Haar Distribution in PyMC3

haar.py

def HaarDistribution(name, dim, model=None):
    def transform(x):
        pi = np.pi

        def component(i):
            result = T.cos(pi * x[i])
            for j in range(i):
                result *= T.sin(pi * x[j])
            return result
            
        result = [component(i) for i in range(dim - 2)]

        z = T.sin(pi * x[0])
        for i in range(1, dim - 2):
            z *= T.sin(pi * x[i])

        result += [z * T.cos(2 * pi * x[dim - 2])]
        result += [z * T.sin(2 * pi * x[dim - 2])]
        return T.as_tensor_variable(result)
    
    angles = pm.distributions.Uniform('angles', shape=dim - 1)
    return pm.Deterministic(name, transform(angles), model=model)

Not the prettiest nor the most pythonic, but it works ;)