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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 Bmatrix(C):
 """ Calculate a matrix which is effectively the square root of the correlation matrix C Parameters C : 2d array A covariance matrix Returns ------- B : 2d array A matrix B such the B.dot(B') = inv(C) """

    # this version of finding the square root of the inverse matrix
    # suggested by Cath Trott
    L, Q = eigh(C)
    # force very small eigenvalues to have some minimum non-zero value
    minL = 1e-9*L[-1]
    L[L < minL] = minL
    S = np.diag(1 / np.sqrt(L))
    B = Q.dot(S)
    return B