Hi Alan,
> > My friend clarifies: "It's not the efficiency of doxygen that's the
> > question. The problem is that you can add fields to objects as you go in
> > Python so you need to do a deep analysis of the code to determine the
>class
> > structure which you don't have to do with C++ (or Java)."
>
>That's true it's one of the "benefits" of a dynamic language, but it does
>make the code harder to parse.
Is this not just evidence of a very bad Python coding style?
Should we not always declare *all* class fields in the class definition
by assigning to them, even if the assignment is token or dummy
i.e. 'None', "", [], {} etc.
> > He mentioned numbers like maybe ~20+x slower on lines-of-code
> > for Python vs C++.
>
>That sounds high, I would have expected no more than 5-10 times longer.
>But of course against that we have the advantage that there will be far
>fewer
>lines to parse in a Python project, typically only a third or a quarter of
>the number of lines - sometimes less than that.
Can you cite us a literature source for saying that Python is 3-4x more
expressive per line-of-code than C++?
Would come in handy for evangelizing.
> > A second friend of mine who is an XML/Java enthusiast echoed similar
> > comments about scalability in large builds with weakly-typed dynamic
> > languages such as Python.
>
>The concept of a "large build" doesn't really exist in an interpreted
>language like Python. OTOH I probably wouldn't usePython for a
>very large project - say over a million lines of code in C++ - for a
>variety of other reasons. eg. Python could do it but the coordination of
>multi team projects becomes harder without tools like static type
>checking.
Has anyone ever tried pragmas for hinting about member types as I suggested?
Stephen
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