I’m now busy reading the great book Learning Python by Mark Lutz. I must say it reminds the C++ bible: The C++ Programming Language by Bjarne Stroustrup. The thing I like about this book – a very thorough content, covering each and every major part of Python programming language. Being a C++ developer, I like to know what happens behind the scence when you write code. And this “Learning Python”, though being much more steadier than, for example, another great book Dive Into Python, covers language in much more detail, leaving behind domain specific information like Web-services, XML handling, etc.. That’s very nice 🙂

While reading a chapter on functions basics, it came to my mind, that Python polymorphism reminds the so-called “static polymorphism” found in C++ templates. The idioms behind both are very much the same. In both cases you define a function that accepts arguments, regardless of their type until they follow some predefined protocol. This way of programming OOP is different from common polymorphism found in static-typed languages.

Usually if you want to make use of polymorphism in C++, you write something like this:

struct IBase
    virtual void func() = 0;
    virtual ~IBase() = 0 {}
struct SomeClass : public IBase
    virtual void func() { std::cout << "Hello there!\n"}
[...] //Skip
void someFancyFunction(IBase* arg)
[...] //Skip
int main()
    IBase* pBase = new SomeClass;
    [...] //Skip

Here, the function someFancyFunction can only accept arguments that undoubtedly follow the IBase interface. If you try to pass some param unrelated to IBase, the compiler will report an error. This kind of polymorphism is powerful and widely-used, but it encourages a lot of boilerplate code and the code-base tends to extend hugely. It also puts a set of constraints on a developer, preventing from code from flexibility.

On other hand, there is what’s called “static polymorphism” in C++, and it’s the main tool of OOP in Python. Here’s the code example in C++:

template<typename Type>
void fancyFunction(Type& t)

Now, the fancyFunction will happily accept any argument as long as its underlying type has the func method and also provides the post-increment operator. This construction, though less readable, gives much more flexibility to the developer. To use the function, one doesn’t need to derive a new type from some basic interface and implement all of its abstract methods. The only obligation is to define methods used inside fancyFunction. That’s a great leap forward and generally a superb feature of C++.

As, for Python, here polymorphism follows the C++ “static polymorphism” idiom, but it allows to achieve the same goals with less effort. Naturally, functions in Python don’t allow to declare types for arguments, so there’s no need to use cumbersome template<> constructions. In fact, all functions and operations in Python follow static polymorphism – actual receiver of a function call or operand is detected at the point of use in runtime.

To sum up, here’s the list of differences and likenesses of static polymorphism in Python and C++:

  • Both are very similar in behaviour – concrete data types are not specified in function definition, and any type can be accepted until it follows the protocol used inside the function
  • Both are very powerful indeed: C++ templates can be applied for such great things as Curiously Recurring Pattern. On other hand Python classes be defined dynamically in runtime and can be compound in metaclasses.
  • Python and C++ handle dynamic polymorphism differently – in C++ each use of a template with different kind of type actually generates a parallel family of functions and correctness of its use is checked at compile time, saving cost of dispatching via virtual table, but increasing amount of code. In Python actual types are resolved in runtime much like what is done when virtual functions are used in usual polymorphism in C++ – interpreter checks whether the typed passed to function contains needed method or data member, influencing the execution time, but leaving the code size as is.

These parallels between languages  helped me to understand clearly what is polymorphism in Python and how it should be used. As a general rule, you don’t check types in Python methods – testing input arguments is considered lame and redundant here.

P.S. Here’s a little code-snippet that I consider particularly exciting:

def func(x):
   class D(x):
      def foo(self):
         print "Oy!"

class C(object):
    def foo(self):
       print "Hello!"

z = func(C)
z.foo() # prints "Oy!"

GPL PyQt with LGPL Qt

July 4, 2009

UPD: Actually, it turned out that if you don’t want to mess with manual installation of PyQt from sources, and also don’t wish to make fixes in its code, there are easy to use Windows installers on official PyQt page (GPL only). For Python 2.6 it can be downloaded here. Installation process is smooth and runs without any problems. And it DOES work from the scratch with LGPL version of Qt, remaining GPL itself though.

While configuring environment for developing my CD/DVD burner I ran into an issue with PyQt licensing. From my experience and what I heard previously and read in Internet, I was sure that “free” version of PyQt can be used with the “free” version of Qt library. But despite the fact I have LGPL Qt 4.5.2 installed on my Windows machine, the PyQt configure.py script kept telling me that those two libraries have inconsistent licenses. The error message lacks details:

Error: This version of PyQt and the Desktop edition of Qt have incompatible licenses.

That seemed kind of strange to me, so I started a little investigation. I found the initial (I suppose so) request for LGPL version of PyQt: [PyQt] LGPL license. There’s a rather long discussion, even with participation of PyQt author – Phil Thompson, which resulted in nothing. The main thought is that Phill was going to consider – should or not there be PyQt under LGPL.

Since that post was a somewhat outdated, headed back to PyQt site to check current situation and read the following:

PyQt, unlike Qt, is not available under the LGPL.

That stroke me, since I’m not sure if Qt is distributed under GPL at all nowadays. The thoughts started crowding in my head – how to overcome this obstacle (in a good sence). But then finally I’ve read the section named “Compatibility with Qt licenses”:

The GPL version of PyQt can be used with both the LGPL and GPL versions of Qt.

So there should be no problem develop in GPL PyQt with LGPL Qt library. But the configure.py script was resilient – “licenses are different!”. And I decided to dig into installation script with IDLE debugger. It turned out that during initial configuration, PyQt detects my LGPL QT as “Desktop” version and its own – as “GPL”, consequently the comparison of these two fails.

At that moment I decided try to just comment out the fancy function checkLicenses() – and it worked out! Now, after 1,5 hours of investigation, I have a working version of GPL PyQt with LGPL Qt!

P.S. Perhaps, there’s some additional info about how to make GPL PyQt work with LGPL Qt – I don’t know, as I wasn’t able to find any. So I made a fix that I think is a problem with PyQt installation script relying on information found at official site. It’s perhaps an inappropriate method, and I discourage you from using it 😉

And, as it is stated in PyQt’s FAQ:

Riverbank is a product based software development company that depends on sales to fund PyQt’s continued development. […] An LGPL version of PyQt, while probably increasing the number of users, would result in a reduction in income and therefore our ability to fund future development.

So, no luck for us who wish to use PyQt under LGPL license.

Introductory post

June 30, 2009

Hello everyone. Just a few words about myself and what’s this blog about.

I’m a software developer with deep knowledge in C++, and it happens that I’m highly interested in Python programming language, the idiom behind it, the ease of use, etc.

So, lately my interest in Python overcame my native laziness and I finally decided to dive into it. And I’m going to post here my tale of mind-switching from C++ to Python.

Hopefully, someone will find the info here useful.

So, let’s start 🙂