<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def fit_similarity(fit1, fit2):
 """ Distance apart for vectors, given in standard deviations """

    cov1 = aligned_covariance(fit1)
    cov2 = aligned_covariance(fit2)
    if fit2.axes[2,2] < 0:
        cov2 *= -1
    v0 = fit1.normal-fit2.normal
    cov0 = cov1+cov2 # Axes are aligned, so no covariances

    # Find distance of point from center

    # Decompose covariance matrix
    U,s,V = N.linalg.svd(cov0)
    rotated = dot(V,v0) # rotate vector into PCA space
    val = rotated**2/N.sqrt(s)
    return N.sqrt(val.sum())