1. Extending Python with C or C++¶
It is quite easy to add new built-in modules to Python, if you know how to program in C. Such extension modules can do two things that can’t be done directly in Python: they can implement new built-in object types, and they can call C library functions and system calls.
To support extensions, the Python API (Application Programmers Interface) defines a set of functions, macros and variables that provide access to most aspects of the Python run-time system. The Python API is incorporated in a C source file by including the header "Python.h" .
The compilation of an extension module depends on its intended use as well as on your system setup; details are given in later chapters.
The C extension interface is specific to CPython, and extension modules do not work on other Python implementations. In many cases, it is possible to avoid writing C extensions and preserve portability to other implementations. For example, if your use case is calling C library functions or system calls, you should consider using the ctypes module or the cffi library rather than writing custom C code. These modules let you write Python code to interface with C code and are more portable between implementations of Python than writing and compiling a C extension module.
1.1. A Simple Example¶
Let’s create an extension module called spam (the favorite food of Monty Python fans…) and let’s say we want to create a Python interface to the C library function system() 1. This function takes a null-terminated character string as argument and returns an integer. We want this function to be callable from Python as follows:
Begin by creating a file spammodule.c . (Historically, if a module is called spam , the C file containing its implementation is called spammodule.c ; if the module name is very long, like spammify , the module name can be just spammify.c .)
The first two lines of our file can be:
which pulls in the Python API (you can add a comment describing the purpose of the module and a copyright notice if you like).
Since Python may define some pre-processor definitions which affect the standard headers on some systems, you must include Python.h before any standard headers are included.
It is recommended to always define PY_SSIZE_T_CLEAN before including Python.h . See Extracting Parameters in Extension Functions for a description of this macro.
All user-visible symbols defined by Python.h have a prefix of Py or PY , except those defined in standard header files. For convenience, and since they are used extensively by the Python interpreter, "Python.h" includes a few standard header files: <stdio.h> , <string.h> , <errno.h> , and <stdlib.h> . If the latter header file does not exist on your system, it declares the functions malloc() , free() and realloc() directly.
The next thing we add to our module file is the C function that will be called when the Python expression spam.system(string) is evaluated (we’ll see shortly how it ends up being called):
There is a straightforward translation from the argument list in Python (for example, the single expression "ls -l" ) to the arguments passed to the C function. The C function always has two arguments, conventionally named self and args.
The self argument points to the module object for module-level functions; for a method it would point to the object instance.
The args argument will be a pointer to a Python tuple object containing the arguments. Each item of the tuple corresponds to an argument in the call’s argument list. The arguments are Python objects — in order to do anything with them in our C function we have to convert them to C values. The function PyArg_ParseTuple() in the Python API checks the argument types and converts them to C values. It uses a template string to determine the required types of the arguments as well as the types of the C variables into which to store the converted values. More about this later.
PyArg_ParseTuple() returns true (nonzero) if all arguments have the right type and its components have been stored in the variables whose addresses are passed. It returns false (zero) if an invalid argument list was passed. In the latter case it also raises an appropriate exception so the calling function can return NULL immediately (as we saw in the example).
1.2. Intermezzo: Errors and Exceptions¶
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.
The Python API defines a number of functions to set various types of exceptions.
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_IOError ). 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. For this, you usually declare a static object variable at the beginning of your file:
and initialize it in your module’s initialization function ( PyInit_spam() ) with an exception object:
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.
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:
1.3. Back to the Example¶
Going back to our example function, you should now be able to understand this statement:
It returns NULL (the error indicator for functions returning object pointers) if an error is detected in the argument list, relying on the exception set by PyArg_ParseTuple() . Otherwise the string value of the argument has been copied to the local variable command . This is a pointer assignment and you are not supposed to modify the string to which it points (so in Standard C, the variable command should properly be declared as const char *command ).
The next statement is a call to the Unix function system() , passing it the string we just got from PyArg_ParseTuple() :
Our spam.system() function must return the value of sts as a Python object. This is done using the function PyLong_FromLong() .
In this case, it will return an integer object. (Yes, even integers are objects on the heap in Python!)
If you have a C function that returns no useful argument (a function returning void ), the corresponding Python function must return None . You need this idiom to do so (which is implemented by the Py_RETURN_NONE macro):
Py_None is the C name for the special Python object None . It is a genuine Python object rather than a NULL pointer, which means “error” in most contexts, as we have seen.
1.4. The Module’s Method Table and Initialization Function¶
I promised to show how spam_system() is called from Python programs. First, we need to list its name and address in a “method table”:
Note the third entry ( METH_VARARGS ). This is a flag telling the interpreter the calling convention to be used for the C function. It should normally always be METH_VARARGS or METH_VARARGS | METH_KEYWORDS ; a value of 0 means that an obsolete variant of PyArg_ParseTuple() is used.
When using only METH_VARARGS , the function should expect the Python-level parameters to be passed in as a tuple acceptable for parsing via PyArg_ParseTuple() ; more information on this function is provided below.
The METH_KEYWORDS bit may be set in the third field if keyword arguments should be passed to the function. 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 method table must be referenced in the module definition structure:
This structure, in turn, must be passed to the interpreter in the module’s initialization function. The initialization function must be named PyInit_name() , where name is the name of the module, and should be the only non- static item defined in the module file:
Note that PyMODINIT_FUNC declares the function as PyObject * return type, declares any special linkage declarations required by the platform, and for C++ declares the function as extern "C" .
When the Python program imports module spam for the first time, PyInit_spam() is called. (See below for comments about embedding Python.) It calls PyModule_Create() , which returns a module object, and inserts built-in function objects into the newly created module based upon the table (an array of PyMethodDef structures) found in the module definition. PyModule_Create() returns a pointer to the module object that it creates. It may abort with a fatal error for certain errors, or return NULL if the module could not be initialized satisfactorily. The init function must return the module object to its caller, so that it then gets inserted into sys.modules .
When embedding Python, the PyInit_spam() function is not called automatically unless there’s an entry in the PyImport_Inittab table. To add the module to the initialization table, use PyImport_AppendInittab() , optionally followed by an import of the module:
Removing entries from sys.modules or importing compiled modules into multiple interpreters within a process (or following a fork() without an intervening exec() ) can create problems for some extension modules. Extension module authors should exercise caution when initializing internal data structures.
A more substantial example module is included in the Python source distribution as Modules/xxmodule.c . This file may be used as a template or simply read as an example.
Unlike our spam example, xxmodule uses multi-phase initialization (new in Python 3.5), where a PyModuleDef structure is returned from PyInit_spam , and creation of the module is left to the import machinery. For details on multi-phase initialization, see PEP 489.
1.5. Compilation and Linkage¶
There are two more things to do before you can use your new extension: compiling and linking it with the Python system. If you use dynamic loading, the details may depend on the style of dynamic loading your system uses; see the chapters about building extension modules (chapter Building C and C++ Extensions ) and additional information that pertains only to building on Windows (chapter Building C and C++ Extensions on Windows ) for more information about this.
If you can’t use dynamic loading, or 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. Luckily, this is very simple on Unix: just 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:
and rebuild the interpreter by running make in the toplevel directory. You can also run make in the Modules/ subdirectory, but then you must first rebuild Makefile there by running ‘make Makefile’. (This is necessary each time you change the Setup file.)
If your module requires additional libraries to link with, these can be listed on the line in the configuration file as well, for instance:
1.6. Calling Python Functions from C¶
So far we have 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.
Fortunately, the Python interpreter is easily called recursively, and there is a standard interface to call a Python function. (I won’t dwell on how to call the Python parser with a particular string as input — if you’re interested, have a look at the implementation of the -c command line option in Modules/main.c from the Python source code.)
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:
This function must be registered with the interpreter using the METH_VARARGS flag; this is described in section The Module’s Method Table and Initialization Function . The PyArg_ParseTuple() function and its arguments are documented in section Extracting Parameters in Extension Functions .
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 Reference Counts .
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:
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:
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:
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.
1.7. Extracting Parameters in Extension Functions¶
The PyArg_ParseTuple() function is declared as follows:
The arg argument must be a tuple object containing an argument list passed from Python to a C function. The format argument must be a format string, whose syntax is explained in Parsing arguments and building values in the Python/C API Reference Manual. The remaining arguments must be addresses of variables whose type is determined by the format string.
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!
Note that any Python object references which are provided to the caller are borrowed references; do not decrement their reference count!
Some example calls:
1.8. Keyword Parameters for Extension Functions¶
The PyArg_ParseTupleAndKeywords() function is declared as follows:
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.
Nested tuples cannot be parsed when using keyword arguments! Keyword parameters passed in which are not present in the kwlist will cause TypeError to be raised.
Here is an example module which uses keywords, based on an example by Geoff Philbrick (philbrick @ hks . com):
1.9. Building Arbitrary Values¶
This function is the counterpart to PyArg_ParseTuple() . It is declared as follows:
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):
1.10. Reference Counts¶
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 re-use 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.
Common causes of memory leaks are unusual paths through the code. For instance, a function may allocate a block of memory, do some calculation, and then free the block again. Now a change in the requirements for the function may add a test to the calculation that detects an error condition and can return prematurely from the function. It’s easy to forget to free the allocated memory block when taking this premature exit, especially when it is added later to the code. Such leaks, once introduced, often go undetected for a long time: the error exit is taken only in a small fraction of all calls, and most modern machines have plenty of virtual memory, so the leak only becomes apparent in a long-running process that uses the leaking function frequently. Therefore, it’s important to prevent leaks from happening by having a coding convention or strategy that minimizes this kind of errors.
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.
While Python uses the traditional reference counting implementation, it also offers a cycle detector that works to detect reference cycles. This allows applications to not worry about creating direct or indirect circular references; these are the weakness of garbage collection implemented using only reference counting. Reference cycles consist of objects which contain (possibly indirect) references to themselves, so that each object in the cycle has a reference count which is non-zero. Typical reference counting implementations are not able to reclaim the memory belonging to any objects in a reference cycle, or referenced from the objects in the cycle, even though there are no further references to the cycle itself.
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.
1.10.1. Reference Counting in Python¶
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 2 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 3.
The advantage of borrowing over owning a reference is that you don’t need to take care of disposing of the reference on all possible paths through the code — in other words, with a borrowed reference you don’t run the risk of leaking when a premature exit is taken. The disadvantage of borrowing over owning is that there are some subtle situations where in seemingly correct code a borrowed reference can be used after the owner from which it was borrowed has in fact disposed of it.
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).
1.10.2. Ownership Rules¶
Whenever an object reference is passed into or out of a function, it is part of the function’s interface specification whether ownership is transferred with the reference or not.
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() .
The object reference returned from a C function that is called from Python must be an owned reference — ownership is transferred from the function to its caller.
1.10.3. Thin Ice¶
There are a few situations where seemingly harmless use of a borrowed reference can lead to problems. These all have to do with implicit invocations of the interpreter, which can cause the owner of a reference to dispose of it.
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:
This function first borrows a reference to list[0] , then replaces list[1] with the value 0 , and finally prints the borrowed reference. Looks harmless, right? But it’s not!
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.
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 .
The solution, once you know the source of the problem, is easy: temporarily increment the reference count. The correct version of the function reads:
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:
1.10.4. NULL Pointers¶
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 4.
It is a severe error to ever let a NULL pointer “escape” to the Python user.
1.11. Writing Extensions in C++¶
It is possible to write extension modules in C++. Some restrictions apply. If the main program (the Python interpreter) is compiled and linked by the C compiler, global or static objects with constructors cannot be used. This is not a problem if the main program is linked by the C++ compiler. Functions that will be called by the Python interpreter (in particular, module initialization functions) have to be declared using extern "C" . It is unnecessary to enclose the Python header files in extern "C" <. >— they use this form already if the symbol __cplusplus is defined (all recent C++ compilers define this symbol).
1.12. Providing a C API for an Extension Module¶
Many extension modules just provide new functions and types to be used from Python, but sometimes the code in an extension module can be useful for other extension modules. For example, an extension module could implement a type “collection” which works like lists without order. Just like the standard Python list type has a C API which permits extension modules to create and manipulate lists, this new collection type should have a set of C functions for direct manipulation from other extension modules.
At first sight this seems easy: just write the functions (without declaring them static , of course), provide an appropriate header file, and document the C API. And in fact this would work if all extension modules were always linked statically with the Python interpreter. When modules are used as shared libraries, however, the symbols defined in one module may not be visible to another module. The details of visibility depend on the operating system; some systems use one global namespace for the Python interpreter and all extension modules (Windows, for example), whereas others require an explicit list of imported symbols at module link time (AIX is one example), or offer a choice of different strategies (most Unices). And even if symbols are globally visible, the module whose functions one wishes to call might not have been loaded yet!
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 (as discussed in section The Module’s Method Table and Initialization Function ). 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.
There are many ways in which Capsules can be used to export the C API of an extension module. Each function could get its own Capsule, or all C API pointers could be stored in an array whose address is published in a Capsule. And the various tasks of storing and retrieving the pointers can be distributed in different ways between the module providing the code and the client modules.
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.
In particular, Capsules used to expose C APIs should be given a name following this convention:
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 section A Simple Example . 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:
Python изнутри. Структуры процесса
1. Введение
2. Объекты. Голова
3. Объекты. Хвост
4. Структуры процесса
Продолжаем перевод цикла статей о внутренностях Питона. Если вы хоть раз задавались вопросом «а как же оно устроено?», обязательно читайте. Автор проливает свет на многие интересные и важные аспекты устройства языка.
В предыдущих частях мы говорили об объектной системе Питона. Тема ещё не исчерпана, но давайте пойдём дальше.
Когда я размышляю о реализации Питона, я представляю себе огромный конвейер, по которому движутся коды машинных операций, которые затем попадают в гигантский завод, где повсюду возвышаются градирни и башенные краны, — и меня просто переполняет желание подойти поближе. В этой части мы поговорим о структурах состояния интерпретатора и состояния потока ( ./Python/pystate.c ). Сейчас нам нужно заложить фундамент, чтобы потом было легче понять, как исполняется байткод. Совсем скоро мы узнаем, как устроены фреймы, пространства имён и объекты кода. Но для начала давайте поговорим о тех структурах данных, которые связывают всё воедино. Учтите, я предполагаю наличие хотя бы поверхностного понимания устройства операционных систем и знания хотя бы таких терминов, как ядро, процесс, поток и т. п.
Во многих операционных системах пользовательский код исполняется в потоках, которые живут в процессах (это верно для большинства *nix-систем и для «современных» версий Windows). Ядро ответственно за подготовку и удаление процессов и потоков, а также за определение того, какой поток на каком логическом CPU будет исполняться. Когда процесс вызывает функцию Py_Initialize , на сцену выходит другая абстракция, интерпретатор. Любой Python-код, запускаемый в процессе, привязан к интерпретатору. Об интерпретаторе можно думать как об основе всех прочих концепций, которые мы будем обсуждать. Питон поддерживает инициализацию двух (и более) интерпретаторов в одном процессе. Несмотря на то, что эта возможность редко используется на практике, я буду её учитывать. Как было сказано, код исполняется в потоке (или потоках). Не исключение и виртуальная машина Питона (VM). При этом сама VM имеет поддержку потоков, т.е. у Питона есть своя абстракция для представления потоков. Реализация этой абстракции полностью полагается на механизмы ядра. Таким образом, и ядро, и Питон имеют представление о каждом из Python-потоков. Эти потоки управляются ядром и исполняются как отдельные потоки параллельно всем прочим потокам в системе. Ну… почти параллельно.
До сих пор мы не обращали внимания на слона в нашей посудной лавке. Зовут слона GIL (Global Interpreter Lock). По некоторым причинам многие аспекты CPython непотокобезопасны. У этого есть и преимущества (например, упрощение реализации и гарантированная атомарность многих операторов Питона), и недостатки. Основной недостаток — необходимость в механизме, предотвращающем параллельное выполнение потоков Питона, так как без такого механизма возможно повреждение данных. GIL — это блокировка уровня процесса, которую поток обязан захватить, если ему необходимо выполнять Python-код. Это ограничивает количество одновременно выполняющихся Python-потоков на одном логическом CPU до одного. Python-потоки реализуют кооперативную многозадачность, добровольно освобождая GIL и предоставляя другим потокам возможность поработать. Этот функционал встроен в цикл исполнения, т.е. не нужно специально задумываться об этой блокировке при написании обычных скриптов и некоторых расширений (им кажется, что они работают непрерывно). Учтите, что в то время, пока поток не использует API Питона (со многими такое бывает), он может работать параллельно другим Python-потокам. Чуть позже мы ещё обсудим GIL, а те, кому не терпится, могут почитать презентацию Дэвида Бизли.
Мы помним о концепциях процесса (абстракция ОС), интерпретатора (абстракция Питона) и потока (абстракция как ОС, так и Питона). Сейчас мы проделаем следующий путь: начнём с одной операции и закончим целым процессом.
Давайте ещё раз посмотрим на байткод, генерируемый выражением spam = eggs — 1 (что такое diss ):
Помимо операции BINARY_SUBTRACT , которая и выполняет всю работу, мы видим операции LOAD_NAME (eggs) и STORE_NAME (spam) . Очевидно, что для выполнения этих операций нужно место: eggs нужно откуда-то вытащить, а spam нужно куда-то убрать. На это место ссылаются внутренние структуры данных, в которых исполняется код — фрейм-объекты и объекты кода. Когда вы запускаете Python-код, на самом деле исполняются фреймы (вспомните ceval.c : PyEval_EvalFrameEx ). Сейчас мы смешиваем понятия фрейм-объектов и объектов кода, так пока проще. Разницу между этими структурами поймём чуть позже. Сейчас нас больше всего интересует поле f_back фрейм-объекта. Во фрейме n это поле указывает на фрейм n-1 , т.е. на фрейм, который вызвал текущий фрейм (первый фрейм в потоке указывает на NULL ).
Стек фреймов уникален для каждого потока и связан со специфичной потоку структурой ./Include.h/pystate.h : PyThreadState , которая содержит указатель на текущий исполняемый фрейм потока (самый последний вызванный фрейм, вершина стека). Структура PyThreadState выделяется и инициализируется для каждого Python-потока в процессе функцией _PyThreadState_Prealloc прямо перед тем, как созданный поток запрашивается из ОС ( ./Modules/_threadmodule.c : thread_PyThread_start_new_thread и >>> from _thread import start_new_thread ). В процессе могут создаваться и такие потоки, которые не управляются интерпретатором; у них нет структуры PyThreadState , и они не должны обращаться к Python API. Такое бывает в основном во встраиваемых приложениях. Но такие потоки можно «питонизировать», чтобы появилась возможность исполнять в них Python-код, при этом нужно создать новую структуру PyThreadState . В случае, если запущен один интерпретатор, можно воспользоваться API для такой миграции потока. Если интерпретаторов несколько, придётся делать это вручную. Наконец, примерно так же, как и каждый фрейм связан через указатель с предыдущим, состояния потоков объединены связным списком указателей PyThreadState *next .
Список структур потоков связан со структурой интерпретатора, в котором находятся потоки. Структура интерпретатора определена в ./Include.h/pystate.h : PyInterpreterState . Создаётся она при вызове функции Py_Initialize , которая инициализирует в процессе виртуальную машину Питона, или при вызове функции Py_NewInterpreter , в которой создаётся новая структура интерпретатора (в случае, если в процессе не один интерпретатор). Для лучшего понимания напомню, что Py_NewInterpreter возвращает не структуру интерпретатора, а структуру PyThreadState только что созданного потока для нового интерпретатора. Создавать новый интерпретатор без единого потока в нём не имеет особого смысла, так же, как и нет смысла в процессах без потоков в них. Структуры интерпретаторов в процессе связаны друг с другом так же, как и структуры потоков в интерпретаторе.
В целом, наше путешествие от единственной операции к целому процессу закончено: операции находятся в исполняющихся объектах кода (в то время как «неисполняющиеся» объекты лежат где-то рядом, как обычные данные), объекты кода находятся в исполняющихся фреймах, которые находятся в Python-потоках, а потоки в свою очередь принадлежат интерпретатору. На корень всей этой структуры ссылается статическая переменная ./Python/pystate.c : interp_head . Указывает она на структуру первого интерпретатора (через неё доступны все остальные интерпретаторы, потоки и т.д.). Мьютекс head_mutex защищает от повреждения этих структур конкурирующими изменениями из разных потоков (уточняю, что это не GIL, а обычный мьютекс для структур интерпретатора и потоков). Эта блокировка контролируется макросами HEAD_LOCK и HEAD_UNLOCK . Как правило, к переменной interp_head обращаются в случае, если нужно добавить новый или удалить существующий интерпретатор или поток. Если в процессе не один интерпретатор, то по этой переменной доступна структура не обязательно того интерпретатора, в котором находится исполняемый в текущий момент поток.
Надёжнее пользоваться переменной ./Python/pystate.c : _PyThreadState_Current , которая указывает на структуру исполняемого потока (при этом нужно учесть некоторые условия). То есть, чтобы добраться до своего интерпретатора, коду нужна структура его потока, из которой уже можно вытащить интерпретатор. Для доступа к этой переменной (взять текущее значение или сменить его, сохранив старое) есть функции, для работы которых необходимо удерживать GIL. Это важно, и это одна из тех проблем, которые возникают из-за отсутствия потокобезопасности в CPython. Значение переменной _PyThreadState_Current устанавливается в структуре нового потока во время инициализации Питона или во время создания нового потока. Когда Python-поток впервые запускается после начальной загрузки, он полагается на то, что: а) он удерживает GIL и б) значение переменной _PyThreadState_Current корректно. В этот момент поток не должен отдавать GIL. Сначала он должен сохранить куда-нибудь значение _PyThreadState_Current , чтобы при следующем захвате GIL можно было восстановить нужное значение переменной и продолжить работу. Благодаря такому поведению _PyThreadState_Current всегда указывает на выполняющийся в данный момент поток. Для реализации этого поведения есть макросы Py_BEGIN_ALLOW_THREADS и Py_END_ALLOW_THREADS . О GIL и API для работы с ним можно говорить часами, и было бы интересно сравнить CPython с другими реализациями (например, Jython или IronPython, в которых потоки выполняются одновременно). Но давайте пока отложим эту тему.
На схеме я указал связи между структурами одного процесса, в котором запущены два интерпретатора с двумя потоками у каждого, которые ссылаются на свои стеки фреймов. 
Красиво, да? Так. Всё обсудили, но до сих пор непонятно, в чём смысл этих структур. Зачем они нужны? Что в них интересного? Я не хочу усложнять, поэтому только коротко расскажу о некоторых функциях. В структуре интерпретатора, например, есть поля, предназначенные для работы с импортируемыми модулями; указатели, необходимые для работы с юникодом; поле флагов динамического компоновщика и поле связанное с использованием TSC для профилирования (смотрите предпоследний пункт здесь).
Некоторые поля структуры потока связаны с деталями исполнения этого потока. Например, поля recursion_depth , overflow и recursion_critical нужны для того, чтобы поймать слишком глубокую рекурсию и выбросить исключение RuntimeError до того, как переполнится стек нижележащей платформы, и рухнет весь процесс. Там же находятся поля, связанные с профилированием, трассировкой и обработкой исключений и словарь для хранения всякого хлама.
Думаю, на этом рассказ о структуре Python-процесса можно закончить. Надеюсь, всё понятно. В следующих постах мы перейдём к настоящему хардкору и поговорим о фрейм-объектах, пространствах имён и объектах кода. Готовьтесь.
spam 0.6.1.3
Spam is a piece of Python software built upon NumPy and SciPy for the analysis and manipulation of 3D and 2D data sets in material science, be they from x-ray tomography, random fields or any other source.
A number of common functions are provided that are either lacking or slow in Numpy and Scipy, which are expected to be used by users within new python scripts. These functions are in the tools/ directory, and include tools to work with random fields, morphological operations, digital image correlation, and labelled images. Some of spam’s functions transparently call C/C++ functions for speed.
Some user-callable scipts are also provided – they are more complex pieces of code that combine a number of functions and which have a command-line interface. For the moment the scripts are 3 different image correlation techniques.
Email Spam Classification in Python
Hello fellow learner! In this tutorial, we will talk about how to achieve the classification of spam emails with the help of the dataset which will be loaded using scikit-learn in Python programming language.
Introduction to Email Spam
We all know that billions of spam are sent every day to user email accounts and more than 90% of these spam emails are malicious and cause major harm to the user.
Don’t the spams get annoying to you as well? They get pretty annoying to me for sure! Sometimes even some important mails get transferred to spam and as a result, some important information is left unread with the fear of getting harmed by the spam emails.
And did you know that one out of every 1,000 e-mails contains malware charges? And hence it is important for us to learn how can we ourselves classify our emails as safe and unsafe.
Implementing Email Spam Classifier in Python
Let’s get right into the steps to implement an email spam classification algorithm using Python. This will help you understand the backend working of a very basic spam classifier. The algorithms used in the real world are way more advanced compared to the algorithm I’ve described below. But you sure can use this as a starting point for your journey.
1. Importing Modules and Loading Data
First, we import all the necessary required modules into our program. The code for the same is as follows:
We would require some basic machine learning modules such as numpy, pandas, and matplotlib. Along with these, we would require some sklearn models and features.
The next step involves loading the dataset with the help of the pandas module imported earlier. The dataset we would be using is the spam.csv data file which can be found here.
The dataset we loaded has 5572 email samples along with 2 unique labels namely, spam and ham .
2. Training and Testing Data
After loading we have to separate the data into training and testing data.
The separation of data into training and testing data includes two steps:
- Separating the x and y data as the email text and labels respectively
- Splitting the x and y data into four different datasets namely x_train,y_train,x_test, and y_test based on the 80:20 rule.
The separation of data into x and y data is done in the following code:
3. Extracting Important Features
The next step is to get only the important words/features from the whole dataset. To achieve this, we will make use of the CountVectorizer function in order to vectorize the words of the training dataset.
4. Building and Training The Model
The most important step involves building and training the model for the dataset we created earlier. The code for the same is as follows:
The final step includes computing the overall accuracy of our model on the testing dataset.
We ended up achieving an accuracy of 98.744% which is great!!
Conclusion
Implementing an email classification system is a great next step in developing the technology and making emails more secure.