Correct way to implement Discrete and Stationary Wavelet Transform?
In the Haar wavelet case, I just used the following NumPy:

# assume x is passed in as a variable
haar_transform = zeros(N)
i = arange(N/2, dtype=int)
haar_tranform[i]      = x[2*i] + x[2*i + 1]
haar_transform[i+N/2] = x[2*i] - x[2*i + 1]


I believe the mathematical description includes some scaling by $1/\sqrt{2}$.

I've learned that while academic papers may describe something very complicated there's a simple way to compute it.