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In otherwords: *.h/*.c --[??POSSIBLE??]--> *.pxd/*.pyx
OK. I’ve done (I hope) enough digging around the Internet - but I think this is a good question so I’ll ask it straight.
There are a few related questions (e.g. Generate python bindings, what methods/programs to use or Wrapping a C library in Python: C, Cython or ctypes? ) but which don't quite sum up the situation that I’m asking which is perhaps for a more “high-level” approach (and specifically for an existing library, not generating new C from python).
I’ve got a little bit of experience of this myself having wrapped a wee bit of code before using Cython. Cython gets the thumbs up for speed and maintainability. That’s OK in my book for small/single bits of code - but, this time I’ve got a bit more on my plate…
And following the first of the three great virtues of a programmer - I want to do this with as minimal effort as possible.
So the real question here is how can I ease the creation by automated means of the .pxd, and possibly .pyx, files (i.e. to save time and not slip up miss-typing something).
This here seems to be the only real hint/note about how to do this - but most of the projects on it are defunct, old or sourceforge. Many only seem to work for C++ (this is C I'm doing here).
Does anyone still use them? Recently? Has anyone got a workflow or best practice for doing this? Am I simply just better doing it by hand?
My library is well defined by a set of header files. One containing defs of all the C struct/types and another containing prototypes for all the functions. But it's loooonnnggg...
Thanks for any tips.
UPDATE (25th August, 2015):
Right, so over the last few months when I had a spare moment, I tried:
CFFI (thank for #David pointing that out) - has a noble aim of "to call C code from Python without learning a 3rd language: existing alternatives require users to learn domain specific language (Cython, SWIG) or API (ctypes)” - but it didn’t quite fit the bill as it involved a fair degree of embedded C code in the actual python files (or loading it in). This would be a pretty manual process to do for a large library. Maybe I missed something…
SWIG is the granddaddy of Python binding, and is pretty solid. Fundamentally though, it is not “hands off” as I understand it - i.e. you need a separate specification file. For example, you have to edit all your C header files to indicate building a python module with a #define SWIG_FILE_WITH_INIT or use other annotations. SIP has the same issue here. You don’t auto-generate from the headers, you modify them to include your own directives and annotations and create a complete specification file.
cwrap - I’m on a Mac so I used this version for clang. https://github.com/geggo/cwrap Really poor doc - but using the source I finally got it to run and it generated…. an empty .pyx file from a pretty simple header of structs. Not so good.
xdress - This showed promise. The website is down so the docs are actually seemingly here. There’s an impressive amount of work gone into it and it looks straightforward to use. But it needed all the llvm headers (and a correctly linked version of clang). I had to use brew install llvm —with-clang. There is a xdressclang-3.5 branch, but it doesn’t seem to have enough fixes done. I tried tapping homebrew/versions for an earlier version of clang (install llvm33 / llvm34) and that got it built. Anyway, I digress… it worked great for a simple example, but the resulting ctypes files for the full library was pretty garbled and refused to build. Something in the AST C->Python is a bit awry...
ctypesgen wasn’t one I had encountered in the original search. The documentation is pretty sparse - or you might call it concise. It hasn’t seemingly had much work done on it the last 4 years either (and people enquiring on the issues list if the developers are ever going to further the project). I’ve tried running it, but sadly it seems to fall over with what I suspect/seems like issues with the Clang compiler cdefs.h use of _attribute_. I’ve tried things like -std=c11 but to no avail.
In conclusion, out of all the ones I’ve looked at I think xdress came the closest to the fully automated generation of python bindings. It worked fine for the simple examples given, but couldn’t handle the more complex existing library headers, with all the complexities of forward declarations, enumerated types, void pointers… It seems a well designed and (for a while) well maintained project, so there is possibly some way to circumvent these issues if someone were to take it on again.
Still, the question remains, does anyone have a robust toolchain for generating python wrappers from C headers automatically? I think the reality is there always has to be a bit of manual work, and for that CFFI looks the most “modern” approach (one of the best overviews/comparisons I encountered is here) - yet it always involves a specially edited cdef() version of any header files (e.g. Using Python's CFFI and excluding system headers).
I find ctypesgen great for autogeneration. I'm only using it with one or two python modules that I hope to open source, and I've been happy so far. Here's a quick example using it with zlib, but I also just tried it successfully with a few other libraries:
(Edit: I know you mentioned ctypesgen has problems on a mac, so maybe it needs someone to tweak it to work on OSX - I don't have OSX at home or I'd try it.)
Get ctypesgen:
git clone https://github.com/davidjamesca/ctypesgen.git
Run short script to call ctypesgen (replace zlib info with another library):
import os
ZLIB_INC_DIR = "/usr/include"
ZLIB_LIB_DIR = "/usr/lib/x86_64-linux-gnu"
ZLIB_LIB = "libz.so"
ZLIB_HEADERS = "/usr/include/zlib.h"
# Set location of ctypesgen.py
ctypesgen_path = 'ctypesgen/ctypesgen.py'
wrapper_filename = 'zlib.py'
cmd = "LD_LIBRARY_PATH={} {} -I {} -L {} -l {} {} -o {}".format(
ZLIB_LIB_DIR, ctypesgen_path, ZLIB_INC_DIR, ZLIB_LIB_DIR, ZLIB_LIB,
ZLIB_HEADERS, wrapper_filename)
print(cmd)
os.system(cmd)
Usage example:
python
>>> import zlib
>>> zlib.compress("asdfasdfasdfasdfasdf")
'x\x9cK,NIKD\xc3\x00T\xfb\x08\x17'
Is there a language based on S-expressions with powerful macros that allows as seamless integration with Python as Clojure with JVM?
I want to try using such syntax and features while having access to all usual python libraries (including PyQt).
I've been working a project to do this: psil. I have a series of blog posts talking about what I've done. Here's the short manifesto:
Psil is a new general-purpose programming language in the Lisp family of languages. Psil is implemented on top of Python, allowing easy access to existing Python libraries.
Best features from Lisp and Scheme
Complete language in its own right
Built upon the Python standard libraries
Strong interoperability with Python code
The reality hasn't quite caught up to the vision; for example I don't think there is a way to declare new classes in Psil code that can be used from Python. But at least for functions, it's mostly there.
Note that Psil is built completely on Python 3, and there is no Python 2 version. I don't know whether there is a PyQt for Python 3.
While these aren't exactly what you're looking for, check:
CLPython - an implementation of Python in Common Lisp
(An ((Even Better) Lisp) Interpreter (in Python))
Check out Boo; it's a python-inspired language that runs on the CLR, with built-in support for full macros. If that's what you're missing from Lisp, give it a shot. A friend swears by it.
How practical would it be to use Cython as the primary programming language for a game?
I am a experienced Python programmer and I absolutely love it, but I'm admittedly a novice when it comes to game programming specifically. I know that typically Python is considered too slow to do any serious game programming, which is why Cython is interesting to me. With Cython I can use a Python-like language with the speed of C.
I understand that I'll probably need to learn a bit of C/C++ anyway, but it seems like Cython would speed up development time quite a bit in comparison.
So, is it practical? And would I still be able to use C/C++ libraries like OpenGL, OpenAL, and Bullet Physics?
If you're working with a combination like that and your goal is to write a 3D game, you'd probably get better mileage out of a ready-made 3D engine with mature physics and audio bindings and a Python API like OGRE 3D or Panda3D. Even if you don't, this post about using Cython with Panda3D may be helpful.
I'm not sure about now, but back in 2007, the trade-off between the two was basically that:
Panda3D was better-documented and designed from the ground-up to be a C++ accelerated Python engine (apparently they made some API design decisions that don't occur to C++ engine projects) and, predictably, had a more mature Python API.
PyOgre was built on top of a much more advanced engine and had a larger and more vibrant community.
...however it's quite possible that may have changed, given that, passage of time aside, in 2007, Panda3D was still under a GPL-incompatible license and that drove off a lot of people. (Myself included)
I'm the developer for the Ignifuga Game Engine, it's 2D oriented and Python/Cython/SDL based. What I generally do is develop the code in Python, and then profile the engine to see if there are some obvious bottlenecks (the main loop, the rendering code are good candidates), and convert those modules to Cython. I then run all the code (Python and Cython based) through Cython, and compile it statically against SDL.
Another of Cython's big "pluses" is that binding to SDL, or any C based library, is almost trivial.
Regarding threads, the engine is currently single threaded with cooperative multitasking via Greenlets, though this comes from a design decision to mitigate potential multi threaded pitfalls that unexperienced developers might fall into, rather than a limitation on Cython's part.
at this date (12th of April 2011) unixmab83 is wrong.
Cython doesn't forbid the use of threads, you just needs to use the no_gil special statements.
Beside the bindins of c++ is now functional in cython.
We do use it for something which is close to gamedev. So while I cannot be final on this, cython is a valid candidate.
I've found that a lot of the time, especially for larger libraries, you wind up spending a tremendous amount of time just configuring the Cython project to build, knowing which structures to import, bridging the C code into Python in either direction etc. While Cython is a nice stopgap (and significantly more pleasant than pure C/C++ development), the amount of C++ you'd have to learn to effectively use it basically means you're going to have to bite the bullet and learn C++ anyway.
How about PyGame?
I know Cython and you do not have to know C/C++.
You will use static typing but very easy.
The hardest part is to get the compiling working, I think on Windows this is done over visual studio thing.
There is something like a standard library including math for example. The speed gain is not too big but this depends on your scope.
ctypes was much faster (pure C) but the connection to Python was very slow so that i decided to look for Cython which can still be dynamic.
For speed gain in a game Cython will be the right choice but i would name this performance also limited.
Threads!!! A good modern game must use threads. Cython practically forbids their use, holding to GIL (global interpreter lock) the entire time, making your code run in sequence.
If you are not writing a huge game, than Python/Cython is okay. But Cython is no good as a modern language without good thread support.
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I know a bunch of scripting languages, (python, ruby, lua, php) but I don't know any compiled languages like C/C++ , I wanted to try and speed up some python code using cython, which is essentially a python -> C compiler, aimed at creating C extensions for python. Basically you code in a stricter version of python which compiles into C -> native code.
here's the problem, I don't know C, yet the cython documentation is aimed at people who obviously already know C (nothing is explained, only presented), and is of no help to me, I need to know if there are any good cython tutorials aimed at python programmers, or if I'm gonna have to learn C before I learn Cython.
bear in mind I'm a competent python programmer, i would much rather learn cython from the perspective of the language I'm already good at, rather than learn a whole new language in order to learn cython.
1) PLEASE don't recommend psyco
edit: ANY information that will help understand the oficial cython docs is useful information
cython is good at two different things
Interfacing with C language libraries
Speeding up Python code
It probably gets more exposure from 1. hence the emphasis on the tutorial materials you've found towards C stuff. It sounds like you want to use it like 2. though.
From my experience with cython you can just try compiling your python programs and see if it works. It will get a bit faster (maybe). To get a lot faster you need to selectively turn python types into C types. This starts to bring out the power of cython.
If you look at the official tutorial you need to study where they've used the cdef keyword.
So to recap
Make your existing python program compile with cython with as few changes as possible
Declare some variables as cdef and make it work again
If not fast enough go to step 2.
I'm sorry that isn't a pointer to a tutorial, but it should give you a direction to go in!
Learn C! (Sorry -- irresistible.)
Seriously, though, it seems like you mostly need to know about C variable types (C types, if you will) in order to use cdef effectively.
Later on, if you do decide to bite the bullet and learn C properly, treat yourself to a copy of Kernighan and Ritchie, or K & R, available on Amazon.
Have you seen this: http://www.perrygeo.net/wordpress/?p=116 seems like a pretty good overview. You could also have a look at the source in pyzmq and gevent - they use Cython for their core code.
Ben
Cython does support concurrency (you can use native POSIX threads with c, that can be compiled in extent ion module) , you just need to be careful enough to not to modify any python objects when GIL is released and keep in mind the interpreter itself is not thread safe. You can also use multiprocessing with python to use more cores for parallelism which can in turn use your compiled cython extensions to speed up even more. But all in all you definitely have to know c programming model , static types etc
You can do a lot of very useful things with Cython if you can answer the following C quiz...
(1) What is a double? What is an int?
(2) What does the word "compile" mean?
(3) What is a header (.h) file?
To answer these questions you don't need to read a whole C book! ...maybe chapter 1.
Once you can pass that quiz, try again with the tutorial.
What I usually do is start with pure python code, and add Cython elements bit by bit. In that situation, you can learn the Cython features bit by bit. For example I don't understand C strings, because so far I have not tried to cythonize code that involves strings. When I do, I will first look up how strings work in C, and then second look up how strings work in Cython.
Again, once you've gotten started with Cython, you will now and then run into some complication that requires learning slightly more C. And of course the more C you know, the more dextrous you will be with taking full advantage of Cython, not to mention troubleshooting if something goes wrong. But that shouldn't make you reluctant to start!
Cython does not support threads well at all. It holds the GIL (Global Intrepreter Lock) the entire time! This makes your code thread-safe by (virtually) disabling concurrent execution. So I wouldn't use it for general purpose development.
About all the C that you really need to know is:
C types are much faster than Python types (adding to C ints or doubles can be done in a single clock cycle) but less safe (they are not arbitrarily sized and may silently overflow).
C function (cdef) calls are much faster than Python (def) function calls (but are less flexible).
This will get you most of the way there. If you want to eke out that last 10-20% speedup for most applications, there's no getting around knowing C, and how modern processes work (pointers, cache, ...).
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Right now I'm developing mostly in C/C++, but I wrote some small utilities in Python to automatize some tasks and I really love it as language (especially the productivity).
Except for the performances (a problem that could be sometimes solved thanks to the ease of interfacing Python with C modules), do you think it is proper for production use in the development of stand-alone complex applications (think for example to a word processor or a graphic tool)?
What IDE would you suggest? The IDLE provided with Python is not enough even for small projects in my opinion.
We've used IronPython to build our flagship spreadsheet application (40kloc production code - and it's Python, which IMO means loc per feature is low) at Resolver Systems, so I'd definitely say it's ready for production use of complex apps.
There are two ways in which this might not be a useful answer to you :-)
We're using IronPython, not the more usual CPython. This gives us the huge advantage of being able to use .NET class libraries. I may be setting myself up for flaming here, but I would say that I've never really seen a CPython application that looked "professional" - so having access to the WinForms widget set was a huge win for us. IronPython also gives us the advantage of being able to easily drop into C# if we need a performance boost. (Though to be honest we have never needed to do that. All of our performance problems to date have been because we chose dumb algorithms rather than because the language was slow.) Using C# from IP is much easier than writing a C Extension for CPython.
We're an Extreme Programming shop, so we write tests before we write code. I would not write production code in a dynamic language without writing the tests first; the lack of a compile step needs to be covered by something, and as other people have pointed out, refactoring without it can be tough. (Greg Hewgill's answer suggests he's had the same problem. On the other hand, I don't think I would write - or especially refactor - production code in any language these days without writing the tests first - but YMMV.)
Re: the IDE - we've been pretty much fine with each person using their favourite text editor; if you prefer something a bit more heavyweight then WingIDE is pretty well-regarded.
You'll find mostly two answers to that – the religous one (Yes! Of course! It's the best language ever!) and the other religious one (you gotta be kidding me! Python? No... it's not mature enough). I will maybe skip the last religion (Python?! Use Ruby!). The truth, as always, is far from obvious.
Pros: it's easy, readable, batteries included, has lots of good libraries for pretty much everything. It's expressive and dynamic typing makes it more concise in many cases.
Cons: as a dynamic language, has way worse IDE support (proper syntax completion requires static typing, whether explicit in Java or inferred in SML), its object system is far from perfect (interfaces, anyone?) and it is easy to end up with messy code that has methods returning either int or boolean or object or some sort under unknown circumstances.
My take – I love Python for scripting, automation, tiny webapps and other simple well defined tasks. In my opinion it is by far the best dynamic language on the planet. That said, I would never use it any dynamically typed language to develop an application of substantial size.
Say – it would be fine to use it for Stack Overflow, which has three developers and I guess no more than 30k lines of code. For bigger things – first your development would be super fast, and then once team and codebase grow things are slowing down more than they would with Java or C#. You need to offset lack of compilation time checks by writing more unittests, refactorings get harder cause you never know what your refacoring broke until you run all tests or even the whole big app, etc.
Now – decide on how big your team is going to be and how big the app is supposed to be once it is done. If you have 5 or less people and the target size is roughly Stack Overflow, go ahead, write in Python. You will finish in no time and be happy with good codebase. But if you want to write second Google or Yahoo, you will be much better with C# or Java.
Side-note on C/C++ you have mentioned: if you are not writing performance critical software (say massive parallel raytracer that will run for three months rendering a film) or a very mission critical system (say Mars lander that will fly three years straight and has only one chance to land right or you lose $400mln) do not use it. For web apps, most desktop apps, most apps in general it is not a good choice. You will die debugging pointers and memory allocation in complex business logic.
In my opinion python is more than ready for developing complex applications. I see pythons strength more on the server side than writing graphical clients. But have a look at http://www.resolversystems.com/. They develop a whole spreadsheet in python using the .net ironpython port.
If you are familiar with eclipse have a look at pydev which provides auto-completion and debugging support for python with all the other eclipse goodies like svn support. The guy developing it has just been bought by aptana, so this will be solid choice for the future.
#Marcin
Cons: as a dynamic language, has way
worse IDE support (proper syntax
completion requires static typing,
whether explicit in Java or inferred
in SML),
You are right, that static analysis may not provide full syntax completion for dynamic languages, but I thing pydev gets the job done very well. Further more I have a different development style when programming python. I have always an ipython session open and with one F5 I do not only get the perfect completion from ipython, but object introspection and manipulation as well.
But if you want to write second Google
or Yahoo, you will be much better with
C# or Java.
Google just rewrote jaiku to work on top of App Engine, all in python. And as far as I know they use a lot of python inside google too.
I really like python, it's usually my language of choice these days for small (non-gui) stuff that I do on my own.
However, for some larger Python projects I've tackled, I'm finding that it's not quite the same as programming in say, C++. I was working on a language parser, and needed to represent an AST in Python. This is certainly within the scope of what Python can do, but I had a bit of trouble with some refactoring. I was changing the representation of my AST and changing methods and classes around a lot, and I found I missed the strong typing that would be available to me in a C++ solution. Python's duck typing was almost too flexible and I found myself adding a lot of assert code to try to check my types as the program ran. And then I couldn't really be sure that everything was properly typed unless I had 100% code coverage testing (which I didn't at the time).
Actually, that's another thing that I miss sometimes. It's possible to write syntactically correct code in Python that simply won't run. The compiler is incapable of telling you about it until it actually executes the code, so in infrequently-used code paths such as error handlers you can easily have unseen bugs lurking around. Even code that's as simple as printing an error message with a % format string can fail at runtime because of mismatched types.
I haven't used Python for any GUI stuff so I can't comment on that aspect.
Python is considered (among Python programmers :) to be a great language for rapid prototyping. There's not a lot of extraneous syntax getting in the way of your thought processes, so most of the work you do tends to go into the code. (There's far less idioms required to be involved in writing good Python code than in writing good C++.)
Given this, most Python (CPython) programmers ascribe to the "premature optimization is the root of all evil" philosophy. By writing high-level (and significantly slower) Python code, one can optimize the bottlenecks out using C/C++ bindings when your application is nearing completion. At this point it becomes more clear what your processor-intensive algorithms are through proper profiling. This way, you write most of the code in a very readable and maintainable manner while allowing for speedups down the road. You'll see several Python library modules written in C for this very reason.
Most graphics libraries in Python (i.e. wxPython) are just Python wrappers around C++ libraries anyway, so you're pretty much writing to a C++ backend.
To address your IDE question, SPE (Stani's Python Editor) is a good IDE that I've used and Eclipse with PyDev gets the job done as well. Both are OSS, so they're free to try!
[Edit] #Marcin: Have you had experience writing > 30k LOC in Python? It's also funny that you should mention Google's scalability concerns, since they're Python's biggest supporters! Also a small organization called NASA also uses Python frequently ;) see "One coder and 17,000 Lines of Code Later".
Nothing to add to the other answers, besides that if you choose python you must use something like pylint which nobody mentioned so far.
One way to judge what python is used for is to look at what products use python at the moment. This wikipedia page has a long list including various web frameworks, content management systems, version control systems, desktop apps and IDEs.
As it says here - "Some of the largest projects that use Python are the Zope application server, YouTube, and the original BitTorrent client. Large organizations that make use of Python include Google, Yahoo!, CERN and NASA. ITA uses Python for some of its components."
So in short, yes, it is "proper for production use in the development of stand-alone complex applications". So are many other languages, with various pros and cons. Which is the best language for your particular use case is too subjective to answer, so I won't try, but often the answer will be "the one your developers know best".
Refactoring is inevitable on larger codebases and the lack of static typing makes this much harder in python than in statically typed languages.
And as far as I know they use a lot of python inside google too.
Well i'd hope so, the maker of python still works at google if i'm not mistaken?
As for the use of Python, i think it's a great language for stand-alone apps. It's heavily used in a lot of Linux programs, and there are a few nice widget sets out there to aid in the development of GUI's.
Python is a delight to use. I use it routinely and also write a lot of code for work in C#. There are two drawbacks to writing UI code in Python. one is that there is not a single ui framework that is accepted by the majority of the community. when you write in c# the .NET runtime and class libraries are all meant to work together. With Python every UI library has at's own semantics which are often at odds with the pythonic mindset in which you are trying to write your program. I am not blaming the library writers. I've tried several libraries (wxwidgets, PythonWin[Wrapper around MFC], Tkinter), When doing so I often felt that I was writing code in a language other than Python (despite the fact that it was python) because the libraries aren't exactly pythonic they are a port from another language be it c, c++, tk.
So for me I will write UI code in .NET (for me C#) because of the IDE & the consistency of the libraries. But when I can I will write business logic in python because it is more clear and more fun.
I know I'm probably stating the obvious, but don't forget that the quality of the development team and their familiarity with the technology will have a major impact on your ability to deliver.
If you have a strong team, then it's probably not an issue if they're familiar. But if you have people who are more 9 to 5'rs who aren't familiar with the technology, they will need more support and you'd need to make a call if the productivity gains are worth whatever the cost of that support is.
I had only one python experience, my trash-cli project.
I know that probably some or all problems depends of my inexperience with python.
I found frustrating these things:
the difficult of finding a good IDE for free
the limited support to automatic refactoring
Moreover:
the need of introduce two level of grouping packages and modules confuses me.
it seems to me that there is not a widely adopted code naming convention
it seems to me that there are some standard library APIs docs that are incomplete
the fact that some standard libraries are not fully object oriented annoys me
Although some python coders tell me that they does not have these problems, or they say these are not problems.
Try Django or Pylons, write a simple app with both of them and then decide which one suits you best. There are others (like Turbogears or Werkzeug) but those are the most used.