smop 0.26.2

With less than five thousands lines of python code

SMOP does not pretend to compete with such polished products as matlab or octave. Yet, it is not a toy. There is an attempt to follow the original matlab semantics as close as possible. Matlab language definition (never published afaik) is full of dark corners, and SMOP tries to follow matlab as precisely as possible.

There is a price, too.

The generated sources are matlabic, rather than pythonic, which means that library maintainers must be fluent in both languages, and the old development environment must be kept around.

Should the generated program be pythonic or matlabic?

For example should array indexing start with zero (pythonic) or with one (matlabic)?

I beleive now that some matlabic accent is unavoidable in the generated python sources. Imagine matlab program is using regular expressions, matlab style. We are not going to translate them to python style, and that code will remain forever as a reminder of the program’s matlab origin.

Another example. Matlab code opens a file; fopen returns -1 on error. Pythonic code would raise exception, but we are not going to do that. Instead, we will live with the accent, and smop takes this to the extreme — the matlab program remains mostly unchanged.

It turns out that generating matlabic` allows for moving much of the project complexity out of the compiler (which is already complicated enough) and into the runtime library, where there is almost no interaction between the library parts.

Working example: solver.m

We will translate solver.m to present a sample of smop features. The program was borrowed from the matlab programming competition in 2004 (Moving Furniture). For the impatient, it is possible to compile and run the example without installing smop:

$ tar zxvf smop-0.25.4.tar.gz
$ cd smop-0.25.4/smop
$ python main.py solver.m
$ python go.py

To the left is solver.m. To the right is a.py — its translation to python. Though only 30 lines long, this example shows many of the complexities of converting matlab code to python.

01   function mv = solver(ai,af,w)  01 def solver_(ai,af,w,nargout=1):
02   nBlocks = max(ai(:));          02     nBlocks=max_(ai[:])
03   [m,n] = size(ai);              03     m,n=size_(ai,nargout=2)

04   I = [0  1  0 -1];              04     I=matlabarray([0,1,0,- 1])
05   J = [1  0 -1  0];              05     J=matlabarray([1,0,- 1,0])
06   a = ai;                        06     a=copy_(ai)
07   mv = [];                       07     mv=matlabarray([])

08   while ~isequal(af,a)           08     while not isequal_(af,a):
09     bid = ceil(rand*nBlocks);    09         bid=ceil_(rand_() * nBlocks)
10     [i,j] = find(a==bid);        10         i,j=find_(a == bid,nargout=2)
11     r = ceil(rand*4);            11         r=ceil_(rand_() * 4)
12     ni = i + I(r);               12         ni=i + I[r]
13     nj = j + J(r);               13         nj=j + J[r]

14     if (ni<1) || (ni>m) ||       14         if (ni < 1) or (ni > m) or
               (nj<1) || (nj>n)                            (nj < 1) or (nj > n):
15         continue                 15             continue
16     end                          16
17     if a(ni,nj)>0                17         if a[ni,nj] > 0:
18         continue                 18           continue
19     end                          19
20     [ti,tj] = find(af==bid);     20         ti,tj=find_(af == bid,nargout=2)
21     d = (ti-i)^2 + (tj-j)^2;     21         d=(ti - i) ** 2 + (tj - j) ** 2
22     dn = (ti-ni)^2 + (tj-nj)^2;  22         dn=(ti - ni) ** 2 + (tj - nj) ** 2
23     if (d<dn) && (rand>0.05)     23         if (d < dn) and (rand_() > 0.05):
24         continue                 24             continue
25     end                          25
26     a(ni,nj) = bid;              26         a[ni,nj]=bid
27     a(i,j) = 0;                  27         a[i,j]=0
28     mv(end+1,[1 2]) = [bid r];   28         mv[mv.shape[0] + 1,[1,2]]=[bid,r]
29  end                             29
30                                  30     return mv

Which one is faster — python or octave? I don’t know.

Doing reliable performance measurements is notoriously hard, and is of low priority for me now. Instead, I wrote a simple driver go.m and go.py and rewrote rand so that python and octave versions run the same code. Then I ran the above example on my laptop. The results are twice as fast for the python version. What does it mean? Probably nothing. YMMV.

ai = zeros(10,10);
af = ai;

ai(1,1)=2;
ai(2,2)=3;
ai(3,3)=4;
ai(4,4)=5;
ai(5,5)=1;

af(9,9)=1;
af(8,8)=2;
af(7,7)=3;
af(6,6)=4;
af(10,10)=5;

tic;
mv = solver(ai,af,0);
toc

$ cd smop
$ make check

lei@dilbert ~/smop-github/smop $ python main.py -h
SMOP compiler version 0.25.1
Usage: smop [options] file-list
    Options:
    -V --version
    -X --exclude=FILES      Ignore files listed in comma-separated list FILES
    -d --dot=REGEX          For functions whose names match REGEX, save debugging
                            information in "dot" format (see www.graphviz.org).
                            You need an installation of graphviz to use --dot
                            option.  Use "dot" utility to create a pdf file.
                            For example:
                                $ python main.py fastsolver.m -d "solver|cbest"
                                $ dot -Tpdf -o resolve_solver.pdf resolve_solver.dot
    -h --help
    -o --output=FILENAME    By default create file named a.py
    -o- --output=-          Use standard output
    -s --strict             Stop on the first error
    -v --verbose

01   ok = 0                         01 def solver_(c):
02   if c                           02     if c:
03      ok = f00                    03         ok = f00()

  .. code:: matlab