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Running Python remotely from Matlab
{:.no_toc}
* TOC {:toc}The goal
We want to integrate Python files and code into our Matlab workflow.
Questions to David Rotermund
Check if you are ready
Versions of Python Compatible with MATLAB Products by Release
You need to ask yourself or better Matlab if it is using the correct Python. You can check this via:
>> pyenv
ans =
PythonEnvironment with properties:
Version: "3.11"
Executable: "/data_1/davrot/P3.11/bin/python3"
Library: "libpython3.11.so.1.0"
Home: "/data_1/davrot/P3.11"
Status: NotLoaded
ExecutionMode: InProcess
If this is wrong (which it isn't in my case) then you can change it (temporarily?) with
>> pyenv('Version','/data_1/davrot/P3.11/bin/python3')
ans =
PythonEnvironment with properties:
Version: "3.11"
Executable: "/data_1/davrot/P3.11/bin/python3"
Library: "libpython3.11.so.1.0"
Home: "/data_1/davrot/P3.11"
Status: NotLoaded
ExecutionMode: InProcess
Obviously you need to use your location for your Python installation.
Python help
>> py.help('int')
Help on class int in module builtins:
class int(object)
| int([x]) -> integer
| int(x, base=10) -> integer
[...]
Tuple and random number example
We can crate a python tuple like this:
>> py_dim = py.tuple({py.int(10), py.int(100)})
py_dim =
Python tuple with values:
(10, 100)
Use string, double or cell function to convert to a MATLAB array.
Now we can use numpy to generate random numbers:
>> py_dim = py.tuple({py.int(10), py.int(100)})
>> rng = py.numpy.random.default_rng();
>> a = rng.random(py_dim);
>> py.print(py.type(a))
<class 'numpy.ndarray'>
>> py.print(a.shape)
(10, 100)
>> py.print(a.dtype)
float64
>> whos
>> whos
Name Size Bytes Class Attributes
a 1x1 8 py.numpy.ndarray
ans 1x1 8 matlab.pyclient.PythonEnvironment
py_dim 1x2 8 py.tuple
rng 1x1 8 py.numpy.random._generator.Generator
Alternatively this is also possible:
>> b = rng.random(cell({int32(10),int32(100)}));
User defined Python modules
Our very own Python function in the file mtest_1.py:
import numpy as np
def mysquared(input:np.ndarray) -> np.ndarray:
output = input**2
return output
Now Matlab allows us to do this:
>> x = (1:1:10);
>> x_np = py.numpy.array(x);
>> y_np = py.mtest_1.mysquared(x_np);
>> y = double(x_np).^2;
>> sum(sum(abs(y-double(y_np))))
ans =
0
If you change the py file, then you need to clean it from the memory via
>> clear classes
Future David here: Well, I had to do this for a file gauss_smear.py with a function gauss_smear:
if ~exist('mod', 'var')
mod = py.importlib.import_module('gauss_smear');
end
py.importlib.reload(mod);
clearvars -except mod
mod.gauss_smear(2.0, 0.1)
Save a numpy file with Matlab
In Matlab we save data into numpy file:
>> a = rand(100,10);
>> a_np = py.numpy.array(a);
>> py.numpy.save("test_1.npy",a_np);
Now we can load it into Python:
import numpy as np
a = np.load("test_1.npy")
print(type(a)) # --> <class 'numpy.ndarray'>
print(a.shape) # --> (100, 10)
print(a.dtype) # --> float64
Loading a numpy file with Matlab
Under Python we generate a file:
import numpy as np
myrng = np.random.default_rng()
a = myrng.random((100, 10))
np.save("test_2.npy", a)
And under Matlab we load it:
>> a_np = py.numpy.load("test_2.npy");
>> a = double(a_np);
>> whos
Name Size Bytes Class Attributes
a 100x10 8000 double
a_np 1x1 8 py.numpy.ndarray