien zestaw zadań do ustawiania
różnych rodzajów  wyjątków.

The most common one is "PyErr_SetString()".  Its arguments are an
exception object and a C string.  The exception object is usually a
predefined object like "PyExc_ZeroDivisionError".  The C string
indicates the cause of the error and is converted to a Python string
object and stored as the "associated value" of the exception.

Another useful function is "PyErr_SetFromErrno()", which only takes an
exception argument and constructs the associated value by inspection
of the global variable "errno".  The most general function is
"PyErr_SetObject()", which takes two object arguments, the exception
and its associated value.  You don't need to "Py_INCREF()" the objects
passed to any of these functions.

You can test non-destructively whether an exception has been set with
"PyErr_Occurred()".  This returns the current exception object, or
"NULL" if no exception has occurred.  You normally don't need to call
"PyErr_Occurred()" to see whether an error occurred in a function
call, since you should be able to tell from the return value.

When a function *f* that calls another function *g* detects that the
latter fails, *f* should itself return an error value (usually "NULL"
or "-1").  It should *not* call one of the "PyErr_*" functions --- one
has already been called by *g*. *f*'s caller is then supposed to also
return an error indication to *its* caller, again *without* calling
"PyErr_*", and so on --- the most detailed cause of the error was
already reported by the function that first detected it.  Once the
error reaches the Python interpreter's main loop, this aborts the
currently executing Python code and tries to find an exception handler
specified by the Python programmer.

(There are situations where a module can actually give a more detailed
error message by calling another "PyErr_*" function, and in such cases
it is fine to do so.  As a general rule, however, this is not
necessary, and can cause information about the cause of the error to
be lost: most operations can fail for a variety of reasons.)

To ignore an exception set by a function call that failed, the
exception condition must be cleared explicitly by calling
"PyErr_Clear()".  The only time C code should call "PyErr_Clear()" is
if it doesn't want to pass the error on to the interpreter but wants
to handle it completely by itself (possibly by trying something else,
or pretending nothing went wrong).

Every failing "malloc()" call must be turned into an exception --- the
direct caller of "malloc()" (or "realloc()") must call
"PyErr_NoMemory()" and return a failure indicator itself.  All the
object-creating functions (for example, "PyLong_FromLong()") already
do this, so this note is only relevant to those who call "malloc()"
directly.

Also note that, with the important exception of "PyArg_ParseTuple()"
and friends, functions that return an integer status usually return a
positive value or zero for success and "-1" for failure, like Unix
system calls.

Finally, be careful to clean up garbage (by making "Py_XDECREF()" or
"Py_DECREF()" calls for objects you have already created) when you
return an error indicator!

The choice of which exception to raise is entirely yours.  There are
predeclared C objects corresponding to all built-in Python exceptions,
such as "PyExc_ZeroDivisionError", which you can use directly. Of
course, you should choose exceptions wisely --- don't use
"PyExc_TypeError" to mean that a file couldn't be opened (that should
probably be "PyExc_OSError"). If something's wrong with the argument
list, the "PyArg_ParseTuple()" function usually raises
"PyExc_TypeError".  If you have an argument whose value must be in a
particular range or must satisfy other conditions, "PyExc_ValueError"
is appropriate.

You can also define a new exception that is unique to your module. The
simplest way to do this is to declare a static global object variable
at the beginning of the file:

   static PyObject *SpamError = NULL;

and initialize it by calling "PyErr_NewException()" in the module's
"Py_mod_exec" function ("spam_module_exec()"):

   SpamError = PyErr_NewException("spam.error", NULL, NULL);

Since "SpamError" is a global variable, it will be overwritten every
time the module is reinitialized, when the "Py_mod_exec" function is
called.

For now, let's avoid the issue: we will block repeated initialization
by raising an "ImportError":

   static PyObject *SpamError = NULL;

   static int
   spam_module_exec(PyObject *m)
   {
       if (SpamError != NULL) {
           PyErr_SetString(PyExc_ImportError,
                           "cannot initialize spam module more than once");
           return -1;
       }
       SpamError = PyErr_NewException("spam.error", NULL, NULL);
       if (PyModule_AddObjectRef(m, "SpamError", SpamError) < 0) {
           return -1;
       }

       return 0;
   }

   static PyModuleDef_Slot spam_module_slots[] = {
       {Py_mod_exec, spam_module_exec},
       {0, NULL}
   };

   static struct PyModuleDef spam_module = {
       .m_base = PyModuleDef_HEAD_INIT,
       .m_name = "spam",
       .m_size = 0,  // non-negative
       .m_slots = spam_module_slots,
   };

   PyMODINIT_FUNC
   PyInit_spam(void)
   {
       return PyModuleDef_Init(&spam_module);
   }

Note that the Python name for the exception object is "spam.error".
The "PyErr_NewException()" function may create a class with the base
class being "Exception" (unless another class is passed in instead of
"NULL"), described in Built-in Exceptions.

Note also that the "SpamError" variable retains a reference to the
newly created exception class; this is intentional!  Since the
exception could be removed from the module by external code, an owned
reference to the class is needed to ensure that it will not be
discarded, causing "SpamError" to become a dangling pointer. Should it
become a dangling pointer, C code which raises the exception could
cause a core dump or other unintended side effects.

For now, the "Py_DECREF()" call to remove this reference is missing.
Even when the Python interpreter shuts down, the global "SpamError"
variable will not be garbage-collected. It will "leak". We did,
however, ensure that this will happen at most once per process.

We discuss the use of "PyMODINIT_FUNC" as a function return type later
in this sample.

The "spam.error" exception can be raised in your extension module
using a call to "PyErr_SetString()" as shown below:

   static PyObject *
   spam_system(PyObject *self, PyObject *args)
   {
       const char *command;
       int sts;

       if (!PyArg_ParseTuple(args, "s", &command))
           return NULL;
       sts = system(command);
       if (sts < 0) {
           PyErr_SetString(SpamError, "System command failed");
           return NULL;
       }
       return PyLong_FromLong(sts);
   }


Embedding an extension
======================

If you want to make your module a permanent part of the Python
interpreter, you will have to change the configuration setup and
rebuild the interpreter.  On Unix, place your file ("spammodule.c" for
example) in the "Modules/" directory of an unpacked source
distribution, add a line to the file "Modules/Setup.local" describing
your file:

   spam spammodule.o

i przebuduj program interpretujący przez uruchomienie programu
**make** w katalogu głównym instalacji. Możesz także uruchomić program
**make** w podkatalogu "Modules/", ale wtedy musisz najpierw
przebudować plik "Makefile" tam przez uruchomienie programu **make**
Makefile'. To jest konieczne za każdym razem gdy zmieniasz plik
"Setup".)

If your module requires additional libraries to link with, these can
be listed on the line in the configuration file as well, for instance:

   spam spammodule.o -lX11


Wywoływanie zadań języka pytonowskiego z C
==========================================

The tutorial concentrated on making C functions callable from Python.
The reverse is also useful: calling Python functions from C. This is
especially the case for libraries that support so-called "callback"
functions.  If a C interface makes use of callbacks, the equivalent
Python often needs to provide a callback mechanism to the Python
programmer; the implementation will require calling the Python
callback functions from a C callback.  Other uses are also imaginable.

Szczęśliwie, program interpretujący polecenia języka pytonowskiego
jest łatwo wywoływany rekursywnie i istnieje standardowy sprzęg aby
wywołać zadanie języka pytonowskiego. (Nie będę rozpisywał się o tym
jak wywołać czytnik języka pytonowskiego z konkretnym ciągiem znaków
na wejściu --- jeśli jesteś zainteresowany, spójrz na wypełnienie
opcji "-c" wiersza polecenia w "Modules/main.c" z kodu źródłowego
języka pytonowskiego.)

Calling a Python function is easy.  First, the Python program must
somehow pass you the Python function object.  You should provide a
function (or some other interface) to do this.  When this function is
called, save a pointer to the Python function object (be careful to
"Py_INCREF()" it!) in a global variable --- or wherever you see fit.
For example, the following function might be part of a module
definition:

   static PyObject *my_callback = NULL;

   static PyObject *
   my_set_callback(PyObject *dummy, PyObject *args)
   {
       PyObject *result = NULL;
       PyObject *temp;

       if (PyArg_ParseTuple(args, "O:set_callback", &temp)) {
           if (!PyCallable_Check(temp)) {
               PyErr_SetString(PyExc_TypeError, "parameter must be callable");
               return NULL;
           }
           Py_XINCREF(temp);         /* Add a reference to new callback */
           Py_XDECREF(my_callback);  /* Dispose of previous callback */
           my_callback = temp;       /* Remember new callback */
           /* Boilerplate to return "None" */
           Py_INCREF(Py_None);
           result = Py_None;
       }
       return result;
   }

This function must be registered with the interpreter using the
"METH_VARARGS" flag in "PyMethodDef.ml_flags".  The
"PyArg_ParseTuple()" function and its arguments are documented in
section Wydobywanie parametrów w zadaniach rozszerzających.

The macros "Py_XINCREF()" and "Py_XDECREF()" increment/decrement the
reference count of an object and are safe in the presence of "NULL"
pointers (but note that *temp* will not be  "NULL" in this context).
More info on them in section Liczby odniesień.

Later, when it is time to call the function, you call the C function
"PyObject_CallObject()".  This function has two arguments, both
pointers to arbitrary Python objects: the Python function, and the
argument list.  The argument list must always be a tuple object, whose
length is the number of arguments.  To call the Python function with
no arguments, pass in "NULL", or an empty tuple; to call it with one
argument, pass a singleton tuple. "Py_BuildValue()" returns a tuple
when its format string consists of zero or more format codes between
parentheses.  For example:

   int arg;
   PyObject *arglist;
   PyObject *result;
   ...
   arg = 123;
   ...
   /* Time to call the callback */
   arglist = Py_BuildValue("(i)", arg);
   result = PyObject_CallObject(my_callback, arglist);
   Py_DECREF(arglist);

"PyObject_CallObject()" returns a Python object pointer: this is the
return value of the Python function.  "PyObject_CallObject()" is
"reference-count-neutral" with respect to its arguments.  In the
example a new tuple was created to serve as the argument list, which
is "Py_DECREF()"-ed immediately after the "PyObject_CallObject()"
call.

The return value of "PyObject_CallObject()" is "new": either it is a
brand new object, or it is an existing object whose reference count
has been incremented.  So, unless you want to save it in a global
variable, you should somehow "Py_DECREF()" the result, even
(especially!) if you are not interested in its value.

Before you do this, however, it is important to check that the return
value isn't "NULL".  If it is, the Python function terminated by
raising an exception. If the C code that called
"PyObject_CallObject()" is called from Python, it should now return an
error indication to its Python caller, so the interpreter can print a
stack trace, or the calling Python code can handle the exception. If
this is not possible or desirable, the exception should be cleared by
calling "PyErr_Clear()".  For example:

   if (result == NULL)
       return NULL; /* Pass error back */
   ...use result...
   Py_DECREF(result);

Depending on the desired interface to the Python callback function,
you may also have to provide an argument list to
"PyObject_CallObject()".  In some cases the argument list is also
provided by the Python program, through the same interface that
specified the callback function.  It can then be saved and used in the
same manner as the function object.  In other cases, you may have to
construct a new tuple to pass as the argument list.  The simplest way
to do this is to call "Py_BuildValue()".  For example, if you want to
pass an integral event code, you might use the following code:

   PyObject *arglist;
   ...
   arglist = Py_BuildValue("(l)", eventcode);
   result = PyObject_CallObject(my_callback, arglist);
   Py_DECREF(arglist);
   if (result == NULL)
       return NULL; /* Pass error back */
   /* Here maybe use the result */
   Py_DECREF(result);

Note the placement of "Py_DECREF(arglist)" immediately after the call,
before the error check!  Also note that strictly speaking this code is
not complete: "Py_BuildValue()" may run out of memory, and this should
be checked.

You may also call a function with keyword arguments by using
"PyObject_Call()", which supports arguments and keyword arguments.  As
in the above example, we use "Py_BuildValue()" to construct the
dictionary.

   PyObject *dict;
   ...
   dict = Py_BuildValue("{s:i}", "name", val);
   result = PyObject_Call(my_callback, NULL, dict);
   Py_DECREF(dict);
   if (result == NULL)
       return NULL; /* Pass error back */
   /* Here maybe use the result */
   Py_DECREF(result);


Wydobywanie parametrów w zadaniach rozszerzających
==================================================

The tutorial uses a ""METH_O"" function, which is limited to a single
Python argument. If you want more, you can use "METH_VARARGS" instead.
With this flag, the C function will receive a "tuple" of arguments
instead of a single object.

For unpacking the tuple, CPython provides the "PyArg_ParseTuple()"
function, declared as follows:

   int PyArg_ParseTuple(PyObject *arg, const char *format, ...);

Parametr *arg* musi być przedmiotem - krotką zawierającym listę
parametrów z języka pytonowskiego dla zadania C. Parametr *format*
musi być ciągiem formatu, którego składnia jest wyjaśniona w
Pobieranie kolejnych rzeczy podanych na wejściu i konstruowanie
wartości. w podręczniku użytkownika API Python/C. Pozostałe parametry
muszą być adresami zmiennych których rodzaj jest określony przez ciąg
formatujący.

For example, to receive a single Python "str" object and turn it into
a C buffer, you would use ""s"" as the format string:

   const char *command;
   if (!PyArg_ParseTuple(args, "s", &command)) {
       return NULL;
   }

If an error is detected in the argument list, "PyArg_ParseTuple()"
returns "NULL" (the error indicator for functions returning object
pointers); your function may return "NULL", relying on the exception
set by "PyArg_ParseTuple()".

Note that while "PyArg_ParseTuple()" checks that the Python arguments
have the required types, it cannot check the validity of the addresses
of C variables passed to the call: if you make mistakes there, your
code will probably crash or at least overwrite random bits in memory.
So be careful!

Zauważ, że dowolne odniesienia do przedmiotów języka pytonowskiego,
które są dostarczone wołającemu są *pożyczonymi* odniesieniami; nie
zmniejszaj liczby tych odniesień.

Pewne przykładowe wywołania:

   #include <Python.h>

   int ok;
   int i, j;
   long k, l;
   const char *s;
   Py_ssize_t size;

   ok = PyArg_ParseTuple(args, ""); /* No arguments */
       /* Python call: f() */

   ok = PyArg_ParseTuple(args, "s", &s); /* A string */
       /* Possible Python call: f('whoops!') */

   ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */
       /* Possible Python call: f(1, 2, 'three') */

   ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size);
       /* A pair of ints and a string, whose size is also returned */
       /* Possible Python call: f((1, 2), 'three') */

   {
       const char *file;
       const char *mode = "r";
       int bufsize = 0;
       ok = PyArg_ParseTuple(args, "s|si", &file, &mode, &bufsize);
       /* A string, and optionally another string and an integer */
       /* Possible Python calls:
          f('spam')
          f('spam', 'w')
          f('spam', 'wb', 100000) */
   }

   {
       int left, top, right, bottom, h, v;
       ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)",
                &left, &top, &right, &bottom, &h, &v);
       /* A rectangle and a point */
       /* Possible Python call:
          f(((0, 0), (400, 300)), (10, 10)) */
   }

   {
       Py_complex c;
       ok = PyArg_ParseTuple(args, "D:myfunction", &c);
       /* a complex, also providing a function name for errors */
       /* Possible Python call: myfunction(1+2j) */
   }


Parametry kluczowe dla zadań rozszerzających
============================================

If you also want your function to accept *keyword arguments*, use the
"METH_KEYWORDS" flag in combination with "METH_VARARGS".
("METH_KEYWORDS" can also be used with other flags; see its
documentation for the allowed combinations.)

In this case, the C function should accept a third "PyObject *"
parameter which will be a dictionary of keywords. Use
"PyArg_ParseTupleAndKeywords()" to parse the arguments to such a
function.

The "PyArg_ParseTupleAndKeywords()" function is declared as follows:

   int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,
                                   const char *format, char * const *kwlist, ...);

The *arg* and *format* parameters are identical to those of the
"PyArg_ParseTuple()" function.  The *kwdict* parameter is the
dictionary of keywords received as the third parameter from the Python
runtime.  The *kwlist* parameter is a "NULL"-terminated list of
strings which identify the parameters; the names are matched with the
type information from *format* from left to right.  On success,
"PyArg_ParseTupleAndKeywords()" returns true, otherwise it returns
false and raises an appropriate exception.

Informacja:

  Zagnieżdźone krotki nie mogą być wczytane gdy używane są parametry
  słów kluczowych! Parametry słów kluczowych przekazane do zadania
  które nie są obecne na liście *kwlist* spowodują że wyjątek
  "TypeError" zostanie zgłoszony.

Tu jest przykładowy moduł który używa słów kluczowych, oparty na
przykładzie Geoffa Philbricka (philbrick@hks.com):

   #define PY_SSIZE_T_CLEAN
   #include <Python.h>

   static PyObject *
   keywdarg_parrot(PyObject *self, PyObject *args, PyObject *keywds)
   {
       int voltage;
       const char *state = "a stiff";
       const char *action = "voom";
       const char *type = "Norwegian Blue";

       static char *kwlist[] = {"voltage", "state", "action", "type", NULL};

       if (!PyArg_ParseTupleAndKeywords(args, keywds, "i|sss", kwlist,
                                        &voltage, &state, &action, &type))
           return NULL;

       printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",
              action, voltage);
       printf("-- Lovely plumage, the %s -- It's %s!\n", type, state);

       Py_RETURN_NONE;
   }

   static PyMethodDef keywdarg_methods[] = {
       /* The cast of the function is necessary since PyCFunction values
        * only take two PyObject* parameters, and keywdarg_parrot() takes
        * three.
        */
       {"parrot", (PyCFunction)(void(*)(void))keywdarg_parrot, METH_VARARGS | METH_KEYWORDS,
        "Print a lovely skit to standard output."},
       {NULL, NULL, 0, NULL}   /* sentinel */
   };


Budowanie dowolnych wartości
============================

This function is the counterpart to "PyArg_ParseTuple()".  It is
declared as follows:

   PyObject *Py_BuildValue(const char *format, ...);

It recognizes a set of format units similar to the ones recognized by
"PyArg_ParseTuple()", but the arguments (which are input to the
function, not output) must not be pointers, just values.  It returns a
new Python object, suitable for returning from a C function called
from Python.

One difference with "PyArg_ParseTuple()": while the latter requires
its first argument to be a tuple (since Python argument lists are
always represented as tuples internally), "Py_BuildValue()" does not
always build a tuple.  It builds a tuple only if its format string
contains two or more format units. If the format string is empty, it
returns "None"; if it contains exactly one format unit, it returns
whatever object is described by that format unit.  To force it to
return a tuple of size 0 or one, parenthesize the format string.

Examples (to the left the call, to the right the resulting Python
value):

   Py_BuildValue("")                        None
   Py_BuildValue("i", 123)                  123
   Py_BuildValue("iii", 123, 456, 789)      (123, 456, 789)
   Py_BuildValue("s", "hello")              'hello'
   Py_BuildValue("y", "hello")              b'hello'
   Py_BuildValue("ss", "hello", "world")    ('hello', 'world')
   Py_BuildValue("s#", "hello", 4)          'hell'
   Py_BuildValue("y#", "hello", 4)          b'hell'
   Py_BuildValue("()")                      ()
   Py_BuildValue("(i)", 123)                (123,)
   Py_BuildValue("(ii)", 123, 456)          (123, 456)
   Py_BuildValue("(i,i)", 123, 456)         (123, 456)
   Py_BuildValue("[i,i]", 123, 456)         [123, 456]
   Py_BuildValue("{s:i,s:i}",
                 "abc", 123, "def", 456)    {'abc': 123, 'def': 456}
   Py_BuildValue("((ii)(ii)) (ii)",
                 1, 2, 3, 4, 5, 6)          (((1, 2), (3, 4)), (5, 6))


Liczby odniesień
================

In languages like C or C++, the programmer is responsible for dynamic
allocation and deallocation of memory on the heap.  In C, this is done
using the functions "malloc()" and "free()".  In C++, the operators
"new" and "delete" are used with essentially the same meaning and
we'll restrict the following discussion to the C case.

Every block of memory allocated with "malloc()" should eventually be
returned to the pool of available memory by exactly one call to
"free()". It is important to call "free()" at the right time.  If a
block's address is forgotten but "free()" is not called for it, the
memory it occupies cannot be reused until the program terminates.
This is called a *memory leak*.  On the other hand, if a program calls
"free()" for a block and then continues to use the block, it creates a
conflict with reuse of the block through another "malloc()" call.
This is called *using freed memory*. It has the same bad consequences
as referencing uninitialized data --- core dumps, wrong results,
mysterious crashes.

Typowymi przyczynami wycieków pamięci są nietypowe ścieżki przejścia
przez kod. Dla przykładu, zadanie może zaalokować blok pamięci,
wykonać pewne obliczenia, a potem uwolnić ten blok jeszcze raz. Teraz
zmiana w wymaganiach dla zadania może dodać test do obliczenia który
wykrywa warunek błędu i może wrócić wcześniej z zadania. Łatwo jest
zapomnieć aby uwolnić zaalokowany blok pamięci podczas wybierania tej
drogi wcześniejszego zakończenia, szczególnie gdy jest dodawane
później do kodu. Takie przecieki, gdy raz wprowadzone, często uchodzą
niewykryte przez długi czas: błędne wyjście jest wybierane tylko w
małym wycinku wszystkich wywołań, i większość nowoczesnych maszyn ma
mnóstwo wirtualnej pamięci, tak że wyciek staje się widoczny tylko w
długo działającym procesie który używa cieknącego zadania często.
Dlatego też, jest to ważne aby zapobiegać wyciekom przed ich
nastąpieniem, przez powzięcie konwencji kodowania lub strategii która
minimalizuje ten rodzaj błędu.

Since Python makes heavy use of "malloc()" and "free()", it needs a
strategy to avoid memory leaks as well as the use of freed memory.
The chosen method is called *reference counting*.  The principle is
simple: every object contains a counter, which is incremented when a
reference to the object is stored somewhere, and which is decremented
when a reference to it is deleted. When the counter reaches zero, the
last reference to the object has been deleted and the object is freed.

An alternative strategy is called *automatic garbage collection*.
(Sometimes, reference counting is also referred to as a garbage
collection strategy, hence my use of "automatic" to distinguish the
two.)  The big advantage of automatic garbage collection is that the
user doesn't need to call "free()" explicitly.  (Another claimed
advantage is an improvement in speed or memory usage --- this is no
hard fact however.)  The disadvantage is that for C, there is no truly
portable automatic garbage collector, while reference counting can be
implemented portably (as long as the functions "malloc()" and "free()"
are available --- which the C Standard guarantees). Maybe some day a
sufficiently portable automatic garbage collector will be available
for C. Until then, we'll have to live with reference counts.

Podczas gdy język pytonowski używa tradycyjnego wypełnienia zliczania
odniesień, on także oferuje wykrywanie cykli, które pracuje aby
wykrywać cykliczne odniesienia. To pozwala aplikacjom nie martwić się
o tworzenie bezpośrednich lub pośrednich cyklicznych odniesień; to są
słabości wypełnienia zbiórki śmieci opartego jedynie na zliczaniu
odniesień. Cykle odniesień składają się z przedmiotów które zawierają
(możliwie pośrednio) odniesienia do samych siebie, tak że każdy
przedmiot w cyklu ma liczbę odniesień która jest nie-zerowa. Typowe
wypełnienia zliczające odniesienia nie są w stanie przejąć z powrotem
pamięci należącej do któregokolwiek z przedmiotów w cyklu odniesień,
ani do której odnosi się któryś z przedmiotów w cyklu, nawet jeśli nie
ma więcej odniesień do cyklu samego w sobie.

The cycle detector is able to detect garbage cycles and can reclaim
them. The "gc" module exposes a way to run the detector (the
"collect()" function), as well as configuration interfaces and the
ability to disable the detector at runtime.


Zliczanie odniesień w języku pytonowskim
----------------------------------------

There are two macros, "Py_INCREF(x)" and "Py_DECREF(x)", which handle
the incrementing and decrementing of the reference count.
"Py_DECREF()" also frees the object when the count reaches zero. For
flexibility, it doesn't call "free()" directly --- rather, it makes a
call through a function pointer in the object's *type object*.  For
this purpose (and others), every object also contains a pointer to its
type object.

The big question now remains: when to use "Py_INCREF(x)" and
"Py_DECREF(x)"? Let's first introduce some terms.  Nobody "owns" an
object; however, you can *own a reference* to an object.  An object's
reference count is now defined as the number of owned references to
it.  The owner of a reference is responsible for calling "Py_DECREF()"
when the reference is no longer needed.  Ownership of a reference can
be transferred.  There are three ways to dispose of an owned
reference: pass it on, store it, or call "Py_DECREF()". Forgetting to
dispose of an owned reference creates a memory leak.

It is also possible to *borrow* [1] a reference to an object.  The
borrower of a reference should not call "Py_DECREF()".  The borrower
must not hold on to the object longer than the owner from which it was
borrowed. Using a borrowed reference after the owner has disposed of
it risks using freed memory and should be avoided completely [2].

Zaletą pożyczania ponad posiadaniem odniesienia jest to że nie
potrzebujesz zaprzątać swojej uwagi pozbyciem się odniesienia na
wszystkich możliwych ścieżkach przejścia przez kod --- innymi słowy, z
pożyczonym odniesieniem nie musisz ryzykować wycieku gdy nastąpi
przedwczesne wyjście z programu. Wadą pożyczania ponad posiadaniem
jest to że istnieją pewne szczególne sytuacje gdzie w wydawałoby się
poprawnym kodzie pożyczone odniesienie może być użyte po tym jak
właściciel od którego zostało ono pożyczone faktycznie pozbył się go.

A borrowed reference can be changed into an owned reference by calling
"Py_INCREF()".  This does not affect the status of the owner from
which the reference was borrowed --- it creates a new owned reference,
and gives full owner responsibilities (the new owner must dispose of
the reference properly, as well as the previous owner).


Zasady właścicielskie
---------------------

Zawsze gdy odniesienie do przedmiotu jest przekazywane do lub z
zadania, jest częścią specyfiki sprzęgu zadania to czy własność jest
przekazywana z odniesieniem czy też nie.

Most functions that return a reference to an object pass on ownership
with the reference.  In particular, all functions whose function it is
to create a new object, such as "PyLong_FromLong()" and
"Py_BuildValue()", pass ownership to the receiver.  Even if the object
is not actually new, you still receive ownership of a new reference to
that object.  For instance, "PyLong_FromLong()" maintains a cache of
popular values and can return a reference to a cached item.

Many functions that extract objects from other objects also transfer
ownership with the reference, for instance "PyObject_GetAttrString()".
The picture is less clear, here, however, since a few common routines
are exceptions: "PyTuple_GetItem()", "PyList_GetItem()",
"PyDict_GetItem()", and "PyDict_GetItemString()" all return references
that you borrow from the tuple, list or dictionary.

The function "PyImport_AddModule()" also returns a borrowed reference,
even though it may actually create the object it returns: this is
possible because an owned reference to the object is stored in
"sys.modules".

When you pass an object reference into another function, in general,
the function borrows the reference from you --- if it needs to store
it, it will use "Py_INCREF()" to become an independent owner.  There
are exactly two important exceptions to this rule: "PyTuple_SetItem()"
and "PyList_SetItem()".  These functions take over ownership of the
item passed to them --- even if they fail!  (Note that
"PyDict_SetItem()" and friends don't take over ownership --- they are
"normal.")

When a C function is called from Python, it borrows references to its
arguments from the caller.  The caller owns a reference to the object,
so the borrowed reference's lifetime is guaranteed until the function
returns.  Only when such a borrowed reference must be stored or passed
on, it must be turned into an owned reference by calling
"Py_INCREF()".

Odniesienie do przedmiotu zwrócone z zadania C które jest wywołane z
poziomu języka pytonowskiego musi być posiadanym odniesieniem ---
prawo własności jest przekazywane z zadania do wywołującego to
ostatnie.


Cienki lód
----------

Istnieje kilka sytuacji gdzie wydawałoby się nieszkodliwe użycie
pożyczonych odniesień może prowadzić do kłopotów. Wszystkie one mają
do czynienia z niejawnymi wezwaniami programu interpretującego
polecenia języka pytonowskiego, które mogą powodować że właściciel
odniesienia pozbędzie się go.

The first and most important case to know about is using "Py_DECREF()"
on an unrelated object while borrowing a reference to a list item.
For instance:

   void
   bug(PyObject *list)
   {
       PyObject *item = PyList_GetItem(list, 0);

       PyList_SetItem(list, 1, PyLong_FromLong(0L));
       PyObject_Print(item, stdout, 0); /* BUG! */
   }

To zadanie najpierw pożycza odniesienie do "list[0]", potem zamienia
"list[1]" na wartość "0", i ostatecznie wypisuje pożyczone
odniesienie. Wydaje się nieszkodliwe, czyż nie? A jednak jest!

Let's follow the control flow into "PyList_SetItem()".  The list owns
references to all its items, so when item 1 is replaced, it has to
dispose of the original item 1.  Now let's suppose the original item 1
was an instance of a user-defined class, and let's further suppose
that the class defined a "__del__()" method.  If this class instance
has a reference count of 1, disposing of it will call its "__del__()"
method. Internally, "PyList_SetItem()" calls "Py_DECREF()" on the
replaced item, which invokes replaced item's corresponding
"tp_dealloc" function. During deallocation, "tp_dealloc" calls
"tp_finalize", which is mapped to the "__del__()" method for class
instances (see **PEP 442**). This entire sequence happens
synchronously within the "PyList_SetItem()" call.

Since it is written in Python, the "__del__()" method can execute
arbitrary Python code.  Could it perhaps do something to invalidate
the reference to "item" in "bug()"?  You bet!  Assuming that the list
passed into "bug()" is accessible to the "__del__()" method, it could
execute a statement to the effect of "del list[0]", and assuming this
was the last reference to that object, it would free the memory
associated with it, thereby invalidating "item".

Rozwiązanie, gdy znasz już źródło problemu, jest łatwe: tymczasowo
zwiększyć ilość odniesień. Poprawna wersja zadania równa jest:

   void
   no_bug(PyObject *list)
   {
       PyObject *item = PyList_GetItem(list, 0);

       Py_INCREF(item);
       PyList_SetItem(list, 1, PyLong_FromLong(0L));
       PyObject_Print(item, stdout, 0);
       Py_DECREF(item);
   }

This is a true story.  An older version of Python contained variants
of this bug and someone spent a considerable amount of time in a C
debugger to figure out why his "__del__()" methods would fail...

The second case of problems with a borrowed reference is a variant
involving threads.  Normally, multiple threads in the Python
interpreter can't get in each other's way, because there is a *global
lock* protecting Python's entire object space. However, it is possible
to temporarily release this lock using the macro
"Py_BEGIN_ALLOW_THREADS", and to re-acquire it using
"Py_END_ALLOW_THREADS".  This is common around blocking I/O calls, to
let other threads use the processor while waiting for the I/O to
complete. Obviously, the following function has the same problem as
the previous one:

   void
   bug(PyObject *list)
   {
       PyObject *item = PyList_GetItem(list, 0);
       Py_BEGIN_ALLOW_THREADS
       ...some blocking I/O call...
       Py_END_ALLOW_THREADS
       PyObject_Print(item, stdout, 0); /* BUG! */
   }


Puste wskaźniki (NULL)
----------------------

In general, functions that take object references as arguments do not
expect you to pass them "NULL" pointers, and will dump core (or cause
later core dumps) if you do so.  Functions that return object
references generally return "NULL" only to indicate that an exception
occurred.  The reason for not testing for "NULL" arguments is that
functions often pass the objects they receive on to other function ---
if each function were to test for "NULL", there would be a lot of
redundant tests and the code would run more slowly.

It is better to test for "NULL" only at the "source:" when a pointer
that may be "NULL" is received, for example, from "malloc()" or from a
function that may raise an exception.

The macros "Py_INCREF()" and "Py_DECREF()" do not check for "NULL"
pointers --- however, their variants "Py_XINCREF()" and "Py_XDECREF()"
do.

The macros for checking for a particular object type
("Pytype_Check()") don't check for "NULL" pointers --- again, there is
much code that calls several of these in a row to test an object
against various different expected types, and this would generate
redundant tests.  There are no variants with "NULL" checking.

The C function calling mechanism guarantees that the argument list
passed to C functions ("args" in the examples) is never "NULL" --- in
fact it guarantees that it is always a tuple [3].

It is a severe error to ever let a "NULL" pointer "escape" to the
Python user.


Pisanie rozszerzeń w C++
========================

Jest możliwe pisanie modułów rozszerzających w C++. Niektóre
ograniczenia obowiązują. Jeśli główny program (program interpretujący
polecenia języka pytonowskiego) jest kompilowany i łączony przez
kompilator języka C, nadrzędne lub statyczne przedmioty z
konstruktorami nie mogą być używane. To nie jest problemem jeśli
główny program jest łączony przez kompilator C++. Zadania które będą
wezwane przez program interpretujący polecenia języka pytonowskiego (w
szczególności, zadania inicjujące moduł) muszą być deklarowane
używając "extern "C"". Nie jest to konieczne aby zawierać plik
nagłówkowy języka pytonowskiego w "extern "C" {...}" --- one używają
już tej formy jeśli symbol "__cplusplus" jest zdefiniowany (wszystkie
niedawne kompilatory C++ definiują ten symbol).


Dostarczanie sprzęgu programowania aplikacji (API) języka C dla modułu rozszerzającego
======================================================================================

Wiele modułów rozszerzających po prostu dostarcza nowych zadań i typów
aby były używane z języka pytonowskiego, ale czasami kod w module
rozszerzającym może być użyteczny dla innych rozszerzających modułów.
Na przykład, moduł rozszerzający mógłby wypełniać typ "kolekcji" który
działałby jak lista bez wprowadzonego porządku. Tak jak standardowy
typ listy języka pytonowskiego posiada sprzęg programowania aplikacji
języka C, który pozwala modułom rozszerzającym tworzenie i zmianę
list, ten nowy typ kolekcji powinien mieć zbiór zadań C dla
bezpośrednich zmian z innych modułów rozszerzających.

Na pierwszy rzut oka to wydaje się proste: napisać zadania (bez
deklarowania ich jako "statycznych", oczywiście), dostarczyć
odpowiedni plik nagłówkowy, i udokumentować API języka C. I faktycznie
to mogłoby zadziałać jeśli wszystkie rozszerzające moduły byłyby
zawsze złączone statycznie z interpreterem Pythona. Gdy moduły są
używane jako współdzielone biblioteki, jednakże, symbole zdefiniowane
w jednym module mogą nie być widoczne dla innych modułów. Szczegóły
widoczności zależą od systemu operacyjnego; niektóre systemy używają
jednej nadrzędnej przestrzeni nazw dla interpretera Pythona i
wszystkich modułów rozszerzających (dla Windows, na przykład), podczas
gdy inne wymagają jawnej listy importowanych symboli w czasie łączenia
modułów (AIX jest jednym z przykładów), lub oferują wybór różnych
strategii (większość Unix-ów). I nawet jeśli symbole są widoczne
nadrzędnie, moduł którego zadania ktoś chciałby uruchomić mogły nie
zostać jeszcze załadowane!

Portability therefore requires not to make any assumptions about
symbol visibility. This means that all symbols in extension modules
should be declared "static", except for the module's initialization
function, in order to avoid name clashes with other extension modules.
And it means that symbols that *should* be accessible from other
extension modules must be exported in a different way.

Python provides a special mechanism to pass C-level information
(pointers) from one extension module to another one: Capsules. A
Capsule is a Python data type which stores a pointer (void*).
Capsules can only be created and accessed via their C API, but they
can be passed around like any other Python object. In particular,
they can be assigned to a name in an extension module's namespace.
Other extension modules can then import this module, retrieve the
value of this name, and then retrieve the pointer from the Capsule.

Istnieje wiele sposobów w jakie kapsuły mogą być używane aby wystawiać
na zewnątrz sprzęgi programowania aplikacji (API) języka C dla danego
modułu rozszerzającego. Każde zadanie mogłoby dostać swoją własną
kapsułę, lub wszystkie wskaźniki sprzęgu programowania aplikacji (API)
języka C mogłyby być zachowane w tabeli której adres byłby
opublikowany w kapsule. A różne zadania zachowania i odbioru
wskaźników mogłyby być rozprowadzone na różne sposoby pomiędzy moduły
dostarczające kod i moduły odbierające.

Whichever method you choose, it's important to name your Capsules
properly. The function "PyCapsule_New()" takes a name parameter (const
char*); you're permitted to pass in a "NULL" name, but we strongly
encourage you to specify a name.  Properly named Capsules provide a
degree of runtime type-safety; there is no feasible way to tell one
unnamed Capsule from another.

W szczególności, kapsułom używanym do wystawiania sprzęgów
programowania aplikacji języka C ( - z ang. - API) powinna być nadana
nazwa stosująca się do następującej konwencji:

   modulename.attributename

The convenience function "PyCapsule_Import()" makes it easy to load a
C API provided via a Capsule, but only if the Capsule's name matches
this convention.  This behavior gives C API users a high degree of
certainty that the Capsule they load contains the correct C API.

The following example demonstrates an approach that puts most of the
burden on the writer of the exporting module, which is appropriate for
commonly used library modules. It stores all C API pointers (just one
in the example!) in an array of void pointers which becomes the value
of a Capsule. The header file corresponding to the module provides a
macro that takes care of importing the module and retrieving its C API
pointers; client modules only have to call this macro before accessing
the C API.

The exporting module is a modification of the "spam" module from the
tutorial. The function "spam.system()" does not call the C library
function "system()" directly, but a function "PySpam_System()", which
would of course do something more complicated in reality (such as
adding "spam" to every command). This function "PySpam_System()" is
also exported to other extension modules.

The function "PySpam_System()" is a plain C function, declared
"static" like everything else:

   static int
   PySpam_System(const char *command)
   {
       return system(command);
   }

The function "spam_system()" is modified in a trivial way:

   static PyObject *
   spam_system(PyObject *self, PyObject *args)
   {
       const char *command;
       int sts;

       if (!PyArg_ParseTuple(args, "s", &command))
           return NULL;
       sts = PySpam_System(command);
       return PyLong_FromLong(sts);
   }

Na początku modułu, zaraz za linią

   #include <Python.h>

muszą być dodane dwie linie:

   #define SPAM_MODULE
   #include "spammodule.h"

The "#define" is used to tell the header file that it is being
included in the exporting module, not a client module. Finally, the
module's "mod_exec" function must take care of initializing the C API
pointer array:

   static int
   spam_module_exec(PyObject *m)
   {
       static void *PySpam_API[PySpam_API_pointers];
       PyObject *c_api_object;

       /* Initialize the C API pointer array */
       PySpam_API[PySpam_System_NUM] = (void *)PySpam_System;

       /* Create a Capsule containing the API pointer array's address */
       c_api_object = PyCapsule_New((void *)PySpam_API, "spam._C_API", NULL);

       if (PyModule_Add(m, "_C_API", c_api_object) < 0) {
           return -1;
       }

       return 0;
   }

Note that "PySpam_API" is declared "static"; otherwise the pointer
array would disappear when "PyInit_spam()" terminates!

Większa część pracy jest wykonywana w pliku nagłówkowym
"spammodule.h", który wygląda następująco:

   #ifndef Py_SPAMMODULE_H
   #define Py_SPAMMODULE_H
   #ifdef __cplusplus
   extern "C" {
   #endif

   /* Header file for spammodule */

   /* C API functions */
   #define PySpam_System_NUM 0
   #define PySpam_System_RETURN int
   #define PySpam_System_PROTO (const char *command)

   /* Total number of C API pointers */
   #define PySpam_API_pointers 1


   #ifdef SPAM_MODULE
   /* This section is used when compiling spammodule.c */

   static PySpam_System_RETURN PySpam_System PySpam_System_PROTO;

   #else
   /* This section is used in modules that use spammodule's API */

   static void **PySpam_API;

   #define PySpam_System \
    (*(PySpam_System_RETURN (*)PySpam_System_PROTO) PySpam_API[PySpam_System_NUM])

   /* Return -1 on error, 0 on success.
    * PyCapsule_Import will set an exception if there's an error.
    */
   static int
   import_spam(void)
   {
       PySpam_API = (void **)PyCapsule_Import("spam._C_API", 0);
       return (PySpam_API != NULL) ? 0 : -1;
   }

   #endif

   #ifdef __cplusplus
   }
   #endif

   #endif /* !defined(Py_SPAMMODULE_H) */

All that a client module must do in order to have access to the
function "PySpam_System()" is to call the function (or rather macro)
"import_spam()" in its "mod_exec" function:

   static int
   client_module_exec(PyObject *m)
   {
       if (import_spam() < 0) {
           return -1;
       }
       /* additional initialization can happen here */
       return 0;
   }

Główną wadą tego podejścia jest to, że plik "spammodule.h" jest raczej
skomplikowany. Jednakże podstawowa struktura jest taka sama dla
każdego zadania które jest wystawiane na zewnątrz więc trzeba się tego
uczyć tylko raz.

Ostatecznie warto wspomnieć, że kapsuły dają dodatkowe możliwości
działania, które są szczególnie użyteczne dla umieszczania i
zabierania miejsca w pamięci wskaźników zachowywanych w kapsule.
Szczegóły są opisane w podręczniku użytkownika API Python/C w
rozdziale Capsules i w wypełnieniu programowym kapsuł (plików
"Include/pycapsule.h" i "Objects/pycapsule.c" w dystrybucji źródłowej
kodu Pythona).

-[ Przypisy ]-

[1] Metafora "pożyczania" odniesienia nie jest do końca poprawna:
    właściciel wciąż ma kopię odniesienia.

[2] Sprawdzanie że liczba odniesień jest przynajmniej 1 **nie działa**
    --- liczba odniesień sama w sobie może być w uwolnionej pamięci i
    dlatego może być ponownie użyta dla innego przedmiotu!

[3] Te gwarancje nie są w mocy gdy używasz "starego" sposobu
    wywoływania --- to jest wciąż znajdowane w dużej części
    istniejącego kodu.
