def anneal(start_temp=1.0, n=10000, advent=None, scurve_k=10.0, scurve_bias=0.3):
    import math
    temp = start_temp
    cooling_rate = (start_temp / float(n))
    if advent is None:
        advent = randcal()
    else:
        advent = advent.copy()
    bestscore = current_score = score(advent)
    best = advent.copy()
    count = 0

    step = 1.0 / n
    try:
        while count < n:
            i = np.random.randint(0,24)
            j = np.random.randint(0,24)
            advent[i/6,i%6], advent[j/6,j%6] = advent[j/6,j%6], advent[i/6,i%6]
            newscore = score(advent)
            if newscore < current_score or np.random.rand() < temp:
                current_score = newscore
            else:
                advent[i/6,i%6], advent[j/6,j%6] = advent[j/6,j%6], advent[i/6,i%6]

            # Cool temperature along s-curve (needs a more robust sigmoid func!)
            # Seems to perform better than a linear decrease.
            sigmoid = 1 - 1.0 / (
                1 + math.e ** (
                    -scurve_k * (step * count - scurve_bias)
                )
            )
            temp = start_temp * sigmoid

            if current_score < bestscore:
                best = advent.copy()
                bestscore = current_score

            count += 1
            if count % 100 == 0:
                print count, temp, bestscore, current_score
    except:
        print 'Error. Best so far was:', (best, bestscore)
        raise

    return (best, bestscore)