code: def assemble_covariance(error, correlation):
      
  
      covariance = []
      for n in range(0, len(error)):
          covariance.append([])
          for m in range(0, len(error)):
              covariance[n].append(correlation[n][m]*error[n]*error[m])
      return _n.array(covariance)
docs: This takes an error vector and a correlation matrix and assembles the covariance