Dear scientists,

I have a very simple algorithm that implements the *Iterative Bayesian Unfolding* algorithms and that deals with lists. Here is the code:

```
def IBU(m,t0,Rin,n):
""" this is the implementation of IBU.
args:
m (list): the measured distribution.
t0 (list): the prior truth spectrum often chosen as a uniform distribution.
Rin (ndarray): the response matrix.
n (int): number of iterations.
Returns:
returns a list representing the approximate true distribution.
"""
tn = t0
for i in range(n):
Rjitni = [np.array(Rin[:][i])*tn[i] for i in range(len(tn))]
Pm_given_t = Rjitni / np.matmul(Rin,tn)
tn = np.dot(Pm_given_t,m)
pass
return tn
```

So I wonder if such a Tufold implementation is possible.

(i.e. you give it the list representing the measured distribution and the algorithm corrects it via Tufold and gives you the corrected distribution).

Thaks for helping