Little Python to C++ Compiler
For the impatient: this probably does NOT do what you want, yet. Check back in a couple of months time.
Pyxie is intended to be a simple Python to C++ compiler, with a target of compiling python code such that it can run on a microcontroller - like Arduino, MSP430 or ARM mbed type devices.
The name is a play on words. Specifically, Python to C++ - can be py2cc or pycc. If you try pronouncing “pycc” it can be “pic”, “py cc” or pyc-c”. The final one leads to Pixie.
This is unlikely to ever be a completely general python to C++ compiler - if you’re after than look at Shed Skin, or things like Cython, Pyrex, and PyPy. (in terms of diminishing similarity) The difference this project has from those is that this project assumes a very small target device. Something along the lines of an Atmega 8A, Atmega 328 or more capable.
This is also a difference from MicroPython - which is designed to run on microcontrollers larger than the Atmega 8A.
In the past I’ve written a test driven compiler suite, so I’ll be following the same approach here. It did consider actually making Pyxie use that as a frontend, but for the moment, I’d like python compatibility.
Huh? What job does this do?
It allows a user to write code in a familiar high level language that can then be compiled to run on an arbitrary embedded system - that is devices with very low power CPUs and very little memory.
In particular, one thing it should do (when complete) help support The Scout Association’s “Digital Maker” badge, but that’s some way off!
What does it do?
What it does do:
- Recognise python programs with simple assigment & print statements
- Parse those to an AST
- Can represent equivalent C programs using a concrete C representation (CST)
- Can translate the AST to the CST and then generate C++ code from the CST
That means it can compile one very very simple type of python program that looks like this…
greeting = “hello” name = “world”
print greeting, name
… into the equivalent C program.
Yes, that’s not a lot. But on the flipside, it’s a starting point.
- Many moons ago, I made a generic language parser which I called SWP (semantic
- whitespace parser), or Gloop.
It was an experiment to see if you could write a parser that had no keywords, or similar, in a completely test driven fashion. ie a bit like a parser for a Lisp like language that would look like python or ruby. It turns out that you can and there’s lots of interesting things that arise if you do. (Best seen in the slideshare link)
Which version of Python?
Well, it won’t be a complete subset of any particular python - it will probably be based around the intersection points in python 2 and 3. It will be, by definition, a non-dynamic subset - at least at first
(The language definition is coming)
Why write this?
Personally, having built something simpler in the past, I know I’d find it useful. (I use python rather than C++ often because I can write more quicker with the former). Also, I work with kids in my spare time, and it opens up options there.
I’ve written something like this for work last year, but that was much more limited and restricted in both aspiration and implementation. This rewrite is something I’ve done on my own time, with my own tools, from scratch, which allows me to share this with others.
Two major changes:
- This aims to be a more rounded implementation
- This performs transforms from an AST (abstract syntax tree) to a CCR (concrete code representation), rather than munging code directly from a concrete parse tree.
That potentially allows other things, like creation of visual representations of programs from code as well.
Is this part of any larger project?
Not directly. If anything, it’s a continuation of the personal itch around SWP from about 10 years ago. Unlike that though, it’s much, much better structured.
One thing that may happen though is the ability to take python classes and derive iotoy device implementations/interfaces directly. (since iotoy was inspired heavily by python introspection) That’s quite some time off.
Michael Sparks, April 2015
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