Mlab is a high-level python to Matlab bridge that lets Matlab look like a normal python library

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

This python library is based on the work of original mlabwrap project

http://mlabwrap.sourceforge.net/

and Dani Valevski (from Dana Pe'er's lab):

http://code.google.com/p/danapeerlab/source/browse/trunk/freecell/depends/common/python/matlabpipe.py

Primer

Quick installation:

pip install mlab

Start working with the library by picking a MATLAB release that you have locally installed:

from mlab.releases import latest_release

from matlab import matlabroot

print matlabroot()

where latest_release is a MlabWrap instance, matlabroot is wrapper around MATLAB function.

Please note that matlab module is dynamically created instance, which is in this case referencing

latest_release object.

MATLAB installation discovery mechanism is implemented by mlab.releases module in such a way, that

you have to specify the release version you want to use first, by importing it. Only then you can

import from matlab module:

from mlab.releases import R2010b

from matlab import matlabroot

Also see mlab.releases.get_available_releases().

Contents

Primer

Description

Related

News

License

Download

Installation

Linux

Windows

Documentation

Tutorial

Comparison to other existing modules

What's Missing?

Implementation Notes

Troubleshooting

Strange hangs under Matlab R2008a

matlab not in path

"Can't open engine"

"`GLIBCXX_3.4.9' not found" on importing mlab (or similar)

Old Matlab version

OS X

Notes on running

Windows

Function Handles and callbacks into python

Directly manipulating variables in Matlab space

Support and Feedback

Credits

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

mlab is a repackaging effort to make things up-to-date.

Related

Thereis is a copy of mlabwrap v1.1-pre (http://mlabwrap.sourceforge.net/) patched

as described here:

http://sourceforge.net/mailarchive/message.php?msg_id=27312822

with a patch fixing the error:

mlabraw.cpp:225: *error*: invalid conversion from const mwSize* to const int*

Also note that in Ubuntu you need to sudo apt-get install csh

For details see

http://github.com/aweinstein/mlabwrap

News

2014-08-26 1.1.3 Applying patch to add support for Windows via COM.

Credits to Sergey Matyunin, Amro@stackoverflow

2013-07-26 1.1.1 Repacking a library as mlab project. Including code

for Windows (matlabraw.cpp is off for now).

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.

License

mlab (and mlabwrap) is under MIT license, see LICENSE.txt. mlabraw is under a BSD-style

license, see the mlabraw.cpp.

Download

<http://github.com/ewiger/mlab>

Installation

mlab should work with python>=2.7 (downto python 2.2, with minor coaxing) and

either numpy (recommended) or Numeric (obsolete) installed and Matlab 6, 6.5,

7.x and 8.x under Linux, OS X and Windows (see OS X) on 32- or 64-bit

machines.

Linux

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.

Windows

Assuming you have python 2.7.5 (e.g. C:Python27) and setuptools

("easy_install.exe") installed and on your PATH.

1) Download and install numpy package. You can use packages provided by

Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/pythonlibs/ Also see official

SciPy website for latest status, it might that:

easy_install.exe numpy

would do the trick.

You would also need The PyWin32 module by Mark Hammond:

<string>:175: (WARNING/2) Literal block expected; none found.

<string>:5: (INFO/1) Enumerated list start value not ordinal-1: "2" (ordinal 2)

easy_install.exe pywin32

also see Windows in Troubleshooting.

Documentation

for lazy people

>>> from mlab.releases import latest_release as matlab

>>> matlab.plot([1,2,3],'-o')

ugly-plot

a slightly prettier example

>>> from mlab.releases import latest_release as matlab

>>> from numpy import *

>>> xx = arange(-2*pi, 2*pi, 0.2)

>>> mlab.surf(subtract.outer(sin(xx),cos(xx)))

surface-plot

for a complete description:

Just run pydoc mlab.

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 mlab 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 mlab 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

StackOverflow tagged query); 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

<string>:529: (INFO/1) Duplicate implicit target name: "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

Post your questions directly on Stack overflow with tags matlab, mlab

and python

Credits

Alejandro Weinstein for patches of 1.1pre

https://github.com/aweinstein/mlabwrap

Alexander Schmolck and Vivek Rathod for mlabwrap:

http://mlabwrap.sourceforge.net/

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.

This python library is based on the work of original mlabwrap project

http://mlabwrap.sourceforge.net/

and Dani Valevski (from Dana Pe'er's lab):

http://code.google.com/p/danapeerlab/source/browse/trunk/freecell/depends/common/python/matlabpipe.py

Primer

Quick installation:

pip install mlab

Start working with the library by picking a MATLAB release that you have locally installed:

from mlab.releases import latest_release

from matlab import matlabroot

print matlabroot()

where latest_release is a MlabWrap instance, matlabroot is wrapper around MATLAB function.

Please note that matlab module is dynamically created instance, which is in this case referencing

latest_release object.

MATLAB installation discovery mechanism is implemented by mlab.releases module in such a way, that

you have to specify the release version you want to use first, by importing it. Only then you can

import from matlab module:

from mlab.releases import R2010b

from matlab import matlabroot

Also see mlab.releases.get_available_releases().

Contents

Primer

Description

Related

News

License

Download

Installation

Linux

Windows

Documentation

Tutorial

Comparison to other existing modules

What's Missing?

Implementation Notes

Troubleshooting

Strange hangs under Matlab R2008a

matlab not in path

"Can't open engine"

"`GLIBCXX_3.4.9' not found" on importing mlab (or similar)

Old Matlab version

OS X

Notes on running

Windows

Function Handles and callbacks into python

Directly manipulating variables in Matlab space

Support and Feedback

Credits

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

mlab is a repackaging effort to make things up-to-date.

Related

Thereis is a copy of mlabwrap v1.1-pre (http://mlabwrap.sourceforge.net/) patched

as described here:

http://sourceforge.net/mailarchive/message.php?msg_id=27312822

with a patch fixing the error:

mlabraw.cpp:225: *error*: invalid conversion from const mwSize* to const int*

Also note that in Ubuntu you need to sudo apt-get install csh

For details see

http://github.com/aweinstein/mlabwrap

News

2014-08-26 1.1.3 Applying patch to add support for Windows via COM.

Credits to Sergey Matyunin, Amro@stackoverflow

2013-07-26 1.1.1 Repacking a library as mlab project. Including code

for Windows (matlabraw.cpp is off for now).

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.

License

mlab (and mlabwrap) is under MIT license, see LICENSE.txt. mlabraw is under a BSD-style

license, see the mlabraw.cpp.

Download

<http://github.com/ewiger/mlab>

Installation

mlab should work with python>=2.7 (downto python 2.2, with minor coaxing) and

either numpy (recommended) or Numeric (obsolete) installed and Matlab 6, 6.5,

7.x and 8.x under Linux, OS X and Windows (see OS X) on 32- or 64-bit

machines.

Linux

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.

Windows

Assuming you have python 2.7.5 (e.g. C:Python27) and setuptools

("easy_install.exe") installed and on your PATH.

1) Download and install numpy package. You can use packages provided by

Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/pythonlibs/ Also see official

SciPy website for latest status, it might that:

easy_install.exe numpy

would do the trick.

You would also need The PyWin32 module by Mark Hammond:

<string>:175: (WARNING/2) Literal block expected; none found.

<string>:5: (INFO/1) Enumerated list start value not ordinal-1: "2" (ordinal 2)

easy_install.exe pywin32

also see Windows in Troubleshooting.

Documentation

for lazy people

>>> from mlab.releases import latest_release as matlab

>>> matlab.plot([1,2,3],'-o')

ugly-plot

a slightly prettier example

>>> from mlab.releases import latest_release as matlab

>>> from numpy import *

>>> xx = arange(-2*pi, 2*pi, 0.2)

>>> mlab.surf(subtract.outer(sin(xx),cos(xx)))

surface-plot

for a complete description:

Just run pydoc mlab.

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 mlab 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 mlab 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

StackOverflow tagged query); 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

<string>:529: (INFO/1) Duplicate implicit target name: "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

Post your questions directly on Stack overflow with tags matlab, mlab

and python

Credits

Alejandro Weinstein for patches of 1.1pre

https://github.com/aweinstein/mlabwrap

Alexander Schmolck and Vivek Rathod for mlabwrap:

http://mlabwrap.sourceforge.net/

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.

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