# mlabwrap v1.1-pre

Copyright: 2003-2009 Alexander Schmolck and Vivek Rathod 2009-10-26

## Description

Mlabwrap is a high-level python to Matlab® bridge that lets Matlab look like a normal python library.

Thanks for your terrific work on this very-useful Python tool!

—George A. Blaha, Senior Systems Engineer, Raytheon Integrated Defense Systems

## News

2009-10-26 1.1 fixes an incorrect declaration in mlabraw.cpp that caused compilation problems for some users and incorporates a setup.py fix for windows suggested by Alan Brooks. More significantly there is a new spiffy logo!

2009-09-14 1.1-pre finally brings N-D array support, thanks to Vivek Rathod who joined the project! Also fixed a missing import for saveVarsInMat (thanks to Nicolas Pinto).

Since a few people have run into problems that appear to relate to compiling Matlab® C-extensions in general and aren't mlabwrap-specific, I should probably stress that in case of any problems that look C-related, verifying whether engdemo.c works is a great litmus test (see Troubleshooting ).

2009-03-23 1.0.1 is finally out. This is a minor release that fixes some annoying but mostly minor bugs in mlabwrap (it also slightly improves the indexing support for proxy-objects, but the exact semantics are still subject to change.)

• installation is now easier, in particularly LD_LIBRARY_PATH no longer needs to be set and some quoting issues with the matlab call during installation have been addressed.
• sparse Matlab® matrices are now handled correctly (mlab.sparse([0,0,0,0]) will now return a proxy for a sparse double matrix, rather than incorrectly treat at as plain double array and return junk or crash).
• replaced the (optional) use of the outdated netcdf package for the unit-tests with homegrown matlab helper class.
• several bugs squashed (length of mlabraw.eval'ed strings is checked, better error-messages etc.) and some small documentation improvements and quite a few code clean-ups.

Many thanks to Iain Murray at Toronto and Nicolas Pinto at MIT for letting themselves be roped into helping me test my stupidly broken release candidates.

(P.S. the activity stats are bogus -- look at the release dates).

## Installation

If you're lucky (linux, Matlab binary in PATH):

python setup.py install


(As usual, if you want to install just in your homedir add --prefix=$HOME; and make sure your PYTHONPATH is set accordingly.) If things do go awry, see Troubleshooting. Although I myself use only linux, mlabwrap should work with python>=2.4 (even downto python 2.2, with minor coaxing) and either numpy (recommended) or Numeric (obsolete) installed and Matlab 6, 6.5 or 7.x under Linux, OS X® and Windows® (see OS X) on 32- or 64-bit machines. ## Documentation • for lazy people >>> from mlabwrap import mlab; mlab.plot([1,2,3],'-o')  • a slightly prettier example >>> from mlabwrap import mlab; from numpy import * >>> xx = arange(-2*pi, 2*pi, 0.2) >>> mlab.surf(subtract.outer(sin(xx),cos(xx)))  • for a complete description: Just run pydoc mlabwrap. • for people who like tutorials: see below ### Tutorial [This is adapted from an email I wrote someone who asked me about mlabwrap. Compatibility Note: Since matlab is becoming increasingly less double-centric, the default conversion rules might change in post 1.0 mlabwrap; so whilst using mlab.plot([1,2,3]) rather than mlab.plot(array([1.,2.,3.])) is fine for interactive use as in the tutorial below, the latter is recommended for production code.] Legend: [...] = omitted output Let's say you want to do use Matlab® to calculate the singular value decomposition of a matrix. So first you import the mlab pseudo-module and Numeric: >>> from mlabwrap import mlab >>> import numpy  Now you want to find out what the right function is, so you simply do: >>> mlab.lookfor('singular value') GSVD Generalized Singular Value Decompostion. SVD Singular value decomposition. [...]  Then you look up what svd actually does, just as you'd look up the docstring of a python function: >>> help(mlab.svd) mlab_command(*args, **kwargs) SVD Singular value decomposition. [U,S,V] = SVD(X) produces a diagonal matrix S, of the same dimension as X and with nonnegative diagonal elements in [...]  Then you try it out: >>> mlab.svd(array([[1,2], [1,3]])) array([[ 3.86432845], [ 0.25877718]])  Notice that we only got 'U' back -- that's because python hasn't got something like Matlab's multiple value return. Since Matlab functions can have completely different behavior depending on how many output parameters are requested, you have to specify explicitly if you want more than 1. So to get 'U' and also 'S' and 'V' you'd do: >>> U, S, V = mlab.svd([[1,2],[1,3]], nout=3)  The only other possible catch is that Matlab (to a good approximation) basically represents everything as a double matrix. So there are no scalars, or 'flat' vectors. They correspond to 1x1 and 1xN matrices respectively. So, when you pass a flat vector or a scalar to a mlab-function, it is autoconverted. Also, integer values are automatically converted to double floats. Here is an example: >>> mlab.abs(-1) array([ [ 1.]])  Strings also work as expected: >>> mlab.upper('abcde') 'ABCDE'  However, although matrices and strings should cover most needs and can be directly converted, Matlab functions can also return structs or indeed classes and other types that cannot be converted into python equivalents. However, rather than just giving up, mlabwrap just hides this fact from the user by using proxies: E.g. to create a netlab neural net with 2 input, 3 hidden and 1 output node: >>> net = mlab.mlp(2,3,1,'logistic')  Looking at net reveals that is a proxy: >>> net <MLabObjectProxy of matlab-class: 'struct'; internal name: 'PROXY_VAL0__'; has parent: no> type: 'mlp' nin: 3 nhidden: 3 nout: 3 nwts: 24 outfn: 'linear' w1: [3x3 double] b1: [0.0873 -0.0934 0.3629] w2: [3x3 double] b2: [-0.6681 0.3572 0.8118]  When net or other proxy objects a passed to mlab functions, they are automatically converted into the corresponding Matlab-objects. So to obtain a trained network on the 'xor'-problem, one can simply do: >>> net = mlab.mlptrain(net, [[1,1], [0,0], [1,0], [0,1]], [0,0,1,1], 1000)  And test with: >>> mlab.mlpfwd(net2, [[1,0]]) array([ [ 1.]]) >>> mlab.mlpfwd(net2, [[1,1]]) array([ [ 7.53175454e-09]])  As previously mentioned, normally you shouldn't notice at all when you are working with proxy objects; they can even be pickled (!), although that is still somewhat experimental. mlabwrap also offers proper error handling and exceptions! So trying to pass only one input to a net with 2 input nodes raises an Exception: >>> mlab.mlpfwd(net2, 1) Traceback (most recent call last): [...] mlabraw.error: Error using ==> mlpfwd Dimension of inputs 1 does not match number of model inputs 2  Warning messages (and messages to stdout) are also displayed: >>> mlab.log(0) Warning: Log of zero. array([ [ -inf]])  ### Comparison to other existing modules To get a vague impression just how high-level all this, consider attempting to do something similar to the first example with pymat (upon which the underlying mlabraw interface to Matlab® is based). this: >>> A, B, C = mlab.svd([[1,2],[1,3]], 0, nout=3)  becomes this: >>> session = pymat.open() >>> pymat.put(session, "X", [[1,2], [1,3]]) >>> pymat.put(session, "cheap", 0) >>> pymat.eval(session, '[A, B, C] = svd(X, cheap)') >>> A = pymat.get(session, 'A') >>> B = pymat.get(session, 'B') >>> C = pymat.get(session, 'C')  Plus, there is virtually no error-reporting at all, if something goes wrong in the eval step, you'll only notice because the subsequent get mysteriously fails. And of course something more fancy like the netlab example above (which uses proxies to represent matlab class instances in python) would be impossible to accomplish in pymat in a similar manner. However should you need low-level access, then that is equally available (and with error reporting); basically just replace pymat with mlabraw above and use mlab._session as session), i.e >>> from mlabwrap import mlab >>> import mlabraw >>> mlabraw.put(mlab._session, "X", [[1,2], [1,3]]) [...]  Before you resort to this you should ask yourself if it's really a good idea; the inherent overhead associated with Matlab's C interface appears to be quite high, so the additional python overhead shouldn't normally matter much -- if efficiency becomes an issue it's probably better to try to chunk together several matlab commands in an .m-file in order to reduce the number of matlab calls. If you're looking for a way to execute "raw" matlab for specific purposes, mlab._do is probably a better idea. The low-level mlabraw API is much more likely to change in completely backwards incompatible ways in future versions of mlabwrap. You've been warned. ### What's Missing? • Handling of as arrays of (array) rank 3 or more as well as non-double/complex arrays (currently everything is converted to double/complex for passing to Matlab and passing non-double/complex from Matlab is not not supported). Both should be reasonably easy to implement, but not that many people have asked for it and I haven't got around to it yet. • Better support for cells. • Thread-safety. If you think there's a need please let me know (on the project mailing list); at the moment you can /probably/ get away with using one seperate MlabWrap object per thread without implementing your own locking, but even that hasn't been tested. ### Implementation Notes So how does it all work? I've got a C extension module (a heavily bug-fixed and somewhat modified version of pymat, an open-source, low-level python-matlab interface) to take care of opening Matlab sessions, sending Matlab commands as strings to a running Matlab session and and converting Numeric arrays (and sequences and strings...) to Matlab matrices and vice versa. On top of this I then built a pure python module that with various bells and whistles gives the impression of providing a Matlab "module". This is done by a class that manages a single Matlab session (of which mlab is an instance) and creates methods with docstrings on-the-fly. Thus, on the first call of mlab.abs(1), the wrapper looks whether there already is a matching function in the cache. If not, the docstring for abs is looked up in Matlab and Matlab's flimsy introspection abilities are used to determine the number of output arguments (0 or more), then a function with the right docstring is dynamically created and assigned to mlab.abs. This function takes care of the conversion of all input parameters and the return values, using proxies where necessary. Proxy are a bit more involved and the proxy pickling scheme uses Matlab's save command to create a binary version of the proxy's contents which is then pickled, together with the proxy object by python itself. Hope that gives a vague idea, for more info study the source. ### Troubleshooting #### Strange hangs under Matlab® R2008a It looks like this particular version of matlab might be broken (I was able to reproduced the problem with just a stripped down engdemo.c under 64-bit linux). R2008b is reported to be working correctly (as are several earlier versions). #### matlab not in path setup.py will call matlab in an attempt to query the version and other information relevant for installation, so it has to be in your PATH unless you specify everything by hand in setup.py. Of course to be able to use mlabwrap in any way matlab will have to be in your path anyway (unless that is you set the environment variable MLABRAW_CMD_STR that specifies how exactly Matlab® should be called). #### "Can't open engine" If you see something like mlabraw.error: Unable to start MATLAB(TM) engine then you may be using an incompatible C++ compiler (or version), or if you're using unix you might not have csh installed under /bin/csh, see below. Try if you can get the engdemo.c file to work that comes with your Matlab installation -- engdemo provides detailed instructions, but in a nutshell: copy it to a directory where you have write access and do (assuming Matlab is installed in /opt/MatlabR14 and you're running unix, otherwise modify as requird): mex -f /opt/MatlabR14/bin/engopts.sh engdemo.c ./engdemo  if you get Can't start MATLAB engine chances are you're trying to use a compiler version that's not in Mathworks's list of compatible compilers or something else with your compiler/Matlab installation is broken that needs to be resolved before you can successfully build mlabwrap. Chances are that you or you institution pays a lot of money to the Mathworks, so they should be happy to give you some tech support. Here's what some user who recently (2007-02-04) got Matlab 7.04's mex support to work under Ubuntu Edgy after an exchange with support reported back; apart from installing gcc-3.2.3, he did the following: The code I'd run (from within Matlab) is... > mex -setup; # then select: 2 - gcc Mex options > optsfile = [matlabroot '/bin/engopts.sh']; > mex -v -f optsfile 'engdemo.c'; > !./engdemo;  Update John Bender reports that under unix csh needs to be installed in /bin/csh for the matlab external engine to work -- since many linux distros don't install csh by default, you might have to do something like sudo apt-get install csh (e.g. under ubuntu or other debian-based systems). He also pointed out this helpful engdemo troubleshooting page at the Mathworks(tm) site. #### "GLIBCXX_3.4.9' not found" on importing mlab (or similar) As above, first try to see if you can get engdemo.c to work, because as long as even the examples that come with Matlab® don't compile, chances of mlabwrap compiling are rather slim. On the plus-side if the problem isn't mlabwrap specific, The Mathworks® and/or Matlab®-specific support forums should be able to help. #### Old Matlab version If you get something like this on python setup.py install: mlabraw.cpp:634: engGetVariable' undeclared (first use this function)  Then you're presumably using an old version of Matlab (i.e. < 6.5); setup.py ought to have detected this though (try adjusting MATLAB_VERSION by hand and write me a bug report). #### OS X Josh Marshall tried it under OS X and sent me the following notes (thanks!). ##### Notes on running • Before running python, run: export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH\$:/Applications/MATLAB701/bin/mac/
export MLABRAW_CMD_STR=/Applications/MATLAB701/bin/matlab


[Edit: I'm not sure DYLD_LIBRARY_PATH modification is still necessary.]

• As far as graphics commands go, the python interpreter will need to be run from within the X11 xterm to be able to display anything to the screen. ie, the command for lazy people

>>> from mlabwrap import mlab; mlab.plot([1,2,3],'-o')


won't work unless python is run from an xterm, and the matlab startup string is changed to:

export MLABRAW_CMD_STR="/Applications/MATLAB701/bin/matlab -nodesktop"


#### Windows

I'm thankfully not using windows myself, but I try to keep mlabwrap working under windows, for which I depend on the feedback from windows users.

Since there are several popular C++ compilers under windows, you might have to tell setup.py which one you'd like to use (unless it's VC 7).

George A. Blaha sent me a patch for Borland C++ support; search for "Borland C++" in setup.py and follow the instructions.

Dylan T Walker writes mingw32 will also work fine, but for some reason (distuils glitch?) the following invocation is required:

> setup.py build --compiler=mingw32
> setup.py install --skip-build


#### Function Handles and callbacks into python

People sometimes try to pass a python function to a matlab function (e.g. mlab.fzero(lambda x: x**2-2, 0)) which will result in an error messages because callbacks into python are not implemented (I'm not even it would even be feasible). Whilst there is no general workaround, in some cases you can just create an equivalent matlab function on the fly, e.g. do something like this: mlab.fzero(mlab.eval('@(x) x^2-2', 0)).

#### Directly manipulating variables in Matlab® space

In certain (rare!) certain cases it might be necessary to directly access or set a global variable in matlab. In these cases you can use mlab._get('SOME_VAR') and mlab._set('SOME_VAR', somevalue).

## Support and Feedback

Private email is OK, but the preferred way is via the project mailing list

## Credits

Andrew Sterian for writing pymat without which this module would never have existed.

Matthew Brett contributed numpy compatibility and nice setup.py improvements (which I adapted a bit) to further reduce the need for manual user intervention for installation.

I'm only using linux myself -- so I gratefully acknowledge the help of Windows and OS X users to get things running smoothly under these OSes as well; particularly those who provided patches to setup.py or mlabraw.cpp (Joris van Zwieten, George A. Blaha and others).

Matlab is a registered trademark of The Mathworks.