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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def regularDTW(distMat, norm=True):
 """Use a local distance matrix to perform dynamic time warping. Parameters distMat : 2D array Local distance matrix. Returns ------- float Total unweighted distance of the optimal path through the local distance matrix. """

    sLen, tLen = distMat.shape
    totalDistance = zeros((sLen, tLen))
    totalDistance[0:sLen, 0:tLen] = distMat

    minDirection = zeros((sLen, tLen))

    for i in range(1, sLen):
        totalDistance[i, 0] = totalDistance[i, 0] + totalDistance[i - 1, 0]

    for j in range(1, tLen):
        totalDistance[0, j] = totalDistance[0, j] + totalDistance[0, j - 1]

    for i in range(1, sLen):
        for j in range(1, tLen):
            # direction,minPrevDistance = min(enumerate([totalDistance[i,j],totalDistance[i,j+1],totalDistance[i+1,j]]), key=operator.itemgetter(1))
            # totalDistance[i+1,j+1] = totalDistance[i+1,j+1] + minPrevDistance
            # minDirection[i,j] = direction
            minDirection[i, j], totalDistance[i, j] = min(
                enumerate([totalDistance[i - 1, j - 1] + 2 * totalDistance[i, j],
                           totalDistance[i - 1, j] + totalDistance[i, j],
                           totalDistance[i, j - 1] + totalDistance[i, j]]), key=operator.itemgetter(1))
    if norm:
        return totalDistance[sLen - 1, tLen - 1] / (sLen + tLen)
    return totalDistance[sLen - 1, tLen - 1]