def foo(n: int):
def foo(int n):
http://docs.python.org/py3k/reference/compound_stmts.html#fu... http://www.python.org/dev/peps/pep-3107/ http://www.acooke.org/pytyp/
I have been using this recipe: http://code.activestate.com/recipes/572161/ to add type checking to Python for a while now, so it's nice to see a more mature and developed library that does the same (and more.)
In your code examples I didn't see any decorators around the functions that were being typechecked, so how do you then enforce the type checking?
In addition, is pytyp as fully-featured as the recipe above? (The recipe is short but well-written.) The comments at the beginning of the recipe shows a bunch of examples that demonstrate all that it can do.
pyptyp is pretty comprehensive. for example, Rec(a=Seq(Opt(int)),b=Alt(float,str)) is the type for something like a dict where a is a list that contains ints or None and b is either a float or a string.
BUT it's pure python so it's not at all fast. i wouldn't use type checking throughout a codebase - only for debugging, or tests.
it's perhaps more useful as a start for building other things. for example, it includes mapping from JSON to python objects - it can use type annotations to guide the construction of python objects from JSON lists and maps. but on the other hand the internals are quite complex (i have been working with it recently, after not using it for a year or so, and it's taken some effort to understand everything).
> Mypy will get rid of the Global Interpreter Lock (GIL)
>Details of the concurrency model are still undecided, though
The GIL is an optimization though, and so the performance hit from removing it on single threaded code is prohibitive. You can watch any of David Beazley's talks on the GIL for more info.
We don't believe that it is practical to adapt the CPython VM incrementally for running parallel workloads efficiently. Instead, we are going to get rid of the GIL by having a new VM for running mypy code. The VM will be based on the Alore VM, but it will use compiled native code from the start (the Alore VM uses a bytecode interpeter). It will use a garbage collector instead of reference counting (CPython primarily uses reference counting), and it will also support a modified C Python extension API that will support proper multithreading without a GIL.
The CPython VM will still be used to run Python code and modules, and it will still be limited to running a single thread at a time (most of the time). Objects passed between the VMs will have to be copied or accessed using proxy objects, so there will be some performance overhead compared to native mypy modules. We plan to port commonly used, performance-sensitive Python modules to mypy and the modified C API to minimize performance bottlenecks.
The initial compiler will compile into C or use LLVM for the back end
This is .. a rather fundamental point. More critically, there is no link that I could see to any code.
It is an interesting idea, but it seems a little too under-cooked to merit much discussion right now. However, one way that this could get really good traction is if it were to support Cython code (which doesn't seem to be the case, from the FAQ)
Access to Python libs
Mypy will support accessing Python modules from mypy programs, running in the stock CPython virtual machine for the best compatibility. You can also access your existing Python code.
This would actually be even more compelling that I initially thought, since you pointed out that mypy run in stock CPython (I overlooked that and assumed it would have separate interpreter). All hypothetical, of course -- where's the github link?
Also, what about things like metaclasses, changing methods of classes at runtime, etc? It seems like it won't be much like Python at all, and certainly not compatible, if it is to be efficient!
It seems to me that Python is fundamentally dynamic in nature, not just programatically, but also in terms of its type system.
Cobra was an attempt at a Python-like language with static compilation and type annotation. But so far, I don't see a mass exodus from Python to Cobra.
If mypy isn't going to be fully Python compatible, why wouldn't people who were unhappy with Python just switch to another statically typed/compiled language that already exists?
Has anything like this been attempted?
There's an old question: which benefits of static typing are needed - and are they worth it? (to the extent that static typed languages are successful). Performance is obviously important. But if dynamically typed languages were fast enough for most needs, would the bulk of users switch to them? (note: it doesn't matter if static typing is faster still, just that dynamic typing is fast enough.)
Would it follow the long-term trend, of performance being traded for developer productivity? The real question behind this being whether dynamic typing actually increases developer productivity (and if so, for which tasks). Obviously, dynamic typing requires fewer keystrokes, less up-front design, and is more flexible (when iterating lean/agile). But static typing prevents some bugs, aids documentation, and helps manage projects as they increase in size.
However, this may be moot. We're no longer getting the long-term increases in single-core performance, so this trade-off may no longer apply. And, note: we do actually have some excess performance, and what has happened is in some cases it is absorbed by dynamic languages (python/ruby on serverside), and it is used to create smaller computers, lower performance, using less wattage (smartphones/tablets). iPhones use objective-c, androids use a java variant.
Maybe we've reached a point of equilibrium in the trade-off, if we include the end-user's demands?
BTW: I'm personally at home with stating typing, but I can't remember the last time it prevented a bug for me. (other people using it in others ways might have different experiences).
PS: to give another answer to your question: Flash's ActionScript 3.0. It's javascript plus optional static typing. Coincidentally, flash is dying/dead.
Personally I'd prefer explicit dynamic types and implicit type inference, but that's a personal preference. From working with C# and F# in my day job, F# is much more graceful without the 'var' everywhere that you get with C#'s inference.
sub fib (Int $n) returns Void {
my Int ($a, $b) = 0, 1;
while $a < $n {
say $a;
($a, $b) = ($b, $a + $b);
}
}
def foo( often int n ):
Once all call sites stop using the dynamic version, one can move on to strict typing (and remove the "often" qualifier), if such is the goal.
Think of it like the old "register" hint in C code (now obsolete).
Nit-pick: This post seem to say the dynamic typing and duck typing are the same thing. They're not. Duck-typing means looking up method names runtime by name, and not compile time by interface. Dynamic typing is a much bigger concept.
> Performance > Static typing can give you high, scalable and predictable efficiency, without the slow warm-up seen in many JIT compilers. These are important for interactive applications and games, for example.
static vs dynamic typing has not much to do with performance, at least as exposed here: the most widespread JIT out there is for a static language: Java, all the while both Python and Ruby can be compiled AOT (both to bytecode and to native code). Now type information could add some data for static analysis, which may bring some performance benefits but given how static typing is optional, such analysis (and following benefits) would only go so far.
> Compile-time and runtime type checking > Static typing makes it easier to find bugs and with less debugging (and with less staring at long stack traces)
Well, for the breadth and depth of Java and C# stack traces I have enjoyed, they can be equally confusing, just as dynamic typing stack traces can be equally informative. More often than not, a confounding stack trace is a code smell of some architectural issue.
> Grow your programs from dynamic to static typing > You can develop programs with dynamic types and only add static typing after your code has matured. This way you do not have maintain type declarations in initial development when the code is still changing rapidly.
This sounds interesting, but it also sounds like in reality, few areas would end up being typed and probably not enough for static typing to be meaningful. Humans are lazy, and programmers even more (that's why we tell machines to do stuff for us after all). I'm thinking a bit like for test suites, written after the fact or too late in the game. I guess that, just like tests, responsible developers will end up doing the right thing.
Maybe it's just the way it's worded and the project fundamentals are solid, but this sounds too much like a bullet list of "why dynamic typing is bad" (not that they're a silver bullet either) from someone who does not grok dynamic typing (and form the about page that does not seem to be the case, which only adds to my unease).
Anyway I'll keep mypy in my peripheral radar.
How is mypy different from Cython?
Cython is a variant of Python that supports compilation to efficient C modules. Mypy differs in the following aspects, among others:
Mypy will have a powerful type system that can detect many type errors while supporting a very Python-like programming model. Cython has simpler types that primarily serve to speed up code.
Mypy will be able to speed up most programs, even programs that heavily use object-oriented features. Cython is primarily focused on speeding up numerical code and tight loops.
Mypy will have new virtual machine that allows speeding up all parts of the VM, including standard libraries and the garbage collector. Cython uses the normal Python VM.
Cython supports accessing C functions directly and many features are defined in terms of translating them to C. Mypy is not bound to any particular target language and can support both C and Java backends, for example. However, accessing C library functionality in mypy will not be as easy as in Cython.
Boo - http://boo.codehaus.org/
Shed-skin - http://code.google.com/p/shedskin/
Both are projects that take a crack at adding or creating a static type system in concert with Python like syntax. The above take very different approaches.
Remember, the ideas was not to have CPython and CPython-without-GIL, it was to have one unified GIL-less CPython.
For that, a 2x performance drop would not be acceptable for most code.
And I don't see why performance is suddenly used as a deal breaker for a language primarily used for scripting and other non-CPU-bound purposes.
I speculate that the performance issue wasn't really the most important reason for rejecting the patch, but more so not wanting to deal with the complexity of maintaining the patch. It's a shame because the future is definitely going to have lots of cores, and not all problems are well-suited to multiprocessing.
People want to use Python for lots of CPU bound processes. A big Python niche is numeric and scientific computing.
You say that it is "primarily used for scripting and other non-CPU-bound purposes", but this is also a consequence of it being slow. If it was faster it would open MORE uses. LUA and C# got the games action. Go gets attention on the networking apps end, etc...