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Transplant is an easy way of calling Matlab from Python.

import transplant
matlab = transplant.Matlab()
# call Matlab functions:
length = matlab.numel([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 Maps, and Numpy arrays are converted do native Matlab matrices.

All Matlab functions and objects can be accessed from Python.

Transplant is licensed under the terms of the BSD 3-clause license
(c) 2014 Bastian Bechtold

open-issues closed-issues open-prs closed-prs


  • Should now reliably raise an error if Matlab dies unexpectedly.
  • Keyword arguments are now automatically translated to string-value pairs in Matlab.
  • close was renamed exit. Even though Python typically uses close to close files and connections, this conflicts with Matlab's own close function.
  • Matlab will now start Matlab at the current working directory.
  • Transplant can now be installed through pip install transplant.
  • You can now use jvm=False and desktop=False to auto-supply common command line arguments for Matlab.


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 in PATH, use Matlab(executable='/path/to/matlab').

By default, Matlab is called with -nodesktop and -nosplash (and -minimize on Windows), so no IDE or splash screen show up. You can change this by setting desktop=True.

You can start Matlab without loading the Java-based GUI system ('-nojvm') by setting jvm=False. This will speed up startup considerably, but you won't be able to open figures any more.

If you want to start Matlab with additional command line arguments, you can supply them like this: Matlab(arguments=['-c licensefile']).

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=''). 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 Matlab running on Windows isn'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:

Python Argument Matlab Type
str char vector
float double scalar
int an int{8,16,32,64} scalar
True/False logical scalar
None []
list cell
dict containers.Map
transplant.MatlabStruct(dict) struct
numpy.ndarray double matrix
scipy.sparse sparse matrix
proxy object original object
proxy function original function

Return values are treated similarly:

Matlab Return Value Python Type
char vector str
numeric scalar number
logical scalar True/False
[] None
cell list
struct or containers.Map dict
numeric matrix numpy.ndarray
sparse matrix scipy.sparse
function proxy function
object proxy object

If the function returns a function handle or an object, a matching Python functions/objects will be created that forwards every access to Matlab. Objects 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 Matlab 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.

All other keyword arguments are transparently translated to key-value pairs in Matlab, i.e. matlab.struct(a=1, b=2) is another way of writing matlab.struct('a', 1, 'b', 2).

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 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 exit 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 exit 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 exit method or the context manager to make sure the processes are stopped correctly.


  1. Install the zeromq library on your computer and add it to your PATH. Alternatively, Transplant automatically uses conda's zeromq if you use conda.
  2. Install Transplant using pip install transplant. This will install pyzmq, numpy and msgpack as dependencies.

If you want to run Transplant over the network, the remote Matlab has to have access to ZMQ.m and transplant_remote.m and the zeromq library and has to be reachable through SSH.


  1. Install the latest version of zeromq through your package manager. Install version 4 (often called 5).
  2. Make sure that Matlab is using the system's version of libstdc++. If it is using an incompatible version, starting Transplant might fail with an error like GLIBCXX_3.4.21 not found. If you experience this, disable Matlab's own libstdc++ either by removing/renaming $MATLABROOT/sys/os/glnxa64/libstdc++, or by installing matlab-support (if you are running Ubuntu).


  1. Install the latest version of zeromq from here: OR through conda.
  2. Install a compiler. See here for a list of supported compilers: Matlab needs a compiler in order to load and use the ZeroMQ library using loadlibrary.


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 2 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.
  • del_proxy removes a cached object.
  • 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 specially formatted Msgpack/JSON arrays 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. In Msgpack, the data is not base64-encoded.

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.


  • I get errors with integer numbers Many Matlab functions crash if called with integers. Convert your numbers to float in Python to fix this problem.
  • How do I pass structs to Matlab? Since Matlab structs can't use arbitrary keys, all Python dictionaries are converted to Matlab containers.Map instead of structs. Wrap your dicts in transplant.MatlabStruct in Python to have them converted to structs. Note that this will change all invalid keys to whatever Matlab thinks is an appropriate key name using matlab.lang.makeValidName.
  • I get errors like GLIBCXX_3.4.21 not found Matlab's version of libstdc++ is incompatible with your OS's version. See INSTALLATION GUIDE FOR LINUX for details.
  • Does Transplant work in Python 2.7? No, it does not.
  • How to integrate Transplant with Jupyter? Use the provided, to get %%matlab cell magic.


I know of two programs that try to do similar things as Transplant:

  • Mathwork's own MATLAB Engine API for Python provides a CPython extension for calling Matlab code from some versions of Python. In my experience, it is significantly slower than Transplant, less feature-complete (no support for non-scalar structs, objects, methods, packages, numpy), and more cumbersome to use (all arguments and return values need to be wrapped in a matlab.double instead of Numpy Arrays). For a comparison of the two, here are two blog posts on the topic: Intro to Transplant, Transplant speed.
  • Oct2Py calls Octave from Python. It is very similar to Transplant, but uses Octave instead of Matlab. This has huge benefits in startup time, but of course doesn't support all Matlab code.


Transplant is a side project of mine that I use for running cross-language experiments on a small compute cluster. As such, my usage of Transplant is very narrow, and I do not see bugs that don't happen in my typical usage. That said, I have used Transplant for hundreds of hours, and hundreds of Gigabytes of data without errors.

If you find a bug, or would like to discuss a new feature, or would like to contribute code, please open an issue on Github.

I do not have a Windows machine to test Transplant. Windows support might contain bugs, but at least one user has used it on Windows in the past. If you are hitting problems on Windows, please open an issue on Github.

Running Transplant over the network might contain bugs. If you are hitting problems, please open an issue on Github.

Finally, I would like to remind you that I am developing this project for free, and in my spare time. While I try to be as accomodating as possible, I can not guarantee a timely response to issues. Publishing Open Source Software on Github does not imply an obligation to fix your problem right now. Please be civil.

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