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# 概述
Python 的应用编程接口（API）使得 C 和 C++ 程序员可以在多个层级上访问 Python 解释器。该 API 在 C++ 中同样可用，但为简化描述，通常将其称为 Python/C API。使用 Python/C API 有两个基本的理由。第一个理由是为了特定目的而编写 *扩展模块*；它们是扩展 Python 解释器功能的 C 模块。这可能是最常见的使用场景。第二个理由是将 Python 用作更大规模应用的组件；这种技巧通常被称为在一个应用中 *embedding* Python。
编写扩展模块的过程相对来说更易于理解，可以通过“菜谱”的形式分步骤介绍。使用某些工具可在一定程度上自动化这一过程。虽然人们在其他应用中嵌入 Python 的做法早已有之，但嵌入 Python 的过程没有编写扩展模块那样方便直观。
许多 API 函数在你嵌入或是扩展 Python 这两种场景下都能发挥作用；此外，大多数嵌入 Python 的应用程序也需要提供自定义扩展，因此在尝试在实际应用中嵌入 Python 之前先熟悉编写扩展应该会是个好主意。
## 代码标准
如果你想要编写可包含于 CPython 的 C 代码，你 **必须** 遵循在 [**PEP 7**](https://www.python.org/dev/peps/pep-0007) \[https://www.python.org/dev/peps/pep-0007\] 中定义的指导原则和标准。这些指导原则适用于任何你所要扩展的 Python 版本。在编写你自己的第三方扩展模块时可以不必遵循这些规范，除非你准备在日后向 Python 贡献这些模块。
## 包含文件
使用 Python/C API 所需要的全部函数、类型和宏定义可通过下面这行语句包含到你的代码之中：
```
#define PY_SSIZE_T_CLEAN
#include <Python.h>
```
这意味着包含以下标准头文件：`<stdio.h>`，`<string.h>`，`<errno.h>`，`<limits.h>`，`<assert.h>` 和 `<stdlib.h>`（如果可用）。
注解
由于 Python 可能会定义一些能在某些系统上影响标准头文件的预处理器定义，因此在包含任何标准头文件之前，你 *必须* 先包含 `Python.h`。
It is recommended to always define `PY_SSIZE_T_CLEAN` before including `Python.h`. See [语句解释及变量编译](arg.xhtml#arg-parsing) for a description of this macro.
Python.h 所定义的全部用户可见名称（由包含的标准头文件所定义的除外）都带有前缀 `Py` 或者 `_Py`。以 `_Py` 打头的名称是供 Python 实现内部使用的，不应被扩展编写者使用。结构成员名称没有保留前缀。
**重要：** 用户代码永远不应该定义以 `Py` `或 ``_Py` 开头的名称。这会使读者感到困惑，并危及用户代码对未来 Python 版本的可移植性，因为未来版本可能会额外定义以这些前缀之一开头的名称。
头文件通常会与 Python 一起安装。在 Unix 上，它们位于以下目录：`prefix/include/pythonversion/` 和 `exec_prefix/include/pythonversion/`，其中 `prefix` 和 `exec_prefix` 是由向 Python 的 **configure** 脚本传入的对应形参所定义，而 *version* 则为 `'%d.%d' % sys.version_info[:2]`。在 Windows 上，头文件安装于 `prefix/include`，其中 `prefix` 是向安装程序指定的安装目录。
要包含头文件，请将两个目录（如果不同）都放到你所用编译器的包含搜索路径中。请 *不要* 将父目录放入搜索路径然后使用 `#include <pythonX.Y/Python.h>`；这将使得多平台编译不可用，因为 `prefix` 下平台无关的头文件需要包含来自 `exec_prefix` 下特定平台的头文件。
C++ 用户应该注意，尽管 API 是完全使用 C 来定义的，但头文件正确地将入口点声明为 `extern "C"`，因此 API 在 C++ 中使用此 API 不必再做任何特殊处理。
## 有用的宏
Python 头文件中定义了一些有用的宏。许多是在靠近它们被使用的地方定义的（例如 [`Py_RETURN_NONE`](none.xhtml#c.Py_RETURN_NONE "Py_RETURN_NONE")）。其他更为通用的则定义在这里。这里所显示的并不是一个完整的列表。
`Py_UNREACHABLE`()这个可以在你有一个不打算被触及的代码路径时使用。例如，当一个 `switch` 语句中所有可能的值都已被 `case` 子句覆盖了，就可将其用在 `default:` 子句中。当你非常想在某个位置放一个 `assert(0)` 或 `abort()` 调用时也可以用这个。
3\.7 新版功能.
`Py_ABS`(x)返回 `x` 的绝对值。
3\.3 新版功能.
`Py_MIN`(x, y)返回 `x` 和 `y` 当中的最小值。
3\.3 新版功能.
`Py_MAX`(x, y)返回 `x` 和 `y` 当中的最大值。
3\.3 新版功能.
`Py_STRINGIFY`(x)将 `x` 转换为 C 字符串。例如 `Py_STRINGIFY(123)` 返回 `"123"`。
3\.4 新版功能.
`Py_MEMBER_SIZE`(type, member)返回结构 (`type`) `member` 的大小，以字节表示。
3\.6 新版功能.
`Py_CHARMASK`(c)参数必须为 \[-128, 127\] 或 \[0, 255\] 范围内的字符或整数类型。这个宏将 `c` 强制转换为 `unsigned char` 返回。
`Py_GETENV`(s)类似 `getenv(s)` 但会在以下情况返回 *NULL* ：如果通过命令行传入 [`-E`](../using/cmdline.xhtml#cmdoption-e) （即设置了 `Py_IgnoreEnvironmentFlag` ）。
`Py_UNUSED`(arg)这个可用于函数定义中未使用的参数以隐藏编译器警告，例如 `PyObject* func(PyObject *Py_UNUSED(ignored))`。
3\.4 新版功能.
## 对象、类型和引用计数
大多数 Python/C API 函数都有一个或多个参数以及一个 [`PyObject*`](structures.xhtml#c.PyObject "PyObject") 类型的返回值。此类型是一个指针，指向表示一个任意 Python 对象的不透明数据类型。由于在大多数情况下（例如赋值、作用域规则和参数传递） Python 语言都会以同样的方式处理所有 Python 对象类型，因此它们由一个单独的 C 类型来表示是很适宜的。几乎所有 Python 对象都生存在堆上：你绝不会声明一个 [`PyObject`](structures.xhtml#c.PyObject "PyObject") 类型的自动或静态变量，只有 [`PyObject*`](structures.xhtml#c.PyObject "PyObject") 类型的指针变量可以被声明。唯一的例外是 type 对象；由于此种对象永远不能被释放，所以它们通常是静态 [`PyTypeObject`](type.xhtml#c.PyTypeObject "PyTypeObject") 对象。
所有 Python 对象（甚至 Python 整数）都有一个 *type* 和一个 *reference count*。对象的类型确定它是什么类型的对象（例如整数、列表或用户定义函数；还有更多，如 [标准类型层级结构](../reference/datamodel.xhtml#types) 中所述）。对于每个众所周知的类型，都有一个宏来检查对象是否属于该类型；例如，当（且仅当） *a* 所指的对象是 Python 列表时 `PyList_Check(a)` 为真。
### 引用计数
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()`](refcounting.xhtml#c.Py_INCREF "Py_INCREF") to increment an object's reference count by one, and [`Py_DECREF()`](refcounting.xhtml#c.Py_DECREF "Py_DECREF") to decrement it by one. The [`Py_DECREF()`](refcounting.xhtml#c.Py_DECREF "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(Py_ssize_t) >= sizeof(void*)`). 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()`](refcounting.xhtml#c.Py_DECREF "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()`](refcounting.xhtml#c.Py_DECREF "Py_DECREF") when they are done with the result; this soon becomes second nature.
#### Reference Count Details
The reference count behavior of functions in the Python/C API is best explained in terms of *ownership of references*. Ownership pertains to references, never to objects (objects are not owned: they are always shared). "Owning a reference" means being responsible for calling Py\_DECREF on it when the reference is no longer needed. Ownership can also be transferred, meaning that the code that receives ownership of the reference then becomes responsible for eventually decref'ing it by calling [`Py_DECREF()`](refcounting.xhtml#c.Py_DECREF "Py_DECREF") or [`Py_XDECREF()`](refcounting.xhtml#c.Py_XDECREF "Py_XDECREF")when it's no longer needed---or passing on this responsibility (usually to its caller). 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 in a reference to an object, there are two possibilities: the function *steals* a reference to the object, or it does not. *Stealing a reference* means that when you pass a reference to a function, that function assumes that it now owns that reference, and you are not responsible for it any longer.
Few functions steal references; the two notable exceptions are [`PyList_SetItem()`](list.xhtml#c.PyList_SetItem "PyList_SetItem") and [`PyTuple_SetItem()`](tuple.xhtml#c.PyTuple_SetItem "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, PyLong_FromLong(1L));
PyTuple_SetItem(t, 1, PyLong_FromLong(2L));
PyTuple_SetItem(t, 2, PyUnicode_FromString("three"));
```
Here, [`PyLong_FromLong()`](long.xhtml#c.PyLong_FromLong "PyLong_FromLong") returns a new reference which is immediately stolen by [`PyTuple_SetItem()`](tuple.xhtml#c.PyTuple_SetItem "PyTuple_SetItem"). When you want to keep using an object although the reference to it will be stolen, use [`Py_INCREF()`](refcounting.xhtml#c.Py_INCREF "Py_INCREF") to grab another reference before calling the reference-stealing function.
Incidentally, [`PyTuple_SetItem()`](tuple.xhtml#c.PyTuple_SetItem "PyTuple_SetItem") is the *only* way to set tuple items; [`PySequence_SetItem()`](sequence.xhtml#c.PySequence_SetItem "PySequence_SetItem") and [`PyObject_SetItem()`](object.xhtml#c.PyObject_SetItem "PyObject_SetItem") refuse to do this since tuples are an immutable data type. You should only use [`PyTuple_SetItem()`](tuple.xhtml#c.PyTuple_SetItem "PyTuple_SetItem") for tuples that you are creating yourself.
Equivalent code for populating a list can be written using [`PyList_New()`](list.xhtml#c.PyList_New "PyList_New")and [`PyList_SetItem()`](list.xhtml#c.PyList_SetItem "PyList_SetItem").
However, in practice, you will rarely use these ways of creating and populating a tuple or list. There's a generic function, [`Py_BuildValue()`](arg.xhtml#c.Py_BuildValue "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 *tuple, *list;
tuple = Py_BuildValue("(iis)", 1, 2, "three");
list = Py_BuildValue("[iis]", 1, 2, "three");
```
It is much more common to use [`PyObject_SetItem()`](object.xhtml#c.PyObject_SetItem "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)
{
Py_ssize_t i, n;
n = PyObject_Length(target);
if (n < 0)
return -1;
for (i = 0; i < n; i++) {
PyObject *index = PyLong_FromSsize_t(i);
if (!index)
return -1;
if (PyObject_SetItem(target, index, item) < 0) {
Py_DECREF(index);
return -1;
}
Py_DECREF(index);
}
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 reference 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()`](object.xhtml#c.PyObject_GetItem "PyObject_GetItem") and [`PySequence_GetItem()`](sequence.xhtml#c.PySequence_GetItem "PySequence_GetItem"), always return a new reference (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* (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()`](list.xhtml#c.PyList_GetItem "PyList_GetItem"), you don't own the reference --- but if you obtain the same item from the same list using [`PySequence_GetItem()`](sequence.xhtml#c.PySequence_GetItem "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()`](list.xhtml#c.PyList_GetItem "PyList_GetItem"), and once using [`PySequence_GetItem()`](sequence.xhtml#c.PySequence_GetItem "PySequence_GetItem").
```
long
sum_list(PyObject *list)
{
Py_ssize_t i, n;
long total = 0, value;
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 (!PyLong_Check(item)) continue; /* Skip non-integers */
value = PyLong_AsLong(item);
if (value == -1 && PyErr_Occurred())
/* Integer too big to fit in a C long, bail out */
return -1;
total += value;
}
return total;
}
```
```
long
sum_sequence(PyObject *sequence)
{
Py_ssize_t i, n;
long total = 0, value;
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 (PyLong_Check(item)) {
value = PyLong_AsLong(item);
Py_DECREF(item);
if (value == -1 && PyErr_Occurred())
/* Integer too big to fit in a C long, bail out */
return -1;
total += value;
}
else {
Py_DECREF(item); /* Discard reference ownership */
}
}
return total;
}
```
### 类型
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.
## 异常
Python程序员只需要处理特定需要处理的错误异常；未处理的异常会自动传递给调用者，然后传递给调用者的调用者，依此类推，直到他们到达顶级解释器，在那里将它们报告给用户并伴随堆栈回溯。
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. If not documented otherwise, this indicator is either *NULL* or `-1`, depending on the function's return type. 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()`](exceptions.xhtml#c.PyErr_Occurred "PyErr_Occurred"). These exceptions are always explicitly documented.
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()`](exceptions.xhtml#c.PyErr_Occurred "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()`](exceptions.xhtml#c.PyErr_SetString "PyErr_SetString") is the most common (though not the most general) function to set the exception state, and [`PyErr_Clear()`](exceptions.xhtml#c.PyErr_Clear "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 result of `sys.exc_info()`; however, they are not the same: the Python objects represent the last exception being handled by a Python [`try`](../reference/compound_stmts.xhtml#try) ... [`except`](../reference/compound_stmts.xhtml#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_info()` 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()`](../library/sys.xhtml#sys.exc_info "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 this 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 = PyLong_FromLong(0L);
if (item == NULL)
goto error;
}
const_one = PyLong_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()`](exceptions.xhtml#c.PyErr_ExceptionMatches "PyErr_ExceptionMatches") and [`PyErr_Clear()`](exceptions.xhtml#c.PyErr_Clear "PyErr_Clear") to handle specific exceptions, and the use of [`Py_XDECREF()`](refcounting.xhtml#c.Py_XDECREF "Py_XDECREF") to dispose of owned references that may be *NULL* (note the `'X'` in the name; [`Py_DECREF()`](refcounting.xhtml#c.Py_DECREF "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.
## 嵌入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()`](init.xhtml#c.Py_Initialize "Py_Initialize"). This initializes the table of loaded modules, and creates the fundamental modules [`builtins`](../library/builtins.xhtml#module-builtins "builtins: The module that provides the built-in namespace."), [`__main__`](../library/__main__.xhtml#module-__main__ "__main__: The environment where the top-level script is run."), and [`sys`](../library/sys.xhtml#module-sys "sys: Access system-specific parameters and functions."). It also initializes the module search path (`sys.path`).
[`Py_Initialize()`](init.xhtml#c.Py_Initialize "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_SetArgvEx(argc, argv, updatepath)`after the call to [`Py_Initialize()`](init.xhtml#c.Py_Initialize "Py_Initialize").
On most systems (in particular, on Unix and Windows, although the details are slightly different), [`Py_Initialize()`](init.xhtml#c.Py_Initialize "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/pythonX.Y` 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/pythonX.Y`. (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`](../using/cmdline.xhtml#envvar-PYTHONHOME), or insert additional directories in front of the standard path by setting [`PYTHONPATH`](../using/cmdline.xhtml#envvar-PYTHONPATH).
The embedding application can steer the search by calling `Py_SetProgramName(file)` *before* calling [`Py_Initialize()`](init.xhtml#c.Py_Initialize "Py_Initialize"). Note that [`PYTHONHOME`](../using/cmdline.xhtml#envvar-PYTHONHOME) still overrides this and [`PYTHONPATH`](../using/cmdline.xhtml#envvar-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()`](init.xhtml#c.Py_GetPath "Py_GetPath"), [`Py_GetPrefix()`](init.xhtml#c.Py_GetPrefix "Py_GetPrefix"), [`Py_GetExecPrefix()`](init.xhtml#c.Py_GetExecPrefix "Py_GetExecPrefix"), and [`Py_GetProgramFullPath()`](init.xhtml#c.Py_GetProgramFullPath "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()`](init.xhtml#c.Py_Initialize "Py_Initialize")) or the application is simply done with its use of Python and wants to free memory allocated by Python. This can be accomplished by calling [`Py_FinalizeEx()`](init.xhtml#c.Py_FinalizeEx "Py_FinalizeEx"). The function [`Py_IsInitialized()`](init.xhtml#c.Py_IsInitialized "Py_IsInitialized") returns true if Python is currently in the initialized state. More information about these functions is given in a later chapter. Notice that [`Py_FinalizeEx()`](init.xhtml#c.Py_FinalizeEx "Py_FinalizeEx")does *not* free all memory allocated by the Python interpreter, e.g. memory allocated by extension modules currently cannot be released.
## 调试构建
Python can be built with several macros to enable extra checks of the interpreter and extension modules. These checks tend to add a large amount of overhead to the runtime so they are not enabled by default.
A full list of the various types of debugging builds is in the file `Misc/SpecialBuilds.txt` in the Python source distribution. Builds are available that support tracing of reference counts, debugging the memory allocator, or low-level profiling of the main interpreter loop. Only the most frequently-used builds will be described in the remainder of this section.
Compiling the interpreter with the `Py_DEBUG` macro defined produces what is generally meant by "a debug build" of Python. `Py_DEBUG` is enabled in the Unix build by adding `--with-pydebug` to the `./configure` command. It is also implied by the presence of the not-Python-specific `_DEBUG` macro. When `Py_DEBUG` is enabled in the Unix build, compiler optimization is disabled.
除了前面描述的引用计数调试之外，还执行以下额外检查：
- 额外检查将添加到对象分配器。
- 额外的检查将添加到解析器和编译器中。
- Downcasts from wide types to narrow types are checked for loss of information.
- 许多断言被添加到字典和集合实现中。另外，集合对象需要 `test_c_api()` 方法。
- 输入参数的完整性检查被添加到框架创建中。
- 使用已知的无效模式初始化整型的存储，以捕获对未初始化数字的引用。
- 添加底层跟踪和额外的异常检查到虚拟机的运行时中。
- Extra checks are added to the memory arena implementation.
- 添加额外调试到线程模块。
这里可能没有提到的额外的检查。
Defining `Py_TRACE_REFS` enables reference tracing. When defined, a circular doubly linked list of active objects is maintained by adding two extra fields to every [`PyObject`](structures.xhtml#c.PyObject "PyObject"). Total allocations are tracked as well. Upon exit, all existing references are printed. (In interactive mode this happens after every statement run by the interpreter.) Implied by `Py_DEBUG`.
有关更多详细信息，请参阅Python源代码中的 `Misc/SpecialBuilds.txt` 。
### 导航
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- 模块 difflib 是一个计算差异的助手
- textwrap — Text wrapping and filling
- unicodedata — Unicode 数据库
- stringprep — Internet String Preparation
- readline — GNU readline interface
- rlcompleter — GNU readline的完成函数
- 二进制数据服务
- struct — Interpret bytes as packed binary data
- codecs — Codec registry and base classes
- 数据类型
- datetime — 基础日期/时间数据类型
- calendar — General calendar-related functions
- collections — 容器数据类型
- collections.abc — 容器的抽象基类
- heapq — 堆队列算法
- bisect — Array bisection algorithm
- array — Efficient arrays of numeric values
- weakref — 弱引用
- types — Dynamic type creation and names for built-in types
- copy — 浅层 (shallow) 和深层 (deep) 复制操作
- pprint — 数据美化输出
- reprlib — Alternate repr() implementation
- enum — Support for enumerations
- 数字和数学模块
- numbers — 数字的抽象基类
- math — 数学函数
- cmath — Mathematical functions for complex numbers
- decimal — 十进制定点和浮点运算
- fractions — 分数
- random — 生成伪随机数
- statistics — Mathematical statistics functions
- 函数式编程模块
- itertools — 为高效循环而创建迭代器的函数
- functools — 高阶函数和可调用对象上的操作
- operator — 标准运算符替代函数
- 文件和目录访问
- pathlib — 面向对象的文件系统路径
- os.path — 常见路径操作
- fileinput — Iterate over lines from multiple input streams
- stat — Interpreting stat() results
- filecmp — File and Directory Comparisons
- tempfile — Generate temporary files and directories
- glob — Unix style pathname pattern expansion
- fnmatch — Unix filename pattern matching
- linecache — Random access to text lines
- shutil — High-level file operations
- macpath — Mac OS 9 路径操作函数
- 数据持久化
- pickle —— Python 对象序列化
- copyreg — Register pickle support functions
- shelve — Python object persistence
- marshal — Internal Python object serialization
- dbm — Interfaces to Unix “databases”
- sqlite3 — SQLite 数据库 DB-API 2.0 接口模块
- 数据压缩和存档
- zlib — 与 gzip 兼容的压缩
- gzip — 对 gzip 格式的支持
- bz2 — 对 bzip2 压缩算法的支持
- lzma — 用 LZMA 算法压缩
- zipfile — 在 ZIP 归档中工作
- tarfile — Read and write tar archive files
- 文件格式
- csv — CSV 文件读写
- configparser — Configuration file parser
- netrc — netrc file processing
- xdrlib — Encode and decode XDR data
- plistlib — Generate and parse Mac OS X .plist files
- 加密服务
- hashlib — 安全哈希与消息摘要
- hmac — 基于密钥的消息验证
- secrets — Generate secure random numbers for managing secrets
- 通用操作系统服务
- os — 操作系统接口模块
- io — 处理流的核心工具
- time — 时间的访问和转换
- argparse — 命令行选项、参数和子命令解析器
- getopt — C-style parser for command line options
- 模块 logging — Python 的日志记录工具
- logging.config — 日志记录配置
- logging.handlers — Logging handlers
- getpass — 便携式密码输入工具
- curses — 终端字符单元显示的处理
- curses.textpad — Text input widget for curses programs
- curses.ascii — Utilities for ASCII characters
- curses.panel — A panel stack extension for curses
- platform — Access to underlying platform's identifying data
- errno — Standard errno system symbols
- ctypes — Python 的外部函数库
- 并发执行
- threading — 基于线程的并行
- multiprocessing — 基于进程的并行
- concurrent 包
- concurrent.futures — 启动并行任务
- subprocess — 子进程管理
- sched — 事件调度器
- queue — 一个同步的队列类
- _thread — 底层多线程 API
- _dummy_thread — _thread 的替代模块
- dummy_threading — 可直接替代 threading 模块。
- contextvars — Context Variables
- Context Variables
- Manual Context Management
- asyncio support
- 网络和进程间通信
- asyncio — 异步 I/O
- socket — 底层网络接口
- ssl — TLS/SSL wrapper for socket objects
- select — Waiting for I/O completion
- selectors — 高级 I/O 复用库
- asyncore — 异步socket处理器
- asynchat — 异步 socket 指令/响应 处理器
- signal — Set handlers for asynchronous events
- mmap — Memory-mapped file support
- 互联网数据处理
- email — 电子邮件与 MIME 处理包
- json — JSON 编码和解码器
- mailcap — Mailcap file handling
- mailbox — Manipulate mailboxes in various formats
- mimetypes — Map filenames to MIME types
- base64 — Base16, Base32, Base64, Base85 数据编码
- binhex — 对binhex4文件进行编码和解码
- binascii — 二进制和 ASCII 码互转
- quopri — Encode and decode MIME quoted-printable data
- uu — Encode and decode uuencode files
- 结构化标记处理工具
- html — 超文本标记语言支持
- html.parser — 简单的 HTML 和 XHTML 解析器
- html.entities — HTML 一般实体的定义
- XML处理模块
- xml.etree.ElementTree — The ElementTree XML API
- xml.dom — The Document Object Model API
- xml.dom.minidom — Minimal DOM implementation
- xml.dom.pulldom — Support for building partial DOM trees
- xml.sax — Support for SAX2 parsers
- xml.sax.handler — Base classes for SAX handlers
- xml.sax.saxutils — SAX Utilities
- xml.sax.xmlreader — Interface for XML parsers
- xml.parsers.expat — Fast XML parsing using Expat
- 互联网协议和支持
- webbrowser — 方便的Web浏览器控制器
- cgi — Common Gateway Interface support
- cgitb — Traceback manager for CGI scripts
- wsgiref — WSGI Utilities and Reference Implementation
- urllib — URL 处理模块
- urllib.request — 用于打开 URL 的可扩展库
- urllib.response — Response classes used by urllib
- urllib.parse — Parse URLs into components
- urllib.error — Exception classes raised by urllib.request
- urllib.robotparser — Parser for robots.txt
- http — HTTP 模块
- http.client — HTTP协议客户端
- ftplib — FTP protocol client
- poplib — POP3 protocol client
- imaplib — IMAP4 protocol client
- nntplib — NNTP protocol client
- smtplib —SMTP协议客户端
- smtpd — SMTP Server
- telnetlib — Telnet client
- uuid — UUID objects according to RFC 4122
- socketserver — A framework for network servers
- http.server — HTTP 服务器
- http.cookies — HTTP state management
- http.cookiejar — Cookie handling for HTTP clients
- xmlrpc — XMLRPC 服务端与客户端模块
- xmlrpc.client — XML-RPC client access
- xmlrpc.server — Basic XML-RPC servers
- ipaddress — IPv4/IPv6 manipulation library
- 多媒体服务
- audioop — Manipulate raw audio data
- aifc — Read and write AIFF and AIFC files
- sunau — 读写 Sun AU 文件
- wave — 读写WAV格式文件
- chunk — Read IFF chunked data
- colorsys — Conversions between color systems
- imghdr — 推测图像类型
- sndhdr — 推测声音文件的类型
- ossaudiodev — Access to OSS-compatible audio devices
- 国际化
- gettext — 多语种国际化服务
- locale — 国际化服务
- 程序框架
- turtle — 海龟绘图
- cmd — 支持面向行的命令解释器
- shlex — Simple lexical analysis
- Tk图形用户界面(GUI)
- tkinter — Tcl/Tk的Python接口
- tkinter.ttk — Tk themed widgets
- tkinter.tix — Extension widgets for Tk
- tkinter.scrolledtext — 滚动文字控件
- IDLE
- 其他图形用户界面（GUI）包
- 开发工具
- typing — 类型标注支持
- pydoc — Documentation generator and online help system
- doctest — Test interactive Python examples
- unittest — 单元测试框架
- unittest.mock — mock object library
- unittest.mock 上手指南
- 2to3 - 自动将 Python 2 代码转为 Python 3 代码
- test — Regression tests package for Python
- test.support — Utilities for the Python test suite
- test.support.script_helper — Utilities for the Python execution tests
- 调试和分析
- bdb — Debugger framework
- faulthandler — Dump the Python traceback
- pdb — The Python Debugger
- The Python Profilers
- timeit — 测量小代码片段的执行时间
- trace — Trace or track Python statement execution
- tracemalloc — Trace memory allocations
- 软件打包和分发
- distutils — 构建和安装 Python 模块
- ensurepip — Bootstrapping the pip installer
- venv — 创建虚拟环境
- zipapp — Manage executable Python zip archives
- Python运行时服务
- sys — 系统相关的参数和函数
- sysconfig — Provide access to Python's configuration information
- builtins — 内建对象
- main — 顶层脚本环境
- warnings — Warning control
- dataclasses — 数据类
- contextlib — Utilities for with-statement contexts
- abc — 抽象基类
- atexit — 退出处理器
- traceback — Print or retrieve a stack traceback
- future — Future 语句定义
- gc — 垃圾回收器接口
- inspect — 检查对象
- site — Site-specific configuration hook
- 自定义 Python 解释器
- code — Interpreter base classes
- codeop — Compile Python code
- 导入模块
- zipimport — Import modules from Zip archives
- pkgutil — Package extension utility
- modulefinder — 查找脚本使用的模块
- runpy — Locating and executing Python modules
- importlib — The implementation of import
- Python 语言服务
- parser — Access Python parse trees
- ast — 抽象语法树
- symtable — Access to the compiler's symbol tables
- symbol — 与 Python 解析树一起使用的常量
- token — 与Python解析树一起使用的常量
- keyword — 检验Python关键字
- tokenize — Tokenizer for Python source
- tabnanny — 模糊缩进检测
- pyclbr — Python class browser support
- py_compile — Compile Python source files
- compileall — Byte-compile Python libraries
- dis — Python 字节码反汇编器
- pickletools — Tools for pickle developers
- 杂项服务
- formatter — Generic output formatting
- Windows系统相关模块
- msilib — Read and write Microsoft Installer files
- msvcrt — Useful routines from the MS VC++ runtime
- winreg — Windows 注册表访问
- winsound — Sound-playing interface for Windows
- Unix 专有服务
- posix — The most common POSIX system calls
- pwd — 用户密码数据库
- spwd — The shadow password database
- grp — The group database
- crypt — Function to check Unix passwords
- termios — POSIX style tty control
- tty — 终端控制功能
- pty — Pseudo-terminal utilities
- fcntl — The fcntl and ioctl system calls
- pipes — Interface to shell pipelines
- resource — Resource usage information
- nis — Interface to Sun's NIS (Yellow Pages)
- Unix syslog 库例程
- 被取代的模块
- optparse — Parser for command line options
- imp — Access the import internals
- 未创建文档的模块
- 平台特定模块
- 扩展和嵌入 Python 解释器
- 推荐的第三方工具
- 不使用第三方工具创建扩展
- 使用 C 或 C++ 扩展 Python
- 自定义扩展类型：教程
- 定义扩展类型：已分类主题
- 构建C/C++扩展
- 在Windows平台编译C和C++扩展
- 在更大的应用程序中嵌入 CPython 运行时
- Embedding Python in Another Application
- Python/C API 参考手册
- 概述
- 代码标准
- 包含文件
- 有用的宏
- 对象、类型和引用计数
- 异常
- 嵌入Python
- 调试构建
- 稳定的应用程序二进制接口
- The Very High Level Layer
- Reference Counting
- 异常处理
- Printing and clearing
- 抛出异常
- Issuing warnings
- Querying the error indicator
- Signal Handling
- Exception Classes
- Exception Objects
- Unicode Exception Objects
- Recursion Control
- 标准异常
- 标准警告类别
- 工具
- 操作系统实用程序
- 系统功能
- 过程控制
- 导入模块
- Data marshalling support
- 语句解释及变量编译
- 字符串转换与格式化
- 反射
- 编解码器注册与支持功能
- 抽象对象层
- Object Protocol
- 数字协议
- Sequence Protocol
- Mapping Protocol
- 迭代器协议
- 缓冲协议
- Old Buffer Protocol
- 具体的对象层
- 基本对象
- 数值对象
- 序列对象
- 容器对象
- 函数对象
- 其他对象
- Initialization, Finalization, and Threads
- 在Python初始化之前
- 全局配置变量
- Initializing and finalizing the interpreter
- Process-wide parameters
- Thread State and the Global Interpreter Lock
- Sub-interpreter support
- Asynchronous Notifications
- Profiling and Tracing
- Advanced Debugger Support
- Thread Local Storage Support
- 内存管理
- 概述
- 原始内存接口
- Memory Interface
- 对象分配器
- 默认内存分配器
- Customize Memory Allocators
- The pymalloc allocator
- tracemalloc C API
- 示例
- 对象实现支持
- 在堆中分配对象
- Common Object Structures
- Type 对象
- Number Object Structures
- Mapping Object Structures
- Sequence Object Structures
- Buffer Object Structures
- Async Object Structures
- 使对象类型支持循环垃圾回收
- API 和 ABI 版本管理
- 分发 Python 模块
- 关键术语
- 开源许可与协作
- 安装工具
- 阅读指南
- 我该如何...？
- ...为我的项目选择一个名字？
- ...创建和分发二进制扩展？
- 安装 Python 模块
- 关键术语
- 基本使用
- 我应如何 ...？
- ... 在 Python 3.4 之前的 Python 版本中安装 pip ？
- ... 只为当前用户安装软件包？
- ... 安装科学计算类 Python 软件包？
- ... 使用并行安装的多个 Python 版本？
- 常见的安装问题
- 在 Linux 的系统 Python 版本上安装
- 未安装 pip
- 安装二进制编译扩展
- Python 常用指引
- 将 Python 2 代码迁移到 Python 3
- 简要说明
- 详情
- 将扩展模块移植到 Python 3
- 条件编译
- 对象API的更改
- 模块初始化和状态
- CObject 替换为 Capsule
- 其他选项
- Curses Programming with Python
- What is curses?
- Starting and ending a curses application
- Windows and Pads
- Displaying Text
- User Input
- For More Information
- 实现描述器
- 摘要
- 定义和简介
- 描述器协议
- 发起调用描述符
- 描述符示例
- Properties
- 函数和方法
- Static Methods and Class Methods
- 函数式编程指引
- 概述
- 迭代器
- 生成器表达式和列表推导式
- 生成器
- 内置函数
- itertools 模块
- The functools module
- Small functions and the lambda expression
- Revision History and Acknowledgements
- 引用文献
- 日志 HOWTO
- 日志基础教程
- 进阶日志教程
- 日志级别
- 有用的处理程序
- 记录日志中引发的异常
- 使用任意对象作为消息
- 优化
- 日志操作手册
- 在多个模块中使用日志
- 在多线程中使用日志
- 使用多个日志处理器和多种格式化
- 在多个地方记录日志
- 日志服务器配置示例
- 处理日志处理器的阻塞
- Sending and receiving logging events across a network
- Adding contextual information to your logging output
- Logging to a single file from multiple processes
- Using file rotation
- Use of alternative formatting styles
- Customizing LogRecord
- Subclassing QueueHandler - a ZeroMQ example
- Subclassing QueueListener - a ZeroMQ example
- An example dictionary-based configuration
- Using a rotator and namer to customize log rotation processing
- A more elaborate multiprocessing example
- Inserting a BOM into messages sent to a SysLogHandler
- Implementing structured logging
- Customizing handlers with dictConfig()
- Using particular formatting styles throughout your application
- Configuring filters with dictConfig()
- Customized exception formatting
- Speaking logging messages
- Buffering logging messages and outputting them conditionally
- Formatting times using UTC (GMT) via configuration
- Using a context manager for selective logging
- 正则表达式HOWTO
- 概述
- 简单模式
- 使用正则表达式
- 更多模式能力
- 修改字符串
- 常见问题
- 反馈
- 套接字编程指南
- 套接字
- 创建套接字
- 使用一个套接字
- 断开连接
- 非阻塞的套接字
- 排序指南
- 基本排序
- 关键函数
- Operator 模块函数
- 升序和降序
- 排序稳定性和排序复杂度
- 使用装饰-排序-去装饰的旧方法
- 使用 cmp 参数的旧方法
- 其它
- Unicode 指南
- Unicode 概述
- Python's Unicode Support
- Reading and Writing Unicode Data
- Acknowledgements
- 如何使用urllib包获取网络资源
- 概述
- Fetching URLs
- 处理异常
- info and geturl
- Openers and Handlers
- Basic Authentication
- Proxies
- Sockets and Layers
- 脚注
- Argparse 教程
- 概念
- 基础
- 位置参数介绍
- Introducing Optional arguments
- Combining Positional and Optional arguments
- Getting a little more advanced
- Conclusion
- ipaddress模块介绍
- 创建 Address/Network/Interface 对象
- 审查 Address/Network/Interface 对象
- Network 作为 Address 列表
- 比较
- 将IP地址与其他模块一起使用
- 实例创建失败时获取更多详细信息
- Argument Clinic How-To
- The Goals Of Argument Clinic
- Basic Concepts And Usage
- Converting Your First Function
- Advanced Topics
- 使用 DTrace 和 SystemTap 检测CPython
- Enabling the static markers
- Static DTrace probes
- Static SystemTap markers
- Available static markers
- SystemTap Tapsets
- 示例
- Python 常见问题
- Python常见问题
- 一般信息
- 现实世界中的 Python
- 编程常见问题
- 一般问题
- 核心语言
- 数字和字符串
- 性能
- 序列（元组/列表）
- 对象
- 模块
- 设计和历史常见问题
- 为什么Python使用缩进来分组语句？
- 为什么简单的算术运算得到奇怪的结果？
- 为什么浮点计算不准确？
- 为什么Python字符串是不可变的？
- 为什么必须在方法定义和调用中显式使用“self”？
- 为什么不能在表达式中赋值？
- 为什么Python对某些功能（例如list.index()）使用方法来实现，而其他功能（例如len(List)）使用函数实现？
- 为什么 join()是一个字符串方法而不是列表或元组方法？
- 异常有多快？
- 为什么Python中没有switch或case语句？
- 难道不能在解释器中模拟线程，而非得依赖特定于操作系统的线程实现吗？
- 为什么lambda表达式不能包含语句？
- 可以将Python编译为机器代码，C或其他语言吗？
- Python如何管理内存？
- 为什么CPython不使用更传统的垃圾回收方案？
- CPython退出时为什么不释放所有内存？
- 为什么有单独的元组和列表数据类型？
- 列表是如何在CPython中实现的？
- 字典是如何在CPython中实现的？
- 为什么字典key必须是不可变的？
- 为什么 list.sort() 没有返回排序列表？
- 如何在Python中指定和实施接口规范？
- 为什么没有goto？
- 为什么原始字符串（r-strings）不能以反斜杠结尾？
- 为什么Python没有属性赋值的“with”语句？
- 为什么 if/while/def/class语句需要冒号？
- 为什么Python在列表和元组的末尾允许使用逗号？
- 代码库和插件 FAQ
- 通用的代码库问题
- 通用任务
- 线程相关
- 输入输出
- 网络 / Internet 编程
- 数据库
- 数学和数字
- 扩展/嵌入常见问题
- 可以使用C语言中创建自己的函数吗？
- 可以使用C++语言中创建自己的函数吗？
- C很难写，有没有其他选择？
- 如何从C执行任意Python语句？
- 如何从C中评估任意Python表达式？
- 如何从Python对象中提取C的值？
- 如何使用Py_BuildValue()创建任意长度的元组？
- 如何从C调用对象的方法？
- 如何捕获PyErr_Print()（或打印到stdout / stderr的任何内容）的输出？
- 如何从C访问用Python编写的模块？
- 如何从Python接口到C ++对象？
- 我使用Setup文件添加了一个模块，为什么make失败了？
- 如何调试扩展？
- 我想在Linux系统上编译一个Python模块，但是缺少一些文件。为什么?
- 如何区分“输入不完整”和“输入无效”？
- 如何找到未定义的g++符号__builtin_new或__pure_virtual？
- 能否创建一个对象类，其中部分方法在C中实现，而其他方法在Python中实现（例如通过继承）？
- Python在Windows上的常见问题
- 我怎样在Windows下运行一个Python程序？
- 我怎么让 Python 脚本可执行？
- 为什么有时候 Python 程序会启动缓慢？
- 我怎样使用Python脚本制作可执行文件？
- *.pyd 文件和DLL文件相同吗？
- 我怎样将Python嵌入一个Windows程序？
- 如何让编辑器不要在我的 Python 源代码中插入 tab ？
- 如何在不阻塞的情况下检查按键？
- 图形用户界面（GUI）常见问题
- 图形界面常见问题
- Python 是否有平台无关的图形界面工具包？
- 有哪些Python的GUI工具是某个平台专用的？
- 有关Tkinter的问题
- “为什么我的电脑上安装了 Python ？”
- 什么是Python？
- 为什么我的电脑上安装了 Python ？
- 我能删除 Python 吗？
- 术语对照表
- 文档说明
- Python 文档贡献者
- 解决 Bug
- 文档错误
- 使用 Python 的错误追踪系统
- 开始为 Python 贡献您的知识
- 版权
- 历史和许可证
- 软件历史
- 访问Python或以其他方式使用Python的条款和条件
- Python 3.7.3 的 PSF 许可协议
- Python 2.0 的 BeOpen.com 许可协议
- Python 1.6.1 的 CNRI 许可协议
- Python 0.9.0 至 1.2 的 CWI 许可协议
- 集成软件的许可和认可
- Mersenne Twister
- 套接字
- Asynchronous socket services
- Cookie management
- Execution tracing
- UUencode and UUdecode functions
- XML Remote Procedure Calls
- test_epoll
- Select kqueue
- SipHash24
- strtod and dtoa
- OpenSSL
- expat
- libffi
- zlib
- cfuhash
- libmpdec