Motivation for re-implementing R (GNU S) in PyPy (perhaps?)
Well, R is free and open sourced. It is based on S and Splus, which the developers decided to close-source after a while (after achieving success). R is also known as GNU S for that matter. IMHO, R itself is a simple language and a relatively simple platform. It is a language that manipulates data but philosophically, it is not a statistical or mathematical language. The R core team seems to adhere to the view that R is a programming language where (by chance and intent) a lot of statistical tools are built on. Hence, most of the statistical libraries for R are written in R. I believe that the entire Bioconductor is written in R. Hence, the motivation for considering implementing or re-implementing R in PyPy is "natively" to bring in the strength of R (huge amounts of statistical libraries and data sets) into Python. I have to admit that my skills in this area (langauage interpreters/compilers) is rather limited to undergraduate level but I'm willing to learn more. So I'm currently reading codes on language interpreters for esoteric languages like BrainFuck simply because they are tiny but provides the relevant concepts. So, anyone like to support the idea of (re-)implementing R on PyPy? I'll need some "emotional" strength to go on this route. Also willing to hear other radical options... Another thing of interest to me is implementing modeling/simulation languages in PyPy as it will help in my future career route but my knowledge of even the simulation languages are rather limited. Please advice and comment. Cheers maurice
Em 23/04/2007, às 21:47, Maurice Ling escreveu:
So, if the language is open source, quite simple and the only advantage would be to have huge amounts of libraries... Why not just make a code conversion from R to Numpy + Python, a full featured language, used in a lot more places. Then you can get the libraries and also a bigger comunity. Just remember that making an interpreter of R using pypy will not bring anything to python, as they would be diferent interpreters that just happen to be inplemented on the same platform. It would keep the same distance as R as it is today (implemented in C I suppose) and Python (also made in C if you consider CPython). So I would probably say, do the Numpy work on pypy than make a translator R2Py so you can run your favorite R library on top of your optimized Numpy implementation.
Mine to, but there is a lot of really brainy people on the project... and they really help (specially if you could go to some sprint)
Making Numpy work as we are saying (making it really optimized and cache friendly) is probably going to help your career even more.
Please advice and comment.
My two cents. -- Leonardo Santagada santagada@gmail.com
This could be a nice idea - make R codes run on Numpy + Python. My line of thought is that since there is an implementation of Numpy (I do not know how complete it is) in PyPy already (pypy/rpython/numpy), implementing a means to get R codes to run in RPython will be just as equally good, since RPython are essentially complete Python programs. Kinda killing 2 birds with a stone, or so.
I think this line of argument can be made against every language interpreter implemented in PyPy, essentially everything can be interfaced at C. IMHO, what PyPy offers is the language-optimization-platform modularization. This may be loafy goal but sure worth the effort. I am not sure what making an interpreter of X (where X can be any language, such as javascript or prolog) using PyPy will really bring anything to Python than implementing X interpreter directly on Python itself (without using PyPy). Please enlighten me on this. Cheers maurice
Em 23/04/2007, às 21:47, Maurice Ling escreveu:
So, if the language is open source, quite simple and the only advantage would be to have huge amounts of libraries... Why not just make a code conversion from R to Numpy + Python, a full featured language, used in a lot more places. Then you can get the libraries and also a bigger comunity. Just remember that making an interpreter of R using pypy will not bring anything to python, as they would be diferent interpreters that just happen to be inplemented on the same platform. It would keep the same distance as R as it is today (implemented in C I suppose) and Python (also made in C if you consider CPython). So I would probably say, do the Numpy work on pypy than make a translator R2Py so you can run your favorite R library on top of your optimized Numpy implementation.
Mine to, but there is a lot of really brainy people on the project... and they really help (specially if you could go to some sprint)
Making Numpy work as we are saying (making it really optimized and cache friendly) is probably going to help your career even more.
Please advice and comment.
My two cents. -- Leonardo Santagada santagada@gmail.com
This could be a nice idea - make R codes run on Numpy + Python. My line of thought is that since there is an implementation of Numpy (I do not know how complete it is) in PyPy already (pypy/rpython/numpy), implementing a means to get R codes to run in RPython will be just as equally good, since RPython are essentially complete Python programs. Kinda killing 2 birds with a stone, or so.
I think this line of argument can be made against every language interpreter implemented in PyPy, essentially everything can be interfaced at C. IMHO, what PyPy offers is the language-optimization-platform modularization. This may be loafy goal but sure worth the effort. I am not sure what making an interpreter of X (where X can be any language, such as javascript or prolog) using PyPy will really bring anything to Python than implementing X interpreter directly on Python itself (without using PyPy). Please enlighten me on this. Cheers maurice
participants (3)
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Leonardo Santagada -
Maurice Ling -
Simon Burton