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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 decompose_covariance(c):
 """ This decomposes a covariance matrix into an error vector and a correlation matrix """


    # make it a kickass copy of the original
    c = _n.array(c)

    # first get the error vector
    e = []
    for n in range(0, len(c[0])): e.append(_n.sqrt(c[n][n]))

    # now cycle through the matrix, dividing by e[1]*e[2]
    for n in range(0, len(c[0])):
        for m in range(0, len(c[0])):
            c[n][m] = c[n][m] / (e[n]*e[m])

    return [_n.array(e), _n.array(c)]