# Modelling a spring interpolation

I have parameters $$T$$ for tension, $$b$$ for bounciness and $$P_t$$ for target value that should be approached as t goes to infinity.

Currently I have written an equation like so:

$$\ddot{f}(t)=\frac{T(P_t-f(t))-\ddot{f}(t)}{b}$$

Which is taken from code that I have written. However, while the code works, this equation does not get me a function that is similar to the results shown by my code. Where have I gone wrong into translating the python code into a mathematical function of time?

This is my code:

g_velocity = 0.0

# note: fr = from, from is just a taken keyword in python

def lerp(fr, to, amount):
return fr + (amount * (to - fr))

def spring_interp(fr, to, bounciness, tension):
global g_velocity
g_velocity = lerp(g_velocity, (to - fr) * tension, 1.0 / bounciness)
return fr + g_velocity

# usage (example values):
# the value will then oscillate, dampened over time and approach the target_value

value = 1.0
target_value = 8.0
while True:
value = spring_interp(value, target_value, 8.0, 0.4)
print(value)


Edit:
Yes, the correct function I am trying to solve would be
$$\ddot{f}(t)=\frac{T(P_t-f(t))-\dot{f}(t)}{b}$$

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If I understand correctly, you are trying to solve the initial value problem $$\ddot{f}(t) = \frac{T (P_t - f(t))}{1 + b}\\ f(0) = v_0\\ \dot{f}(0) = g_0$$ Note that I re-arranged your equation by collecting all the $$\ddot{f}(t)$$ terms to the left-hand side. This has the solution $$f(t) = P_t + (v_0-P_t) \cos\left(t \sqrt{\frac{T}{\sqrt{1+b}}}\right) + \frac{g_0}{T} \sqrt{(b+1) T} \sin\left(t \sqrt{\frac{T}{1+b}}\right)$$ This solution does not damp such that $$\lim_{t\rightarrow \infty} f(t) = P_t$$, but instead oscillates indefinitely.
I suspect the ODE problem you might have been expecting to solve is $$\ddot{f}(t) = \frac{T(P_t - f(t)) - \dot{f}(t)}{b}\\ f(0) = v_0\\ \dot{f}(0) = g_0$$ This system does have the property that $$\lim_{t\rightarrow \infty} f(t) = P_t$$
Here's a numerical solution for this second system I obtained using scipy's solve_ivp:  I think you're numerical implementation might be a forward Euler method with $$\Delta t = 1$$?