Beginning Python - From Novice to Professional

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CHAPTER 17 ■ EXTENDING PYTHON 365 After these darkly magical incantations, you should end up with a highly useful file called _palindrome.so. This is your shared library, which can be imported directly into Python (if it’s put somewhere in your PYTHONPATH, for example in the current directory): >>> import _palindrome >>> dir(_palindrome) ['__doc__', '__file__', '__name__', 'is_palindrome'] >>> _palindrome.is_palindrome('ipreferpi') 1 >>> _palindrome.is_palindrome('notlob') 0 In older versions of SWIG, that would be all there was to it. Recent versions of SWIG, however, generate some wrapping code in Python as well (the file palindrome.py, remember). This wrapper code imports the _palindrome module and takes care of a bit of checking. If you’d rather skip that, you could just remove the palindrome.py file and link your library directly into a file named palindrome.so. Using the wrapper code works just as well as using the shared library: >>> import palindrome >>> from palindrome import is_palindrome >>> if is_palindrome('abba'): ... print 'Wow -- that never occurred to me...' ... Wow -- that never occurred to me... A Shortcut Through the Magic Forest of Compilers If you think the compilation process can be a bit arcane, you’re not alone. If you automate the compilation (say, using a makefile) users will have to configure the setup by specifying where their Python installation is, which specific options to use with their compiler, and, not the least, which compiler to use. You can avoid this elegantly by using Distutils, which I discuss in more detail in the next chapter. In fact, it has direct support for SWIG, so you don’t even have to run that manually. You just write the code and the interface file, and run your Distutils script. For more information about this magic, see the section “Compiling Extensions” in Chapter 18. Hacking It on Your Own SWIG does quite a bit of magic behind the scenes, but not all of it is strictly necessary. If you want to get close to the metal and grind your teeth on the processor, so to speak, you can certainly write your wrapper code yourself—or simply write your C code so that it uses the Python C API directly. The Python C API is described in documents “Extending and Embedding the Python Interpreter” (a tutorial) and “Python/C API Reference Manual” (a reference) by Guido van Rossum (both available from http://python.org/doc). There is quite a bit of information to swallow in these documents, but if you know some C programming, the first one (“Extending and Embedding . . .”) includes a fairly gentle introduction. I’ll try to be even gentler (and briefer) here. If you’re curious about what I’m leaving out (which is rather a lot), you should take a look at the docs.
366 CHAPTER 17 ■ EXTENDING PYTHON Reference Counting If you haven’t worked with it before, reference counting will probably be one of the most foreign concepts you’ll encounter in this section, although it’s not really all that complicated. In Python, memory used is dealt with automatically—you just create objects, and they disappear when you no longer use them. In C, this isn’t the case: you have to explicitly deallocate objects (or, rather, chunks of memory) that you’re no longer using. If you don’t, your program may start hogging more and more memory, and you have what’s called a memory leak. When writing Python extensions, you have access to the same tools Python uses “under the hood” to manage memory, one of which is reference counting. The idea is that as long as some parts of your code have references to an object (that is, in C-speak, pointers pointing to it), it should not be deallocated. However, once the number of references to an object hits zero, the number can no longer increase—there is no code that can create new references to it, and it’s just “free floating” in memory. At this point, it’s safe to deallocate it. Reference counting automates this process: You follow a set of rules where you increment or decrement the reference count for an object under various circumstances (through a part of the Python API), and if the count ever goes to zero, the object is automatically deallocated. This means that no single piece of code has the sole responsibility for managing an object. You can create an object, return it from a function, and forget about it, safe in the knowledge that it will disappear when it is no longer needed. You use two macros called Py_INCREF and Py_DECREF to increment and decrement the reference count of an object, respectively. You can find detailed information about how to use these in the Python documentation (http://python.org/doc/ext/refcounts.html); here is the gist of it: • You can’t own an object, but you can own a reference to it. The reference count of an object is the number of owned references to it. • If you own a reference, you are responsible for calling Py_DECREF when you no longer need the reference. • If you borrow a reference temporarily, you should not call Py_DECREF when you’re finished with the object; that’s the responsibility of the owner. (You should certainly never use a borrowed reference after the owner has disposed of it. See the “Thin Ice” sections in the documentation for some more advice on staying safe.) • You can change a borrowed reference into an owned reference by calling Py_INCREF. This creates a new owned reference; the original owner still owns the original reference. • When you receive an object as a parameter, it’s up to you whether you want the ownership of its reference transferred (for example, if you’re going to store it somewhere) or whether you simply want to borrow it. This should be documented clearly. If your function is called from Python, it’s safe to simply borrow—the object will live for the duration of the function call. If, however, your function is called from C, this cannot be guaranteed, and you might want to create an owned reference, and release it when you’re finished.
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