code: def fit_similarity(fit1, fit2):
      
      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())
docs: Distance apart for vectors, given in
      standard deviations