Metadata-Version: 1.1
Name: mlab
Version: 1.1.4
Summary: Mlab is a high-level python to Matlab bridge that lets Matlab look like a normal python library
Home-page: https://github.com/ewiger/mlab
Author: Yauhen Yakimovich
Author-email: eugeny.yakimovitch@gmail.com
License: MIT
Download-URL: https://github.com/ewiger/mlab/tarball/master
Description: 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.
Platform: UNKNOWN
