Transplant is an easy way of calling Matlab from Python.

 import transplant
 matlab = transplant.Matlab()
 # call Matlab functions:
 n, m = matlab.size([1, 2, 3])
 magic = matlab.magic(2)
 spectrum = matlab.fft(numpy.random.randn(100))
 # inject variables into Matlab:
 matlab.signal = numpy.zeros(100)

Python lists are converted to cell arrays in Matlab, dicts are converted to stucts, and numpy matrices are converted do native Matlab matrices.

All Matlab functions and objects can be accessed from Python.

matlab = transplant.Matlab()

Will start a Matlab session and connect to it. This will take a few seconds while Matlab starts up. All of Matlab's output will go to the standard output and will appear interspersed with Python output. Standard input is suppressed to make REPLs work, so Matlab's input function will not work.

By default, this will try to call matlab on the command line. If you want to use a different version of Matlab, or matlab is not available on the command line, use Matlab(executable='/path/to/matlab').

By default, Matlab is called with -nodesktop and -nosplash, so no IDE or splash screen show up. If you want to use different arguments, you can supply them like this: Matlab(arguments=('-nodesktop', '-nosplash', '-c licensefile' , '-nojvm')). Note that '-nojvm' will speed up startup considerably, but you won't be able to open figures any more.

By default, Matlab will be started on the local machine. To start Matlab on a different computer, supply the IP address of that computer: Matlab(address='172.168.1.5'). This only works if that computer is reachable through ssh, Matlab is available on the other computer's command line, and transplant is in the other Matlab's path.

Note that due to a limitation of Matlab on Windows, command line output from Matlabs running on Windows aren't visible to Transplant.

matlab.disp("Hello, World")

Will call Matlab's disp function with the argument 'Hello, World'. It is equivalent to disp('Hello, World') in Matlab. Return values will be returned to Python, and errors will be converted to Python errors (Matlab stack traces will be given, too!).

Input arguments are converted to Matlab data structures:

• Strings and numbers stay strings and numbers
• True and False become logical(1) and logical(0)
• None becomes []
• Lists become cell arrays
• Dictionaries become containers.Map
• Numpy arrays become matrices

If the function returns a function handle or an object, a matching Python functions/objects will be created that forwards every access to Matlab. These objects and functions can also be handed back to Matlab and will work as intended.

f = matlab.figure() # create a Figure object
f.Visible = 'off' # modify a property of the Figure object
matlab.set(f, 'Visible', 'on') # pass the Figure object to a function

In Matlab, some functions behave differently depending on the number of output arguments. By default, Transplant uses the Matlab function nargout to figure out the number of return values for a function. If nargout can not determine the number of output arguments either, Matlab functions will return the value of ans after the function call.

In some cases, nargout will report a wrong number of output arguments. For example nargout profile will say 1, but x = profile('on') will raise an error that too few output arguments were used. To fix this, every function has a keyword argument nargout, which can be used in these cases: matlab.profile('on', nargout=0) calls profile on with no output arguments. s, f, t, p = matlab.spectrogram(numpy.random.randn(1000), nargout=4) returns all four output arguments of spectrogram.

When working with plots, note that the Matlab program does not wait for drawing on its own. Use matlab.drawnow() to make figures appear.

Note that functions are not called in the base workspace. Functions that access the current non-lexical workspace (this is very rare) will therefore not work as expected. For example, matlab.truth = 42, matlab.exist('truth') will not find the truth variable. Use matlab.evalin('base', "exist('truth')", nargout=1) instead in this case.

If you hit Ctrl-C, the KeyboardInterrupt will be applied to both Python and Matlab, stopping any currently running function. Due to a limitation of Matlab, the error and stack trace of that function will be lost.

The way multidimensional arrays are indexed in Matlab and Python are fundamentally different. Thankfully, the two-dimensional case works as expected:

           Python         |        Matlab
--------------------------+------------------------
 array([[  1,   2,   3],  |     1   2   3
        [ 10,  20,  30]]) |    10  20  30

In both languages, this array has the shape (2, 3).

With higher-dimension arrays, this becomes harder. The next array is again identical:

           Python         |        Matlab
--------------------------+------------------------
 array([[[  1,   2],      | (:,:,1) =
         [  3,   4]],     |              1    3
                          |             10   30
        [[ 10,  20],      |            100  300
         [ 30,  40]],     | (:,:,2) =
                          |              2    4
        [[100, 200],      |             20   40
         [300, 400]]])    |            200  400

Even though they look radically different, they both have the same shape (3, 2, 2), and are indexed in the same way. The element at position a, b, c in Python is the same as the element at position a+1, b+1, c+1 in Matlab (+1 due to zero-based/one-based indexing).

You can think about the difference in presentation like this: Python displays multidimensional arrays as [n,:,:], whereas Matlab displays them as (:,:,n).

Matlab processes end when the Matlab instance goes out of scope or is explicitly closed using the close method. Alternatively, the Matlab class can be used as a context manager, which will properly clean up after itself.

If you are not using the context manager or the close method, you will notice that some Matlab processes don't die when you expect them to die. If you are running the regular python interpreter, chances are that the Matlab process is still referenced to in sys.last_traceback, which holds the value of the last exception that was raised. Your Matlab process will die once the next exception is raised.

If you are running ipython, though, all bets are off. I have noticed that ipython keeps all kinds of references to all kinds of things. Sometimes, %reset will clear them, sometimes it won't. Sometimes they only go away when ipython quits. And sometimes, even stopping ipython doesn't kill it (how is this even possible?). This can be quite annoying. Use the close method or the context manager to make sure the processes are stopped correctly.

Transplant opens Matlab as a subprocess (optionally over SSH), then connects to it via 0MQ in a request-response pattern. Matlab then runs the transplant remote and starts listening for messages. Now, Python can send messages to Matlab, and Matlab will respond. Roundtrip time for sending/receiving and encoding/decoding values from Python to Matlab and back is about 3-7 ms.

All messages are Msgpack-encoded or JSON-encoded objects. You can choose between Msgpack (faster) and JSON (slower, human-readable) using the msgformat attribute of the Matlab constructor. There are seven messages types used by Python:

• set_global and get_global set and retrieve a global variable.
• set_proxy and get_proxy and del_proxy to interact with cached Matlab objects.
• call calls a Matlab function with some function arguments and returns the result.
• die tells Matlab to shut down.

Matlab can then respond with one of three message types:

• ack for successful execution.
• value for return values.
• error if there was an error during execution.

In addition to the regular Msgpack/JSON data types, transplant uses a specially formatted Msgpack/JSON array for transmitting numerical matrices as binary data. A numerical 2x2 32-bit integer matrix containing [[1, 2], [3, 4]] would be encoded as ["__matrix__", "int32", [2, 2], "AQAAAAIAAAADAAAABAAAA==\n"], where "int32" is the data type, [2, 2] is the matrix shape and the long string is the base64-encoded matrix content. This allows for efficient data exchange and prevents rounding errors due to JSON serialization.

When Matlab returns a function handle, it is encoded as ["__function__", func2str(f)]. When Matlab returns an object, it caches its value and returns ["__object__", cache_idx]. These arrays are translated back to their original Matlab values if passed to Matlab.

Note that this project includes a Msgpack serializer/parser, a JSON serializer/parser, and a Base64 encoder/decoder in pure Matlab.

1. Install the zeromq library on your computer in a path that is known to Matlab.

2. Add ZMQ.m, transplantzmq.h, and transplant_remote.m to your Matlab path.

3. On the Python side, make sure to have PyZMQ and Numpy installed as well.

4. If matlab is not reachable in your shell, give the full path to your Matlab executable to the Matlab constructor.

5. If you intend to start Matlab on a remote computer, make sure that computer is reachable through SSH and fullfills the above steps.

1. Install the latest version of zeromq from here: http://zeromq.org/distro:microsoft-windows

2. Rename libzmq-v90-mt-4_0_4.dll to libzmq.dll

3. Make sure that the libary is in Matlab's path (use pathtool or add addpath('path/to/zeromq/bin') to your matlabrc.m

4. Install a compiler. See here for a list of supported compilers: http://uk.mathworks.com/support/compilers/R2016a/
   Matlab needs a compiler in order to load and use the ZeroMQ library using loadlibrary.

5. Now, manually run a new Matlab session, and verify that you can manually use loadlibrary('libzmq.dll', 'transplantzmq.h') without getting any errors/warnings.

MATLAB (R) is copyright of the Mathworks

Copyright (c) 2014 Bastian Bechtold All rights reserved.

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