Transplant 0.8.11

Description:

Transplant 0.8.11

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

RECENT CHANGES

Fixes for finding libzmq on Windows (Thank you, hardmstar)
Now correctly encodes bool ndarrays as logical arrays (thank you, Júlio)
Fixes working with Matlab packages (Thank you, dani-l)
Fixes recursion at Matlab shutdown (Thank you, dani-l)
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.

STARTING 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='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 Matlab running on Windows isn’t visible to Transplant.

CALLING MATLAB
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.

MATRIX DIMENSIONS
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).

STOPPING MATLAB
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.

INSTALLATION

Install the zeromq library on your computer and add it to your
PATH. Alternatively, Transplant automatically uses conda’s
zeromq if you use conda.
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.

INSTALLATION GUIDE FOR LINUX

Install the latest version of zeromq through your package manager.
Install version 4 (often called 5).
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).

INSTALLATION GUIDE FOR WINDOWS

Install the latest version of zeromq from here:
http://zeromq.org/distro:microsoft-windows OR through conda.
Install a compiler. See here for a list of supported compilers:
http://uk.mathworks.com/support/compilers/R2017a/ Matlab needs a
compiler in order to load and use the ZeroMQ library using
loadlibrary.

HOW DOES IT WORK?
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.

FAQ

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 transplant_magic.py, to get %%matlab cell
magic.

SIMILAR PROGRAMS
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.

KNOWN ISSUES
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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