Let X be a square matrix. We want to force it to be Hermitian, that is: self-conjugate-transpose. X = X^H = conj(X^T). To do this in Python with numpy is easy:
X = 0.5*(X + np.conj(X.T))
I haven't found in NumPy a single function that does it in a single experssion f(x).
The question is should I define a new function to do it? E.g.
def make_hermitian(X):
return 0.5*(X + np.conj(X.T))
(one can come up with short name, e.g. "make_h" or "herm" or "selfconj").
Pros: more readable code, one operation in shorter form. If one uses shorter name it saves writing when repeated many times, and makes modification in this operation far more easy and comfortable (need to change only in place).
Cons: replaces a very short and straight-forward expression which is self-evident.
What is more appropriate way of programming: define a new function or just write the explicit expression repeatedly?
I would say it depends on how many times you need to reuse that function.
If it's more than twice, then definitely make a function. If it's only once or twice, I would say it's up to you. If you choose to go with no function, add a short comment specifying what such piece of code is supposed to do.
My preference in any case would be defining a function with a meaningful name, because if anyone else is going to / supposed to read the code, they may not know or remember how to achieve a Hermitian matrix, and hence the math alone ain't going to be sufficient.
On the other hand, a meaningful function name will tell them clearly what it's going on, and they can google after what a Hermitian matrix is.