We don't use simulink or anything like that - we just use the typical linear algebra stuff: Markov chains, finding eigenvalues, solving linear systems, etc.
Maybe R and Numpy are getting better, but try to invert a matrix, find and eigenvalue, find the solution to a polynomial, fit an equation, etc Not to mention the most advanced things outside Matlab
Simulink is a whole different world inside Matlab as well
Here's something that doesn't look like it's covered by NumPy 100% http://www.mathworks.de/de/help/signal/ug/iir-filter-design....
Yeah that's exactly the kind of stuff I meant when I was talking about pulse coding and such. Although the last time I had to do some filtering stuff I did manage to use python+scipy, but yeah it hasn't progressed as far as matlab in a lot of those niche engineering areas yet.
Where I've found matlab to still be ahead is in the area of domain specific engineering. For example, if you want to manually pulse code a signal using some specific modulation scheme, matlab has a tool for just that. In python you still have to manually write a bit, interface it to the fitting routine, and convolve it across your signal.
All I ever did in matlab involved matrices (usually differential equations with matrix coefficients and other things of that nature) and matrices are super easy to use in MATLAB:
A = [1 2 3
4 5 6];
A = [1 2 3; 4 5 6];
A = array([[1,2,3],[4,5,6]])
Even if you're doing complicated operations because matrices are the essential building blocks of MATLAB it is easy to use, not so much in python.
I tried building my FEA (finite element analysis) project in python after i finished programming it in matlab it and found it to frustrating to continue.