I would still recommend mlabwrap as the solution for this.
I use mlabwrap on a regular (weekly?) basis, on both Linux and Windows, across several different versions of Python, and several different versions of Matlab. To answer your specific questions:
- mlabwrap will reliably perform across platforms, Python, and Matlab versions. It does however have limitations, and it will reliably fail when pushed past those limitations. Usually, these can be worked around.
- See my answer here for more information on calling Matlab functions vs. Matlab scripts through mlabwrap. This answer also describes how to workaround one of the primary limitations of mlabwrap, which is that not all Matlab objects can be directly converted into Python objects.
- I don't know anything about calling Matlab using win32com.
I have used mlabwrap in what I'll term 'Python-primary' style, where the majority of the programming in Python, using Matlab as a library for specific mathematical functions that aren't available in scipy/numpy, and in a 'Matlab-primary' style, the majority of the programming is in Matlab, and the final results are importedinto Python for use in some external process.
For Python-primary, the thing to keep in mind is that not all Matlab functions will return Python-readable data. mlabwrap will return a MLabObjectProxy object from these functions. These commonly occur when you use Matlab functions to create objects that are passed into other Matlab functions to actually process the data. For example, you can use the digital signal processing toolbox to create a Welch spectrum object which you can then use to get the power spectrum of your data. Theoretically, you can pass these MLabObjectProxies into Matlab functions that require them. In my experience the more you pass these back and forth, the more likely you are to find a bug in mlabwrap. What you can do instead is write a simple Matlab wrapper function obtains the object, processes the data, and then returns appropriate output as an array.
You can also get around problems with the MLabObjectProxies by using the low-level commands in mlabwrap. For example, if I have a matlab_struct that is a struct array with field matlab_struct.label, and I only want the labels on the Python side, I can do the following:
# place matlab_struct into the Matlab workspace
mlab._set('matlab_struct', matlab_struct)
# convert the labels into a cell array
matlab_struct_labels = mlab.eval('{matlab_struct.labels}')
The main low-level commands available are mlab._set('variable_name', variable), mlab.eval('command string'), and mlab.get('variable_name').
If I'm doing a lot of heavy-duty processing in Matlab, say in a toolbox or plugin that isn't available elsewhere, I'll write what I call 'Matlab-primary' code, where I try to avoid passing data back and forth through mlabwrap, instead manipulating variables in the Matlab workspace by calling .m scripts, saving the resulting output to a data file, and importing that into my Python code.
Good luck!
