Support in Matlab

MATLAB provides built-in support for calling Python libraries through its Python Interface. This allows users to use Python functions, classes, and modules directly from MATLAB, making it easy to integrate Python-based scientific computing, machine learning, and deep learning libraries into MATLAB workflows. MATLAB interacts with Python by adding the py. prefix, which allows MATLAB to call the needed Python library seamlessly. One can also execute Python statements in the Python interpreter directly from MATLAB using the pyrun or pyrunfile functions. For detail, we refer the users to url{https://www.mathworks.com/help/matlab/call-python-libraries.html}

If you use Python virtual environments, ensure MATLAB detects it:

>> pe = pyenv('Version', 'C:\Users\YourUser\Anaconda3\envs\your_env\python.exe');

This allows MATLAB to use Python packages installed in the virtual environment.

>> pe =
PythonEnvironment with properties:

        Version: "3.10"
    Executable: "/software/python/anaconda3/bin/python3"
        Library: "/software/python/anaconda3/lib/libpython3.10.so"
            Home: "/software/python/anaconda3"
        Status: NotLoaded
    ExecutionMode: InProcess

You can call Python’s built-in functions using py.<module>.<function>.

>> result = py.math.sqrt(16);
>> disp(result)

>> version = py.sys.version;
>> disp(version)

MATLAB automatically converts data types when calling Python functions. However, sometimes explicit conversion is required.

MATLAB Type	Python Type
double	float
char (string)	str
cell array	list
struct	dict
logical	bool

To use an external library like NumPy or SciPy, ensure it’s installed in your Python environment.

Calling NumPy’s array and Computing the Sum

>> numpy = py.importlib.import_module('numpy'); % Import the numpy module
>> a = numpy.array([1, 2, 3, 4, 5]);  % Create a numpy array
>> s = numpy.sum(a);  % Compute sum
>> disp(double(s));   % Convert to MATLAB double

Check if your PyTorch is loaded properly:

>> torch = py.importlib.import_module('torch');
>> torch.cuda.is_available()

To use Pychop in your MATLAB environment, similarly, simply load the Pychop module:

>> pc = py.importlib.import_module('pychop');
>> ch = pc.Chop(exp_bits=5, sig_bits=10, rmode=1)
>> X = rand(100, 100);
>> X_q = ch(X);

Or more specifically, use

>> np = py.importlib.import_module('numpy');
>> pc = py.importlib.import_module('pychop');
>> ch = pc.Chop(exp_bits=5, sig_bits=10, rmode=1)
>> X = np.random.randn(int32(100), int32(100));
>> X_q = ch(X);