Matlab to Python converter
Project description
Summary
SMOP stands for Small Matlab/Octave to Python compiler. It is supposed to help those who attempt migration from Matlab to Python. Despite the similarities between the two languages, there are enough differences to make manual translation too time consuming.
SMOP is not a polished product, nor a replacement to Octave and Matlab. Taking into account its size (less than 3000 lines), this is not surprizing. There are no toolboxes. Small everyday functions (max, length, etc.) are recognized and supported, but that’s all.
SMOP is written in Python, using PLY – Python Lex/Yacc for lexical analysis and parsing, and numpy for runtime environment. SMOP is platformindependent, but is tested only on Linux. It is a commandline utility.
Example
It is possible to run an example without installing smop. Just unzip it somewhere, and cd there. In your current directory you will find a bunch of .py files and a file named fastsolver.m. It is taken from the winning submission to Matlab programming competition in 2004 (Moving Furniture http://www.mathworks.cn/matlabcentral/contest/contests/12/submissions/29989).
Now type python main.py fastsolver.m o fastsolver.py. If you don’t specify the output file with o option, it is written to a.py. Each time a function is translated, its name is written.
lei@fuji:~/smop/smop$ python main.py fastsolver.m
fastsolver.m
solver
cbest
mainsolver
imoves
easysolver
localfiddler
findoverlaps
dijkstra
improve
TLL79
solverA
solver1
movefrompos
onemove
solver2
SearchPath
Faster10IntReps2
matrixsolver
outoftheway
ismember1
ismember2
setdiff
unique
sub2ind
randperm
perms
itTakesAThief
movefurniture
findshortestpath
dealWall1
lei@fuji:~/smop/smop$
The entire submission contains 2093 lines, and it is automatically translated to Python by smop. These are the good news. The bad news are that generating the code is not enough to run the program, so there are no performance numbers yet.
While the submission itself — the solver program — does not use graphics, the envelope code that is responsible to run the submission, collect and display the results, does. So about 100 lines of the envelope must be rewritten by hand.
Many standard functions are not yet implemented — rand, find, and others. They are on the issues list.
Some matlab constructs, especially creating arrays by out of bound assignment, are used in the submission, but not yet supported by smop. Meanwhile, these lines should be rewritten in the Matlab code.
01 function moves=solver(A,B,w0)
02 [moves,optmove,optscore]=cbest(A,B,w0);
03 curscore=sum(w0(moves(:,1)));
04 lots=1;
05 if length(moves)optmove<20curscore/optscore<1.05
06 lots=2; return
07 else
08 lenw=length(w0);
09 [xx,nseq]=sort(rand(1,lenw));
10 A1=A;
11 B1=B;
12 w01=w0;
13 for i=1:lenw
14 A1(A==i)=nseq(i);
15 B1(B==i)=nseq(i);
16 w01(nseq(i))=w0(i);
17 end;
18 [moves2,optmove,optscore]=cbest(A1,B1,w01);
becomes
01 def solver(A,B,w0):
02 moves,optmove,optscore = cbest(A,B,w0)
03 curscore=np.sum(w0[(moves[:,0]1)])
04 lots=1
05 if max(moves.shape)  optmove < 20 or curscore / optscore < 1.05:
06 lots=2
07 return moves
08 else:
09 lenw=max(w0.shape)
10 xx,nseq=sort(rand(1,lenw))
11 A1=A
12 B1=B
13 w01=w0
14 for i in range(1,(lenw+1)):
15 A1[A == i]=nseq[(i1)]
16 B1[B == i]=nseq[(i1)]
17 w01[(nseq[(i1)]1)]=w0[(i1)]
18 moves2,optmove,optscore = cbest(A1,B1,w01)
Now some random notes.
Line 03. Functions vs. arrays ambiguity is correctly resolved: for example, sum is a function, but w0 and moves are arrays.
Line 09, Matlab function length is correctly inlined as max(w0.shape) — that is the maximum of the array dimensions.
For some functions, such as abs, max, and others, there are both a builtin version and a different numpy version, and it is important to use the right one. In line 03 np.sum is used, but in line 09 builtin max. This is correct.
Line 10. Functions rand and sort are not yet implemented.
Lines 1516. There is some support to boolean indexing.
Lines 1517. Array subscripts are modified to start with zero index. Note that if all subscripts were decremented, it would break boolean indexing.
Line 13. Range specification in Matlab includes the upper bound. In Python, it doesn’t. So for i=1:lenw becomes for i in range(1,(lenw+1)). Extra parentheses are known as issue #1.
The table below tries to summarize various features.
Implemented features 


Lexical and syntactical analysis 
Mostly complete, including some weird Matlab features 
Name resolution 
For each occurrence of a variable, find a set of its possible definitions 
Inlining of small functions 

Array subscripts translated from 1based (Matlab and Fortran style) to 0based (C and Python style) 
Also, end subscript implemented 
from:step:to translated to from:to:step 

Upper bound is n+1 
Unimplemented features 


Structs 
To be implemented as soon as cc possible. 
Arrays silently become C=style (rows first). 
In some cases it may break the code. Not detected. 
Function handles and lambda expressions 
Handles break the heuristic that tells between function calls and array references. 
Graphics, 
Never 
Autoexpanding arrays 
Unlike other languages, matlab allows outofbounds assignment. As MathWorks tries to phase out this feature, there is a lot of legacy code depending on it. 
Sparse matrices 
Have good chances of being implemented, especially taking into account that scipy have several implementations to choose from. 
Full support for boolean indexing. Currently, some expressions don’t work 
For example, x(x>0.5) = 1 works, but y=x>0.5; x(y)=1 does not work. 
Command syntax 
Too complex to support 
Type, rank and shape inference 

Strings 
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