code: def cov_to_correlation(cov):
      
      err = np.sqrt(np.diag(cov))
      errinv = np.ones_like(err) * np.nan
      m = np.isfinite(err) & (err != 0)
      errinv[m] = 1. / err[m]
      corr = np.array(cov)
      return corr * np.outer(errinv, errinv)
docs: Compute the correlation matrix given the covariance matrix.
  
      Parameters
      ----------
      cov : `~numpy.ndarray`
          N x N matrix of covariances among N parameters.
  
      Returns
      -------
      corr : `~numpy.ndarray`
          N x N matrix of correlations among N parameters.