实现多个嵌套函数

Question

I have a interesting equation I want to defined that has a Fourier transform with two equations inside of it.

I'm not trying to solve for the differential equation, I can just represent as a normal function, L.

The other equations in this large equation are

Now as you can, these are some hefty functions to correlate. I'm not very experienced with nested functions and I don't know how to define what in order. When i say in order, i mean that the most efficient way possible that computer understands correctly.

I would think to start out by defining my first function, L, but i don't know what to implement first, my first function, the fourier transform, or the functions inside the fourier transform then the fourier transfrom. Any similar examples and tips will be greatly appreciated.

Thank you.

Edit: I'm not asking anybody to code it all for me, i just want to understand what will be more efficient. And yes there are values that have meaning, but you can treat them as constants.

Answer 1

Generally: Start with the innermost parts that doesn't actually need to call any custom functions anymore. Ideally test them by executing them with some mock data, to ensure they work as expected. Then move on to the functions that require only the functions that you already defined. Again, test run if possible. Rinse and repeat until you have defined the outermost function.

In your particular case, the three sub-equations are all independent, so you can implement them in any order (though I'd still start with whatever you feel is simplest). Then just plug them into the Fourier transform.

Answer 2

Pardon me for not using Latex features. Let's say I have two equations

a/b = c
c+d = e

If you need to find e , you need to arrange equation 2 and plug it into equation 1

a/b = e-d

then rearrange

e = d + a/b

then define your function

def get_var_e(a, b, d):
   return d + a/b

Now if you need to get out a , the representation and choice of top-level equation will be completely different. You get the idea.