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pythonfunctionpartialsymbolic-mathfunctools

Create a function object from a function and a partial function


I am new using function generators. I am working with the following function:

def math_model(n: float):
    def crrcn(x, t0: float, amp: float, tau: float, offset: float, dt: float, k: float):
        crrcn = np.zeros(np.shape(x))
        for i, a in enumerate(x):
            if a < (t0 + dt):
                crrcn[i] = offset
            else: 
                crrcn[i] = offset + k * amp * (math.exp(n) / (n ** n)) * ((a - (t0 + dt)) / tau) ** n * math.exp( - (a - (t0 + dt)) / tau) 
        return crrcn
    return crrcn

which defines a mathematical model I will use later to fit some data with scipy curve_fit. I wanted to use this same model to build a more complicated one with the following line.

model = partial(math_model(N), dt = 0, k = 1) + math_model(N)

which gives me:

TypeError: unsupported operand type(s) for +: 'functools.partial' and 'function'

From which I understand that I cannot build a function from two functions using this operator and as far as I know there are no function operands in python. How can one build a function from other functions without explicitly evaluating them?


Solution

  • PREVIOUS ANSWER:

    This seems like a misunderstanding of partial.

    partial(...) returns a new function. It does not execute it, it just creates it.

    So your line model = partial(math_model(N), dt = 0, k = 1) + math_model(N) is invalid, because you are basically doing a a + b operation, where a is ... a function :)

    What you may wish to do is simply applying the model. That can be done using math_model(N)(dt = 0, k = 1).

    So

    model = math_model(N)(dt = 0, k = 1) + math_model(N)

    may do the trick

    NEW EDIT: It seems that I misunderstood you. You actually wish to create a function by combining two functions. This is therefore some kind of symbolic reasoning. There are a few libraries out there for this, the most advanced that I know is SymPy. Or if your functions have only one argument, you can use my mini_lambda

    For example with mini-lambda:

    from mini_lambda import x
    
    f1 = x ** 2
    f2 = 5 * x
    f3 = f1 + f2
    
    print(f3.to_string())
    print(f3.evaluate(1))
    

    yields

    x ** 2 + 5 * x
    6