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Matlab to Python converter

Project description

``SMOP`` is Small Matlab and Octave to Python compiler.
``SMOP`` translates matlab to python. Despite obvious similarities
between matlab and numeric python, there are enough differences to
make manual translation infeasible in real life. ``SMOP`` generates
human-readable python, which also appears to be faster than octave.
Just how fast? Timing results for "Moving furniture" are shown
in Table 1. It seems that for this program, translation to python
resulted in about two times speedup, and additional two times speedup
was achieved by compiling ``SMOP`` run-time library ``runtime.py``
to C, using `cython`. This pseudo-benchmark measures scalar
performance, and my interpretation is that scalar computations are
of less interest to the octave team.

======================================== ==================
octave-3.8.1 190 ms
---------------------------------------- ------------------
smop+python-2.7 80 ms
---------------------------------------- ------------------
smop+python-2.7+cython-0.20.1 40 ms
---------------------------------------- ------------------
Table 1. ``SMOP`` performance
======================================== ==================

Version 0.27 release notes
==========================
Line numbering information is included in the output.
Enabled by default. Disabled with ``--no-numbers``.

Block comments are preserved now.
Lines, containing anything but a comment, are
preserved. Enabled by default.
Disable with ``--no-comments``.

New command-line options

Special ``%!`` comments are partially supported now.
They are mostly useful in testing, and
are used by Octave library and test suite. Disabled
by default. Enabled with ``--testing-mode``.

News
====


June 19,2016
After a year-long vacation I am back to active development.
My first goal is adopting the Octave runtime and test suite.

.. October 23, 2014
Downloaded ``mybench`` -- a collection of 20 or so
micro-benchmarks originally meant to compare matlab and
octave performance. After succesfully running the first nine,
the geometric mean of the speedup is 0.36, which is cool.

.. == ======== ====== =========== =======
// name octave smop speedup
== ======== ====== =========== =======
1 rand 2.58 0.36 0.14
2 randn 2.26 1.04 0.46
3 primes 0.35 0.17 0.49
4 fft2 2.75 1.13 0.41
5 square 4.24 0
6 inv 4.38 2.26 0.53
7 eig 17.95 9.09 0.51
8 qr 3.06 1.83 0.60
9 shur 5.98 2.31 0.39
10 roots 8.31 2.02 0.24
== ======== ====== =========== =======

October 15, 2014
Version 0.26.3 is available for beta testing.
Next version 0.27 is planned to compile octave
``scripts`` library, which contains over 120 KLOC in
almost 1,000 matlab files. There are 13 compilation
errors with smop 0.26.3 .


Installation
============

+ Network installation is the best method if you just want it to
run the example::

$ easy_install smop --user

+ Install from the sources if you are behind a firewall::

$ tar zxvf smop.tar.gz
$ cd smop
$ python setup.py install --user

+ Fork github repository if you need the latest fixes.

+ Finally, it is possible to use smop without doing the installation,
but only if you already installed the dependences -- numpy
and networkx::

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

Working example
===============

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).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.

.. code:: matlab

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)

==== =========================================================================
02 Matlab uses round brackets both for array indexing and
for function calls. To figure out which is which,
SMOP computes local use-def information, and then
applies the following rule: undefined names are
functions, while defined are arrays.
---- -------------------------------------------------------------------------
03 Matlab function ``size`` returns variable number of
return values, which corresponds to returning a tuple
in python. Since python functions are unaware of the
expected number of return values, their number must be
explicitly passed in ``nargout``.
==== =========================================================================

.. code:: matlab

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([])

==== =========================================================================
04 Matlab array indexing starts with one; python indexing
starts with zero. New class ``matlabarray`` derives from
``ndarray``, but exposes matlab array behaviour. For
example, ``matlabarray`` instances always have at least
two dimensions -- the shape of ``I`` and ``J`` is [1 4].
---- -------------------------------------------------------------------------
06 Matlab array assignment implies copying; python
assignment implies data sharing. We use explicit copy
here.
---- -------------------------------------------------------------------------
07 Empty ``matlabarray`` object is created, and then
extended at line 28. Extending arrays by
out-of-bounds assignment is deprecated in matlab, but
is widely used never the less. Python ``ndarray``
can't be resized except in some special cases.
Instances of ``matlabarray`` can be resized except
where it is too expensive.
==== =========================================================================

.. code:: matlab

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]

==== =========================================================================
09 Matlab functions of zero arguments, such as
``rand``, can be used without parentheses. In python,
parentheses are required. To detect such cases, used
but undefined variables are assumed to be functions.
---- -------------------------------------------------------------------------
10 The expected number of return values from the matlab
function ``find`` is explicitly passed in ``nargout``.
---- -------------------------------------------------------------------------
12 Variables I and J contain instances of the new class
``matlabarray``, which among other features uses one
based array indexing.
==== =========================================================================

.. code:: matlab

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

Implementation status
=====================

.. Table 3. Not compiled

.. =========================== =====================================
stft.m missing semicolon
datenum.m missing semicolon
orderfields.m
legend.m
pack.m premature EOF
unpack.m premature EOF
__unimplemented__.m premature EOF
assert.m
optimset.m
=========================== =====================================


Random remarks
==============
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.

.. missing standard library and toolboxes
.. missing grapphics library

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.

.. code:: matlab

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

Running the test suite::

$ cd smop
$ make check
$ make test

Command-line options
--------------------

.. code:: sh

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

---------------------------------------------------------------------

.. vim: tw=80

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